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notebooks/Data_Exploration.ipynb
|
###Markdown
Exploring the files with PandasMany statistical Python packages can deal with numpy Arrays.Numpy Arrays however are not always easy to use. Pandas is a package that provides a dataframe interface, similar to what R uses as the main data structure.Since Pandas has become so popular, many packages accept both pd.DataFrames and numpy Arrays.
###Code
import os
from dotenv import load_dotenv, find_dotenv
# find .env automagically by walking up directories until it's found
dotenv_path = find_dotenv()
# load up the entries as environment variables
load_dotenv(dotenv_path)
###Output
_____no_output_____
###Markdown
First some environment variablesWe now use the files that are stored in the RAW directory.If we decide to change the data format by changing names, adding features, created summary data frames etc., we will save those files in the INTERIM directory.
###Code
PROJECT_DIR = os.path.dirname(dotenv_path)
RAW_DATA_DIR = PROJECT_DIR + os.environ.get("RAW_DATA_DIR")
INTERIM_DATA_DIR = PROJECT_DIR + os.environ.get("INTERIM_DATA_DIR")
files=os.environ.get("FILES").split()
print("Project directory is : {0}".format(PROJECT_DIR))
print("Raw data directory is : {0}".format(RAW_DATA_DIR))
print("Interim directory is : {0}".format(INTERIM_DATA_DIR))
###Output
Project directory is : /home/gsentveld/lunch_and_learn
Raw data directory is : /home/gsentveld/lunch_and_learn/data/raw
Interim directory is : /home/gsentveld/lunch_and_learn/data/interim
###Markdown
Importing pandas and matplotlib.pyplot
###Code
# The following jupyter notebook magic makes the plots appear in the notebook.
# If you run in batch mode, you have to save your plots as images.
%matplotlib inline
# matplotlib.pyplot is traditionally imported as plt
import matplotlib.pyplot as plt
# Pandas is traditionaly imported as pd.
import pandas as pd
from pylab import rcParams
# some display options to size the figures. feel free to experiment
pd.set_option('display.max_columns', 25)
rcParams['figure.figsize'] = (17, 7)
###Output
_____no_output_____
###Markdown
Reading a file in PandasReading a CSV file is really easy in Pandas. There are several formats that Pandas can deal with.|Format Type|Data Description|Reader|Writer||---|---|---|---||text|CSV|read_csv|to_csv||text|JSON|read_json|to_json||text|HTML|read_html|to_html||text|Local clipboard|read_clipboard|to_clipboard||binary|MS Excel|read_excel|to_excel||binary|HDF5 Format|read_hdf|to_hdf||binary|Feather Format|read_feather|to_feather||binary|Msgpack|read_msgpack|to_msgpack||binary|Stata|read_stata|to_stata||binary|SAS|read_sas |||binary|Python Pickle Format|read_pickle|to_pickle||SQL|SQL|read_sql|to_sql||SQL|Google Big Query|read_gbq|to_gbq| We will use pd.read_csv().As you will see, the Jupyter notebook prints out a very nice rendition of the DataFrame object that is the result
###Code
#family=pd.read_csv(RAW_DATA_DIR+'/familyxx.csv')
#persons=pd.read_csv(RAW_DATA_DIR+'/personsx.csv')
samadult=pd.read_csv(RAW_DATA_DIR+'/samadult.csv')
samadult.columns.values.tolist()
features=[x for x in samadult.columns.values.tolist() if x.startswith('ALDURA')]
import numpy as np
np.sum(samadult.WKDAYR.notnull() & (samadult['WKDAYR']<900))
np.sum(samadult.ALDURA17.notnull() & (samadult['ALDURA17']<90) )
features=[
'ALDURA3',
#'ALDURA4',
#'ALDURA6',
#'ALDURA7',
#'ALDURA8',
'ALDURA11',
#'ALDURA17',
#'ALDURA20',
#'ALDURA21',
#'ALDURA22',
#'ALDURA23',
#'ALDURA24',
#'ALDURA27',
#'ALDURA28',
'ALDURA29',
'ALDURA33']
target='ALDURA17'
ADD_INDICATORS=False
ADD_POLYNOMIALS=True
LOG_X=True
LOG_Y=False
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
np.random.seed(42)
reg=LinearRegression()
data=samadult[samadult.ALDURA17.notnull() & (samadult['ALDURA17']<90)]
X=data[features]
X.shape
# turn years since into the "nth" year of
# then fill with 0 otherwise
X=X+1
X=X.fillna(0)
if LOG_X:
X=np.log1p(X)
if ADD_INDICATORS:
indicator_names=[x+"_I" for x in features]
indicators=pd.DataFrame()
for feature in features:
indicators[feature+"_I"]=data[feature].notnull().astype(int)
X=pd.concat([X, indicators], axis=1, join_axes=[X.index])
from sklearn.preprocessing import PolynomialFeatures
if ADD_POLYNOMIALS:
poly=PolynomialFeatures(degree=2, interaction_only=True)
X=poly.fit_transform(X)
X.shape
y=data[target]
y=y+1
y=y.fillna(0)
if LOG_Y:
y=np.log1p(y)
y.head()
reg.fit(X,y)
y_pred=reg.predict(X)
score=r2_score(y, y_pred)
import matplotlib.pyplot as plt
plt.plot(y,y_pred,marker='.', linestyle='None', alpha=0.5 )
plt.xlabel('Y Train')
plt.ylabel('Y Predict')
plt.show()
score
from sklearn.linear_model import Ridge
ridge=Ridge(alpha=0.7, normalize=True)
ridge.fit(X,y)
y_pred=ridge.predict(X)
def display_plot(cv_scores, cv_scores_std):
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot(alpha_space, cv_scores)
std_error = cv_scores_std / np.sqrt(10)
ax.fill_between(alpha_space, cv_scores + std_error, cv_scores - std_error, alpha=0.2)
ax.set_ylabel('CV Score +/- Std Error')
ax.set_xlabel('Alpha')
ax.axhline(np.max(cv_scores), linestyle='--', color='.5')
ax.set_xlim([alpha_space[0], alpha_space[-1]])
ax.set_xscale('log')
plt.show()
# Import necessary modules
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
# Setup the array of alphas and lists to store scores
alpha_space = np.logspace(-4, 0, 50)
ridge_scores = []
ridge_scores_std = []
# Create a ridge regressor: ridge
ridge = Ridge(normalize=True)
# Compute scores over range of alphas
for alpha in alpha_space:
# Specify the alpha value to use: ridge.alpha
ridge.alpha = alpha
# Perform 10-fold CV: ridge_cv_scores
ridge_cv_scores = cross_val_score(ridge, X, y, cv=10)
# Append the mean of ridge_cv_scores to ridge_scores
ridge_scores.append(np.mean(ridge_cv_scores))
# Append the std of ridge_cv_scores to ridge_scores_std
ridge_scores_std.append(np.std(ridge_cv_scores))
# Display the plot
display_plot(ridge_scores, ridge_scores_std)
from sklearn.decomposition import PCA
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import KMeans
fig = plt.figure(4, figsize=(8, 6))
plt.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=40, azim=20)
plt.cla()
pca = PCA(n_components=3)
pca.fit(X)
X_pca = pca.transform(X)
kmean=KMeans(n_clusters=4)
kmean.fit(X_pca)
y_lab=kmean.labels_
# Reorder the labels to have colors matching the cluster results
ax.scatter(X_pca[:, 0], X_pca[:, 1], X_pca[:, 2], label=y_lab,c=y_lab+1, cmap=plt.cm.spectral)
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
plt.legend(bbox_to_anchor=(0, 1), loc='upper right', ncol=7)
plt.show()
y_lab
pca = PCA(n_components=2)
pca.fit(X)
X_pca2 = pca.transform(X)
kmean=KMeans(n_clusters=4)
kmean.fit(X_pca2)
y_lab2=kmean.labels_
#plt.cla()
#plt.figure()
markers=[',',]
case=1
x_special=X_pca2[y_lab2==case]
c_special=y_lab2[y_lab2==case]
x_other=X_pca2[y_lab2!=case]
c_other=y_lab2[y_lab2!=case]
plt.scatter(x_special[:,0],x_special[:,1], c=c_special, marker='+')
plt.scatter(x_other[:,0],x_other[:,1], c=c_other, marker='.')
plt.show()
y_lab2[:5]
for i in range(0,4):
y_case=y[y_lab2==i]
lab_mean=np.mean(y_case)
lab_std=np.std(y_case)
lab_perc=np.percentile(y_case, [2.5, 97.5])
print("For case {}, the mean is {} and the std = {} and the 95% confidence interval = {}".format(i,lab_mean, lab_std, lab_perc))
###Output
For case 0, the mean is 20.43243243243243 and the std = 18.205346022885752 and the 95% confidence interval = [ 1.9 57.4]
For case 1, the mean is 20.97983870967742 and the std = 16.63106537350679 and the 95% confidence interval = [ 1.175 61. ]
For case 2, the mean is 24.357142857142858 and the std = 12.361270986761973 and the 95% confidence interval = [ 7.675 51.325]
For case 3, the mean is 19.341796875 and the std = 16.382702479177244 and the 95% confidence interval = [ 1. 61.]
###Markdown
Power plants
###Code
plants = pd.read_csv('../data/starter_pack/gppd/gppd_120_pr.csv')
plants.columns
plants = pd.read_csv('../data/starter_pack/gppd/gppd_120_pr.csv')
plants = plants[['capacity_mw', 'estimated_generation_gwh', 'primary_fuel', '.geo']]
coordinates = pd.json_normalize(plants['.geo'].apply(json.loads))['coordinates']
plants[['longitude', 'latitude']] = pd.DataFrame(coordinates.values.tolist(), index= coordinates.index)
plants.drop('.geo', axis=1, inplace=True)
plants.describe()
ax = sns.countplot(x="primary_fuel", data=plants)
plants['potential_max_generation_gwh'] = plants['capacity_mw']*365*24/1000
plants['capacity_fraction'] = plants['capacity_mw']/plants['capacity_mw'].sum()
plants['generation_fraction'] = plants['estimated_generation_gwh']/plants['estimated_generation_gwh'].sum()
plants
###Output
_____no_output_____
###Markdown
I calculated the maxium potential generation using the capacity_mw. In some cases, the estimated annual generation in the dataset exceeded the maximum potential annual generation, which is not possible. I looked at the code that was used to generate the estimated annual generation, which seemed to be a reasonable method. The issues might have been with the input data. Therefore, I am using the potential max generation adjusted by a capacity factor to estimate annual generation.
###Code
plants[['primary_fuel','capacity_mw']].groupby(['primary_fuel']).describe()
plants[['primary_fuel','estimated_generation_gwh']].groupby(['primary_fuel']).describe()
###Output
_____no_output_____
###Markdown
NO2
###Code
def plot_scaled(file_name):
vmin, vmax = np.nanpercentile(file_name, (5,95)) # 5-95% stretch
img_plt = plt.imshow(file_name, cmap='gray', vmin=vmin, vmax=vmax)
plt.show()
image = '../data/starter_pack/s5p_no2/tif/s5p_no2_20180708T172237_20180714T190743.tif'
image_band = rio.open(image).read(1)
plot_scaled(image_band)
#overlay_image_on_puerto_rico(image,band_layer=1)
###Output
_____no_output_____
###Markdown
Plot 1 dimensional data
###Code
ds = xr.open_dataset('/Users/kasmith/Code/kaggle_ds4g/data/starter_pack/s5p_no2/no2_1year.nc')
ds
no2_1d = ds.isel(lat=15, lon=15)
no2_1d.NO2_column_number_density.plot()
no2_1d.cloud_fraction.plot()
no2_2d = ds.NO2_column_number_density.isel(time=0)
no2_2d
no2_2d.plot()
map_proj = ccrs.LambertConformal(central_longitude=-65, central_latitude=18)
ax = plt.axes(projection=ccrs.LambertConformal(central_longitude=-65, central_latitude=18))
no2_2d.plot.contourf(ax=ax, transform=ccrs.PlateCarree());
ax.coastlines()
ax.set_extent([-67.5, -65, 17.5, 19])
ax.set_aspect("equal")
###Output
_____no_output_____
###Markdown
GFS
###Code
ds_gfs = xr.open_dataset('/Users/kasmith/Code/kaggle_ds4g/data/starter_pack/gfs/gfs_1year.nc')
ds_gfs
ds_gfs_temp2m_1d = ds_gfs.temperature_2m_above_ground.isel(lat=15, lon=15)
ds_gfs_temp2m_1d.plot()
###Output
_____no_output_____
###Markdown
GLDAS
###Code
ds_gldas = xr.open_dataset('/Users/kasmith/Code/kaggle_ds4g/data/starter_pack/gldas/gldas_1year.nc')
ds_gldas
ds_gldas_tair_1d = ds_gldas.Tair_f_inst.isel(lat=100, lon=100)
ds_gldas_tair_1d
ds_gldas_tair_1d.plot()
###Output
_____no_output_____
|
tutorials/1_Installing_the_UMLS.ipynb
|
###Markdown
I. Installing the Unified Medical Language System (UMLS)
###Code
%load_ext autoreload
%autoreload 2
import sys
sys.path.insert(0,'../../trove')
###Output
_____no_output_____
###Markdown
A. Download the UMLS Release FilesTrove requires access to the [Unified Medical Language System (UMLS)](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) which is freely available after signing up for an account with the National Library of Medicine (NLM). Visit the link below and download the latest "UMLS Metathesaurus Files" release [2020AB](https://download.nlm.nih.gov/umls/kss/2020AB/umls-2020AB-metathesaurus.zip). This file is quite large (5.3 GB compressed), so it may take some time to download. **Please note, "full" release zip files are currently not supported.**Alternatively, if you have an existing API KEY you can use the following script to download the zip file from the command line. See https://documentation.uts.nlm.nih.gov/automating-downloads.html for technical details on NLM authentication. python download_umls.py \ --apikey XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX \ --url https://download.nlm.nih.gov/umls/kss/2020AB/umls-2020AB-metathesaurus.zip B. InstallationCurrently there are 2 ways to initalize the UMLS:- From the source zip file (e.g., `umls-2020AB-metathesaurus.zip`)- From the Rich Release Format (RRF) files generated by [MetamorphoSys](https://www.ncbi.nlm.nih.gov/books/NBK9683/) - **TBD** An existing database instanceDepending on your machine, this should take 2-5 minutes. Option 1: Install from NLM Zip File
###Code
%%time
from trove.labelers.umls import UMLS
# setup defaults
UMLS.config(
cache_root = "~/.trove/umls2020AB",
backend = 'pandas'
)
NLM_ZIPFILE_PATH = "umls-2020AB-metathesaurus.zip"
if not UMLS.is_initalized():
print("Initializing the UMLS from zip file...")
UMLS.init_from_nlm_zip(NLM_ZIPFILE_PATH)
###Output
Initializing the UMLS from zip file...
CPU times: user 2min 1s, sys: 1min, total: 3min 2s
Wall time: 3min 47s
###Markdown
Option 2: Install from RRF FilesIf you have installed the UMLS before using [MetamorphoSys](https://www.ncbi.nlm.nih.gov/books/NBK9683/) to create custom vocabulary subsets you can directly use the generated RRF files.
###Code
%%time
RRF_FILE_PATH = ""
if not UMLS.is_initalized():
print("Initializing the UMLS from RRFs...")
UMLS.init_from_rrfs(RRF_FILE_PATH)
###Output
CPU times: user 131 µs, sys: 311 µs, total: 442 µs
Wall time: 384 µs
###Markdown
Option 3: Install from an Existing Database Instance**TBD**: If you have a live UMLS database instance running, you can initialize Trove as follows.
###Code
# if not UMLS.is_initalized():
# UMLS.init_from_dbconn(engine='mysql', dbname='UMLS2020AB')
###Output
_____no_output_____
###Markdown
3. Test the InstallationHere we apply some common term transformations. This should run in 2-4 minutes.
###Code
%%time
from trove.labelers.umls import UMLS
from trove.transforms import SmartLowercase
# english stopwords
stopwords = set(open('data/stopwords.txt','r').read().splitlines())
stopwords = stopwords.union(set([t[0].upper() + t[1:] for t in stopwords]))
# options for filtering terms
config = {
"type_mapping" : "TUI", # TUI = semantic types, CUI = concept ids
'min_char_len' : 2,
'max_tok_len' : 8,
'min_dict_size' : 500,
'stopwords' : stopwords,
'transforms' : [SmartLowercase()],
'languages' : {"ENG"},
'filter_sabs' : {"SNOMEDCT_VET"},
'filter_rgx' : r'''^[-+]*[0-9]+([.][0-9]+)*$''' # filter numbers
}
umls = UMLS(**config)
###Output
CPU times: user 1min 25s, sys: 6.59 s, total: 1min 32s
Wall time: 1min 28s
###Markdown
Look at semantic type assignments for an example term `acetaminophen` from the Medical Subject Headings (MeSH®) terminology.
###Code
from trove.labelers.umls import SemanticGroups
semgroups = SemanticGroups()
stys = umls.terminologies['MSH']['acetaminophen']
print(stys)
print([semgroups.types[sty] for sty in stys])
###Output
{'T109', 'T121'}
['Organic Chemical', 'Pharmacologic Substance']
|
Projects/Projects/A Visual History of Nobel Prize Winners/notebook.ipynb
|
###Markdown
1. The most Nobel of PrizesThe Nobel Prize is perhaps the world's most well known scientific award. Except for the honor, prestige and substantial prize money the recipient also gets a gold medal showing Alfred Nobel (1833 - 1896) who established the prize. Every year it's given to scientists and scholars in the categories chemistry, literature, physics, physiology or medicine, economics, and peace. The first Nobel Prize was handed out in 1901, and at that time the Prize was very Eurocentric and male-focused, but nowadays it's not biased in any way whatsoever. Surely. Right?Well, we're going to find out! The Nobel Foundation has made a dataset available of all prize winners from the start of the prize, in 1901, to 2016. Let's load it in and take a look.
###Code
# Loading in required libraries
# ... YOUR CODE FOR TASK 1 ...
import numpy as np
import pandas as pd
import seaborn as sns
# Reading in the Nobel Prize data
nobel = pd.read_csv('datasets/nobel.csv')
# Taking a look at the first several winners
# ... YOUR CODE FOR TASK 1 ...
nobel.head(6)
###Output
_____no_output_____
###Markdown
2. So, who gets the Nobel Prize?Just looking at the first couple of prize winners, or Nobel laureates as they are also called, we already see a celebrity: Wilhelm Conrad Röntgen, the guy who discovered X-rays. And actually, we see that all of the winners in 1901 were guys that came from Europe. But that was back in 1901, looking at all winners in the dataset, from 1901 to 2016, which sex and which country is the most commonly represented? (For country, we will use the birth_country of the winner, as the organization_country is NaN for all shared Nobel Prizes.)
###Code
# Display the number of (possibly shared) Nobel Prizes handed
# out between 1901 and 2016
# ... YOUR CODE FOR TASK 2 ...
display(len(nobel))
# Display the number of prizes won by male and female recipients.
# ... YOUR CODE FOR TASK 2 ...
display(nobel['sex'].value_counts())
# Display the number of prizes won by the top 10 nationalities.
# ... YOUR CODE FOR TASK 2 ...
nobel['birth_country'].value_counts().head(10)
###Output
_____no_output_____
###Markdown
3. USA dominanceNot so surprising perhaps: the most common Nobel laureate between 1901 and 2016 was a man born in the United States of America. But in 1901 all the winners were European. When did the USA start to dominate the Nobel Prize charts?
###Code
nobel.info()
nobel.year
np.floor(1953/10)
#np.floor((nobel.year/10)) * 10
# Calculating the proportion of USA born winners per decade
nobel['usa_born_winner'] = nobel['birth_country'] == 'United States of America'
nobel['decade'] = (np.floor(nobel.year/10) * 10).astype('int64')
prop_usa_winners = nobel.groupby('decade', as_index = False)['usa_born_winner'].mean()
# Display the proportions of USA born winners per decade
# ... YOUR CODE FOR TASK 3 ...
prop_usa_winners
###Output
_____no_output_____
###Markdown
4. USA dominance, visualizedA table is OK, but to see when the USA started to dominate the Nobel charts we need a plot!
###Code
# Setting the plotting theme
sns.set()
# and setting the size of all plots.
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [11, 7]
# Plotting USA born winners
ax = sns.lineplot(x = 'decade', y = 'usa_born_winner', data = prop_usa_winners)
# Adding %-formatting to the y-axis
from matplotlib.ticker import PercentFormatter
# ... YOUR CODE FOR TASK 4 ...
ax.yaxis.set_major_formatter(PercentFormatter(1.0))
###Output
_____no_output_____
###Markdown
5. What is the gender of a typical Nobel Prize winner?So the USA became the dominating winner of the Nobel Prize first in the 1930s and had kept the leading position ever since. But one group that was in the lead from the start, and never seems to let go, are men. Maybe it shouldn't come as a shock that there is some imbalance between how many male and female prize winners there are, but how significant is this imbalance? And is it better or worse within specific prize categories like physics, medicine, literature, etc.?
###Code
nobel.head()
# Calculating the proportion of female laureates per decade
nobel['female_winner'] = nobel['sex'] == 'Female'
prop_female_winners = nobel.groupby(['decade', 'category'], as_index = False)['female_winner'].mean()
# Plotting USA born winners with % winners on the y-axis
# ... YOUR CODE FOR TASK 5 ...
ax = sns.lineplot(x = 'decade', y = 'female_winner', hue = 'category', data = prop_female_winners)
# Adding %-formatting to the y-axis
from matplotlib.ticker import PercentFormatter
# ... YOUR CODE FOR TASK 4 ...
ax.yaxis.set_major_formatter(PercentFormatter(1.0))
###Output
_____no_output_____
###Markdown
6. The first woman to win the Nobel PrizeThe plot above is a bit messy as the lines are overplotting. But it does show some interesting trends and patterns. Overall the imbalance is pretty large with physics, economics, and chemistry having the largest imbalance. Medicine has a somewhat positive trend, and since the 1990s the literature prize is also now more balanced. The big outlier is the peace prize during the 2010s, but keep in mind that this just covers the years 2010 to 2016.Given this imbalance, who was the first woman to receive a Nobel Prize? And in what category?
###Code
# Picking out the first woman to win a Nobel Prize
# ... YOUR CODE FOR TASK 5 ...
nobel[nobel['female_winner']].nsmallest(1, 'year')
###Output
_____no_output_____
###Markdown
7. Repeat laureatesFor most scientists/writers/activists a Nobel Prize would be the crowning achievement of a long career. But for some people, one is just not enough, and few have gotten it more than once. Who are these lucky few? (Having won no Nobel Prize myself, I'll assume it's just about luck.)
###Code
# Selecting the laureates that have received 2 or more prizes.
# ... YOUR CODE FOR TASK 5 ...
nobel.groupby('full_name').filter(lambda group: len(group) >= 2)
###Output
_____no_output_____
###Markdown
8. How old are you when you get the prize?The list of repeat winners contains some illustrious names! We again meet Marie Curie, who got the prize in physics for discovering radiation and in chemistry for isolating radium and polonium. John Bardeen got it twice in physics for transistors and superconductivity, Frederick Sanger got it twice in chemistry, and Linus Carl Pauling got it first in chemistry and later in peace for his work in promoting nuclear disarmament. We also learn that organizations also get the prize as both the Red Cross and the UNHCR have gotten it twice.But how old are you generally when you get the prize?
###Code
#nobel['birth_date'] = pd.to_datetime(nobel['birth_date'])
#nobel['year'] - nobel['birth_date'].dt.year
# Converting birth_date from String to datetime
nobel['birth_date'] = pd.to_datetime(nobel['birth_date'])
# Calculating the age of Nobel Prize winners
nobel['age'] = nobel['year'] - nobel['birth_date'].dt.year
# Plotting the age of Nobel Prize winners
# ... YOUR CODE FOR TASK 8 ...
sns.lmplot(x = 'year', y = 'age', data = nobel)
###Output
_____no_output_____
###Markdown
9. Age differences between prize categoriesThe plot above shows us a lot! We see that people use to be around 55 when they received the price, but nowadays the average is closer to 65. But there is a large spread in the laureates' ages, and while most are 50+, some are very young.We also see that the density of points is much high nowadays than in the early 1900s -- nowadays many more of the prizes are shared, and so there are many more winners. We also see that there was a disruption in awarded prizes around the Second World War (1939 - 1945). Let's look at age trends within different prize categories.
###Code
# Same plot as above, but separate plots for each type of Nobel Prize
# ... YOUR CODE FOR TASK 9 ...
sns.lmplot(x = 'year', y = 'age', row = 'category', data = nobel)
###Output
_____no_output_____
###Markdown
10. Oldest and youngest winnersMore plots with lots of exciting stuff going on! We see that both winners of the chemistry, medicine, and physics prize have gotten older over time. The trend is strongest for physics: the average age used to be below 50, and now it's almost 70. Literature and economics are more stable. We also see that economics is a newer category. But peace shows an opposite trend where winners are getting younger! In the peace category we also a winner around 2010 that seems exceptionally young. This begs the questions, who are the oldest and youngest people ever to have won a Nobel Prize?
###Code
# The oldest winner of a Nobel Prize as of 2016
# ... YOUR CODE FOR TASK 10 ...
display(nobel.nlargest(1, 'age'))
# The youngest winner of a Nobel Prize as of 2016
# ... YOUR CODE FOR TASK 10 ...
display(nobel.nsmallest(1, 'age'))
###Output
_____no_output_____
###Markdown
11. You get a prize!Hey! You get a prize for making it to the very end of this notebook! It might not be a Nobel Prize, but I made it myself in paint so it should count for something. But don't despair, Leonid Hurwicz was 90 years old when he got his prize, so it might not be too late for you. Who knows.Before you leave, what was again the name of the youngest winner ever who in 2014 got the prize for "[her] struggle against the suppression of children and young people and for the right of all children to education"?
###Code
# The name of the youngest winner of the Nobel Prize as of 2016
youngest_winner = 'Malala Yousafzai'
###Output
_____no_output_____
|
original_code/ch06.ipynb
|
###Markdown
Data Loading, Storage,
###Code
import numpy as np
import pandas as pd
np.random.seed(12345)
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
np.set_printoptions(precision=4, suppress=True)
###Output
_____no_output_____
###Markdown
Reading and Writing Data in Text Format
###Code
!cat examples/ex1.csv
df = pd.read_csv('examples/ex1.csv')
df
pd.read_table('examples/ex1.csv', sep=',')
!cat examples/ex2.csv
pd.read_csv('examples/ex2.csv', header=None)
pd.read_csv('examples/ex2.csv', names=['a', 'b', 'c', 'd', 'message'])
names = ['a', 'b', 'c', 'd', 'message']
pd.read_csv('examples/ex2.csv', names=names, index_col='message')
!cat examples/csv_mindex.csv
parsed = pd.read_csv('examples/csv_mindex.csv',
index_col=['key1', 'key2'])
parsed
list(open('examples/ex3.txt'))
result = pd.read_table('examples/ex3.txt', sep='\s+')
result
!cat examples/ex4.csv
pd.read_csv('examples/ex4.csv', skiprows=[0, 2, 3])
!cat examples/ex5.csv
result = pd.read_csv('examples/ex5.csv')
result
pd.isnull(result)
result = pd.read_csv('examples/ex5.csv', na_values=['NULL'])
result
sentinels = {'message': ['foo', 'NA'], 'something': ['two']}
pd.read_csv('examples/ex5.csv', na_values=sentinels)
###Output
_____no_output_____
###Markdown
Reading Text Files in Pieces
###Code
pd.options.display.max_rows = 10
result = pd.read_csv('examples/ex6.csv')
result
pd.read_csv('examples/ex6.csv', nrows=5)
chunker = pd.read_csv('examples/ex6.csv', chunksize=1000)
chunker
chunker = pd.read_csv('examples/ex6.csv', chunksize=1000)
tot = pd.Series([])
for piece in chunker:
tot = tot.add(piece['key'].value_counts(), fill_value=0)
tot = tot.sort_values(ascending=False)
tot[:10]
###Output
_____no_output_____
###Markdown
Writing Data to Text Format
###Code
data = pd.read_csv('examples/ex5.csv')
data
data.to_csv('examples/out.csv')
!cat examples/out.csv
import sys
data.to_csv(sys.stdout, sep='|')
data.to_csv(sys.stdout, na_rep='NULL')
data.to_csv(sys.stdout, index=False, header=False)
data.to_csv(sys.stdout, index=False, columns=['a', 'b', 'c'])
dates = pd.date_range('1/1/2000', periods=7)
ts = pd.Series(np.arange(7), index=dates)
ts.to_csv('examples/tseries.csv')
!cat examples/tseries.csv
###Output
_____no_output_____
###Markdown
Working with Delimited Formats
###Code
!cat examples/ex7.csv
import csv
f = open('examples/ex7.csv')
reader = csv.reader(f)
for line in reader:
print(line)
with open('examples/ex7.csv') as f:
lines = list(csv.reader(f))
header, values = lines[0], lines[1:]
data_dict = {h: v for h, v in zip(header, zip(*values))}
data_dict
###Output
_____no_output_____
###Markdown
class my_dialect(csv.Dialect): lineterminator = '\n' delimiter = ';' quotechar = '"' quoting = csv.QUOTE_MINIMAL reader = csv.reader(f, dialect=my_dialect) reader = csv.reader(f, delimiter='|') with open('mydata.csv', 'w') as f: writer = csv.writer(f, dialect=my_dialect) writer.writerow(('one', 'two', 'three')) writer.writerow(('1', '2', '3')) writer.writerow(('4', '5', '6')) writer.writerow(('7', '8', '9')) JSON Data
###Code
obj = """
{"name": "Wes",
"places_lived": ["United States", "Spain", "Germany"],
"pet": null,
"siblings": [{"name": "Scott", "age": 30, "pets": ["Zeus", "Zuko"]},
{"name": "Katie", "age": 38,
"pets": ["Sixes", "Stache", "Cisco"]}]
}
"""
import json
result = json.loads(obj)
result
asjson = json.dumps(result)
siblings = pd.DataFrame(result['siblings'], columns=['name', 'age'])
siblings
!cat examples/example.json
data = pd.read_json('examples/example.json')
data
print(data.to_json())
print(data.to_json(orient='records'))
###Output
_____no_output_____
###Markdown
XML and HTML: Web Scraping conda install lxmlpip install beautifulsoup4 html5lib
###Code
tables = pd.read_html('examples/fdic_failed_bank_list.html')
len(tables)
failures = tables[0]
failures.head()
close_timestamps = pd.to_datetime(failures['Closing Date'])
close_timestamps.dt.year.value_counts()
###Output
_____no_output_____
###Markdown
Parsing XML with lxml.objectify 373889 Metro-North Railroad Escalator Availability Percent of the time that escalators are operational systemwide. The availability rate is based on physical observations performed the morning of regular business days only. This is a new indicator the agency began reporting in 2009. 2011 12 Service Indicators M U % 1 97.00 97.00
###Code
from lxml import objectify
path = 'datasets/mta_perf/Performance_MNR.xml'
parsed = objectify.parse(open(path))
root = parsed.getroot()
data = []
skip_fields = ['PARENT_SEQ', 'INDICATOR_SEQ',
'DESIRED_CHANGE', 'DECIMAL_PLACES']
for elt in root.INDICATOR:
el_data = {}
for child in elt.getchildren():
if child.tag in skip_fields:
continue
el_data[child.tag] = child.pyval
data.append(el_data)
perf = pd.DataFrame(data)
perf.head()
from io import StringIO
tag = '<a href="http://www.google.com">Google</a>'
root = objectify.parse(StringIO(tag)).getroot()
root
root.get('href')
root.text
###Output
_____no_output_____
###Markdown
Binary Data Formats
###Code
frame = pd.read_csv('examples/ex1.csv')
frame
frame.to_pickle('examples/frame_pickle')
pd.read_pickle('examples/frame_pickle')
!rm examples/frame_pickle
###Output
_____no_output_____
###Markdown
Using HDF5 Format
###Code
frame = pd.DataFrame({'a': np.random.randn(100)})
store = pd.HDFStore('mydata.h5')
store['obj1'] = frame
store['obj1_col'] = frame['a']
store
store['obj1']
store.put('obj2', frame, format='table')
store.select('obj2', where=['index >= 10 and index <= 15'])
store.close()
frame.to_hdf('mydata.h5', 'obj3', format='table')
pd.read_hdf('mydata.h5', 'obj3', where=['index < 5'])
os.remove('mydata.h5')
###Output
_____no_output_____
###Markdown
Reading Microsoft Excel Files
###Code
xlsx = pd.ExcelFile('examples/ex1.xlsx')
pd.read_excel(xlsx, 'Sheet1')
frame = pd.read_excel('examples/ex1.xlsx', 'Sheet1')
frame
writer = pd.ExcelWriter('examples/ex2.xlsx')
frame.to_excel(writer, 'Sheet1')
writer.save()
frame.to_excel('examples/ex2.xlsx')
!rm examples/ex2.xlsx
###Output
_____no_output_____
###Markdown
Interacting with Web APIs
###Code
import requests
url = 'https://api.github.com/repos/pandas-dev/pandas/issues'
resp = requests.get(url)
resp
data = resp.json()
data[0]['title']
issues = pd.DataFrame(data, columns=['number', 'title',
'labels', 'state'])
issues
###Output
_____no_output_____
###Markdown
Interacting with Databases
###Code
import sqlite3
query = """
CREATE TABLE test
(a VARCHAR(20), b VARCHAR(20),
c REAL, d INTEGER
);"""
con = sqlite3.connect('mydata.sqlite')
con.execute(query)
con.commit()
data = [('Atlanta', 'Georgia', 1.25, 6),
('Tallahassee', 'Florida', 2.6, 3),
('Sacramento', 'California', 1.7, 5)]
stmt = "INSERT INTO test VALUES(?, ?, ?, ?)"
con.executemany(stmt, data)
con.commit()
cursor = con.execute('select * from test')
rows = cursor.fetchall()
rows
cursor.description
pd.DataFrame(rows, columns=[x[0] for x in cursor.description])
import sqlalchemy as sqla
db = sqla.create_engine('sqlite:///mydata.sqlite')
pd.read_sql('select * from test', db)
!rm mydata.sqlite
###Output
_____no_output_____
|
zjj2.ipynb
|
###Markdown
作业
###Code
def zjj():
a = ['http://www.17k.com/book/2822756.html','http://www.17k.com/book/2822754.html']
import requests
header = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.79 Safari/537.36'}
for i in range(2):
url = a[i]
response = requests.get(url,headers=header)
file_path='C:\\Users\\Administrator\\Desktop\\hanshu'+str(i)+'.html'
with open(file_path,'a',encoding='utf-8')as f:
f.write(response.text)
print(i)
zjj()
###Output
1
|
sagemaker-deployment/Mini-Projects/IMDB Sentiment Analysis - XGBoost (Batch Transform).ipynb
|
###Markdown
Sentiment Analysis Using XGBoost in SageMaker_Deep Learning Nanodegree Program | Deployment_---As our first example of using Amazon's SageMaker service we will construct a random tree model to predict the sentiment of a movie review. You may have seen a version of this example in a pervious lesson although it would have been done using the sklearn package. Instead, we will be using the XGBoost package as it is provided to us by Amazon. InstructionsSome template code has already been provided for you, and you will need to implement additional functionality to successfully complete this notebook. You will not need to modify the included code beyond what is requested. Sections that begin with '**TODO**' in the header indicate that you need to complete or implement some portion within them. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a ` TODO: ...` comment. Please be sure to read the instructions carefully!In addition to implementing code, there may be questions for you to answer which relate to the task and your implementation. Each section where you will answer a question is preceded by a '**Question:**' header. Carefully read each question and provide your answer below the '**Answer:**' header by editing the Markdown cell.> **Note**: Code and Markdown cells can be executed using the **Shift+Enter** keyboard shortcut. In addition, a cell can be edited by typically clicking it (double-click for Markdown cells) or by pressing **Enter** while it is highlighted. Step 1: Downloading the dataThe dataset we are going to use is very popular among researchers in Natural Language Processing, usually referred to as the [IMDb dataset](http://ai.stanford.edu/~amaas/data/sentiment/). It consists of movie reviews from the website [imdb.com](http://www.imdb.com/), each labeled as either '**pos**itive', if the reviewer enjoyed the film, or '**neg**ative' otherwise.> Maas, Andrew L., et al. [Learning Word Vectors for Sentiment Analysis](http://ai.stanford.edu/~amaas/data/sentiment/). In _Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies_. Association for Computational Linguistics, 2011.We begin by using some Jupyter Notebook magic to download and extract the dataset.
###Code
%mkdir ../data
!wget -O ../data/aclImdb_v1.tar.gz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -zxf ../data/aclImdb_v1.tar.gz -C ../data
###Output
mkdir: cannot create directory ‘../data’: File exists
--2020-08-01 13:13:55-- http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Resolving ai.stanford.edu (ai.stanford.edu)... 171.64.68.10
Connecting to ai.stanford.edu (ai.stanford.edu)|171.64.68.10|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84125825 (80M) [application/x-gzip]
Saving to: ‘../data/aclImdb_v1.tar.gz’
../data/aclImdb_v1. 100%[===================>] 80.23M 9.87MB/s in 12s
2020-08-01 13:14:08 (6.67 MB/s) - ‘../data/aclImdb_v1.tar.gz’ saved [84125825/84125825]
###Markdown
Step 2: Preparing the dataThe data we have downloaded is split into various files, each of which contains a single review. It will be much easier going forward if we combine these individual files into two large files, one for training and one for testing.
###Code
import os
import glob
def read_imdb_data(data_dir='../data/aclImdb'):
data = {}
labels = {}
for data_type in ['train', 'test']:
data[data_type] = {}
labels[data_type] = {}
for sentiment in ['pos', 'neg']:
data[data_type][sentiment] = []
labels[data_type][sentiment] = []
path = os.path.join(data_dir, data_type, sentiment, '*.txt')
files = glob.glob(path)
for f in files:
with open(f) as review:
data[data_type][sentiment].append(review.read())
# Here we represent a positive review by '1' and a negative review by '0'
labels[data_type][sentiment].append(1 if sentiment == 'pos' else 0)
assert len(data[data_type][sentiment]) == len(labels[data_type][sentiment]), \
"{}/{} data size does not match labels size".format(data_type, sentiment)
return data, labels
data, labels = read_imdb_data()
print("IMDB reviews: train = {} pos / {} neg, test = {} pos / {} neg".format(
len(data['train']['pos']), len(data['train']['neg']),
len(data['test']['pos']), len(data['test']['neg'])))
from sklearn.utils import shuffle
def prepare_imdb_data(data, labels):
"""Prepare training and test sets from IMDb movie reviews."""
#Combine positive and negative reviews and labels
data_train = data['train']['pos'] + data['train']['neg']
data_test = data['test']['pos'] + data['test']['neg']
labels_train = labels['train']['pos'] + labels['train']['neg']
labels_test = labels['test']['pos'] + labels['test']['neg']
#Shuffle reviews and corresponding labels within training and test sets
data_train, labels_train = shuffle(data_train, labels_train)
data_test, labels_test = shuffle(data_test, labels_test)
# Return a unified training data, test data, training labels, test labets
return data_train, data_test, labels_train, labels_test
train_X, test_X, train_y, test_y = prepare_imdb_data(data, labels)
print("IMDb reviews (combined): train = {}, test = {}".format(len(train_X), len(test_X)))
train_X[100]
###Output
_____no_output_____
###Markdown
Step 3: Processing the dataNow that we have our training and testing datasets merged and ready to use, we need to start processing the raw data into something that will be useable by our machine learning algorithm. To begin with, we remove any html formatting that may appear in the reviews and perform some standard natural language processing in order to homogenize the data.
###Code
import nltk
nltk.download("stopwords")
from nltk.corpus import stopwords
from nltk.stem.porter import *
stemmer = PorterStemmer()
import re
from bs4 import BeautifulSoup
def review_to_words(review):
text = BeautifulSoup(review, "html.parser").get_text() # Remove HTML tags
text = re.sub(r"[^a-zA-Z0-9]", " ", text.lower()) # Convert to lower case
words = text.split() # Split string into words
words = [w for w in words if w not in stopwords.words("english")] # Remove stopwords
words = [PorterStemmer().stem(w) for w in words] # stem
return words
import pickle
cache_dir = os.path.join("../cache", "sentiment_analysis") # where to store cache files
os.makedirs(cache_dir, exist_ok=True) # ensure cache directory exists
def preprocess_data(data_train, data_test, labels_train, labels_test,
cache_dir=cache_dir, cache_file="preprocessed_data.pkl"):
"""Convert each review to words; read from cache if available."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = pickle.load(f)
print("Read preprocessed data from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Preprocess training and test data to obtain words for each review
#words_train = list(map(review_to_words, data_train))
#words_test = list(map(review_to_words, data_test))
words_train = [review_to_words(review) for review in data_train]
words_test = [review_to_words(review) for review in data_test]
# Write to cache file for future runs
if cache_file is not None:
cache_data = dict(words_train=words_train, words_test=words_test,
labels_train=labels_train, labels_test=labels_test)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
pickle.dump(cache_data, f)
print("Wrote preprocessed data to cache file:", cache_file)
else:
# Unpack data loaded from cache file
words_train, words_test, labels_train, labels_test = (cache_data['words_train'],
cache_data['words_test'], cache_data['labels_train'], cache_data['labels_test'])
return words_train, words_test, labels_train, labels_test
# Preprocess data
train_X, test_X, train_y, test_y = preprocess_data(train_X, test_X, train_y, test_y)
###Output
Wrote preprocessed data to cache file: preprocessed_data.pkl
###Markdown
Extract Bag-of-Words featuresFor the model we will be implementing, rather than using the reviews directly, we are going to transform each review into a Bag-of-Words feature representation. Keep in mind that 'in the wild' we will only have access to the training set so our transformer can only use the training set to construct a representation.
###Code
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.externals import joblib
# joblib is an enhanced version of pickle that is more efficient for storing NumPy arrays
def extract_BoW_features(words_train, words_test, vocabulary_size=5000,
cache_dir=cache_dir, cache_file="bow_features.pkl"):
"""Extract Bag-of-Words for a given set of documents, already preprocessed into words."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = joblib.load(f)
print("Read features from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Fit a vectorizer to training documents and use it to transform them
# NOTE: Training documents have already been preprocessed and tokenized into words;
# pass in dummy functions to skip those steps, e.g. preprocessor=lambda x: x
vectorizer = CountVectorizer(max_features=vocabulary_size,
preprocessor=lambda x: x, tokenizer=lambda x: x) # already preprocessed
features_train = vectorizer.fit_transform(words_train).toarray()
# Apply the same vectorizer to transform the test documents (ignore unknown words)
features_test = vectorizer.transform(words_test).toarray()
# NOTE: Remember to convert the features using .toarray() for a compact representation
# Write to cache file for future runs (store vocabulary as well)
if cache_file is not None:
vocabulary = vectorizer.vocabulary_
cache_data = dict(features_train=features_train, features_test=features_test,
vocabulary=vocabulary)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
joblib.dump(cache_data, f)
print("Wrote features to cache file:", cache_file)
else:
# Unpack data loaded from cache file
features_train, features_test, vocabulary = (cache_data['features_train'],
cache_data['features_test'], cache_data['vocabulary'])
# Return both the extracted features as well as the vocabulary
return features_train, features_test, vocabulary
# Extract Bag of Words features for both training and test datasets
train_X, test_X, vocabulary = extract_BoW_features(train_X, test_X)
###Output
Wrote features to cache file: bow_features.pkl
###Markdown
Step 4: Classification using XGBoostNow that we have created the feature representation of our training (and testing) data, it is time to start setting up and using the XGBoost classifier provided by SageMaker. (TODO) Writing the datasetThe XGBoost classifier that we will be using requires the dataset to be written to a file and stored using Amazon S3. To do this, we will start by splitting the training dataset into two parts, the data we will train the model with and a validation set. Then, we will write those datasets to a file and upload the files to S3. In addition, we will write the test set input to a file and upload the file to S3. This is so that we can use SageMakers Batch Transform functionality to test our model once we've fit it.
###Code
import pandas as pd
# TODO: Split the train_X and train_y arrays into the DataFrames val_X, train_X and val_y, train_y. Make sure that
# val_X and val_y contain 10 000 entries while train_X and train_y contain the remaining 15 000 entries.
val_X = pd.DataFrame(train_X[:10000])
train_X = pd.DataFrame(train_X[10000:])
val_y = pd.DataFrame(train_y[:10000])
train_y = pd.DataFrame(train_y[10000:])
###Output
_____no_output_____
###Markdown
The documentation for the XGBoost algorithm in SageMaker requires that the saved datasets should contain no headers or index and that for the training and validation data, the label should occur first for each sample.For more information about this and other algorithms, the SageMaker developer documentation can be found on __[Amazon's website.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
# First we make sure that the local directory in which we'd like to store the training and validation csv files exists.
data_dir = '../data/xgboost'
if not os.path.exists(data_dir):
os.makedirs(data_dir)
# First, save the test data to test.csv in the data_dir directory. Note that we do not save the associated ground truth
# labels, instead we will use them later to compare with our model output.
pd.DataFrame(test_X).to_csv(os.path.join(data_dir, 'test.csv'), header=False, index=False)
# TODO: Save the training and validation data to train.csv and validation.csv in the data_dir directory.
# Make sure that the files you create are in the correct format.
pd.concat([train_y, train_X], axis=1).to_csv(os.path.join(data_dir, 'train.csv'), header=False, index=False)
pd.concat([val_y, val_X], axis=1).to_csv(os.path.join(data_dir, 'validation.csv'), header=False, index=False)
# To save a bit of memory we can set text_X, train_X, val_X, train_y and val_y to None.
test_X = train_X = val_X = train_y = val_y = None
###Output
_____no_output_____
###Markdown
(TODO) Uploading Training / Validation files to S3Amazon's S3 service allows us to store files that can be access by both the built-in training models such as the XGBoost model we will be using as well as custom models such as the one we will see a little later.For this, and most other tasks we will be doing using SageMaker, there are two methods we could use. The first is to use the low level functionality of SageMaker which requires knowing each of the objects involved in the SageMaker environment. The second is to use the high level functionality in which certain choices have been made on the user's behalf. The low level approach benefits from allowing the user a great deal of flexibility while the high level approach makes development much quicker. For our purposes we will opt to use the high level approach although using the low-level approach is certainly an option.Recall the method `upload_data()` which is a member of object representing our current SageMaker session. What this method does is upload the data to the default bucket (which is created if it does not exist) into the path described by the key_prefix variable. To see this for yourself, once you have uploaded the data files, go to the S3 console and look to see where the files have been uploaded.For additional resources, see the __[SageMaker API documentation](http://sagemaker.readthedocs.io/en/latest/)__ and in addition the __[SageMaker Developer Guide.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
import sagemaker
session = sagemaker.Session() # Store the current SageMaker session
# S3 prefix (which folder will we use)
prefix = 'sentiment-xgboost'
# TODO: Upload the test.csv, train.csv and validation.csv files which are contained in data_dir to S3 using sess.upload_data().
test_location = session.upload_data(os.path.join(data_dir, 'test.csv'), key_prefix=prefix)
val_location = session.upload_data(os.path.join(data_dir, 'validation.csv'), key_prefix=prefix)
train_location = session.upload_data(os.path.join(data_dir, 'train.csv'), key_prefix=prefix)
###Output
_____no_output_____
###Markdown
(TODO) Creating the XGBoost modelNow that the data has been uploaded it is time to create the XGBoost model. To begin with, we need to do some setup. At this point it is worth discussing what a model is in SageMaker. It is easiest to think of a model of comprising three different objects in the SageMaker ecosystem, which interact with one another.- Model Artifacts- Training Code (Container)- Inference Code (Container)The Model Artifacts are what you might think of as the actual model itself. For example, if you were building a neural network, the model artifacts would be the weights of the various layers. In our case, for an XGBoost model, the artifacts are the actual trees that are created during training.The other two objects, the training code and the inference code are then used to manipulate the training artifacts. More precisely, the training code uses the training data that is provided and creates the model artifacts, while the inference code uses the model artifacts to make predictions on new data.The way that SageMaker runs the training and inference code is by making use of Docker containers. For now, think of a container as being a way of packaging code up so that dependencies aren't an issue.
###Code
from sagemaker import get_execution_role
# Our current execution role is required when creating the model as the training
# and inference code will need to access the model artifacts.
role = get_execution_role()
# We need to retrieve the location of the container which is provided by Amazon for using XGBoost.
# As a matter of convenience, the training and inference code both use the same container.
from sagemaker.amazon.amazon_estimator import get_image_uri
container = get_image_uri(session.boto_region_name, 'xgboost')
# TODO: Create a SageMaker estimator using the container location determined in the previous cell.
# It is recommended that you use a single training instance of type ml.m4.xlarge. It is also
# recommended that you use 's3://{}/{}/output'.format(session.default_bucket(), prefix) as the
# output path.
xgb = sagemaker.estimator.Estimator(container, # The image name of the training container
role, # The IAM role to use (our current role in this case)
train_instance_count=1, # The number of instances to use for training
train_instance_type='ml.m4.xlarge', # The type of instance to use for training
output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),
# Where to save the output (the model artifacts)
sagemaker_session=session) # The current SageMaker session
# TODO: Set the XGBoost hyperparameters in the xgb object. Don't forget that in this case we have a binary
# label so we should be using the 'binary:logistic' objective.
xgb.set_hyperparameters(max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
objective='binary:logistic',
early_stopping_rounds=10,
num_round=200)
###Output
Parameter image_name will be renamed to image_uri in SageMaker Python SDK v2.
###Markdown
Fit the XGBoost modelNow that our model has been set up we simply need to attach the training and validation datasets and then ask SageMaker to set up the computation.
###Code
s3_input_train = sagemaker.s3_input(s3_data=train_location, content_type='csv')
s3_input_validation = sagemaker.s3_input(s3_data=val_location, content_type='csv')
xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})
###Output
2020-08-01 15:03:35 Starting - Starting the training job...
2020-08-01 15:03:37 Starting - Launching requested ML instances......
2020-08-01 15:04:39 Starting - Preparing the instances for training...
2020-08-01 15:05:34 Downloading - Downloading input data...
2020-08-01 15:05:50 Training - Downloading the training image.[34mArguments: train[0m
[34m[2020-08-01:15:06:10:INFO] Running standalone xgboost training.[0m
[34m[2020-08-01:15:06:10:INFO] File size need to be processed in the node: 238.47mb. Available memory size in the node: 8482.08mb[0m
[34m[2020-08-01:15:06:10:INFO] Determined delimiter of CSV input is ','[0m
[34m[15:06:10] S3DistributionType set as FullyReplicated[0m
[34m[15:06:12] 15000x5000 matrix with 75000000 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,[0m
[34m[2020-08-01:15:06:12:INFO] Determined delimiter of CSV input is ','[0m
[34m[15:06:12] S3DistributionType set as FullyReplicated[0m
[34m[15:06:13] 10000x5000 matrix with 50000000 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,[0m
[34m[15:06:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 44 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[0]#011train-error:0.298667#011validation-error:0.2987[0m
[34mMultiple eval metrics have been passed: 'validation-error' will be used for early stopping.
[0m
[34mWill train until validation-error hasn't improved in 10 rounds.[0m
[34m[15:06:18] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[1]#011train-error:0.2844#011validation-error:0.2802[0m
[34m[15:06:20] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 42 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[2]#011train-error:0.283133#011validation-error:0.2808[0m
2020-08-01 15:06:30 Training - Training image download completed. Training in progress.[34m[15:06:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[3]#011train-error:0.272867#011validation-error:0.2717[0m
[34m[15:06:23] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[4]#011train-error:0.27#011validation-error:0.2675[0m
[34m[15:06:24] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[5]#011train-error:0.2574#011validation-error:0.255[0m
[34m[15:06:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[6]#011train-error:0.245733#011validation-error:0.245[0m
[34m[15:06:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[7]#011train-error:0.240533#011validation-error:0.2426[0m
[34m[15:06:28] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[8]#011train-error:0.2368#011validation-error:0.2373[0m
[34m[15:06:29] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[9]#011train-error:0.225933#011validation-error:0.2276[0m
[34m[15:06:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[10]#011train-error:0.2192#011validation-error:0.2212[0m
[34m[15:06:32] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[11]#011train-error:0.212067#011validation-error:0.2169[0m
[34m[15:06:33] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 38 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[12]#011train-error:0.208267#011validation-error:0.2159[0m
[34m[15:06:34] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[13]#011train-error:0.2048#011validation-error:0.2111[0m
[34m[15:06:36] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 32 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[14]#011train-error:0.199867#011validation-error:0.2079[0m
[34m[15:06:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[15]#011train-error:0.197933#011validation-error:0.2056[0m
[34m[15:06:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[16]#011train-error:0.193533#011validation-error:0.2012[0m
[34m[15:06:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[17]#011train-error:0.189733#011validation-error:0.2004[0m
[34m[15:06:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[18]#011train-error:0.187667#011validation-error:0.1985[0m
[34m[15:06:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[19]#011train-error:0.1848#011validation-error:0.1955[0m
[34m[15:06:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[20]#011train-error:0.1828#011validation-error:0.1947[0m
[34m[15:06:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[21]#011train-error:0.181533#011validation-error:0.1935[0m
[34m[15:06:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[22]#011train-error:0.1796#011validation-error:0.1934[0m
[34m[15:06:47] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[23]#011train-error:0.177733#011validation-error:0.1925[0m
[34m[15:06:49] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[24]#011train-error:0.176867#011validation-error:0.1917[0m
[34m[15:06:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[25]#011train-error:0.1744#011validation-error:0.1897[0m
[34m[15:06:51] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[26]#011train-error:0.1724#011validation-error:0.1867[0m
[34m[15:06:52] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[27]#011train-error:0.169733#011validation-error:0.185[0m
[34m[15:06:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[28]#011train-error:0.167267#011validation-error:0.1849[0m
[34m[15:06:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[29]#011train-error:0.165#011validation-error:0.1832[0m
[34m[15:06:56] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[30]#011train-error:0.162733#011validation-error:0.18[0m
[34m[15:06:58] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[31]#011train-error:0.160067#011validation-error:0.1798[0m
[34m[15:06:59] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[32]#011train-error:0.160067#011validation-error:0.1786[0m
[34m[15:07:00] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[33]#011train-error:0.1586#011validation-error:0.1781[0m
[34m[15:07:02] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[34]#011train-error:0.1578#011validation-error:0.1769[0m
[34m[15:07:03] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[35]#011train-error:0.156267#011validation-error:0.1752[0m
[34m[15:07:04] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 32 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[36]#011train-error:0.155533#011validation-error:0.1733[0m
[34m[15:07:05] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 36 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[37]#011train-error:0.1542#011validation-error:0.1742[0m
[34m[15:07:07] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[38]#011train-error:0.151#011validation-error:0.1717[0m
[34m[15:07:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[39]#011train-error:0.149467#011validation-error:0.1706[0m
[34m[15:07:09] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[40]#011train-error:0.148#011validation-error:0.1704[0m
[34m[15:07:11] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[41]#011train-error:0.1466#011validation-error:0.1706[0m
[34m[15:07:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[42]#011train-error:0.145667#011validation-error:0.1691[0m
[34m[15:07:13] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[43]#011train-error:0.144067#011validation-error:0.1682[0m
[34m[15:07:15] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[44]#011train-error:0.143667#011validation-error:0.1669[0m
[34m[15:07:16] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[45]#011train-error:0.142133#011validation-error:0.1666[0m
[34m[15:07:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[46]#011train-error:0.1412#011validation-error:0.1667[0m
[34m[15:07:18] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[47]#011train-error:0.14#011validation-error:0.1659[0m
[34m[15:07:20] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 16 pruned nodes, max_depth=5[0m
[34m[48]#011train-error:0.139467#011validation-error:0.1648[0m
[34m[15:07:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[49]#011train-error:0.138733#011validation-error:0.1654[0m
[34m[15:07:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[50]#011train-error:0.138733#011validation-error:0.1651[0m
[34m[15:07:24] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[51]#011train-error:0.137733#011validation-error:0.1652[0m
[34m[15:07:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[52]#011train-error:0.136467#011validation-error:0.1647[0m
[34m[15:07:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[53]#011train-error:0.136667#011validation-error:0.1624[0m
[34m[15:07:28] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[54]#011train-error:0.1338#011validation-error:0.1629[0m
[34m[15:07:29] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[55]#011train-error:0.1342#011validation-error:0.1629[0m
[34m[15:07:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[56]#011train-error:0.132867#011validation-error:0.1629[0m
[34m[15:07:31] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[57]#011train-error:0.132#011validation-error:0.1625[0m
[34m[15:07:33] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[58]#011train-error:0.131533#011validation-error:0.1603[0m
[34m[15:07:34] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 18 pruned nodes, max_depth=5[0m
[34m[59]#011train-error:0.1304#011validation-error:0.1601[0m
[34m[15:07:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 16 pruned nodes, max_depth=5[0m
[34m[60]#011train-error:0.130067#011validation-error:0.1595[0m
[34m[15:07:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[61]#011train-error:0.128333#011validation-error:0.1607[0m
[34m[15:07:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[62]#011train-error:0.127067#011validation-error:0.1608[0m
[34m[15:07:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[63]#011train-error:0.1258#011validation-error:0.1603[0m
[34m[15:07:40] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[64]#011train-error:0.1266#011validation-error:0.1612[0m
[34m[15:07:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 18 pruned nodes, max_depth=5[0m
[34m[65]#011train-error:0.125#011validation-error:0.1614[0m
[34m[15:07:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[66]#011train-error:0.123667#011validation-error:0.1609[0m
[34m[15:07:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[67]#011train-error:0.123467#011validation-error:0.1605[0m
[34m[15:07:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[68]#011train-error:0.1236#011validation-error:0.1597[0m
[34m[15:07:47] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[69]#011train-error:0.122667#011validation-error:0.159[0m
[34m[15:07:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[70]#011train-error:0.121867#011validation-error:0.1583[0m
[34m[15:07:49] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[71]#011train-error:0.121533#011validation-error:0.1581[0m
[34m[15:07:51] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 20 pruned nodes, max_depth=5[0m
[34m[72]#011train-error:0.120867#011validation-error:0.1571[0m
[34m[15:07:52] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[73]#011train-error:0.1216#011validation-error:0.1555[0m
[34m[15:07:53] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[74]#011train-error:0.121267#011validation-error:0.1561[0m
[34m[15:07:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[75]#011train-error:0.1204#011validation-error:0.1559[0m
[34m[15:07:56] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[76]#011train-error:0.118#011validation-error:0.1543[0m
[34m[15:07:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 22 pruned nodes, max_depth=5[0m
[34m[77]#011train-error:0.1176#011validation-error:0.1541[0m
[34m[15:07:59] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[78]#011train-error:0.1174#011validation-error:0.1547[0m
[34m[15:08:00] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[79]#011train-error:0.117067#011validation-error:0.1533[0m
[34m[15:08:01] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[80]#011train-error:0.116733#011validation-error:0.1538[0m
[34m[15:08:02] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[81]#011train-error:0.115467#011validation-error:0.1521[0m
[34m[15:08:04] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[82]#011train-error:0.1152#011validation-error:0.1522[0m
[34m[15:08:05] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[83]#011train-error:0.114933#011validation-error:0.153[0m
[34m[15:08:06] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[84]#011train-error:0.115#011validation-error:0.1528[0m
[34m[15:08:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[85]#011train-error:0.114667#011validation-error:0.1517[0m
[34m[15:08:09] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[86]#011train-error:0.1138#011validation-error:0.1517[0m
[34m[15:08:10] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[87]#011train-error:0.113667#011validation-error:0.152[0m
[34m[15:08:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[88]#011train-error:0.1124#011validation-error:0.1508[0m
[34m[15:08:13] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[89]#011train-error:0.111#011validation-error:0.1504[0m
[34m[15:08:14] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[90]#011train-error:0.109667#011validation-error:0.1501[0m
[34m[15:08:16] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[91]#011train-error:0.108667#011validation-error:0.1499[0m
[34m[15:08:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[92]#011train-error:0.1082#011validation-error:0.1494[0m
[34m[15:08:18] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[93]#011train-error:0.107867#011validation-error:0.1492[0m
[34m[15:08:19] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[94]#011train-error:0.107733#011validation-error:0.1488[0m
[34m[15:08:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[95]#011train-error:0.106867#011validation-error:0.1496[0m
[34m[15:08:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[96]#011train-error:0.106933#011validation-error:0.1494[0m
[34m[15:08:23] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[97]#011train-error:0.1068#011validation-error:0.1497[0m
[34m[15:08:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[98]#011train-error:0.1064#011validation-error:0.1502[0m
[34m[15:08:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[99]#011train-error:0.105667#011validation-error:0.1502[0m
[34m[15:08:27] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[100]#011train-error:0.104467#011validation-error:0.1502[0m
[34m[15:08:29] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[101]#011train-error:0.104333#011validation-error:0.1496[0m
[34m[15:08:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[102]#011train-error:0.103867#011validation-error:0.1488[0m
[34m[15:08:31] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[103]#011train-error:0.1036#011validation-error:0.1475[0m
[34m[15:08:33] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[104]#011train-error:0.102867#011validation-error:0.147[0m
[34m[15:08:34] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[105]#011train-error:0.102#011validation-error:0.1476[0m
[34m[15:08:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[106]#011train-error:0.1016#011validation-error:0.1474[0m
[34m[15:08:36] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[107]#011train-error:0.1016#011validation-error:0.1482[0m
[34m[15:08:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[108]#011train-error:0.1014#011validation-error:0.148[0m
[34m[15:08:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[109]#011train-error:0.1006#011validation-error:0.1481[0m
[34m[15:08:40] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[110]#011train-error:0.100933#011validation-error:0.1472[0m
[34m[15:08:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[111]#011train-error:0.100267#011validation-error:0.1467[0m
[34m[15:08:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 16 pruned nodes, max_depth=5[0m
[34m[112]#011train-error:0.099733#011validation-error:0.1474[0m
[34m[15:08:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[113]#011train-error:0.100267#011validation-error:0.147[0m
[34m[15:08:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[114]#011train-error:0.0996#011validation-error:0.147[0m
[34m[15:08:47] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[115]#011train-error:0.099267#011validation-error:0.147[0m
[34m[15:08:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[116]#011train-error:0.099#011validation-error:0.1477[0m
[34m[15:08:49] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[117]#011train-error:0.0982#011validation-error:0.1472[0m
[34m[15:08:51] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[118]#011train-error:0.098#011validation-error:0.1459[0m
[34m[15:08:52] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[119]#011train-error:0.097867#011validation-error:0.1454[0m
[34m[15:08:53] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[120]#011train-error:0.097333#011validation-error:0.1458[0m
[34m[15:08:54] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[121]#011train-error:0.097#011validation-error:0.1454[0m
[34m[15:08:56] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[122]#011train-error:0.096267#011validation-error:0.1454[0m
[34m[15:08:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 16 pruned nodes, max_depth=5[0m
[34m[123]#011train-error:0.095533#011validation-error:0.1452[0m
[34m[15:08:58] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[124]#011train-error:0.0948#011validation-error:0.145[0m
[34m[15:09:00] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[125]#011train-error:0.095333#011validation-error:0.1449[0m
[34m[15:09:01] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[126]#011train-error:0.093867#011validation-error:0.1454[0m
[34m[15:09:02] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[127]#011train-error:0.093467#011validation-error:0.145[0m
[34m[15:09:04] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[128]#011train-error:0.093333#011validation-error:0.1452[0m
[34m[15:09:05] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 22 pruned nodes, max_depth=5[0m
[34m[129]#011train-error:0.092733#011validation-error:0.1454[0m
[34m[15:09:06] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[130]#011train-error:0.0924#011validation-error:0.145[0m
[34m[15:09:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[131]#011train-error:0.092267#011validation-error:0.1448[0m
[34m[15:09:09] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 18 pruned nodes, max_depth=5[0m
[34m[132]#011train-error:0.091867#011validation-error:0.1446[0m
[34m[15:09:10] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[133]#011train-error:0.092067#011validation-error:0.1448[0m
[34m[15:09:11] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[134]#011train-error:0.091267#011validation-error:0.1446[0m
[34m[15:09:13] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 2 pruned nodes, max_depth=5[0m
[34m[135]#011train-error:0.090933#011validation-error:0.1445[0m
[34m[15:09:14] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[136]#011train-error:0.090867#011validation-error:0.1446[0m
[34m[15:09:15] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[137]#011train-error:0.09#011validation-error:0.1444[0m
[34m[15:09:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[138]#011train-error:0.089067#011validation-error:0.1447[0m
[34m[15:09:18] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[139]#011train-error:0.088933#011validation-error:0.144[0m
[34m[15:09:19] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[140]#011train-error:0.0898#011validation-error:0.1435[0m
[34m[15:09:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[141]#011train-error:0.0894#011validation-error:0.1428[0m
[34m[15:09:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[142]#011train-error:0.089467#011validation-error:0.143[0m
[34m[15:09:23] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[143]#011train-error:0.0896#011validation-error:0.1433[0m
[34m[15:09:24] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[144]#011train-error:0.088933#011validation-error:0.143[0m
[34m[15:09:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[145]#011train-error:0.088667#011validation-error:0.1428[0m
[34m[15:09:27] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[146]#011train-error:0.0876#011validation-error:0.1428[0m
[34m[15:09:28] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[147]#011train-error:0.087467#011validation-error:0.1424[0m
[34m[15:09:30] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[148]#011train-error:0.0874#011validation-error:0.1427[0m
[34m[15:09:31] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[149]#011train-error:0.087333#011validation-error:0.1434[0m
[34m[15:09:32] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 22 pruned nodes, max_depth=5[0m
[34m[150]#011train-error:0.0874#011validation-error:0.1429[0m
[34m[15:09:34] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[151]#011train-error:0.0874#011validation-error:0.1427[0m
[34m[15:09:35] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 4 pruned nodes, max_depth=5[0m
[34m[152]#011train-error:0.086867#011validation-error:0.1426[0m
[34m[15:09:36] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[153]#011train-error:0.0862#011validation-error:0.1415[0m
[34m[15:09:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[154]#011train-error:0.086333#011validation-error:0.1412[0m
[34m[15:09:39] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[155]#011train-error:0.086467#011validation-error:0.1416[0m
[34m[15:09:40] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 10 pruned nodes, max_depth=5[0m
[34m[156]#011train-error:0.086267#011validation-error:0.1414[0m
[34m[15:09:41] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[157]#011train-error:0.085533#011validation-error:0.1416[0m
[34m[15:09:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 12 pruned nodes, max_depth=5[0m
[34m[158]#011train-error:0.085133#011validation-error:0.1419[0m
[34m[15:09:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 8 pruned nodes, max_depth=5[0m
[34m[159]#011train-error:0.0856#011validation-error:0.1421[0m
[34m[15:09:45] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[160]#011train-error:0.085467#011validation-error:0.1427[0m
[34m[15:09:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 6 pruned nodes, max_depth=5[0m
[34m[161]#011train-error:0.085333#011validation-error:0.1426[0m
[34m[15:09:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[162]#011train-error:0.085867#011validation-error:0.143[0m
[34m[15:09:49] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 14 pruned nodes, max_depth=5[0m
[34m[163]#011train-error:0.085067#011validation-error:0.1428[0m
[34m[15:09:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 16 pruned nodes, max_depth=5[0m
[34m[164]#011train-error:0.085467#011validation-error:0.1426[0m
[34mStopping. Best iteration:[0m
[34m[154]#011train-error:0.086333#011validation-error:0.1412
[0m
2020-08-01 15:10:00 Uploading - Uploading generated training model
2020-08-01 15:10:00 Completed - Training job completed
Training seconds: 266
Billable seconds: 266
###Markdown
(TODO) Testing the modelNow that we've fit our XGBoost model, it's time to see how well it performs. To do this we will use SageMakers Batch Transform functionality. Batch Transform is a convenient way to perform inference on a large dataset in a way that is not realtime. That is, we don't necessarily need to use our model's results immediately and instead we can peform inference on a large number of samples. An example of this in industry might be peforming an end of month report. This method of inference can also be useful to us as it means we can perform inference on our entire test set. To perform a Batch Transformation we need to first create a transformer objects from our trained estimator object.
###Code
# TODO: Create a transformer object from the trained model. Using an instance count of 1 and an instance type of
# ml.m4.xlarge should be more than enough.
xgb_transformer = xgb.transformer(instance_count = 1, instance_type = 'ml.m4.xlarge')
###Output
Parameter image will be renamed to image_uri in SageMaker Python SDK v2.
###Markdown
Next we actually perform the transform job. When doing so we need to make sure to specify the type of data we are sending so that it is serialized correctly in the background. In our case we are providing our model with csv data so we specify `text/csv`. Also, if the test data that we have provided is too large to process all at once then we need to specify how the data file should be split up. Since each line is a single entry in our data set we tell SageMaker that it can split the input on each line.
###Code
# TODO: Start the transform job. Make sure to specify the content type and the split type of the test data.
xgb_transformer.transform(test_location, content_type='text/csv', split_type='Line')
###Output
_____no_output_____
###Markdown
Currently the transform job is running but it is doing so in the background. Since we wish to wait until the transform job is done and we would like a bit of feedback we can run the `wait()` method.
###Code
xgb_transformer.wait()
###Output
....................[34mArguments: serve[0m
[34m[2020-08-01 15:18:40 +0000] [1] [INFO] Starting gunicorn 19.7.1[0m
[34m[2020-08-01 15:18:40 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)[0m
[34m[2020-08-01 15:18:40 +0000] [1] [INFO] Using worker: gevent[0m
[34m[2020-08-01 15:18:40 +0000] [38] [INFO] Booting worker with pid: 38[0m
[34m[2020-08-01 15:18:40 +0000] [39] [INFO] Booting worker with pid: 39[0m
[34m[2020-08-01 15:18:40 +0000] [40] [INFO] Booting worker with pid: 40[0m
[34m[2020-08-01 15:18:40 +0000] [41] [INFO] Booting worker with pid: 41[0m
[34m[2020-08-01:15:18:40:INFO] Model loaded successfully for worker : 38[0m
[34m[2020-08-01:15:18:40:INFO] Model loaded successfully for worker : 39[0m
[34m[2020-08-01:15:18:41:INFO] Model loaded successfully for worker : 41[0m
[34m[2020-08-01:15:18:41:INFO] Model loaded successfully for worker : 40[0m
[34m[2020-08-01:15:19:05:INFO] Sniff delimiter as ','[0m
[34m[2020-08-01:15:19:05:INFO] Determined delimiter of CSV input is ','[0m
[34m[2020-08-01:15:19:06:INFO] Sniff delimiter as ','[0m
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###Markdown
Now the transform job has executed and the result, the estimated sentiment of each review, has been saved on S3. Since we would rather work on this file locally we can perform a bit of notebook magic to copy the file to the `data_dir`.
###Code
!aws s3 cp --recursive $xgb_transformer.output_path $data_dir
###Output
download: s3://sagemaker-eu-west-2-519526115051/xgboost-2020-08-01-15-15-16-778/test.csv.out to ../data/xgboost/test.csv.out
###Markdown
The last step is now to read in the output from our model, convert the output to something a little more usable, in this case we want the sentiment to be either `1` (positive) or `0` (negative), and then compare to the ground truth labels.
###Code
predictions = pd.read_csv(os.path.join(data_dir, 'test.csv.out'), header=None)
predictions = [round(num) for num in predictions.squeeze().values]
from sklearn.metrics import accuracy_score
accuracy_score(test_y, predictions)
###Output
_____no_output_____
###Markdown
Optional: Clean upThe default notebook instance on SageMaker doesn't have a lot of excess disk space available. As you continue to complete and execute notebooks you will eventually fill up this disk space, leading to errors which can be difficult to diagnose. Once you are completely finished using a notebook it is a good idea to remove the files that you created along the way. Of course, you can do this from the terminal or from the notebook hub if you would like. The cell below contains some commands to clean up the created files from within the notebook.
###Code
# First we will remove all of the files contained in the data_dir directory
!rm $data_dir/*
# And then we delete the directory itself
!rmdir $data_dir
# Similarly we will remove the files in the cache_dir directory and the directory itself
!rm $cache_dir/*
!rmdir $cache_dir
###Output
_____no_output_____
###Markdown
Sentiment Analysis Using XGBoost in SageMaker_Deep Learning Nanodegree Program | Deployment_---As our first example of using Amazon's SageMaker service we will construct a random tree model to predict the sentiment of a movie review. You may have seen a version of this example in a pervious lesson although it would have been done using the sklearn package. Instead, we will be using the XGBoost package as it is provided to us by Amazon. InstructionsSome template code has already been provided for you, and you will need to implement additional functionality to successfully complete this notebook. You will not need to modify the included code beyond what is requested. Sections that begin with '**TODO**' in the header indicate that you need to complete or implement some portion within them. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a ` TODO: ...` comment. Please be sure to read the instructions carefully!In addition to implementing code, there may be questions for you to answer which relate to the task and your implementation. Each section where you will answer a question is preceded by a '**Question:**' header. Carefully read each question and provide your answer below the '**Answer:**' header by editing the Markdown cell.> **Note**: Code and Markdown cells can be executed using the **Shift+Enter** keyboard shortcut. In addition, a cell can be edited by typically clicking it (double-click for Markdown cells) or by pressing **Enter** while it is highlighted. Step 1: Downloading the dataThe dataset we are going to use is very popular among researchers in Natural Language Processing, usually referred to as the [IMDb dataset](http://ai.stanford.edu/~amaas/data/sentiment/). It consists of movie reviews from the website [imdb.com](http://www.imdb.com/), each labeled as either '**pos**itive', if the reviewer enjoyed the film, or '**neg**ative' otherwise.> Maas, Andrew L., et al. [Learning Word Vectors for Sentiment Analysis](http://ai.stanford.edu/~amaas/data/sentiment/). In _Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies_. Association for Computational Linguistics, 2011.We begin by using some Jupyter Notebook magic to download and extract the dataset.
###Code
%mkdir ../data
!wget -O ../data/aclImdb_v1.tar.gz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -zxf ../data/aclImdb_v1.tar.gz -C ../data
###Output
mkdir: cannot create directory ‘../data’: File exists
--2019-04-30 13:04:42-- http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Resolving ai.stanford.edu (ai.stanford.edu)... 171.64.68.10
Connecting to ai.stanford.edu (ai.stanford.edu)|171.64.68.10|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84125825 (80M) [application/x-gzip]
Saving to: ‘../data/aclImdb_v1.tar.gz’
../data/aclImdb_v1. 100%[===================>] 80.23M 12.5MB/s in 7.6s
2019-04-30 13:04:50 (10.6 MB/s) - ‘../data/aclImdb_v1.tar.gz’ saved [84125825/84125825]
###Markdown
Step 2: Preparing the dataThe data we have downloaded is split into various files, each of which contains a single review. It will be much easier going forward if we combine these individual files into two large files, one for training and one for testing.
###Code
import os
import glob
def read_imdb_data(data_dir='../data/aclImdb'):
data = {}
labels = {}
for data_type in ['train', 'test']:
data[data_type] = {}
labels[data_type] = {}
for sentiment in ['pos', 'neg']:
data[data_type][sentiment] = []
labels[data_type][sentiment] = []
path = os.path.join(data_dir, data_type, sentiment, '*.txt')
files = glob.glob(path)
for f in files:
with open(f) as review:
data[data_type][sentiment].append(review.read())
# Here we represent a positive review by '1' and a negative review by '0'
labels[data_type][sentiment].append(1 if sentiment == 'pos' else 0)
assert len(data[data_type][sentiment]) == len(labels[data_type][sentiment]), \
"{}/{} data size does not match labels size".format(data_type, sentiment)
return data, labels
data, labels = read_imdb_data()
print("IMDB reviews: train = {} pos / {} neg, test = {} pos / {} neg".format(
len(data['train']['pos']), len(data['train']['neg']),
len(data['test']['pos']), len(data['test']['neg'])))
from sklearn.utils import shuffle
def prepare_imdb_data(data, labels):
"""Prepare training and test sets from IMDb movie reviews."""
#Combine positive and negative reviews and labels
data_train = data['train']['pos'] + data['train']['neg']
data_test = data['test']['pos'] + data['test']['neg']
labels_train = labels['train']['pos'] + labels['train']['neg']
labels_test = labels['test']['pos'] + labels['test']['neg']
#Shuffle reviews and corresponding labels within training and test sets
data_train, labels_train = shuffle(data_train, labels_train)
data_test, labels_test = shuffle(data_test, labels_test)
# Return a unified training data, test data, training labels, test labets
return data_train, data_test, labels_train, labels_test
train_X, test_X, train_y, test_y = prepare_imdb_data(data, labels)
print("IMDb reviews (combined): train = {}, test = {}".format(len(train_X), len(test_X)))
train_X[100]
###Output
_____no_output_____
###Markdown
Step 3: Processing the dataNow that we have our training and testing datasets merged and ready to use, we need to start processing the raw data into something that will be useable by our machine learning algorithm. To begin with, we remove any html formatting that may appear in the reviews and perform some standard natural language processing in order to homogenize the data.
###Code
import nltk
nltk.download("stopwords")
from nltk.corpus import stopwords
from nltk.stem.porter import *
stemmer = PorterStemmer()
import re
from bs4 import BeautifulSoup
def review_to_words(review):
text = BeautifulSoup(review, "html.parser").get_text() # Remove HTML tags
text = re.sub(r"[^a-zA-Z0-9]", " ", text.lower()) # Convert to lower case
words = text.split() # Split string into words
words = [w for w in words if w not in stopwords.words("english")] # Remove stopwords
words = [PorterStemmer().stem(w) for w in words] # stem
return words
import pickle
cache_dir = os.path.join("../cache", "sentiment_analysis") # where to store cache files
os.makedirs(cache_dir, exist_ok=True) # ensure cache directory exists
def preprocess_data(data_train, data_test, labels_train, labels_test,
cache_dir=cache_dir, cache_file="preprocessed_data.pkl"):
"""Convert each review to words; read from cache if available."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = pickle.load(f)
print("Read preprocessed data from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Preprocess training and test data to obtain words for each review
#words_train = list(map(review_to_words, data_train))
#words_test = list(map(review_to_words, data_test))
words_train = [review_to_words(review) for review in data_train]
words_test = [review_to_words(review) for review in data_test]
# Write to cache file for future runs
if cache_file is not None:
cache_data = dict(words_train=words_train, words_test=words_test,
labels_train=labels_train, labels_test=labels_test)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
pickle.dump(cache_data, f)
print("Wrote preprocessed data to cache file:", cache_file)
else:
# Unpack data loaded from cache file
words_train, words_test, labels_train, labels_test = (cache_data['words_train'],
cache_data['words_test'], cache_data['labels_train'], cache_data['labels_test'])
return words_train, words_test, labels_train, labels_test
# Preprocess data
train_X, test_X, train_y, test_y = preprocess_data(train_X, test_X, train_y, test_y)
###Output
Wrote preprocessed data to cache file: preprocessed_data.pkl
###Markdown
Extract Bag-of-Words featuresFor the model we will be implementing, rather than using the reviews directly, we are going to transform each review into a Bag-of-Words feature representation. Keep in mind that 'in the wild' we will only have access to the training set so our transformer can only use the training set to construct a representation.
###Code
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.externals import joblib
# joblib is an enhanced version of pickle that is more efficient for storing NumPy arrays
def extract_BoW_features(words_train, words_test, vocabulary_size=5000,
cache_dir=cache_dir, cache_file="bow_features.pkl"):
"""Extract Bag-of-Words for a given set of documents, already preprocessed into words."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = joblib.load(f)
print("Read features from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Fit a vectorizer to training documents and use it to transform them
# NOTE: Training documents have already been preprocessed and tokenized into words;
# pass in dummy functions to skip those steps, e.g. preprocessor=lambda x: x
vectorizer = CountVectorizer(max_features=vocabulary_size,
preprocessor=lambda x: x, tokenizer=lambda x: x) # already preprocessed
features_train = vectorizer.fit_transform(words_train).toarray()
# Apply the same vectorizer to transform the test documents (ignore unknown words)
features_test = vectorizer.transform(words_test).toarray()
# NOTE: Remember to convert the features using .toarray() for a compact representation
# Write to cache file for future runs (store vocabulary as well)
if cache_file is not None:
vocabulary = vectorizer.vocabulary_
cache_data = dict(features_train=features_train, features_test=features_test,
vocabulary=vocabulary)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
joblib.dump(cache_data, f)
print("Wrote features to cache file:", cache_file)
else:
# Unpack data loaded from cache file
features_train, features_test, vocabulary = (cache_data['features_train'],
cache_data['features_test'], cache_data['vocabulary'])
# Return both the extracted features as well as the vocabulary
return features_train, features_test, vocabulary
# Extract Bag of Words features for both training and test datasets
train_X, test_X, vocabulary = extract_BoW_features(train_X, test_X)
###Output
Wrote features to cache file: bow_features.pkl
###Markdown
Step 4: Classification using XGBoostNow that we have created the feature representation of our training (and testing) data, it is time to start setting up and using the XGBoost classifier provided by SageMaker. (TODO) Writing the datasetThe XGBoost classifier that we will be using requires the dataset to be written to a file and stored using Amazon S3. To do this, we will start by splitting the training dataset into two parts, the data we will train the model with and a validation set. Then, we will write those datasets to a file and upload the files to S3. In addition, we will write the test set input to a file and upload the file to S3. This is so that we can use SageMakers Batch Transform functionality to test our model once we've fit it.
###Code
import pandas as pd
# Split the train_X and train_y arrays into the DataFrames val_X, train_X and val_y, train_y. Make sure that
# val_X and val_y contain 10 000 entires while train_X and train_y contain the remaining 15 000 entries.
val_X = pd.DataFrame(train_X[:10000])
train_X = pd.DataFrame(train_X[10000:])
val_y = pd.DataFrame(train_y[:10000])
train_y = pd.DataFrame(train_y[10000:])
###Output
_____no_output_____
###Markdown
The documentation for the XGBoost algorithm in SageMaker requires that the saved datasets should contain no headers or index and that for the training and validation data, the label should occur first for each sample.For more information about this and other algorithms, the SageMaker developer documentation can be found on __[Amazon's website.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
# First we make sure that the local directory in which we'd like to store the training and validation csv files exists.
data_dir = '../data/xgboost'
if not os.path.exists(data_dir):
os.makedirs(data_dir)
# First, save the test data to test.csv in the data_dir directory. Note that we do not save the associated ground truth
# labels, instead we will use them later to compare with our model output.
pd.DataFrame(test_X).to_csv(os.path.join(data_dir, 'test.csv'), header=False, index=False)
# Save the training and validation data to train.csv and validation.csv in the data_dir directory.
# Make sure that the files you create are in the correct format.
pd.concat([val_y, val_X], axis=1).to_csv(os.path.join(data_dir, 'validation.csv'), header=False, index=False)
pd.concat([train_y, train_X], axis=1).to_csv(os.path.join(data_dir, 'train.csv'), header=False, index=False)
# To save a bit of memory we can set text_X, train_X, val_X, train_y and val_y to None.
test_X = train_X = val_X = train_y = val_y = None
###Output
_____no_output_____
###Markdown
(TODO) Uploading Training / Validation files to S3Amazon's S3 service allows us to store files that can be access by both the built-in training models such as the XGBoost model we will be using as well as custom models such as the one we will see a little later.For this, and most other tasks we will be doing using SageMaker, there are two methods we could use. The first is to use the low level functionality of SageMaker which requires knowing each of the objects involved in the SageMaker environment. The second is to use the high level functionality in which certain choices have been made on the user's behalf. The low level approach benefits from allowing the user a great deal of flexibility while the high level approach makes development much quicker. For our purposes we will opt to use the high level approach although using the low-level approach is certainly an option.Recall the method `upload_data()` which is a member of object representing our current SageMaker session. What this method does is upload the data to the default bucket (which is created if it does not exist) into the path described by the key_prefix variable. To see this for yourself, once you have uploaded the data files, go to the S3 console and look to see where the files have been uploaded.For additional resources, see the __[SageMaker API documentation](http://sagemaker.readthedocs.io/en/latest/)__ and in addition the __[SageMaker Developer Guide.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
import sagemaker
session = sagemaker.Session() # Store the current SageMaker session
# S3 prefix (which folder will we use)
prefix = 'sentiment-xgboost'
# Upload the test.csv, train.csv and validation.csv files which are contained in data_dir to S3 using sess.upload_data().
test_location = session.upload_data(os.path.join(data_dir, 'test.csv'), key_prefix=prefix)
val_location = session.upload_data(os.path.join(data_dir, 'validation.csv'), key_prefix=prefix)
train_location = session.upload_data(os.path.join(data_dir, 'train.csv'), key_prefix=prefix)
###Output
_____no_output_____
###Markdown
(TODO) Creating the XGBoost modelNow that the data has been uploaded it is time to create the XGBoost model. To begin with, we need to do some setup. At this point it is worth discussing what a model is in SageMaker. It is easiest to think of a model of comprising three different objects in the SageMaker ecosystem, which interact with one another.- Model Artifacts- Training Code (Container)- Inference Code (Container)The Model Artifacts are what you might think of as the actual model itself. For example, if you were building a neural network, the model artifacts would be the weights of the various layers. In our case, for an XGBoost model, the artifacts are the actual trees that are created during training.The other two objects, the training code and the inference code are then used the manipulate the training artifacts. More precisely, the training code uses the training data that is provided and creates the model artifacts, while the inference code uses the model artifacts to make predictions on new data.The way that SageMaker runs the training and inference code is by making use of Docker containers. For now, think of a container as being a way of packaging code up so that dependencies aren't an issue.
###Code
from sagemaker import get_execution_role
# Our current execution role is require when creating the model as the training
# and inference code will need to access the model artifacts.
role = get_execution_role()
# We need to retrieve the location of the container which is provided by Amazon for using XGBoost.
# As a matter of convenience, the training and inference code both use the same container.
from sagemaker.amazon.amazon_estimator import get_image_uri
container = get_image_uri(session.boto_region_name, 'xgboost')
# Create a SageMaker estimator using the container location determined in the previous cell.
# It is recommended that you use a single training instance of type ml.m4.xlarge. It is also
# recommended that you use 's3://{}/{}/output'.format(session.default_bucket(), prefix) as the
# output path.
xgb = sagemaker.estimator.Estimator(container, # The location of the container we wish to use
role, # What is our current IAM Role
train_instance_count=1, # How many compute instances
train_instance_type='ml.m4.xlarge', # What kind of compute instances
output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),
sagemaker_session=session)
# Set the XGBoost hyperparameters in the xgb object. Don't forget that in this case we have a binary
# label so we should be using the 'binary:logistic' objective.
xgb.set_hyperparameters(max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
silent=0,
objective='binary:logistic',
early_stopping_rounds=10,
num_round=500)
###Output
_____no_output_____
###Markdown
Fit the XGBoost modelNow that our model has been set up we simply need to attach the training and validation datasets and then ask SageMaker to set up the computation.
###Code
s3_input_train = sagemaker.s3_input(s3_data=train_location, content_type='csv')
s3_input_validation = sagemaker.s3_input(s3_data=val_location, content_type='csv')
xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})
###Output
INFO:sagemaker:Creating training-job with name: xgboost-2019-04-30-14-17-48-879
###Markdown
(TODO) Testing the modelNow that we've fit our XGBoost model, it's time to see how well it performs. To do this we will use SageMakers Batch Transform functionality. Batch Transform is a convenient way to perform inference on a large dataset in a way that is not realtime. That is, we don't necessarily need to use our model's results immediately and instead we can peform inference on a large number of samples. An example of this in industry might be peforming an end of month report. This method of inference can also be useful to us as it means to can perform inference on our entire test set. To perform a Batch Transformation we need to first create a transformer objects from our trained estimator object.
###Code
# Create a transformer object from the trained model. Using an instance count of 1 and an instance type of ml.m4.xlarge
# should be more than enough.
xgb_transformer = xgb.transformer(instance_count = 1, instance_type = 'ml.m4.xlarge')
###Output
INFO:sagemaker:Creating model with name: xgboost-2019-04-30-14-17-48-879
###Markdown
Next we actually perform the transform job. When doing so we need to make sure to specify the type of data we are sending so that it is serialized correctly in the background. In our case we are providing our model with csv data so we specify `text/csv`. Also, if the test data that we have provided is too large to process all at once then we need to specify how the data file should be split up. Since each line is a single entry in our data set we tell SageMaker that it can split the input on each line.
###Code
# Start the transform job. Make sure to specify the content type and the split type of the test data.
xgb_transformer.transform(test_location, content_type='text/csv', split_type='Line')
###Output
INFO:sagemaker:Creating transform job with name: xgboost-2019-04-30-14-24-40-426
###Markdown
Currently the transform job is running but it is doing so in the background. Since we wish to wait until the transform job is done and we would like a bit of feedback we can run the `wait()` method.
###Code
xgb_transformer.wait()
###Output
........................................................!
###Markdown
Now the transform job has executed and the result, the estimated sentiment of each review, has been saved on S3. Since we would rather work on this file locally we can perform a bit of notebook magic to copy the file to the `data_dir`.
###Code
!aws s3 cp --recursive $xgb_transformer.output_path $data_dir
###Output
Completed 256.0 KiB/368.9 KiB (3.0 MiB/s) with 1 file(s) remaining
Completed 368.9 KiB/368.9 KiB (4.2 MiB/s) with 1 file(s) remaining
download: s3://sagemaker-ap-northeast-1-926798200699/xgboost-2019-04-30-14-24-40-426/test.csv.out to ../data/xgboost/test.csv.out
###Markdown
The last step is now to read in the output from our model, convert the output to something a little more usable, in this case we want the sentiment to be either `1` (positive) or `0` (negative), and then compare to the ground truth labels.
###Code
predictions = pd.read_csv(os.path.join(data_dir, 'test.csv.out'), header=None)
predictions = [round(num) for num in predictions.squeeze().values]
from sklearn.metrics import accuracy_score
accuracy_score(test_y, predictions)
###Output
_____no_output_____
###Markdown
Optional: Clean upThe default notebook instance on SageMaker doesn't have a lot of excess disk space available. As you continue to complete and execute notebooks you will eventually fill up this disk space, leading to errors which can be difficult to diagnose. Once you are completely finished using a notebook it is a good idea to remove the files that you created along the way. Of course, you can do this from the terminal or from the notebook hub if you would like. The cell below contains some commands to clean up the created files from within the notebook.
###Code
# First we will remove all of the files contained in the data_dir directory
!rm $data_dir/*
# And then we delete the directory itself
!rmdir $data_dir
# Similarly we will remove the files in the cache_dir directory and the directory itself
!rm $cache_dir/*
!rmdir $cache_dir
###Output
_____no_output_____
###Markdown
Sentiment Analysis Using XGBoost in SageMaker_Deep Learning Nanodegree Program | Deployment_---As our first example of using Amazon's SageMaker service we will construct a random tree model to predict the sentiment of a movie review. You may have seen a version of this example in a pervious lesson although it would have been done using the sklearn package. Instead, we will be using the XGBoost package as it is provided to us by Amazon. InstructionsSome template code has already been provided for you, and you will need to implement additional functionality to successfully complete this notebook. You will not need to modify the included code beyond what is requested. Sections that begin with '**TODO**' in the header indicate that you need to complete or implement some portion within them. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a ` TODO: ...` comment. Please be sure to read the instructions carefully!In addition to implementing code, there may be questions for you to answer which relate to the task and your implementation. Each section where you will answer a question is preceded by a '**Question:**' header. Carefully read each question and provide your answer below the '**Answer:**' header by editing the Markdown cell.> **Note**: Code and Markdown cells can be executed using the **Shift+Enter** keyboard shortcut. In addition, a cell can be edited by typically clicking it (double-click for Markdown cells) or by pressing **Enter** while it is highlighted. Step 1: Downloading the dataThe dataset we are going to use is very popular among researchers in Natural Language Processing, usually referred to as the [IMDb dataset](http://ai.stanford.edu/~amaas/data/sentiment/). It consists of movie reviews from the website [imdb.com](http://www.imdb.com/), each labeled as either '**pos**itive', if the reviewer enjoyed the film, or '**neg**ative' otherwise.> Maas, Andrew L., et al. [Learning Word Vectors for Sentiment Analysis](http://ai.stanford.edu/~amaas/data/sentiment/). In _Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies_. Association for Computational Linguistics, 2011.We begin by using some Jupyter Notebook magic to download and extract the dataset.
###Code
%mkdir ../data
!wget -O ../data/aclImdb_v1.tar.gz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -zxf ../data/aclImdb_v1.tar.gz -C ../data
###Output
--2020-05-11 14:40:43-- http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Resolving ai.stanford.edu (ai.stanford.edu)... 171.64.68.10
Connecting to ai.stanford.edu (ai.stanford.edu)|171.64.68.10|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84125825 (80M) [application/x-gzip]
Saving to: ‘../data/aclImdb_v1.tar.gz’
../data/aclImdb_v1. 100%[===================>] 80.23M 28.3MB/s in 2.8s
2020-05-11 14:40:46 (28.3 MB/s) - ‘../data/aclImdb_v1.tar.gz’ saved [84125825/84125825]
###Markdown
Step 2: Preparing the dataThe data we have downloaded is split into various files, each of which contains a single review. It will be much easier going forward if we combine these individual files into two large files, one for training and one for testing.
###Code
import os
import glob
def read_imdb_data(data_dir='../data/aclImdb'):
data = {}
labels = {}
for data_type in ['train', 'test']:
data[data_type] = {}
labels[data_type] = {}
for sentiment in ['pos', 'neg']:
data[data_type][sentiment] = []
labels[data_type][sentiment] = []
path = os.path.join(data_dir, data_type, sentiment, '*.txt')
files = glob.glob(path)
for f in files:
with open(f) as review:
data[data_type][sentiment].append(review.read())
# Here we represent a positive review by '1' and a negative review by '0'
labels[data_type][sentiment].append(1 if sentiment == 'pos' else 0)
assert len(data[data_type][sentiment]) == len(labels[data_type][sentiment]), \
"{}/{} data size does not match labels size".format(data_type, sentiment)
return data, labels
data, labels = read_imdb_data()
print("IMDB reviews: train = {} pos / {} neg, test = {} pos / {} neg".format(
len(data['train']['pos']), len(data['train']['neg']),
len(data['test']['pos']), len(data['test']['neg'])))
from sklearn.utils import shuffle
def prepare_imdb_data(data, labels):
"""Prepare training and test sets from IMDb movie reviews."""
#Combine positive and negative reviews and labels
data_train = data['train']['pos'] + data['train']['neg']
data_test = data['test']['pos'] + data['test']['neg']
labels_train = labels['train']['pos'] + labels['train']['neg']
labels_test = labels['test']['pos'] + labels['test']['neg']
#Shuffle reviews and corresponding labels within training and test sets
data_train, labels_train = shuffle(data_train, labels_train)
data_test, labels_test = shuffle(data_test, labels_test)
# Return a unified training data, test data, training labels, test labets
return data_train, data_test, labels_train, labels_test
train_X, test_X, train_y, test_y = prepare_imdb_data(data, labels)
print("IMDb reviews (combined): train = {}, test = {}".format(len(train_X), len(test_X)))
train_X[100]
###Output
_____no_output_____
###Markdown
Step 3: Processing the dataNow that we have our training and testing datasets merged and ready to use, we need to start processing the raw data into something that will be useable by our machine learning algorithm. To begin with, we remove any html formatting that may appear in the reviews and perform some standard natural language processing in order to homogenize the data.
###Code
import nltk
nltk.download("stopwords")
from nltk.corpus import stopwords
from nltk.stem.porter import *
stemmer = PorterStemmer()
import re
from bs4 import BeautifulSoup
def review_to_words(review):
text = BeautifulSoup(review, "html.parser").get_text() # Remove HTML tags
text = re.sub(r"[^a-zA-Z0-9]", " ", text.lower()) # Convert to lower case
words = text.split() # Split string into words
words = [w for w in words if w not in stopwords.words("english")] # Remove stopwords
words = [PorterStemmer().stem(w) for w in words] # stem
return words
import pickle
cache_dir = os.path.join("../cache", "sentiment_analysis") # where to store cache files
os.makedirs(cache_dir, exist_ok=True) # ensure cache directory exists
def preprocess_data(data_train, data_test, labels_train, labels_test,
cache_dir=cache_dir, cache_file="preprocessed_data.pkl"):
"""Convert each review to words; read from cache if available."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = pickle.load(f)
print("Read preprocessed data from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Preprocess training and test data to obtain words for each review
#words_train = list(map(review_to_words, data_train))
#words_test = list(map(review_to_words, data_test))
words_train = [review_to_words(review) for review in data_train]
words_test = [review_to_words(review) for review in data_test]
# Write to cache file for future runs
if cache_file is not None:
cache_data = dict(words_train=words_train, words_test=words_test,
labels_train=labels_train, labels_test=labels_test)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
pickle.dump(cache_data, f)
print("Wrote preprocessed data to cache file:", cache_file)
else:
# Unpack data loaded from cache file
words_train, words_test, labels_train, labels_test = (cache_data['words_train'],
cache_data['words_test'], cache_data['labels_train'], cache_data['labels_test'])
return words_train, words_test, labels_train, labels_test
# Preprocess data
train_X, test_X, train_y, test_y = preprocess_data(train_X, test_X, train_y, test_y)
###Output
Read preprocessed data from cache file: preprocessed_data.pkl
###Markdown
Extract Bag-of-Words featuresFor the model we will be implementing, rather than using the reviews directly, we are going to transform each review into a Bag-of-Words feature representation. Keep in mind that 'in the wild' we will only have access to the training set so our transformer can only use the training set to construct a representation.
###Code
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.externals import joblib
# joblib is an enhanced version of pickle that is more efficient for storing NumPy arrays
def extract_BoW_features(words_train, words_test, vocabulary_size=5000,
cache_dir=cache_dir, cache_file="bow_features.pkl"):
"""Extract Bag-of-Words for a given set of documents, already preprocessed into words."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = joblib.load(f)
print("Read features from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Fit a vectorizer to training documents and use it to transform them
# NOTE: Training documents have already been preprocessed and tokenized into words;
# pass in dummy functions to skip those steps, e.g. preprocessor=lambda x: x
vectorizer = CountVectorizer(max_features=vocabulary_size,
preprocessor=lambda x: x, tokenizer=lambda x: x) # already preprocessed
features_train = vectorizer.fit_transform(words_train).toarray()
# Apply the same vectorizer to transform the test documents (ignore unknown words)
features_test = vectorizer.transform(words_test).toarray()
# NOTE: Remember to convert the features using .toarray() for a compact representation
# Write to cache file for future runs (store vocabulary as well)
if cache_file is not None:
vocabulary = vectorizer.vocabulary_
cache_data = dict(features_train=features_train, features_test=features_test,
vocabulary=vocabulary)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
joblib.dump(cache_data, f)
print("Wrote features to cache file:", cache_file)
else:
# Unpack data loaded from cache file
features_train, features_test, vocabulary = (cache_data['features_train'],
cache_data['features_test'], cache_data['vocabulary'])
# Return both the extracted features as well as the vocabulary
return features_train, features_test, vocabulary
# Extract Bag of Words features for both training and test datasets
train_X, test_X, vocabulary = extract_BoW_features(train_X, test_X)
###Output
Read features from cache file: bow_features.pkl
###Markdown
Step 4: Classification using XGBoostNow that we have created the feature representation of our training (and testing) data, it is time to start setting up and using the XGBoost classifier provided by SageMaker. (TODO) Writing the datasetThe XGBoost classifier that we will be using requires the dataset to be written to a file and stored using Amazon S3. To do this, we will start by splitting the training dataset into two parts, the data we will train the model with and a validation set. Then, we will write those datasets to a file and upload the files to S3. In addition, we will write the test set input to a file and upload the file to S3. This is so that we can use SageMakers Batch Transform functionality to test our model once we've fit it.
###Code
import pandas as pd
from sklearn.model_selection import train_test_split
# TODO: Split the train_X and train_y arrays into the DataFrames val_X, train_X and val_y, train_y. Make sure that
# val_X and val_y contain 10 000 entires while train_X and train_y contain the remaining 15 000 entries.
train_X = pd.DataFrame(train_X, columns=vocabulary)
train_y = pd.DataFrame(train_y)
train_X, val_X, train_y, val_y = train_test_split(train_X, train_y, test_size=10000, random_state=42)
###Output
_____no_output_____
###Markdown
The documentation for the XGBoost algorithm in SageMaker requires that the saved datasets should contain no headers or index and that for the training and validation data, the label should occur first for each sample.For more information about this and other algorithms, the SageMaker developer documentation can be found on __[Amazon's website.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
# First we make sure that the local directory in which we'd like to store the training and validation csv files exists.
data_dir = '../data/xgboost'
if not os.path.exists(data_dir):
os.makedirs(data_dir)
# First, save the test data to test.csv in the data_dir directory. Note that we do not save the associated ground truth
# labels, instead we will use them later to compare with our model output.
pd.DataFrame(test_X).to_csv(os.path.join(data_dir, 'test.csv'), header=False, index=False)
# TODO: Save the training and validation data to train.csv and validation.csv in the data_dir directory.
# Make sure that the files you create are in the correct format.
pd.concat([val_y, val_X], axis=1).to_csv(os.path.join(data_dir, 'validation.csv'), header=False, index=False)
pd.concat([train_y, train_X], axis=1).to_csv(os.path.join(data_dir, 'train.csv'), header=False, index=False)
# To save a bit of memory we can set text_X, train_X, val_X, train_y and val_y to None.
test_X = train_X = val_X = train_y = val_y = None
###Output
_____no_output_____
###Markdown
(TODO) Uploading Training / Validation files to S3Amazon's S3 service allows us to store files that can be access by both the built-in training models such as the XGBoost model we will be using as well as custom models such as the one we will see a little later.For this, and most other tasks we will be doing using SageMaker, there are two methods we could use. The first is to use the low level functionality of SageMaker which requires knowing each of the objects involved in the SageMaker environment. The second is to use the high level functionality in which certain choices have been made on the user's behalf. The low level approach benefits from allowing the user a great deal of flexibility while the high level approach makes development much quicker. For our purposes we will opt to use the high level approach although using the low-level approach is certainly an option.Recall the method `upload_data()` which is a member of object representing our current SageMaker session. What this method does is upload the data to the default bucket (which is created if it does not exist) into the path described by the key_prefix variable. To see this for yourself, once you have uploaded the data files, go to the S3 console and look to see where the files have been uploaded.For additional resources, see the __[SageMaker API documentation](http://sagemaker.readthedocs.io/en/latest/)__ and in addition the __[SageMaker Developer Guide.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
import sagemaker
session = sagemaker.Session() # Store the current SageMaker session
# S3 prefix (which folder will we use)
prefix = 'sentiment-xgboost'
# TODO: Upload the test.csv, train.csv and validation.csv files which are contained in data_dir to S3 using sess.upload_data().
test_location = session.upload_data(os.path.join(data_dir, 'test.csv'), key_prefix=prefix)
val_location = session.upload_data(os.path.join(data_dir, 'validation.csv'), key_prefix=prefix)
train_location = session.upload_data(os.path.join(data_dir, 'train.csv'), key_prefix=prefix)
###Output
_____no_output_____
###Markdown
(TODO) Creating the XGBoost modelNow that the data has been uploaded it is time to create the XGBoost model. To begin with, we need to do some setup. At this point it is worth discussing what a model is in SageMaker. It is easiest to think of a model of comprising three different objects in the SageMaker ecosystem, which interact with one another.- Model Artifacts- Training Code (Container)- Inference Code (Container)The Model Artifacts are what you might think of as the actual model itself. For example, if you were building a neural network, the model artifacts would be the weights of the various layers. In our case, for an XGBoost model, the artifacts are the actual trees that are created during training.The other two objects, the training code and the inference code are then used the manipulate the training artifacts. More precisely, the training code uses the training data that is provided and creates the model artifacts, while the inference code uses the model artifacts to make predictions on new data.The way that SageMaker runs the training and inference code is by making use of Docker containers. For now, think of a container as being a way of packaging code up so that dependencies aren't an issue.
###Code
from sagemaker import get_execution_role
# Our current execution role is require when creating the model as the training
# and inference code will need to access the model artifacts.
role = get_execution_role()
# We need to retrieve the location of the container which is provided by Amazon for using XGBoost.
# As a matter of convenience, the training and inference code both use the same container.
from sagemaker.amazon.amazon_estimator import get_image_uri
container = get_image_uri(session.boto_region_name, 'xgboost', '0.90-2')
# TODO: Create a SageMaker estimator using the container location determined in the previous cell.
# It is recommended that you use a single training instance of type ml.m4.xlarge. It is also
# recommended that you use 's3://{}/{}/output'.format(session.default_bucket(), prefix) as the
# output path.
xgb = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.m4.xlarge',
output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),
sagemaker_session=session)
# TODO: Set the XGBoost hyperparameters in the xgb object. Don't forget that in this case we have a binary
# label so we should be using the 'binary:logistic' objective.
xgb.set_hyperparameters(max_depth=8,
eta=0.1,
objective='binary:logistic',
early_stopping_rounds=10,
num_round=500)
###Output
_____no_output_____
###Markdown
Fit the XGBoost modelNow that our model has been set up we simply need to attach the training and validation datasets and then ask SageMaker to set up the computation.
###Code
s3_input_train = sagemaker.s3_input(s3_data=train_location, content_type='csv')
s3_input_validation = sagemaker.s3_input(s3_data=val_location, content_type='csv')
xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})
###Output
2020-05-12 03:22:16 Starting - Starting the training job...
2020-05-12 03:22:17 Starting - Launching requested ML instances.........
2020-05-12 03:23:48 Starting - Preparing the instances for training...
2020-05-12 03:24:39 Downloading - Downloading input data...
2020-05-12 03:24:57 Training - Downloading the training image...
2020-05-12 03:25:28 Training - Training image download completed. Training in progress..[34mINFO:sagemaker-containers:Imported framework sagemaker_xgboost_container.training[0m
[34mINFO:sagemaker-containers:Failed to parse hyperparameter objective value binary:logistic to Json.[0m
[34mReturning the value itself[0m
[34mINFO:sagemaker-containers:No GPUs detected (normal if no gpus installed)[0m
[34mINFO:sagemaker_xgboost_container.training:Running XGBoost Sagemaker in algorithm mode[0m
[34mINFO:root:Determined delimiter of CSV input is ','[0m
[34mINFO:root:Determined delimiter of CSV input is ','[0m
[34mINFO:root:Determined delimiter of CSV input is ','[0m
[34m[03:25:32] 15000x5000 matrix with 75000000 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,[0m
[34mINFO:root:Determined delimiter of CSV input is ','[0m
[34m[03:25:33] 10000x5000 matrix with 50000000 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,[0m
[34mINFO:root:Single node training.[0m
[34mINFO:root:Train matrix has 15000 rows[0m
[34mINFO:root:Validation matrix has 10000 rows[0m
[34m[0]#011train-error:0.248533#011validation-error:0.2838[0m
[34m[1]#011train-error:0.243733#011validation-error:0.2774[0m
[34m[2]#011train-error:0.2346#011validation-error:0.2679[0m
[34m[3]#011train-error:0.224733#011validation-error:0.2626[0m
[34m[4]#011train-error:0.225667#011validation-error:0.2642[0m
[34m[5]#011train-error:0.224133#011validation-error:0.2637[0m
[34m[6]#011train-error:0.215467#011validation-error:0.2564[0m
[34m[7]#011train-error:0.2152#011validation-error:0.252[0m
[34m[8]#011train-error:0.211733#011validation-error:0.2501[0m
[34m[9]#011train-error:0.206#011validation-error:0.2469[0m
[34m[10]#011train-error:0.204267#011validation-error:0.2444[0m
[34m[11]#011train-error:0.200467#011validation-error:0.2393[0m
[34m[12]#011train-error:0.1952#011validation-error:0.2378[0m
[34m[13]#011train-error:0.192533#011validation-error:0.2349[0m
[34m[14]#011train-error:0.187933#011validation-error:0.2329[0m
[34m[15]#011train-error:0.186733#011validation-error:0.2317[0m
[34m[16]#011train-error:0.183867#011validation-error:0.23[0m
[34m[17]#011train-error:0.179867#011validation-error:0.227[0m
[34m[18]#011train-error:0.177733#011validation-error:0.2249[0m
[34m[19]#011train-error:0.172933#011validation-error:0.2234[0m
[34m[20]#011train-error:0.169533#011validation-error:0.2189[0m
[34m[21]#011train-error:0.166733#011validation-error:0.217[0m
[34m[22]#011train-error:0.164467#011validation-error:0.215[0m
[34m[23]#011train-error:0.161467#011validation-error:0.2135[0m
[34m[24]#011train-error:0.1598#011validation-error:0.2143[0m
[34m[25]#011train-error:0.1568#011validation-error:0.2124[0m
[34m[26]#011train-error:0.154467#011validation-error:0.2095[0m
[34m[27]#011train-error:0.150533#011validation-error:0.2064[0m
[34m[28]#011train-error:0.148333#011validation-error:0.2058[0m
[34m[29]#011train-error:0.1458#011validation-error:0.2042[0m
[34m[30]#011train-error:0.144333#011validation-error:0.2034[0m
[34m[31]#011train-error:0.1408#011validation-error:0.2007[0m
[34m[32]#011train-error:0.137667#011validation-error:0.2001[0m
[34m[33]#011train-error:0.133467#011validation-error:0.1987[0m
[34m[34]#011train-error:0.1322#011validation-error:0.1983[0m
[34m[35]#011train-error:0.130867#011validation-error:0.1964[0m
[34m[36]#011train-error:0.127267#011validation-error:0.1948[0m
[34m[37]#011train-error:0.125333#011validation-error:0.1938[0m
[34m[38]#011train-error:0.1238#011validation-error:0.1923[0m
[34m[39]#011train-error:0.1222#011validation-error:0.1918[0m
[34m[40]#011train-error:0.120533#011validation-error:0.1912[0m
[34m[41]#011train-error:0.120067#011validation-error:0.1921[0m
[34m[42]#011train-error:0.118267#011validation-error:0.1901[0m
[34m[43]#011train-error:0.115867#011validation-error:0.1902[0m
[34m[44]#011train-error:0.115133#011validation-error:0.1883[0m
[34m[45]#011train-error:0.113667#011validation-error:0.1879[0m
[34m[46]#011train-error:0.1124#011validation-error:0.1882[0m
[34m[47]#011train-error:0.111333#011validation-error:0.1872[0m
[34m[48]#011train-error:0.1104#011validation-error:0.1857[0m
[34m[49]#011train-error:0.1082#011validation-error:0.1838[0m
[34m[50]#011train-error:0.1074#011validation-error:0.1832[0m
[34m[51]#011train-error:0.105667#011validation-error:0.1823[0m
[34m[52]#011train-error:0.104133#011validation-error:0.1811[0m
[34m[53]#011train-error:0.103667#011validation-error:0.1811[0m
[34m[54]#011train-error:0.1018#011validation-error:0.1808[0m
[34m[55]#011train-error:0.101667#011validation-error:0.1809[0m
[34m[56]#011train-error:0.1008#011validation-error:0.1806[0m
[34m[57]#011train-error:0.098267#011validation-error:0.1796[0m
[34m[58]#011train-error:0.097267#011validation-error:0.1793[0m
[34m[59]#011train-error:0.096467#011validation-error:0.1791[0m
[34m[60]#011train-error:0.095933#011validation-error:0.1784[0m
[34m[61]#011train-error:0.095267#011validation-error:0.1774[0m
[34m[62]#011train-error:0.094933#011validation-error:0.1764[0m
[34m[63]#011train-error:0.094067#011validation-error:0.1753[0m
[34m[64]#011train-error:0.092267#011validation-error:0.1743[0m
[34m[65]#011train-error:0.091667#011validation-error:0.1745[0m
[34m[66]#011train-error:0.091267#011validation-error:0.1739[0m
[34m[67]#011train-error:0.090267#011validation-error:0.1736[0m
[34m[68]#011train-error:0.0896#011validation-error:0.1732[0m
[34m[69]#011train-error:0.0886#011validation-error:0.1739[0m
[34m[70]#011train-error:0.087867#011validation-error:0.1729[0m
[34m[71]#011train-error:0.0876#011validation-error:0.1725[0m
[34m[72]#011train-error:0.086933#011validation-error:0.1718[0m
[34m[73]#011train-error:0.0862#011validation-error:0.1709[0m
[34m[74]#011train-error:0.085533#011validation-error:0.1715[0m
[34m[75]#011train-error:0.0848#011validation-error:0.1711[0m
[34m[76]#011train-error:0.084067#011validation-error:0.1699[0m
[34m[77]#011train-error:0.082867#011validation-error:0.1694[0m
[34m[78]#011train-error:0.081933#011validation-error:0.1688[0m
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[34m[80]#011train-error:0.080267#011validation-error:0.1676[0m
[34m[81]#011train-error:0.079533#011validation-error:0.1672[0m
[34m[82]#011train-error:0.079067#011validation-error:0.1661[0m
[34m[83]#011train-error:0.078267#011validation-error:0.1665[0m
[34m[84]#011train-error:0.078333#011validation-error:0.1661[0m
[34m[85]#011train-error:0.077533#011validation-error:0.167[0m
[34m[86]#011train-error:0.077#011validation-error:0.1669[0m
[34m[87]#011train-error:0.076667#011validation-error:0.1666[0m
[34m[88]#011train-error:0.075867#011validation-error:0.1664[0m
[34m[89]#011train-error:0.075467#011validation-error:0.1657[0m
[34m[90]#011train-error:0.075067#011validation-error:0.1658[0m
[34m[91]#011train-error:0.074133#011validation-error:0.1651[0m
[34m[92]#011train-error:0.0736#011validation-error:0.1649[0m
[34m[93]#011train-error:0.072733#011validation-error:0.1637[0m
[34m[94]#011train-error:0.072467#011validation-error:0.1634[0m
[34m[95]#011train-error:0.071667#011validation-error:0.163[0m
[34m[96]#011train-error:0.0712#011validation-error:0.1632[0m
[34m[97]#011train-error:0.071067#011validation-error:0.1631[0m
[34m[98]#011train-error:0.070733#011validation-error:0.1629[0m
[34m[99]#011train-error:0.070533#011validation-error:0.1624[0m
[34m[100]#011train-error:0.069467#011validation-error:0.1622[0m
[34m[101]#011train-error:0.068267#011validation-error:0.1619[0m
[34m[102]#011train-error:0.067933#011validation-error:0.161[0m
[34m[103]#011train-error:0.067667#011validation-error:0.161[0m
[34m[104]#011train-error:0.066467#011validation-error:0.1618[0m
[34m[105]#011train-error:0.066333#011validation-error:0.161[0m
[34m[106]#011train-error:0.0658#011validation-error:0.1606[0m
[34m[107]#011train-error:0.0652#011validation-error:0.1604[0m
[34m[108]#011train-error:0.065067#011validation-error:0.1605[0m
[34m[109]#011train-error:0.064333#011validation-error:0.1597[0m
[34m[110]#011train-error:0.063533#011validation-error:0.1596[0m
[34m[111]#011train-error:0.063133#011validation-error:0.1603[0m
###Markdown
(TODO) Testing the modelNow that we've fit our XGBoost model, it's time to see how well it performs. To do this we will use SageMakers Batch Transform functionality. Batch Transform is a convenient way to perform inference on a large dataset in a way that is not realtime. That is, we don't necessarily need to use our model's results immediately and instead we can peform inference on a large number of samples. An example of this in industry might be peforming an end of month report. This method of inference can also be useful to us as it means to can perform inference on our entire test set. To perform a Batch Transformation we need to first create a transformer objects from our trained estimator object.
###Code
# TODO: Create a transformer object from the trained model. Using an instance count of 1 and an instance type of ml.m4.xlarge
# should be more than enough.
xgb_transformer = xgb.transformer(instance_count = 1, instance_type = 'ml.m4.xlarge')
###Output
_____no_output_____
###Markdown
Next we actually perform the transform job. When doing so we need to make sure to specify the type of data we are sending so that it is serialized correctly in the background. In our case we are providing our model with csv data so we specify `text/csv`. Also, if the test data that we have provided is too large to process all at once then we need to specify how the data file should be split up. Since each line is a single entry in our data set we tell SageMaker that it can split the input on each line.
###Code
# TODO: Start the transform job. Make sure to specify the content type and the split type of the test data.
xgb_transformer.transform(test_location, content_type='text/csv', split_type='Line')
###Output
_____no_output_____
###Markdown
Currently the transform job is running but it is doing so in the background. Since we wish to wait until the transform job is done and we would like a bit of feedback we can run the `wait()` method.
###Code
xgb_transformer.wait()
###Output
......................[32m2020-05-12T03:34:27.922:[sagemaker logs]: MaxConcurrentTransforms=4, MaxPayloadInMB=6, BatchStrategy=MULTI_RECORD[0m
[34m[2020-05-12 03:34:23 +0000] [15] [INFO] Starting gunicorn 19.10.0[0m
[34m[2020-05-12 03:34:23 +0000] [15] [INFO] Listening at: unix:/tmp/gunicorn.sock (15)[0m
[34m[2020-05-12 03:34:23 +0000] [15] [INFO] Using worker: gevent[0m
[34m[2020-05-12 03:34:23 +0000] [22] [INFO] Booting worker with pid: 22[0m
[34m[2020-05-12 03:34:23 +0000] [23] [INFO] Booting worker with pid: 23[0m
[34m[2020-05-12 03:34:23 +0000] [24] [INFO] Booting worker with pid: 24[0m
[34m[2020-05-12 03:34:23 +0000] [28] [INFO] Booting worker with pid: 28[0m
[34m[2020-05-12:03:34:27:INFO] No GPUs detected (normal if no gpus installed)[0m
[34m169.254.255.130 - - [12/May/2020:03:34:27 +0000] "GET /ping HTTP/1.1" 200 0 "-" "Go-http-client/1.1"[0m
[34m[2020-05-12:03:34:27:INFO] No GPUs detected (normal if no gpus installed)[0m
[34m169.254.255.130 - - [12/May/2020:03:34:27 +0000] "GET /execution-parameters HTTP/1.1" 200 84 "-" "Go-http-client/1.1"[0m
[35m[2020-05-12 03:34:23 +0000] [15] [INFO] Starting gunicorn 19.10.0[0m
[35m[2020-05-12 03:34:23 +0000] [15] [INFO] Listening at: unix:/tmp/gunicorn.sock (15)[0m
[35m[2020-05-12 03:34:23 +0000] [15] [INFO] Using worker: gevent[0m
[35m[2020-05-12 03:34:23 +0000] [22] [INFO] Booting worker with pid: 22[0m
[35m[2020-05-12 03:34:23 +0000] [23] [INFO] Booting worker with pid: 23[0m
[35m[2020-05-12 03:34:23 +0000] [24] [INFO] Booting worker with pid: 24[0m
[35m[2020-05-12 03:34:23 +0000] [28] [INFO] Booting worker with pid: 28[0m
[35m[2020-05-12:03:34:27:INFO] No GPUs detected (normal if no gpus installed)[0m
[35m169.254.255.130 - - [12/May/2020:03:34:27 +0000] "GET /ping HTTP/1.1" 200 0 "-" "Go-http-client/1.1"[0m
[35m[2020-05-12:03:34:27:INFO] No GPUs detected (normal if no gpus installed)[0m
[35m169.254.255.130 - - [12/May/2020:03:34:27 +0000] "GET /execution-parameters HTTP/1.1" 200 84 "-" "Go-http-client/1.1"[0m
[34m[2020-05-12:03:34:31:INFO] No GPUs detected (normal if no gpus installed)[0m
[34m[2020-05-12:03:34:31:INFO] No GPUs detected (normal if no gpus installed)[0m
[34m[2020-05-12:03:34:31:INFO] Determined delimiter of CSV input is ','[0m
[34m[2020-05-12:03:34:31:INFO] Determined delimiter of CSV input is ','[0m
[34m[2020-05-12:03:34:31:INFO] Determined delimiter of CSV input is ','[0m
[34m[2020-05-12:03:34:31:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-05-12:03:34:31:INFO] No GPUs detected (normal if no gpus installed)[0m
[35m[2020-05-12:03:34:31:INFO] No GPUs detected (normal if no gpus installed)[0m
[35m[2020-05-12:03:34:31:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-05-12:03:34:31:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-05-12:03:34:31:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-05-12:03:34:31:INFO] Determined delimiter of CSV input is ','[0m
[34m169.254.255.130 - - [12/May/2020:03:34:34 +0000] "POST /invocations HTTP/1.1" 200 12208 "-" "Go-http-client/1.1"[0m
[34m169.254.255.130 - - [12/May/2020:03:34:34 +0000] "POST /invocations HTTP/1.1" 200 12225 "-" "Go-http-client/1.1"[0m
[34m169.254.255.130 - - [12/May/2020:03:34:34 +0000] "POST /invocations HTTP/1.1" 200 12171 "-" "Go-http-client/1.1"[0m
[34m169.254.255.130 - - [12/May/2020:03:34:34 +0000] "POST /invocations HTTP/1.1" 200 12206 "-" "Go-http-client/1.1"[0m
[35m169.254.255.130 - - [12/May/2020:03:34:34 +0000] "POST /invocations HTTP/1.1" 200 12208 "-" "Go-http-client/1.1"[0m
[35m169.254.255.130 - - [12/May/2020:03:34:34 +0000] "POST /invocations HTTP/1.1" 200 12225 "-" "Go-http-client/1.1"[0m
[35m169.254.255.130 - - [12/May/2020:03:34:34 +0000] "POST /invocations HTTP/1.1" 200 12171 "-" "Go-http-client/1.1"[0m
[35m169.254.255.130 - - [12/May/2020:03:34:34 +0000] "POST /invocations HTTP/1.1" 200 12206 "-" "Go-http-client/1.1"[0m
[34m[2020-05-12:03:34:35:INFO] Determined delimiter of CSV input is ','[0m
[34m[2020-05-12:03:34:35:INFO] Determined delimiter of CSV input is ','[0m
[34m[2020-05-12:03:34:35:INFO] Determined delimiter of CSV input is ','[0m
[34m[2020-05-12:03:34:35:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-05-12:03:34:35:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-05-12:03:34:35:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-05-12:03:34:35:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-05-12:03:34:35:INFO] Determined delimiter of CSV input is ','[0m
[34m169.254.255.130 - - [12/May/2020:03:34:38 +0000] "POST /invocations HTTP/1.1" 200 12227 "-" "Go-http-client/1.1"[0m
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###Markdown
Now the transform job has executed and the result, the estimated sentiment of each review, has been saved on S3. Since we would rather work on this file locally we can perform a bit of notebook magic to copy the file to the `data_dir`.
###Code
!aws s3 cp --recursive $xgb_transformer.output_path $data_dir
###Output
Completed 256.0 KiB/473.5 KiB (4.0 MiB/s) with 1 file(s) remaining
Completed 473.5 KiB/473.5 KiB (7.1 MiB/s) with 1 file(s) remaining
download: s3://sagemaker-us-east-2-577314450857/sagemaker-xgboost-2020-05-12-03-31-01-217/test.csv.out to ../data/xgboost/test.csv.out
###Markdown
The last step is now to read in the output from our model, convert the output to something a little more usable, in this case we want the sentiment to be either `1` (positive) or `0` (negative), and then compare to the ground truth labels.
###Code
predictions = pd.read_csv(os.path.join(data_dir, 'test.csv.out'), header=None)
predictions = [round(num) for num in predictions.squeeze().values]
from sklearn.metrics import accuracy_score
accuracy_score(test_y, predictions)
###Output
_____no_output_____
###Markdown
Optional: Clean upThe default notebook instance on SageMaker doesn't have a lot of excess disk space available. As you continue to complete and execute notebooks you will eventually fill up this disk space, leading to errors which can be difficult to diagnose. Once you are completely finished using a notebook it is a good idea to remove the files that you created along the way. Of course, you can do this from the terminal or from the notebook hub if you would like. The cell below contains some commands to clean up the created files from within the notebook.
###Code
# First we will remove all of the files contained in the data_dir directory
!rm $data_dir/*
# And then we delete the directory itself
!rmdir $data_dir
# Similarly we will remove the files in the cache_dir directory and the directory itself
!rm $cache_dir/*
!rmdir $cache_dir
###Output
_____no_output_____
###Markdown
Sentiment Analysis Using XGBoost in SageMaker_Deep Learning Nanodegree Program | Deployment_---As our first example of using Amazon's SageMaker service we will construct a random tree model to predict the sentiment of a movie review. You may have seen a version of this example in a pervious lesson although it would have been done using the sklearn package. Instead, we will be using the XGBoost package as it is provided to us by Amazon. InstructionsSome template code has already been provided for you, and you will need to implement additional functionality to successfully complete this notebook. You will not need to modify the included code beyond what is requested. Sections that begin with '**TODO**' in the header indicate that you need to complete or implement some portion within them. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a ` TODO: ...` comment. Please be sure to read the instructions carefully!In addition to implementing code, there may be questions for you to answer which relate to the task and your implementation. Each section where you will answer a question is preceded by a '**Question:**' header. Carefully read each question and provide your answer below the '**Answer:**' header by editing the Markdown cell.> **Note**: Code and Markdown cells can be executed using the **Shift+Enter** keyboard shortcut. In addition, a cell can be edited by typically clicking it (double-click for Markdown cells) or by pressing **Enter** while it is highlighted. Step 1: Downloading the dataThe dataset we are going to use is very popular among researchers in Natural Language Processing, usually referred to as the [IMDb dataset](http://ai.stanford.edu/~amaas/data/sentiment/). It consists of movie reviews from the website [imdb.com](http://www.imdb.com/), each labeled as either '**pos**itive', if the reviewer enjoyed the film, or '**neg**ative' otherwise.> Maas, Andrew L., et al. [Learning Word Vectors for Sentiment Analysis](http://ai.stanford.edu/~amaas/data/sentiment/). In _Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies_. Association for Computational Linguistics, 2011.We begin by using some Jupyter Notebook magic to download and extract the dataset.
###Code
%mkdir ../data
!wget -O ../data/aclImdb_v1.tar.gz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -zxf ../data/aclImdb_v1.tar.gz -C ../data
###Output
mkdir: cannot create directory ‘../data’: File exists
--2019-06-09 05:47:57-- http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Resolving ai.stanford.edu (ai.stanford.edu)... 171.64.68.10
Connecting to ai.stanford.edu (ai.stanford.edu)|171.64.68.10|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84125825 (80M) [application/x-gzip]
Saving to: ‘../data/aclImdb_v1.tar.gz’
../data/aclImdb_v1. 100%[===================>] 80.23M 41.8MB/s in 1.9s
2019-06-09 05:47:59 (41.8 MB/s) - ‘../data/aclImdb_v1.tar.gz’ saved [84125825/84125825]
###Markdown
Step 2: Preparing the dataThe data we have downloaded is split into various files, each of which contains a single review. It will be much easier going forward if we combine these individual files into two large files, one for training and one for testing.
###Code
import os
import glob
def read_imdb_data(data_dir='../data/aclImdb'):
data = {}
labels = {}
for data_type in ['train', 'test']:
data[data_type] = {}
labels[data_type] = {}
for sentiment in ['pos', 'neg']:
data[data_type][sentiment] = []
labels[data_type][sentiment] = []
path = os.path.join(data_dir, data_type, sentiment, '*.txt')
files = glob.glob(path)
for f in files:
with open(f) as review:
data[data_type][sentiment].append(review.read())
# Here we represent a positive review by '1' and a negative review by '0'
labels[data_type][sentiment].append(1 if sentiment == 'pos' else 0)
assert len(data[data_type][sentiment]) == len(labels[data_type][sentiment]), \
"{}/{} data size does not match labels size".format(data_type, sentiment)
return data, labels
data, labels = read_imdb_data()
print("IMDB reviews: train = {} pos / {} neg, test = {} pos / {} neg".format(
len(data['train']['pos']), len(data['train']['neg']),
len(data['test']['pos']), len(data['test']['neg'])))
from sklearn.utils import shuffle
def prepare_imdb_data(data, labels):
"""Prepare training and test sets from IMDb movie reviews."""
#Combine positive and negative reviews and labels
data_train = data['train']['pos'] + data['train']['neg']
data_test = data['test']['pos'] + data['test']['neg']
labels_train = labels['train']['pos'] + labels['train']['neg']
labels_test = labels['test']['pos'] + labels['test']['neg']
#Shuffle reviews and corresponding labels within training and test sets
data_train, labels_train = shuffle(data_train, labels_train)
data_test, labels_test = shuffle(data_test, labels_test)
# Return a unified training data, test data, training labels, test labets
return data_train, data_test, labels_train, labels_test
train_X, test_X, train_y, test_y = prepare_imdb_data(data, labels)
print("IMDb reviews (combined): train = {}, test = {}".format(len(train_X), len(test_X)))
train_X[100]
###Output
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###Markdown
Step 3: Processing the dataNow that we have our training and testing datasets merged and ready to use, we need to start processing the raw data into something that will be useable by our machine learning algorithm. To begin with, we remove any html formatting that may appear in the reviews and perform some standard natural language processing in order to homogenize the data.
###Code
import nltk
nltk.download("stopwords")
from nltk.corpus import stopwords
from nltk.stem.porter import *
stemmer = PorterStemmer()
import re
from bs4 import BeautifulSoup
def review_to_words(review):
text = BeautifulSoup(review, "html.parser").get_text() # Remove HTML tags
text = re.sub(r"[^a-zA-Z0-9]", " ", text.lower()) # Convert to lower case
words = text.split() # Split string into words
words = [w for w in words if w not in stopwords.words("english")] # Remove stopwords
words = [PorterStemmer().stem(w) for w in words] # stem
return words
import pickle
cache_dir = os.path.join("../cache", "sentiment_analysis") # where to store cache files
os.makedirs(cache_dir, exist_ok=True) # ensure cache directory exists
def preprocess_data(data_train, data_test, labels_train, labels_test,
cache_dir=cache_dir, cache_file="preprocessed_data.pkl"):
"""Convert each review to words; read from cache if available."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = pickle.load(f)
print("Read preprocessed data from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Preprocess training and test data to obtain words for each review
#words_train = list(map(review_to_words, data_train))
#words_test = list(map(review_to_words, data_test))
words_train = [review_to_words(review) for review in data_train]
words_test = [review_to_words(review) for review in data_test]
# Write to cache file for future runs
if cache_file is not None:
cache_data = dict(words_train=words_train, words_test=words_test,
labels_train=labels_train, labels_test=labels_test)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
pickle.dump(cache_data, f)
print("Wrote preprocessed data to cache file:", cache_file)
else:
# Unpack data loaded from cache file
words_train, words_test, labels_train, labels_test = (cache_data['words_train'],
cache_data['words_test'], cache_data['labels_train'], cache_data['labels_test'])
return words_train, words_test, labels_train, labels_test
# Preprocess data
train_X, test_X, train_y, test_y = preprocess_data(train_X, test_X, train_y, test_y)
###Output
Wrote preprocessed data to cache file: preprocessed_data.pkl
###Markdown
Extract Bag-of-Words featuresFor the model we will be implementing, rather than using the reviews directly, we are going to transform each review into a Bag-of-Words feature representation. Keep in mind that 'in the wild' we will only have access to the training set so our transformer can only use the training set to construct a representation.
###Code
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.externals import joblib
# joblib is an enhanced version of pickle that is more efficient for storing NumPy arrays
def extract_BoW_features(words_train, words_test, vocabulary_size=5000,
cache_dir=cache_dir, cache_file="bow_features.pkl"):
"""Extract Bag-of-Words for a given set of documents, already preprocessed into words."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = joblib.load(f)
print("Read features from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Fit a vectorizer to training documents and use it to transform them
# NOTE: Training documents have already been preprocessed and tokenized into words;
# pass in dummy functions to skip those steps, e.g. preprocessor=lambda x: x
vectorizer = CountVectorizer(max_features=vocabulary_size,
preprocessor=lambda x: x, tokenizer=lambda x: x) # already preprocessed
features_train = vectorizer.fit_transform(words_train).toarray()
# Apply the same vectorizer to transform the test documents (ignore unknown words)
features_test = vectorizer.transform(words_test).toarray()
# NOTE: Remember to convert the features using .toarray() for a compact representation
# Write to cache file for future runs (store vocabulary as well)
if cache_file is not None:
vocabulary = vectorizer.vocabulary_
cache_data = dict(features_train=features_train, features_test=features_test,
vocabulary=vocabulary)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
joblib.dump(cache_data, f)
print("Wrote features to cache file:", cache_file)
else:
# Unpack data loaded from cache file
features_train, features_test, vocabulary = (cache_data['features_train'],
cache_data['features_test'], cache_data['vocabulary'])
# Return both the extracted features as well as the vocabulary
return features_train, features_test, vocabulary
# Extract Bag of Words features for both training and test datasets
train_X, test_X, vocabulary = extract_BoW_features(train_X, test_X)
###Output
Wrote features to cache file: bow_features.pkl
###Markdown
Step 4: Classification using XGBoostNow that we have created the feature representation of our training (and testing) data, it is time to start setting up and using the XGBoost classifier provided by SageMaker. (TODO) Writing the datasetThe XGBoost classifier that we will be using requires the dataset to be written to a file and stored using Amazon S3. To do this, we will start by splitting the training dataset into two parts, the data we will train the model with and a validation set. Then, we will write those datasets to a file and upload the files to S3. In addition, we will write the test set input to a file and upload the file to S3. This is so that we can use SageMakers Batch Transform functionality to test our model once we've fit it.
###Code
import pandas as pd
# TODO: Split the train_X and train_y arrays into the DataFrames val_X, train_X and val_y, train_y. Make sure that
# val_X and val_y contain 10 000 entires while train_X and train_y contain the remaining 15 000 entries.
num_entries = 10000
val_X = pd.DataFrame(train_X[:num_entries])
val_y = pd.DataFrame(train_y[:num_entries])
train_X = pd.DataFrame(train_X[num_entries:])
train_y = pd.DataFrame(train_y[num_entries:])
###Output
_____no_output_____
###Markdown
The documentation for the XGBoost algorithm in SageMaker requires that the saved datasets should contain no headers or index and that for the training and validation data, the label should occur first for each sample.For more information about this and other algorithms, the SageMaker developer documentation can be found on __[Amazon's website.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
# First we make sure that the local directory in which we'd like to store the training and validation csv files exists.
data_dir = '../data/xgboost'
if not os.path.exists(data_dir):
os.makedirs(data_dir)
# First, save the test data to test.csv in the data_dir directory. Note that we do not save the associated ground truth
# labels, instead we will use them later to compare with our model output.
pd.DataFrame(test_X).to_csv(os.path.join(data_dir, 'test.csv'), header=False, index=False)
# TODO: Save the training and validation data to train.csv and validation.csv in the data_dir directory.
# Make sure that the files you create are in the correct format.
pd.concat([val_y, val_X], axis=1).to_csv(os.path.join(data_dir, 'validation.csv'), header=False, index=False)
pd.concat([train_y, train_X], axis=1).to_csv(os.path.join(data_dir, 'train.csv'), header=False, index=False)
# To save a bit of memory we can set text_X, train_X, val_X, train_y and val_y to None.
test_X = train_X = val_X = train_y = val_y = None
###Output
_____no_output_____
###Markdown
(TODO) Uploading Training / Validation files to S3Amazon's S3 service allows us to store files that can be access by both the built-in training models such as the XGBoost model we will be using as well as custom models such as the one we will see a little later.For this, and most other tasks we will be doing using SageMaker, there are two methods we could use. The first is to use the low level functionality of SageMaker which requires knowing each of the objects involved in the SageMaker environment. The second is to use the high level functionality in which certain choices have been made on the user's behalf. The low level approach benefits from allowing the user a great deal of flexibility while the high level approach makes development much quicker. For our purposes we will opt to use the high level approach although using the low-level approach is certainly an option.Recall the method `upload_data()` which is a member of object representing our current SageMaker session. What this method does is upload the data to the default bucket (which is created if it does not exist) into the path described by the key_prefix variable. To see this for yourself, once you have uploaded the data files, go to the S3 console and look to see where the files have been uploaded.For additional resources, see the __[SageMaker API documentation](http://sagemaker.readthedocs.io/en/latest/)__ and in addition the __[SageMaker Developer Guide.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
import sagemaker
session = sagemaker.Session() # Store the current SageMaker session
# S3 prefix (which folder will we use)
prefix = 'sentiment-xgboost'
# TODO: Upload the test.csv, train.csv and validation.csv files which are contained in data_dir to S3 using sess.upload_data().
test_location = session.upload_data(os.path.join(data_dir, 'test.csv'), key_prefix=prefix)
val_location = session.upload_data(os.path.join(data_dir, 'validation.csv'), key_prefix=prefix)
train_location = session.upload_data(os.path.join(data_dir, 'train.csv'), key_prefix=prefix)
###Output
_____no_output_____
###Markdown
(TODO) Creating the XGBoost modelNow that the data has been uploaded it is time to create the XGBoost model. To begin with, we need to do some setup. At this point it is worth discussing what a model is in SageMaker. It is easiest to think of a model of comprising three different objects in the SageMaker ecosystem, which interact with one another.- Model Artifacts- Training Code (Container)- Inference Code (Container)The Model Artifacts are what you might think of as the actual model itself. For example, if you were building a neural network, the model artifacts would be the weights of the various layers. In our case, for an XGBoost model, the artifacts are the actual trees that are created during training.The other two objects, the training code and the inference code are then used the manipulate the training artifacts. More precisely, the training code uses the training data that is provided and creates the model artifacts, while the inference code uses the model artifacts to make predictions on new data.The way that SageMaker runs the training and inference code is by making use of Docker containers. For now, think of a container as being a way of packaging code up so that dependencies aren't an issue.
###Code
from sagemaker import get_execution_role
# Our current execution role is require when creating the model as the training
# and inference code will need to access the model artifacts.
# tells you what resources you have access to
role = get_execution_role()
# We need to retrieve the location of the container which is provided by Amazon for using XGBoost.
# As a matter of convenience, the training and inference code both use the same container.
from sagemaker.amazon.amazon_estimator import get_image_uri
container = get_image_uri(session.boto_region_name, 'xgboost')
# TODO: Create a SageMaker estimator using the container location determined in the previous cell.
# It is recommended that you use a single training instance of type ml.m4.xlarge. It is also
# recommended that you use 's3://{}/{}/output'.format(session.default_bucket(), prefix) as the
# output path.
xgb = sagemaker.estimator.Estimator(
container,
role,
train_instance_count=1,
train_instance_type='ml.m4.xlarge',
output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),
sagemaker_session=session)
# TODO: Set the XGBoost hyperparameters in the xgb object. Don't forget that in this case we have a binary
# label so we should be using the 'binary:logistic' objective.
xgb.set_hyperparameters(max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
objective='binary:logistic',
early_stopping_rounds=10,
num_round=200)
###Output
_____no_output_____
###Markdown
Fit the XGBoost modelNow that our model has been set up we simply need to attach the training and validation datasets and then ask SageMaker to set up the computation.
###Code
s3_input_train = sagemaker.s3_input(s3_data=train_location, content_type='csv')
s3_input_validation = sagemaker.s3_input(s3_data=val_location, content_type='csv')
xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})
###Output
2019-06-09 07:08:02 Starting - Starting the training job...
2019-06-09 07:08:03 Starting - Launching requested ML instances......
2019-06-09 07:09:07 Starting - Preparing the instances for training...
2019-06-09 07:09:54 Downloading - Downloading input data...
2019-06-09 07:10:14 Training - Downloading the training image..
[31mArguments: train[0m
[31m[2019-06-09:07:10:34:INFO] Running standalone xgboost training.[0m
[31m[2019-06-09:07:10:34:INFO] File size need to be processed in the node: 238.47mb. Available memory size in the node: 8458.72mb[0m
[31m[2019-06-09:07:10:34:INFO] Determined delimiter of CSV input is ','[0m
[31m[07:10:34] S3DistributionType set as FullyReplicated[0m
[31m[07:10:36] 15000x5000 matrix with 75000000 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,[0m
[31m[2019-06-09:07:10:36:INFO] Determined delimiter of CSV input is ','[0m
[31m[07:10:36] S3DistributionType set as FullyReplicated[0m
[31m[07:10:37] 10000x5000 matrix with 50000000 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,[0m
[31m[07:10:40] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 38 extra nodes, 10 pruned nodes, max_depth=5[0m
[31m[0]#011train-error:0.2988#011validation-error:0.2975[0m
[31mMultiple eval metrics have been passed: 'validation-error' will be used for early stopping.
[0m
[31mWill train until validation-error hasn't improved in 10 rounds.[0m
[31m[07:10:42] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 6 pruned nodes, max_depth=5[0m
[31m[1]#011train-error:0.281067#011validation-error:0.2818[0m
[31m[07:10:43] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 36 extra nodes, 12 pruned nodes, max_depth=5[0m
[31m[2]#011train-error:0.282533#011validation-error:0.2785[0m
[31m[07:10:44] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[3]#011train-error:0.272267#011validation-error:0.2725[0m
[31m[07:10:46] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 2 pruned nodes, max_depth=5[0m
[31m[4]#011train-error:0.2574#011validation-error:0.2597[0m
2019-06-09 07:10:33 Training - Training image download completed. Training in progress.[31m[07:10:47] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 36 extra nodes, 2 pruned nodes, max_depth=5[0m
[31m[5]#011train-error:0.2602#011validation-error:0.2624[0m
[31m[07:10:48] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 36 extra nodes, 2 pruned nodes, max_depth=5[0m
[31m[6]#011train-error:0.251867#011validation-error:0.2565[0m
[31m[07:10:50] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 14 pruned nodes, max_depth=5[0m
[31m[7]#011train-error:0.245267#011validation-error:0.2495[0m
[31m[07:10:51] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 2 pruned nodes, max_depth=5[0m
[31m[8]#011train-error:0.231#011validation-error:0.238[0m
[31m[07:10:52] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[9]#011train-error:0.231#011validation-error:0.2369[0m
[31m[07:10:53] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 2 pruned nodes, max_depth=5[0m
[31m[10]#011train-error:0.226533#011validation-error:0.2329[0m
[31m[07:10:55] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 12 pruned nodes, max_depth=5[0m
[31m[11]#011train-error:0.220467#011validation-error:0.2266[0m
[31m[07:10:56] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 6 pruned nodes, max_depth=5[0m
[31m[12]#011train-error:0.2144#011validation-error:0.2254[0m
[31m[07:10:57] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 10 pruned nodes, max_depth=5[0m
[31m[13]#011train-error:0.209667#011validation-error:0.2216[0m
[31m[07:10:58] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[14]#011train-error:0.204867#011validation-error:0.2164[0m
[31m[07:11:00] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 8 pruned nodes, max_depth=5[0m
[31m[15]#011train-error:0.2018#011validation-error:0.2129[0m
[31m[07:11:01] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 36 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[16]#011train-error:0.199067#011validation-error:0.2091[0m
[31m[07:11:02] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 6 pruned nodes, max_depth=5[0m
[31m[17]#011train-error:0.1944#011validation-error:0.2068[0m
[31m[07:11:03] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 38 extra nodes, 2 pruned nodes, max_depth=5[0m
[31m[18]#011train-error:0.191467#011validation-error:0.2066[0m
[31m[07:11:05] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[19]#011train-error:0.1914#011validation-error:0.2047[0m
[31m[07:11:06] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 16 pruned nodes, max_depth=5[0m
[31m[20]#011train-error:0.188133#011validation-error:0.2016[0m
[31m[07:11:07] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[21]#011train-error:0.186733#011validation-error:0.1994[0m
[31m[07:11:08] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 10 pruned nodes, max_depth=5[0m
[31m[22]#011train-error:0.183333#011validation-error:0.1984[0m
[31m[07:11:10] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 18 pruned nodes, max_depth=5[0m
[31m[23]#011train-error:0.181133#011validation-error:0.1964[0m
[31m[07:11:11] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 20 pruned nodes, max_depth=5[0m
[31m[24]#011train-error:0.180533#011validation-error:0.1966[0m
[31m[07:11:12] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 6 pruned nodes, max_depth=5[0m
[31m[25]#011train-error:0.177133#011validation-error:0.1928[0m
[31m[07:11:13] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 6 pruned nodes, max_depth=5[0m
[31m[26]#011train-error:0.174267#011validation-error:0.1925[0m
[31m[07:11:15] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 6 pruned nodes, max_depth=5[0m
[31m[27]#011train-error:0.1718#011validation-error:0.1898[0m
[31m[07:11:16] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[28]#011train-error:0.171533#011validation-error:0.1885[0m
[31m[07:11:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5[0m
[31m[29]#011train-error:0.169467#011validation-error:0.1866[0m
[31m[07:11:19] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 6 pruned nodes, max_depth=5[0m
[31m[30]#011train-error:0.1652#011validation-error:0.1844[0m
[31m[07:11:20] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 10 pruned nodes, max_depth=5[0m
[31m[31]#011train-error:0.164867#011validation-error:0.1815[0m
[31m[07:11:21] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[32]#011train-error:0.162133#011validation-error:0.1824[0m
[31m[07:11:22] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 32 extra nodes, 10 pruned nodes, max_depth=5[0m
[31m[33]#011train-error:0.160467#011validation-error:0.1811[0m
[31m[07:11:24] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 10 pruned nodes, max_depth=5[0m
[31m[34]#011train-error:0.160333#011validation-error:0.182[0m
[31m[07:11:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 10 pruned nodes, max_depth=5[0m
[31m[35]#011train-error:0.1586#011validation-error:0.1795[0m
[31m[07:11:26] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 4 pruned nodes, max_depth=5[0m
[31m[36]#011train-error:0.157267#011validation-error:0.1789[0m
###Markdown
(TODO) Testing the modelNow that we've fit our XGBoost model, it's time to see how well it performs. To do this we will use SageMakers Batch Transform functionality. Batch Transform is a convenient way to perform inference on a large dataset in a way that is not realtime. That is, we don't necessarily need to use our model's results immediately and instead we can peform inference on a large number of samples. An example of this in industry might be peforming an end of month report. This method of inference can also be useful to us as it means to can perform inference on our entire test set. To perform a Batch Transformation we need to first create a transformer objects from our trained estimator object.
###Code
# TODO: Create a transformer object from the trained model. Using an instance count of 1 and an instance type of ml.m4.xlarge
# should be more than enough.
xgb_transformer = xgb.transformer(instance_count=1, instance_type='ml.m4.xlarge')
###Output
_____no_output_____
###Markdown
Next we actually perform the transform job. When doing so we need to make sure to specify the type of data we are sending so that it is serialized correctly in the background. In our case we are providing our model with csv data so we specify `text/csv`. Also, if the test data that we have provided is too large to process all at once then we need to specify how the data file should be split up. Since each line is a single entry in our data set we tell SageMaker that it can split the input on each line.
###Code
# TODO: Start the transform job. Make sure to specify the content type and the split type of the test data.
xgb_transformer.transform(test_location, content_type='text/csv', split_type='Line')
###Output
_____no_output_____
###Markdown
Currently the transform job is running but it is doing so in the background. Since we wish to wait until the transform job is done and we would like a bit of feedback we can run the `wait()` method.
###Code
xgb_transformer.wait()
###Output
.............................................!
###Markdown
Now the transform job has executed and the result, the estimated sentiment of each review, has been saved on S3. Since we would rather work on this file locally we can perform a bit of notebook magic to copy the file to the `data_dir`.
###Code
!aws s3 cp --recursive $xgb_transformer.output_path $data_dir
###Output
Completed 256.0 KiB/370.2 KiB (2.6 MiB/s) with 1 file(s) remaining
Completed 370.2 KiB/370.2 KiB (3.6 MiB/s) with 1 file(s) remaining
download: s3://sagemaker-us-west-2-550802670303/xgboost-2019-06-09-07-14-55-455/test.csv.out to ../data/xgboost/test.csv.out
###Markdown
The last step is now to read in the output from our model, convert the output to something a little more usable, in this case we want the sentiment to be either `1` (positive) or `0` (negative), and then compare to the ground truth labels.
###Code
predictions = pd.read_csv(os.path.join(data_dir, 'test.csv.out'), header=None)
predictions = [round(num) for num in predictions.squeeze().values]
from sklearn.metrics import accuracy_score
accuracy_score(test_y, predictions)
###Output
_____no_output_____
###Markdown
Optional: Clean upThe default notebook instance on SageMaker doesn't have a lot of excess disk space available. As you continue to complete and execute notebooks you will eventually fill up this disk space, leading to errors which can be difficult to diagnose. Once you are completely finished using a notebook it is a good idea to remove the files that you created along the way. Of course, you can do this from the terminal or from the notebook hub if you would like. The cell below contains some commands to clean up the created files from within the notebook.
###Code
# First we will remove all of the files contained in the data_dir directory
!rm $data_dir/*
# And then we delete the directory itself
!rmdir $data_dir
# Similarly we will remove the files in the cache_dir directory and the directory itself
!rm $cache_dir/*
!rmdir $cache_dir
###Output
_____no_output_____
###Markdown
Sentiment Analysis Using XGBoost in SageMaker_Deep Learning Nanodegree Program | Deployment_---As our first example of using Amazon's SageMaker service we will construct a random tree model to predict the sentiment of a movie review. You may have seen a version of this example in a pervious lesson although it would have been done using the sklearn package. Instead, we will be using the XGBoost package as it is provided to us by Amazon. InstructionsSome template code has already been provided for you, and you will need to implement additional functionality to successfully complete this notebook. You will not need to modify the included code beyond what is requested. Sections that begin with '**TODO**' in the header indicate that you need to complete or implement some portion within them. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a ` TODO: ...` comment. Please be sure to read the instructions carefully!In addition to implementing code, there may be questions for you to answer which relate to the task and your implementation. Each section where you will answer a question is preceded by a '**Question:**' header. Carefully read each question and provide your answer below the '**Answer:**' header by editing the Markdown cell.> **Note**: Code and Markdown cells can be executed using the **Shift+Enter** keyboard shortcut. In addition, a cell can be edited by typically clicking it (double-click for Markdown cells) or by pressing **Enter** while it is highlighted. Step 1: Downloading the dataThe dataset we are going to use is very popular among researchers in Natural Language Processing, usually referred to as the [IMDb dataset](http://ai.stanford.edu/~amaas/data/sentiment/). It consists of movie reviews from the website [imdb.com](http://www.imdb.com/), each labeled as either '**pos**itive', if the reviewer enjoyed the film, or '**neg**ative' otherwise.> Maas, Andrew L., et al. [Learning Word Vectors for Sentiment Analysis](http://ai.stanford.edu/~amaas/data/sentiment/). In _Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies_. Association for Computational Linguistics, 2011.We begin by using some Jupyter Notebook magic to download and extract the dataset.
###Code
%mkdir ../data
!wget -O ../data/aclImdb_v1.tar.gz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -zxf ../data/aclImdb_v1.tar.gz -C ../data
###Output
mkdir: cannot create directory ‘../data’: File exists
--2020-04-18 22:31:58-- http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Resolving ai.stanford.edu (ai.stanford.edu)... 171.64.68.10
Connecting to ai.stanford.edu (ai.stanford.edu)|171.64.68.10|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84125825 (80M) [application/x-gzip]
Saving to: ‘../data/aclImdb_v1.tar.gz’
../data/aclImdb_v1. 100%[===================>] 80.23M 22.9MB/s in 4.1s
2020-04-18 22:32:02 (19.8 MB/s) - ‘../data/aclImdb_v1.tar.gz’ saved [84125825/84125825]
###Markdown
Step 2: Preparing the dataThe data we have downloaded is split into various files, each of which contains a single review. It will be much easier going forward if we combine these individual files into two large files, one for training and one for testing.
###Code
import os
import glob
def read_imdb_data(data_dir='../data/aclImdb'):
data = {}
labels = {}
for data_type in ['train', 'test']:
data[data_type] = {}
labels[data_type] = {}
for sentiment in ['pos', 'neg']:
data[data_type][sentiment] = []
labels[data_type][sentiment] = []
path = os.path.join(data_dir, data_type, sentiment, '*.txt')
files = glob.glob(path)
for f in files:
with open(f) as review:
data[data_type][sentiment].append(review.read())
# Here we represent a positive review by '1' and a negative review by '0'
labels[data_type][sentiment].append(1 if sentiment == 'pos' else 0)
assert len(data[data_type][sentiment]) == len(labels[data_type][sentiment]), \
"{}/{} data size does not match labels size".format(data_type, sentiment)
return data, labels
data, labels = read_imdb_data()
print("IMDB reviews: train = {} pos / {} neg, test = {} pos / {} neg".format(
len(data['train']['pos']), len(data['train']['neg']),
len(data['test']['pos']), len(data['test']['neg'])))
from sklearn.utils import shuffle
def prepare_imdb_data(data, labels):
"""Prepare training and test sets from IMDb movie reviews."""
#Combine positive and negative reviews and labels
data_train = data['train']['pos'] + data['train']['neg']
data_test = data['test']['pos'] + data['test']['neg']
labels_train = labels['train']['pos'] + labels['train']['neg']
labels_test = labels['test']['pos'] + labels['test']['neg']
#Shuffle reviews and corresponding labels within training and test sets
data_train, labels_train = shuffle(data_train, labels_train)
data_test, labels_test = shuffle(data_test, labels_test)
# Return a unified training data, test data, training labels, test labets
return data_train, data_test, labels_train, labels_test
train_X, test_X, train_y, test_y = prepare_imdb_data(data, labels)
print("IMDb reviews (combined): train = {}, test = {}".format(len(train_X), len(test_X)))
train_X[100]
train_y[100]
###Output
_____no_output_____
###Markdown
Step 3: Processing the dataNow that we have our training and testing datasets merged and ready to use, we need to start processing the raw data into something that will be useable by our machine learning algorithm. To begin with, we remove any html formatting that may appear in the reviews and perform some standard natural language processing in order to homogenize the data.
###Code
import nltk
nltk.download("stopwords")
from nltk.corpus import stopwords
from nltk.stem.porter import *
stemmer = PorterStemmer()
import re
from bs4 import BeautifulSoup
def review_to_words(review):
text = BeautifulSoup(review, "html.parser").get_text() # Remove HTML tags
text = re.sub(r"[^a-zA-Z0-9]", " ", text.lower()) # Convert to lower case
words = text.split() # Split string into words
words = [w for w in words if w not in stopwords.words("english")] # Remove stopwords
words = [PorterStemmer().stem(w) for w in words] # stem
return words
import pickle
cache_dir = os.path.join("../cache", "sentiment_analysis") # where to store cache files
os.makedirs(cache_dir, exist_ok=True) # ensure cache directory exists
def preprocess_data(data_train, data_test, labels_train, labels_test,
cache_dir=cache_dir, cache_file="preprocessed_data.pkl"):
"""Convert each review to words; read from cache if available."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = pickle.load(f)
print("Read preprocessed data from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Preprocess training and test data to obtain words for each review
#words_train = list(map(review_to_words, data_train))
#words_test = list(map(review_to_words, data_test))
words_train = [review_to_words(review) for review in data_train]
words_test = [review_to_words(review) for review in data_test]
# Write to cache file for future runs
if cache_file is not None:
cache_data = dict(words_train=words_train, words_test=words_test,
labels_train=labels_train, labels_test=labels_test)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
pickle.dump(cache_data, f)
print("Wrote preprocessed data to cache file:", cache_file)
else:
# Unpack data loaded from cache file
words_train, words_test, labels_train, labels_test = (cache_data['words_train'],
cache_data['words_test'], cache_data['labels_train'], cache_data['labels_test'])
return words_train, words_test, labels_train, labels_test
# Preprocess data
train_X, test_X, train_y, test_y = preprocess_data(train_X, test_X, train_y, test_y)
###Output
Wrote preprocessed data to cache file: preprocessed_data.pkl
###Markdown
Extract Bag-of-Words featuresFor the model we will be implementing, rather than using the reviews directly, we are going to transform each review into a Bag-of-Words feature representation. Keep in mind that 'in the wild' we will only have access to the training set so our transformer can only use the training set to construct a representation.
###Code
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.externals import joblib
# joblib is an enhanced version of pickle that is more efficient for storing NumPy arrays
def extract_BoW_features(words_train, words_test, vocabulary_size=5000,
cache_dir=cache_dir, cache_file="bow_features.pkl"):
"""Extract Bag-of-Words for a given set of documents, already preprocessed into words."""
# If cache_file is not None, try to read from it first
cache_data = None
if cache_file is not None:
try:
with open(os.path.join(cache_dir, cache_file), "rb") as f:
cache_data = joblib.load(f)
print("Read features from cache file:", cache_file)
except:
pass # unable to read from cache, but that's okay
# If cache is missing, then do the heavy lifting
if cache_data is None:
# Fit a vectorizer to training documents and use it to transform them
# NOTE: Training documents have already been preprocessed and tokenized into words;
# pass in dummy functions to skip those steps, e.g. preprocessor=lambda x: x
vectorizer = CountVectorizer(max_features=vocabulary_size,
preprocessor=lambda x: x, tokenizer=lambda x: x) # already preprocessed
features_train = vectorizer.fit_transform(words_train).toarray()
# Apply the same vectorizer to transform the test documents (ignore unknown words)
features_test = vectorizer.transform(words_test).toarray()
# NOTE: Remember to convert the features using .toarray() for a compact representation
# Write to cache file for future runs (store vocabulary as well)
if cache_file is not None:
vocabulary = vectorizer.vocabulary_
cache_data = dict(features_train=features_train, features_test=features_test,
vocabulary=vocabulary)
with open(os.path.join(cache_dir, cache_file), "wb") as f:
joblib.dump(cache_data, f)
print("Wrote features to cache file:", cache_file)
else:
# Unpack data loaded from cache file
features_train, features_test, vocabulary = (cache_data['features_train'],
cache_data['features_test'], cache_data['vocabulary'])
# Return both the extracted features as well as the vocabulary
return features_train, features_test, vocabulary
# Extract Bag of Words features for both training and test datasets
train_X, test_X, vocabulary = extract_BoW_features(train_X, test_X)
###Output
Wrote features to cache file: bow_features.pkl
###Markdown
Step 4: Classification using XGBoostNow that we have created the feature representation of our training (and testing) data, it is time to start setting up and using the XGBoost classifier provided by SageMaker. (TODO) Writing the datasetThe XGBoost classifier that we will be using requires the dataset to be written to a file and stored using Amazon S3. To do this, we will start by splitting the training dataset into two parts, the data we will train the model with and a validation set. Then, we will write those datasets to a file and upload the files to S3. In addition, we will write the test set input to a file and upload the file to S3. This is so that we can use SageMakers Batch Transform functionality to test our model once we've fit it.
###Code
import pandas as pd
# TODO: Split the train_X and train_y arrays into the DataFrames val_X, train_X and val_y, train_y. Make sure that
# val_X and val_y contain 10 000 entires while train_X and train_y contain the remaining 15 000 entries.
val_X = pd.DataFrame(train_X[:10000])
train_X = pd.DataFrame(train_X[10000:])
val_y = pd.DataFrame(train_y[:10000])
train_y = pd.DataFrame(train_y[10000:])
###Output
_____no_output_____
###Markdown
The documentation for the XGBoost algorithm in SageMaker requires that the saved datasets should contain no headers or index and that for the training and validation data, the label should occur first for each sample.For more information about this and other algorithms, the SageMaker developer documentation can be found on __[Amazon's website.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
# First we make sure that the local directory in which we'd like to store the training and validation csv files exists.
data_dir = '../data/xgboost'
if not os.path.exists(data_dir):
os.makedirs(data_dir)
# First, save the test data to test.csv in the data_dir directory. Note that we do not save the associated ground truth
# labels, instead we will use them later to compare with our model output.
pd.DataFrame(test_X).to_csv(os.path.join(data_dir, 'test.csv'), header=False, index=False)
# TODO: Save the training and validation data to train.csv and validation.csv in the data_dir directory.
# Make sure that the files you create are in the correct format.
pd.concat([val_y, val_X],axis=1).to_csv(os.path.join(data_dir, 'validation.csv'), header=False, index=False)
pd.concat([train_y, train_X],axis=1).to_csv(os.path.join(data_dir, 'train.csv'), header=False, index=False)
# To save a bit of memory we can set text_X, train_X, val_X, train_y and val_y to None.
test_X = train_X = val_X = train_y = val_y = None
###Output
_____no_output_____
###Markdown
(TODO) Uploading Training / Validation files to S3Amazon's S3 service allows us to store files that can be access by both the built-in training models such as the XGBoost model we will be using as well as custom models such as the one we will see a little later.For this, and most other tasks we will be doing using SageMaker, there are two methods we could use. The first is to use the low level functionality of SageMaker which requires knowing each of the objects involved in the SageMaker environment. The second is to use the high level functionality in which certain choices have been made on the user's behalf. The low level approach benefits from allowing the user a great deal of flexibility while the high level approach makes development much quicker. For our purposes we will opt to use the high level approach although using the low-level approach is certainly an option.Recall the method `upload_data()` which is a member of object representing our current SageMaker session. What this method does is upload the data to the default bucket (which is created if it does not exist) into the path described by the key_prefix variable. To see this for yourself, once you have uploaded the data files, go to the S3 console and look to see where the files have been uploaded.For additional resources, see the __[SageMaker API documentation](http://sagemaker.readthedocs.io/en/latest/)__ and in addition the __[SageMaker Developer Guide.](https://docs.aws.amazon.com/sagemaker/latest/dg/)__
###Code
import sagemaker
session = sagemaker.Session() # Store the current SageMaker session
# S3 prefix (which folder will we use)
prefix = 'sentiment-xgboost'
# TODO: Upload the test.csv, train.csv and validation.csv files which are contained in data_dir to S3 using sess.upload_data().
test_location = None
val_location = None
train_location = None
test_location = session.upload_data(os.path.join(data_dir, 'test.csv'), key_prefix=prefix)
val_location = session.upload_data(os.path.join(data_dir, 'validation.csv'), key_prefix=prefix)
train_location = session.upload_data(os.path.join(data_dir, 'train.csv'), key_prefix=prefix)
###Output
_____no_output_____
###Markdown
(TODO) Creating the XGBoost modelNow that the data has been uploaded it is time to create the XGBoost model. To begin with, we need to do some setup. At this point it is worth discussing what a model is in SageMaker. It is easiest to think of a model of comprising three different objects in the SageMaker ecosystem, which interact with one another.- Model Artifacts- Training Code (Container)- Inference Code (Container)The Model Artifacts are what you might think of as the actual model itself. For example, if you were building a neural network, the model artifacts would be the weights of the various layers. In our case, for an XGBoost model, the artifacts are the actual trees that are created during training.The other two objects, the training code and the inference code are then used the manipulate the training artifacts. More precisely, the training code uses the training data that is provided and creates the model artifacts, while the inference code uses the model artifacts to make predictions on new data.The way that SageMaker runs the training and inference code is by making use of Docker containers. For now, think of a container as being a way of packaging code up so that dependencies aren't an issue.
###Code
from sagemaker import get_execution_role
# Our current execution role is require when creating the model as the training
# and inference code will need to access the model artifacts.
role = get_execution_role()
# We need to retrieve the location of the container which is provided by Amazon for using XGBoost.
# As a matter of convenience, the training and inference code both use the same container.
from sagemaker.amazon.amazon_estimator import get_image_uri
container = get_image_uri(session.boto_region_name, 'xgboost')
# TODO: Create a SageMaker estimator using the container location determined in the previous cell.
# It is recommended that you use a single training instance of type ml.m4.xlarge. It is also
# recommended that you use 's3://{}/{}/output'.format(session.default_bucket(), prefix) as the
# output path.
xgb = None
xgb = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.m4.xlarge',
output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),
sagemaker_session=session)
# TODO: Set the XGBoost hyperparameters in the xgb object. Don't forget that in this case we have a binary
# label so we should be using the 'binary:logistic' objective.
xgb.set_hyperparameters(max_depth=5,
eta=0.2,
gamme=4,
min_child_weight=6,
subsample=0.8,
silent=0,
objective='binary:logistic',
early_stopping_rounds=10,
num_round=500)
###Output
_____no_output_____
###Markdown
Fit the XGBoost modelNow that our model has been set up we simply need to attach the training and validation datasets and then ask SageMaker to set up the computation.
###Code
s3_input_train = sagemaker.s3_input(s3_data=train_location, content_type='csv')
s3_input_validation = sagemaker.s3_input(s3_data=val_location, content_type='csv')
xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})
###Output
2020-04-18 23:39:39 Starting - Starting the training job...
2020-04-18 23:39:40 Starting - Launching requested ML instances...
2020-04-18 23:40:37 Starting - Preparing the instances for training......
2020-04-18 23:41:40 Downloading - Downloading input data...
2020-04-18 23:41:57 Training - Downloading the training image..[34mArguments: train[0m
[34m[2020-04-18:23:42:17:INFO] Running standalone xgboost training.[0m
[34m[2020-04-18:23:42:17:INFO] File size need to be processed in the node: 1.87mb. Available memory size in the node: 8487.85mb[0m
[34m[2020-04-18:23:42:17:INFO] Determined delimiter of CSV input is ','[0m
[34m[23:42:17] S3DistributionType set as FullyReplicated[0m
[34m[23:42:17] 15000x36 matrix with 540000 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,[0m
[34m[2020-04-18:23:42:17:INFO] Determined delimiter of CSV input is ','[0m
[34m[23:42:17] S3DistributionType set as FullyReplicated[0m
[34m[23:42:17] 10000x36 matrix with 360000 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 52 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[0]#011train-error:0.465867#011validation-error:0.4823[0m
[34mMultiple eval metrics have been passed: 'validation-error' will be used for early stopping.
[0m
[34mWill train until validation-error hasn't improved in 10 rounds.[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[1]#011train-error:0.457867#011validation-error:0.4739[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[2]#011train-error:0.454533#011validation-error:0.4778[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[3]#011train-error:0.452467#011validation-error:0.4823[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 46 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[4]#011train-error:0.450933#011validation-error:0.4765[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[5]#011train-error:0.449067#011validation-error:0.4759[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 48 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[6]#011train-error:0.4444#011validation-error:0.4766[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[7]#011train-error:0.4424#011validation-error:0.4751[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 34 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[8]#011train-error:0.4408#011validation-error:0.4764[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 40 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[9]#011train-error:0.438#011validation-error:0.4779[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 42 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[10]#011train-error:0.437133#011validation-error:0.4794[0m
[34m[23:42:17] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5[0m
[34m[11]#011train-error:0.4378#011validation-error:0.478[0m
[34mStopping. Best iteration:[0m
[34m[1]#011train-error:0.457867#011validation-error:0.4739
[0m
2020-04-18 23:42:29 Uploading - Uploading generated training model
2020-04-18 23:42:29 Completed - Training job completed
Training seconds: 49
Billable seconds: 49
###Markdown
(TODO) Testing the modelNow that we've fit our XGBoost model, it's time to see how well it performs. To do this we will use SageMakers Batch Transform functionality. Batch Transform is a convenient way to perform inference on a large dataset in a way that is not realtime. That is, we don't necessarily need to use our model's results immediately and instead we can peform inference on a large number of samples. An example of this in industry might be peforming an end of month report. This method of inference can also be useful to us as it means to can perform inference on our entire test set. To perform a Batch Transformation we need to first create a transformer objects from our trained estimator object.
###Code
# TODO: Create a transformer object from the trained model. Using an instance count of 1 and an instance type of ml.m4.xlarge
# should be more than enough.
xgb_transformer = xgb.transformer(instance_count = 1, instance_type= 'ml.m4.xlarge')
###Output
_____no_output_____
###Markdown
Next we actually perform the transform job. When doing so we need to make sure to specify the type of data we are sending so that it is serialized correctly in the background. In our case we are providing our model with csv data so we specify `text/csv`. Also, if the test data that we have provided is too large to process all at once then we need to specify how the data file should be split up. Since each line is a single entry in our data set we tell SageMaker that it can split the input on each line.
###Code
# TODO: Start the transform job. Make sure to specify the content type and the split type of the test data.
xgb_transformer.transform(test_location, content_type='text/csv', split_type='Line')
###Output
_____no_output_____
###Markdown
Currently the transform job is running but it is doing so in the background. Since we wish to wait until the transform job is done and we would like a bit of feedback we can run the `wait()` method.
###Code
xgb_transformer.wait()
###Output
....................[34mArguments: serve[0m
[34m[2020-04-18 23:46:24 +0000] [1] [INFO] Starting gunicorn 19.7.1[0m
[34m[2020-04-18 23:46:24 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)[0m
[34m[2020-04-18 23:46:24 +0000] [1] [INFO] Using worker: gevent[0m
[34m[2020-04-18 23:46:24 +0000] [38] [INFO] Booting worker with pid: 38[0m
[34m[2020-04-18 23:46:24 +0000] [39] [INFO] Booting worker with pid: 39[0m
[34m[2020-04-18 23:46:24 +0000] [40] [INFO] Booting worker with pid: 40[0m
[34m[2020-04-18 23:46:24 +0000] [41] [INFO] Booting worker with pid: 41[0m
[34m[2020-04-18:23:46:24:INFO] Model loaded successfully for worker : 38[0m
[35mArguments: serve[0m
[35m[2020-04-18 23:46:24 +0000] [1] [INFO] Starting gunicorn 19.7.1[0m
[35m[2020-04-18 23:46:24 +0000] [1] [INFO] Listening at: http://0.0.0.0:8080 (1)[0m
[35m[2020-04-18 23:46:24 +0000] [1] [INFO] Using worker: gevent[0m
[35m[2020-04-18 23:46:24 +0000] [38] [INFO] Booting worker with pid: 38[0m
[35m[2020-04-18 23:46:24 +0000] [39] [INFO] Booting worker with pid: 39[0m
[35m[2020-04-18 23:46:24 +0000] [40] [INFO] Booting worker with pid: 40[0m
[35m[2020-04-18 23:46:24 +0000] [41] [INFO] Booting worker with pid: 41[0m
[35m[2020-04-18:23:46:24:INFO] Model loaded successfully for worker : 38[0m
[34m[2020-04-18:23:46:24:INFO] Model loaded successfully for worker : 40[0m
[34m[2020-04-18:23:46:24:INFO] Model loaded successfully for worker : 39[0m
[34m[2020-04-18:23:46:24:INFO] Model loaded successfully for worker : 41[0m
[35m[2020-04-18:23:46:24:INFO] Model loaded successfully for worker : 40[0m
[35m[2020-04-18:23:46:24:INFO] Model loaded successfully for worker : 39[0m
[35m[2020-04-18:23:46:24:INFO] Model loaded successfully for worker : 41[0m
[34m[2020-04-18:23:46:27:INFO] Sniff delimiter as ','[0m
[34m[2020-04-18:23:46:27:INFO] Determined delimiter of CSV input is ','[0m
[35m[2020-04-18:23:46:27:INFO] Sniff delimiter as ','[0m
[35m[2020-04-18:23:46:27:INFO] Determined delimiter of CSV input is ','[0m
[32m2020-04-18T23:46:27.415:[sagemaker logs]: MaxConcurrentTransforms=4, MaxPayloadInMB=6, BatchStrategy=MULTI_RECORD[0m
###Markdown
Now the transform job has executed and the result, the estimated sentiment of each review, has been saved on S3. Since we would rather work on this file locally we can perform a bit of notebook magic to copy the file to the `data_dir`.
###Code
!aws s3 cp --recursive $xgb_transformer.output_path $data_dir
###Output
Completed 256.0 KiB/365.7 KiB (3.4 MiB/s) with 1 file(s) remaining
Completed 365.7 KiB/365.7 KiB (4.7 MiB/s) with 1 file(s) remaining
download: s3://sagemaker-us-east-2-244377630672/xgboost-2020-04-18-23-43-11-980/test.csv.out to ../data/xgboost/test.csv.out
###Markdown
The last step is now to read in the output from our model, convert the output to something a little more usable, in this case we want the sentiment to be either `1` (positive) or `0` (negative), and then compare to the ground truth labels.
###Code
predictions = pd.read_csv(os.path.join(data_dir, 'test.csv.out'), header=None)
predictions = [round(num) for num in predictions.squeeze().values]
from sklearn.metrics import accuracy_score
accuracy_score(test_y, predictions)
###Output
_____no_output_____
###Markdown
Optional: Clean upThe default notebook instance on SageMaker doesn't have a lot of excess disk space available. As you continue to complete and execute notebooks you will eventually fill up this disk space, leading to errors which can be difficult to diagnose. Once you are completely finished using a notebook it is a good idea to remove the files that you created along the way. Of course, you can do this from the terminal or from the notebook hub if you would like. The cell below contains some commands to clean up the created files from within the notebook.
###Code
# First we will remove all of the files contained in the data_dir directory
!rm $data_dir/*
# And then we delete the directory itself
!rmdir $data_dir
# Similarly we will remove the files in the cache_dir directory and the directory itself
!rm $cache_dir/*
!rmdir $cache_dir
###Output
_____no_output_____
|
tests/practice/pda_ch-09_Analyzing Textual Data and Social Media.ipynb
|
###Markdown
Analyzing Textual Data and Social Media
###Code
#load watermark
%load_ext watermark
%watermark -a 'Gopala KR' -u -d -v -p watermark,numpy,pandas,matplotlib,nltk,sklearn,tensorflow,theano,mxnet,chainer,seaborn,keras,tflearn,bokeh,gensim
###Output
/srv/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.
Using TensorFlow backend.
/srv/venv/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()
if d.decorator_argspec is not None), _inspect.getargspec(target))
###Markdown
Import nltk
###Code
!pip install nltk
import nltk
nltk.download('stopwords')
###Output
[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...
[nltk_data] Unzipping corpora/stopwords.zip.
###Markdown
Filtering out stopwords, names, and numbers Load English stopwords and print some of the words
###Code
sw = set(nltk.corpus.stopwords.words('english'))
print("Stop words:", list(sw)[:7])
###Output
Stop words: ['am', 'yourselves', 'about', 'o', 'they', 'why', 'because']
###Markdown
Load Gutenberg corpopra and print some of the filenames
###Code
nltk.download('gutenberg')
gb = nltk.corpus.gutenberg
print("Gutenberg files:\n", gb.fileids()[-5:])
###Output
[nltk_data] Downloading package gutenberg to /home/jovyan/nltk_data...
[nltk_data] Unzipping corpora/gutenberg.zip.
Gutenberg files:
['milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']
###Markdown
Extract sentences from milton-paradise.txt file
###Code
nltk.download('punkt')
text_sent = gb.sents("milton-paradise.txt")[:2]
print("Unfiltered:", text_sent)
###Output
[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...
[nltk_data] Unzipping tokenizers/punkt.zip.
Unfiltered: [['[', 'Paradise', 'Lost', 'by', 'John', 'Milton', '1667', ']'], ['Book', 'I']]
###Markdown
Filter out the stopwords from extracted sentences
###Code
nltk.download('averaged_perceptron_tagger')
for sent in text_sent:
filtered = [w for w in sent if w.lower() not in sw]
print("Filtered:\n", filtered)
tagged = nltk.pos_tag(filtered)
print("Tagged:\n", tagged)
words= []
for word in tagged:
if word[1] != 'NNP' and word[1] != 'CD':
words.append(word[0])
print("Words:\n",words)
###Output
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data] /home/jovyan/nltk_data...
[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.
Filtered:
['[', 'Paradise', 'Lost', 'John', 'Milton', '1667', ']']
Tagged:
[('[', 'JJ'), ('Paradise', 'NNP'), ('Lost', 'NNP'), ('John', 'NNP'), ('Milton', 'NNP'), ('1667', 'CD'), (']', 'NN')]
Words:
['[', ']']
Filtered:
['Book']
Tagged:
[('Book', 'NN')]
Words:
['Book']
###Markdown
Bag of words model Import scikit-learn
###Code
import sklearn as sk
###Output
_____no_output_____
###Markdown
Load two documents from NLTK Gutenberg corpus
###Code
hamlet = gb.raw("shakespeare-hamlet.txt")
macbeth = gb.raw("shakespeare-macbeth.txt")
###Output
_____no_output_____
###Markdown
Create the feature vector by omitting English stopwords
###Code
cv = sk.feature_extraction.text.CountVectorizer(stop_words='english')
print("Feature vector:\n", cv.fit_transform([hamlet, macbeth]).toarray())
###Output
Feature vector:
[[ 1 0 1 ... 14 0 1]
[ 0 1 0 ... 1 1 0]]
###Markdown
Print a small selection of the features found
###Code
print("Features:\n", cv.get_feature_names()[:5])
###Output
Features:
['1599', '1603', 'abhominably', 'abhorred', 'abide']
###Markdown
Analyzing word frequencies
###Code
import nltk
import string
gb = nltk.corpus.gutenberg
words = gb.words("shakespeare-caesar.txt")
sw = set(nltk.corpus.stopwords.words('english'))
punctuation = set(string.punctuation)
filtered = [w.lower() for w in words if w.lower() not in sw and w.lower() not in punctuation]
fd = nltk.FreqDist(filtered)
print("Words", list(fd.keys())[:5])
print("Counts", list(fd.values())[:5])
print("Max", fd.max())
print("Count", fd['d'])
fd = nltk.FreqDist(nltk.bigrams(filtered))
print("Bigrams", list(fd.keys())[:5])
print("Counts", list(fd.values())[:5])
print("Bigram Max", fd.max())
print("Bigram count", fd[('let', 'vs')])
###Output
Words ['tragedie', 'julius', 'caesar', 'william', 'shakespeare']
Counts [2, 1, 190, 1, 1]
Max caesar
Count 0
Bigrams [('tragedie', 'julius'), ('julius', 'caesar'), ('caesar', 'william'), ('william', 'shakespeare'), ('shakespeare', '1599')]
Counts [1, 1, 1, 1, 1]
Bigram Max ('let', 'vs')
Bigram count 16
###Markdown
Naive Bayesian
###Code
import nltk
import string
import random
sw = set(nltk.corpus.stopwords.words('english'))
punctuation = set(string.punctuation)
def word_features(word):
return {'len': len(word)}
def isStopword(word):
return word in sw or word in punctuation
gb = nltk.corpus.gutenberg
words = gb.words("shakespeare-caesar.txt")
labeled_words = ([(word.lower(), isStopword(word.lower())) for
word in words])
random.seed(42)
random.shuffle(labeled_words)
print(labeled_words[:5])
featuresets = [(word_features(n), word) for (n, word) in
labeled_words]
cutoff = int(.9 * len(featuresets))
train_set, test_set = featuresets[:cutoff], featuresets[cutoff:]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print("'behold' class", classifier.classify(word_features('behold')))
print("'the' class", classifier.classify(word_features('the')))
print("Accuracy", nltk.classify.accuracy(classifier, test_set))
print(classifier.show_most_informative_features(5))
###Output
[('i', True), ('is', True), ('in', True), ('he', True), ('ambitious', False)]
'behold' class False
'the' class True
Accuracy 0.8521671826625387
Most Informative Features
len = 7 False : True = 77.8 : 1.0
len = 6 False : True = 52.2 : 1.0
len = 1 True : False = 51.8 : 1.0
len = 2 True : False = 10.9 : 1.0
len = 5 False : True = 10.9 : 1.0
None
###Markdown
Sentiment Analysis
###Code
import random
from nltk.corpus import movie_reviews
from nltk.corpus import stopwords
from nltk import FreqDist
from nltk import NaiveBayesClassifier
from nltk.classify import accuracy
import string
nltk.download('movie_reviews')
labeled_docs = [(list(movie_reviews.words(fid)), cat)
for cat in movie_reviews.categories()
for fid in movie_reviews.fileids(cat)]
random.seed(42)
random.shuffle(labeled_docs)
review_words = movie_reviews.words()
print("# Review Words", len(review_words))
sw = set(stopwords.words('english'))
punctuation = set(string.punctuation)
def isStopWord(word):
return word in sw or word in punctuation
filtered = [w.lower() for w in review_words if not isStopWord(w.lower())]
print("# After filter", len(filtered))
words = FreqDist(filtered)
N = int(.05 * len(words.keys()))
word_features = list(words.keys())[:N]
def doc_features(doc):
doc_words = FreqDist(w for w in doc if not isStopWord(w))
features = {}
for word in word_features:
features['count (%s)' % word] = (doc_words.get(word, 0))
return features
featuresets = [(doc_features(d), c) for (d,c) in labeled_docs]
train_set, test_set = featuresets[200:], featuresets[:200]
classifier = NaiveBayesClassifier.train(train_set)
print("Accuracy", accuracy(classifier, test_set))
print(classifier.show_most_informative_features())
###Output
[nltk_data] Downloading package movie_reviews to
[nltk_data] /home/jovyan/nltk_data...
[nltk_data] Unzipping corpora/movie_reviews.zip.
# Review Words 1583820
###Markdown
Creating Word Clouds
###Code
from nltk.corpus import movie_reviews
from nltk.corpus import stopwords
from nltk import FreqDist
import string
sw = set(stopwords.words('english'))
punctuation = set(string.punctuation)
def isStopWord(word):
return word in sw or word in punctuation
review_words = movie_reviews.words()
filtered = [w.lower() for w in review_words if not isStopWord(w.lower())]
words = FreqDist(filtered)
N = int(.01 * len(words.keys()))
tags = list(words.keys())[:N]
for tag in tags:
print(tag, ':', words[tag])
from nltk.corpus import movie_reviews
from nltk.corpus import stopwords
from nltk.corpus import names
from nltk import FreqDist
from sklearn.feature_extraction.text import TfidfVectorizer
import itertools
import pandas as pd
import numpy as np
import string
nltk.download('names')
sw = set(stopwords.words('english'))
punctuation = set(string.punctuation)
all_names = set([name.lower() for name in names.words()])
def isStopWord(word):
return (word in sw or word in punctuation) or not word.isalpha() or word in all_names
review_words = movie_reviews.words()
filtered = [w.lower() for w in review_words if not isStopWord(w.lower())]
words = FreqDist(filtered)
texts = []
for fid in movie_reviews.fileids():
texts.append(" ".join([w.lower() for w in movie_reviews.words(fid) if not isStopWord(w.lower()) and words[w.lower()] > 1]))
vectorizer = TfidfVectorizer(stop_words='english')
matrix = vectorizer.fit_transform(texts)
sums = np.array(matrix.sum(axis=0)).ravel()
ranks = []
for word, val in zip(vectorizer.get_feature_names(), sums):
ranks.append((word, val))
df = pd.DataFrame(ranks, columns=["term", "tfidf"])
df = df.sort_values(['tfidf'])
print(df.head())
N = int(.01 * len(df))
df = df.tail(N)
for term, tfidf in zip(df["term"].values, df["tfidf"].values):
print(term, ":", tfidf)
###Output
_____no_output_____
###Markdown
Social Network Analysis
###Code
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
print([s for s in dir(nx) if s.endswith('graph')])
G = nx.davis_southern_women_graph()
plt.hist(list(nx.degree(G).values()))
plt.show()
plt.figure(figsize=(8,8))
pos = nx.spring_layout(G)
nx.draw(G, node_size=10)
nx.draw_networkx_labels(G, pos)
plt.show()
plt.figure(figsize=(8,8))
pos = nx.spring_layout(G, node_size=2)
nx.draw(G)
nx.draw_networkx_labels(G, pos)
plt.show()
###Output
_____no_output_____
|
notebooks/Ugrainium-Pb.ipynb
|
###Markdown
IntroductionThis Morglorb recipe uses groupings of ingredients to try to cover nutritional requirements with enough overlap that a single ingredient with quality issues does not cause a failure for the whole recipe. An opimizer is used to find the right amount of each ingredient to fulfill the nutritional and practical requirements. To Do* Nutrients without an upper limit should have the upper limit constraint removed* Add constraints for the NIH essential protein combinations as a limit* Add a radar graph for vitamins showing the boundry between RDI and UL* Add a radar graph for vitamins without an upper limit but showing the RDI* Add a radar graph for essential proteins showing the range between RDI and UL* Add a radar graph for essential proteins without an upper limit, but showing the RDI as the lower limit* Add a radar graph pair for non-essential proteins with the above UL and no UL pairing* Add equality constraints for at least energy, and macro nutrients if possible
###Code
# Import all of the helper libraries
from scipy.optimize import minimize
from scipy.optimize import Bounds
from scipy.optimize import least_squares, lsq_linear, dual_annealing, minimize
import pandas as pd
import numpy as np
import os
import json
from math import e, log, log10
import matplotlib.pyplot as plt
import seaborn as sns
from ipysheet import from_dataframe, to_dataframe
#!pip install seaborn
#!pip install ipysheet
#!pip install ipywidgets
# Setup the notebook context
data_dir = '../data'
pd.set_option('max_columns', 70)
###Output
_____no_output_____
###Markdown
Our DataThe [tables](https://docs.google.com/spreadsheets/d/104Y7kH4OzmfsM-v2MSEoc7cIgv0aAMT2sQLAmgkx8R8/editgid=442191411) containing our ingredients nutrition profile are held in Google Sheets.The sheet names are "Ingredients" and "Nutrition Profile"
###Code
# Download our nutrition profile data from Google Sheets
# For the technique to download the sheet data, see third post in SO: https://stackoverflow.com/questions/33713084/download-link-for-google-spreadsheets-csv-export-with-multiple-sheets
# The "Ugrainium PB" spreadsheet
#google_spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1tUi3XqIf_hO_2SR3nXY455SYm-uQp3ljq1TnS-Forpo/export?format=csv&id=1tUi3XqIf_hO_2SR3nXY455SYm-uQp3ljq1TnS-Forpo'
google_spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1Qr4MvDsJczSwaCH641YPpj6Tj8Bn2WxkkKkUG9jEKcA/export?format=csv&id=1Qr4MvDsJczSwaCH641YPpj6Tj8Bn2WxkkKkUG9jEKcA'
google_spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1Qr4MvDsJczSwaCH641YPpj6Tj8Bn2WxkkKkUG9jEKcA/export?format=csv&id=1Qr4MvDsJczSwaCH641YPpj6Tj8Bn2WxkkKkUG9jEKcA'
ingredient_tab_url = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vR46GO190pKxg8hm80NE5mLc2hDKDLJ7ZCLbIuIvx71gikSCI079pYhnljclZ7ltsr4SF_zuPoXCeso/pub?gid=1812860789&single=true&output=csv'
nutrition_tab_url = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vR46GO190pKxg8hm80NE5mLc2hDKDLJ7ZCLbIuIvx71gikSCI079pYhnljclZ7ltsr4SF_zuPoXCeso/pub?gid=624419712&single=true&output=csv'
nutrition_tab = '624419712'
ingredient_tab = '1812860789'
#https://doc-10-1s-sheets.googleusercontent.com/export/e8bjv0c29nvvcp2d4u4g8eb4dk/rbt3ilqpe70qof3dr8c62kvclo/1624425540000/116012584642359142100/116012584642359142100/1tUi3XqIf_hO_2SR3nXY455SYm-uQp3ljq1TnS-Forpo?format=csv&id=1tUi3XqIf_hO_2SR3nXY455SYm-uQp3ljq1TnS-Forpo&gid=442191411&dat=AMvwolYUsRdJY9vOf-gxvJzBUHP6nUeiUVIFk43G-JxzZtXcnd29P62FmMzIfl8rxpLxNjFWO3Ope1Mf9aAFnlX7PBKwK3xdAFEbH2Itn169rlxUDXXZxNlYSZrFxnpapNhEC_iaU64qyLIrMf4nUNFJ1frcLcuDax5r19eOGPVCTCql6GYbNR-3tfgbYhGQx35FEV3fvhjfl8YBMIoGI8_ktliC_uhMsloYGgd3gi9WkcGM3AtCyC70nW2v_X9FxHfcH_F1OX17MNMD35iW446-6-eZPYAdrvNFNgY40fUxsBd4z2-wYam8KjV9MisN4vrxNbjy3yTSw5l3ZEzHtmVHLmxMqGeIb8iwjAFxS4WQJHkBI0XtP1a7TkErCov0bPaMoFM7JqyftqSv8Vv5tlHpLKd5rwxjgR8qs3HYkm5hxM9FVa3EiTe5MjXe-ihEZtgerXJi-s6SXOpLz5ILqlQ1ucFV8dO45Yr5-Tc12DDCt9dDC1hH9WCZOEz36__3cJyAzJ4zysRYcgJ3c8a9M97dncWhcf5Rzelv-84P1Sc7xpSpDNYjV3fl8vH6FCm7V77buFHiVJJyzdr4U_05atA_EYyLek8Csof9QMWIiyZW98oEcsqGz6wMN1lI64zDO5_ghUMo6Np0CRZfn_EJf09sMpJZuo6Nzx0Gqzs7bilwiryhQXo81iVc8cI6-rF4t6_bGfIw8iNIInspbSCJO2KXvaLvTiw
nutrition_tab_url = f'{google_spreadsheet_url}&gid={nutrition_tab}'
#ingredient_tab_url = f'{google_spreadsheet_url}&gid={ingredient_tab}'
nutrition_profile_df = pd.read_csv(nutrition_tab_url, index_col=0, verbose=True)
for col in ['RDI', 'UL', 'Target Scale', 'Target', 'Weight']:
nutrition_profile_df[col] = nutrition_profile_df[col].astype(float)
nutrition_profile_df = nutrition_profile_df.transpose()
ingredients_df = pd.read_csv(ingredient_tab_url, index_col=0, verbose=True).transpose()
# convert all values to float
for col in ingredients_df.columns:
ingredients_df[col] = ingredients_df[col].astype(float)
#ingredients_df
###Output
_____no_output_____
###Markdown
Problem SetupLet's cast our data into the from $\vec{y} = A \vec{x} + \vec{b}$ where $A$ is our ingredients data, $\vec{x}$ is the quantity of each ingredient for our recipe, and $\vec{b}$ is the nutrition profile.The problem to be solved is to find the quantity of each ingredient which will optimally satisfy the nutrition profile, or in our model, to minimize: $|A \vec{x} - \vec{b}|$.There are some nutrients we only want to track, but not optimize. For example, we want to know how much cholesterol is contained in our recipe, but we don't want to constrain our result to obtain a specific amount of cholesterol as a goal. The full list of ingredients are named: A_full, and b_full. The values to optimized are named: A and b
###Code
b_full = nutrition_profile_df
A_full = ingredients_df.transpose()
A = ingredients_df.transpose()[nutrition_profile_df.loc['Report Only'] == False].astype(float)
b_full = nutrition_profile_df.loc['Target']
b = nutrition_profile_df.loc['Target'][nutrition_profile_df.loc['Report Only'] == False].astype(float)
ul = nutrition_profile_df.loc['UL'][nutrition_profile_df.loc['Report Only'] == False].astype(float)
rdi = nutrition_profile_df.loc['RDI'][nutrition_profile_df.loc['Report Only'] == False].astype(float)
weight = nutrition_profile_df.loc['Weight'][nutrition_profile_df.loc['Report Only'] == False]
ul_full = nutrition_profile_df.loc['UL']
rdi_full = nutrition_profile_df.loc['RDI']
#A_full
# Constrain ingredients before the optimization process. Many of the ingredients are required for non-nutritional purposes
# or are being limited to enhance flavor
#
# The bounds units are in fractions of 100g / day, i.e.: 0.5 represents 50g / day, of the ingredient
#bounds_df = pd.DataFrame(index=ingredients_df.index, data={'lower': 0.0, 'upper': np.inf})
bounds_df = pd.DataFrame(index=ingredients_df.index, data={'lower': 0.0, 'upper': 1.0e6})
bounds_df.loc['Guar gum'] = [1.0 * .01, 1.0 * .01 + .0001]
bounds_df.loc['Xanthan Gum'] = [1.0 * .01, 1.0 * .01 + .0001]
bounds_df.loc['Alpha-galactosidase enzyme (Beano)'] = [1.0, 1.0 + .0001]
bounds_df.loc['Multivitamin'] = [1.0, 1.0 + .0001]
bounds_df.loc['Corn flour, nixtamalized'] = [0, 1.0]
bounds_df.loc['Whey protein'] = [0.0,0.15]
bounds_df.loc['Ascorbic acid'] = [0.01, 0.01 + .0001]
bounds_df.loc['Peanut butter'] = [0.70, 5.0]
bounds_df.loc['Wheat bran, crude'] = [0.25, 5.0]
bounds_df.loc['Flaxseed, fresh ground'] = [0.25, 5.0]
bounds_df.loc['Choline Bitartrate'] = [0.0, 0.05]
bounds_df.loc['Potassium chloride'] = [0.0, 0.15]
bounds_df.loc['Canola oil'] = [0.01, 5.0] # Minimum amount set to allow mixing of gums, dry pills, and lecithin
lower = bounds_df.lower.values
upper = bounds_df.upper.values
lower.shape, upper.shape
x0 = np.array(lower)
bounds = pd.DataFrame( data = {'lower': lower, 'upper': upper}, dtype=float)
a = 100.; b = 2.; c = a; k = 10
a = 20.; b = 2.; c = a; k = 10
a = 5.; b = 0.1 ; c = a; k = 5
#a = 12.; b = 0.1 ; c = 10.; k = 10.
#u0 = (rdi + np.log(rdi)); u0.name = 'u0'
#u0 = rdi * (1 + log(a))
u0 = rdi / (1 - log(k) / a)
u1 = ul / (log(k) / c + 1)
#u1 = ul - np.log(ul); u1.name = 'u1'
# Some objectives are singular without an upper/lower bound
u1.loc[u0 > u1] = (u0 + u1) / 2.0
#u = pd.concat([limits, pd.Series(y0,scale_limits.index, name='y0')], axis=1)
def obj(x):
y0 = A.dot(x.transpose())
obj_vec = (np.exp(a * (u0 - y0)/u0) + np.exp(b * (y0 - u0)/u0) + np.nan_to_num(np.exp(c * (y0 - u1)/u1))) * weight
#print(f'obj_vec: {obj_vec[0]}, y0: {y0[0]}, u0: {u0[0]}')
return(np.sum(obj_vec))
rdi[26], u0[26], u1[26], ul[26]
rdi[0:5], u0[0:5], u1[0:5], ul[0:5]
#np.log(rdi)[26]
#u1
#rdi[u0 > u1], u0[u0 > u1], u1[u0 > u1], ul[u0 > u1]
u0['Calories (kcal)'], u1['Calories (kcal)']
solution = minimize(obj, x0, method='SLSQP', bounds=list(zip(lower, upper)), options = {'maxiter': 1000})
solution.success
A_full.dot(solution.x).astype(int)
# Scale the ingredient nutrient amounts for the given quantity of each ingredient given by the optimizer
solution_df = A_full.transpose().mul(solution.x, axis=0) # Scale each nutrient vector per ingredient by the amount of the ingredient
solution_df.insert(0, 'Quantity (g)', solution.x * 100) # Scale to 100 g since that is basis for the nutrient quantities
# Add a row showing the sum of the scaled amount of each nutrient
total = solution_df.sum()
total.name = 'Total'
solution_df = solution_df.append(total)
# Plot the macro nutrient profile
# The ratio of Calories for protein:carbohydrates:fat is 4:4:9 kcal/g
pc = solution_df['Protein (g)']['Total'] * 4.0
cc = solution_df['Carbohydrates (g)']['Total'] * 4.0
fc = solution_df['Total Fat (g)']['Total'] * 9.0
tc = pc + cc + fc
p_pct = int(round(pc / tc * 100))
c_pct = int(round(cc / tc * 100))
f_pct = int(round(fc / tc * 100))
(p_pct, c_pct, f_pct)
# create data
names=f'Protein Cal {p_pct}%', f'Carbohydrates Cal {c_pct}%', f'Fat Cal {f_pct}%',
size=[p_pct, c_pct, f_pct]
fig = plt.figure(figsize=(10, 5))
fig.add_subplot(1,2,1)
# Create a circle for the center of the plot
my_circle=plt.Circle( (0,0), 0.5, color='white')
# Give color names
cmap = plt.get_cmap('Spectral')
sm = plt.cm.ScalarMappable(cmap=cmap)
colors = ['yellow','orange','red']
plt.pie(size, labels=names, colors=colors)
#p=plt.gcf()
#p.gca().add_artist(my_circle)
fig.gca().add_artist(my_circle)
#plt.show()
fig.add_subplot(1,2,2)
barWidth = 1
fs = [solution_df['Soluble Fiber (g)']['Total']]
fi = [solution_df['Insoluble Fiber (g)']['Total']]
plt.bar([0], fs, color='blue', edgecolor='white', width=barWidth, label=['Soluble Fiber (g)'])
plt.bar([0], fi, bottom=fs, color='green', edgecolor='white', width=barWidth, label=['Insoluble Fiber (g)'])
plt.show()
# Also show the Omega-3, Omega-6 ratio
# Saturated:Monounsaturated:Polyunsaturated ratios
# Prepare data as a whole for plotting by normalizing and scaling
amounts = solution_df
total = A_full.dot(solution.x) #solution_df.loc['Total']
# Normalize as a ratio beyond RDI
norm = (total) / rdi_full
norm_ul = (ul_full) / rdi_full
nuts = pd.concat([pd.Series(norm.values, name='value'), pd.Series(norm.index, name='name')], axis=1)
# Setup categories of nutrients and a common plotting function
vitamins = ['Vitamin A (IU)','Vitamin B6 (mg)','Vitamin B12 (ug)','Vitamin C (mg)','Vitamin D (IU)',
'Vitamin E (IU)','Vitamin K (ug)','Thiamin (mg)','Riboflavin (mg)','Niacin (mg)','Folate (ug)','Pantothenic Acid (mg)','Biotin (ug)','Choline (mg)']
minerals = ['Calcium (g)','Chloride (g)','Chromium (ug)','Copper (mg)','Iodine (ug)','Iron (mg)',
'Magnesium (mg)','Manganese (mg)','Molybdenum (ug)','Phosphorus (g)','Potassium (g)','Selenium (ug)','Sodium (g)','Sulfur (g)','Zinc (mg)']
essential_aminoacids = ['Cystine (mg)','Histidine (mg)','Isoleucine (mg)','Leucine (mg)','Lysine (mg)',
'Methionine (mg)','Phenylalanine (mg)','Threonine (mg)','Tryptophan (mg)','Valine (mg)']
other_aminoacids = ['Tyrosine (mg)','Arginine (mg)','Alanine (mg)','Aspartic acid (mg)','Glutamic acid (mg)','Glycine (mg)','Proline (mg)','Serine (mg)','Hydroxyproline (mg)']
def plot_group(nut_names, title):
nut_names_short = [s.split(' (')[0] for s in nut_names] # Snip off the units from the nutrient names
# Create a bar to indicate an upper limit
ul_bar = (norm_ul * 1.04)[nut_names]
ul_bar[ul_full[nut_names].isnull() == True] = 0
# Create a bar to mask the UL bar so just the end is exposed
ul_mask = norm_ul[nut_names]
ul_mask[ul_full[nut_names].isnull() == True] = 0
n = [] # normalized values for each bar
for x, mx in zip(norm[nut_names], ul_mask.values):
if mx == 0: # no upper limit
if x < 1.0:
n.append(1.0 - (x / 2.0))
else:
n.append(0.50)
else:
n.append(1.0 - (log10(x) / log10(mx)))
clrs = sm.to_rgba(n, norm=False)
g = sns.barplot(x=ul_bar.values, y=nut_names_short, color='red')
g.set_xscale('log')
sns.barplot(x=ul_mask.values, y=nut_names_short, color='white')
bax = sns.barplot(x=norm[nut_names], y=nut_names_short, label="Total", palette=clrs)
# Add a legend and informative axis label
g.set( ylabel="",xlabel="Nutrient Mass / RDI (Red Band is UL)", title=title)
#sns.despine(left=True, bottom=True)
# Construct a group of bar charts for each nutrient group
# Setup the colormap for each bar
cmap = plt.get_cmap('Spectral')
sm = plt.cm.ScalarMappable(cmap=cmap)
#fig = plt.figure(figsize=plt.figaspect(3.))
fig = plt.figure(figsize=(20, 20))
fig.add_subplot(4, 1, 1)
plot_group(vitamins,'Vitamin amounts relative to RDI')
fig.add_subplot(4, 1, 2)
plot_group(minerals,'Mineral amounts relative to RDI')
fig.add_subplot(4, 1, 3)
plot_group(essential_aminoacids,'Essential amino acid amounts relative to RDI')
fig.add_subplot(4, 1, 4)
plot_group(other_aminoacids,'Other amino acid amounts relative to RDI')
#fig.show()
fig.tight_layout()
#solu_amount = (solution_df['Quantity (g)'] * 14).astype(int)
pd.options.display.float_format = "{:,.2f}".format
solu_amount = solution_df['Quantity (g)']
solu_amount.index.name = 'Ingredient'
#solu_amount['Batch (g)'] = solu_amount * 14.0
solu_amount.reset_index()
recipe = pd.DataFrame(solu_amount)
recipe['Batch (g)'] = recipe['Quantity (g)'] * 14.0
recipe
A_full.dot(solution.x).astype(int)['Calories (kcal)']
recipe['Quantity (g)']['Total']
calories = A_full.dot(solution.x).astype(int)['Calories (kcal)']
weight = recipe['Quantity (g)']['Total'] - recipe['Quantity (g)']['Vitamin K'] - recipe['Quantity (g)']['Alpha-galactosidase enzyme (Beano)'] - recipe['Quantity (g)']['Multivitamin'] - recipe['Quantity (g)']['Calcium and vitamin D']
cal_by_100g = int(calories / weight * 100.)
'Calories per 100 g:', cal_by_100g
'Volume of 1/7 of pitcher:', 2000 / 7
meal = pd.DataFrame.from_dict({'Calories (kcal)': range(1000, 2600, 100)})
meal['Measure (g)'] = round(meal['Calories (kcal)'] / cal_by_100g * 100)
print('Amount to measure daily for calorie goal: ')
meal
###Output
Amount to measure daily for calorie goal:
|
spam/03_spam_data_slicing_tutorial.ipynb
|
###Markdown
✂️ Snorkel Intro Tutorial: _Data Slicing_In real-world applications, some model outcomes are often more important than others — e.g. vulnerable cyclist detections in an autonomous driving task, or, in our running **spam** application, potentially malicious link redirects to external websites.Traditional machine learning systems optimize for overall quality, which may be too coarse-grained.Models that achieve high overall performance might produce unacceptable failure rates on critical slices of the data — data subsets that might correspond to vulnerable cyclist detection in an autonomous driving task, or in our running spam detection application, external links to potentially malicious websites.In this tutorial, we:1. **Introduce _Slicing Functions (SFs)_** as a programming interface1. **Monitor** application-critical data subsets2. **Improve model performance** on slices First, we'll set up our notebook for reproducibility and proper logging.
###Code
import logging
import os
from snorkel.utils import set_seed
# For reproducibility
os.environ["PYTHONHASHSEED"] = "0"
set_seed(111)
# Make sure we're running from the spam/ directory
if os.path.basename(os.getcwd()) == "snorkel-tutorials":
os.chdir("spam")
# To visualize logs
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
###Output
_____no_output_____
###Markdown
If you want to display all comment text untruncated, change `DISPLAY_ALL_TEXT` to `True` below.
###Code
import pandas as pd
DISPLAY_ALL_TEXT = False
pd.set_option("display.max_colwidth", 0 if DISPLAY_ALL_TEXT else 50)
###Output
_____no_output_____
###Markdown
_Note:_ this tutorial differs from the labeling tutorial in that we use ground truth labels in the train split for demo purposes.SFs are intended to be used *after the training set has already been labeled* by LFs (or by hand) in the training data pipeline.
###Code
from utils import load_spam_dataset
df_train, df_valid, df_test = load_spam_dataset(load_train_labels=True, split_dev=False)
###Output
_____no_output_____
###Markdown
1. Write slicing functionsWe leverage *slicing functions* (SFs), which output binary _masks_ indicating whether an data point is in the slice or not.Each slice represents some noisily-defined subset of the data (corresponding to an SF) that we'd like to programmatically monitor. In the following cells, we use the [`@slicing_function()`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slicing_function.htmlsnorkel.slicing.slicing_function) decorator to initialize an SF that identifies shortened links the spam dataset.These links could redirect us to potentially dangerous websites, and we don't want our users to click them!To select the subset of shortened links in our dataset, we write a regex that checks for the commonly-used `.ly` extension.You'll notice that the `short_link` SF is a heuristic, like the other programmatic ops we've defined, and may not fully cover the slice of interest.That's okay — in last section, we'll show how a model can handle this in Snorkel.
###Code
import re
from snorkel.slicing import slicing_function
@slicing_function()
def short_link(x):
"""Returns whether text matches common pattern for shortened ".ly" links."""
return bool(re.search(r"\w+\.ly", x.text))
sfs = [short_link]
###Output
_____no_output_____
###Markdown
Visualize slices With a utility function, [`slice_dataframe`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slice_dataframe.htmlsnorkel.slicing.slice_dataframe), we can visualize data points belonging to this slice in a `pandas.DataFrame`.
###Code
from snorkel.slicing import slice_dataframe
short_link_df = slice_dataframe(df_valid, short_link)
short_link_df[["text", "label"]]
###Output
_____no_output_____
###Markdown
2. Monitor slice performance with [`Scorer.score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) In this section, we'll demonstrate how we might monitor slice performance on the `short_link` slice — this approach is compatible with _any modeling framework_. Train a simple classifierFirst, we featurize the data — as you saw in the introductory Spam tutorial, we can extract simple bag-of-words features and store them as numpy arrays.
###Code
from sklearn.feature_extraction.text import CountVectorizer
from utils import df_to_features
vectorizer = CountVectorizer(ngram_range=(1, 1))
X_train, Y_train = df_to_features(vectorizer, df_train, "train")
X_valid, Y_valid = df_to_features(vectorizer, df_valid, "valid")
X_test, Y_test = df_to_features(vectorizer, df_test, "test")
###Output
_____no_output_____
###Markdown
We define a `LogisticRegression` model from `sklearn` and show how we might visualize these slice-specific scores.
###Code
from sklearn.linear_model import LogisticRegression
sklearn_model = LogisticRegression(C=0.001, solver="liblinear")
sklearn_model.fit(X=X_train, y=Y_train)
print(f"Test set accuracy: {100 * sklearn_model.score(X_test, Y_test):.1f}%")
from snorkel.utils import preds_to_probs
preds_test = sklearn_model.predict(X_test)
probs_test = preds_to_probs(preds_test, 2)
###Output
_____no_output_____
###Markdown
Store slice metadata in `S` We apply our list of `sfs` to the data using an SF applier.For our data format, we leverage the [`PandasSFApplier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.PandasSFApplier.htmlsnorkel.slicing.PandasSFApplier).The output of the `applier` is an [`np.recarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html) which stores vectors in named fields indicating whether each of $n$ data points belongs to the corresponding slice.
###Code
from snorkel.slicing import PandasSFApplier
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
###Output
0%| | 0/250 [00:00<?, ?it/s]
###Markdown
Now, we initialize a [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer) using the desired `metrics`.
###Code
from snorkel.analysis import Scorer
scorer = Scorer(metrics=["accuracy", "f1"])
###Output
_____no_output_____
###Markdown
Using the [`score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) method, we can see both `overall` and slice-specific performance.
###Code
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Despite high overall performance, the `short_link` slice performs poorly here! Write additional slicing functions (SFs)Slices are dynamic — as monitoring needs grow or change with new data distributions or application needs, an ML pipeline might require dozens, or even hundreds, of slices.We'll take inspiration from the labeling tutorial to write additional slicing functions.We demonstrate how the same powerful preprocessors and utilities available for labeling functions can be leveraged for slicing functions.
###Code
from snorkel.slicing import SlicingFunction, slicing_function
from snorkel.preprocess import preprocessor
# Keyword-based SFs
def keyword_lookup(x, keywords):
return any(word in x.text.lower() for word in keywords)
def make_keyword_sf(keywords):
return SlicingFunction(
name=f"keyword_{keywords[0]}",
f=keyword_lookup,
resources=dict(keywords=keywords),
)
keyword_subscribe = make_keyword_sf(keywords=["subscribe"])
keyword_please = make_keyword_sf(keywords=["please", "plz"])
# Regex-based SF
@slicing_function()
def regex_check_out(x):
return bool(re.search(r"check.*out", x.text, flags=re.I))
# Heuristic-based SF
@slicing_function()
def short_comment(x):
"""Ham comments are often short, such as 'cool video!'"""
return len(x.text.split()) < 5
# Leverage preprocessor in SF
from textblob import TextBlob
@preprocessor(memoize=True)
def textblob_sentiment(x):
scores = TextBlob(x.text)
x.polarity = scores.sentiment.polarity
return x
@slicing_function(pre=[textblob_sentiment])
def textblob_polarity(x):
return x.polarity > 0.9
###Output
_____no_output_____
###Markdown
Again, we'd like to visualize data points in a particular slice. This time, we'll inspect the `textblob_polarity` slice.Most data points with high-polarity sentiments are strong opinions about the video — hence, they are usually relevant to the video, and the corresponding labels are $0$.We might define a slice here for *product and marketing reasons*, it's important to make sure that we don't misclassify very positive comments from good users.
###Code
polarity_df = slice_dataframe(df_valid, textblob_polarity)
polarity_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
We can evaluate performance on _all SFs_ using the model-agnostic [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel-analysis-scorer).
###Code
extra_sfs = [
keyword_subscribe,
keyword_please,
regex_check_out,
short_comment,
textblob_polarity,
]
sfs = [short_link] + extra_sfs
slice_names = [sf.name for sf in sfs]
###Output
_____no_output_____
###Markdown
Let's see how the `sklearn` model we learned before performs on these new slices!
###Code
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Looks like some do extremely well on our small test set, while others do decently.At the very least, we may want to monitor these to make sure that as we iterate to improve certain slices like `short_link`, we don't hurt the performance of others.Next, we'll introduce a model that helps us to do this balancing act automatically! 3. Improve slice performanceIn the following section, we demonstrate a modeling approach that we call _Slice-based Learning,_ which improves performance by adding extra slice-specific representational capacity to whichever model we're using.Intuitively, we'd like to model to learn *representations that are better suited to handle data points in this slice*.In our approach, we model each slice as a separate "expert task" in the style of [multi-task learning](https://github.com/snorkel-team/snorkel-tutorials/blob/master/multitask/multitask_tutorial.ipynb); for further details of how slice-based learning works under the hood, check out the [code](https://github.com/snorkel-team/snorkel/blob/master/snorkel/slicing/utils.py) (with paper coming soon)!In other approaches, one might attempt to increase slice performance with techniques like _oversampling_ (i.e. with PyTorch's [`WeightedRandomSampler`](https://pytorch.org/docs/stable/data.htmltorch.utils.data.WeightedRandomSampler)), effectively shifting the training distribution towards certain populations.This might work with small number of slices, but with hundreds or thousands or production slices at scale, it could quickly become intractable to tune upsampling weights per slice. Set up modeling pipeline with [`SlicingClassifier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.SlicingClassifier.html)Snorkel supports performance monitoring on slices using discriminative models from [`snorkel.slicing`](https://snorkel.readthedocs.io/en/master/packages/slicing.html).To demonstrate this functionality, we'll first set up a the datasets + modeling pipeline in the PyTorch-based [`snorkel.classification`](https://snorkel.readthedocs.io/en/master/packages/classification.html) package. First, we initialize a dataloaders for each split.
###Code
from utils import create_dict_dataloader
BATCH_SIZE = 64
train_dl = create_dict_dataloader(
X_train, Y_train, "train", batch_size=BATCH_SIZE, shuffle=True
)
valid_dl = create_dict_dataloader(
X_valid, Y_valid, "valid", batch_size=BATCH_SIZE, shuffle=False
)
test_dl = create_dict_dataloader(
X_test, Y_test, "test", batch_size=BATCH_SIZE, shuffle=True
)
###Output
_____no_output_____
###Markdown
We'll now initialize a [`SlicingClassifier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.SlicingClassifier.html):* `base_architecture`: We define a simple Multi-Layer Perceptron (MLP) in Pytorch to serve as the primary representation architecture. We note that the `BinarySlicingClassifier` is **agnostic to the base architecture** — you might leverage a Transformer model for text, or a ResNet for images.* `head_dim`: identifies the final output feature dimension of the `base_architecture`* `slice_names`: Specify the slices that we plan to train on with this classifier.
###Code
from snorkel.slicing import SlicingClassifier
from utils import get_pytorch_mlp
# Define model architecture
bow_dim = X_train.shape[1]
hidden_dim = bow_dim
mlp = get_pytorch_mlp(hidden_dim=hidden_dim, num_layers=2)
# Init slice model
slice_model = SlicingClassifier(
base_architecture=mlp, head_dim=hidden_dim, slice_names=[sf.name for sf in sfs]
)
###Output
_____no_output_____
###Markdown
Monitor slice performance _during training_Using Snorkel's [`Trainer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/classification/snorkel.classification.Trainer.html), we fit to `train_dl`, and validate on `valid_dl`.We note that we can monitor slice-specific performance during training — this is a powerful way to track especially critical subsets of the data.If logging in `Tensorboard` (i.e. [`snorkel.classification.TensorboardWritier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/classification/snorkel.classification.TensorBoardWriter.html)), we would visualize individual loss curves and validation metrics to debug convegence for specific slices.
###Code
from snorkel.classification import Trainer
# For demonstration purposes, we set n_epochs=2
trainer = Trainer(lr=1e-4, n_epochs=2)
trainer.fit(slice_model, [train_dl, valid_dl])
###Output
Epoch 0:: 0%| | 0/25 [00:00<?, ?it/s]
###Markdown
Representation learning with slices To cope with scale, we will attempt to learn and combine many slice-specific representations with an attention mechanism.(For details about this approach, please see our technical report — coming soon!) First, we'll generate the remaining `S` matrixes with the new set of slicing functions.
###Code
applier = PandasSFApplier(sfs)
S_train = applier.apply(df_train)
S_valid = applier.apply(df_valid)
###Output
0%| | 0/1586 [00:00<?, ?it/s]
###Markdown
In order to train using slice information, we'd like to initialize a **slice-aware dataloader**.To do this, we can use [`slice_model.make_slice_dataloader`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.SlicingClassifier.htmlsnorkel.slicing.SlicingClassifier.predict) to add slice labels to an existing dataloader.Under the hood, this method leverages slice metadata to add slice labels to the appropriate fields such that it's compatible with the initialized [`SliceClassifier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.SlicingClassifier.htmlsnorkel-slicing-slicingclassifier).
###Code
train_dl_slice = slice_model.make_slice_dataloader(
train_dl.dataset, S_train, shuffle=True, batch_size=BATCH_SIZE
)
valid_dl_slice = slice_model.make_slice_dataloader(
valid_dl.dataset, S_valid, shuffle=False, batch_size=BATCH_SIZE
)
test_dl_slice = slice_model.make_slice_dataloader(
test_dl.dataset, S_test, shuffle=False, batch_size=BATCH_SIZE
)
###Output
_____no_output_____
###Markdown
We train a single model initialized with all slice tasks.
###Code
from snorkel.classification import Trainer
# For demonstration purposes, we set n_epochs=2
trainer = Trainer(n_epochs=2, lr=1e-4, progress_bar=True)
trainer.fit(slice_model, [train_dl_slice, valid_dl_slice])
###Output
Epoch 0:: 0%| | 0/25 [00:00<?, ?it/s]
###Markdown
At inference time, the primary task head (`spam_task`) will make all final predictions.We'd like to evaluate all the slice heads on the original task head — [`score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.SlicingClassifier.htmlsnorkel.slicing.SlicingClassifier.score_slices) remaps all slice-related labels, denoted `spam_task_slice:{slice_name}_pred`, to be evaluated on the `spam_task`.
###Code
slice_model.score_slices([valid_dl_slice, test_dl_slice], as_dataframe=True)
###Output
_____no_output_____
###Markdown
✂️ Snorkel Intro Tutorial: _Data Slicing_In real-world applications, some model outcomes are often more important than others — e.g. vulnerable cyclist detections in an autonomous driving task, or, in our running **spam** application, potentially malicious link redirects to external websites.Traditional machine learning systems optimize for overall quality, which may be too coarse-grained.Models that achieve high overall performance might produce unacceptable failure rates on critical slices of the data — data subsets that might correspond to vulnerable cyclist detection in an autonomous driving task, or in our running spam detection application, external links to potentially malicious websites.In this tutorial, we:1. **Introduce _Slicing Functions (SFs)_** as a programming interface1. **Monitor** application-critical data subsets2. **Improve model performance** on slices First, we'll set up our notebook for reproducibility and proper logging.
###Code
import logging
import os
import numpy as np
import random
import torch
# For reproducibility
os.environ["PYTHONHASHSEED"] = "0"
SEED = 123
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
# Make sure we're running from the spam/ directory
if os.path.basename(os.getcwd()) == "snorkel-tutorials":
os.chdir("spam")
# To visualize logs
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
###Output
_____no_output_____
###Markdown
If you want to display all comment text untruncated, change `DISPLAY_ALL_TEXT` to `True` below.
###Code
import pandas as pd
DISPLAY_ALL_TEXT = False
pd.set_option("display.max_colwidth", 0 if DISPLAY_ALL_TEXT else 50)
###Output
_____no_output_____
###Markdown
_Note:_ this tutorial differs from the labeling tutorial in that we use ground truth labels in the train split for demo purposes.SFs are intended to be used *after the training set has already been labeled* by LFs (or by hand) in the training data pipeline.
###Code
from utils import load_spam_dataset
df_train, df_valid, df_test = load_spam_dataset(load_train_labels=True, split_dev=False)
###Output
_____no_output_____
###Markdown
1. Write slicing functionsWe leverage *slicing functions* (SFs), which output binary _masks_ indicating whether an data point is in the slice or not.Each slice represents some noisily-defined subset of the data (corresponding to an SF) that we'd like to programmatically monitor. In the following cells, we use the [`@slicing_function()`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slicing_function.htmlsnorkel.slicing.slicing_function) decorator to initialize an SF that identifies shortened links the spam dataset.These links could redirect us to potentially dangerous websites, and we don't want our users to click them!To select the subset of shortened links in our dataset, we write a regex that checks for the commonly-used `.ly` extension.You'll notice that the `short_link` SF is a heuristic, like the other programmatic ops we've defined, and may not fully cover the slice of interest.That's okay — in last section, we'll show how a model can handle this in Snorkel.
###Code
import re
from snorkel.slicing import slicing_function
@slicing_function()
def short_link(x):
"""Returns whether text matches common pattern for shortened ".ly" links."""
return bool(re.search(r"\w+\.ly", x.text))
sfs = [short_link]
###Output
_____no_output_____
###Markdown
Visualize slices With a utility function, [`slice_dataframe`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slice_dataframe.htmlsnorkel.slicing.slice_dataframe), we can visualize data points belonging to this slice in a `pandas.DataFrame`.
###Code
from snorkel.slicing import slice_dataframe
short_link_df = slice_dataframe(df_valid, short_link)
short_link_df[["text", "label"]]
###Output
_____no_output_____
###Markdown
2. Monitor slice performance with [`Scorer.score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) In this section, we'll demonstrate how we might monitor slice performance on the `short_link` slice — this approach is compatible with _any modeling framework_. Train a simple classifierFirst, we featurize the data — as you saw in the introductory Spam tutorial, we can extract simple bag-of-words features and store them as numpy arrays.
###Code
from sklearn.feature_extraction.text import CountVectorizer
from utils import df_to_features
vectorizer = CountVectorizer(ngram_range=(1, 1))
X_train, Y_train = df_to_features(vectorizer, df_train, "train")
X_valid, Y_valid = df_to_features(vectorizer, df_valid, "valid")
X_test, Y_test = df_to_features(vectorizer, df_test, "test")
###Output
_____no_output_____
###Markdown
We define a `LogisticRegression` model from `sklearn` and show how we might visualize these slice-specific scores.
###Code
from sklearn.linear_model import LogisticRegression
sklearn_model = LogisticRegression(C=0.001, solver="liblinear")
sklearn_model.fit(X=X_train, y=Y_train)
print(f"Test set accuracy: {100 * sklearn_model.score(X_test, Y_test):.1f}%")
from snorkel.utils import preds_to_probs
preds_test = sklearn_model.predict(X_test)
probs_test = preds_to_probs(preds_test, 2)
###Output
_____no_output_____
###Markdown
Store slice metadata in `S` We apply our list of `sfs` to the data using an SF applier.For our data format, we leverage the [`PandasSFApplier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.PandasSFApplier.htmlsnorkel.slicing.PandasSFApplier).The output of the `applier` is an [`np.recarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html) which stores vectors in named fields indicating whether each of $n$ data points belongs to the corresponding slice.
###Code
from snorkel.slicing import PandasSFApplier
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
###Output
0%| | 0/250 [00:00<?, ?it/s]
###Markdown
Now, we initialize a [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer) using the desired `metrics`.
###Code
from snorkel.analysis import Scorer
scorer = Scorer(metrics=["accuracy", "f1"])
###Output
_____no_output_____
###Markdown
Using the [`score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) method, we can see both `overall` and slice-specific performance.
###Code
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Despite high overall performance, the `short_link` slice performs poorly here! Write additional slicing functions (SFs)Slices are dynamic — as monitoring needs grow or change with new data distributions or application needs, an ML pipeline might require dozens, or even hundreds, of slices.We'll take inspiration from the labeling tutorial to write additional slicing functions.We demonstrate how the same powerful preprocessors and utilities available for labeling functions can be leveraged for slicing functions.
###Code
from snorkel.slicing import SlicingFunction, slicing_function
from snorkel.preprocess import preprocessor
# Keyword-based SFs
def keyword_lookup(x, keywords):
return any(word in x.text.lower() for word in keywords)
def make_keyword_sf(keywords):
return SlicingFunction(
name=f"keyword_{keywords[0]}",
f=keyword_lookup,
resources=dict(keywords=keywords),
)
keyword_subscribe = make_keyword_sf(keywords=["subscribe"])
keyword_please = make_keyword_sf(keywords=["please", "plz"])
# Regex-based SF
@slicing_function()
def regex_check_out(x):
return bool(re.search(r"check.*out", x.text, flags=re.I))
# Heuristic-based SF
@slicing_function()
def short_comment(x):
"""Ham comments are often short, such as 'cool video!'"""
return len(x.text.split()) < 5
# Leverage preprocessor in SF
from textblob import TextBlob
@preprocessor(memoize=True)
def textblob_sentiment(x):
scores = TextBlob(x.text)
x.polarity = scores.sentiment.polarity
return x
@slicing_function(pre=[textblob_sentiment])
def textblob_polarity(x):
return x.polarity > 0.9
###Output
_____no_output_____
###Markdown
Again, we'd like to visualize data points in a particular slice. This time, we'll inspect the `textblob_polarity` slice.Most data points with high-polarity sentiments are strong opinions about the video — hence, they are usually relevant to the video, and the corresponding labels are $0$.We might define a slice here for *product and marketing reasons*, it's important to make sure that we don't misclassify very positive comments from good users.
###Code
polarity_df = slice_dataframe(df_valid, textblob_polarity)
polarity_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
We can evaluate performance on _all SFs_ using the model-agnostic [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel-analysis-scorer).
###Code
extra_sfs = [
keyword_subscribe,
keyword_please,
regex_check_out,
short_comment,
textblob_polarity,
]
sfs = [short_link] + extra_sfs
slice_names = [sf.name for sf in sfs]
###Output
_____no_output_____
###Markdown
Let's see how the `sklearn` model we learned before performs on these new slices!
###Code
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Looks like some do extremely well on our small test set, while others do decently.At the very least, we may want to monitor these to make sure that as we iterate to improve certain slices like `short_link`, we don't hurt the performance of others.Next, we'll introduce a model that helps us to do this balancing act automatically! 3. Improve slice performanceIn the following section, we demonstrate a modeling approach that we call _Slice-based Learning,_ which improves performance by adding extra slice-specific representational capacity to whichever model we're using.Intuitively, we'd like to model to learn *representations that are better suited to handle data points in this slice*.In our approach, we model each slice as a separate "expert task" in the style of [multi-task learning](https://github.com/snorkel-team/snorkel-tutorials/blob/master/multitask/multitask_tutorial.ipynb); for further details of how slice-based learning works under the hood, check out the [code](https://github.com/snorkel-team/snorkel/blob/master/snorkel/slicing/utils.py) (with paper coming soon)!In other approaches, one might attempt to increase slice performance with techniques like _oversampling_ (i.e. with PyTorch's [`WeightedRandomSampler`](https://pytorch.org/docs/stable/data.htmltorch.utils.data.WeightedRandomSampler)), effectively shifting the training distribution towards certain populations.This might work with small number of slices, but with hundreds or thousands or production slices at scale, it could quickly become intractable to tune upsampling weights per slice. Set up modeling pipeline with [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html)Snorkel supports performance monitoring on slices using discriminative models from [`snorkel.slicing`](https://snorkel.readthedocs.io/en/master/packages/slicing.html).To demonstrate this functionality, we'll first set up a the datasets + modeling pipeline in the PyTorch-based [`snorkel.classification`](https://snorkel.readthedocs.io/en/master/packages/classification.html) package. First, we initialize a dataloaders for each split.
###Code
from utils import create_dict_dataloader
BATCH_SIZE = 64
train_dl = create_dict_dataloader(
X_train, Y_train, "train", batch_size=BATCH_SIZE, shuffle=True
)
valid_dl = create_dict_dataloader(
X_valid, Y_valid, "valid", batch_size=BATCH_SIZE, shuffle=False
)
test_dl = create_dict_dataloader(
X_test, Y_test, "test", batch_size=BATCH_SIZE, shuffle=True
)
###Output
_____no_output_____
###Markdown
We'll now initialize a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html):* `base_architecture`: We define a simple Multi-Layer Perceptron (MLP) in Pytorch to serve as the primary representation architecture. We note that the `BinarySlicingClassifier` is **agnostic to the base architecture** — you might leverage a Transformer model for text, or a ResNet for images.* `head_dim`: identifies the final output feature dimension of the `base_architecture`* `slice_names`: Specify the slices that we plan to train on with this classifier.
###Code
from snorkel.slicing import SliceAwareClassifier
from utils import get_pytorch_mlp
# Define model architecture
bow_dim = X_train.shape[1]
hidden_dim = bow_dim
mlp = get_pytorch_mlp(hidden_dim=hidden_dim, num_layers=2)
# Init slice model
slice_model = SliceAwareClassifier(
base_architecture=mlp, head_dim=hidden_dim, slice_names=[sf.name for sf in sfs]
)
###Output
_____no_output_____
###Markdown
Monitor slice performance _during training_Using Snorkel's [`Trainer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/classification/snorkel.classification.Trainer.html), we fit to `train_dl`, and validate on `valid_dl`.We note that we can monitor slice-specific performance during training — this is a powerful way to track especially critical subsets of the data.If logging in `Tensorboard` (i.e. [`snorkel.classification.TensorboardWritier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/classification/snorkel.classification.TensorBoardWriter.html)), we would visualize individual loss curves and validation metrics to debug convegence for specific slices.
###Code
from snorkel.classification import Trainer
# For demonstration purposes, we set n_epochs=2
trainer = Trainer(lr=1e-4, n_epochs=2)
trainer.fit(slice_model, [train_dl, valid_dl])
###Output
Epoch 0:: 0%| | 0/25 [00:00<?, ?it/s]
###Markdown
Representation learning with slices To cope with scale, we will attempt to learn and combine many slice-specific representations with an attention mechanism.(Please see our [Section 3 of our technical report](https://arxiv.org/abs/1909.06349) for details on this approach). First, we'll generate the remaining `S` matrixes with the new set of slicing functions.
###Code
applier = PandasSFApplier(sfs)
S_train = applier.apply(df_train)
S_valid = applier.apply(df_valid)
###Output
0%| | 0/1586 [00:00<?, ?it/s]
###Markdown
In order to train using slice information, we'd like to initialize a **slice-aware dataloader**.To do this, we can use [`slice_model.make_slice_dataloader`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.predict) to add slice labels to an existing dataloader.Under the hood, this method leverages slice metadata to add slice labels to the appropriate fields such that it's compatible with the initialized [`SliceClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel-slicing-slicingclassifier).
###Code
train_dl_slice = slice_model.make_slice_dataloader(
train_dl.dataset, S_train, shuffle=True, batch_size=BATCH_SIZE
)
valid_dl_slice = slice_model.make_slice_dataloader(
valid_dl.dataset, S_valid, shuffle=False, batch_size=BATCH_SIZE
)
test_dl_slice = slice_model.make_slice_dataloader(
test_dl.dataset, S_test, shuffle=False, batch_size=BATCH_SIZE
)
###Output
_____no_output_____
###Markdown
We train a single model initialized with all slice tasks.
###Code
from snorkel.classification import Trainer
# For demonstration purposes, we set n_epochs=2
trainer = Trainer(n_epochs=2, lr=1e-4, progress_bar=True)
trainer.fit(slice_model, [train_dl_slice, valid_dl_slice])
###Output
Epoch 0:: 0%| | 0/25 [00:00<?, ?it/s]
###Markdown
At inference time, the primary task head (`spam_task`) will make all final predictions.We'd like to evaluate all the slice heads on the original task head — [`score_slices`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.score_slices) remaps all slice-related labels, denoted `spam_task_slice:{slice_name}_pred`, to be evaluated on the `spam_task`.
###Code
slice_model.score_slices([valid_dl_slice, test_dl_slice], as_dataframe=True)
###Output
_____no_output_____
###Markdown
✂️ Snorkel Intro Tutorial: _Data Slicing_In real-world applications, some model outcomes are often more important than others — e.g. vulnerable cyclist detections in an autonomous driving task, or, in our running **spam** application, potentially malicious link redirects to external websites.Traditional machine learning systems optimize for overall quality, which may be too coarse-grained.Models that achieve high overall performance might produce unacceptable failure rates on critical slices of the data — data subsets that might correspond to vulnerable cyclist detection in an autonomous driving task, or in our running spam detection application, external links to potentially malicious websites.In this tutorial, we:1. **Introduce _Slicing Functions (SFs)_** as a programming interface1. **Monitor** application-critical data subsets2. **Improve model performance** on slices First, we'll set up our notebook for reproducibility and proper logging.
###Code
import logging
import os
import numpy as np
import random
import torch
# For reproducibility
os.environ["PYTHONHASHSEED"] = "0"
SEED = 123
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
# Make sure we're running from the spam/ directory
if os.path.basename(os.getcwd()) == "snorkel-tutorials":
os.chdir("spam")
# To visualize logs
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
###Output
_____no_output_____
###Markdown
If you want to display all comment text untruncated, change `DISPLAY_ALL_TEXT` to `True` below.
###Code
import pandas as pd
DISPLAY_ALL_TEXT = False
pd.set_option("display.max_colwidth", 0 if DISPLAY_ALL_TEXT else 50)
###Output
_____no_output_____
###Markdown
_Note:_ this tutorial differs from the labeling tutorial in that we use ground truth labels in the train split for demo purposes.SFs are intended to be used *after the training set has already been labeled* by LFs (or by hand) in the training data pipeline.
###Code
from utils import load_spam_dataset
df_train, df_test = load_spam_dataset(load_train_labels=True)
###Output
_____no_output_____
###Markdown
1. Write slicing functionsWe leverage *slicing functions* (SFs), which output binary _masks_ indicating whether an data point is in the slice or not.Each slice represents some noisily-defined subset of the data (corresponding to an SF) that we'd like to programmatically monitor. In the following cells, we use the [`@slicing_function()`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slicing_function.htmlsnorkel.slicing.slicing_function) decorator to initialize an SF that identifies short commentsYou'll notice that the `short_comment` SF is a heuristic, like the other programmatic ops we've defined, and may not fully cover the slice of interest.That's okay — in last section, we'll show how a model can handle this in Snorkel.
###Code
import re
from snorkel.slicing import slicing_function
@slicing_function()
def short_comment(x):
"""Ham comments are often short, such as 'cool video!'"""
return len(x.text.split()) < 5
sfs = [short_comment]
###Output
_____no_output_____
###Markdown
Visualize slices With a utility function, [`slice_dataframe`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slice_dataframe.htmlsnorkel.slicing.slice_dataframe), we can visualize data points belonging to this slice in a `pandas.DataFrame`.
###Code
from snorkel.slicing import slice_dataframe
short_comment_df = slice_dataframe(df_test, short_comment)
short_comment_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
2. Monitor slice performance with [`Scorer.score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) In this section, we'll demonstrate how we might monitor slice performance on the `short_comment` slice — this approach is compatible with _any modeling framework_. Train a simple classifierFirst, we featurize the data — as you saw in the introductory Spam tutorial, we can extract simple bag-of-words features and store them as numpy arrays.
###Code
from sklearn.feature_extraction.text import CountVectorizer
from utils import df_to_features
vectorizer = CountVectorizer(ngram_range=(1, 1))
X_train, Y_train = df_to_features(vectorizer, df_train, "train")
X_test, Y_test = df_to_features(vectorizer, df_test, "test")
###Output
_____no_output_____
###Markdown
We define a `LogisticRegression` model from `sklearn`.
###Code
from sklearn.linear_model import LogisticRegression
sklearn_model = LogisticRegression(C=0.001, solver="liblinear")
sklearn_model.fit(X=X_train, y=Y_train)
from snorkel.utils import preds_to_probs
preds_test = sklearn_model.predict(X_test)
probs_test = preds_to_probs(preds_test, 2)
from sklearn.metrics import f1_score
print(f"Test set F1: {100 * f1_score(Y_test, preds_test):.1f}%")
###Output
Test set F1: 92.5%
###Markdown
Store slice metadata in `S` We apply our list of `sfs` to the data using an SF applier.For our data format, we leverage the [`PandasSFApplier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.PandasSFApplier.htmlsnorkel.slicing.PandasSFApplier).The output of the `applier` is an [`np.recarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html) which stores vectors in named fields indicating whether each of $n$ data points belongs to the corresponding slice.
###Code
from snorkel.slicing import PandasSFApplier
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
###Output
0%| | 0/250 [00:00<?, ?it/s]
###Markdown
Now, we initialize a [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer) using the desired `metrics`.
###Code
from snorkel.analysis import Scorer
scorer = Scorer(metrics=["f1"])
###Output
_____no_output_____
###Markdown
Using the [`score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) method, we can see both `overall` and slice-specific performance.
###Code
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Despite high overall performance, the `short_comment` slice performs poorly here! Write additional slicing functions (SFs)Slices are dynamic — as monitoring needs grow or change with new data distributions or application needs, an ML pipeline might require dozens, or even hundreds, of slices.We'll take inspiration from the labeling tutorial to write additional slicing functions.We demonstrate how the same powerful preprocessors and utilities available for labeling functions can be leveraged for slicing functions.
###Code
from snorkel.slicing import SlicingFunction, slicing_function
from snorkel.preprocess import preprocessor
# Keyword-based SFs
def keyword_lookup(x, keywords):
return any(word in x.text.lower() for word in keywords)
def make_keyword_sf(keywords):
return SlicingFunction(
name=f"keyword_{keywords[0]}",
f=keyword_lookup,
resources=dict(keywords=keywords),
)
keyword_please = make_keyword_sf(keywords=["please", "plz"])
# Regex-based SFs
@slicing_function()
def regex_check_out(x):
return bool(re.search(r"check.*out", x.text, flags=re.I))
@slicing_function()
def short_link(x):
"""Returns whether text matches common pattern for shortened ".ly" links."""
return bool(re.search(r"\w+\.ly", x.text))
# Leverage preprocessor in SF
from textblob import TextBlob
@preprocessor(memoize=True)
def textblob_sentiment(x):
scores = TextBlob(x.text)
x.polarity = scores.sentiment.polarity
return x
@slicing_function(pre=[textblob_sentiment])
def textblob_polarity(x):
return x.polarity > 0.9
###Output
_____no_output_____
###Markdown
Again, we'd like to visualize data points in a particular slice. This time, we'll inspect the `textblob_polarity` slice.Most data points with high-polarity sentiments are strong opinions about the video — hence, they are usually relevant to the video, and the corresponding labels are $0$ (not spam).We might define a slice here for *product and marketing reasons*, it's important to make sure that we don't misclassify very positive comments from good users.
###Code
polarity_df = slice_dataframe(df_test, textblob_polarity)
polarity_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
We can evaluate performance on _all SFs_ using the model-agnostic [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel-analysis-scorer).
###Code
extra_sfs = [keyword_please, regex_check_out, short_link, textblob_polarity]
sfs = [short_comment] + extra_sfs
slice_names = [sf.name for sf in sfs]
###Output
_____no_output_____
###Markdown
Let's see how the `sklearn` model we learned before performs on these new slices.
###Code
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Looks like some do extremely well on our small test set, while others do decently.At the very least, we may want to monitor these to make sure that as we iterate to improve certain slices like `short_comment`, we don't hurt the performance of others.Next, we'll introduce a model that helps us to do this balancing act automatically. 3. Improve slice performanceIn the following section, we demonstrate a modeling approach that we call _Slice-based Learning,_ which improves performance by adding extra slice-specific representational capacity to whichever model we're using.Intuitively, we'd like to model to learn *representations that are better suited to handle data points in this slice*.In our approach, we model each slice as a separate "expert task" in the style of [multi-task learning](https://github.com/snorkel-team/snorkel-tutorials/blob/master/multitask/multitask_tutorial.ipynb); for further details of how slice-based learning works under the hood, check out the [code](https://github.com/snorkel-team/snorkel/blob/master/snorkel/slicing/utils.py) (with paper coming soon)!In other approaches, one might attempt to increase slice performance with techniques like _oversampling_ (i.e. with PyTorch's [`WeightedRandomSampler`](https://pytorch.org/docs/stable/data.htmltorch.utils.data.WeightedRandomSampler)), effectively shifting the training distribution towards certain populations.This might work with small number of slices, but with hundreds or thousands or production slices at scale, it could quickly become intractable to tune upsampling weights per slice. Constructing a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html) To cope with scale, we will attempt to learn and combine many slice-specific representations with an attention mechanism.(Please see our [Section 3 of our technical report](https://arxiv.org/abs/1909.06349) for details on this approach). First we'll initialize a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html):* `base_architecture`: We define a simple Multi-Layer Perceptron (MLP) in Pytorch to serve as the primary representation architecture. We note that the `BinarySlicingClassifier` is **agnostic to the base architecture** — you might leverage a Transformer model for text, or a ResNet for images.* `head_dim`: identifies the final output feature dimension of the `base_architecture`* `slice_names`: Specify the slices that we plan to train on with this classifier.
###Code
from snorkel.slicing import SliceAwareClassifier
from utils import get_pytorch_mlp
# Define model architecture
bow_dim = X_train.shape[1]
hidden_dim = bow_dim
mlp = get_pytorch_mlp(hidden_dim=hidden_dim, num_layers=2)
# Initialize slice model
slice_model = SliceAwareClassifier(
base_architecture=mlp,
head_dim=hidden_dim,
slice_names=[sf.name for sf in sfs],
scorer=scorer,
)
###Output
_____no_output_____
###Markdown
Next, we'll generate the remaining `S` matrixes with the new set of slicing functions.
###Code
applier = PandasSFApplier(sfs)
S_train = applier.apply(df_train)
S_test = applier.apply(df_test)
###Output
0%| | 0/1586 [00:00<?, ?it/s]
###Markdown
In order to train using slice information, we'd like to initialize a **slice-aware dataloader**.To do this, we can use [`slice_model.make_slice_dataloader`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.predict) to add slice labels to an existing dataloader.Under the hood, this method leverages slice metadata to add slice labels to the appropriate fields such that it will be compatible with our model, a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel-slicing-slicingclassifier).
###Code
from utils import create_dict_dataloader
BATCH_SIZE = 64
train_dl = create_dict_dataloader(X_train, Y_train, "train")
train_dl_slice = slice_model.make_slice_dataloader(
train_dl.dataset, S_train, shuffle=True, batch_size=BATCH_SIZE
)
test_dl = create_dict_dataloader(X_test, Y_test, "train")
test_dl_slice = slice_model.make_slice_dataloader(
test_dl.dataset, S_test, shuffle=False, batch_size=BATCH_SIZE
)
###Output
_____no_output_____
###Markdown
Representation learning with slices Using Snorkel's [`Trainer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/classification/snorkel.classification.Trainer.html), we fit our classifier with the training set dataloader.
###Code
from snorkel.classification import Trainer
# For demonstration purposes, we set n_epochs=2
trainer = Trainer(n_epochs=2, lr=1e-4, progress_bar=True)
trainer.fit(slice_model, [train_dl_slice])
###Output
Epoch 0:: 0%| | 0/25 [00:00<?, ?it/s]
###Markdown
At inference time, the primary task head (`spam_task`) will make all final predictions.We'd like to evaluate all the slice heads on the original task head — [`score_slices`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.score_slices) remaps all slice-related labels, denoted `spam_task_slice:{slice_name}_pred`, to be evaluated on the `spam_task`.
###Code
slice_model.score_slices([test_dl_slice], as_dataframe=True)
###Output
_____no_output_____
###Markdown
✂️ Snorkel Intro Tutorial: _Data Slicing_In real-world applications, some model outcomes are often more important than others — e.g. vulnerable cyclist detections in an autonomous driving task, or, in our running **spam** application, potentially malicious link redirects to external websites.Traditional machine learning systems optimize for overall quality, which may be too coarse-grained.Models that achieve high overall performance might produce unacceptable failure rates on critical slices of the data — data subsets that might correspond to vulnerable cyclist detection in an autonomous driving task, or in our running spam detection application, external links to potentially malicious websites.In this tutorial, we:1. **Introduce _Slicing Functions (SFs)_** as a programming interface1. **Monitor** application-critical data subsets2. **Improve model performance** on slices First, we'll set up our notebook for reproducibility and proper logging.
###Code
import logging
import os
import numpy as np
import random
import torch
# For reproducibility
os.environ["PYTHONHASHSEED"] = "0"
SEED = 123
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
# Make sure we're running from the spam/ directory
if os.path.basename(os.getcwd()) == "snorkel-tutorials":
os.chdir("spam")
# To visualize logs
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
###Output
_____no_output_____
###Markdown
If you want to display all comment text untruncated, change `DISPLAY_ALL_TEXT` to `True` below.
###Code
import pandas as pd
DISPLAY_ALL_TEXT = True
pd.set_option("display.max_colwidth", 0 if DISPLAY_ALL_TEXT else 50)
###Output
_____no_output_____
###Markdown
_Note:_ this tutorial differs from the labeling tutorial in that we use ground truth labels in the train split for demo purposes.SFs are intended to be used *after the training set has already been labeled* by LFs (or by hand) in the training data pipeline.
###Code
from utils import load_spam_dataset
df_train, df_test = load_spam_dataset(load_train_labels=True)
###Output
2021-08-09 05:29:34.760571: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
###Markdown
1. Write slicing functionsWe leverage *slicing functions* (SFs), which output binary _masks_ indicating whether an data point is in the slice or not.Each slice represents some noisily-defined subset of the data (corresponding to an SF) that we'd like to programmatically monitor. In the following cells, we use the [`@slicing_function()`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slicing_function.htmlsnorkel.slicing.slicing_function) decorator to initialize an SF that identifies short commentsYou'll notice that the `short_comment` SF is a heuristic, like the other programmatic ops we've defined, and may not fully cover the slice of interest.That's okay — in last section, we'll show how a model can handle this in Snorkel.
###Code
import re
from snorkel.slicing import slicing_function
@slicing_function()
def short_comment(x):
"""Ham comments are often short, such as 'cool video!'"""
return len(x.text.split()) < 5
sfs = [short_comment]
###Output
_____no_output_____
###Markdown
Visualize slices With a utility function, [`slice_dataframe`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slice_dataframe.htmlsnorkel.slicing.slice_dataframe), we can visualize data points belonging to this slice in a `pandas.DataFrame`.
###Code
from snorkel.slicing import slice_dataframe
short_comment_df = slice_dataframe(df_test, short_comment)
short_comment_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
2. Monitor slice performance with [`Scorer.score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) In this section, we'll demonstrate how we might monitor slice performance on the `short_comment` slice — this approach is compatible with _any modeling framework_. Train a simple classifierFirst, we featurize the data — as you saw in the introductory Spam tutorial, we can extract simple bag-of-words features and store them as numpy arrays.
###Code
from sklearn.feature_extraction.text import CountVectorizer
from utils import df_to_features
vectorizer = CountVectorizer(ngram_range=(1, 1))
X_train, Y_train = df_to_features(vectorizer, df_train, "train")
X_test, Y_test = df_to_features(vectorizer, df_test, "test")
###Output
_____no_output_____
###Markdown
We define a `LogisticRegression` model from `sklearn`.
###Code
from sklearn.linear_model import LogisticRegression
sklearn_model = LogisticRegression(C=0.001, solver="liblinear")
sklearn_model.fit(X=X_train, y=Y_train)
from snorkel.utils import preds_to_probs
preds_test = sklearn_model.predict(X_test)
probs_test = preds_to_probs(preds_test, 2)
from sklearn.metrics import f1_score
print(f"Test set F1: {100 * f1_score(Y_test, preds_test):.1f}%")
###Output
Test set F1: 92.5%
###Markdown
Store slice metadata in `S` We apply our list of `sfs` to the data using an SF applier.For our data format, we leverage the [`PandasSFApplier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.PandasSFApplier.htmlsnorkel.slicing.PandasSFApplier).The output of the `applier` is an [`np.recarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html) which stores vectors in named fields indicating whether each of $n$ data points belongs to the corresponding slice.
###Code
from snorkel.slicing import PandasSFApplier
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
###Output
100%|██████████████████████████████████████| 250/250 [00:00<00:00, 50980.94it/s]
###Markdown
Now, we initialize a [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer) using the desired `metrics`.
###Code
from snorkel.analysis import Scorer
scorer = Scorer(metrics=["f1"])
###Output
_____no_output_____
###Markdown
Using the [`score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) method, we can see both `overall` and slice-specific performance.
###Code
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Despite high overall performance, the `short_comment` slice performs poorly here! Write additional slicing functions (SFs)Slices are dynamic — as monitoring needs grow or change with new data distributions or application needs, an ML pipeline might require dozens, or even hundreds, of slices.We'll take inspiration from the labeling tutorial to write additional slicing functions.We demonstrate how the same powerful preprocessors and utilities available for labeling functions can be leveraged for slicing functions.
###Code
from snorkel.slicing import SlicingFunction, slicing_function
from snorkel.preprocess import preprocessor
# Keyword-based SFs
def keyword_lookup(x, keywords):
return any(word in x.text.lower() for word in keywords)
def make_keyword_sf(keywords):
return SlicingFunction(
name=f"keyword_{keywords[0]}",
f=keyword_lookup,
resources=dict(keywords=keywords),
)
keyword_please = make_keyword_sf(keywords=["please", "plz"])
# Regex-based SFs
@slicing_function()
def regex_check_out(x):
return bool(re.search(r"check.*out", x.text, flags=re.I))
@slicing_function()
def short_link(x):
"""Returns whether text matches common pattern for shortened ".ly" links."""
return bool(re.search(r"\w+\.ly", x.text))
# Leverage preprocessor in SF
from textblob import TextBlob
@preprocessor(memoize=True)
def textblob_sentiment(x):
scores = TextBlob(x.text)
x.polarity = scores.sentiment.polarity
return x
@slicing_function(pre=[textblob_sentiment])
def textblob_polarity(x):
return x.polarity > 0.9
###Output
_____no_output_____
###Markdown
Again, we'd like to visualize data points in a particular slice. This time, we'll inspect the `textblob_polarity` slice.Most data points with high-polarity sentiments are strong opinions about the video — hence, they are usually relevant to the video, and the corresponding labels are $0$ (not spam).We might define a slice here for *product and marketing reasons*, it's important to make sure that we don't misclassify very positive comments from good users.
###Code
polarity_df = slice_dataframe(df_test, textblob_polarity)
polarity_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
We can evaluate performance on _all SFs_ using the model-agnostic [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel-analysis-scorer).
###Code
extra_sfs = [keyword_please, regex_check_out, short_link, textblob_polarity]
sfs = [short_comment] + extra_sfs
slice_names = [sf.name for sf in sfs]
###Output
_____no_output_____
###Markdown
Let's see how the `sklearn` model we learned before performs on these new slices.
###Code
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Looks like some do extremely well on our small test set, while others do decently.At the very least, we may want to monitor these to make sure that as we iterate to improve certain slices like `short_comment`, we don't hurt the performance of others.Next, we'll introduce a model that helps us to do this balancing act automatically. 3. Improve slice performanceIn the following section, we demonstrate a modeling approach that we call _Slice-based Learning,_ which improves performance by adding extra slice-specific representational capacity to whichever model we're using.Intuitively, we'd like to model to learn *representations that are better suited to handle data points in this slice*.In our approach, we model each slice as a separate "expert task" in the style of [multi-task learning](https://github.com/snorkel-team/snorkel-tutorials/blob/master/multitask/multitask_tutorial.ipynb); for further details of how slice-based learning works under the hood, check out the [code](https://github.com/snorkel-team/snorkel/blob/master/snorkel/slicing/utils.py) (with paper coming soon)!In other approaches, one might attempt to increase slice performance with techniques like _oversampling_ (i.e. with PyTorch's [`WeightedRandomSampler`](https://pytorch.org/docs/stable/data.htmltorch.utils.data.WeightedRandomSampler)), effectively shifting the training distribution towards certain populations.This might work with small number of slices, but with hundreds or thousands or production slices at scale, it could quickly become intractable to tune upsampling weights per slice. Constructing a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html) To cope with scale, we will attempt to learn and combine many slice-specific representations with an attention mechanism.(Please see our [Section 3 of our technical report](https://arxiv.org/abs/1909.06349) for details on this approach). First we'll initialize a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html):* `base_architecture`: We define a simple Multi-Layer Perceptron (MLP) in Pytorch to serve as the primary representation architecture. We note that the `BinarySlicingClassifier` is **agnostic to the base architecture** — you might leverage a Transformer model for text, or a ResNet for images.* `head_dim`: identifies the final output feature dimension of the `base_architecture`* `slice_names`: Specify the slices that we plan to train on with this classifier.
###Code
from snorkel.slicing import SliceAwareClassifier
from utils import get_pytorch_mlp
# Define model architecture
bow_dim = X_train.shape[1]
hidden_dim = bow_dim
mlp = get_pytorch_mlp(hidden_dim=hidden_dim, num_layers=2)
# Initialize slice model
slice_model = SliceAwareClassifier(
base_architecture=mlp,
head_dim=hidden_dim,
slice_names=[sf.name for sf in sfs],
scorer=scorer,
)
###Output
_____no_output_____
###Markdown
Next, we'll generate the remaining `S` matrixes with the new set of slicing functions.
###Code
applier = PandasSFApplier(sfs)
S_train = applier.apply(df_train)
S_test = applier.apply(df_test)
###Output
100%|████████████████████████████████████| 1586/1586 [00:00<00:00, 11030.58it/s]
100%|██████████████████████████████████████| 250/250 [00:00<00:00, 13241.94it/s]
###Markdown
In order to train using slice information, we'd like to initialize a **slice-aware dataloader**.To do this, we can use [`slice_model.make_slice_dataloader`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.predict) to add slice labels to an existing dataloader.Under the hood, this method leverages slice metadata to add slice labels to the appropriate fields such that it will be compatible with our model, a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel-slicing-slicingclassifier).
###Code
from utils import create_dict_dataloader
BATCH_SIZE = 64
train_dl = create_dict_dataloader(X_train, Y_train, "train")
train_dl_slice = slice_model.make_slice_dataloader(
train_dl.dataset, S_train, shuffle=True, batch_size=BATCH_SIZE
)
test_dl = create_dict_dataloader(X_test, Y_test, "train")
test_dl_slice = slice_model.make_slice_dataloader(
test_dl.dataset, S_test, shuffle=False, batch_size=BATCH_SIZE
)
###Output
_____no_output_____
###Markdown
Representation learning with slices Using Snorkel's [`Trainer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/classification/snorkel.classification.Trainer.html), we fit our classifier with the training set dataloader.
###Code
from snorkel.classification import Trainer
# For demonstration purposes, we set n_epochs=2
trainer = Trainer(n_epochs=2, lr=1e-4, progress_bar=True)
trainer.fit(slice_model, [train_dl_slice])
###Output
Epoch 0:: 100%|█| 25/25 [00:00<00:00, 33.07it/s, model/all/train/loss=0.498, mod
Epoch 1:: 100%|█| 25/25 [00:00<00:00, 36.39it/s, model/all/train/loss=0.249, mod
###Markdown
At inference time, the primary task head (`spam_task`) will make all final predictions.We'd like to evaluate all the slice heads on the original task head — [`score_slices`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.score_slices) remaps all slice-related labels, denoted `spam_task_slice:{slice_name}_pred`, to be evaluated on the `spam_task`.
###Code
slice_model.score_slices([test_dl_slice], as_dataframe=True)
###Output
_____no_output_____
###Markdown
✂️ Snorkel Intro Tutorial: _Data Slicing_In real-world applications, some model outcomes are often more important than others — e.g. vulnerable cyclist detections in an autonomous driving task, or, in our running **spam** application, potentially malicious link redirects to external websites.Traditional machine learning systems optimize for overall quality, which may be too coarse-grained.Models that achieve high overall performance might produce unacceptable failure rates on critical slices of the data — data subsets that might correspond to vulnerable cyclist detection in an autonomous driving task, or in our running spam detection application, external links to potentially malicious websites.In this tutorial, we:1. **Introduce _Slicing Functions (SFs)_** as a programming interface1. **Monitor** application-critical data subsets2. **Improve model performance** on slices First, we'll set up our notebook for reproducibility and proper logging.
###Code
import logging
import os
import numpy as np
import random
import torch
# For reproducibility
os.environ["PYTHONHASHSEED"] = "0"
SEED = 123
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
# Make sure we're running from the spam/ directory
if os.path.basename(os.getcwd()) == "snorkel-tutorials":
os.chdir("spam")
# To visualize logs
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
###Output
_____no_output_____
###Markdown
If you want to display all comment text untruncated, change `DISPLAY_ALL_TEXT` to `True` below.
###Code
import pandas as pd
DISPLAY_ALL_TEXT = False
pd.set_option("display.max_colwidth", 0 if DISPLAY_ALL_TEXT else 50)
###Output
_____no_output_____
###Markdown
_Note:_ this tutorial differs from the labeling tutorial in that we use ground truth labels in the train split for demo purposes.SFs are intended to be used *after the training set has already been labeled* by LFs (or by hand) in the training data pipeline.
###Code
from utils import load_spam_dataset
df_train, df_test = load_spam_dataset(load_train_labels=True)
###Output
_____no_output_____
###Markdown
1. Write slicing functionsWe leverage *slicing functions* (SFs), which output binary _masks_ indicating whether an data point is in the slice or not.Each slice represents some noisily-defined subset of the data (corresponding to an SF) that we'd like to programmatically monitor. In the following cells, we use the [`@slicing_function()`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slicing_function.htmlsnorkel.slicing.slicing_function) decorator to initialize an SF that identifies short commentsYou'll notice that the `short_comment` SF is a heuristic, like the other programmatic ops we've defined, and may not fully cover the slice of interest.That's okay — in last section, we'll show how a model can handle this in Snorkel.
###Code
import re
from snorkel.slicing import slicing_function
@slicing_function()
def short_comment(x):
"""Ham comments are often short, such as 'cool video!'"""
return len(x.text.split()) < 5
sfs = [short_comment]
###Output
_____no_output_____
###Markdown
Visualize slices With a utility function, [`slice_dataframe`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slice_dataframe.htmlsnorkel.slicing.slice_dataframe), we can visualize data points belonging to this slice in a `pandas.DataFrame`.
###Code
from snorkel.slicing import slice_dataframe
short_comment_df = slice_dataframe(df_test, short_comment)
short_comment_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
2. Monitor slice performance with [`Scorer.score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) In this section, we'll demonstrate how we might monitor slice performance on the `short_comment` slice — this approach is compatible with _any modeling framework_. Train a simple classifierFirst, we featurize the data — as you saw in the introductory Spam tutorial, we can extract simple bag-of-words features and store them as numpy arrays.
###Code
from sklearn.feature_extraction.text import CountVectorizer
from utils import df_to_features
vectorizer = CountVectorizer(ngram_range=(1, 1))
X_train, Y_train = df_to_features(vectorizer, df_train, "train")
X_test, Y_test = df_to_features(vectorizer, df_test, "test")
###Output
_____no_output_____
###Markdown
We define a `LogisticRegression` model from `sklearn`.
###Code
from sklearn.linear_model import LogisticRegression
sklearn_model = LogisticRegression(C=0.001, solver="liblinear")
sklearn_model.fit(X=X_train, y=Y_train)
from snorkel.utils import preds_to_probs
preds_test = sklearn_model.predict(X_test)
probs_test = preds_to_probs(preds_test, 2)
from sklearn.metrics import f1_score
print(f"Test set F1: {100 * f1_score(Y_test, preds_test):.1f}%")
###Output
Test set F1: 92.5%
###Markdown
Store slice metadata in `S` We apply our list of `sfs` to the data using an SF applier.For our data format, we leverage the [`PandasSFApplier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.PandasSFApplier.htmlsnorkel.slicing.PandasSFApplier).The output of the `applier` is an [`np.recarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html) which stores vectors in named fields indicating whether each of $n$ data points belongs to the corresponding slice.
###Code
from snorkel.slicing import PandasSFApplier
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
###Output
0%| | 0/250 [00:00<?, ?it/s]
###Markdown
Now, we initialize a [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer) using the desired `metrics`.
###Code
from snorkel.analysis import Scorer
scorer = Scorer(metrics=["f1"])
###Output
_____no_output_____
###Markdown
Using the [`score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) method, we can see both `overall` and slice-specific performance.
###Code
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Despite high overall performance, the `short_comment` slice performs poorly here! Write additional slicing functions (SFs)Slices are dynamic — as monitoring needs grow or change with new data distributions or application needs, an ML pipeline might require dozens, or even hundreds, of slices.We'll take inspiration from the labeling tutorial to write additional slicing functions.We demonstrate how the same powerful preprocessors and utilities available for labeling functions can be leveraged for slicing functions.
###Code
from snorkel.slicing import SlicingFunction, slicing_function
from snorkel.preprocess import preprocessor
# Keyword-based SFs
def keyword_lookup(x, keywords):
return any(word in x.text.lower() for word in keywords)
def make_keyword_sf(keywords):
return SlicingFunction(
name=f"keyword_{keywords[0]}",
f=keyword_lookup,
resources=dict(keywords=keywords),
)
keyword_please = make_keyword_sf(keywords=["please", "plz"])
# Regex-based SFs
@slicing_function()
def regex_check_out(x):
return bool(re.search(r"check.*out", x.text, flags=re.I))
@slicing_function()
def short_link(x):
"""Returns whether text matches common pattern for shortened ".ly" links."""
return bool(re.search(r"\w+\.ly", x.text))
# Leverage preprocessor in SF
from textblob import TextBlob
@preprocessor(memoize=True)
def textblob_sentiment(x):
scores = TextBlob(x.text)
x.polarity = scores.sentiment.polarity
return x
@slicing_function(pre=[textblob_sentiment])
def textblob_polarity(x):
return x.polarity > 0.9
###Output
_____no_output_____
###Markdown
Again, we'd like to visualize data points in a particular slice. This time, we'll inspect the `textblob_polarity` slice.Most data points with high-polarity sentiments are strong opinions about the video — hence, they are usually relevant to the video, and the corresponding labels are $0$ (not spam).We might define a slice here for *product and marketing reasons*, it's important to make sure that we don't misclassify very positive comments from good users.
###Code
polarity_df = slice_dataframe(df_test, textblob_polarity)
polarity_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
We can evaluate performance on _all SFs_ using the model-agnostic [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel-analysis-scorer).
###Code
extra_sfs = [keyword_please, regex_check_out, short_link, textblob_polarity]
sfs = [short_comment] + extra_sfs
slice_names = [sf.name for sf in sfs]
###Output
_____no_output_____
###Markdown
Let's see how the `sklearn` model we learned before performs on these new slices.
###Code
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Looks like some do extremely well on our small test set, while others do decently.At the very least, we may want to monitor these to make sure that as we iterate to improve certain slices like `short_comment`, we don't hurt the performance of others.Next, we'll introduce a model that helps us to do this balancing act automatically. 3. Improve slice performanceIn the following section, we demonstrate a modeling approach that we call _Slice-based Learning,_ which improves performance by adding extra slice-specific representational capacity to whichever model we're using.Intuitively, we'd like to model to learn *representations that are better suited to handle data points in this slice*.In our approach, we model each slice as a separate "expert task" in the style of [multi-task learning](https://github.com/snorkel-team/snorkel-tutorials/blob/master/multitask/multitask_tutorial.ipynb); for further details of how slice-based learning works under the hood, check out the [code](https://github.com/snorkel-team/snorkel/blob/master/snorkel/slicing/utils.py) (with paper coming soon)!In other approaches, one might attempt to increase slice performance with techniques like _oversampling_ (i.e. with PyTorch's [`WeightedRandomSampler`](https://pytorch.org/docs/stable/data.htmltorch.utils.data.WeightedRandomSampler)), effectively shifting the training distribution towards certain populations.This might work with small number of slices, but with hundreds or thousands or production slices at scale, it could quickly become intractable to tune upsampling weights per slice. Constructing a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html) To cope with scale, we will attempt to learn and combine many slice-specific representations with an attention mechanism.(Please see our [Section 3 of our technical report](https://arxiv.org/abs/1909.06349) for details on this approach). First we'll initialize a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html):* `base_architecture`: We define a simple Multi-Layer Perceptron (MLP) in Pytorch to serve as the primary representation architecture. We note that the `BinarySlicingClassifier` is **agnostic to the base architecture** — you might leverage a Transformer model for text, or a ResNet for images.* `head_dim`: identifies the final output feature dimension of the `base_architecture`* `slice_names`: Specify the slices that we plan to train on with this classifier.
###Code
from snorkel.slicing import SliceAwareClassifier
from utils import get_pytorch_mlp
# Define model architecture
bow_dim = X_train.shape[1]
hidden_dim = bow_dim
mlp = get_pytorch_mlp(hidden_dim=hidden_dim, num_layers=2)
# Initialize slice model
slice_model = SliceAwareClassifier(
base_architecture=mlp,
head_dim=hidden_dim,
slice_names=[sf.name for sf in sfs],
scorer=scorer,
)
###Output
_____no_output_____
###Markdown
Next, we'll generate the remaining `S` matrixes with the new set of slicing functions.
###Code
applier = PandasSFApplier(sfs)
S_train = applier.apply(df_train)
S_test = applier.apply(df_test)
###Output
0%| | 0/1586 [00:00<?, ?it/s]
###Markdown
In order to train using slice information, we'd like to initialize a **slice-aware dataloader**.To do this, we can use [`slice_model.make_slice_dataloader`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.predict) to add slice labels to an existing dataloader.Under the hood, this method leverages slice metadata to add slice labels to the appropriate fields such that it will be compatible with our model, a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel-slicing-slicingclassifier).
###Code
from utils import create_dict_dataloader
BATCH_SIZE = 64
train_dl = create_dict_dataloader(X_train, Y_train, "train")
train_dl_slice = slice_model.make_slice_dataloader(
train_dl.dataset, S_train, shuffle=True, batch_size=BATCH_SIZE
)
test_dl = create_dict_dataloader(X_test, Y_test, "train")
test_dl_slice = slice_model.make_slice_dataloader(
test_dl.dataset, S_test, shuffle=False, batch_size=BATCH_SIZE
)
###Output
_____no_output_____
###Markdown
Representation learning with slices Using Snorkel's [`Trainer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/classification/snorkel.classification.Trainer.html), we fit our classifier with the training set dataloader.
###Code
from snorkel.classification import Trainer
# For demonstration purposes, we set n_epochs=2
trainer = Trainer(n_epochs=2, lr=1e-4, progress_bar=True)
trainer.fit(slice_model, [train_dl_slice])
###Output
Epoch 0:: 0%| | 0/25 [00:00<?, ?it/s]
###Markdown
At inference time, the primary task head (`spam_task`) will make all final predictions.We'd like to evaluate all the slice heads on the original task head — [`score_slices`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.score_slices) remaps all slice-related labels, denoted `spam_task_slice:{slice_name}_pred`, to be evaluated on the `spam_task`.
###Code
slice_model.score_slices([test_dl_slice], as_dataframe=True)
###Output
_____no_output_____
###Markdown
✂️ Snorkel Intro Tutorial: _Data Slicing_In real-world applications, some model outcomes are often more important than others — e.g. vulnerable cyclist detections in an autonomous driving task, or, in our running **spam** application, potentially malicious link redirects to external websites.Traditional machine learning systems optimize for overall quality, which may be too coarse-grained.Models that achieve high overall performance might produce unacceptable failure rates on critical slices of the data — data subsets that might correspond to vulnerable cyclist detection in an autonomous driving task, or in our running spam detection application, external links to potentially malicious websites.In this tutorial, we:1. **Introduce _Slicing Functions (SFs)_** as a programming interface1. **Monitor** application-critical data subsets2. **Improve model performance** on slices First, we'll set up our notebook for reproducibility and proper logging.
###Code
import logging
import os
import numpy as np
import random
import torch
# For reproducibility
os.environ["PYTHONHASHSEED"] = "0"
SEED = 123
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
# Make sure we're running from the spam/ directory
if os.path.basename(os.getcwd()) == "snorkel-tutorials":
os.chdir("spam")
# To visualize logs
logger = logging.getLogger()
logger.setLevel(logging.WARNING)
###Output
_____no_output_____
###Markdown
If you want to display all comment text untruncated, change `DISPLAY_ALL_TEXT` to `True` below.
###Code
import pandas as pd
DISPLAY_ALL_TEXT = False
pd.set_option("display.max_colwidth", 0 if DISPLAY_ALL_TEXT else 50)
###Output
_____no_output_____
###Markdown
_Note:_ this tutorial differs from the labeling tutorial in that we use ground truth labels in the train split for demo purposes.SFs are intended to be used *after the training set has already been labeled* by LFs (or by hand) in the training data pipeline.
###Code
from utils import load_spam_dataset
df_train, df_test = load_spam_dataset(load_train_labels=True)
###Output
_____no_output_____
###Markdown
1. Write slicing functionsWe leverage *slicing functions* (SFs), which output binary _masks_ indicating whether an data point is in the slice or not.Each slice represents some noisily-defined subset of the data (corresponding to an SF) that we'd like to programmatically monitor. In the following cells, we use the [`@slicing_function()`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slicing_function.htmlsnorkel.slicing.slicing_function) decorator to initialize an SF that identifies short commentsYou'll notice that the `short_comment` SF is a heuristic, like the other programmatic ops we've defined, and may not fully cover the slice of interest.That's okay — in last section, we'll show how a model can handle this in Snorkel.
###Code
import re
from snorkel.slicing import slicing_function
@slicing_function()
def short_comment(x):
"""Ham comments are often short, such as 'cool video!'"""
return len(x.text.split()) < 5
sfs = [short_comment]
###Output
_____no_output_____
###Markdown
Visualize slices With a utility function, [`slice_dataframe`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.slice_dataframe.htmlsnorkel.slicing.slice_dataframe), we can visualize data points belonging to this slice in a `pandas.DataFrame`.
###Code
from snorkel.slicing import slice_dataframe
short_comment_df = slice_dataframe(df_test, short_comment)
short_comment_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
2. Monitor slice performance with [`Scorer.score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) In this section, we'll demonstrate how we might monitor slice performance on the `short_comment` slice — this approach is compatible with _any modeling framework_. Train a simple classifierFirst, we featurize the data — as you saw in the introductory Spam tutorial, we can extract simple bag-of-words features and store them as numpy arrays.
###Code
from sklearn.feature_extraction.text import CountVectorizer
from utils import df_to_features
vectorizer = CountVectorizer(ngram_range=(1, 1))
X_train, Y_train = df_to_features(vectorizer, df_train, "train")
X_test, Y_test = df_to_features(vectorizer, df_test, "test")
###Output
_____no_output_____
###Markdown
We define a `LogisticRegression` model from `sklearn`.
###Code
from sklearn.linear_model import LogisticRegression
sklearn_model = LogisticRegression(C=0.001, solver="liblinear")
sklearn_model.fit(X=X_train, y=Y_train)
from snorkel.utils import preds_to_probs
preds_test = sklearn_model.predict(X_test)
probs_test = preds_to_probs(preds_test, 2)
from sklearn.metrics import f1_score
print(f"Test set F1: {100 * f1_score(Y_test, preds_test):.1f}%")
###Output
Test set F1: 92.5%
###Markdown
Store slice metadata in `S` We apply our list of `sfs` to the data using an SF applier.For our data format, we leverage the [`PandasSFApplier`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/slicing/snorkel.slicing.PandasSFApplier.htmlsnorkel.slicing.PandasSFApplier).The output of the `applier` is an [`np.recarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html) which stores vectors in named fields indicating whether each of $n$ data points belongs to the corresponding slice.
###Code
from snorkel.slicing import PandasSFApplier
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
###Output
100%|██████████| 250/250 [00:00<00:00, 19888.02it/s]
###Markdown
Now, we initialize a [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer) using the desired `metrics`.
###Code
from snorkel.analysis import Scorer
scorer = Scorer(metrics=["f1"])
###Output
_____no_output_____
###Markdown
Using the [`score_slices`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel.analysis.Scorer.score_slices) method, we can see both `overall` and slice-specific performance.
###Code
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Despite high overall performance, the `short_comment` slice performs poorly here! Write additional slicing functions (SFs)Slices are dynamic — as monitoring needs grow or change with new data distributions or application needs, an ML pipeline might require dozens, or even hundreds, of slices.We'll take inspiration from the labeling tutorial to write additional slicing functions.We demonstrate how the same powerful preprocessors and utilities available for labeling functions can be leveraged for slicing functions.
###Code
from snorkel.slicing import SlicingFunction, slicing_function
from snorkel.preprocess import preprocessor
# Keyword-based SFs
def keyword_lookup(x, keywords):
return any(word in x.text.lower() for word in keywords)
def make_keyword_sf(keywords):
return SlicingFunction(
name=f"keyword_{keywords[0]}",
f=keyword_lookup,
resources=dict(keywords=keywords),
)
keyword_please = make_keyword_sf(keywords=["please", "plz"])
# Regex-based SFs
@slicing_function()
def regex_check_out(x):
return bool(re.search(r"check.*out", x.text, flags=re.I))
@slicing_function()
def short_link(x):
"""Returns whether text matches common pattern for shortened ".ly" links."""
return bool(re.search(r"\w+\.ly", x.text))
# Leverage preprocessor in SF
from textblob import TextBlob
@preprocessor(memoize=True)
def textblob_sentiment(x):
scores = TextBlob(x.text)
x.polarity = scores.sentiment.polarity
return x
@slicing_function(pre=[textblob_sentiment])
def textblob_polarity(x):
return x.polarity > 0.9
###Output
_____no_output_____
###Markdown
Again, we'd like to visualize data points in a particular slice. This time, we'll inspect the `textblob_polarity` slice.Most data points with high-polarity sentiments are strong opinions about the video — hence, they are usually relevant to the video, and the corresponding labels are $0$ (not spam).We might define a slice here for *product and marketing reasons*, it's important to make sure that we don't misclassify very positive comments from good users.
###Code
polarity_df = slice_dataframe(df_test, textblob_polarity)
polarity_df[["text", "label"]].head()
###Output
_____no_output_____
###Markdown
We can evaluate performance on _all SFs_ using the model-agnostic [`Scorer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/analysis/snorkel.analysis.Scorer.htmlsnorkel-analysis-scorer).
###Code
extra_sfs = [keyword_please, regex_check_out, short_link, textblob_polarity]
sfs = [short_comment] + extra_sfs
slice_names = [sf.name for sf in sfs]
###Output
_____no_output_____
###Markdown
Let's see how the `sklearn` model we learned before performs on these new slices.
###Code
applier = PandasSFApplier(sfs)
S_test = applier.apply(df_test)
scorer.score_slices(
S=S_test, golds=Y_test, preds=preds_test, probs=probs_test, as_dataframe=True
)
###Output
_____no_output_____
###Markdown
Looks like some do extremely well on our small test set, while others do decently.At the very least, we may want to monitor these to make sure that as we iterate to improve certain slices like `short_comment`, we don't hurt the performance of others.Next, we'll introduce a model that helps us to do this balancing act automatically. 3. Improve slice performanceIn the following section, we demonstrate a modeling approach that we call _Slice-based Learning,_ which improves performance by adding extra slice-specific representational capacity to whichever model we're using.Intuitively, we'd like to model to learn *representations that are better suited to handle data points in this slice*.In our approach, we model each slice as a separate "expert task" in the style of [multi-task learning](https://github.com/snorkel-team/snorkel-tutorials/blob/master/multitask/multitask_tutorial.ipynb); for further details of how slice-based learning works under the hood, check out the [code](https://github.com/snorkel-team/snorkel/blob/master/snorkel/slicing/utils.py) (with paper coming soon)!In other approaches, one might attempt to increase slice performance with techniques like _oversampling_ (i.e. with PyTorch's [`WeightedRandomSampler`](https://pytorch.org/docs/stable/data.htmltorch.utils.data.WeightedRandomSampler)), effectively shifting the training distribution towards certain populations.This might work with small number of slices, but with hundreds or thousands or production slices at scale, it could quickly become intractable to tune upsampling weights per slice. Constructing a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html) To cope with scale, we will attempt to learn and combine many slice-specific representations with an attention mechanism.(Please see our [Section 3 of our technical report](https://arxiv.org/abs/1909.06349) for details on this approach). First we'll initialize a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.html):* `base_architecture`: We define a simple Multi-Layer Perceptron (MLP) in Pytorch to serve as the primary representation architecture. We note that the `BinarySlicingClassifier` is **agnostic to the base architecture** — you might leverage a Transformer model for text, or a ResNet for images.* `head_dim`: identifies the final output feature dimension of the `base_architecture`* `slice_names`: Specify the slices that we plan to train on with this classifier.
###Code
from snorkel.slicing import SliceAwareClassifier
from utils import get_pytorch_mlp
# Define model architecture
bow_dim = X_train.shape[1]
hidden_dim = bow_dim
mlp = get_pytorch_mlp(hidden_dim=hidden_dim, num_layers=2)
# Initialize slice model
slice_model = SliceAwareClassifier(
base_architecture=mlp,
head_dim=hidden_dim,
slice_names=[sf.name for sf in sfs],
scorer=scorer,
)
###Output
_____no_output_____
###Markdown
Next, we'll generate the remaining `S` matrixes with the new set of slicing functions.
###Code
applier = PandasSFApplier(sfs)
S_train = applier.apply(df_train)
S_test = applier.apply(df_test)
###Output
100%|██████████| 1586/1586 [00:01<00:00, 942.16it/s]
100%|██████████| 250/250 [00:00<00:00, 4942.43it/s]
###Markdown
In order to train using slice information, we'd like to initialize a **slice-aware dataloader**.To do this, we can use [`slice_model.make_slice_dataloader`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.predict) to add slice labels to an existing dataloader.Under the hood, this method leverages slice metadata to add slice labels to the appropriate fields such that it will be compatible with our model, a [`SliceAwareClassifier`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel-slicing-slicingclassifier).
###Code
from utils import create_dict_dataloader
BATCH_SIZE = 64
train_dl = create_dict_dataloader(X_train, Y_train, "train")
train_dl_slice = slice_model.make_slice_dataloader(
train_dl.dataset, S_train, shuffle=True, batch_size=BATCH_SIZE
)
test_dl = create_dict_dataloader(X_test, Y_test, "train")
test_dl_slice = slice_model.make_slice_dataloader(
test_dl.dataset, S_test, shuffle=False, batch_size=BATCH_SIZE
)
###Output
_____no_output_____
###Markdown
Representation learning with slices Using Snorkel's [`Trainer`](https://snorkel.readthedocs.io/en/master/packages/_autosummary/classification/snorkel.classification.Trainer.html), we fit our classifier with the training set dataloader.
###Code
from snorkel.classification import Trainer
# For demonstration purposes, we set n_epochs=2
trainer = Trainer(n_epochs=2, lr=1e-4, progress_bar=True)
trainer.fit(slice_model, [train_dl_slice])
###Output
Epoch 0:: 100%|██████████| 25/25 [00:24<00:00, 1.01it/s, model/all/train/loss=0.5, model/all/train/lr=0.0001]
Epoch 1:: 100%|██████████| 25/25 [00:25<00:00, 1.01s/it, model/all/train/loss=0.257, model/all/train/lr=0.0001]
###Markdown
At inference time, the primary task head (`spam_task`) will make all final predictions.We'd like to evaluate all the slice heads on the original task head — [`score_slices`](https://snorkel.readthedocs.io/en/v0.9.3/packages/_autosummary/slicing/snorkel.slicing.SliceAwareClassifier.htmlsnorkel.slicing.SliceAwareClassifier.score_slices) remaps all slice-related labels, denoted `spam_task_slice:{slice_name}_pred`, to be evaluated on the `spam_task`.
###Code
slice_model.score_slices([test_dl_slice], as_dataframe=True)
###Output
_____no_output_____
|
03-Features/Features.ipynb
|
###Markdown
Подготовка данных или Feature [Preprocessing](https://hackernoon.com/what-steps-should-one-take-while-doing-data-preprocessing-502c993e1caa)[Preprocessing](https://hackernoon.com/what-steps-should-one-take-while-doing-data-preprocessing-502c993e1caa) Здесь мы рассмотрим несколько важных моментов связанных с подготовкой данных к обучению.
###Code
%matplotlib inline
import numpy as np
import seaborn as sns
from sklearn import metrics
import matplotlib.pyplot as plt
from setup_libs import *
###Output
_____no_output_____
###Markdown
Feature Engineering (Создание новых признаков) Под этим словосочетанием подразумевают создание и подготовку признаков объектов, чтобы они лучше подходили под алгоритмы и давали наилучший результат. Рассмотрим пример концентрических окружностей.
###Code
from random import gauss
num_samples = 1000
theta = np.linspace(0, 2*np.pi, num_samples)
r1 = 1
r2 = 2
rng = np.random.RandomState(1)
circle = np.hstack([np.cos(theta).reshape((-1, 1)) + (rng.randn(num_samples)[:,np.newaxis] / 8),
np.sin(theta).reshape((-1, 1)) + (rng.randn(num_samples)[:,np.newaxis] / 8)])
lil = r1 * circle
big = r2 * circle
X = np.vstack([lil, big])
y = np.hstack([np.zeros(num_samples), np.ones(num_samples)])
# plots
plt.figure(figsize=(7,6))
plt.scatter(lil[:,0],lil[:,1])
plt.scatter(big[:,0],big[:,1])
plt.grid()
###Output
_____no_output_____
###Markdown
Давайте попробуем классифицировать их деревом
###Code
from sklearn.tree import DecisionTreeClassifier as DTC
clf = DTC()
clf.fit(X, y)
clf.tree_.max_depth
plot_model(X, y, clf)
###Output
_____no_output_____
###Markdown
Глубина дерева - 9, несмотря на такую простую с первого взгляда классификацию. Можно ли что-то с этим сделать?А давайте добавим радиус в наши данные.
###Code
r = X[:,0]**2 + X[:,1]**2
X_new = np.hstack([X, r.reshape([-1,1])])
clf2 = DTC()
clf2.fit(X_new, y)
clf2.tree_.max_depth
cross_val_score(clf, X, y, cv=5, scoring=metrics.scorer.accuracy_scorer)
cross_val_score(clf, X_new, y, cv=5, scoring=metrics.scorer.accuracy_scorer)
###Output
_____no_output_____
###Markdown
Добавив логичный признак, который мы предположили, мы сумели классифицировать данные гораздо более простой моделью, да еще и с намного лучшим качеством. Этот процесс и называется `Feature Engineering`. Линейная Регрессия и Polynomial Features Собственно, с помощью создания фич мы можем заставить работать линейную регрессию. Вернемся к примеру с прошлого семинара.
###Code
def plot_reg(X, y, clf_dtc, X_test, dim=0):
clf_dtc.fit(X, y)
Y_test = clf_dtc.predict(X_test)
plt.figure(figsize=(8, 6))
plt.scatter(X[:,dim], y, cmap='bwr', s=50, alpha=1)
plt.plot(X_test[:,dim], Y_test, color='r', alpha=1)
plt.grid()
def f(x):
return np.sqrt(x) + np.sin(x)
vf = np.vectorize(f)
rng = np.random.RandomState(1)
X_reg = np.arange(0, 10, 0.2)[:, np.newaxis]
y_reg = vf(X_reg) + (rng.rand(50)[:,np.newaxis] / 2)#добавляем шумы
plt.figure(figsize=(6, 5))
plt.scatter(X_reg, y_reg, cmap='bwr', s=50, alpha=1)
plt.xlabel('x')
plt.ylabel('y')
plt.grid()
from sklearn.linear_model import LinearRegression as LR
reg_lr = LR()
X_test = np.arange(0, 10, 0.5)[:,np.newaxis]
plot_reg(X_reg, y_reg, reg_lr, X_test)
###Output
_____no_output_____
###Markdown
Теперь добавим функции второй, третей и четвертых степеней.
###Code
X_reg_new = np.hstack([X_reg, X_reg**2, X_reg**3, X_reg**4])
X_test_new = np.hstack([X_test, X_test**2, X_test**3, X_test**4])
from sklearn.linear_model import LinearRegression as LR
reg_lr = LR()
plot_reg(X_reg_new, y_reg, reg_lr, X_test_new)
###Output
_____no_output_____
###Markdown
В изначальной линейной регрессии мы находимся в пространстве линейных алгоритмов:$$a(x) = a_0x_0 + a_1x_1 + \ldots a_nx_n$$ Но теперь место фич у нас заняли полиномиальные функции и мы получили степенную функцию:$$a(x) = a_0 + a_1x^1 + \ldots a_nx^n$$ Чтобы не делать это вручную, есть механизм `PolynomialFeatures`
###Code
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=8)
X_reg2 = poly.fit_transform(X_reg)
X_test2 = poly.fit_transform(X_test)
from sklearn.linear_model import LinearRegression as LR
reg_lr = LR()
plot_reg(X_reg2, y_reg, reg_lr, X_test2, dim=1)
###Output
_____no_output_____
###Markdown
С другой стороны мы можем догадаться, что тут не степенные функции и использовать те функции, которые нужны.
###Code
X_reg3 = np.hstack([X_reg, X_reg**0.5, np.sin(X_reg)])
X_test3 = np.hstack([X_test, X_test**0.5, np.sin(X_test)])
from sklearn.linear_model import LinearRegression as LR
reg_lr = LR()
plot_reg(X_reg3, y_reg, reg_lr, X_test3, dim=0)
###Output
_____no_output_____
###Markdown
Масштабирование (Scaling) Рассмотрим такую ситуацию: у вас есть данные о дороге с 2мя признаками: `длина участка дороги` и `толщина слоя асфальта`. Обе величины даются в одной метрике - метры. И нам нужно бинарно классифицировать качество дороги: `хорошая`,`плохая`.
###Code
X = np.array([[10000, 7],[11000, 6],[9000, 5],[5000, 2],[6000, 1],[13000, 2],[14000, 1]])
y = np.array([1,1,1,0,0,0,0]) # 1 - хорошие, 0 - плохие
plt.scatter(X[:,0], X[:,1], c=y, cmap='bwr', s=50, alpha=1)
plt.xlabel('длина дороги')
plt.ylabel('толщина дороги')
plt.grid()
###Output
_____no_output_____
###Markdown
Из логики нам будет очевидно, что первый признак практически бесполезен, а второй играет ключевую роль. Но, если запустить KNN или любой другой `метрический` алгоритм, мы получим обратную ситуацию, признак который в абсолюте больше - **важнее**, чем признак, который очень мало отличается.
###Code
model = KNN(3)
model.fit(X,y)
X_bad = [[10000, 1]]
y_pred = model.predict(X_bad)
X_new = np.vstack([X, X_bad])
y_new = np.hstack([y, y_pred])
plt.scatter(X_new[:,0], X_new[:,1], c=y_new, cmap='bwr', s=50, alpha=1)
plt.xlabel('длина дороги')
plt.ylabel('толщина дороги')
plt.grid()
###Output
_____no_output_____
###Markdown
Почему новая точка классифицировалась неправильно? Потому что, с точки зрения эвклидовой метрики, до `хороших` ей действительно `ближе`.$$\rho((6000, 7), (10000, 1)) = \sqrt{(6000 - 10000)^2 + (7-1)^2} = \sqrt{6^2} << \rho((6000, 1), (10000, 1)) = \sqrt{4000^2 + (1-1)^2}$$ Чтобы справиться с этой проблемой важно `масштабировать` численные признаки, чтобы они были корректно сравнимы.Есть 2 подхода к масштабированию:* Нормализация (MinMax Scaling) $$x^* = \frac{x-x_{min}}{x_{max} - x_{min}}$$* Стандартизация (Standart Scaling) $$x^* = \frac{x-\mu}{\sigma}$$, где $\mu$ - среднее, а $\sigma$ - стандартное отклонение
###Code
from sklearn.preprocessing import StandardScaler, MinMaxScaler
stan = StandardScaler().fit(X)
norm = MinMaxScaler().fit(X)
X_st = stan.transform(X)
X_norm = norm.transform(X)
###Output
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###Markdown
Метод `fit` подбирает параметры масштабирования, а метод `transform` непосредственно преобразует значения.
###Code
plt.scatter(X_st[:,0], X_st[:,1], c=y, cmap='bwr', s=50, alpha=1)
plt.title('Standart')
plt.xlabel('длина дороги')
plt.ylabel('толщина дороги')
plt.grid()
###Output
_____no_output_____
###Markdown
И вот теперь мы можем верно классифицировать данные, не забыв трансформировать точку, которую предсказываем
###Code
model = KNN(3)
model.fit(X_st,y)
X_bad = stan.transform([[10000, 1]])
y_pred = model.predict(X_bad)
X_new = np.vstack([X_st, X_bad])
y_new = np.hstack([y, y_pred])
plt.scatter(X_new[:,0], X_new[:,1], c=y_new, cmap='bwr', s=50, alpha=1)
plt.title('Standart')
plt.xlabel('длина дороги')
plt.ylabel('толщина дороги')
plt.grid()
###Output
_____no_output_____
###Markdown
Интересный факт, что любые древесные алгоритмы: DecisionTree, RandomForest - не требуют масштабирования. Потому что они работают на пороговых правилах и все признаки у них остаются независимыми. Резюме: Хороший паттерн делать масштабирование - `всегда`. Потому что для метрических алгоритмов оно необходимо, а для деревьев оно ничего не портит. Категориальные признаки Окей, с числовыми признаками разобрались, но ведь доставать необходимую информацию нужно еще и из категориальных признаков. Как с ними можно работать? Label Encoding Рассмотрим выборку UCI bank, в которой большая часть признаков – категориальные.
###Code
import pandas as pd
df = pd.read_csv('data/bank_train.csv')
labels = pd.read_csv('data/bank_train_target.csv', header=None)
df.head()
###Output
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###Markdown
Чтобы найти решение, давайте рассмотрим признак education:
###Code
df['job'].value_counts().plot.barh();
###Output
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###Markdown
Естественным решением такой проблемы было бы однозначное отображение каждого значения в уникальное число. К примеру, мы могли бы преобразовать `university.degree` в 0, а `basic.9y` в 1. Эту простую операцию приходится делать часто, поэтому в модуле `preprocessing` библиотеки `sklearn` именно для этой задачи реализован класс `LabelEncoder`:
###Code
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
label_encoder = LabelEncoder()
###Output
_____no_output_____
###Markdown
Метод `fit` этого класса находит все уникальные значения и строит таблицу для соответствия каждой категории некоторому числу, а метод `transform` непосредственно преобразует значения в числа.
###Code
mapped_education = pd.Series(label_encoder.fit_transform(df['education']))
mapped_education.value_counts().plot.barh()
print(dict(enumerate(label_encoder.classes_)))
###Output
{0: 'basic.4y', 1: 'basic.6y', 2: 'basic.9y', 3: 'high.school', 4: 'illiterate', 5: 'professional.course', 6: 'university.degree', 7: 'unknown'}
###Markdown
Что произойдет, если у нас появятся данные с другими категориями?
###Code
print(len("25,20,RL,NA,8246,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,8,1968,2001,Gable,CompShg,Plywood,Plywood,None,0,TA,Gd,CBlock,TA,TA,Mn,Rec,188,ALQ,668,204,1060,GasA,Ex,Y,SBrkr,1060,0,0,1060,1,0,1,0,3,1,Gd,6,Typ,1,TA,Attchd,1968,Unf,1,270,TA,TA,Y,406,90,0,0,0,0,NA,MnPrv,NA,0,5,2010,WD,Normal,154000".split(',')))
###Output
81
###Markdown
Таким образом, при использовании этого подхода мы всегда должны быть уверены, что признак не может принимать неизвестных ранее значений. К этой проблеме мы вернемся чуть позже, а сейчас заменим весь столбец education на преобразованный:
###Code
df['education'] = mapped_education
df.head()
###Output
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###Markdown
Продолжим преобразование для всех столбцов, имеющих тип `object` – именно этот тип задается в pandas для таких данных.
###Code
categorical_columns = df.columns[df.dtypes == 'object'].union(['education'])
for column in categorical_columns:
df[column] = label_encoder.fit_transform(df[column])
df.head()
###Output
_____no_output_____
###Markdown
Основная проблема такого представления заключается в том, что числовой код создал евклидово представление для данных.К примеру, нами неявным образом была введена алгебра над значениями работы - мы можем вычесть работу клиента 1 из работы клиента 2:
###Code
df.loc[1].job - df.loc[2].job
###Output
_____no_output_____
###Markdown
Конечно же, эта операция не имеет никакого смысла. Но именно на этом основаны метрики близости объектов, что делает бессмысленным применение метода ближайшего соседа на данных в таком виде. Аналогичным образом, никакого смысла не будет иметь применение линейных моделей.Пример внизу: у класса 1 `precision` = 0, `recall`=0, `f1-score`=0 -никакая классификация не осуществялется.
###Code
import warnings
warnings.filterwarnings('ignore')
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
def logistic_regression_accuracy_on(dataframe, labels):
features = dataframe.as_matrix()
train_features, test_features, train_labels, test_labels = \
train_test_split(features, labels)
logit = LogisticRegression()
logit.fit(train_features, train_labels)
return classification_report(test_labels, logit.predict(test_features))
print(logistic_regression_accuracy_on(df[categorical_columns], labels))
###Output
precision recall f1-score support
0 0.89 1.00 0.94 6126
1 0.00 0.00 0.00 773
accuracy 0.89 6899
macro avg 0.44 0.50 0.47 6899
weighted avg 0.79 0.89 0.84 6899
###Markdown
Однако для древесных моделей `LabelEncoding` вполне сгодится. Для того, чтобы мы смогли применять линейные модели на таких данных нам необходим другой метод, который называется One-Hot Encoding One-Hot EncodingПредположим, что некоторый признак может принимать 10 разных значений. В этом случае one hot encoding подразумевает создание 10 признаков, все из которых равны нулю *за исключением одного*. На позицию, соответствующую численному значению признака мы помещаем 1:
###Code
one_hot_example = pd.DataFrame([{i: 0 for i in range(10)}])
one_hot_example.loc[0, 6] = 1
one_hot_example
###Output
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###Markdown
По умолчанию `OneHotEncoder` преобразует данные в разреженную матрицу, чтобы не расходовать память на хранение многочисленных нулей. Однако в этом примере размер данных не является для нас проблемой, поэтому мы будем использовать "плотное" представление.
###Code
onehot_encoder = OneHotEncoder(sparse=False)
encoded_categorical_columns = pd.DataFrame(onehot_encoder.fit_transform(df[categorical_columns]))
encoded_categorical_columns.head()
###Output
_____no_output_____
###Markdown
Мы получили 53 столбца - именно столько различных уникальных значений могут принимать категориальные столбцы исходной выборки. Преобразованные с помощью One-Hot Encoding данные начинают обретать смысл для линейной модели:
###Code
print(logistic_regression_accuracy_on(encoded_categorical_columns, labels))
###Output
precision recall f1-score support
0 0.91 0.99 0.95 6139
1 0.66 0.18 0.28 760
accuracy 0.90 6899
macro avg 0.78 0.58 0.61 6899
weighted avg 0.88 0.90 0.87 6899
###Markdown
Проблема возникает когда, количество различных значений у категориального признака сильно возрастает. В этом случае `OneHotEncoding` станет очень затратным по памяти (даже если будем использовать разряженные таблицы). Но в целом лучше всегда использовать `OneHot`. Хэширование признаков (Hashing trick)С течением времени категориальные признаки могут принимать новых значений. Это затрудняет использование уже обученных моделей на новых данных. Кроме того, `LabelEncoder` подразумевает предварительный анализ всей выборки и хранение построенных отображений в памяти, что затрудняет работу в режиме больших данных.Для решения этих проблем существует более простой подход к векторизации категориальных признаков, основанный на хэшировании, известный как hashing trick. Хэш-функции могут помочь нам в задаче поиска уникальных кодов для различных значений признака, к примеру:
###Code
for s in ('university.degree', 'high.school', 'illiterate'):
print(s, '->', hash(s))
###Output
university.degree -> -68740083847602807
high.school -> 4321647289974080590
illiterate -> -3642350026650764040
###Markdown
Отрицательные и настолько большие по модулю значения нам не подойдут. Ограничим область значений хэш-функции:
###Code
hash_space = 25
for s in ('university.degree', 'high.school', 'illiterate'):
print(s, '->', hash(s) % hash_space)
###Output
university.degree -> 18
high.school -> 15
illiterate -> 10
###Markdown
Представим, что у нас в выборке есть холостой студент, которому позвонили в понедельник, тогда его вектор признаков будет сформирован аналогично One-Hot Encoding, но в едином пространстве фиксированного размера для всех признаков:
###Code
hashing_example = pd.DataFrame([{i: 0.0 for i in range(hash_space)}])
for s in ('job=student', 'marital=single', 'day_of_week=mon'):
print(s, '->', hash(s) % hash_space)
hashing_example.loc[0, hash(s) % hash_space] = 1
hashing_example
###Output
job=student -> 16
marital=single -> 1
day_of_week=mon -> 0
###Markdown
Стоит обратить внимание, что в этом примере хэшировались не только значения признаков, а пары **название признака + значение признака**. Это необходимо, чтобы разделить одинаковые значения разных признаков между собой, к примеру:
###Code
assert hash('no') == hash('no')
assert hash('housing=no') != hash('loan=no')
###Output
_____no_output_____
###Markdown
Может ли произойти коллизия хэш-функции, то есть совпадение кодов для двух разных значений? Нетрудно доказать, что при достаточном размере пространства хэширования это происходит редко, но даже в тех случаях, когда это происходит, это не будет приводить к существенному ухудшению качества классификации или регрессии.Возможно, вы спросите: "а что за хрень вообще происходит?", и покажется, что при хэшировании признаков страдает здравый смысл. Возможно, но эта эвристика – по сути, единственный подход к тому, чтобы работать с категориальными признаками, у которых много уникальных значений. Более того, эта техника себя хорошо зарекомендовала по результатами на практике. Подробней про хэширование признаков (learning to hash) можно почитать в [этом](https://arxiv.org/abs/1509.05472) обзоре, а также в [материалах](https://github.com/esokolov/ml-course-hse/blob/master/2016-fall/lecture-notes/lecture06-linclass.pdf) Евгения Соколова. Blending И на финал мы познакомимся с внутренним устройством классификаторов и попытаемся сделать свой смешанный классификатор.Часто на практике оказывается возможным увеличить качество предсказания путем смешивания разных моделей. Это происходит за счет того, что разные модели могут хранить в себе разные скрытые зависимости от данных, которые могут вместе дать синергию.Посмотрим на наш небольшой класс классификатора.
###Code
from sklearn.base import BaseEstimator, ClassifierMixin
class BlendClassifier(BaseEstimator, ClassifierMixin):# предок класса всех классификаторов
def __init__(self, clf1, clf2, alpha=0.5):
self.clf1 = clf1
self.clf2 = clf2
self.alpha = alpha #параметр смешивания
def fit(self, X, y): #обучаем классификатор
self.X_ = X
self.y_ = y
self.clf1.fit(X, y)
self.clf2.fit(X, y)
return self
def predict(self, X): #возвращаем значения
predict1 = self.clf1.predict(X)
predict2 = self.clf2.predict(X)
return np.round(predict1 * self.alpha + predict2 * (1 - self.alpha))
def predict_proba(self, X): #возвращаем вероятности
predict1 = self.clf1.predict_proba(X)
predict2 = self.clf2.predict_proba(X)
return predict1 * self.alpha + predict2 * (1 - self.alpha)
from sklearn.linear_model import SGDClassifier as SGD
from sklearn.ensemble import RandomForestClassifier as RF
clf = BlendClassifier(
clf1 = SGD(loss = 'log'),
clf2 = RF(n_estimators = 50)
)
clf.fit(iris.data, iris.target)
###Output
_____no_output_____
###Markdown
Подготовка данных или Feature [Preprocessing](https://hackernoon.com/what-steps-should-one-take-while-doing-data-preprocessing-502c993e1caa)[Preprocessing](https://hackernoon.com/what-steps-should-one-take-while-doing-data-preprocessing-502c993e1caa) Здесь мы рассмотрим несколько важных моментов связанных с подготовкой данных к обучению.
###Code
%matplotlib inline
import numpy as np
import seaborn as sns
from sklearn import metrics
import matplotlib.pyplot as plt
from setup_libs import *
###Output
_____no_output_____
###Markdown
Feature Engineering (Создание новых признаков) Под этим словосочетанием подразумевают создание и подготовку признаков объектов, чтобы они лучше подходили под алгоритмы и давали наилучший результат. Рассмотрим пример концентрических окружностей.
###Code
from random import gauss
num_samples = 1000
theta = np.linspace(0, 2*np.pi, num_samples)
r1 = 1
r2 = 2
rng = np.random.RandomState(1)
circle = np.hstack([np.cos(theta).reshape((-1, 1)) + (rng.randn(num_samples)[:,np.newaxis] / 8),
np.sin(theta).reshape((-1, 1)) + (rng.randn(num_samples)[:,np.newaxis] / 8)])
lil = r1 * circle
big = r2 * circle
X = np.vstack([lil, big])
y = np.hstack([np.zeros(num_samples), np.ones(num_samples)])
# plots
plt.figure(figsize=(7,6))
plt.scatter(lil[:,0],lil[:,1])
plt.scatter(big[:,0],big[:,1])
plt.grid()
###Output
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###Markdown
Давайте попробуем классифицировать их деревом
###Code
from sklearn.tree import DecisionTreeClassifier as DTC
clf = DTC()
clf.fit(X, y)
clf.tree_.max_depth
plot_model(X, y, clf)
###Output
_____no_output_____
###Markdown
Глубина дерева - 9, несмотря на такую простую с первого взгляда классификацию. Можно ли что-то с этим сделать?А давайте добавим радиус в наши данные.
###Code
r = X[:,0]**2 + X[:,1]**2
X_new = np.hstack([X, r.reshape([-1,1])])
clf2 = DTC()
clf2.fit(X_new, y)
clf2.tree_.max_depth
cross_val_score(clf, X, y, cv=5, scoring=metrics.scorer.accuracy_scorer)
cross_val_score(clf, X_new, y, cv=5, scoring=metrics.scorer.accuracy_scorer)
###Output
_____no_output_____
###Markdown
Добавив логичный признак, который мы предположили, мы сумели классифицировать данные гораздо более простой моделью, да еще и с намного лучшим качеством. Этот процесс и называется `Feature Engineering`. Линейная Регрессия и Polynomial Features Собственно, с помощью создания фич мы можем заставить работать линейную регрессию. Вернемся к примеру с прошлого семинара.
###Code
def plot_reg(X, y, clf_dtc, X_test, dim=0):
clf_dtc.fit(X, y)
Y_test = clf_dtc.predict(X_test)
plt.figure(figsize=(8, 6))
plt.scatter(X[:,dim], y, cmap='bwr', s=50, alpha=1)
plt.plot(X_test[:,dim], Y_test, color='r', alpha=1)
plt.grid()
def f(x):
return np.sqrt(x) + np.sin(x)
vf = np.vectorize(f)
rng = np.random.RandomState(1)
X_reg = np.arange(0, 10, 0.2)[:, np.newaxis]
y_reg = vf(X_reg) + (rng.rand(50)[:,np.newaxis] / 2)#добавляем шумы
plt.figure(figsize=(6, 5))
plt.scatter(X_reg, y_reg, cmap='bwr', s=50, alpha=1)
plt.xlabel('x')
plt.ylabel('y')
plt.grid()
from sklearn.linear_model import LinearRegression as LR
reg_lr = LR()
X_test = np.arange(0, 10, 0.5)[:,np.newaxis]
plot_reg(X_reg, y_reg, reg_lr, X_test)
###Output
_____no_output_____
###Markdown
Теперь добавим функции второй, третей и четвертых степеней.
###Code
X_reg_new = np.hstack([X_reg, X_reg**2, X_reg**3, X_reg**4])
X_test_new = np.hstack([X_test, X_test**2, X_test**3, X_test**4])
from sklearn.linear_model import LinearRegression as LR
reg_lr = LR()
plot_reg(X_reg_new, y_reg, reg_lr, X_test_new)
###Output
_____no_output_____
###Markdown
В изначальной линейной регрессии мы находимся в пространстве линейных алгоритмов:$$a(x) = a_0x_0 + a_1x_1 + \ldots a_nx_n$$ Но теперь место фич у нас заняли полиномиальные функции и мы получили степенную функцию:$$a(x) = a_0 + a_1x^1 + \ldots a_nx^n$$ Чтобы не делать это вручную, есть механизм `PolynomialFeatures`
###Code
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=8)
X_reg2 = poly.fit_transform(X_reg)
X_test2 = poly.fit_transform(X_test)
from sklearn.linear_model import LinearRegression as LR
reg_lr = LR()
plot_reg(X_reg2, y_reg, reg_lr, X_test2, dim=1)
###Output
_____no_output_____
###Markdown
С другой стороны мы можем догадаться, что тут не степенные функции и использовать те функции, которые нужны.
###Code
X_reg3 = np.hstack([X_reg, X_reg**0.5, np.sin(X_reg)])
X_test3 = np.hstack([X_test, X_test**0.5, np.sin(X_test)])
from sklearn.linear_model import LinearRegression as LR
reg_lr = LR()
plot_reg(X_reg3, y_reg, reg_lr, X_test3, dim=0)
###Output
_____no_output_____
###Markdown
Масштабирование (Scaling) Рассмотрим такую ситуацию: у вас есть данные о дороге с 2мя признаками: `длина участка дороги` и `толщина слоя асфальта`. Обе величины даются в одной метрике - метры. И нам нужно бинарно классифицировать качество дороги: `хорошая`,`плохая`.
###Code
X = np.array([[10000, 7],[11000, 6],[9000, 5],[5000, 2],[6000, 1],[13000, 2],[14000, 1]])
y = np.array([1,1,1,0,0,0,0]) # 1 - хорошие, 0 - плохие
plt.scatter(X[:,0], X[:,1], c=y, cmap='bwr', s=50, alpha=1)
plt.xlabel('длина дороги')
plt.ylabel('толщина дороги')
plt.grid()
###Output
_____no_output_____
###Markdown
Из логики нам будет очевидно, что первый признак практически бесполезен, а второй играет ключевую роль. Но, если запустить KNN или любой другой `метрический` алгоритм, мы получим обратную ситуацию, признак который в абсолюте больше - **важнее**, чем признак, который очень мало отличается.
###Code
model = KNN(3)
model.fit(X,y)
X_bad = [[10000, 1]]
y_pred = model.predict(X_bad)
X_new = np.vstack([X, X_bad])
y_new = np.hstack([y, y_pred])
plt.scatter(X_new[:,0], X_new[:,1], c=y_new, cmap='bwr', s=50, alpha=1)
plt.xlabel('длина дороги')
plt.ylabel('толщина дороги')
plt.grid()
###Output
_____no_output_____
###Markdown
Почему новая точка классифицировалась неправильно? Потому что, с точки зрения эвклидовой метрики, до `хороших` ей действительно `ближе`.$$\rho((6000, 7), (10000, 1)) = \sqrt{(6000 - 10000)^2 + (7-1)^2} = \sqrt{6^2} << \rho((6000, 1), (10000, 1)) = \sqrt{4000^2 + (1-1)^2}$$ Чтобы справиться с этой проблемой важно `масштабировать` численные признаки, чтобы они были корректно сравнимы.Есть 2 подхода к масштабированию:* Нормализация (MinMax Scaling) $$x^* = \frac{x-x_{min}}{x_{max} - x_{min}}$$* Стандартизация (Standart Scaling) $$x^* = \frac{x-\mu}{\sigma}$$, где $\mu$ - среднее, а $\sigma$ - стандартное отклонение
###Code
from sklearn.preprocessing import StandardScaler, MinMaxScaler
stan = StandardScaler().fit(X)
norm = MinMaxScaler().fit(X)
X_st = stan.transform(X)
X_norm = norm.transform(X)
###Output
_____no_output_____
###Markdown
Метод `fit` подбирает параметры масштабирования, а метод `transform` непосредственно преобразует значения.
###Code
plt.scatter(X_st[:,0], X_st[:,1], c=y, cmap='bwr', s=50, alpha=1)
plt.title('Standart')
plt.xlabel('длина дороги')
plt.ylabel('толщина дороги')
plt.grid()
###Output
_____no_output_____
###Markdown
И вот теперь мы можем верно классифицировать данные, не забыв трансформировать точку, которую предсказываем
###Code
model = KNN(3)
model.fit(X_st,y)
X_bad = stan.transform([[10000, 1]])
y_pred = model.predict(X_bad)
X_new = np.vstack([X_st, X_bad])
y_new = np.hstack([y, y_pred])
plt.scatter(X_new[:,0], X_new[:,1], c=y_new, cmap='bwr', s=50, alpha=1)
plt.title('Standart')
plt.xlabel('длина дороги')
plt.ylabel('толщина дороги')
plt.grid()
###Output
_____no_output_____
###Markdown
Интересный факт, что любые древесные алгоритмы: DecisionTree, RandomForest - не требуют масштабирования. Потому что они работают на пороговых правилах и все признаки у них остаются независимыми. Резюме: Хороший паттерн делать масштабирование - `всегда`. Потому что для метрических алгоритмов оно необходимо, а для деревьев оно ничего не портит. Категориальные признаки Окей, с числовыми признаками разобрались, но ведь доставать необходимую информацию нужно еще и из категориальных признаков. Как с ними можно работать? Label Encoding Рассмотрим выборку UCI bank, в которой большая часть признаков – категориальные.
###Code
import pandas as pd
df = pd.read_csv('bank_train.csv')
labels = pd.read_csv('bank_train_target.csv', header=None)
df.head()
###Output
_____no_output_____
###Markdown
Чтобы найти решение, давайте рассмотрим признак education:
###Code
df['job'].value_counts().plot.barh();
###Output
_____no_output_____
###Markdown
Естественным решением такой проблемы было бы однозначное отображение каждого значения в уникальное число. К примеру, мы могли бы преобразовать `university.degree` в 0, а `basic.9y` в 1. Эту простую операцию приходится делать часто, поэтому в модуле `preprocessing` библиотеки `sklearn` именно для этой задачи реализован класс `LabelEncoder`:
###Code
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
label_encoder = LabelEncoder()
###Output
_____no_output_____
###Markdown
Метод `fit` этого класса находит все уникальные значения и строит таблицу для соответствия каждой категории некоторому числу, а метод `transform` непосредственно преобразует значения в числа.
###Code
mapped_education = pd.Series(label_encoder.fit_transform(df['education']))
mapped_education.value_counts().plot.barh()
print(dict(enumerate(label_encoder.classes_)))
###Output
{0: 'basic.4y', 1: 'basic.6y', 2: 'basic.9y', 3: 'high.school', 4: 'illiterate', 5: 'professional.course', 6: 'university.degree', 7: 'unknown'}
###Markdown
Что произойдет, если у нас появятся данные с другими категориями?
###Code
try:
label_encoder.transform(df['education'].replace('high.school', 'high_school'))
except Exception as e:
print('Error:', e)
###Output
Error: y contains previously unseen labels: 'high_school'
###Markdown
Таким образом, при использовании этого подхода мы всегда должны быть уверены, что признак не может принимать неизвестных ранее значений. К этой проблеме мы вернемся чуть позже, а сейчас заменим весь столбец education на преобразованный:
###Code
df['education'] = mapped_education
df.head()
###Output
_____no_output_____
###Markdown
Продолжим преобразование для всех столбцов, имеющих тип `object` – именно этот тип задается в pandas для таких данных.
###Code
categorical_columns = df.columns[df.dtypes == 'object'].union(['education'])
for column in categorical_columns:
df[column] = label_encoder.fit_transform(df[column])
df.head()
###Output
_____no_output_____
###Markdown
Основная проблема такого представления заключается в том, что числовой код создал евклидово представление для данных.К примеру, нами неявным образом была введена алгебра над значениями работы - мы можем вычесть работу клиента 1 из работы клиента 2:
###Code
df.loc[1].job - df.loc[2].job
###Output
_____no_output_____
###Markdown
Конечно же, эта операция не имеет никакого смысла. Но именно на этом основаны метрики близости объектов, что делает бессмысленным применение метода ближайшего соседа на данных в таком виде. Аналогичным образом, никакого смысла не будет иметь применение линейных моделей.Пример внизу: у класса 1 `precision` = 0, `recall`=0, `f1-score`=0 -никакая классификация не осуществялется.
###Code
import warnings
warnings.filterwarnings('ignore')
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
def logistic_regression_accuracy_on(dataframe, labels):
features = dataframe.as_matrix()
train_features, test_features, train_labels, test_labels = \
train_test_split(features, labels)
logit = LogisticRegression()
logit.fit(train_features, train_labels)
return classification_report(test_labels, logit.predict(test_features))
print(logistic_regression_accuracy_on(df[categorical_columns], labels))
###Output
precision recall f1-score support
0 0.89 1.00 0.94 6126
1 0.00 0.00 0.00 773
accuracy 0.89 6899
macro avg 0.44 0.50 0.47 6899
weighted avg 0.79 0.89 0.84 6899
###Markdown
Однако для древесных моделей `LabelEncoding` вполне сгодится. Для того, чтобы мы смогли применять линейные модели на таких данных нам необходим другой метод, который называется One-Hot Encoding One-Hot EncodingПредположим, что некоторый признак может принимать 10 разных значений. В этом случае one hot encoding подразумевает создание 10 признаков, все из которых равны нулю *за исключением одного*. На позицию, соответствующую численному значению признака мы помещаем 1:
###Code
one_hot_example = pd.DataFrame([{i: 0 for i in range(10)}])
one_hot_example.loc[0, 6] = 1
one_hot_example
###Output
_____no_output_____
###Markdown
По умолчанию `OneHotEncoder` преобразует данные в разреженную матрицу, чтобы не расходовать память на хранение многочисленных нулей. Однако в этом примере размер данных не является для нас проблемой, поэтому мы будем использовать "плотное" представление.
###Code
onehot_encoder = OneHotEncoder(sparse=False)
encoded_categorical_columns = pd.DataFrame(onehot_encoder.fit_transform(df[categorical_columns]))
encoded_categorical_columns.head()
###Output
_____no_output_____
###Markdown
Мы получили 53 столбца - именно столько различных уникальных значений могут принимать категориальные столбцы исходной выборки. Преобразованные с помощью One-Hot Encoding данные начинают обретать смысл для линейной модели:
###Code
print(logistic_regression_accuracy_on(encoded_categorical_columns, labels))
###Output
precision recall f1-score support
0 0.91 0.99 0.95 6139
1 0.66 0.18 0.28 760
accuracy 0.90 6899
macro avg 0.78 0.58 0.61 6899
weighted avg 0.88 0.90 0.87 6899
###Markdown
Проблема возникает когда, количество различных значений у категориального признака сильно возрастает. В этом случае `OneHotEncoding` станет очень затратным по памяти (даже если будем использовать разряженные таблицы). Но в целом лучше всегда использовать `OneHot`. Хэширование признаков (Hashing trick)С течением времени категориальные признаки могут принимать новых значений. Это затрудняет использование уже обученных моделей на новых данных. Кроме того, `LabelEncoder` подразумевает предварительный анализ всей выборки и хранение построенных отображений в памяти, что затрудняет работу в режиме больших данных.Для решения этих проблем существует более простой подход к векторизации категориальных признаков, основанный на хэшировании, известный как hashing trick. Хэш-функции могут помочь нам в задаче поиска уникальных кодов для различных значений признака, к примеру:
###Code
for s in ('university.degree', 'high.school', 'illiterate'):
print(s, '->', hash(s))
###Output
university.degree -> -68740083847602807
high.school -> 4321647289974080590
illiterate -> -3642350026650764040
###Markdown
Отрицательные и настолько большие по модулю значения нам не подойдут. Ограничим область значений хэш-функции:
###Code
hash_space = 25
for s in ('university.degree', 'high.school', 'illiterate'):
print(s, '->', hash(s) % hash_space)
###Output
university.degree -> 18
high.school -> 15
illiterate -> 10
###Markdown
Представим, что у нас в выборке есть холостой студент, которому позвонили в понедельник, тогда его вектор признаков будет сформирован аналогично One-Hot Encoding, но в едином пространстве фиксированного размера для всех признаков:
###Code
hashing_example = pd.DataFrame([{i: 0.0 for i in range(hash_space)}])
for s in ('job=student', 'marital=single', 'day_of_week=mon'):
print(s, '->', hash(s) % hash_space)
hashing_example.loc[0, hash(s) % hash_space] = 1
hashing_example
###Output
job=student -> 16
marital=single -> 1
day_of_week=mon -> 0
###Markdown
Стоит обратить внимание, что в этом примере хэшировались не только значения признаков, а пары **название признака + значение признака**. Это необходимо, чтобы разделить одинаковые значения разных признаков между собой, к примеру:
###Code
assert hash('no') == hash('no')
assert hash('housing=no') != hash('loan=no')
###Output
_____no_output_____
###Markdown
Может ли произойти коллизия хэш-функции, то есть совпадение кодов для двух разных значений? Нетрудно доказать, что при достаточном размере пространства хэширования это происходит редко, но даже в тех случаях, когда это происходит, это не будет приводить к существенному ухудшению качества классификации или регрессии.Возможно, вы спросите: "а что за хрень вообще происходит?", и покажется, что при хэшировании признаков страдает здравый смысл. Возможно, но эта эвристика – по сути, единственный подход к тому, чтобы работать с категориальными признаками, у которых много уникальных значений. Более того, эта техника себя хорошо зарекомендовала по результатами на практике. Подробней про хэширование признаков (learning to hash) можно почитать в [этом](https://arxiv.org/abs/1509.05472) обзоре, а также в [материалах](https://github.com/esokolov/ml-course-hse/blob/master/2016-fall/lecture-notes/lecture06-linclass.pdf) Евгения Соколова. Blending И на финал мы познакомимся с внутренним устройством классификаторов и попытаемся сделать свой смешанный классификатор.Часто на практике оказывается возможным увеличить качество предсказания путем смешивания разных моделей. Это происходит за счет того, что разные модели могут хранить в себе разные скрытые зависимости от данных, которые могут вместе дать синергию.Посмотрим на наш небольшой класс классификатора.
###Code
from sklearn.base import BaseEstimator, ClassifierMixin
class BlendClassifier(BaseEstimator, ClassifierMixin):# предок класса всех классификаторов
def __init__(self, clf1, clf2, alpha=0.5):
self.clf1 = clf1
self.clf2 = clf2
self.alpha = alpha #параметр смешивания
def fit(self, X, y): #обучаем классификатор
self.X_ = X
self.y_ = y
self.clf1.fit(X, y)
self.clf2.fit(X, y)
return self
def predict(self, X): #возвращаем значения
predict1 = self.clf1.predict(X)
predict2 = self.clf2.predict(X)
return np.round(predict1 * self.alpha + predict2 * (1 - self.alpha))
def predict_proba(self, X): #возвращаем вероятности
predict1 = self.clf1.predict_proba(X)
predict2 = self.clf2.predict_proba(X)
return predict1 * self.alpha + predict2 * (1 - self.alpha)
from sklearn.linear_model import SGDClassifier as SGD
from sklearn.ensemble import RandomForestClassifier as RF
clf = BlendClassifier(
clf1 = SGD(loss = 'log'),
clf2 = RF(n_estimators = 50)
)
clf.fit(iris.data, iris.target)
###Output
_____no_output_____
|
validation/comparisons/comparison_galah_dr2/galah_dr3_comparison_dr2.ipynb
|
###Markdown
GALAH DR3 - Comparison to DR2 Author(s): Sven Buder (SB, WG4) History:200228 SB Created The aim of this notebook is to provide a comparison of GALAH DR3 with respect to GALAH DR2
###Code
# Preamble for notebook
# Compatibility with Python 3
from __future__ import (absolute_import, division, print_function)
try:
%matplotlib inline
%config InlineBackend.figure_format='retina'
except:
pass
# Basic packages
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
import os
import sys
import glob
import pickle
import pandas
# Packages to work with FITS and (IDL) SME.out files
import astropy.io.fits as pyfits
from astropy.table import Table
from scipy.io.idl import readsav
# Matplotlib and associated packages for plotting
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib.transforms import Bbox,TransformedBbox
from matplotlib.image import BboxImage
from matplotlib.legend_handler import HandlerBase
from matplotlib._png import read_png
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib.colors import ListedColormap
import matplotlib.colors as colors
params = {
'font.family' : 'sans',
'font.size' : 17,
'axes.labelsize' : 20,
'ytick.labelsize' : 16,
'xtick.labelsize' : 16,
'legend.fontsize' : 20,
'text.usetex' : True,
'text.latex.preamble': [r'\usepackage{upgreek}', r'\usepackage{amsmath}'],
}
plt.rcParams.update(params)
_parula_data = [[0.2081, 0.1663, 0.5292],
[0.2116238095, 0.1897809524, 0.5776761905],
[0.212252381, 0.2137714286, 0.6269714286],
[0.2081, 0.2386, 0.6770857143],
[0.1959047619, 0.2644571429, 0.7279],
[0.1707285714, 0.2919380952, 0.779247619],
[0.1252714286, 0.3242428571, 0.8302714286],
[0.0591333333, 0.3598333333, 0.8683333333],
[0.0116952381, 0.3875095238, 0.8819571429],
[0.0059571429, 0.4086142857, 0.8828428571],
[0.0165142857, 0.4266, 0.8786333333],
[0.032852381, 0.4430428571, 0.8719571429],
[0.0498142857, 0.4585714286, 0.8640571429],
[0.0629333333, 0.4736904762, 0.8554380952],
[0.0722666667, 0.4886666667, 0.8467],
[0.0779428571, 0.5039857143, 0.8383714286],
[0.079347619, 0.5200238095, 0.8311809524],
[0.0749428571, 0.5375428571, 0.8262714286],
[0.0640571429, 0.5569857143, 0.8239571429],
[0.0487714286, 0.5772238095, 0.8228285714],
[0.0343428571, 0.5965809524, 0.819852381],
[0.0265, 0.6137, 0.8135],
[0.0238904762, 0.6286619048, 0.8037619048],
[0.0230904762, 0.6417857143, 0.7912666667],
[0.0227714286, 0.6534857143, 0.7767571429],
[0.0266619048, 0.6641952381, 0.7607190476],
[0.0383714286, 0.6742714286, 0.743552381],
[0.0589714286, 0.6837571429, 0.7253857143],
[0.0843, 0.6928333333, 0.7061666667],
[0.1132952381, 0.7015, 0.6858571429],
[0.1452714286, 0.7097571429, 0.6646285714],
[0.1801333333, 0.7176571429, 0.6424333333],
[0.2178285714, 0.7250428571, 0.6192619048],
[0.2586428571, 0.7317142857, 0.5954285714],
[0.3021714286, 0.7376047619, 0.5711857143],
[0.3481666667, 0.7424333333, 0.5472666667],
[0.3952571429, 0.7459, 0.5244428571],
[0.4420095238, 0.7480809524, 0.5033142857],
[0.4871238095, 0.7490619048, 0.4839761905],
[0.5300285714, 0.7491142857, 0.4661142857],
[0.5708571429, 0.7485190476, 0.4493904762],
[0.609852381, 0.7473142857, 0.4336857143],
[0.6473, 0.7456, 0.4188],
[0.6834190476, 0.7434761905, 0.4044333333],
[0.7184095238, 0.7411333333, 0.3904761905],
[0.7524857143, 0.7384, 0.3768142857],
[0.7858428571, 0.7355666667, 0.3632714286],
[0.8185047619, 0.7327333333, 0.3497904762],
[0.8506571429, 0.7299, 0.3360285714],
[0.8824333333, 0.7274333333, 0.3217],
[0.9139333333, 0.7257857143, 0.3062761905],
[0.9449571429, 0.7261142857, 0.2886428571],
[0.9738952381, 0.7313952381, 0.266647619],
[0.9937714286, 0.7454571429, 0.240347619],
[0.9990428571, 0.7653142857, 0.2164142857],
[0.9955333333, 0.7860571429, 0.196652381],
[0.988, 0.8066, 0.1793666667],
[0.9788571429, 0.8271428571, 0.1633142857],
[0.9697, 0.8481380952, 0.147452381],
[0.9625857143, 0.8705142857, 0.1309],
[0.9588714286, 0.8949, 0.1132428571],
[0.9598238095, 0.9218333333, 0.0948380952],
[0.9661, 0.9514428571, 0.0755333333],
[0.9763, 0.9831, 0.0538]]
parula = ListedColormap(_parula_data, name='parula')
parula_zero = _parula_data[0]
parula_0 = ListedColormap(_parula_data, name='parula_0')
parula_0.set_bad((1,1,1))
parula_r = ListedColormap(_parula_data[::-1], name='parula_r')
willi_blau = [0.0722666667, 0.4886666667, 0.8467]
# read in galah dr3
dr3 = Table.read('../../../catalogs/GALAH_DR3_main.fits')
dr3['A_Li'] = dr3['Li_fe'] + dr3['fe_h'] + 1.05
pdr3 = Table.to_pandas(dr3)
# read in galah dr2
dr2 = Table.read('GALAH_DR2.1_catalog.fits')
dr2['a_li'] = dr2['li_fe'] + dr2['fe_h'] + 1.05
pdr2 = Table.to_pandas(dr2)
# merge them via pandas.merge
df = pandas.merge(pdr3, pdr2, on=['sobject_id','sobject_id'], how='left')
# select the unflagged stars
good_dr3 = (dr3['flag_sp'] == 0)
good_dr2 = (dr2['flag_cannon'] == 0)
good_df = (df['flag_sp'] == 0) & (df['flag_cannon'] == 0)
f, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(15,5))
giants = good_df & (df['teff_x'] < 5500) & (df['logg_x'] < 3.5)
dwarfs = good_df & ((df['teff_x'] > 5500) | (df['logg_x'] > 3.5))
ax1.hist2d(
df['fe_h_y'][good_df],
df['fe_h_x'][good_df]-df['fe_h_y'][good_df],
bins=(np.linspace(-1.5,1.0,100),np.linspace(-0.25,0.25,100)),
cmin=1
);
ax2.hist2d(
df['fe_h_y'][giants],
df['fe_h_x'][giants]-df['fe_h_y'][giants],
bins=(np.linspace(-1.5,1.0,100),np.linspace(-0.25,0.25,100)),
cmin=1
);
ax3.hist2d(
df['fe_h_y'][dwarfs],
df['fe_h_x'][dwarfs]-df['fe_h_y'][dwarfs],
bins=(np.linspace(-1.5,1.0,100),np.linspace(-0.25,0.25,100)),
cmin=1
);
def plot_tefflogg_fehalphafe_fehali(dr2=dr2, dr3=dr3, use='all'):
"""
We plot 6 panels
a) Teff vs. logg for GALAH DR2
b) [Fe/H] vs. [alpha/Fe] for GALAH DR2
c) [Fe/H] vs. A(Li) for GALAH DR2
d) Teff vs. logg for GALAH DR3
e) [Fe/H] vs. [alpha/Fe] for GALAH DR3
f) [Fe/H] vs. A(Li) for GALAH DR3
"""
fig, gs = plt.subplots(2,3,figsize=(15,7))
teff_lim = (3000,8250)
logg_lim = (-0.5,5.5)
feh_lim = (-2.75,1)
alpha_lim = (-0.5,1.0)
li_lim = (-1.5,4.5)
props = dict(boxstyle='round', facecolor='w', alpha=0.75)
kwargs_tefflogg = dict(
cmin=1,rasterized=True,
bins=(np.linspace(teff_lim[0],teff_lim[1],100),np.linspace(logg_lim[0],logg_lim[1],100))
)
kwargs_fehalphafe = dict(
cmin=1,rasterized=True,
bins=(np.linspace(feh_lim[0],feh_lim[1],100),np.linspace(alpha_lim[0],alpha_lim[1],100))
)
kwargs_fehali = dict(
cmin=1,rasterized=True,
bins=(np.linspace(feh_lim[0],feh_lim[1],100),np.linspace(li_lim[0],li_lim[1],100))
)
"""
dr2 teff vs. logg
"""
ax1 = gs[0,0]
dr2_all = np.isfinite(dr2['teff']) & np.isfinite(dr2['logg'])
ax1.hist2d(
dr2['teff'][dr2_all],
dr2['logg'][dr2_all],
cmap='Blues',
norm=LogNorm(),
**kwargs_tefflogg
)
dr2_unflagged = (dr2['flag_cannon'] == 0)
p1,x,y,s1 = ax1.hist2d(
dr2['teff'][dr2_unflagged],
dr2['logg'][dr2_unflagged],
cmap=parula,
norm=LogNorm(),
**kwargs_tefflogg
)
ax1.set_xlabel(r'$T_\text{eff}$ GALAH DR2')
ax1.set_ylabel(r'$\log g$ GALAH DR2')
ax1.set_xlim(teff_lim[1],teff_lim[0])
ax1.set_ylim(logg_lim[1],logg_lim[0])
c1 = plt.colorbar(s1, ax=ax1)
c1.set_label('Nr. Unflagged Spectra')
"""
dr3 teff vs. logg
"""
ax2 = gs[1,0]
dr3_all = np.isfinite(dr3['teff']) & np.isfinite(dr3['logg'])
ax2.hist2d(
dr3['teff'][dr3_all],
dr3['logg'][dr3_all],
cmap='Blues',
norm=LogNorm(),
**kwargs_tefflogg
)
dr3_unflagged = (dr3['flag_sp'] == 0)
p2,x,y,s2 = ax2.hist2d(
dr3['teff'][dr3_unflagged],
dr3['logg'][dr3_unflagged],
cmap=parula,
norm=LogNorm(),
**kwargs_tefflogg
)
ax2.set_xlabel(r'$T_\text{eff}$ GALAH DR3')
ax2.set_ylabel(r'$\log g$ GALAH DR3')
ax2.set_xlim(teff_lim[1],teff_lim[0])
ax2.set_ylim(logg_lim[1],logg_lim[0])
c2 = plt.colorbar(s2, ax=ax2)
c2.set_label('Nr. Unflagged Spectra')
ax1.text(0.025, 0.975, 'a)', transform=ax1.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
ax2.text(0.025, 0.975, 'd)', transform=ax2.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
"""
dr2 fe_h vs. alpha_fe
"""
ax1 = gs[0,1]
dr2_all = np.isfinite(dr2['fe_h']) & np.isfinite(dr2['alpha_fe'])
ax1.hist2d(
dr2['fe_h'][dr2_all],
dr2['alpha_fe'][dr2_all],
cmap='Blues',
norm=LogNorm(),
**kwargs_fehalphafe
)
dr2_unflagged = (dr2['flag_cannon'] == 0) & np.isfinite(dr2['alpha_fe'])
p1,x,y,s1 = ax1.hist2d(
dr2['fe_h'][dr2_unflagged],
dr2['alpha_fe'][dr2_unflagged],
cmap=parula,
norm=LogNorm(),
**kwargs_fehalphafe
)
ax1.set_xlabel(r'[Fe/H] GALAH DR2')
ax1.set_ylabel(r'[$\alpha$/Fe] GALAH DR2')
ax1.set_xlim(feh_lim[0],feh_lim[1])
ax1.set_ylim(alpha_lim[0],alpha_lim[1])
c1 = plt.colorbar(s1, ax=ax1)
c1.set_label('Nr. Unflagged Spectra')
"""
dr3 fe_h vs. alpha_fe
"""
ax2 = gs[1,1]
dr3_all = np.isfinite(dr3['fe_h']) & np.isfinite(dr3['alpha_fe'])
ax2.hist2d(
dr3['fe_h'][dr3_all],
dr3['alpha_fe'][dr3_all],
cmap='Blues',
norm=LogNorm(),
**kwargs_fehalphafe
)
dr3_unflagged = (dr3['flag_sp'] == 0) & np.isfinite(dr3['alpha_fe'])
p2,x,y,s2 = ax2.hist2d(
dr3['fe_h'][dr3_unflagged],
dr3['alpha_fe'][dr3_unflagged],
cmap=parula,
norm=LogNorm(),
**kwargs_fehalphafe
)
ax2.set_xlabel(r'[Fe/H] GALAH DR3')
ax2.set_ylabel(r'[$\alpha$/Fe] GALAH DR3')
ax2.set_xlim(feh_lim[0],feh_lim[1])
ax2.set_ylim(alpha_lim[0],alpha_lim[1])
c2 = plt.colorbar(s2, ax=ax2)
c2.set_label('Nr. Unflagged Spectra')
ax1.text(0.025, 0.975, 'b)', transform=ax1.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
ax2.text(0.025, 0.975, 'e)', transform=ax2.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
"""
dr2 fe_h vs. a_li
"""
ax1 = gs[0,2]
dr2_all = np.isfinite(dr2['fe_h']) & np.isfinite(dr2['a_li'])
ax1.hist2d(
dr2['fe_h'][dr2_all],
dr2['a_li'][dr2_all],
cmap='Blues',
norm=LogNorm(),
**kwargs_fehali
)
dr2_unflagged = (dr2['flag_cannon'] == 0) & (dr2['flag_li_fe'] == 0)
p1,x,y,s1 = ax1.hist2d(
dr2['fe_h'][dr2_unflagged],
dr2['a_li'][dr2_unflagged],
cmap=parula,
norm=LogNorm(),
**kwargs_fehali
)
ax1.set_xlabel(r'[Fe/H] GALAH DR2')
ax1.set_ylabel(r'A(Li) GALAH DR2')
ax1.set_xlim(feh_lim[0],feh_lim[1])
ax1.set_ylim(li_lim[0],li_lim[1])
c1 = plt.colorbar(s1, ax=ax1)
c1.set_label('Nr. Unflagged Spectra')
"""
dr3 fe_h vs. alpha_fe
"""
ax2 = gs[1,2]
dr3_all = np.isfinite(dr3['fe_h']) & np.isfinite(dr3['A_Li'])
ax2.hist2d(
dr3['fe_h'][dr3_all],
dr3['A_Li'][dr3_all],
cmap='Blues',
norm=LogNorm(),
**kwargs_fehali
)
dr3_unflagged = (dr3['flag_sp'] == 0) & (dr3['flag_Li_fe']==0)
p2,x,y,s2 = ax2.hist2d(
dr3['fe_h'][dr3_unflagged],
dr3['A_Li'][dr3_unflagged],
cmap=parula,
norm=LogNorm(),
**kwargs_fehali
)
ax2.set_xlabel(r'[Fe/H] GALAH DR3')
ax2.set_ylabel(r'A(Li) GALAH DR3')
ax2.set_xlim(feh_lim[0],feh_lim[1])
ax2.set_ylim(li_lim[0],li_lim[1])
c2 = plt.colorbar(s2, ax=ax2)
c2.set_label('Nr. Unflagged Spectra')
ax1.text(0.025, 0.975, 'c)', transform=ax1.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
ax2.text(0.025, 0.975, 'f)', transform=ax2.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
plt.tight_layout()
return fig
# Create comparison for all stars
fig = plot_tefflogg_fehalphafe_fehali()
fig.savefig('galah_dr3_comparison_dr2.pdf',dpi=200,bbox_inches='tight')
fig.savefig('../../../dr3_release_paper/figures/galah_dr3_comparison_dr2.png',dpi=200,bbox_inches='tight')
###Output
_____no_output_____
|
_notebooks/2021-12-23-Do_natural_language4.ipynb
|
###Markdown
"[Do it 자연어] 4. 문장 쌍 분류하기 + 웹 실습"- author: Seong Yeon Kim - categories: [book, jupyter, Do it, natural language, BERT, tokenizer, web, Classifier] 모델 환경설정
###Code
!pip install ratsnlp
from google.colab import drive
drive.mount('/gdrive', force_remount=True)
from torch.cuda import is_available
import torch
from ratsnlp.nlpbook.classification import ClassificationTrainArguments
args = ClassificationTrainArguments(
pretrained_model_name='beomi/kcbert-base',
downstream_task_name = 'pair-classification', # 문장 쌍 분류를 할 예정이므로
downstream_corpus_name='klue-nli', # 업스테이지 기업이 공게한 KLUE-NLI 데이터로 파인튜닝
downstream_model_dir='/gdrive/My Drive/nlpbook/checkpoint-paircls',
batch_size = 32 if torch.cuda.is_available() else 4,
learning_rate=5e-5,
max_seq_length=64,
epochs = 5,
tpu_cores=0 if torch.cuda.is_available() else 8,
seed = 7,
)
###Output
_____no_output_____
###Markdown
kcbert-base 모델을 klue-nli 데이터로 파인튜닝 합니다.
###Code
from ratsnlp import nlpbook
nlpbook.set_seed(args)
nlpbook.set_logger(args)
###Output
INFO:ratsnlp:Training/evaluation parameters ClassificationTrainArguments(pretrained_model_name='beomi/kcbert-base', downstream_task_name='pair-classification', downstream_corpus_name='klue-nli', downstream_corpus_root_dir='/root/Korpora', downstream_model_dir='/gdrive/My Drive/nlpbook/checkpoint-paircls', max_seq_length=64, save_top_k=1, monitor='min val_loss', seed=7, overwrite_cache=False, force_download=False, test_mode=False, learning_rate=5e-05, epochs=5, batch_size=32, cpu_workers=2, fp16=False, tpu_cores=0)
###Markdown
랜덤 시드와 로거를 설정합니다. 말뭉치 내려받기
###Code
nlpbook.download_downstream_dataset(args)
###Output
Downloading: 100%|██████████| 12.3M/12.3M [00:00<00:00, 37.5MB/s]
Downloading: 100%|██████████| 1.47M/1.47M [00:00<00:00, 35.4MB/s]
###Markdown
말뭉치를 내려받습니다.
###Code
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained(
args.pretrained_model_name,
do_lower_case = False,
)
###Output
_____no_output_____
###Markdown
토크나이저를 구축합니다. 학습 데이터 구축
###Code
from ratsnlp.nlpbook.paircls import KlueNLICorpus
from ratsnlp.nlpbook.classification import ClassificationDataset
corpus = KlueNLICorpus() # json 파일형식의 KLUE-NLI 데이터를 문장(전제+가설)과 레이블(참 거짓 중립)으로 읽음
train_dataset = ClassificationDataset(
args = args,
corpus = corpus,
tokenizer = tokenizer,
mode = 'train',
)
train_dataset[0]
###Output
_____no_output_____
###Markdown
KlueNLICorpus에서 받은 문장들을 미리 설정한 토크나이저로 분리합니다.출력물은 input_ids, attention_mask, token_type_ids, label 총 4개가 나옵니다.이전과 같은 결과인데 짧게 설명하며 input_ids은 토큰 시퀀스를, attention_mask는 패딩 여부를 알려줍니다.token_type_ids은 세그먼트 정보로 첫번째 문장은 0, 두번째 문장은 1, 나머지 패딩은 0을 줍니다.label은 0일때 참, 1일때 거짓, 2일때 중립을 의미합니다.
###Code
from torch.utils.data import DataLoader, RandomSampler
train_dataloader = DataLoader(
train_dataset,
batch_size = args.batch_size,
sampler = RandomSampler(train_dataset, replacement = False),
collate_fn = nlpbook.data_collator, # 뽑은 인스턴스를 배치로 바꿔줌(텐서 형태로)
drop_last = False,
num_workers = args.cpu_workers,
)
###Output
_____no_output_____
###Markdown
학습 데이터 셋으로 로더를 구축했습니다. 배치크기만큼 인스턴스를 랜덤하게 뽑은 뒤 이를 합처 배치를 만듭니다. 평가용 데이터 구축
###Code
from torch.utils.data import SequentialSampler
val_dataset = ClassificationDataset(
args = args,
corpus = corpus,
tokenizer = tokenizer,
mode = 'test',
)
val_dataloader = DataLoader(
val_dataset,
batch_size = args.batch_size,
sampler = SequentialSampler(val_dataset), # 순서대로 추출
collate_fn = nlpbook.data_collator,
drop_last = False,
num_workers = args.cpu_workers,
)
###Output
INFO:ratsnlp:Creating features from dataset file at /root/Korpora/klue-nli
INFO:ratsnlp:loading test data... LOOKING AT /root/Korpora/klue-nli/klue_nli_dev.json
INFO:ratsnlp:tokenize sentences, it could take a lot of time...
INFO:ratsnlp:tokenize sentences [took 1.457 s]
INFO:ratsnlp:*** Example ***
INFO:ratsnlp:sentence A, B: 10명이 함께 사용하기 불편함없이 만족했다. + 10명이 함께 사용하기 불편함이 많았다.
INFO:ratsnlp:tokens: [CLS] 10명 ##이 함께 사용 ##하기 불편 ##함 ##없이 만족 ##했다 . [SEP] 10명 ##이 함께 사용 ##하기 불편 ##함이 많았 ##다 . [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]
INFO:ratsnlp:label: contradiction
INFO:ratsnlp:features: ClassificationFeatures(input_ids=[2, 21000, 4017, 9158, 9021, 8268, 10588, 4421, 8281, 14184, 8258, 17, 3, 21000, 4017, 9158, 9021, 8268, 10588, 11467, 14338, 4020, 17, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], attention_mask=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], token_type_ids=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], label=1)
INFO:ratsnlp:*** Example ***
INFO:ratsnlp:sentence A, B: 10명이 함께 사용하기 불편함없이 만족했다. + 성인 10명이 함께 사용하기 불편함없이 없었다.
INFO:ratsnlp:tokens: [CLS] 10명 ##이 함께 사용 ##하기 불편 ##함 ##없이 만족 ##했다 . [SEP] 성인 10명 ##이 함께 사용 ##하기 불편 ##함 ##없이 없었다 . [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]
INFO:ratsnlp:label: neutral
INFO:ratsnlp:features: ClassificationFeatures(input_ids=[2, 21000, 4017, 9158, 9021, 8268, 10588, 4421, 8281, 14184, 8258, 17, 3, 13246, 21000, 4017, 9158, 9021, 8268, 10588, 4421, 8281, 12629, 17, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], attention_mask=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], token_type_ids=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], label=2)
INFO:ratsnlp:*** Example ***
INFO:ratsnlp:sentence A, B: 10명이 함께 사용하기 불편함없이 만족했다. + 10명이 함께 사용하기에 만족스러웠다.
INFO:ratsnlp:tokens: [CLS] 10명 ##이 함께 사용 ##하기 불편 ##함 ##없이 만족 ##했다 . [SEP] 10명 ##이 함께 사용 ##하기에 만족 ##스러 ##웠다 . [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]
INFO:ratsnlp:label: entailment
INFO:ratsnlp:features: ClassificationFeatures(input_ids=[2, 21000, 4017, 9158, 9021, 8268, 10588, 4421, 8281, 14184, 8258, 17, 3, 21000, 4017, 9158, 9021, 19956, 14184, 13378, 19996, 17, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], attention_mask=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], token_type_ids=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], label=0)
INFO:ratsnlp:*** Example ***
INFO:ratsnlp:sentence A, B: 10층에 건물사람들만 이용하는 수영장과 썬베드들이 있구요. + 건물사람들은 수영장과 썬베드를 이용할 수 있습니다.
INFO:ratsnlp:tokens: [CLS] 10 ##층 ##에 건물 ##사람들 ##만 이용하는 수영 ##장과 썬 ##베 ##드 ##들이 있 ##구요 . [SEP] 건물 ##사람들은 수영 ##장과 썬 ##베 ##드 ##를 이용할 수 있습니다 . [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]
INFO:ratsnlp:label: entailment
INFO:ratsnlp:features: ClassificationFeatures(input_ids=[2, 8240, 4491, 4113, 10828, 9390, 4049, 14502, 26770, 21758, 2060, 4155, 4273, 7967, 2469, 8875, 17, 3, 10828, 11249, 26770, 21758, 2060, 4155, 4273, 4180, 29861, 1931, 8982, 17, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], attention_mask=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], token_type_ids=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], label=0)
INFO:ratsnlp:*** Example ***
INFO:ratsnlp:sentence A, B: 10층에 건물사람들만 이용하는 수영장과 썬베드들이 있구요. + 수영장과 썬베드는 9층에 있습니다.
INFO:ratsnlp:tokens: [CLS] 10 ##층 ##에 건물 ##사람들 ##만 이용하는 수영 ##장과 썬 ##베 ##드 ##들이 있 ##구요 . [SEP] 수영 ##장과 썬 ##베 ##드는 9 ##층 ##에 있습니다 . [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]
INFO:ratsnlp:label: contradiction
INFO:ratsnlp:features: ClassificationFeatures(input_ids=[2, 8240, 4491, 4113, 10828, 9390, 4049, 14502, 26770, 21758, 2060, 4155, 4273, 7967, 2469, 8875, 17, 3, 26770, 21758, 2060, 4155, 8609, 28, 4491, 4113, 8982, 17, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], attention_mask=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], token_type_ids=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], label=1)
INFO:ratsnlp:Saving features into cached file, it could take a lot of time...
INFO:ratsnlp:Saving features into cached file /root/Korpora/klue-nli/cached_test_BertTokenizer_64_klue-nli_pair-classification [took 0.293 s]
###Markdown
입력받은 데이터를 토크나이저로 분리한 후 로더를 이용해 테스트용 배치를 만듭니다. 모델 학습
###Code
from transformers import BertConfig, BertForSequenceClassification
pretrained_model_config = BertConfig.from_pretrained(
args.pretrained_model_name,
num_labels = corpus.num_labels,
)
model = BertForSequenceClassification.from_pretrained(
args.pretrained_model_name,
config = pretrained_model_config
)
###Output
_____no_output_____
###Markdown
pretrained_model_config은 프리트레인을 마친 BERT모델을 기록한 형태입니다.분류할 라벨이 3인것까지 정보를 줍니다.model은 윗 모델에 문서 분류용 태스크 모듈을 덧붙인 모델입니다.
###Code
from ratsnlp.nlpbook.classification import ClassificationTask
task = ClassificationTask(model, args)
###Output
_____no_output_____
###Markdown
앞서 정의한 모델을 학습시킵니다. ClassificationTask 내에는 옵티마이저와 러닝 레이트 스케줄러가 있습니다.
###Code
trainer = nlpbook.get_trainer(args)
trainer
###Output
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:69: UserWarning: Checkpoint directory /gdrive/My Drive/nlpbook/checkpoint-paircls exists and is not empty.
warnings.warn(*args, **kwargs)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
###Markdown
트레이너를 정의했습니다. 트레이너는 GPU/TPU 설정, 로그 및 체크포인트 등 귀찮은 설정을 알아서 해줍니다.
###Code
trainer.fit(
task,
train_dataloader = train_dataloader,
val_dataloaders = val_dataloader
)
###Output
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
--------------------------------------------------------
0 | model | BertForSequenceClassification | 108 M
--------------------------------------------------------
108 M Trainable params
0 Non-trainable params
108 M Total params
435.683 Total estimated model params size (MB)
###Markdown
전제와 가설을 검증하는 웹 서비스 만들기
###Code
from ratsnlp.nlpbook.classification import ClassificationDeployArguments
args = ClassificationDeployArguments(
pretrained_model_name='beomi/kcbert-base',
downstream_model_dir='/gdrive/My Drive/nlpbook/checkpoint-paircls',
max_seq_length=64,
)
###Output
downstream_model_checkpoint_fpath: /gdrive/My Drive/nlpbook/checkpoint-paircls/epoch=1-val_loss=0.82-v1.ckpt
###Markdown
인퍼런스 설정을 해줍니다.
###Code
import torch
from transformers import BertConfig, BertForSequenceClassification
fine_tuned_model_ckpt = torch.load(
args.downstream_model_checkpoint_fpath,
map_location = torch.device('cpu'),
)
###Output
_____no_output_____
###Markdown
체크 포인트를 로드해줍니다. (이전에 만든 모델 업로드)
###Code
from transformers import BertConfig
pretrained_model_config = BertConfig.from_pretrained(
args.pretrained_model_name,
num_labels = 3
)
model = BertForSequenceClassification(pretrained_model_config)
###Output
_____no_output_____
###Markdown
BERT 설절을 로드하고 BERT 모델을 초기화합니다.
###Code
model.load_state_dict({k.replace('model.', ''): v for k, v in fine_tuned_model_ckpt['state_dict'].items()})
model.eval()
###Output
_____no_output_____
###Markdown
초기화한 BERT 모델에 체크 포인트를 주입합니다. 그 후 모델을 평가모드로 바꿉니다.
###Code
def inference_fn(premise, hypothesis):
inputs = tokenizer(
[(premise, hypothesis)],
max_length = args.max_seq_length,
padding = 'max_length',
truncation = True # 잘린 값 처리 여부
)
with torch.no_grad():
outputs = model(**{k: torch.tensor(v) for k, v in inputs.items()})
prob = outputs.logits.softmax(dim=1)
entailment_prob = round(prob[0][0].item(), 2)
contradiction_prob = round(prob[0][1].item(), 2)
neutral_prob = round(prob[0][1].item(), 2)
if torch.argmax(prob) == 0:
pred = '참'
elif torch.argmax(prob) == 1:
pred = '거짓'
else:
pred = '중립'
return {
'premise' : premise,
'hypothesis' : hypothesis,
'prediction' : pred,
'entailment_data': f"참 {entailment_prob}",
'contradiction_data': f"거짓 {contradiction_prob}",
'neutral_data': f"중립 {neutral_prob}",
'entailment_width': f"{entailment_prob*100}%",
'contradiction_width': f"{contradiction_prob*100}%",
'neutral_width': f"{neutral_prob*100}%",
}
###Output
_____no_output_____
###Markdown
전제와 가설을 입력받아 각각 토큰화, 인덱싱을 수행한 것을 파이토치 텐서 자료형으로 변환한뒤 모델에 입력하는 함수를 만듭니다.
###Code
from ratsnlp.nlpbook.classification import get_web_service_app
app = get_web_service_app(inference_fn)
app.run()
###Output
* Serving Flask app "ratsnlp.nlpbook.classification.deploy" (lazy loading)
* Environment: production
[31m WARNING: This is a development server. Do not use it in a production deployment.[0m
[2m Use a production WSGI server instead.[0m
* Debug mode: off
|
notebooks/ch5_loops_iterations_part1.ipynb
|
###Markdown
chapter 5 loops and iterations write a program to say hello 5 times
###Code
n = 5
while n > 0:
print('hello')
n = n - 1
print('done!')
n = 0
while n < 5:
print('hello')
n = n + 1
print('done!')
###Output
hello
hello
hello
hello
hello
done!
###Markdown
* write a program to count-up 1-5 * write a program to count-down 5-0
###Code
n = 1
while n <= 5 :
print(n)
n +=1 # n = n -1
print('done')
n = 5
while n >= 0:
print(n)
n -=1
print('done')
###Output
5
4
3
2
1
0
done
###Markdown
write a program to say hello 5 times each second
###Code
from time import sleep
n = 0
while n < 5:
print('hello')
n +=1
sleep(2)
print('done')
while True:
line = input('> ')
if line == 'exit':
break # go to line 6
print(line)
print('done')
while True:
line = input('> ')
if line[0] == "#":
continue # go to line 1
if line == 'exit':
break # go to line 8
print(line)
print('done')
# indefinite loop
while True:
line = input('> ')
if line[0] == "#":
continue # go to line 1
if line == 'exit':
break # go to line 8
print(line)
print('done')
names = ['Ahmed', 'Adham', 'George', 'Ali', 'Majdi', 'Khitam']
# write code to find names that start with A
for name in names:
if name[0] != 'A':
continue
print(name)
names = ['Ahmed', 'Adham', 'George', 'Ali', 'Majdi', 'Khitam']
# write code to find names that do not start with A
for name in names:
if name[0] == 'A':
continue
print(name)
# loop using iteration variable
numbers = [2, 5, 1, 8]
for n in numbers:
print(n)
# write a program to sum a list
numbers = [2, 5, 1, 8]
total = 0
for n in numbers:
total = total + n
print(total)
numbers[0]
numbers[3]
range(4)
list(range(4))
# loop using index
for i in range(4):
print(numbers[i])
# loop using index
for i in range(len(numbers)):
print(numbers[i])
###Output
2
5
1
8
|
01a_Operators_DataTypes__SupplementaryMaterial.ipynb
|
###Markdown
Introduction to Programming for Engineers Python 3 01a Operators and Data Types SUPPLEMENTARY MATERIAL 1. Algebraic Operators 2. Comments 3. Operator Precedence 4. Readability 5. Variable Assignment 6. `print` 7. Augmented Assignment 8. Naming Variables 9. Comparison Operators 10. Logical Operators 11. Types 12. Numeric Types 13. Type Conversion 1. Algebraic OperatorsWe can use Python like a calculator.__Simple arithmetical operators:__$+$ Addition $-$ Subtraction $*$ Multiplication $/$ Division $//$ Floor division (round down to the next integer)$\%$ Modulo (remainder)$**$ Exponent Practise Exercise 1A : Algebraic OperatorsExpress the following simple expressions using python code. Click on the cell to type in it. Press "Shift" + "Enter" to run the cell. $3 + 8$
###Code
# SOLUTION
a = 1 + 3
print(a)
###Output
4
###Markdown
2. Comments The text beginning with "" is a comment. ```python SOLUTION```These are not computed as part of the program but are there for humans to read to help understand what the code does. 3. Operator PrecedenceThe order in which operations are performed when there are multiple operations in an expressione.g. multiplication before addition. Python follows the usual mathematical rules for precedence. > 1. Parentheses e.g. $(2+4)$1. Exponents e.g. $2^2$1. Multiplication, Division, Floor Division and Modulo (left to right)1. Addition and Subtraction (left to right) - The expression should __evaluate correctly__.- The expression should be __easily readable__. __Easily Readable__Simple enough for someone else reading the code to understand.It is possible to write __code__ that is correct, but might be difficult for someone (including you!) to check. Correct EvaluationA common example: $$\frac{10}{2 \times 50} = 0.1$$
###Code
# Incorrect solution
10 / 2 * 50
# Correct solution
10 / (2 * 50)
###Output
_____no_output_____
###Markdown
Multiplication and division have the same precedence.The expression is evaluated 'left-to-right'. The correct result is acheived by using brackets $()$, as you would when using a calculator. 4. ReadabilityAn example that __evaluates__ the following expression correctly:$$2^{3} \cdot 4 = 32$$but is __not easily readable__:
###Code
# Less easily readable
2**3*5
# More easily readable
(2**3)*4
###Output
_____no_output_____
###Markdown
It is best practise to use spaces between characters to make your code more readable.You will be marked on readbility in your assessment.Start developing good habits now! 5. Variable AssignmentWhen we compute something, we usually want to __store__ the result.This allows us to use it in subsequent computations. We *assign* a value to a *variables* to store the value. Example : The variable `c` is used to 'store' the value `10`.
###Code
c = 10
print(c)
###Output
10
###Markdown
6. `print `The variable `c` is used to 'store' the value `10`. The function `print` is used to display the value of a variable. (We will learn what functions are and how we use them later). We can add some text to the printed inforation. Text data is known as __string data__.__String data__ is surrounded by quotation marks `"...."`In the example below, a string and an integer value are seperated by a comma when using the `print` function:
###Code
c = 10
print("the value of c is", c)
###Output
the value of c is 10
###Markdown
Another way is to embed numerical values within a string using f-strings:
###Code
print(f"the value of c is {c}")
###Output
the value of c is 10
###Markdown
Example: We want to use the value of `A`, found using the first expression, in a subsequent computation to find `d`.>$a = b + c$>$d = a + b$
###Code
b = 11
c = 10
a = b + c
d = a + b
print(d)
###Output
32
###Markdown
Variable assigment allows us to quickly and easily update the value of every expression in which the variable appears by a single line of code to re-assign the variable:Compute $c, d, e$ , 1. where $a = 2 , b = 10$1. where $a = 5 , b = 1$
###Code
a = 2
b = 10
c = a + b
e = a - b
f = a / b
print(c, e, f)
a = 5
b = 1
c = a + b
e = a - b
f = a / b
print(c, e, f)
###Output
6 4 5.0
###Markdown
If we want to change the value of $a$ to $4$ and recompute the sum, we change `a = 2` to as `a = 4` and run the cell again to execute the code. Practise Exercise 6A : Variable AssignmentChange the value of `a` or `b`.Re-run the cell to update the value.(Click on the cell to type in it. Press "Shift" + "Enter" to run the cell.)Then run the cell containing `print(c, e, f)` to view the new value. Practise Exercise 6B : Variable Assignment$y=ax^2+bx+c$ In the cell below find $y$ when $a=1$, $b=1$, $c=-6$, $x=-2$Now change the value of $x$ so that $x = 0$ and re-run the cell to update the value. What value did you get for y this time?
###Code
# create variables a, b, c and x
# e.g. a = 1
#type: print(y) to reveal the answer
###Output
_____no_output_____
###Markdown
7. Augmented AssignmentThe case where the assigned value depends on a previous value of the variable.
###Code
# Example:
a = 2
b = 11
a = a + b
print(a)
###Output
13
###Markdown
This type of expression is not a valid algebraic statement since '`a`' appears on both sides of '`=`'. However, is very common in computer programming. __How it works:__ > `a = a + b` The final value of `a` (left-hand side) is equal to the sum of the initial value of `a` and `b` Shortcuts Augmented assignments can be written in short form.For __addition__:`a = a + b` can be written `a += b`
###Code
# Addition : long version
a = 2
b = 11
a = a + b
print(a)
# Augmented assignment
a = 2
b = 11
a += b
print(a)
###Output
13
13
###Markdown
For __subtraction__:`a = a - b` can be written `a -= b`
###Code
# subtraction : long version
a = 1
b = 4
a = a - b
print(a)
# Augmented assignment
a = 1
b = 4
a -= b
print(a)
###Output
-3
-3
###Markdown
The basic algebraic operators can all be manipulated in the same way to produce a short form of augmented assigment. Practise Exercise 7A : Augmented Assignment In the cells below, use augmented assigment to write a shorter form of the expression shown in the cell. Type `print(a)` to check your answers match. Exercise 7Aa : Multiplication
###Code
# Multiplication : long version
a = 10
c = 2
a = c*a
print(a)
# Write code using Augmented Assigment here:
###Output
20
###Markdown
Exercise 7Ab : Division
###Code
# Division : long version
a = 1
a = a/4
print(a)
# Write code using Augmented Assigment here:
###Output
0.25
###Markdown
Exercise 7Ac : Floor Division
###Code
# Floor division : long version
a = 12
a = a//5
print(a)
# Write code using Augmented Assigment here:
###Output
2
###Markdown
Exercise 7Ad : Floor Division (of negative nunbers)
###Code
# Floor division : long version
a = -12
a = a//5
print(a)
# Write code using Augmented Assigment here:
###Output
-3
###Markdown
__NOTE:__ Floor division always rounds DOWN. $$\frac{-12}{5} = -2.4$$The closest integer __less than__ -2.4 is -3. 8. Naming Variables__It is good practice to use meaningful variable names. __e.g. using '`x`' for time, and '`t`' for position is likely to cause confusion. You will be marked on readbility in your assessment.Start developing good habits now! Problems with poorly considered variable names: 1. You're much more likely to make errors.1. It can be difficult to remember what the program does. 1. It can be difficult for others to understand and use your program. __Different languages have different rules__ for what characters can be used in variable names. In Python variable names can use letters and digits, but cannot start with a digit.e.g. `data5 = 3` $\checkmark$`5data = 3` $\times$ __Python is a case-sensitive language__e.g. the variables '`A`' and '`a`' are different. __Languages have *reserved keywords*__ that cannot be used as variable names as they are used for other purposes. The reserved keywords in Python are:`['False', 'None', 'True', 'and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']` A reserved keyword already has a use assigned to it. Reserved kewords are colored bold green when you type them in the Notebook so you can see if one is being used. If you try to assign something to a reserved keyword, you will get an error e.g. it is not possible to create a variable with the name __`for`__:
###Code
for = 12
###Output
_____no_output_____
###Markdown
Example Use of Reserved Keywords: `print`We can print output from our program using the word `print`.A print call is ended by a newline by default.This is shown below:
###Code
c = 10
print(c)
print(c)
###Output
10
10
###Markdown
However, we can choose a different line ending using the __keyword__ `end`.
###Code
print(c, end=" ")
print(c)
print(c, end="")
print(c)
###Output
10 10
1010
###Markdown
The print function can take mutiple inputs, seperated by commas.The default output seperator is a space.However, we can choose a different seperator using the keyword `sep`.
###Code
print(c, c, c)
print(c, c, c, sep=':')
###Output
10 10 10
10:10:10
###Markdown
If you try to assign something to a reserved keyword, you will get an error e.g. it is not possible to create a variable with the name __`for`__:
###Code
for = 12
###Output
_____no_output_____
###Markdown
__Sometimes it is useful to have variable names that are made up of two words.__ A convention is to separate the words in the variable name using an underscore '`_`'. e.g. a variable name for storing the number of days: ```python num_days = 10``` Can you think of a suitable variable name for each of the following quantities? __temperature____minimum height____side length____class__ 9. Comparison Operators __Boolean:__ A type of variable that can take on one of two values:- true- false __Comparison Operator:__ An operator that is used to compare the values of two variables. __Commonly used comparison operators:__$==$ Equality $!=$ Inequality $>$ Greater than $$>=$ Greater than or equal to $ One way to visualise how a Boolean works is consider the answer when we make a verbal comparison... __Example:__ Comparing variables a and b using comparison operators returns a boolean variable:
###Code
a = 10.0
b = 9.9
# Check if a is equal to b
print("Is a equal to b?")
print(a==b)
# Check if a is more than b.
print("Is a greater than b?")
print(a > b)
###Output
Is a equal to b?
False
Is a greater than b?
True
###Markdown
Practise Exercise 5 : Complete the cell below by writing the __correct comparison operator__ in each eampty `print()` statement:
###Code
a = 14
b = -9
c = 14
# EXAMPLE : Check if a is less than b.
print("Is a less than b?")
print(a < c)
# Check if a is equal to c
print("Is a equal to c?")
print()
# Check if a is not equal to c
print("Is a not equal to c?")
print()
# Check if a is less than or equal to b
print("Is a less than or equal to b?")
print()
# Check if a is less than or equal to c
print("Is a less than or equal to c?")
print()
# Check if two colours are the same
colour0 = 'blue'
colour1 = 'green'
print("Is colour0 the same as colour1?")
print()
###Output
Is a less than b?
False
Is a equal to c?
Is a not equal to c?
Is a less than or equal to b?
Is a less than or equal to c?
Is colour0 the same as colour1?
###Markdown
10. Logical OperatorsThe comparisons we have looked at so far consider two variables.*Logical operators*:> ```python and or not ``` allow us to make multiple comparisons at the same time. The code```pythonX and Y```will evaluate to `True` if statement `X` *and* statement `Y` are both true.Otherwise will evaluate to `False`. The code```pythonX or Y```will evaluate to `True` if statement `X` *or* statement `Y` is true. Otherwise will evaluate to `False`. __Examples:__$10 < 9$ is false$15 < 20$ is true
###Code
print(10 < 9 and 15 < 20)
print(10 < 9 or 15 < 20)
###Output
True
###Markdown
In Python, the 'not' operator negates a statement, e.g.:
###Code
a = 12
b = 7
print(a < b)
print(not a < b)
###Output
False
True
###Markdown
11. Types  __11.1 Booleans__  __11.2 Strings__ All variables have a 'type', which indicates what the variable is, e.g. a number, a string of characters, etc. Type is important because it determines: - how a variable is stored - how it behaves when we perform operations on it - how it interacts with other variables. e.g.multiplication of two real numbers is different from multiplication of two complex numbers. Introspection We can check a variable's type using *introspection*. To check the type of a variable we use the function `type`.
###Code
x = True
print(type(x))
a = 1
print(type(a))
a = "1"
print(type(a))
a = 1.0
print(type(a))
###Output
<class 'bool'>
<class 'int'>
<class 'str'>
<class 'float'>
###Markdown
__Note__ that `1`, `1.0` and `"1"` have different *types*. Complete the cell in your interactive textbook to find the `type` of `a` when it is written as shown below:
###Code
a = 1
a = 1.0
###Output
_____no_output_____
###Markdown
What is the first type? What is the second type? - __bool__ means __Boolean__ variable. - __str__ means __string__ variable. - __int__ means __integer__ variable. - __float__ means __floating point__ variable. This distinction is very important for numerical computations.We will look at the meaning of these different types next... 11.1 BooleansA type of variable that can take on one of two values - true or false. This is the simplest type.
###Code
a = True
b = False
# test will = True if a or b = True
test = a or b
print(test)
print(type(test))
###Output
True
<class 'bool'>
###Markdown
Note: We can use a single instance of the print function to display multiple pieces of information if we sperate them by commas.e.g. `print(item_1, item_2)`
###Code
print(test, type(test))
###Output
True <class 'bool'>
###Markdown
__Re-cap: what does a evaluate to? (`True` or `False`)__
###Code
a = (5 < 6 or 7 > 8)
print(a)
###Output
True
###Markdown
11.2 StringsA string is a collection of characters. A string is created by placing the characters between quotation marks. You may use single or double quotation marks; either is fine e.g. my_string = 'This is a string.' or my_string = "This is a string." __Example:__ Assign a string to a variable, display the string, and then check its type:
###Code
my_string = "This is a string."
print(my_string)
print(type(my_string))
###Output
This is a string.
<class 'str'>
###Markdown
We can perform many different operations on strings. __Example__: Extract a *single* character as a new string:> *__NOTE:__ Python counts from 0.*
###Code
my_string = "This is a string."
# Store the 3rd character of `my_string` as a new variable
s = my_string[2]
print(s)
print(type(s))
###Output
i
<class 'str'>
###Markdown
The number that describes the position of a character is called the *index*.What is the character at index 4?What is the index of the *second* i character?
###Code
my_string = "This is a string."
my_string[4]
###Output
_____no_output_____
###Markdown
This shows that we count spaces as characters. Practise Exercise 11A: Strings `my_string = "This is a string."`In the cell provided: - store the 4th character of `my_string` as a new variable - print the new variable - check that it is a string
###Code
# Store the 4th character as a new variable
# Print the new variable
# Check the type of the new variable
###Output
_____no_output_____
###Markdown
We can extract a *range of* characters as a new string by specifiying the index to __start__ at and the index to __stop__ at:$$\underbrace{\underbrace{t}_{\text{0}} \\underbrace{h}_{\text{1}}\\underbrace{i}_{\text{2}}\\underbrace{s}_{\text{3}}\\underbrace{}_{\text{4}}\\underbrace{i}_{\text{5}}\}_{\text{s}}\underbrace{s}_{\text{6}}\\underbrace{}_{\text{7}}\\underbrace{a}_{\text{8}}\\underbrace{}_{\text{9}}\\underbrace{s}_{\text{10}}\\underbrace{t}_{\text{11}}\\underbrace{r}_{\text{12}}\\underbrace{i}_{\text{13}}\\underbrace{n}_{\text{14}} \\underbrace{g}_{\text{15}} \\underbrace{.}_{\text{16}} \$$The following example stores characters 0 to 5 of `my_string` as new variable, `s`.
###Code
my_string = "This is a string."
# Store the first 6 characters of my_string as new string, s
s = my_string[0:6]
# print
print(s)
# check type
print(type(s))
###Output
This i
<class 'str'>
###Markdown
__Note:__ - The space between the first and second word is counted as the 5th character. - The "stop" value is not included in the range.
###Code
# Store the last 4 characters and print
s = my_string[-4:]
print(s)
###Output
ing.
###Markdown
$$my\_string = \underbrace{t}_{\text{-17}} \\underbrace{h}_{\text{-16}}\\underbrace{i}_{\text{-15}}\\underbrace{s}_{\text{-14}}\\underbrace{}_{\text{-13}}\\underbrace{i}_{\text{-12}}\\underbrace{s}_{\text{-11}}\\underbrace{}_{\text{-10}}\\underbrace{a}_{\text{-9}}\\underbrace{}_{\text{-8}}\\underbrace{s}_{\text{-7}}\\underbrace{t}_{\text{-6}}\\underbrace{r}_{\text{-5}}\\underbrace{\underbrace{i}_{\text{-4}}\\underbrace{n}_{\text{-3}} \\underbrace{g}_{\text{-2}} \\underbrace{.}_{\text{-1}} \}_{\text{s}}$$ __Note:__ - This time we only specify a starting value for the range. - The stopping value is not specified. ```pythons = my_string[-4:] ``` - This means the range ends at the end of the string. Practise Exercise 11B: Strings In the cell provided: - store the last 6 characters of `my_string` as a new variable. - print your new variable
###Code
# Store the last 6 characters as a new variable.
# Print the new varaible
###Output
_____no_output_____
###Markdown
Practise Exercise 11C: Strings In the cell provided: - store 6 characters, starting with the 2nd character of `my_string` (i.e. store: "his is") as a new variable. - print your new variable - Find an alternative way of extracting the same string?
###Code
# Store 6 characters, starting with "h"
print(my_string[1:7])
print(my_string[-16:-10])
# Print the new varaible
###Output
his is
his is
###Markdown
__Example:__ Add strings together.
###Code
start = "Py"
end = "thon"
word = start + end
print(word)
###Output
Python
###Markdown
__Example:__ Add a section of a string to a section of another string:
###Code
start = "Pythagoras"
end = "marathon"
word = start[:2] + end[-4:]
print(word)
###Output
Python
###Markdown
__Note__: We can use a blank space __or__ a 0 to index the first character; either is OK. Practise Exercise 11D : StringsIn the cell below add the variables `start` and `end` to make a sentence.
###Code
start = "The sky is"
end = "blue"
# Add variables start and end to make a new variable and print it
###Output
_____no_output_____
###Markdown
Notice that we need to add a space to seperate the words "is" and "blue". We can do this using a pair of quotation marks, seperated by a space.
###Code
print(start + " " + end)
###Output
The sky is blue
###Markdown
12. Numeric Types   12.1 Integers   12.2 Floating Point   12.3 ScientificNotation  12.4 Complex NumbersNumeric types are particlarly important when solving scientific and engineering problems. Python 3 has three numerical types:- integers (`int`)- floating point numbers (`float`)- complex numbers (`complex`)__Integers:__ Whole numbers. __Floating point:__ Numbers with a decimal place.__Complex numbers:__ Numbers with a real and imaginary part. 12.1 Integers - Integers (`int`) are whole numbers. - They can be postive or negative. - Integers should be used when a value can only take on a whole number e.g. the year, or the number of students following this course. 12.2 Floating Point Numbers that have a decimal point are automatically stored using the `float` type. A number is automatically classed as a float:- if it has a decimal point- if it is written using scientific notation (i.e. using e or E)... Rounding floating point numbers.You can round your answer to a defined number of digits after the decimal point using the `round` function:https://docs.python.org/3/library/functions.htmlround
###Code
a = 0.768567
print(round(a,2))
print(round(a,3))
print(round(a,4))
###Output
0.77
0.769
0.7686
###Markdown
12.3 Scientific Notation In scientific notation, the letter e (or E) symbolises power of ten in the exponent. For example:$$10.45\textrm{e}2 = 10.45 \times 10^{2} = 1045$$$$1.045\textrm{e}3 = 1.045 \times 10^{3} = 1045$$ Examples using scientific notation.
###Code
a = 2e0
print(a, type(a))
b = 2e3
print(b)
c = 2.1E3
print(c)
###Output
2.0 <class 'float'>
2000.0
2100.0
###Markdown
Practical Exercise 12AIn the cell provided write two alternative ways to express 35,000 using scientific notation.
###Code
# Express 35,000 using scientific notation
###Output
_____no_output_____
###Markdown
12.4 Complex NumbersComplex numbers have real and imaginary parts. We can declare a complex number in Python by adding `j` or `J` after the complex part of the number: __Standard mathematical notation.__ __Python notation__ $ a = \underbrace{3}_{\text{real part}} + \underbrace{4j}_{\text{imaginary part}} $ `a = 3 + 4j` __or__ `a = 3 + 4J`
###Code
b = 4 - 3j
print(b, type(b))
###Output
(4-3j) <class 'complex'>
###Markdown
Python determines the type of a number from the way we input it.e.g. It will decide that a number is an `int` if we assign a number with no decimal place: Practise Exercise 12Ba) In the cell provided find the type of the following values: - 3.1 - 2 - `'blue'` __How could you re-write the number 2 so that Python makes it a float?__b) Try changing the way 2 is written and run the cell again to check that the variable type has changed.
###Code
# Find the variable types
###Output
_____no_output_____
###Markdown
13. Type Conversions (Casting)We often want to change between types. Sometimes we need to make sure two variables have the same type in order to perform an operation on them. Sometimes we recieve data of a type that is not directly usable by the program.This is called *type conversion* or *type casting*. Automatic Type ConversionIf we add two integers, the results will be an integer:
###Code
a = 4 # int
b = 15 # int
c = a + b
# print(c, type(c))
###Output
_____no_output_____
###Markdown
However, if we add an int and a float, the result will be a float:
###Code
a = 4 # int
b = 15.0 # float
c = a + b
# print(c, type(c))
###Output
_____no_output_____
###Markdown
If we divide two integers, the result will be a `float`:
###Code
a = 16 # int
b = 4 # int
c = a / b
# print(c, type(c))
###Output
_____no_output_____
###Markdown
When dividing two integers with floor division (or 'integer division') using `//`, the result will be an `int` e.g.
###Code
a = 16 # int
b = 3 # int
c = a // b
# print(c, type(c))
###Output
_____no_output_____
###Markdown
In general: - operations that mix an `int` and `float` will generate a `float`. - operations that mix an `int` or a `float` with `complex` will generate a `complex` type. If in doubt, use `type` to check. Explicit Type ConversionWe can explicitly change (or *cast*) the type.To cast variable a as a different type, write the name of the type, followed by the variable to convert in brackets. __Example: Cast from an int to a float:__
###Code
a = 1
a = float(a)
print(a, type(a))
# If we use a new variable name the original value is unchanged.
a = 1
b = float(a)
# print(a, type(a))
# print(b, type(b))
# If we use the original name, the variable is updated.
a = 1
a = float(a)
# print(a, type(a))
###Output
_____no_output_____
###Markdown
__Try it yourself.__In the cell provided: - cast variable `a` from a float back to an int. - print variable `a` and its type to check your answer
###Code
# cast a as an int
# print a and its type
###Output
_____no_output_____
###Markdown
Note: Take care when casting as the value of the variable may change as well as the type.Here is an example to demmonstrate: In the cell below:1. cast `i` as an `int` and print `i`.1. cast `i` back to a `float` and print `i`.
###Code
i = 1.3 # float
print(i, type(i))
# cast i as an int and print it
i = int(i)
print(i)
i = float(i)
print(i)
###Output
1.3 <class 'float'>
1
1.0
###Markdown
What has happened to the original value of `i`? Note that rounding is applied when converting from a `float` to an `int`; the values after the decimal point are discarded. This type of rounding is called 'round towards zero' or 'truncation'. A common task is converting numerical types to-and-from strings. Examples: - Reading a number from a file where it appears as as a string - User input might be given as a string. __Example: Cast from a float to a string:__
###Code
a = 1.023
b = str(a)
# print(b, type(b))
###Output
_____no_output_____
###Markdown
We can use __format__ to change how a string is displayed...
###Code
r = 0.0123456
s = 0.2345678
# cast as a string
print(str(r))
print('%s' % r)
print('{}'.format(r))
# cast as a string, scientific notation
print('%E' % r)
# specify number of significant figures displayed
print('%.2E, %.1E' % (r, s))
###Output
0.0123456
0.0123456
0.0123456
1.234560E-02
1.23E-02, 2.3E-01
###Markdown
__Example: Cast from a string to a float:__It is important to cast string numbers as either `int`s or `float`s for them to perform correctly in algebraic expressions.Consider the example below:
###Code
a = "15.07"
b = "18.07"
print("As string numbers:")
print("15.07 + 18.07 = ", a + b)
print("When cast from string to float:")
print("15.07 + 18.07 = ", float(a) + float(b))
###Output
As string numbers:
15.07 + 18.07 = 15.0718.07
When cast from string to float:
15.07 + 18.07 = 33.14
###Markdown
Note from the cell above that numbers expressed as strings can be cast as floats *within* algebraic expressions. Only numerical values can be cast as numerical types.e.g. Trying to cast the string `four` as an integer causes an error:
###Code
f = float("four")
###Output
_____no_output_____
|
algoExpert/reverse_linked_list/solution.ipynb
|
###Markdown
Reversed Linked List[link](https://www.algoexpert.io/questions/Reverse%20Linked%20List) My Solution
###Code
def reverseLinkedList(head):
# Write your code here.
prv, cur = None, head
while cur is not None:
nxt = cur.next
cur.next = prv
prv = cur
cur = nxt
return prv
def reverseLinkedList(head):
# Write your code here.
return reverseListHelper(None, head, head.next)
# O(n) time | O(n) space
def reverseListHelper(prevNode, currentNode, nextNode):
currentNode.next = prevNode
if nextNode is None:
return currentNode
return reverseListHelper(currentNode, nextNode, nextNode.next)
###Output
_____no_output_____
###Markdown
Expert Solution
###Code
# O(n) time | O(1) space - where n is the number of nodes in the Linked List
def reverseLinkedList(head):
previousNode, currentNode = None, head
while currentNode is not None:
nextNode = currentNode.next
currentNode.next = previousNode
previousNode = currentNode
currentNode = nextNode
return previousNode
###Output
_____no_output_____
|
site/ru/tutorials/generative/pix2pix.ipynb
|
###Markdown
Copyright 2019 The TensorFlow Authors.Licensed under the Apache License, Version 2.0 (the "License");
###Code
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
###Output
_____no_output_____
###Markdown
Pix2Pix Смотрите на TensorFlow.org Запустите в Google Colab Изучайте код на GitHub Скачайте ноутбук Note: Вся информация в этом разделе переведена с помощью русскоговорящего Tensorflow сообщества на общественных началах. Поскольку этот перевод не является официальным, мы не гарантируем что он на 100% аккуратен и соответствует [официальной документации на английском языке](https://www.tensorflow.org/?hl=en). Если у вас есть предложение как исправить этот перевод, мы будем очень рады увидеть pull request в [tensorflow/docs](https://github.com/tensorflow/docs) репозиторий GitHub. Если вы хотите помочь сделать документацию по Tensorflow лучше (сделать сам перевод или проверить перевод подготовленный кем-то другим), напишите нам на [[email protected] list](https://groups.google.com/a/tensorflow.org/forum/!forum/docs-ru). Это руководство демонстрирует преобразование изображение в изображение с использованием условных GAN, как описано в [Преобразование изображения в изображение с использованием условных состязательных сетей](https://arxiv.org/abs/1611.07004). Используя эту технику, мы можем раскрасить черно-белые фотографии, преобразовать карты Google в Google Earth и т. д. В этом руководстве мы преобразовываем фасады зданий в настоящие здания.В примере мы будем использовать [База данных фасадов CMP](http://cmp.felk.cvut.cz/~tylecr1/facade/), любезно предоставленная [Центром машинного восприятия](http://cmp.felk .cvut.cz/) в [Чешском техническом университете в Праге](https://www.cvut.cz/). Чтобы наш пример был кратким, мы будем использовать предварительно обработанную [копию](https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/) этого набора данных, созданную авторами [paper](https://arxiv.org/abs/1611.07004) выше.Каждая эпоха занимает около 15 секунд на одном графическом процессоре V100.Ниже приведен результат, полученный после обучения модели для 200 эпох. Импорт TensorFlow и других библиотек
###Code
import tensorflow as tf
import os
import time
from matplotlib import pyplot as plt
from IPython import display
!pip install -U tensorboard
###Output
_____no_output_____
###Markdown
Загрузка датасетаВы можете загрузить этот набор данных и аналогичные ему [здесь](https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets). Как упоминалось в [статье](https://arxiv.org/abs/1611.07004), мы применяем случайную рябь и зеркальное отображение к набору обучающих данных.* При случайной ряби размер изображения изменяется до `286 x 286`, а затем случайным образом обрезается до `256 x 256`.* При случайном зеркальном отображении изображение переворачивается по горизонтали, т.е. слева направо.
###Code
_URL = 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/facades.tar.gz'
path_to_zip = tf.keras.utils.get_file('facades.tar.gz',
origin=_URL,
extract=True)
PATH = os.path.join(os.path.dirname(path_to_zip), 'facades/')
BUFFER_SIZE = 400
BATCH_SIZE = 1
IMG_WIDTH = 256
IMG_HEIGHT = 256
def load(image_file):
image = tf.io.read_file(image_file)
image = tf.image.decode_jpeg(image)
w = tf.shape(image)[1]
w = w // 2
real_image = image[:, :w, :]
input_image = image[:, w:, :]
input_image = tf.cast(input_image, tf.float32)
real_image = tf.cast(real_image, tf.float32)
return input_image, real_image
inp, re = load(PATH+'train/100.jpg')
# преобразуем в int чтобы matplotlib мог показать изображение
plt.figure()
plt.imshow(inp/255.0)
plt.figure()
plt.imshow(re/255.0)
def resize(input_image, real_image, height, width):
input_image = tf.image.resize(input_image, [height, width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
real_image = tf.image.resize(real_image, [height, width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return input_image, real_image
def random_crop(input_image, real_image):
stacked_image = tf.stack([input_image, real_image], axis=0)
cropped_image = tf.image.random_crop(
stacked_image, size=[2, IMG_HEIGHT, IMG_WIDTH, 3])
return cropped_image[0], cropped_image[1]
# нормализуем изображение до [-1, 1]
def normalize(input_image, real_image):
input_image = (input_image / 127.5) - 1
real_image = (real_image / 127.5) - 1
return input_image, real_image
@tf.function()
def random_jitter(input_image, real_image):
# ресайз до 286 x 286 x 3
input_image, real_image = resize(input_image, real_image, 286, 286)
# случайная обреска до 256 x 256 x 3
input_image, real_image = random_crop(input_image, real_image)
if tf.random.uniform(()) > 0.5:
# случайное зеркальное отображение
input_image = tf.image.flip_left_right(input_image)
real_image = tf.image.flip_left_right(real_image)
return input_image, real_image
###Output
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###Markdown
Как вы можете видеть на изображениях ниже,- они проходят через случайное зашумливаниеСлучайное зашумливание, описанное в документе, должно1. Изменить размер изображения на больший.2. Произвольно обрезать до нужного размера.3. Произвольно перевернуть изображение по горизонтали.
###Code
plt.figure(figsize=(6, 6))
for i in range(4):
rj_inp, rj_re = random_jitter(inp, re)
plt.subplot(2, 2, i+1)
plt.imshow(rj_inp/255.0)
plt.axis('off')
plt.show()
def load_image_train(image_file):
input_image, real_image = load(image_file)
input_image, real_image = random_jitter(input_image, real_image)
input_image, real_image = normalize(input_image, real_image)
return input_image, real_image
def load_image_test(image_file):
input_image, real_image = load(image_file)
input_image, real_image = resize(input_image, real_image,
IMG_HEIGHT, IMG_WIDTH)
input_image, real_image = normalize(input_image, real_image)
return input_image, real_image
###Output
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###Markdown
Входной конвейер
###Code
train_dataset = tf.data.Dataset.list_files(PATH+'train/*.jpg')
train_dataset = train_dataset.map(load_image_train,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.batch(BATCH_SIZE)
test_dataset = tf.data.Dataset.list_files(PATH+'test/*.jpg')
test_dataset = test_dataset.map(load_image_test)
test_dataset = test_dataset.batch(BATCH_SIZE)
###Output
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###Markdown
Создание генератора * Архитектура генератора - это модифицированная U-Net. * Каждый блок в энкодере(Conv -> Batchnorm -> Leaky ReLU) * Каждый блок в декодере(Transposed Conv -> Batchnorm -> Dropout (применяется к первым трем блокам) -> ReLU) * Между энкодером и декодером есть пропускаемые соединения(как в U-Net).
###Code
OUTPUT_CHANNELS = 3
def downsample(filters, size, apply_batchnorm=True):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
kernel_initializer=initializer, use_bias=False))
if apply_batchnorm:
result.add(tf.keras.layers.BatchNormalization())
result.add(tf.keras.layers.LeakyReLU())
return result
down_model = downsample(3, 4)
down_result = down_model(tf.expand_dims(inp, 0))
print (down_result.shape)
def upsample(filters, size, apply_dropout=False):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
result.add(tf.keras.layers.BatchNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
up_model = upsample(3, 4)
up_result = up_model(down_result)
print (up_result.shape)
def Generator():
inputs = tf.keras.layers.Input(shape=[256,256,3])
down_stack = [
downsample(64, 4, apply_batchnorm=False), # (bs, 128, 128, 64)
downsample(128, 4), # (bs, 64, 64, 128)
downsample(256, 4), # (bs, 32, 32, 256)
downsample(512, 4), # (bs, 16, 16, 512)
downsample(512, 4), # (bs, 8, 8, 512)
downsample(512, 4), # (bs, 4, 4, 512)
downsample(512, 4), # (bs, 2, 2, 512)
downsample(512, 4), # (bs, 1, 1, 512)
]
up_stack = [
upsample(512, 4, apply_dropout=True), # (bs, 2, 2, 1024)
upsample(512, 4, apply_dropout=True), # (bs, 4, 4, 1024)
upsample(512, 4, apply_dropout=True), # (bs, 8, 8, 1024)
upsample(512, 4), # (bs, 16, 16, 1024)
upsample(256, 4), # (bs, 32, 32, 512)
upsample(128, 4), # (bs, 64, 64, 256)
upsample(64, 4), # (bs, 128, 128, 128)
]
initializer = tf.random_normal_initializer(0., 0.02)
last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
strides=2,
padding='same',
kernel_initializer=initializer,
activation='tanh') # (bs, 256, 256, 3)
x = inputs
# понижение размерности
skips = []
for down in down_stack:
x = down(x)
skips.append(x)
skips = reversed(skips[:-1])
# Повышение размерности и установление пропуска соединений
for up, skip in zip(up_stack, skips):
x = up(x)
x = tf.keras.layers.Concatenate()([x, skip])
x = last(x)
return tf.keras.Model(inputs=inputs, outputs=x)
generator = Generator()
tf.keras.utils.plot_model(generator, show_shapes=True, dpi=64)
gen_output = generator(inp[tf.newaxis,...], training=False)
plt.imshow(gen_output[0,...])
###Output
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###Markdown
* **Расчет потерь в генераторе** * Это сигмоидная кросс-энтропия сгенерированных изображений и **массива из них**. * [Статья](https://arxiv.org/abs/1611.07004) также включает потерю L1, которая представляет собой MAE(средняя абсолютная ошибка) между сгенерированным изображением и целевым изображением. * Это позволяет сгенерированному изображению стать структурно похожим на целевое изображение. * Формула для расчета общих потерь генератора loss = gan_loss + LAMBDA * l1_loss, где LAMBDA = 100. Это значение было определено авторами [статьи](https://arxiv.org/abs/1611.07004). Процедура обучения генератора показана ниже:
###Code
LAMBDA = 100
def generator_loss(disc_generated_output, gen_output, target):
gan_loss = loss_object(tf.ones_like(disc_generated_output), disc_generated_output)
# mean absolute error
l1_loss = tf.reduce_mean(tf.abs(target - gen_output))
total_gen_loss = gan_loss + (LAMBDA * l1_loss)
return total_gen_loss, gan_loss, l1_loss
###Output
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###Markdown
 Создание дискриминатора * Дискриминатор - это PatchGAN. * Каждый блок в дискриминаторе это (Conv -> BatchNorm -> Leaky ReLU) * Размерность вывода после последнего слоя: (batch_size, 30, 30, 1) * Каждый патч 30x30 на выходе классифицирует часть входного изображения размером 70x70 (такая архитектура называется PatchGAN). * Дискриминатор получает 2 входа. * Входное изображение и целевое изображение, которое следует классифицировать как реальное. * Входное изображение и сгенерированное изображение(вывод генератора), которое следует классифицировать как подделку. * Мы объединяем эти 2 ввода вместе в коде (`tf.concat ([inp, tar], axis = -1)`)
###Code
def Discriminator():
initializer = tf.random_normal_initializer(0., 0.02)
inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image')
tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image')
x = tf.keras.layers.concatenate([inp, tar]) # (bs, 256, 256, channels*2)
down1 = downsample(64, 4, False)(x) # (bs, 128, 128, 64)
down2 = downsample(128, 4)(down1) # (bs, 64, 64, 128)
down3 = downsample(256, 4)(down2) # (bs, 32, 32, 256)
zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (bs, 34, 34, 256)
conv = tf.keras.layers.Conv2D(512, 4, strides=1,
kernel_initializer=initializer,
use_bias=False)(zero_pad1) # (bs, 31, 31, 512)
batchnorm1 = tf.keras.layers.BatchNormalization()(conv)
leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)
zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (bs, 33, 33, 512)
last = tf.keras.layers.Conv2D(1, 4, strides=1,
kernel_initializer=initializer)(zero_pad2) # (bs, 30, 30, 1)
return tf.keras.Model(inputs=[inp, tar], outputs=last)
discriminator = Discriminator()
tf.keras.utils.plot_model(discriminator, show_shapes=True, dpi=64)
disc_out = discriminator([inp[tf.newaxis,...], gen_output], training=False)
plt.imshow(disc_out[0,...,-1], vmin=-20, vmax=20, cmap='RdBu_r')
plt.colorbar()
###Output
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###Markdown
**Расчет потерь дискриминатора** * Функция потерь дискриминатора принимает 2 входа: **[реальные изображения, сгенерированные изображения]** * real_loss - это сигмоидная кросс-энтропия **реальных изображений** и **массива единиц(поскольку это настоящие изображения)** * generated_loss - сигмоидная кросс-энтропия **сгенерированных изображений** и **массива нулей(поскольку это поддельные изображения)** * В результате **total_loss** - это сумма real_loss и generated_loss
###Code
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def discriminator_loss(disc_real_output, disc_generated_output):
real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)
generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)
total_disc_loss = real_loss + generated_loss
return total_disc_loss
###Output
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###Markdown
Процедура обучения дискриминатора показана ниже.Чтобы узнать больше об архитектуре и гиперпараметрах, вы можете обратиться к [статье](https://arxiv.org/abs/1611.07004).  Определение оптимайзера и сохранения чекпойнтов
###Code
generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
generator=generator,
discriminator=discriminator)
###Output
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###Markdown
Генерация изображенияНапишите функцию для построения изображений во время обучения.* Мы передаем изображения из тестового датасета в генератор.* Генератор преобразует входное изображение в выходное.* Последний шаг - построить прогнозы и **вуаля!** Примечание: аргумент `training` установлен в `True` намеренно, так как нам нужна пакетная статистика при запуске модели на тестовом наборе данных. Если мы используем `training = False`, мы получим накопленную статистику для всего набора данных(чего мы не хотим).
###Code
def generate_images(model, test_input, tar):
prediction = model(test_input, training=True)
plt.figure(figsize=(15,15))
display_list = [test_input[0], tar[0], prediction[0]]
title = ['Input Image', 'Ground Truth', 'Predicted Image']
for i in range(3):
plt.subplot(1, 3, i+1)
plt.title(title[i])
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
for example_input, example_target in test_dataset.take(1):
generate_images(generator, example_input, example_target)
###Output
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###Markdown
Обучение* Для каждого входного изображения генерируем выходное изображение.* Дискриминатор получает входное и сгенерированное изображение в качестве первого входа. Второй вход - это входное и целевое изображения.* Далее рассчитываем потери генератора и дискриминатора.* Затем вычисляем градиенты потерь как для генератора, так и для переменных дискриминатора(входных данных) и применяем их к оптимизатору.* Затем логируем потери в TensorBoard
###Code
EPOCHS = 150
import datetime
log_dir="logs/"
summary_writer = tf.summary.create_file_writer(
log_dir + "fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
@tf.function
def train_step(input_image, target, epoch):
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
gen_output = generator(input_image, training=True)
disc_real_output = discriminator([input_image, target], training=True)
disc_generated_output = discriminator([input_image, gen_output], training=True)
gen_total_loss, gen_gan_loss, gen_l1_loss = generator_loss(disc_generated_output, gen_output, target)
disc_loss = discriminator_loss(disc_real_output, disc_generated_output)
generator_gradients = gen_tape.gradient(gen_total_loss,
generator.trainable_variables)
discriminator_gradients = disc_tape.gradient(disc_loss,
discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(generator_gradients,
generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(discriminator_gradients,
discriminator.trainable_variables))
with summary_writer.as_default():
tf.summary.scalar('gen_total_loss', gen_total_loss, step=epoch)
tf.summary.scalar('gen_gan_loss', gen_gan_loss, step=epoch)
tf.summary.scalar('gen_l1_loss', gen_l1_loss, step=epoch)
tf.summary.scalar('disc_loss', disc_loss, step=epoch)
###Output
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###Markdown
Фактический цикл обучения:* Итерирует по количеству эпох.* В каждую эпоху очищает дисплей и запускает `generate_images`, чтобы показать прогресс.* В каждую эпоху выполняет итерацию по набору тренировочных данных, печатая '.' для каждого примера.* Сохраняет контрольную точку каждые 20 эпох.
###Code
def fit(train_ds, epochs, test_ds):
for epoch in range(epochs):
start = time.time()
display.clear_output(wait=True)
for example_input, example_target in test_ds.take(1):
generate_images(generator, example_input, example_target)
print("Epoch: ", epoch)
# Тренировка
for n, (input_image, target) in train_ds.enumerate():
print('.', end='')
if (n+1) % 100 == 0:
print()
train_step(input_image, target, epoch)
print()
# сохраняем чекпойнт каждые 20 эпох
if (epoch + 1) % 20 == 0:
checkpoint.save(file_prefix = checkpoint_prefix)
print ('Time taken for epoch {} is {} sec\n'.format(epoch + 1,
time.time()-start))
checkpoint.save(file_prefix = checkpoint_prefix)
###Output
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###Markdown
Этот цикл обучения сохраняет логи, которые вы можете просматривать в TensorBoard для отслеживания прогресса обучения. Работая локально, вы запускаете отдельный процесс TensorBoard. В ноутбуке, если вы хотите контролировать процесс с помощью TensorBoard, проще всего запустить программу просмотра перед началом обучения.Чтобы запустить программу просмотра, вставьте в ячейку кода следующее:
###Code
#docs_infra: no_execute
%load_ext tensorboard
%tensorboard --logdir {log_dir}
###Output
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###Markdown
Теперь запустите цикл обучения:
###Code
fit(train_dataset, EPOCHS, test_dataset)
###Output
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###Markdown
Если вы хотите опубликовать результаты TensorBoard _публично_, вы можете загрузить журналы в [TensorBoard.dev](https://tensorboard.dev/), скопировав и выполнив следующее в ячейку кода.Примечание. Для этого требуется учетная запись Google.```!tensorboard dev upload --logdir {log_dir}``` Внимание! Эта команда не завершается. Он предназначен для постоянной загрузки результатов длительных экспериментов. После того, как ваши данные загружены, вам необходимо остановить выполнение команды, выполнив "interrupt execution" в вашем инструменте для работы с ноутбуком(jupyter, colab, etc). Вы можете просмотреть [результаты предыдущего запуска](https://tensorboard.dev/experiment/lZ0C6FONROaUMfjYkVyJqw) этого ноутбука на [TensorBoard.dev](https://tensorboard.dev/).TensorBoard.dev - это управляемый интерфейс для размещения, отслеживания и обмена экспериментами машинного обучения.Он также может быть включен с помощью ``:
###Code
display.IFrame(
src="https://tensorboard.dev/experiment/lZ0C6FONROaUMfjYkVyJqw",
width="100%",
height="1000px")
###Output
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###Markdown
Интерпретация логов из GAN сложнее, чем интерпретация простой классификациии или регрессионной модели. На что следует обратить внимание:* Убедитесь, что ни одна из моделей не «выиграла». Если либо `gen_gan_loss`, либо `disc_loss` становятся очень низким, это показатель того, что одна модель доминирует над другой, и вы не обучаете комбинированную модель успешно.* Значение `log(2) = 0.69` является хорошим показателем для этих потерь, поскольку это значение указывает на неуверенность модели: дискриминатор в среднем одинаково неуверен в обоих вариантах.* Для `disc_loss` значение ниже `0.69` означает, что дискриминатор работает лучше, чем случайно, на объединенном наборе реальных + сгенерированных изображений.* Для `gen_gan_loss` значение ниже `0.69` означает, что генератор работает лучше, чем случайно, обманывая дескриминатор.* По мере обучения значение `gen_l1_loss` должно уменьшаться. Восстановление последнего чекпойнта и тестирование
###Code
!ls {checkpoint_dir}
# восстанавливаем последний чекпойнт из checkpoint_dir
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
###Output
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###Markdown
Генерация с использованием тестового датасета
###Code
# Запускаем обученную модель на нескольких примерах из тестового набора данных
for inp, tar in test_dataset.take(5):
generate_images(generator, inp, tar)
###Output
_____no_output_____
|
notebook/notes_gama_sdss1and2_emline.ipynb
|
###Markdown
Read in GAMA-Legacy catalog
###Code
# read in GAMA-Legacy catalog
cata = Cat.GamaLegacy()
gleg = cata.Read()
cataid = gleg['gama-photo']['cataid'] # GAMA catalog id of each object
ngal = len(cataid)
print('%i galaxies with GAMA-Legacy catalog' % ngal)
#
f_gama_sdss = fits.open(''.join([UT.dat_dir(), 'gama/ExternalSpecAll.fits']))
fdata = f_gama_sdss[1].data
fdata.names
print fdata.field('SPECID')[fdata.field("SURVEY") == 'SDSS'][:10]
print fdata.field('RA')[fdata.field("SURVEY") == 'SDSS'][:10]
print fdata.field('Dec')[fdata.field("SURVEY") == 'SDSS'][:10]
print fdata.field('Z')[fdata.field("SURVEY") == 'SDSS'][:10]
###Output
['85508603036827648' '85508603053604864' '86071544291262464'
'132797727590318080' '92828848596451328' '87197673190326272'
'92828847778562048' '133360699471560704' '131671727867428864'
'80443406383316992']
[214.24123 214.19898 217.29903 135.73344 177.6942 223.20035 176.27012
140.17268 132.29052 179.80848]
[-0.95861 -1.07057 -0.23759 1.77393 -1.96023 0.32503 -1.82894 2.16178
0.84326 -0.80916]
[3.2000e-04 1.2513e-01 3.4500e-02 1.1796e-01 1.9380e-02 3.0463e-01
3.9391e-01 3.0890e-01 1.1031e-01 1.7514e-01]
###Markdown
Read in SDSS specObj
###Code
f_sdss = h5py.File(''.join([UT.dat_dir(), 'sdss/specObj-dr8.hdf5']), 'r')
ra_sdss = f_sdss['plug_ra'].value
dec_sdss = f_sdss['plug_dec'].value
print('%i galaxies with DR8 specObj' % len(ra_sdss))
f_sdss['z'].value.min(), f_sdss['z'].value.max()
m_sdss, m_gleg, d_match = spherematch(ra_sdss, dec_sdss,
gleg['gama-photo']['ra'], gleg['gama-photo']['dec'], 3*0.000277778)
print d_match[:10]
print('%i matching galaxies' % len(m_sdss))
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
sub.scatter(ra_sdss, dec_sdss, c='k', s=5, label='SDSS DR8')
sub.scatter(gleg['gama-photo']['ra'], gleg['gama-photo']['dec'], c='C1', s=4, label='GAMA')
sub.set_xlabel('RA', fontsize=30)
sub.set_xlim([179., 181.])
sub.set_ylabel('Dec', fontsize=30)
sub.set_ylim([-1., 1.])
sub.legend(loc='upper left', markerscale=5, prop={'size':20})
###Output
_____no_output_____
|
make_172_imagenet_6_class_data.ipynb
|
###Markdown
y_label = np.argmax(y_data, axis=1)y_text = ['bed', 'bird', 'cat', 'dog', 'house', 'tree']y_table = {i:text for i, text in enumerate(y_text)}y_table_array = np.array([(i, text) for i, text in enumerate(y_text)]) x_train_temp, x_test, y_train_temp, y_test = train_test_split( x_2d_data, y_label, test_size=0.2, random_state=42, stratify=y_label)x_train, x_val, y_train, y_val = train_test_split( x_train_temp, y_train_temp, test_size=0.25, random_state=42, stratify=y_train_temp)x_train.shape, y_train.shape, x_val.shape, y_val.shape, x_test.shape, y_test.shape
###Code
np.savez_compressed(path.join(base_path, 'imagenet_6_class_172_train_data.npz'),
x_data=x_train, y_data=y_train, y_list=y_list)
np.savez_compressed(path.join(base_path, 'imagenet_6_class_172_val_data.npz'),
x_data=x_val, y_data=y_val, y_list=y_list)
###Output
_____no_output_____
|
lstm_weather.ipynb
|
###Markdown
Transform QCLCD Data Function
###Code
# downloaded weather data from http://www.ncdc.noaa.gov/qclcd/QCLCD
def load_weather_frame(filename):
#load the weather data and make a date
data_raw = pd.read_csv(filename, dtype={'Time': str, 'Date': str})
data_raw['WetBulbCelsius'] = data_raw['WetBulbCelsius'].astype(float)
times = []
for index, row in data_raw.iterrows():
_t = datetime.time(int(row['Time'][:2]), int(row['Time'][:-2]), 0) #2153
_d = datetime.datetime.strptime( row['Date'], "%Y%m%d" ) #20150905
times.append(datetime.datetime.combine(_d, _t))
data_raw['_time'] = pd.Series(times, index=data_raw.index)
df = pd.DataFrame(data_raw, columns=['_time','WetBulbCelsius'])
return df.set_index('_time')
###Output
_____no_output_____
###Markdown
Load The Data as CSV This is QCLCD data for PDX. It is waht will be used to train the model.
###Code
# scale values to reasonable values and convert to float
data_weather = load_weather_frame("data/QCLCD_PDX_20150901.csv")
X, y = load_csvdata(data_weather, TIMESTEPS, seperate=False)
###Output
_____no_output_____
###Markdown
Run The Model and Fit Predictions
###Code
regressor = learn.Estimator(model_fn=lstm_model(TIMESTEPS, RNN_LAYERS, DENSE_LAYERS),
model_dir=LOG_DIR)
# create a lstm instance and validation monitor
validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],
every_n_steps=PRINT_STEPS,
early_stopping_rounds=1000)
regressor.fit(X['train'], y['train'],
monitors=[validation_monitor],
batch_size=BATCH_SIZE,
steps=TRAINING_STEPS)
predicted = regressor.predict(X['test'])
#not used in this example but used for seeing deviations
rmse = np.sqrt(((predicted - y['test']) ** 2).mean(axis=0))
score = mean_squared_error(predicted, y['test'])
print ("MSE: %f" % score)
# plot the data
all_dates = data_weather.index.get_values()
fig, ax = plt.subplots(1)
fig.autofmt_xdate()
predicted_values = predicted.flatten() #already subset
predicted_dates = all_dates[len(all_dates)-len(predicted_values):len(all_dates)]
predicted_series = pd.Series(predicted_values, index=predicted_dates)
plot_predicted, = ax.plot(predicted_series, label='predicted (c)')
test_values = y['test'].flatten()
test_dates = all_dates[len(all_dates)-len(test_values):len(all_dates)]
test_series = pd.Series(test_values, index=test_dates)
plot_test, = ax.plot(test_series, label='2015 (c)')
xfmt = mdates.DateFormatter('%b %d %H')
ax.xaxis.set_major_formatter(xfmt)
# ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d %H')
plt.title('PDX Weather Predictions for 2016 vs 2015')
plt.legend(handles=[plot_predicted, plot_test])
plt.show()
###Output
_____no_output_____
###Markdown
Transform QCLCD Data Function
###Code
# downloaded weather data from http://www.ncdc.noaa.gov/qclcd/QCLCD
def load_weather_frame(filename):
#load the weather data and make a date
data_raw = pd.read_csv(filename, dtype={'Time': str, 'Date': str})
data_raw['WetBulbCelsius'] = data_raw['WetBulbCelsius'].astype(float)
times = []
for index, row in data_raw.iterrows():
_t = datetime.time(int(row['Time'][:2]), int(row['Time'][:-2]), 0) #2153
_d = datetime.datetime.strptime( row['Date'], "%Y%m%d" ) #20150905
times.append(datetime.datetime.combine(_d, _t))
data_raw['_time'] = pd.Series(times, index=data_raw.index)
df = pd.DataFrame(data_raw, columns=['_time','WetBulbCelsius'])
return df.set_index('_time')
###Output
_____no_output_____
###Markdown
Load The Data as CSV This is QCLCD data for PDX. It is what will be used to train the model.
###Code
# scale values to reasonable values and convert to float
data_weather = load_weather_frame("data/QCLCD_PDX_20150901.csv")
X, y = load_csvdata(data_weather, TIMESTEPS, seperate=False)
###Output
_____no_output_____
###Markdown
Run The Model and Fit Predictions
###Code
regressor = learn.SKCompat(learn.Estimator(
model_fn=lstm_model(
TIMESTEPS,
RNN_LAYERS,
DENSE_LAYERS
),
model_dir=LOG_DIR
))
# create a lstm instance and validation monitor
validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],
every_n_steps=PRINT_STEPS,
early_stopping_rounds=1000)
regressor.fit(X['train'], y['train'],
monitors=[validation_monitor],
batch_size=BATCH_SIZE,
steps=TRAINING_STEPS)
predicted = regressor.predict(X['test'])
#not used in this example but used for seeing deviations
rmse = np.sqrt(((predicted - y['test']) ** 2).mean(axis=0))
score = mean_squared_error(predicted, y['test'])
print ("MSE: %f" % score)
# plot the data
all_dates = data_weather.index.get_values()
fig, ax = plt.subplots(1)
fig.autofmt_xdate()
predicted_values = predicted.flatten() #already subset
predicted_dates = all_dates[len(all_dates)-len(predicted_values):len(all_dates)]
predicted_series = pd.Series(predicted_values, index=predicted_dates)
plot_predicted, = ax.plot(predicted_series, label='predicted (c)')
test_values = y['test'].flatten()
test_dates = all_dates[len(all_dates)-len(test_values):len(all_dates)]
test_series = pd.Series(test_values, index=test_dates)
plot_test, = ax.plot(test_series, label='2015 (c)')
xfmt = mdates.DateFormatter('%b %d %H')
ax.xaxis.set_major_formatter(xfmt)
# ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d %H')
plt.title('PDX Weather Predictions for 2016 vs 2015')
plt.legend(handles=[plot_predicted, plot_test])
plt.show()
###Output
_____no_output_____
|
flashlight/app/asr/tutorial/notebooks/FinetuneCTC.ipynb
|
###Markdown
Tutorial on ASR Finetuning with CTC model Let's finetune a pretrained ASR model!Here we provide pre-trained speech recognition model with CTC loss that is trained on many open-sourced datasets. Details can be found in [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://arxiv.org/abs/2010.11745) Step 1: Install `Flashlight`First we install `Flashlight` and its dependencies. Flashlight is built from source with either CPU/CUDA backend and installation takes **~16 minutes**. For installation out of colab notebook please use [link](https://github.com/fairinternal/flashlightbuilding).
###Code
# First, choose backend to build with
backend = 'CUDA' #@param ["CPU", "CUDA"]
# Clone Flashlight
!git clone https://github.com/flashlight/flashlight.git
# install all dependencies for colab notebook
!source flashlight/scripts/colab/colab_install_deps.sh
###Output
_____no_output_____
###Markdown
Build CPU/CUDA Backend of `Flashlight`:- Build from current master. - Builds the ASR app. - Resulting binaries in `/content/flashlight/build/bin/asr`.If using a GPU Colab runtime, build the CUDA backend; else build the CPU backend.
###Code
# export necessary env variables
%env MKLROOT=/opt/intel/mkl
%env ArrayFire_DIR=/opt/arrayfire/share/ArrayFire/cmake
%env DNNL_DIR=/opt/dnnl/dnnl_lnx_2.0.0_cpu_iomp/lib/cmake/dnnl
if backend == "CUDA":
# Total time: ~13 minutes
!cd flashlight && mkdir -p build && cd build && \
cmake .. -DCMAKE_BUILD_TYPE=Release \
-DFL_BUILD_TESTS=OFF \
-DFL_BUILD_EXAMPLES=OFF \
-DFL_BUILD_APP_ASR=ON && \
make -j$(nproc)
elif backend == "CPU":
# Total time: ~14 minutes
!cd flashlight && mkdir -p build && cd build && \
cmake .. -DFL_BACKEND=CPU \
-DCMAKE_BUILD_TYPE=Release \
-DFL_BUILD_TESTS=OFF \
-DFL_BUILD_EXAMPLES=OFF \
-DFL_BUILD_APP_ASR=ON && \
make -j$(nproc)
else:
raise ValueError(f"Unknown backend {backend}")
###Output
_____no_output_____
###Markdown
Let's take a look around.
###Code
# Binaries are located in
!ls flashlight/build/bin/asr
###Output
fl_asr_align fl_asr_tutorial_finetune_ctc
fl_asr_decode fl_asr_tutorial_inference_ctc
fl_asr_test fl_asr_voice_activity_detection_ctc
fl_asr_train
###Markdown
Step 2: Setup Finetuning Downloading the model filesFirst, let's download the pretrained models for finetuning. For acoustic model, you can choose from >Architecture | Params | Criterion | Model Name | Arch Name >---|---|:---|:---:|:---:> Transformer|70Mil|CTC|am_transformer_ctc_stride3_letters_70Mparams.bin |am_transformer_ctc_stride3_letters_70Mparams.arch> Transformer|300Mil|CTC|am_transformer_ctc_stride3_letters_300Mparams.bin | am_transformer_ctc_stride3_letters_300Mparams.arch> Conformer|25Mil|CTC|am_conformer_ctc_stride3_letters_25Mparams.bin|am_conformer_ctc_stride3_letters_25Mparams.arch> Conformer|87Mil|CTC|am_conformer_ctc_stride3_letters_87Mparams.bin|am_conformer_ctc_stride3_letters_87Mparams.arch> Conformer|300Mil|CTC|am_conformer_ctc_stride3_letters_300Mparams.bin| am_conformer_ctc_stride3_letters_300Mparams.archFor demonstration, we will use the model in first row and download the model and its arch file.
###Code
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/am_transformer_ctc_stride3_letters_70Mparams.bin -O model.bin # acoustic model
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/am_transformer_ctc_stride3_letters_70Mparams.arch -O arch.txt # model architecture file
###Output
_____no_output_____
###Markdown
Along with the acoustic model, we will also download the tokens file, lexicon file
###Code
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/tokens.txt -O tokens.txt # tokens (defines predicted tokens)
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/lexicon.txt -O lexicon.txt # lexicon files (defines mapping between words)
###Output
_____no_output_____
###Markdown
Downloading the datasetFor finetuning the model, we provide a limited supervision dataset based on [AMI Corpus](http://groups.inf.ed.ac.uk/ami/corpus/). It consists of 10m, 1hr and 10hr subsets organized as follows. ```dev.lst development set test.lst test set train_10min_0.lst first 10 min foldtrain_10min_1.lsttrain_10min_2.lsttrain_10min_3.lsttrain_10min_4.lsttrain_10min_5.lsttrain_9hr.lst remaining data of the 10h split (10h=1h+9h)```The 10h split is created by combining the data from the 9h split and the 1h split. The 1h split is itself made of 6 folds of 10 min splits.The recipe used for preparing this corpus can be found [here](https://github.com/flashlight/wav2letter/tree/master/data/ami). **You can also use your own dataset to finetune the model instead of AMI Corpus.**
###Code
!rm ami_limited_supervision.tar.gz
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/ami_limited_supervision.tar.gz -O ami_limited_supervision.tar.gz
!tar -xf ami_limited_supervision.tar.gz
!ls ami_limited_supervision
###Output
rm: cannot remove 'ami_limited_supervision.tar.gz': No such file or directory
audio train_10min_0.lst train_10min_3.lst train_9hr.lst
dev.lst train_10min_1.lst train_10min_4.lst
test.lst train_10min_2.lst train_10min_5.lst
###Markdown
Get baseline WER before finetuningBefore proceeding to finetuning, let's test (viterbi) WER on AMI dataset to we have something to compare results after finetuning.
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am model.bin --datadir '' --emission_dir '' --uselexicon false \
--test ami_limited_supervision/test.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1046.72_1047.07, WER: 100%, TER: 50%, total WER: 25.9066%, total TER: 12.6062%, progress (thread 0): 86.8275%]
|T|: m m
|P|: m m
[sample: ES2004c_H01_FEE013_1294.34_1294.69, WER: 0%, TER: 0%, total WER: 25.9063%, total TER: 12.6061%, progress (thread 0): 86.8354%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1302.15_1302.5, WER: 100%, TER: 40%, total WER: 25.9071%, total TER: 12.6065%, progress (thread 0): 86.8434%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1515.72_1516.07, WER: 0%, TER: 0%, total WER: 25.9068%, total TER: 12.6063%, progress (thread 0): 86.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1690.13_1690.48, WER: 0%, TER: 0%, total WER: 25.9065%, total TER: 12.6062%, progress (thread 0): 86.8592%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_2078.48_2078.83, WER: 100%, TER: 40%, total WER: 25.9074%, total TER: 12.6065%, progress (thread 0): 86.8671%]
|T|: o k a y
|P|: i t
[sample: ES2004c_H02_MEE014_2291.54_2291.89, WER: 100%, TER: 100%, total WER: 25.9082%, total TER: 12.6074%, progress (thread 0): 86.875%]
|T|: o h
|P|:
[sample: ES2004d_H00_MEO015_127.17_127.52, WER: 100%, TER: 100%, total WER: 25.9091%, total TER: 12.6078%, progress (thread 0): 86.8829%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_561.86_562.21, WER: 0%, TER: 0%, total WER: 25.9088%, total TER: 12.6077%, progress (thread 0): 86.8908%]
|T|: ' k a y
|P|: k a y
[sample: ES2004d_H00_MEO015_640.16_640.51, WER: 100%, TER: 25%, total WER: 25.9096%, total TER: 12.6078%, progress (thread 0): 86.8987%]
|T|: r i g h t
|P|: l i k e
[sample: ES2004d_H00_MEO015_821.44_821.79, WER: 100%, TER: 80%, total WER: 25.9105%, total TER: 12.6086%, progress (thread 0): 86.9066%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H01_FEE013_822.51_822.86, WER: 100%, TER: 40%, total WER: 25.9113%, total TER: 12.6089%, progress (thread 0): 86.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1778.02_1778.37, WER: 0%, TER: 0%, total WER: 25.911%, total TER: 12.6088%, progress (thread 0): 86.9225%]
|T|: m m h m m
|P|: u | h u h
[sample: IS1009a_H00_FIE088_76.08_76.43, WER: 200%, TER: 80%, total WER: 25.913%, total TER: 12.6096%, progress (thread 0): 86.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_431.19_431.54, WER: 0%, TER: 0%, total WER: 25.9127%, total TER: 12.6094%, progress (thread 0): 86.9383%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_492.57_492.92, WER: 0%, TER: 0%, total WER: 25.9124%, total TER: 12.6094%, progress (thread 0): 86.9462%]
|T|: y e a h
|P|: h e | w a s
[sample: IS1009b_H02_FIO084_232.67_233.02, WER: 200%, TER: 100%, total WER: 25.9144%, total TER: 12.6102%, progress (thread 0): 86.9541%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_361.11_361.46, WER: 0%, TER: 0%, total WER: 25.9141%, total TER: 12.6101%, progress (thread 0): 86.962%]
|T|: y e p
|P|: y e a h
[sample: IS1009b_H00_FIE088_723.57_723.92, WER: 100%, TER: 66.6667%, total WER: 25.9149%, total TER: 12.6104%, progress (thread 0): 86.9699%]
|T|: m m h m m
|P|: e
[sample: IS1009b_H02_FIO084_993.71_994.06, WER: 100%, TER: 100%, total WER: 25.9158%, total TER: 12.6115%, progress (thread 0): 86.9778%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1007.39_1007.74, WER: 0%, TER: 0%, total WER: 25.9155%, total TER: 12.6114%, progress (thread 0): 86.9858%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1079.18_1079.53, WER: 100%, TER: 40%, total WER: 25.9163%, total TER: 12.6117%, progress (thread 0): 86.9937%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1082.71_1083.06, WER: 100%, TER: 40%, total WER: 25.9172%, total TER: 12.612%, progress (thread 0): 87.0016%]
|T|: t h i s | o n e
|P|: t h i s | o n e
[sample: IS1009b_H00_FIE088_1179.44_1179.79, WER: 0%, TER: 0%, total WER: 25.9166%, total TER: 12.6118%, progress (thread 0): 87.0095%]
|T|: y o u ' r e | t h r e e
|P|: i t h r i
[sample: IS1009b_H00_FIE088_1203.06_1203.41, WER: 100%, TER: 75%, total WER: 25.9183%, total TER: 12.6136%, progress (thread 0): 87.0174%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1699.07_1699.42, WER: 0%, TER: 0%, total WER: 25.918%, total TER: 12.6134%, progress (thread 0): 87.0253%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1821.76_1822.11, WER: 0%, TER: 0%, total WER: 25.9177%, total TER: 12.6133%, progress (thread 0): 87.0332%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_527.63_527.98, WER: 100%, TER: 40%, total WER: 25.9185%, total TER: 12.6136%, progress (thread 0): 87.0411%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_546.82_547.17, WER: 100%, TER: 40%, total WER: 25.9194%, total TER: 12.614%, progress (thread 0): 87.049%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_609.9_610.25, WER: 100%, TER: 40%, total WER: 25.9202%, total TER: 12.6143%, progress (thread 0): 87.057%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009c_H00_FIE088_688.49_688.84, WER: 200%, TER: 100%, total WER: 25.9222%, total TER: 12.6153%, progress (thread 0): 87.0649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1303.58_1303.93, WER: 0%, TER: 0%, total WER: 25.9219%, total TER: 12.6152%, progress (thread 0): 87.0728%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H01_FIO087_1514.93_1515.28, WER: 100%, TER: 40%, total WER: 25.9227%, total TER: 12.6155%, progress (thread 0): 87.0807%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1737.76_1738.11, WER: 100%, TER: 40%, total WER: 25.9236%, total TER: 12.6158%, progress (thread 0): 87.0886%]
|T|: a h
|P|: i
[sample: IS1009d_H02_FIO084_734.11_734.46, WER: 100%, TER: 100%, total WER: 25.9244%, total TER: 12.6163%, progress (thread 0): 87.0965%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_742.93_743.28, WER: 0%, TER: 0%, total WER: 25.9241%, total TER: 12.6162%, progress (thread 0): 87.1044%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_743.52_743.87, WER: 0%, TER: 0%, total WER: 25.9238%, total TER: 12.616%, progress (thread 0): 87.1123%]
|T|: y e a h
|P|: w h e r e
[sample: IS1009d_H02_FIO084_953.71_954.06, WER: 100%, TER: 100%, total WER: 25.9247%, total TER: 12.6169%, progress (thread 0): 87.1203%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1243.4_1243.75, WER: 0%, TER: 0%, total WER: 25.9244%, total TER: 12.6168%, progress (thread 0): 87.1282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1323.32_1323.67, WER: 0%, TER: 0%, total WER: 25.9241%, total TER: 12.6166%, progress (thread 0): 87.1361%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_1417.8_1418.15, WER: 100%, TER: 40%, total WER: 25.9249%, total TER: 12.617%, progress (thread 0): 87.144%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_1674.44_1674.79, WER: 100%, TER: 100%, total WER: 25.9258%, total TER: 12.618%, progress (thread 0): 87.1519%]
|T|: m m h m m
|P|: m h
[sample: IS1009d_H02_FIO084_1745.56_1745.91, WER: 100%, TER: 60%, total WER: 25.9266%, total TER: 12.6185%, progress (thread 0): 87.1598%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_506.21_506.56, WER: 0%, TER: 0%, total WER: 25.9263%, total TER: 12.6184%, progress (thread 0): 87.1677%]
|T|: t h e | p e n
|P|: b
[sample: TS3003a_H02_MTD0010ID_1074.9_1075.25, WER: 100%, TER: 100%, total WER: 25.928%, total TER: 12.6199%, progress (thread 0): 87.1756%]
|T|: r i g h t
|P|: r i g h t
[sample: TS3003a_H03_MTD012ME_1219.55_1219.9, WER: 0%, TER: 0%, total WER: 25.9277%, total TER: 12.6197%, progress (thread 0): 87.1835%]
|T|: m m h m m
|P|: m h m
[sample: TS3003a_H01_MTD011UID_1371.45_1371.8, WER: 100%, TER: 40%, total WER: 25.9285%, total TER: 12.62%, progress (thread 0): 87.1915%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_1453.73_1454.08, WER: 0%, TER: 0%, total WER: 25.9282%, total TER: 12.6199%, progress (thread 0): 87.1994%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1295.49_1295.84, WER: 0%, TER: 0%, total WER: 25.9279%, total TER: 12.6199%, progress (thread 0): 87.2073%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1351.56_1351.91, WER: 100%, TER: 50%, total WER: 25.9288%, total TER: 12.62%, progress (thread 0): 87.2152%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_180.89_181.24, WER: 0%, TER: 0%, total WER: 25.9285%, total TER: 12.6199%, progress (thread 0): 87.2231%]
|T|: y e a h
|P|: y e a p
[sample: TS3003c_H00_MTD009PM_1380.5_1380.85, WER: 100%, TER: 25%, total WER: 25.9293%, total TER: 12.62%, progress (thread 0): 87.231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1531.23_1531.58, WER: 0%, TER: 0%, total WER: 25.929%, total TER: 12.6199%, progress (thread 0): 87.2389%]
|T|: t h a t ' s | g o o d
|P|: t h a t ' s g o
[sample: TS3003c_H03_MTD012ME_1567.88_1568.23, WER: 100%, TER: 27.2727%, total WER: 25.9307%, total TER: 12.6203%, progress (thread 0): 87.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1713.34_1713.69, WER: 0%, TER: 0%, total WER: 25.9304%, total TER: 12.6202%, progress (thread 0): 87.2547%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_2280.01_2280.36, WER: 0%, TER: 0%, total WER: 25.9301%, total TER: 12.6201%, progress (thread 0): 87.2627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_306.6_306.95, WER: 0%, TER: 0%, total WER: 25.9298%, total TER: 12.6199%, progress (thread 0): 87.2706%]
|T|: n o
|P|: o
[sample: TS3003d_H00_MTD009PM_819.47_819.82, WER: 100%, TER: 50%, total WER: 25.9307%, total TER: 12.6201%, progress (thread 0): 87.2785%]
|T|: a l r i g h t
|P|:
[sample: TS3003d_H00_MTD009PM_965.64_965.99, WER: 100%, TER: 100%, total WER: 25.9315%, total TER: 12.6216%, progress (thread 0): 87.2864%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H00_MTD009PM_1373.9_1374.25, WER: 0%, TER: 0%, total WER: 25.9312%, total TER: 12.6215%, progress (thread 0): 87.2943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1847.37_1847.72, WER: 0%, TER: 0%, total WER: 25.9309%, total TER: 12.6213%, progress (thread 0): 87.3022%]
|T|: n i n e
|P|: n i n e t h
[sample: TS3003d_H03_MTD012ME_1899.7_1900.05, WER: 100%, TER: 50%, total WER: 25.9318%, total TER: 12.6217%, progress (thread 0): 87.3101%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H01_MTD011UID_2218.91_2219.26, WER: 0%, TER: 0%, total WER: 25.9315%, total TER: 12.6216%, progress (thread 0): 87.318%]
|T|: r i g h t
|P|: b u t
[sample: EN2002a_H03_MEE071_142.16_142.51, WER: 100%, TER: 80%, total WER: 25.9323%, total TER: 12.6224%, progress (thread 0): 87.326%]
|T|: h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_203.92_204.27, WER: 100%, TER: 66.6667%, total WER: 25.9332%, total TER: 12.6228%, progress (thread 0): 87.3339%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_220.21_220.56, WER: 0%, TER: 0%, total WER: 25.9329%, total TER: 12.6226%, progress (thread 0): 87.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1393.11_1393.46, WER: 0%, TER: 0%, total WER: 25.9326%, total TER: 12.6225%, progress (thread 0): 87.3497%]
|T|: n o
|P|: n o
[sample: EN2002a_H02_FEO072_1494.01_1494.36, WER: 0%, TER: 0%, total WER: 25.9323%, total TER: 12.6225%, progress (thread 0): 87.3576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1616.41_1616.76, WER: 0%, TER: 0%, total WER: 25.932%, total TER: 12.6224%, progress (thread 0): 87.3655%]
|T|: y e a h
|P|: y e h
[sample: EN2002a_H01_FEO070_2062.28_2062.63, WER: 100%, TER: 25%, total WER: 25.9328%, total TER: 12.6225%, progress (thread 0): 87.3734%]
|T|: t h e n | u m
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_306.19_306.54, WER: 100%, TER: 114.286%, total WER: 25.9345%, total TER: 12.6242%, progress (thread 0): 87.3813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_679.83_680.18, WER: 0%, TER: 0%, total WER: 25.9342%, total TER: 12.6241%, progress (thread 0): 87.3892%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1132.98_1133.33, WER: 0%, TER: 0%, total WER: 25.9339%, total TER: 12.624%, progress (thread 0): 87.3972%]
|T|: s h o u l d n ' t | n o
|P|: n i
[sample: EN2002b_H01_MEE071_1400.16_1400.51, WER: 100%, TER: 91.6667%, total WER: 25.9356%, total TER: 12.6262%, progress (thread 0): 87.4051%]
|T|: n o
|P|: k n o w
[sample: EN2002b_H02_FEO072_1650.79_1651.14, WER: 100%, TER: 100%, total WER: 25.9364%, total TER: 12.6266%, progress (thread 0): 87.413%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_550.49_550.84, WER: 0%, TER: 0%, total WER: 25.9362%, total TER: 12.6265%, progress (thread 0): 87.4209%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_586.84_587.19, WER: 0%, TER: 0%, total WER: 25.9359%, total TER: 12.6264%, progress (thread 0): 87.4288%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_725.66_726.01, WER: 0%, TER: 0%, total WER: 25.9356%, total TER: 12.6263%, progress (thread 0): 87.4367%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_743.92_744.27, WER: 0%, TER: 0%, total WER: 25.9353%, total TER: 12.6262%, progress (thread 0): 87.4446%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_975_975.35, WER: 0%, TER: 0%, total WER: 25.935%, total TER: 12.6261%, progress (thread 0): 87.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1520.32_1520.67, WER: 0%, TER: 0%, total WER: 25.9347%, total TER: 12.626%, progress (thread 0): 87.4604%]
|T|: s o
|P|:
[sample: EN2002c_H02_MEE071_1667.21_1667.56, WER: 100%, TER: 100%, total WER: 25.9355%, total TER: 12.6264%, progress (thread 0): 87.4684%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_2075.98_2076.33, WER: 0%, TER: 0%, total WER: 25.9352%, total TER: 12.6264%, progress (thread 0): 87.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2182.63_2182.98, WER: 0%, TER: 0%, total WER: 25.9349%, total TER: 12.6262%, progress (thread 0): 87.4842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_352.2_352.55, WER: 0%, TER: 0%, total WER: 25.9346%, total TER: 12.6261%, progress (thread 0): 87.4921%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002d_H03_MEE073_387.18_387.53, WER: 0%, TER: 0%, total WER: 25.934%, total TER: 12.6259%, progress (thread 0): 87.5%]
|T|: m m
|P|:
[sample: EN2002d_H01_FEO072_831.78_832.13, WER: 100%, TER: 100%, total WER: 25.9349%, total TER: 12.6263%, progress (thread 0): 87.5079%]
|T|: n o
|P|: n o
[sample: EN2002d_H03_MEE073_909.36_909.71, WER: 0%, TER: 0%, total WER: 25.9346%, total TER: 12.6263%, progress (thread 0): 87.5158%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002d_H03_MEE073_1113.08_1113.43, WER: 200%, TER: 175%, total WER: 25.9366%, total TER: 12.6278%, progress (thread 0): 87.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1233.84_1234.19, WER: 0%, TER: 0%, total WER: 25.9363%, total TER: 12.6277%, progress (thread 0): 87.5316%]
|T|: i | d o n ' t | k n o w
|P|:
[sample: EN2002d_H01_FEO072_1245.04_1245.39, WER: 100%, TER: 100%, total WER: 25.9388%, total TER: 12.6301%, progress (thread 0): 87.5396%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1798.12_1798.47, WER: 0%, TER: 0%, total WER: 25.9385%, total TER: 12.63%, progress (thread 0): 87.5475%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1926.51_1926.86, WER: 0%, TER: 0%, total WER: 25.9382%, total TER: 12.6299%, progress (thread 0): 87.5554%]
|T|: u h h u h
|P|: u h u h
[sample: EN2002d_H00_FEO070_2027.85_2028.2, WER: 100%, TER: 20%, total WER: 25.9391%, total TER: 12.63%, progress (thread 0): 87.5633%]
|T|: o h | s o r r y
|P|:
[sample: ES2004a_H00_MEO015_255.91_256.25, WER: 100%, TER: 100%, total WER: 25.9407%, total TER: 12.6316%, progress (thread 0): 87.5712%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H01_FEE013_545.3_545.64, WER: 100%, TER: 40%, total WER: 25.9416%, total TER: 12.632%, progress (thread 0): 87.5791%]
|T|: r e a l l y
|P|: r e a l l y
[sample: ES2004a_H01_FEE013_603.88_604.22, WER: 0%, TER: 0%, total WER: 25.9413%, total TER: 12.6318%, progress (thread 0): 87.587%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_635.54_635.88, WER: 100%, TER: 40%, total WER: 25.9421%, total TER: 12.6321%, progress (thread 0): 87.5949%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H03_FEE016_811.15_811.49, WER: 0%, TER: 0%, total WER: 25.9418%, total TER: 12.632%, progress (thread 0): 87.6028%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_954.93_955.27, WER: 100%, TER: 40%, total WER: 25.9427%, total TER: 12.6323%, progress (thread 0): 87.6108%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H03_FEE016_1445.71_1446.05, WER: 100%, TER: 60%, total WER: 25.9435%, total TER: 12.6329%, progress (thread 0): 87.6187%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1995.5_1995.84, WER: 100%, TER: 40%, total WER: 25.9444%, total TER: 12.6332%, progress (thread 0): 87.6266%]
|T|: ' k a y
|P|: t a n
[sample: ES2004c_H03_FEE016_22.85_23.19, WER: 100%, TER: 75%, total WER: 25.9452%, total TER: 12.6338%, progress (thread 0): 87.6345%]
|T|: t h a n k | y o u
|P|:
[sample: ES2004c_H03_FEE016_201.1_201.44, WER: 100%, TER: 100%, total WER: 25.9469%, total TER: 12.6356%, progress (thread 0): 87.6424%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_451.06_451.4, WER: 100%, TER: 25%, total WER: 25.9477%, total TER: 12.6357%, progress (thread 0): 87.6503%]
|T|: n o
|P|: n o t
[sample: ES2004c_H02_MEE014_535.19_535.53, WER: 100%, TER: 50%, total WER: 25.9486%, total TER: 12.6359%, progress (thread 0): 87.6582%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1178.32_1178.66, WER: 0%, TER: 0%, total WER: 25.9483%, total TER: 12.6358%, progress (thread 0): 87.6661%]
|T|: h m m
|P|: m m
[sample: ES2004c_H02_MEE014_1181.26_1181.6, WER: 100%, TER: 33.3333%, total WER: 25.9491%, total TER: 12.6359%, progress (thread 0): 87.674%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1478.47_1478.81, WER: 100%, TER: 50%, total WER: 25.9499%, total TER: 12.6361%, progress (thread 0): 87.682%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1640.61_1640.95, WER: 100%, TER: 40%, total WER: 25.9508%, total TER: 12.6364%, progress (thread 0): 87.6899%]
|T|: m m h m m
|P|: h e
[sample: ES2004c_H03_FEE016_2025.11_2025.45, WER: 100%, TER: 80%, total WER: 25.9516%, total TER: 12.6372%, progress (thread 0): 87.6978%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_2072.31_2072.65, WER: 100%, TER: 40%, total WER: 25.9525%, total TER: 12.6376%, progress (thread 0): 87.7057%]
|T|: y e a h
|P|: y e s
[sample: ES2004d_H00_MEO015_100.76_101.1, WER: 100%, TER: 50%, total WER: 25.9533%, total TER: 12.6379%, progress (thread 0): 87.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H01_FEE013_139.4_139.74, WER: 0%, TER: 0%, total WER: 25.953%, total TER: 12.6378%, progress (thread 0): 87.7215%]
|T|: y e p
|P|: y u p
[sample: ES2004d_H00_MEO015_155.57_155.91, WER: 100%, TER: 33.3333%, total WER: 25.9539%, total TER: 12.6379%, progress (thread 0): 87.7294%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_178.12_178.46, WER: 0%, TER: 0%, total WER: 25.9536%, total TER: 12.6378%, progress (thread 0): 87.7373%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_405.44_405.78, WER: 0%, TER: 0%, total WER: 25.9533%, total TER: 12.6377%, progress (thread 0): 87.7453%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_548.5_548.84, WER: 100%, TER: 40%, total WER: 25.9541%, total TER: 12.638%, progress (thread 0): 87.7532%]
|T|: t h r e e
|P|: t h r e e
[sample: ES2004d_H02_MEE014_646.42_646.76, WER: 0%, TER: 0%, total WER: 25.9538%, total TER: 12.6378%, progress (thread 0): 87.7611%]
|T|: f o u r
|P|: f o r
[sample: ES2004d_H01_FEE013_911.53_911.87, WER: 100%, TER: 25%, total WER: 25.9547%, total TER: 12.638%, progress (thread 0): 87.769%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1135.09_1135.43, WER: 0%, TER: 0%, total WER: 25.9544%, total TER: 12.6378%, progress (thread 0): 87.7769%]
|T|: m m
|P|: h m
[sample: ES2004d_H01_FEE013_1953.89_1954.23, WER: 100%, TER: 50%, total WER: 25.9552%, total TER: 12.638%, progress (thread 0): 87.7848%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_792.79_793.13, WER: 0%, TER: 0%, total WER: 25.9549%, total TER: 12.6379%, progress (thread 0): 87.7927%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_43.16_43.5, WER: 100%, TER: 40%, total WER: 25.9558%, total TER: 12.6383%, progress (thread 0): 87.8006%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_260.98_261.32, WER: 0%, TER: 0%, total WER: 25.9555%, total TER: 12.6381%, progress (thread 0): 87.8085%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_425.4_425.74, WER: 0%, TER: 0%, total WER: 25.9552%, total TER: 12.638%, progress (thread 0): 87.8165%]
|T|: y e p
|P|: o
[sample: IS1009b_H00_FIE088_598.28_598.62, WER: 100%, TER: 100%, total WER: 25.956%, total TER: 12.6386%, progress (thread 0): 87.8244%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_613.29_613.63, WER: 0%, TER: 0%, total WER: 25.9557%, total TER: 12.6385%, progress (thread 0): 87.8323%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1002.09_1002.43, WER: 0%, TER: 0%, total WER: 25.9554%, total TER: 12.6384%, progress (thread 0): 87.8402%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009b_H00_FIE088_1223.66_1224, WER: 200%, TER: 100%, total WER: 25.9574%, total TER: 12.6395%, progress (thread 0): 87.8481%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1225.44_1225.78, WER: 100%, TER: 40%, total WER: 25.9582%, total TER: 12.6398%, progress (thread 0): 87.856%]
|T|: m m
|P|: e a h
[sample: IS1009b_H03_FIO089_1734.63_1734.97, WER: 100%, TER: 150%, total WER: 25.9591%, total TER: 12.6404%, progress (thread 0): 87.8639%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1834.47_1834.81, WER: 0%, TER: 0%, total WER: 25.9588%, total TER: 12.6403%, progress (thread 0): 87.8718%]
|T|: o k a y
|P|: k
[sample: IS1009b_H01_FIO087_1882.63_1882.97, WER: 100%, TER: 75%, total WER: 25.9596%, total TER: 12.6409%, progress (thread 0): 87.8797%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H01_FIO087_1960.82_1961.16, WER: 100%, TER: 40%, total WER: 25.9605%, total TER: 12.6412%, progress (thread 0): 87.8877%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_535.66_536, WER: 100%, TER: 40%, total WER: 25.9613%, total TER: 12.6415%, progress (thread 0): 87.8956%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_575.77_576.11, WER: 100%, TER: 40%, total WER: 25.9621%, total TER: 12.6419%, progress (thread 0): 87.9035%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H03_FIO089_950.04_950.38, WER: 100%, TER: 80%, total WER: 25.963%, total TER: 12.6426%, progress (thread 0): 87.9114%]
|T|: i t | w o r k s
|P|: w u t
[sample: IS1009c_H01_FIO087_1083.82_1084.16, WER: 100%, TER: 87.5%, total WER: 25.9647%, total TER: 12.6441%, progress (thread 0): 87.9193%]
|T|: m m h m m
|P|: y e h
[sample: IS1009c_H03_FIO089_1109.75_1110.09, WER: 100%, TER: 80%, total WER: 25.9655%, total TER: 12.6449%, progress (thread 0): 87.9272%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1603.38_1603.72, WER: 100%, TER: 40%, total WER: 25.9663%, total TER: 12.6452%, progress (thread 0): 87.9351%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1613.06_1613.4, WER: 100%, TER: 40%, total WER: 25.9672%, total TER: 12.6455%, progress (thread 0): 87.943%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_1633.76_1634.1, WER: 0%, TER: 0%, total WER: 25.9669%, total TER: 12.6454%, progress (thread 0): 87.951%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_434.93_435.27, WER: 100%, TER: 40%, total WER: 25.9677%, total TER: 12.6457%, progress (thread 0): 87.9589%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_637.66_638, WER: 100%, TER: 60%, total WER: 25.9686%, total TER: 12.6463%, progress (thread 0): 87.9668%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_649.7_650.04, WER: 0%, TER: 0%, total WER: 25.9683%, total TER: 12.6461%, progress (thread 0): 87.9747%]
|T|: h m m
|P|: r i g h t
[sample: IS1009d_H02_FIO084_674.88_675.22, WER: 100%, TER: 166.667%, total WER: 25.9691%, total TER: 12.6472%, progress (thread 0): 87.9826%]
|T|: o o p s
|P|: u p
[sample: IS1009d_H00_FIE088_1068.99_1069.33, WER: 100%, TER: 75%, total WER: 25.97%, total TER: 12.6478%, progress (thread 0): 87.9905%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H01_FIO087_1509.75_1510.09, WER: 0%, TER: 0%, total WER: 25.9697%, total TER: 12.6477%, progress (thread 0): 87.9984%]
|T|: t h a n k | y o u
|P|: t h a n k | y o u
[sample: TS3003a_H03_MTD012ME_48.1_48.44, WER: 0%, TER: 0%, total WER: 25.9691%, total TER: 12.6475%, progress (thread 0): 88.0063%]
|T|: g u e s s
|P|: y e a f
[sample: TS3003a_H01_MTD011UID_983.96_984.3, WER: 100%, TER: 80%, total WER: 25.9699%, total TER: 12.6482%, progress (thread 0): 88.0142%]
|T|: t h e | p e n
|P|:
[sample: TS3003a_H02_MTD0010ID_1090.49_1090.83, WER: 100%, TER: 100%, total WER: 25.9716%, total TER: 12.6497%, progress (thread 0): 88.0222%]
|T|: t h i n g
|P|: t h i n g
[sample: TS3003a_H00_MTD009PM_1320.19_1320.53, WER: 0%, TER: 0%, total WER: 25.9713%, total TER: 12.6495%, progress (thread 0): 88.0301%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_132.84_133.18, WER: 0%, TER: 0%, total WER: 25.971%, total TER: 12.6495%, progress (thread 0): 88.038%]
|T|: r i g h t
|P|:
[sample: TS3003b_H01_MTD011UID_2086.12_2086.46, WER: 100%, TER: 100%, total WER: 25.9718%, total TER: 12.6505%, progress (thread 0): 88.0459%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2089.85_2090.19, WER: 0%, TER: 0%, total WER: 25.9715%, total TER: 12.6504%, progress (thread 0): 88.0538%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_982.32_982.66, WER: 0%, TER: 0%, total WER: 25.9713%, total TER: 12.6503%, progress (thread 0): 88.0617%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_1007.54_1007.88, WER: 0%, TER: 0%, total WER: 25.971%, total TER: 12.6502%, progress (thread 0): 88.0696%]
|T|: y e a h
|P|: y e h
[sample: TS3003c_H00_MTD009PM_1240.35_1240.69, WER: 100%, TER: 25%, total WER: 25.9718%, total TER: 12.6503%, progress (thread 0): 88.0775%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1413.87_1414.21, WER: 0%, TER: 0%, total WER: 25.9715%, total TER: 12.6501%, progress (thread 0): 88.0854%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1849.58_1849.92, WER: 0%, TER: 0%, total WER: 25.9712%, total TER: 12.65%, progress (thread 0): 88.0934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_595.75_596.09, WER: 0%, TER: 0%, total WER: 25.9709%, total TER: 12.6499%, progress (thread 0): 88.1013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_921.62_921.96, WER: 0%, TER: 0%, total WER: 25.9706%, total TER: 12.6498%, progress (thread 0): 88.1092%]
|T|: u h | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1308.11_1308.45, WER: 50%, TER: 42.8571%, total WER: 25.9712%, total TER: 12.6503%, progress (thread 0): 88.1171%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1388.52_1388.86, WER: 0%, TER: 0%, total WER: 25.9709%, total TER: 12.6502%, progress (thread 0): 88.125%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1802.71_1803.05, WER: 100%, TER: 75%, total WER: 25.9717%, total TER: 12.6508%, progress (thread 0): 88.1329%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1835.63_1835.97, WER: 100%, TER: 25%, total WER: 25.9726%, total TER: 12.6509%, progress (thread 0): 88.1408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2091.81_2092.15, WER: 0%, TER: 0%, total WER: 25.9723%, total TER: 12.6508%, progress (thread 0): 88.1487%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2217.62_2217.96, WER: 0%, TER: 0%, total WER: 25.972%, total TER: 12.6506%, progress (thread 0): 88.1566%]
|T|: m m | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2323.41_2323.75, WER: 50%, TER: 42.8571%, total WER: 25.9725%, total TER: 12.6511%, progress (thread 0): 88.1646%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2420.39_2420.73, WER: 0%, TER: 0%, total WER: 25.9722%, total TER: 12.651%, progress (thread 0): 88.1725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_406.15_406.49, WER: 0%, TER: 0%, total WER: 25.9719%, total TER: 12.6509%, progress (thread 0): 88.1804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_601.54_601.88, WER: 0%, TER: 0%, total WER: 25.9716%, total TER: 12.6508%, progress (thread 0): 88.1883%]
|T|: h m m
|P|: h m
[sample: EN2002a_H02_FEO072_713.14_713.48, WER: 100%, TER: 33.3333%, total WER: 25.9725%, total TER: 12.6509%, progress (thread 0): 88.1962%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_859.83_860.17, WER: 100%, TER: 66.6667%, total WER: 25.9733%, total TER: 12.6513%, progress (thread 0): 88.2041%]
|T|: a l r i g h t
|P|: i t
[sample: EN2002a_H01_FEO070_920.16_920.5, WER: 100%, TER: 71.4286%, total WER: 25.9741%, total TER: 12.6523%, progress (thread 0): 88.212%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1586.73_1587.07, WER: 0%, TER: 0%, total WER: 25.9738%, total TER: 12.6522%, progress (thread 0): 88.2199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1927.75_1928.09, WER: 0%, TER: 0%, total WER: 25.9736%, total TER: 12.652%, progress (thread 0): 88.2279%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1929.86_1930.2, WER: 0%, TER: 0%, total WER: 25.9733%, total TER: 12.6519%, progress (thread 0): 88.2358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2048.6_2048.94, WER: 0%, TER: 0%, total WER: 25.973%, total TER: 12.6518%, progress (thread 0): 88.2437%]
|T|: s o | i t ' s
|P|: t w a
[sample: EN2002a_H03_MEE071_2098.66_2099, WER: 100%, TER: 85.7143%, total WER: 25.9746%, total TER: 12.653%, progress (thread 0): 88.2516%]
|T|: o k a y
|P|: o k
[sample: EN2002a_H00_MEE073_2129.89_2130.23, WER: 100%, TER: 50%, total WER: 25.9755%, total TER: 12.6533%, progress (thread 0): 88.2595%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_144.51_144.85, WER: 0%, TER: 0%, total WER: 25.9752%, total TER: 12.6532%, progress (thread 0): 88.2674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_190.18_190.52, WER: 0%, TER: 0%, total WER: 25.9749%, total TER: 12.6531%, progress (thread 0): 88.2753%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_302.27_302.61, WER: 100%, TER: 66.6667%, total WER: 25.9757%, total TER: 12.6535%, progress (thread 0): 88.2832%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_309.52_309.86, WER: 0%, TER: 0%, total WER: 25.9754%, total TER: 12.6534%, progress (thread 0): 88.2911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_363.35_363.69, WER: 0%, TER: 0%, total WER: 25.9751%, total TER: 12.6533%, progress (thread 0): 88.299%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_409.14_409.48, WER: 0%, TER: 0%, total WER: 25.9749%, total TER: 12.6531%, progress (thread 0): 88.307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_736.4_736.74, WER: 0%, TER: 0%, total WER: 25.9746%, total TER: 12.653%, progress (thread 0): 88.3149%]
|T|: y e a h
|P|: y o
[sample: EN2002b_H03_MEE073_935.86_936.2, WER: 100%, TER: 75%, total WER: 25.9754%, total TER: 12.6536%, progress (thread 0): 88.3228%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002b_H03_MEE073_1252.52_1252.86, WER: 0%, TER: 0%, total WER: 25.9748%, total TER: 12.6534%, progress (thread 0): 88.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1563.14_1563.48, WER: 0%, TER: 0%, total WER: 25.9745%, total TER: 12.6533%, progress (thread 0): 88.3386%]
|T|: y e a h
|P|: y e h
[sample: EN2002b_H00_FEO070_1626.33_1626.67, WER: 100%, TER: 25%, total WER: 25.9754%, total TER: 12.6534%, progress (thread 0): 88.3465%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H01_FEO072_67.59_67.93, WER: 0%, TER: 0%, total WER: 25.9751%, total TER: 12.6533%, progress (thread 0): 88.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_273.14_273.48, WER: 0%, TER: 0%, total WER: 25.9748%, total TER: 12.6532%, progress (thread 0): 88.3623%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_444.82_445.16, WER: 100%, TER: 40%, total WER: 25.9756%, total TER: 12.6535%, progress (thread 0): 88.3703%]
|T|: o r
|P|: e
[sample: EN2002c_H01_FEO072_486.4_486.74, WER: 100%, TER: 100%, total WER: 25.9764%, total TER: 12.6539%, progress (thread 0): 88.3782%]
|T|: o h | w e l l
|P|: h o w
[sample: EN2002c_H03_MEE073_497.23_497.57, WER: 100%, TER: 71.4286%, total WER: 25.9781%, total TER: 12.6549%, progress (thread 0): 88.3861%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002c_H01_FEO072_721.91_722.25, WER: 200%, TER: 14.2857%, total WER: 25.9801%, total TER: 12.6549%, progress (thread 0): 88.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1305.34_1305.68, WER: 0%, TER: 0%, total WER: 25.9798%, total TER: 12.6548%, progress (thread 0): 88.4019%]
|T|: o h
|P|: o h
[sample: EN2002c_H01_FEO072_1925.9_1926.24, WER: 0%, TER: 0%, total WER: 25.9795%, total TER: 12.6547%, progress (thread 0): 88.4098%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2040.49_2040.83, WER: 0%, TER: 0%, total WER: 25.9792%, total TER: 12.6546%, progress (thread 0): 88.4177%]
|T|: m m h m m
|P|: m p
[sample: EN2002c_H03_MEE073_2245.81_2246.15, WER: 100%, TER: 80%, total WER: 25.9801%, total TER: 12.6554%, progress (thread 0): 88.4256%]
|T|: y e a h
|P|: r e a l l y
[sample: EN2002d_H03_MEE073_344.02_344.36, WER: 100%, TER: 100%, total WER: 25.9809%, total TER: 12.6562%, progress (thread 0): 88.4335%]
|T|: w h a t | w a s | i t
|P|: o h | w a s | i t
[sample: EN2002d_H03_MEE073_508.22_508.56, WER: 33.3333%, TER: 27.2727%, total WER: 25.9811%, total TER: 12.6566%, progress (thread 0): 88.4415%]
|T|: y e a h
|P|: g o o d
[sample: EN2002d_H02_MEE071_688.4_688.74, WER: 100%, TER: 100%, total WER: 25.982%, total TER: 12.6574%, progress (thread 0): 88.4494%]
|T|: y e a h
|P|: y e s
[sample: EN2002d_H02_MEE071_715.16_715.5, WER: 100%, TER: 50%, total WER: 25.9828%, total TER: 12.6577%, progress (thread 0): 88.4573%]
|T|: u h
|P|:
[sample: EN2002d_H03_MEE073_1125.77_1126.11, WER: 100%, TER: 100%, total WER: 25.9837%, total TER: 12.6582%, progress (thread 0): 88.4652%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1325.04_1325.38, WER: 100%, TER: 33.3333%, total WER: 25.9845%, total TER: 12.6583%, progress (thread 0): 88.4731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1423.14_1423.48, WER: 0%, TER: 0%, total WER: 25.9842%, total TER: 12.6582%, progress (thread 0): 88.481%]
|T|: n o | w e | p
|P|: s o | w e
[sample: EN2002d_H00_FEO070_1437.94_1438.28, WER: 66.6667%, TER: 42.8571%, total WER: 25.9856%, total TER: 12.6587%, progress (thread 0): 88.4889%]
|T|: w i t h
|P|: w i t h
[sample: EN2002d_H02_MEE071_2073.15_2073.49, WER: 0%, TER: 0%, total WER: 25.9853%, total TER: 12.6586%, progress (thread 0): 88.4968%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_258.31_258.64, WER: 100%, TER: 40%, total WER: 25.9861%, total TER: 12.6589%, progress (thread 0): 88.5048%]
|T|: m m | y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_1048.05_1048.38, WER: 50%, TER: 42.8571%, total WER: 25.9867%, total TER: 12.6594%, progress (thread 0): 88.5127%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_736.03_736.36, WER: 0%, TER: 0%, total WER: 25.9864%, total TER: 12.6593%, progress (thread 0): 88.5206%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1313.86_1314.19, WER: 0%, TER: 0%, total WER: 25.9861%, total TER: 12.6591%, progress (thread 0): 88.5285%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H01_FEE013_1711.46_1711.79, WER: 100%, TER: 200%, total WER: 25.9869%, total TER: 12.66%, progress (thread 0): 88.5364%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004b_H03_FEE016_2208.79_2209.12, WER: 200%, TER: 20%, total WER: 25.9889%, total TER: 12.6601%, progress (thread 0): 88.5443%]
|T|: o k a y
|P|: o k a y
[sample: ES2004b_H03_FEE016_2308.52_2308.85, WER: 0%, TER: 0%, total WER: 25.9886%, total TER: 12.66%, progress (thread 0): 88.5522%]
|T|: y e a h
|P|: y u p
[sample: ES2004c_H02_MEE014_172.32_172.65, WER: 100%, TER: 75%, total WER: 25.9895%, total TER: 12.6606%, progress (thread 0): 88.5601%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_560.34_560.67, WER: 100%, TER: 50%, total WER: 25.9903%, total TER: 12.6608%, progress (thread 0): 88.568%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H01_FEE013_873.37_873.7, WER: 100%, TER: 40%, total WER: 25.9911%, total TER: 12.6611%, progress (thread 0): 88.576%]
|T|: w e l l
|P|: w e l l
[sample: ES2004c_H02_MEE014_925.27_925.6, WER: 0%, TER: 0%, total WER: 25.9908%, total TER: 12.661%, progress (thread 0): 88.5839%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1268.11_1268.44, WER: 0%, TER: 0%, total WER: 25.9905%, total TER: 12.6608%, progress (thread 0): 88.5918%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1965.56_1965.89, WER: 100%, TER: 40%, total WER: 25.9914%, total TER: 12.6612%, progress (thread 0): 88.5997%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H01_FEE013_607.94_608.27, WER: 100%, TER: 40%, total WER: 25.9922%, total TER: 12.6615%, progress (thread 0): 88.6076%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_753.08_753.41, WER: 100%, TER: 33.3333%, total WER: 25.9931%, total TER: 12.6616%, progress (thread 0): 88.6155%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H03_FEE016_876.07_876.4, WER: 100%, TER: 40%, total WER: 25.9939%, total TER: 12.6619%, progress (thread 0): 88.6234%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H00_MEO015_1304.99_1305.32, WER: 0%, TER: 0%, total WER: 25.9936%, total TER: 12.6619%, progress (thread 0): 88.6313%]
|T|: s u r e
|P|: s u r e
[sample: ES2004d_H03_FEE016_1499.95_1500.28, WER: 0%, TER: 0%, total WER: 25.9933%, total TER: 12.6617%, progress (thread 0): 88.6392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1845.84_1846.17, WER: 0%, TER: 0%, total WER: 25.993%, total TER: 12.6616%, progress (thread 0): 88.6471%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1856.75_1857.08, WER: 0%, TER: 0%, total WER: 25.9927%, total TER: 12.6615%, progress (thread 0): 88.6551%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1984.68_1985.01, WER: 0%, TER: 0%, total WER: 25.9924%, total TER: 12.6614%, progress (thread 0): 88.663%]
|T|: m m
|P|: o h
[sample: ES2004d_H01_FEE013_2126.04_2126.37, WER: 100%, TER: 100%, total WER: 25.9933%, total TER: 12.6618%, progress (thread 0): 88.6709%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_55.7_56.03, WER: 0%, TER: 0%, total WER: 25.993%, total TER: 12.6617%, progress (thread 0): 88.6788%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_226.94_227.27, WER: 100%, TER: 40%, total WER: 25.9938%, total TER: 12.662%, progress (thread 0): 88.6867%]
|T|: o k a y
|P|: o k a t
[sample: IS1009a_H03_FIO089_318.12_318.45, WER: 100%, TER: 25%, total WER: 25.9946%, total TER: 12.6621%, progress (thread 0): 88.6946%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_652.91_653.24, WER: 100%, TER: 40%, total WER: 25.9955%, total TER: 12.6624%, progress (thread 0): 88.7025%]
|T|: t h e n
|P|: a n d | t h e n
[sample: IS1009a_H00_FIE088_714.21_714.54, WER: 100%, TER: 100%, total WER: 25.9963%, total TER: 12.6633%, progress (thread 0): 88.7104%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_741.37_741.7, WER: 100%, TER: 40%, total WER: 25.9972%, total TER: 12.6636%, progress (thread 0): 88.7184%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_790.95_791.28, WER: 100%, TER: 40%, total WER: 25.998%, total TER: 12.6639%, progress (thread 0): 88.7263%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_787.47_787.8, WER: 100%, TER: 40%, total WER: 25.9988%, total TER: 12.6642%, progress (thread 0): 88.7342%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1063.18_1063.51, WER: 100%, TER: 40%, total WER: 25.9997%, total TER: 12.6645%, progress (thread 0): 88.7421%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1809.47_1809.8, WER: 0%, TER: 0%, total WER: 25.9994%, total TER: 12.6644%, progress (thread 0): 88.75%]
|T|: m m h m m
|P|: u h
[sample: IS1009b_H03_FIO089_1882.22_1882.55, WER: 100%, TER: 80%, total WER: 26.0002%, total TER: 12.6652%, progress (thread 0): 88.7579%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_408.02_408.35, WER: 100%, TER: 40%, total WER: 26.0011%, total TER: 12.6655%, progress (thread 0): 88.7658%]
|T|: m m h m m
|P|: u h
[sample: IS1009c_H00_FIE088_430.74_431.07, WER: 100%, TER: 80%, total WER: 26.0019%, total TER: 12.6663%, progress (thread 0): 88.7737%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1552.21_1552.54, WER: 0%, TER: 0%, total WER: 26.0016%, total TER: 12.6662%, progress (thread 0): 88.7816%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_383.89_384.22, WER: 100%, TER: 40%, total WER: 26.0024%, total TER: 12.6665%, progress (thread 0): 88.7896%]
|T|: m m h m m
|P|: m
[sample: IS1009d_H03_FIO089_388.95_389.28, WER: 100%, TER: 80%, total WER: 26.0033%, total TER: 12.6673%, progress (thread 0): 88.7975%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_483.18_483.51, WER: 100%, TER: 40%, total WER: 26.0041%, total TER: 12.6676%, progress (thread 0): 88.8054%]
|T|: t h a t ' s | r i g h t
|P|:
[sample: IS1009d_H00_FIE088_757.72_758.05, WER: 100%, TER: 100%, total WER: 26.0058%, total TER: 12.6701%, progress (thread 0): 88.8133%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H01_FIO087_784.18_784.51, WER: 100%, TER: 40%, total WER: 26.0066%, total TER: 12.6704%, progress (thread 0): 88.8212%]
|T|: y e a h
|P|: i t
[sample: IS1009d_H02_FIO084_975.71_976.04, WER: 100%, TER: 100%, total WER: 26.0075%, total TER: 12.6713%, progress (thread 0): 88.8291%]
|T|: m m
|P|: h m
[sample: IS1009d_H02_FIO084_1183.1_1183.43, WER: 100%, TER: 50%, total WER: 26.0083%, total TER: 12.6714%, progress (thread 0): 88.837%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H02_FIO084_1185.85_1186.18, WER: 100%, TER: 40%, total WER: 26.0092%, total TER: 12.6717%, progress (thread 0): 88.8449%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1310.32_1310.65, WER: 100%, TER: 100%, total WER: 26.01%, total TER: 12.6728%, progress (thread 0): 88.8528%]
|T|: u h
|P|: u m
[sample: TS3003a_H01_MTD011UID_915.22_915.55, WER: 100%, TER: 50%, total WER: 26.0108%, total TER: 12.6729%, progress (thread 0): 88.8608%]
|T|: o r | w h o
|P|: o | w h o
[sample: TS3003a_H01_MTD011UID_975.03_975.36, WER: 50%, TER: 16.6667%, total WER: 26.0114%, total TER: 12.673%, progress (thread 0): 88.8687%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1261.99_1262.32, WER: 100%, TER: 50%, total WER: 26.0122%, total TER: 12.6732%, progress (thread 0): 88.8766%]
|T|: s o
|P|: s o
[sample: TS3003b_H03_MTD012ME_280.47_280.8, WER: 0%, TER: 0%, total WER: 26.0119%, total TER: 12.6731%, progress (thread 0): 88.8845%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H00_MTD009PM_842.62_842.95, WER: 100%, TER: 50%, total WER: 26.0128%, total TER: 12.6735%, progress (thread 0): 88.8924%]
|T|: o k a y
|P|: h
[sample: TS3003b_H00_MTD009PM_923.51_923.84, WER: 100%, TER: 100%, total WER: 26.0136%, total TER: 12.6743%, progress (thread 0): 88.9003%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1173.66_1173.99, WER: 0%, TER: 0%, total WER: 26.0133%, total TER: 12.6742%, progress (thread 0): 88.9082%]
|T|: n a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1009.07_1009.4, WER: 100%, TER: 66.6667%, total WER: 26.0141%, total TER: 12.6746%, progress (thread 0): 88.9161%]
|T|: ' k a y
|P|: s o
[sample: TS3003c_H03_MTD012ME_1321.92_1322.25, WER: 100%, TER: 100%, total WER: 26.015%, total TER: 12.6754%, progress (thread 0): 88.924%]
|T|: m m
|P|: a n
[sample: TS3003c_H01_MTD011UID_1395.11_1395.44, WER: 100%, TER: 100%, total WER: 26.0158%, total TER: 12.6758%, progress (thread 0): 88.932%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1507.79_1508.12, WER: 0%, TER: 0%, total WER: 26.0155%, total TER: 12.6757%, progress (thread 0): 88.9399%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1540.01_1540.34, WER: 100%, TER: 40%, total WER: 26.0164%, total TER: 12.676%, progress (thread 0): 88.9478%]
|T|: y e p
|P|: e h
[sample: TS3003c_H00_MTD009PM_1653.43_1653.76, WER: 100%, TER: 66.6667%, total WER: 26.0172%, total TER: 12.6764%, progress (thread 0): 88.9557%]
|T|: u h
|P|: m m
[sample: TS3003c_H01_MTD011UID_1692.63_1692.96, WER: 100%, TER: 100%, total WER: 26.018%, total TER: 12.6768%, progress (thread 0): 88.9636%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2074.03_2074.36, WER: 0%, TER: 0%, total WER: 26.0178%, total TER: 12.6767%, progress (thread 0): 88.9715%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2241.48_2241.81, WER: 0%, TER: 0%, total WER: 26.0175%, total TER: 12.6765%, progress (thread 0): 88.9794%]
|T|: y e a h
|P|: y e r e
[sample: TS3003d_H01_MTD011UID_693.96_694.29, WER: 100%, TER: 50%, total WER: 26.0183%, total TER: 12.6769%, progress (thread 0): 88.9873%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_872.34_872.67, WER: 0%, TER: 0%, total WER: 26.018%, total TER: 12.6768%, progress (thread 0): 88.9953%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_889.13_889.46, WER: 0%, TER: 0%, total WER: 26.0177%, total TER: 12.6767%, progress (thread 0): 89.0032%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_1661.82_1662.15, WER: 0%, TER: 0%, total WER: 26.0174%, total TER: 12.6765%, progress (thread 0): 89.0111%]
|T|: y e a h
|P|: h u h
[sample: TS3003d_H01_MTD011UID_2072.21_2072.54, WER: 100%, TER: 75%, total WER: 26.0183%, total TER: 12.6771%, progress (thread 0): 89.019%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_2085.82_2086.15, WER: 100%, TER: 40%, total WER: 26.0191%, total TER: 12.6774%, progress (thread 0): 89.0269%]
|T|: y e a h
|P|: y e s
[sample: TS3003d_H00_MTD009PM_2164.01_2164.34, WER: 100%, TER: 50%, total WER: 26.0199%, total TER: 12.6778%, progress (thread 0): 89.0348%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2179.8_2180.13, WER: 0%, TER: 0%, total WER: 26.0196%, total TER: 12.6777%, progress (thread 0): 89.0427%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_2461.76_2462.09, WER: 100%, TER: 40%, total WER: 26.0205%, total TER: 12.678%, progress (thread 0): 89.0506%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_299.48_299.81, WER: 0%, TER: 0%, total WER: 26.0202%, total TER: 12.6779%, progress (thread 0): 89.0585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_359.5_359.83, WER: 0%, TER: 0%, total WER: 26.0199%, total TER: 12.6778%, progress (thread 0): 89.0665%]
|T|: w a i t
|P|: w a i t
[sample: EN2002a_H02_FEO072_387.53_387.86, WER: 0%, TER: 0%, total WER: 26.0196%, total TER: 12.6776%, progress (thread 0): 89.0744%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_593.65_593.98, WER: 50%, TER: 55.5556%, total WER: 26.0201%, total TER: 12.6785%, progress (thread 0): 89.0823%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_594.32_594.65, WER: 0%, TER: 0%, total WER: 26.0198%, total TER: 12.6784%, progress (thread 0): 89.0902%]
|T|: y e a h
|P|: y n o
[sample: EN2002a_H01_FEO070_690.97_691.3, WER: 100%, TER: 75%, total WER: 26.0207%, total TER: 12.679%, progress (thread 0): 89.0981%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_1018.98_1019.31, WER: 0%, TER: 0%, total WER: 26.0204%, total TER: 12.6789%, progress (thread 0): 89.106%]
|T|: y e a h
|P|: y e s
[sample: EN2002a_H01_FEO070_1028.68_1029.01, WER: 100%, TER: 50%, total WER: 26.0212%, total TER: 12.6792%, progress (thread 0): 89.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1142.23_1142.56, WER: 0%, TER: 0%, total WER: 26.0209%, total TER: 12.6791%, progress (thread 0): 89.1218%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1361.22_1361.55, WER: 0%, TER: 0%, total WER: 26.0206%, total TER: 12.679%, progress (thread 0): 89.1297%]
|T|: r i g h t
|P|: e h
[sample: EN2002a_H03_MEE071_1383.46_1383.79, WER: 100%, TER: 80%, total WER: 26.0215%, total TER: 12.6798%, progress (thread 0): 89.1377%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1864.48_1864.81, WER: 0%, TER: 0%, total WER: 26.0212%, total TER: 12.6797%, progress (thread 0): 89.1456%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1964.31_1964.64, WER: 0%, TER: 0%, total WER: 26.0209%, total TER: 12.6796%, progress (thread 0): 89.1535%]
|T|: u m
|P|: u m
[sample: EN2002b_H03_MEE073_139.44_139.77, WER: 0%, TER: 0%, total WER: 26.0206%, total TER: 12.6795%, progress (thread 0): 89.1614%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_417.94_418.27, WER: 200%, TER: 175%, total WER: 26.0226%, total TER: 12.681%, progress (thread 0): 89.1693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_420.46_420.79, WER: 0%, TER: 0%, total WER: 26.0223%, total TER: 12.6809%, progress (thread 0): 89.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_705.52_705.85, WER: 0%, TER: 0%, total WER: 26.022%, total TER: 12.6808%, progress (thread 0): 89.1851%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_731.78_732.11, WER: 0%, TER: 0%, total WER: 26.0217%, total TER: 12.6807%, progress (thread 0): 89.193%]
|T|: y e a h | t
|P|: y e a h
[sample: EN2002b_H02_FEO072_1235.77_1236.1, WER: 50%, TER: 33.3333%, total WER: 26.0222%, total TER: 12.681%, progress (thread 0): 89.201%]
|T|: h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_1236.72_1237.05, WER: 100%, TER: 66.6667%, total WER: 26.0231%, total TER: 12.6813%, progress (thread 0): 89.2089%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_1256.5_1256.83, WER: 0%, TER: 0%, total WER: 26.0228%, total TER: 12.6812%, progress (thread 0): 89.2168%]
|T|: h m m
|P|: m
[sample: EN2002b_H02_FEO072_1475.51_1475.84, WER: 100%, TER: 66.6667%, total WER: 26.0236%, total TER: 12.6816%, progress (thread 0): 89.2247%]
|T|: o h | r i g h t
|P|: a l l | r i g h t
[sample: EN2002b_H02_FEO072_1611.8_1612.13, WER: 50%, TER: 37.5%, total WER: 26.0241%, total TER: 12.6821%, progress (thread 0): 89.2326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_93.86_94.19, WER: 0%, TER: 0%, total WER: 26.0238%, total TER: 12.6819%, progress (thread 0): 89.2405%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_245.2_245.53, WER: 0%, TER: 0%, total WER: 26.0235%, total TER: 12.6818%, progress (thread 0): 89.2484%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H01_FEO072_447.06_447.39, WER: 100%, TER: 40%, total WER: 26.0244%, total TER: 12.6822%, progress (thread 0): 89.2563%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002c_H03_MEE073_823.26_823.59, WER: 0%, TER: 0%, total WER: 26.0238%, total TER: 12.6819%, progress (thread 0): 89.2642%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_850.62_850.95, WER: 0%, TER: 0%, total WER: 26.0235%, total TER: 12.6818%, progress (thread 0): 89.2722%]
|T|: t h a t ' s | a
|P|: t h a t ' s | a
[sample: EN2002c_H03_MEE073_903.73_904.06, WER: 0%, TER: 0%, total WER: 26.0229%, total TER: 12.6816%, progress (thread 0): 89.2801%]
|T|: o h
|P|: n o w
[sample: EN2002c_H03_MEE073_962_962.33, WER: 100%, TER: 100%, total WER: 26.0237%, total TER: 12.682%, progress (thread 0): 89.288%]
|T|: y e a h | r i g h t
|P|: y a | r i h
[sample: EN2002c_H03_MEE073_1287.41_1287.74, WER: 100%, TER: 40%, total WER: 26.0254%, total TER: 12.6826%, progress (thread 0): 89.2959%]
|T|: t h a t ' s | t r u e
|P|: t h a t ' s | r u e
[sample: EN2002c_H03_MEE073_1299.73_1300.06, WER: 50%, TER: 9.09091%, total WER: 26.026%, total TER: 12.6825%, progress (thread 0): 89.3038%]
|T|: y o u | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2044.96_2045.29, WER: 0%, TER: 0%, total WER: 26.0254%, total TER: 12.6823%, progress (thread 0): 89.3117%]
|T|: m m
|P|: h m
[sample: EN2002c_H02_MEE071_2072_2072.33, WER: 100%, TER: 50%, total WER: 26.0262%, total TER: 12.6825%, progress (thread 0): 89.3196%]
|T|: o o h
|P|: o o
[sample: EN2002c_H01_FEO072_2817.88_2818.21, WER: 100%, TER: 33.3333%, total WER: 26.0271%, total TER: 12.6826%, progress (thread 0): 89.3275%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002d_H01_FEO072_419.86_420.19, WER: 200%, TER: 14.2857%, total WER: 26.029%, total TER: 12.6827%, progress (thread 0): 89.3354%]
|T|: m m
|P|: i
[sample: EN2002d_H03_MEE073_488.3_488.63, WER: 100%, TER: 100%, total WER: 26.0299%, total TER: 12.6831%, progress (thread 0): 89.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1089.65_1089.98, WER: 0%, TER: 0%, total WER: 26.0296%, total TER: 12.6829%, progress (thread 0): 89.3513%]
|T|: w e l l
|P|: n o
[sample: EN2002d_H03_MEE073_1132.11_1132.44, WER: 100%, TER: 100%, total WER: 26.0304%, total TER: 12.6838%, progress (thread 0): 89.3592%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1131.27_1131.6, WER: 100%, TER: 33.3333%, total WER: 26.0312%, total TER: 12.6839%, progress (thread 0): 89.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1257.89_1258.22, WER: 0%, TER: 0%, total WER: 26.031%, total TER: 12.6838%, progress (thread 0): 89.375%]
|T|: r e a l l y
|P|: r e a l l y
[sample: EN2002d_H01_FEO072_1345.97_1346.3, WER: 0%, TER: 0%, total WER: 26.0307%, total TER: 12.6836%, progress (thread 0): 89.3829%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1429.74_1430.07, WER: 100%, TER: 33.3333%, total WER: 26.0315%, total TER: 12.6838%, progress (thread 0): 89.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1957.8_1958.13, WER: 0%, TER: 0%, total WER: 26.0312%, total TER: 12.6836%, progress (thread 0): 89.3987%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_863.21_863.53, WER: 100%, TER: 40%, total WER: 26.032%, total TER: 12.684%, progress (thread 0): 89.4066%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_895.43_895.75, WER: 100%, TER: 40%, total WER: 26.0329%, total TER: 12.6843%, progress (thread 0): 89.4146%]
|T|: m m
|P|: h m
[sample: ES2004b_H01_FEE013_1140.58_1140.9, WER: 100%, TER: 50%, total WER: 26.0337%, total TER: 12.6845%, progress (thread 0): 89.4225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1182.75_1183.07, WER: 0%, TER: 0%, total WER: 26.0334%, total TER: 12.6843%, progress (thread 0): 89.4304%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1471.32_1471.64, WER: 100%, TER: 40%, total WER: 26.0343%, total TER: 12.6847%, progress (thread 0): 89.4383%]
|T|: s
|P|: s h
[sample: ES2004b_H01_FEE013_1922.51_1922.83, WER: 100%, TER: 100%, total WER: 26.0351%, total TER: 12.6849%, progress (thread 0): 89.4462%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H03_FEE016_1953.34_1953.66, WER: 100%, TER: 60%, total WER: 26.0359%, total TER: 12.6854%, progress (thread 0): 89.4541%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1966.24_1966.56, WER: 0%, TER: 0%, total WER: 26.0356%, total TER: 12.6853%, progress (thread 0): 89.462%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_806.95_807.27, WER: 0%, TER: 0%, total WER: 26.0353%, total TER: 12.6852%, progress (thread 0): 89.4699%]
|T|: o k a y
|P|: o k a
[sample: ES2004c_H03_FEE016_1066.11_1066.43, WER: 100%, TER: 25%, total WER: 26.0362%, total TER: 12.6853%, progress (thread 0): 89.4779%]
|T|: m m
|P|: y e m
[sample: ES2004c_H00_MEO015_1890.24_1890.56, WER: 100%, TER: 100%, total WER: 26.037%, total TER: 12.6857%, progress (thread 0): 89.4858%]
|T|: y e a h
|P|: y e p
[sample: ES2004d_H00_MEO015_341.64_341.96, WER: 100%, TER: 50%, total WER: 26.0379%, total TER: 12.6861%, progress (thread 0): 89.4937%]
|T|: n o
|P|: n o
[sample: ES2004d_H00_MEO015_400.96_401.28, WER: 0%, TER: 0%, total WER: 26.0376%, total TER: 12.686%, progress (thread 0): 89.5016%]
|T|: o o p s
|P|:
[sample: ES2004d_H03_FEE016_430.55_430.87, WER: 100%, TER: 100%, total WER: 26.0384%, total TER: 12.6868%, progress (thread 0): 89.5095%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_774.15_774.47, WER: 0%, TER: 0%, total WER: 26.0381%, total TER: 12.6867%, progress (thread 0): 89.5174%]
|T|: o n e
|P|: w e l l
[sample: ES2004d_H02_MEE014_785.53_785.85, WER: 100%, TER: 133.333%, total WER: 26.0389%, total TER: 12.6875%, progress (thread 0): 89.5253%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_840.47_840.79, WER: 0%, TER: 0%, total WER: 26.0386%, total TER: 12.6875%, progress (thread 0): 89.5332%]
|T|: m m h m m
|P|:
[sample: ES2004d_H03_FEE016_1930.95_1931.27, WER: 100%, TER: 100%, total WER: 26.0395%, total TER: 12.6885%, progress (thread 0): 89.5411%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2146.44_2146.76, WER: 0%, TER: 0%, total WER: 26.0392%, total TER: 12.6884%, progress (thread 0): 89.549%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_290.82_291.14, WER: 100%, TER: 40%, total WER: 26.04%, total TER: 12.6887%, progress (thread 0): 89.557%]
|T|: o o p s
|P|: s
[sample: IS1009a_H03_FIO089_420.14_420.46, WER: 100%, TER: 75%, total WER: 26.0409%, total TER: 12.6893%, progress (thread 0): 89.5649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H01_FIO087_494.72_495.04, WER: 0%, TER: 0%, total WER: 26.0406%, total TER: 12.6892%, progress (thread 0): 89.5728%]
|T|: y e a h
|P|:
[sample: IS1009a_H02_FIO084_577.05_577.37, WER: 100%, TER: 100%, total WER: 26.0414%, total TER: 12.69%, progress (thread 0): 89.5807%]
|T|: m m h m m
|P|: u h h u h
[sample: IS1009a_H02_FIO084_641.8_642.12, WER: 100%, TER: 80%, total WER: 26.0422%, total TER: 12.6908%, progress (thread 0): 89.5886%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_197.33_197.65, WER: 0%, TER: 0%, total WER: 26.0419%, total TER: 12.6906%, progress (thread 0): 89.5965%]
|T|: m m h m m
|P|: u
[sample: IS1009b_H00_FIE088_595.82_596.14, WER: 100%, TER: 100%, total WER: 26.0428%, total TER: 12.6917%, progress (thread 0): 89.6044%]
|T|: m m h m m
|P|: r i h
[sample: IS1009b_H03_FIO089_1073.83_1074.15, WER: 100%, TER: 80%, total WER: 26.0436%, total TER: 12.6925%, progress (thread 0): 89.6123%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1139.48_1139.8, WER: 100%, TER: 40%, total WER: 26.0445%, total TER: 12.6928%, progress (thread 0): 89.6202%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1268.25_1268.57, WER: 100%, TER: 40%, total WER: 26.0453%, total TER: 12.6931%, progress (thread 0): 89.6282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1877.89_1878.21, WER: 0%, TER: 0%, total WER: 26.045%, total TER: 12.693%, progress (thread 0): 89.6361%]
|T|: m m h m m
|P|: m e n
[sample: IS1009b_H03_FIO089_1894.73_1895.05, WER: 100%, TER: 80%, total WER: 26.0458%, total TER: 12.6938%, progress (thread 0): 89.644%]
|T|: h i
|P|: k a y
[sample: IS1009c_H01_FIO087_43.77_44.09, WER: 100%, TER: 150%, total WER: 26.0467%, total TER: 12.6944%, progress (thread 0): 89.6519%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009c_H00_FIE088_405.29_405.61, WER: 200%, TER: 100%, total WER: 26.0487%, total TER: 12.6954%, progress (thread 0): 89.6598%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_524.19_524.51, WER: 100%, TER: 40%, total WER: 26.0495%, total TER: 12.6958%, progress (thread 0): 89.6677%]
|T|: y e s
|P|:
[sample: IS1009c_H01_FIO087_1640_1640.32, WER: 100%, TER: 100%, total WER: 26.0503%, total TER: 12.6964%, progress (thread 0): 89.6756%]
|T|: m e n u
|P|: m e n u
[sample: IS1009d_H00_FIE088_523.67_523.99, WER: 0%, TER: 0%, total WER: 26.05%, total TER: 12.6963%, progress (thread 0): 89.6835%]
|T|: p a r d o n | m e
|P|: b u
[sample: IS1009d_H01_FIO087_699.16_699.48, WER: 100%, TER: 100%, total WER: 26.0517%, total TER: 12.6981%, progress (thread 0): 89.6915%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_799.6_799.92, WER: 100%, TER: 100%, total WER: 26.0525%, total TER: 12.6991%, progress (thread 0): 89.6994%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1056.69_1057.01, WER: 0%, TER: 0%, total WER: 26.0522%, total TER: 12.699%, progress (thread 0): 89.7073%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1722.84_1723.16, WER: 100%, TER: 40%, total WER: 26.0531%, total TER: 12.6993%, progress (thread 0): 89.7152%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1741.45_1741.77, WER: 100%, TER: 40%, total WER: 26.0539%, total TER: 12.6997%, progress (thread 0): 89.7231%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1791.05_1791.37, WER: 0%, TER: 0%, total WER: 26.0536%, total TER: 12.6995%, progress (thread 0): 89.731%]
|T|: y e a h
|P|: h m
[sample: TS3003b_H01_MTD011UID_1651.31_1651.63, WER: 100%, TER: 100%, total WER: 26.0545%, total TER: 12.7004%, progress (thread 0): 89.7389%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_1722.53_1722.85, WER: 100%, TER: 25%, total WER: 26.0553%, total TER: 12.7005%, progress (thread 0): 89.7468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2090.25_2090.57, WER: 0%, TER: 0%, total WER: 26.055%, total TER: 12.7004%, progress (thread 0): 89.7547%]
|T|: m m h m m
|P|: a h
[sample: TS3003c_H03_MTD012ME_2074.3_2074.62, WER: 100%, TER: 80%, total WER: 26.0558%, total TER: 12.7011%, progress (thread 0): 89.7627%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_2134.48_2134.8, WER: 100%, TER: 40%, total WER: 26.0567%, total TER: 12.7015%, progress (thread 0): 89.7706%]
|T|: n a y
|P|: n o
[sample: TS3003d_H01_MTD011UID_417.84_418.16, WER: 100%, TER: 66.6667%, total WER: 26.0575%, total TER: 12.7018%, progress (thread 0): 89.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_694.59_694.91, WER: 0%, TER: 0%, total WER: 26.0572%, total TER: 12.7017%, progress (thread 0): 89.7864%]
|T|: o r | b
|P|: r
[sample: TS3003d_H03_MTD012ME_896.59_896.91, WER: 100%, TER: 75%, total WER: 26.0589%, total TER: 12.7023%, progress (thread 0): 89.7943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1403.4_1403.72, WER: 0%, TER: 0%, total WER: 26.0586%, total TER: 12.7022%, progress (thread 0): 89.8022%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1492.24_1492.56, WER: 0%, TER: 0%, total WER: 26.0583%, total TER: 12.7021%, progress (thread 0): 89.8101%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_2024.39_2024.71, WER: 100%, TER: 100%, total WER: 26.0591%, total TER: 12.7029%, progress (thread 0): 89.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2381.84_2382.16, WER: 0%, TER: 0%, total WER: 26.0588%, total TER: 12.7028%, progress (thread 0): 89.826%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_68.23_68.55, WER: 0%, TER: 0%, total WER: 26.0586%, total TER: 12.7026%, progress (thread 0): 89.8339%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_359.54_359.86, WER: 50%, TER: 42.8571%, total WER: 26.0591%, total TER: 12.7031%, progress (thread 0): 89.8418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_496.88_497.2, WER: 0%, TER: 0%, total WER: 26.0588%, total TER: 12.703%, progress (thread 0): 89.8497%]
|T|: h m m
|P|: y n
[sample: EN2002a_H02_FEO072_504.18_504.5, WER: 100%, TER: 100%, total WER: 26.0596%, total TER: 12.7036%, progress (thread 0): 89.8576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_567.01_567.33, WER: 0%, TER: 0%, total WER: 26.0593%, total TER: 12.7036%, progress (thread 0): 89.8655%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_604.34_604.66, WER: 100%, TER: 40%, total WER: 26.0602%, total TER: 12.7039%, progress (thread 0): 89.8734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_729.25_729.57, WER: 0%, TER: 0%, total WER: 26.0599%, total TER: 12.7038%, progress (thread 0): 89.8813%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_800.36_800.68, WER: 0%, TER: 0%, total WER: 26.0596%, total TER: 12.7036%, progress (thread 0): 89.8892%]
|T|: o h
|P|: n o
[sample: EN2002a_H02_FEO072_856.5_856.82, WER: 100%, TER: 100%, total WER: 26.0604%, total TER: 12.704%, progress (thread 0): 89.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1050.6_1050.92, WER: 0%, TER: 0%, total WER: 26.0601%, total TER: 12.7039%, progress (thread 0): 89.9051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1101.18_1101.5, WER: 0%, TER: 0%, total WER: 26.0598%, total TER: 12.7038%, progress (thread 0): 89.913%]
|T|: o h | n o
|P|: y o u | k n o w
[sample: EN2002a_H01_FEO070_1448.67_1448.99, WER: 100%, TER: 80%, total WER: 26.0615%, total TER: 12.7046%, progress (thread 0): 89.9209%]
|T|: h m m
|P|: y e a h
[sample: EN2002a_H00_MEE073_1448.83_1449.15, WER: 100%, TER: 133.333%, total WER: 26.0623%, total TER: 12.7054%, progress (thread 0): 89.9288%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1537.39_1537.71, WER: 0%, TER: 0%, total WER: 26.0621%, total TER: 12.7053%, progress (thread 0): 89.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1862.6_1862.92, WER: 0%, TER: 0%, total WER: 26.0618%, total TER: 12.7052%, progress (thread 0): 89.9446%]
|T|: y e a h
|P|: i
[sample: EN2002b_H03_MEE073_283.05_283.37, WER: 100%, TER: 100%, total WER: 26.0626%, total TER: 12.706%, progress (thread 0): 89.9525%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_363.29_363.61, WER: 200%, TER: 175%, total WER: 26.0646%, total TER: 12.7075%, progress (thread 0): 89.9604%]
|T|: o h | y e a h
|P|:
[sample: EN2002b_H03_MEE073_679.41_679.73, WER: 100%, TER: 100%, total WER: 26.0662%, total TER: 12.709%, progress (thread 0): 89.9684%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_922.82_923.14, WER: 0%, TER: 0%, total WER: 26.0659%, total TER: 12.7088%, progress (thread 0): 89.9763%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H02_FEO072_1100.2_1100.52, WER: 100%, TER: 40%, total WER: 26.0668%, total TER: 12.7092%, progress (thread 0): 89.9842%]
|T|: g o o d | p o i n t
|P|: t h e | p o i n t
[sample: EN2002b_H03_MEE073_1217.83_1218.15, WER: 50%, TER: 40%, total WER: 26.0673%, total TER: 12.7098%, progress (thread 0): 89.9921%]
|T|: y e a h
|P|: o
[sample: EN2002b_H03_MEE073_1261.76_1262.08, WER: 100%, TER: 100%, total WER: 26.0682%, total TER: 12.7106%, progress (thread 0): 90%]
|T|: h m m
|P|: y n
[sample: EN2002b_H03_MEE073_1598.38_1598.7, WER: 100%, TER: 100%, total WER: 26.069%, total TER: 12.7112%, progress (thread 0): 90.0079%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1611.81_1612.13, WER: 50%, TER: 55.5556%, total WER: 26.0695%, total TER: 12.7121%, progress (thread 0): 90.0158%]
|T|: o k a y
|P|:
[sample: EN2002b_H03_MEE073_1705.25_1705.57, WER: 100%, TER: 100%, total WER: 26.0704%, total TER: 12.713%, progress (thread 0): 90.0237%]
|T|: g o o d
|P|: o k a y
[sample: EN2002c_H01_FEO072_489.73_490.05, WER: 100%, TER: 100%, total WER: 26.0712%, total TER: 12.7138%, progress (thread 0): 90.0316%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_653.98_654.3, WER: 0%, TER: 0%, total WER: 26.0709%, total TER: 12.7137%, progress (thread 0): 90.0396%]
|T|: y e a h
|P|: u h
[sample: EN2002c_H01_FEO072_1352.96_1353.28, WER: 100%, TER: 75%, total WER: 26.0718%, total TER: 12.7143%, progress (thread 0): 90.0475%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_2296.14_2296.46, WER: 100%, TER: 50%, total WER: 26.0726%, total TER: 12.7145%, progress (thread 0): 90.0554%]
|T|: i | t h i n k
|P|: i | t h i n k
[sample: EN2002c_H01_FEO072_2336.35_2336.67, WER: 0%, TER: 0%, total WER: 26.072%, total TER: 12.7143%, progress (thread 0): 90.0633%]
|T|: o h
|P|: i
[sample: EN2002c_H01_FEO072_2368.69_2369.01, WER: 100%, TER: 100%, total WER: 26.0728%, total TER: 12.7147%, progress (thread 0): 90.0712%]
|T|: w h a t
|P|: w e l l
[sample: EN2002c_H01_FEO072_2484.6_2484.92, WER: 100%, TER: 75%, total WER: 26.0737%, total TER: 12.7153%, progress (thread 0): 90.0791%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2800.26_2800.58, WER: 100%, TER: 33.3333%, total WER: 26.0745%, total TER: 12.7154%, progress (thread 0): 90.087%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_2829.54_2829.86, WER: 0%, TER: 0%, total WER: 26.0742%, total TER: 12.7153%, progress (thread 0): 90.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_347.79_348.11, WER: 0%, TER: 0%, total WER: 26.0739%, total TER: 12.7152%, progress (thread 0): 90.1028%]
|T|: i t ' s | g o o d
|P|:
[sample: EN2002d_H00_FEO070_420.4_420.72, WER: 100%, TER: 100%, total WER: 26.0756%, total TER: 12.717%, progress (thread 0): 90.1108%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_574.1_574.42, WER: 100%, TER: 40%, total WER: 26.0764%, total TER: 12.7173%, progress (thread 0): 90.1187%]
|T|: b u t
|P|: u h
[sample: EN2002d_H01_FEO072_590.21_590.53, WER: 100%, TER: 66.6667%, total WER: 26.0773%, total TER: 12.7177%, progress (thread 0): 90.1266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_769.35_769.67, WER: 0%, TER: 0%, total WER: 26.077%, total TER: 12.7176%, progress (thread 0): 90.1345%]
|T|: o h
|P|: n
[sample: EN2002d_H03_MEE073_939.78_940.1, WER: 100%, TER: 100%, total WER: 26.0778%, total TER: 12.718%, progress (thread 0): 90.1424%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_954.69_955.01, WER: 0%, TER: 0%, total WER: 26.0775%, total TER: 12.7179%, progress (thread 0): 90.1503%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1189.57_1189.89, WER: 0%, TER: 0%, total WER: 26.0772%, total TER: 12.7178%, progress (thread 0): 90.1582%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1203.2_1203.52, WER: 0%, TER: 0%, total WER: 26.0769%, total TER: 12.7176%, progress (thread 0): 90.1661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1255.64_1255.96, WER: 0%, TER: 0%, total WER: 26.0766%, total TER: 12.7175%, progress (thread 0): 90.174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1754.3_1754.62, WER: 0%, TER: 0%, total WER: 26.0763%, total TER: 12.7174%, progress (thread 0): 90.182%]
|T|: s o
|P|: s o
[sample: EN2002d_H01_FEO072_1974.1_1974.42, WER: 0%, TER: 0%, total WER: 26.076%, total TER: 12.7173%, progress (thread 0): 90.1899%]
|T|: m m
|P|: h m
[sample: EN2002d_H02_MEE071_2190.35_2190.67, WER: 100%, TER: 50%, total WER: 26.0769%, total TER: 12.7175%, progress (thread 0): 90.1978%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_582.6_582.91, WER: 0%, TER: 0%, total WER: 26.0766%, total TER: 12.7174%, progress (thread 0): 90.2057%]
|T|: m m h m m
|P|: u h | h u h
[sample: ES2004a_H00_MEO015_687.25_687.56, WER: 200%, TER: 100%, total WER: 26.0785%, total TER: 12.7184%, progress (thread 0): 90.2136%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_688.13_688.44, WER: 0%, TER: 0%, total WER: 26.0782%, total TER: 12.7183%, progress (thread 0): 90.2215%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_777.34_777.65, WER: 100%, TER: 40%, total WER: 26.0791%, total TER: 12.7186%, progress (thread 0): 90.2294%]
|T|: y
|P|: y
[sample: ES2004a_H02_MEE014_949.9_950.21, WER: 0%, TER: 0%, total WER: 26.0788%, total TER: 12.7186%, progress (thread 0): 90.2373%]
|T|: n o
|P|: t o
[sample: ES2004b_H03_FEE016_607.28_607.59, WER: 100%, TER: 50%, total WER: 26.0796%, total TER: 12.7188%, progress (thread 0): 90.2453%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H00_MEO015_1612.5_1612.81, WER: 100%, TER: 60%, total WER: 26.0805%, total TER: 12.7193%, progress (thread 0): 90.2532%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_2237.11_2237.42, WER: 0%, TER: 0%, total WER: 26.0802%, total TER: 12.7192%, progress (thread 0): 90.2611%]
|T|: b u t
|P|: b u t
[sample: ES2004c_H02_MEE014_571.73_572.04, WER: 0%, TER: 0%, total WER: 26.0799%, total TER: 12.7191%, progress (thread 0): 90.269%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1321.04_1321.35, WER: 0%, TER: 0%, total WER: 26.0796%, total TER: 12.719%, progress (thread 0): 90.2769%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_1582.98_1583.29, WER: 0%, TER: 0%, total WER: 26.0793%, total TER: 12.7189%, progress (thread 0): 90.2848%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2288.73_2289.04, WER: 0%, TER: 0%, total WER: 26.079%, total TER: 12.7188%, progress (thread 0): 90.2927%]
|T|: m m
|P|: h m
[sample: ES2004d_H00_MEO015_846.15_846.46, WER: 100%, TER: 50%, total WER: 26.0798%, total TER: 12.7189%, progress (thread 0): 90.3006%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1669.25_1669.56, WER: 0%, TER: 0%, total WER: 26.0795%, total TER: 12.7188%, progress (thread 0): 90.3085%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1669.66_1669.97, WER: 0%, TER: 0%, total WER: 26.0792%, total TER: 12.7187%, progress (thread 0): 90.3165%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1825.79_1826.1, WER: 0%, TER: 0%, total WER: 26.0789%, total TER: 12.7186%, progress (thread 0): 90.3244%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1901.99_1902.3, WER: 0%, TER: 0%, total WER: 26.0786%, total TER: 12.7185%, progress (thread 0): 90.3323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1967.13_1967.44, WER: 0%, TER: 0%, total WER: 26.0783%, total TER: 12.7183%, progress (thread 0): 90.3402%]
|T|: w e l l
|P|: y e h
[sample: ES2004d_H02_MEE014_2085.81_2086.12, WER: 100%, TER: 75%, total WER: 26.0792%, total TER: 12.7189%, progress (thread 0): 90.3481%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2220.03_2220.34, WER: 0%, TER: 0%, total WER: 26.0789%, total TER: 12.7188%, progress (thread 0): 90.356%]
|T|: o k a y
|P|: k
[sample: IS1009a_H00_FIE088_90.21_90.52, WER: 100%, TER: 75%, total WER: 26.0797%, total TER: 12.7194%, progress (thread 0): 90.3639%]
|T|: u h
|P|:
[sample: IS1009a_H02_FIO084_451.06_451.37, WER: 100%, TER: 100%, total WER: 26.0806%, total TER: 12.7198%, progress (thread 0): 90.3718%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_702.97_703.28, WER: 0%, TER: 0%, total WER: 26.0803%, total TER: 12.7197%, progress (thread 0): 90.3797%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_795.57_795.88, WER: 0%, TER: 0%, total WER: 26.08%, total TER: 12.7196%, progress (thread 0): 90.3877%]
|T|: m m
|P|: e
[sample: IS1009b_H02_FIO084_218.1_218.41, WER: 100%, TER: 100%, total WER: 26.0808%, total TER: 12.72%, progress (thread 0): 90.3956%]
|T|: m m h m m
|P|: u h m
[sample: IS1009b_H00_FIE088_616.32_616.63, WER: 100%, TER: 60%, total WER: 26.0816%, total TER: 12.7205%, progress (thread 0): 90.4035%]
|T|: y e a h
|P|: y e
[sample: IS1009b_H02_FIO084_1692.92_1693.23, WER: 100%, TER: 50%, total WER: 26.0825%, total TER: 12.7209%, progress (thread 0): 90.4114%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1832.99_1833.3, WER: 0%, TER: 0%, total WER: 26.0822%, total TER: 12.7208%, progress (thread 0): 90.4193%]
|T|: y e s
|P|: i s
[sample: IS1009b_H01_FIO087_1844.23_1844.54, WER: 100%, TER: 66.6667%, total WER: 26.083%, total TER: 12.7211%, progress (thread 0): 90.4272%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H03_FIO089_1904.39_1904.7, WER: 0%, TER: 0%, total WER: 26.0827%, total TER: 12.721%, progress (thread 0): 90.4351%]
|T|: m m h m m
|P|: t a m l e
[sample: IS1009c_H00_FIE088_1140.15_1140.46, WER: 100%, TER: 100%, total WER: 26.0836%, total TER: 12.722%, progress (thread 0): 90.443%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1163.03_1163.34, WER: 100%, TER: 40%, total WER: 26.0844%, total TER: 12.7223%, progress (thread 0): 90.451%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1237.53_1237.84, WER: 100%, TER: 40%, total WER: 26.0852%, total TER: 12.7227%, progress (thread 0): 90.4589%]
|T|: ' k a y
|P|: c a n
[sample: IS1009c_H01_FIO087_1429.75_1430.06, WER: 100%, TER: 75%, total WER: 26.0861%, total TER: 12.7232%, progress (thread 0): 90.4668%]
|T|: m m h m m
|P|: h
[sample: IS1009c_H02_FIO084_1554.5_1554.81, WER: 100%, TER: 80%, total WER: 26.0869%, total TER: 12.724%, progress (thread 0): 90.4747%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H01_FIO087_1580.06_1580.37, WER: 100%, TER: 40%, total WER: 26.0877%, total TER: 12.7243%, progress (thread 0): 90.4826%]
|T|: o h
|P|: u m
[sample: IS1009c_H00_FIE088_1694.98_1695.29, WER: 100%, TER: 100%, total WER: 26.0886%, total TER: 12.7248%, progress (thread 0): 90.4905%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_363.39_363.7, WER: 100%, TER: 40%, total WER: 26.0894%, total TER: 12.7251%, progress (thread 0): 90.4984%]
|T|: m m
|P|: h m
[sample: IS1009d_H02_FIO084_894.86_895.17, WER: 100%, TER: 50%, total WER: 26.0903%, total TER: 12.7253%, progress (thread 0): 90.5063%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_903.74_904.05, WER: 0%, TER: 0%, total WER: 26.09%, total TER: 12.7251%, progress (thread 0): 90.5142%]
|T|: n o
|P|: n o
[sample: IS1009d_H00_FIE088_1011.62_1011.93, WER: 0%, TER: 0%, total WER: 26.0897%, total TER: 12.7251%, progress (thread 0): 90.5222%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1012.22_1012.53, WER: 100%, TER: 40%, total WER: 26.0905%, total TER: 12.7254%, progress (thread 0): 90.5301%]
|T|: m m h m m
|P|: m | h m
[sample: IS1009d_H03_FIO089_1105.97_1106.28, WER: 200%, TER: 40%, total WER: 26.0925%, total TER: 12.7257%, progress (thread 0): 90.538%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1171.27_1171.58, WER: 0%, TER: 0%, total WER: 26.0922%, total TER: 12.7256%, progress (thread 0): 90.5459%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1243.74_1244.05, WER: 100%, TER: 66.6667%, total WER: 26.093%, total TER: 12.726%, progress (thread 0): 90.5538%]
|T|: w h y
|P|: w o w
[sample: IS1009d_H00_FIE088_1442.55_1442.86, WER: 100%, TER: 66.6667%, total WER: 26.0938%, total TER: 12.7264%, progress (thread 0): 90.5617%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1681.21_1681.52, WER: 0%, TER: 0%, total WER: 26.0935%, total TER: 12.7262%, progress (thread 0): 90.5696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_216.48_216.79, WER: 0%, TER: 0%, total WER: 26.0932%, total TER: 12.7261%, progress (thread 0): 90.5775%]
|T|: m e a t
|P|: m e a t
[sample: TS3003a_H03_MTD012ME_595.66_595.97, WER: 0%, TER: 0%, total WER: 26.093%, total TER: 12.726%, progress (thread 0): 90.5854%]
|T|: s o
|P|: s o
[sample: TS3003a_H03_MTD012ME_884.45_884.76, WER: 0%, TER: 0%, total WER: 26.0927%, total TER: 12.7259%, progress (thread 0): 90.5934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1418.64_1418.95, WER: 0%, TER: 0%, total WER: 26.0924%, total TER: 12.7258%, progress (thread 0): 90.6013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_71.8_72.11, WER: 0%, TER: 0%, total WER: 26.0921%, total TER: 12.7257%, progress (thread 0): 90.6092%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_538.03_538.34, WER: 0%, TER: 0%, total WER: 26.0918%, total TER: 12.7256%, progress (thread 0): 90.6171%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1469.03_1469.34, WER: 100%, TER: 100%, total WER: 26.0926%, total TER: 12.726%, progress (thread 0): 90.625%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1715.8_1716.11, WER: 0%, TER: 0%, total WER: 26.0923%, total TER: 12.7259%, progress (thread 0): 90.6329%]
|T|: y e a h
|P|: t h a t
[sample: TS3003b_H00_MTD009PM_1822.91_1823.22, WER: 100%, TER: 75%, total WER: 26.0932%, total TER: 12.7265%, progress (thread 0): 90.6408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_794.24_794.55, WER: 0%, TER: 0%, total WER: 26.0929%, total TER: 12.7264%, progress (thread 0): 90.6487%]
|T|: ' k a y
|P|: k a y
[sample: TS3003c_H00_MTD009PM_934.63_934.94, WER: 100%, TER: 25%, total WER: 26.0937%, total TER: 12.7265%, progress (thread 0): 90.6566%]
|T|: m m
|P|: h m
[sample: TS3003c_H01_MTD011UID_1136.65_1136.96, WER: 100%, TER: 50%, total WER: 26.0945%, total TER: 12.7267%, progress (thread 0): 90.6646%]
|T|: y e a h
|P|: a n c e
[sample: TS3003c_H01_MTD011UID_1444.43_1444.74, WER: 100%, TER: 100%, total WER: 26.0954%, total TER: 12.7275%, progress (thread 0): 90.6725%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1813.2_1813.51, WER: 0%, TER: 0%, total WER: 26.0951%, total TER: 12.7274%, progress (thread 0): 90.6804%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_255.36_255.67, WER: 0%, TER: 0%, total WER: 26.0948%, total TER: 12.7273%, progress (thread 0): 90.6883%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_453.54_453.85, WER: 100%, TER: 50%, total WER: 26.0956%, total TER: 12.7274%, progress (thread 0): 90.6962%]
|T|: y e p
|P|: n o
[sample: TS3003d_H02_MTD0010ID_562.3_562.61, WER: 100%, TER: 100%, total WER: 26.0964%, total TER: 12.728%, progress (thread 0): 90.7041%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H02_MTD0010ID_1317.08_1317.39, WER: 100%, TER: 100%, total WER: 26.0973%, total TER: 12.7289%, progress (thread 0): 90.712%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_1546.63_1546.94, WER: 100%, TER: 40%, total WER: 26.0981%, total TER: 12.7292%, progress (thread 0): 90.7199%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2324.36_2324.67, WER: 0%, TER: 0%, total WER: 26.0978%, total TER: 12.7291%, progress (thread 0): 90.7278%]
|T|: y e a h
|P|: e h
[sample: TS3003d_H01_MTD011UID_2373.13_2373.44, WER: 100%, TER: 50%, total WER: 26.0987%, total TER: 12.7294%, progress (thread 0): 90.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_595.97_596.28, WER: 0%, TER: 0%, total WER: 26.0984%, total TER: 12.7293%, progress (thread 0): 90.7437%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_694.14_694.45, WER: 0%, TER: 0%, total WER: 26.0981%, total TER: 12.7292%, progress (thread 0): 90.7516%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_704.35_704.66, WER: 0%, TER: 0%, total WER: 26.0978%, total TER: 12.7291%, progress (thread 0): 90.7595%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_910.51_910.82, WER: 0%, TER: 0%, total WER: 26.0975%, total TER: 12.7289%, progress (thread 0): 90.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1104.75_1105.06, WER: 0%, TER: 0%, total WER: 26.0972%, total TER: 12.7288%, progress (thread 0): 90.7753%]
|T|: h m m
|P|: h
[sample: EN2002a_H00_MEE073_1619.48_1619.79, WER: 100%, TER: 66.6667%, total WER: 26.098%, total TER: 12.7292%, progress (thread 0): 90.7832%]
|T|: o h
|P|:
[sample: EN2002a_H02_FEO072_1674.71_1675.02, WER: 100%, TER: 100%, total WER: 26.0989%, total TER: 12.7296%, progress (thread 0): 90.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1973.2_1973.51, WER: 0%, TER: 0%, total WER: 26.0986%, total TER: 12.7295%, progress (thread 0): 90.799%]
|T|: o h | r i g h t
|P|: a l | r i g h t
[sample: EN2002a_H03_MEE071_1971.01_1971.32, WER: 50%, TER: 25%, total WER: 26.0991%, total TER: 12.7297%, progress (thread 0): 90.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_346.95_347.26, WER: 0%, TER: 0%, total WER: 26.0988%, total TER: 12.7296%, progress (thread 0): 90.8149%]
|T|: y e a h
|P|: w e l l
[sample: EN2002b_H03_MEE073_372.58_372.89, WER: 100%, TER: 75%, total WER: 26.0996%, total TER: 12.7302%, progress (thread 0): 90.8228%]
|T|: m m
|P|:
[sample: EN2002b_H03_MEE073_565.42_565.73, WER: 100%, TER: 100%, total WER: 26.1005%, total TER: 12.7306%, progress (thread 0): 90.8307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_729.79_730.1, WER: 0%, TER: 0%, total WER: 26.1002%, total TER: 12.7305%, progress (thread 0): 90.8386%]
|T|: y e a h
|P|: r i g h
[sample: EN2002b_H03_MEE073_1214.71_1215.02, WER: 100%, TER: 75%, total WER: 26.101%, total TER: 12.7311%, progress (thread 0): 90.8465%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1323.81_1324.12, WER: 0%, TER: 0%, total WER: 26.1007%, total TER: 12.7309%, progress (thread 0): 90.8544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1461.61_1461.92, WER: 0%, TER: 0%, total WER: 26.1004%, total TER: 12.7308%, progress (thread 0): 90.8623%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1750.66_1750.97, WER: 0%, TER: 0%, total WER: 26.1001%, total TER: 12.7307%, progress (thread 0): 90.8703%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_441.29_441.6, WER: 0%, TER: 0%, total WER: 26.0998%, total TER: 12.7306%, progress (thread 0): 90.8782%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_543.24_543.55, WER: 0%, TER: 0%, total WER: 26.0995%, total TER: 12.7306%, progress (thread 0): 90.8861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_543.24_543.55, WER: 0%, TER: 0%, total WER: 26.0992%, total TER: 12.7305%, progress (thread 0): 90.894%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_982.53_982.84, WER: 100%, TER: 33.3333%, total WER: 26.1001%, total TER: 12.7306%, progress (thread 0): 90.9019%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1524.82_1525.13, WER: 0%, TER: 0%, total WER: 26.0998%, total TER: 12.7305%, progress (thread 0): 90.9098%]
|T|: u m
|P|: u m
[sample: EN2002c_H03_MEE073_1904.01_1904.32, WER: 0%, TER: 0%, total WER: 26.0995%, total TER: 12.7304%, progress (thread 0): 90.9177%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1958.34_1958.65, WER: 0%, TER: 0%, total WER: 26.0992%, total TER: 12.7303%, progress (thread 0): 90.9256%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_2240.67_2240.98, WER: 100%, TER: 40%, total WER: 26.1%, total TER: 12.7306%, progress (thread 0): 90.9335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2358.92_2359.23, WER: 0%, TER: 0%, total WER: 26.0997%, total TER: 12.7305%, progress (thread 0): 90.9415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2477.51_2477.82, WER: 0%, TER: 0%, total WER: 26.0994%, total TER: 12.7304%, progress (thread 0): 90.9494%]
|T|: n o
|P|: n i e
[sample: EN2002c_H02_MEE071_2608.15_2608.46, WER: 100%, TER: 100%, total WER: 26.1003%, total TER: 12.7308%, progress (thread 0): 90.9573%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2724.28_2724.59, WER: 0%, TER: 0%, total WER: 26.1%, total TER: 12.7307%, progress (thread 0): 90.9652%]
|T|: t i c k
|P|: t i k
[sample: EN2002c_H01_FEO072_2878.5_2878.81, WER: 100%, TER: 25%, total WER: 26.1008%, total TER: 12.7308%, progress (thread 0): 90.9731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_391.81_392.12, WER: 0%, TER: 0%, total WER: 26.1005%, total TER: 12.7307%, progress (thread 0): 90.981%]
|T|: y e a h | o k a y
|P|: o k a y
[sample: EN2002d_H00_FEO070_1379.88_1380.19, WER: 50%, TER: 55.5556%, total WER: 26.1011%, total TER: 12.7316%, progress (thread 0): 90.9889%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_1512.76_1513.07, WER: 100%, TER: 40%, total WER: 26.1019%, total TER: 12.7319%, progress (thread 0): 90.9968%]
|T|: s o
|P|: t h e
[sample: EN2002d_H00_FEO070_1542.52_1542.83, WER: 100%, TER: 150%, total WER: 26.1027%, total TER: 12.7325%, progress (thread 0): 91.0047%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1546.76_1547.07, WER: 0%, TER: 0%, total WER: 26.1024%, total TER: 12.7324%, progress (thread 0): 91.0127%]
|T|: s o
|P|: s o
[sample: EN2002d_H03_MEE073_1822.41_1822.72, WER: 0%, TER: 0%, total WER: 26.1021%, total TER: 12.7324%, progress (thread 0): 91.0206%]
|T|: m m
|P|: h m
[sample: EN2002d_H00_FEO070_1872.39_1872.7, WER: 100%, TER: 50%, total WER: 26.103%, total TER: 12.7325%, progress (thread 0): 91.0285%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2114.3_2114.61, WER: 0%, TER: 0%, total WER: 26.1027%, total TER: 12.7324%, progress (thread 0): 91.0364%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_819.96_820.26, WER: 0%, TER: 0%, total WER: 26.1024%, total TER: 12.7323%, progress (thread 0): 91.0443%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1232.15_1232.45, WER: 0%, TER: 0%, total WER: 26.1021%, total TER: 12.7322%, progress (thread 0): 91.0522%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1572.74_1573.04, WER: 100%, TER: 40%, total WER: 26.1029%, total TER: 12.7325%, progress (thread 0): 91.0601%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1784.08_1784.38, WER: 100%, TER: 40%, total WER: 26.1038%, total TER: 12.7328%, progress (thread 0): 91.068%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_2252.76_2253.06, WER: 0%, TER: 0%, total WER: 26.1035%, total TER: 12.7327%, progress (thread 0): 91.076%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_849.84_850.14, WER: 0%, TER: 0%, total WER: 26.1032%, total TER: 12.7326%, progress (thread 0): 91.0839%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1555.92_1556.22, WER: 0%, TER: 0%, total WER: 26.1029%, total TER: 12.7325%, progress (thread 0): 91.0918%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1640.82_1641.12, WER: 100%, TER: 40%, total WER: 26.1037%, total TER: 12.7328%, progress (thread 0): 91.0997%]
|T|: ' k a y
|P|: k e
[sample: ES2004d_H00_MEO015_271.77_272.07, WER: 100%, TER: 75%, total WER: 26.1045%, total TER: 12.7334%, progress (thread 0): 91.1076%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_639.49_639.79, WER: 0%, TER: 0%, total WER: 26.1042%, total TER: 12.7332%, progress (thread 0): 91.1155%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1390.02_1390.32, WER: 0%, TER: 0%, total WER: 26.104%, total TER: 12.7331%, progress (thread 0): 91.1234%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1711.3_1711.6, WER: 0%, TER: 0%, total WER: 26.1037%, total TER: 12.733%, progress (thread 0): 91.1313%]
|T|: m m
|P|: h m
[sample: ES2004d_H01_FEE013_2002_2002.3, WER: 100%, TER: 50%, total WER: 26.1045%, total TER: 12.7332%, progress (thread 0): 91.1392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2096.66_2096.96, WER: 0%, TER: 0%, total WER: 26.1042%, total TER: 12.7331%, progress (thread 0): 91.1472%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_617.96_618.26, WER: 100%, TER: 40%, total WER: 26.105%, total TER: 12.7334%, progress (thread 0): 91.1551%]
|T|: o k a y
|P|: c a n
[sample: IS1009a_H01_FIO087_782.9_783.2, WER: 100%, TER: 75%, total WER: 26.1059%, total TER: 12.734%, progress (thread 0): 91.163%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_182.06_182.36, WER: 0%, TER: 0%, total WER: 26.1056%, total TER: 12.7338%, progress (thread 0): 91.1709%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_574.49_574.79, WER: 100%, TER: 40%, total WER: 26.1064%, total TER: 12.7342%, progress (thread 0): 91.1788%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_707.08_707.38, WER: 100%, TER: 40%, total WER: 26.1072%, total TER: 12.7345%, progress (thread 0): 91.1867%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1025.61_1025.91, WER: 0%, TER: 0%, total WER: 26.1069%, total TER: 12.7344%, progress (thread 0): 91.1946%]
|T|: m m
|P|: m e a n
[sample: IS1009b_H02_FIO084_1366.95_1367.25, WER: 100%, TER: 150%, total WER: 26.1078%, total TER: 12.735%, progress (thread 0): 91.2025%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1371.17_1371.47, WER: 100%, TER: 40%, total WER: 26.1086%, total TER: 12.7353%, progress (thread 0): 91.2104%]
|T|: h m m
|P|: k a y
[sample: IS1009b_H02_FIO084_1604.76_1605.06, WER: 100%, TER: 100%, total WER: 26.1095%, total TER: 12.7359%, progress (thread 0): 91.2184%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1617.66_1617.96, WER: 0%, TER: 0%, total WER: 26.1092%, total TER: 12.7358%, progress (thread 0): 91.2263%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_335.7_336, WER: 0%, TER: 0%, total WER: 26.1089%, total TER: 12.7357%, progress (thread 0): 91.2342%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H03_FIO089_1106.15_1106.45, WER: 100%, TER: 40%, total WER: 26.1097%, total TER: 12.736%, progress (thread 0): 91.2421%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1237.83_1238.13, WER: 0%, TER: 0%, total WER: 26.1094%, total TER: 12.7359%, progress (thread 0): 91.25%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1259.97_1260.27, WER: 0%, TER: 0%, total WER: 26.1091%, total TER: 12.7358%, progress (thread 0): 91.2579%]
|T|: o k a y
|P|: k a m
[sample: IS1009c_H01_FIO087_1297.17_1297.47, WER: 100%, TER: 50%, total WER: 26.1099%, total TER: 12.7362%, progress (thread 0): 91.2658%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_607.11_607.41, WER: 100%, TER: 40%, total WER: 26.1108%, total TER: 12.7365%, progress (thread 0): 91.2737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_769.42_769.72, WER: 0%, TER: 0%, total WER: 26.1105%, total TER: 12.7364%, progress (thread 0): 91.2816%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1062.93_1063.23, WER: 0%, TER: 0%, total WER: 26.1102%, total TER: 12.7362%, progress (thread 0): 91.2896%]
|T|: o n e
|P|: b u t
[sample: IS1009d_H01_FIO087_1482_1482.3, WER: 100%, TER: 100%, total WER: 26.111%, total TER: 12.7369%, progress (thread 0): 91.2975%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H00_FIE088_1528.1_1528.4, WER: 0%, TER: 0%, total WER: 26.1107%, total TER: 12.7368%, progress (thread 0): 91.3054%]
|T|: y e a h
|P|: i
[sample: IS1009d_H02_FIO084_1824.41_1824.71, WER: 100%, TER: 100%, total WER: 26.1116%, total TER: 12.7376%, progress (thread 0): 91.3133%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1853.09_1853.39, WER: 0%, TER: 0%, total WER: 26.1113%, total TER: 12.7375%, progress (thread 0): 91.3212%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_238.31_238.61, WER: 0%, TER: 0%, total WER: 26.111%, total TER: 12.7373%, progress (thread 0): 91.3291%]
|T|: s o
|P|: s o
[sample: TS3003a_H02_MTD0010ID_1080.77_1081.07, WER: 0%, TER: 0%, total WER: 26.1107%, total TER: 12.7373%, progress (thread 0): 91.337%]
|T|: h m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1308.88_1309.18, WER: 100%, TER: 33.3333%, total WER: 26.1115%, total TER: 12.7374%, progress (thread 0): 91.3449%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_1423.96_1424.26, WER: 0%, TER: 0%, total WER: 26.1112%, total TER: 12.7373%, progress (thread 0): 91.3529%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H00_MTD009PM_1463.25_1463.55, WER: 100%, TER: 50%, total WER: 26.1121%, total TER: 12.7377%, progress (thread 0): 91.3608%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_1535.95_1536.25, WER: 0%, TER: 0%, total WER: 26.1118%, total TER: 12.7376%, progress (thread 0): 91.3687%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1635.81_1636.11, WER: 100%, TER: 40%, total WER: 26.1126%, total TER: 12.7379%, progress (thread 0): 91.3766%]
|T|: h m
|P|: i s
[sample: TS3003b_H01_MTD011UID_1834.41_1834.71, WER: 100%, TER: 100%, total WER: 26.1134%, total TER: 12.7383%, progress (thread 0): 91.3845%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1871.5_1871.8, WER: 100%, TER: 100%, total WER: 26.1143%, total TER: 12.7387%, progress (thread 0): 91.3924%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2279.98_2280.28, WER: 0%, TER: 0%, total WER: 26.114%, total TER: 12.7386%, progress (thread 0): 91.4003%]
|T|: s
|P|:
[sample: TS3003d_H01_MTD011UID_856.51_856.81, WER: 100%, TER: 100%, total WER: 26.1148%, total TER: 12.7388%, progress (thread 0): 91.4082%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1057.72_1058.02, WER: 0%, TER: 0%, total WER: 26.1145%, total TER: 12.7387%, progress (thread 0): 91.4161%]
|T|: y e a h
|P|: y e a n
[sample: TS3003d_H02_MTD0010ID_1709.82_1710.12, WER: 100%, TER: 25%, total WER: 26.1153%, total TER: 12.7388%, progress (thread 0): 91.424%]
|T|: y e s
|P|: y e s
[sample: TS3003d_H02_MTD0010ID_1732.89_1733.19, WER: 0%, TER: 0%, total WER: 26.115%, total TER: 12.7387%, progress (thread 0): 91.432%]
|T|: y e a h
|P|: y e h
[sample: TS3003d_H00_MTD009PM_1821.73_1822.03, WER: 100%, TER: 25%, total WER: 26.1159%, total TER: 12.7388%, progress (thread 0): 91.4399%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2250.87_2251.17, WER: 0%, TER: 0%, total WER: 26.1156%, total TER: 12.7387%, progress (thread 0): 91.4478%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2482.46_2482.76, WER: 0%, TER: 0%, total WER: 26.1153%, total TER: 12.7386%, progress (thread 0): 91.4557%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_11.83_12.13, WER: 0%, TER: 0%, total WER: 26.115%, total TER: 12.7385%, progress (thread 0): 91.4636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_48.72_49.02, WER: 0%, TER: 0%, total WER: 26.1147%, total TER: 12.7383%, progress (thread 0): 91.4715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_203.25_203.55, WER: 0%, TER: 0%, total WER: 26.1144%, total TER: 12.7382%, progress (thread 0): 91.4794%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_261.14_261.44, WER: 100%, TER: 33.3333%, total WER: 26.1152%, total TER: 12.7384%, progress (thread 0): 91.4873%]
|T|: w h y | n o t
|P|: w h y | d o n ' t
[sample: EN2002a_H03_MEE071_375.98_376.28, WER: 50%, TER: 42.8571%, total WER: 26.1158%, total TER: 12.7389%, progress (thread 0): 91.4953%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_469.41_469.71, WER: 0%, TER: 0%, total WER: 26.1155%, total TER: 12.7387%, progress (thread 0): 91.5032%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_486.08_486.38, WER: 0%, TER: 0%, total WER: 26.1152%, total TER: 12.7386%, progress (thread 0): 91.5111%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_508.71_509.01, WER: 0%, TER: 0%, total WER: 26.1149%, total TER: 12.7385%, progress (thread 0): 91.519%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_521.78_522.08, WER: 0%, TER: 0%, total WER: 26.1146%, total TER: 12.7384%, progress (thread 0): 91.5269%]
|T|: s o
|P|:
[sample: EN2002a_H03_MEE071_738.15_738.45, WER: 100%, TER: 100%, total WER: 26.1154%, total TER: 12.7388%, progress (thread 0): 91.5348%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_747.85_748.15, WER: 100%, TER: 40%, total WER: 26.1163%, total TER: 12.7391%, progress (thread 0): 91.5427%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_795.73_796.03, WER: 100%, TER: 33.3333%, total WER: 26.1171%, total TER: 12.7393%, progress (thread 0): 91.5506%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_877.67_877.97, WER: 100%, TER: 40%, total WER: 26.1179%, total TER: 12.7396%, progress (thread 0): 91.5585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_918.33_918.63, WER: 0%, TER: 0%, total WER: 26.1176%, total TER: 12.7395%, progress (thread 0): 91.5665%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1889.11_1889.41, WER: 0%, TER: 0%, total WER: 26.1173%, total TER: 12.7393%, progress (thread 0): 91.5744%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H02_FEO072_2116.83_2117.13, WER: 0%, TER: 0%, total WER: 26.1171%, total TER: 12.7392%, progress (thread 0): 91.5823%]
|T|: o h | y e a h
|P|: h e l
[sample: EN2002b_H03_MEE073_211.33_211.63, WER: 100%, TER: 71.4286%, total WER: 26.1187%, total TER: 12.7402%, progress (thread 0): 91.5902%]
|T|: w e ' l l | s e e
|P|: w e l l | s | e
[sample: EN2002b_H00_FEO070_255.08_255.38, WER: 150%, TER: 22.2222%, total WER: 26.1215%, total TER: 12.7404%, progress (thread 0): 91.5981%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_368.42_368.72, WER: 0%, TER: 0%, total WER: 26.1212%, total TER: 12.7402%, progress (thread 0): 91.606%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_391.92_392.22, WER: 100%, TER: 40%, total WER: 26.1221%, total TER: 12.7406%, progress (thread 0): 91.6139%]
|T|: m m
|P|: m
[sample: EN2002b_H03_MEE073_526.11_526.41, WER: 100%, TER: 50%, total WER: 26.1229%, total TER: 12.7407%, progress (thread 0): 91.6218%]
|T|: a l r i g h t
|P|: a l l r i g h t
[sample: EN2002b_H02_FEO072_627.98_628.28, WER: 100%, TER: 14.2857%, total WER: 26.1237%, total TER: 12.7408%, progress (thread 0): 91.6298%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1551.52_1551.82, WER: 0%, TER: 0%, total WER: 26.1234%, total TER: 12.7406%, progress (thread 0): 91.6377%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1611.31_1611.61, WER: 0%, TER: 0%, total WER: 26.1231%, total TER: 12.7405%, progress (thread 0): 91.6456%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1729.42_1729.72, WER: 0%, TER: 0%, total WER: 26.1228%, total TER: 12.7404%, progress (thread 0): 91.6535%]
|T|: i | t h i n k
|P|: i
[sample: EN2002c_H01_FEO072_232.37_232.67, WER: 50%, TER: 85.7143%, total WER: 26.1234%, total TER: 12.7416%, progress (thread 0): 91.6614%]
|T|: y e a h
|P|: y e s
[sample: EN2002c_H02_MEE071_355.18_355.48, WER: 100%, TER: 50%, total WER: 26.1242%, total TER: 12.742%, progress (thread 0): 91.6693%]
|T|: n o | n o
|P|:
[sample: EN2002c_H03_MEE073_543.55_543.85, WER: 100%, TER: 100%, total WER: 26.1259%, total TER: 12.743%, progress (thread 0): 91.6772%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_700.09_700.39, WER: 100%, TER: 40%, total WER: 26.1267%, total TER: 12.7433%, progress (thread 0): 91.6851%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H02_MEE071_1131.17_1131.47, WER: 100%, TER: 66.6667%, total WER: 26.1276%, total TER: 12.7437%, progress (thread 0): 91.693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1143.34_1143.64, WER: 0%, TER: 0%, total WER: 26.1273%, total TER: 12.7436%, progress (thread 0): 91.701%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_1321.8_1322.1, WER: 0%, TER: 0%, total WER: 26.127%, total TER: 12.7435%, progress (thread 0): 91.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1600.62_1600.92, WER: 0%, TER: 0%, total WER: 26.1267%, total TER: 12.7434%, progress (thread 0): 91.7168%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2301.16_2301.46, WER: 0%, TER: 0%, total WER: 26.1264%, total TER: 12.7433%, progress (thread 0): 91.7247%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2443.4_2443.7, WER: 0%, TER: 0%, total WER: 26.1261%, total TER: 12.7432%, progress (thread 0): 91.7326%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H02_MEE071_2847.83_2848.13, WER: 100%, TER: 40%, total WER: 26.1269%, total TER: 12.7435%, progress (thread 0): 91.7405%]
|T|: y e a h
|P|: y e
[sample: EN2002d_H02_MEE071_673.96_674.26, WER: 100%, TER: 50%, total WER: 26.1277%, total TER: 12.7438%, progress (thread 0): 91.7484%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_834.35_834.65, WER: 0%, TER: 0%, total WER: 26.1274%, total TER: 12.7437%, progress (thread 0): 91.7563%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_925.24_925.54, WER: 0%, TER: 0%, total WER: 26.1272%, total TER: 12.7436%, progress (thread 0): 91.7642%]
|T|: u m
|P|: u m
[sample: EN2002d_H01_FEO072_1191.17_1191.47, WER: 0%, TER: 0%, total WER: 26.1269%, total TER: 12.7435%, progress (thread 0): 91.7721%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1205.4_1205.7, WER: 0%, TER: 0%, total WER: 26.1266%, total TER: 12.7434%, progress (thread 0): 91.7801%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H03_MEE073_1221.17_1221.47, WER: 100%, TER: 40%, total WER: 26.1274%, total TER: 12.7437%, progress (thread 0): 91.788%]
|T|: y e a h
|P|: h m
[sample: EN2002d_H00_FEO070_1249.7_1250, WER: 100%, TER: 100%, total WER: 26.1282%, total TER: 12.7445%, progress (thread 0): 91.7959%]
|T|: s u r e
|P|: s u r e
[sample: EN2002d_H03_MEE073_1672.53_1672.83, WER: 0%, TER: 0%, total WER: 26.1279%, total TER: 12.7444%, progress (thread 0): 91.8038%]
|T|: m m
|P|: h m
[sample: EN2002d_H02_MEE071_2192.66_2192.96, WER: 100%, TER: 50%, total WER: 26.1288%, total TER: 12.7446%, progress (thread 0): 91.8117%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_747.66_747.95, WER: 0%, TER: 0%, total WER: 26.1285%, total TER: 12.7444%, progress (thread 0): 91.8196%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_784.99_785.28, WER: 100%, TER: 40%, total WER: 26.1293%, total TER: 12.7448%, progress (thread 0): 91.8275%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_826.06_826.35, WER: 0%, TER: 0%, total WER: 26.129%, total TER: 12.7446%, progress (thread 0): 91.8354%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2019.84_2020.13, WER: 0%, TER: 0%, total WER: 26.1287%, total TER: 12.7445%, progress (thread 0): 91.8434%]
|T|: o k a y
|P|: a
[sample: ES2004b_H02_MEE014_2295.26_2295.55, WER: 100%, TER: 75%, total WER: 26.1296%, total TER: 12.7451%, progress (thread 0): 91.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_316.02_316.31, WER: 0%, TER: 0%, total WER: 26.1293%, total TER: 12.745%, progress (thread 0): 91.8592%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004c_H00_MEO015_1261.19_1261.48, WER: 200%, TER: 20%, total WER: 26.1312%, total TER: 12.745%, progress (thread 0): 91.8671%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H01_FEE013_1987.8_1988.09, WER: 100%, TER: 40%, total WER: 26.1321%, total TER: 12.7454%, progress (thread 0): 91.875%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_2244.26_2244.55, WER: 0%, TER: 0%, total WER: 26.1318%, total TER: 12.7452%, progress (thread 0): 91.8829%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_211.03_211.32, WER: 0%, TER: 0%, total WER: 26.1315%, total TER: 12.7451%, progress (thread 0): 91.8908%]
|T|: r i g h t
|P|: i
[sample: ES2004d_H03_FEE016_810.67_810.96, WER: 100%, TER: 80%, total WER: 26.1323%, total TER: 12.7459%, progress (thread 0): 91.8987%]
|T|: m m
|P|: m h m
[sample: ES2004d_H03_FEE016_1204.42_1204.71, WER: 100%, TER: 50%, total WER: 26.1331%, total TER: 12.7461%, progress (thread 0): 91.9066%]
|T|: u m
|P|:
[sample: ES2004d_H01_FEE013_1227.88_1228.17, WER: 100%, TER: 100%, total WER: 26.134%, total TER: 12.7465%, progress (thread 0): 91.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1634.52_1634.81, WER: 0%, TER: 0%, total WER: 26.1337%, total TER: 12.7463%, progress (thread 0): 91.9225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1787.34_1787.63, WER: 0%, TER: 0%, total WER: 26.1334%, total TER: 12.7462%, progress (thread 0): 91.9304%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1964.63_1964.92, WER: 0%, TER: 0%, total WER: 26.1331%, total TER: 12.7461%, progress (thread 0): 91.9383%]
|T|: o k a y
|P|: o k y
[sample: IS1009a_H03_FIO089_118.76_119.05, WER: 100%, TER: 25%, total WER: 26.1339%, total TER: 12.7462%, progress (thread 0): 91.9462%]
|T|: h m m
|P|: c a s e
[sample: IS1009a_H02_FIO084_803.39_803.68, WER: 100%, TER: 133.333%, total WER: 26.1347%, total TER: 12.7471%, progress (thread 0): 91.9541%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_708.64_708.93, WER: 100%, TER: 40%, total WER: 26.1356%, total TER: 12.7474%, progress (thread 0): 91.962%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_998.44_998.73, WER: 0%, TER: 0%, total WER: 26.1353%, total TER: 12.7473%, progress (thread 0): 91.9699%]
|T|: m m h m m
|P|: h
[sample: IS1009b_H03_FIO089_1311.53_1311.82, WER: 100%, TER: 80%, total WER: 26.1361%, total TER: 12.7481%, progress (thread 0): 91.9778%]
|T|: m m h m m
|P|: a m
[sample: IS1009b_H00_FIE088_1356.24_1356.53, WER: 100%, TER: 80%, total WER: 26.137%, total TER: 12.7488%, progress (thread 0): 91.9858%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_1658.74_1659.03, WER: 100%, TER: 60%, total WER: 26.1378%, total TER: 12.7494%, progress (thread 0): 91.9937%]
|T|: h m m
|P|: m h m
[sample: IS1009b_H02_FIO084_1899.66_1899.95, WER: 100%, TER: 66.6667%, total WER: 26.1386%, total TER: 12.7498%, progress (thread 0): 92.0016%]
|T|: m m h m m
|P|: w h m
[sample: IS1009b_H03_FIO089_1999.16_1999.45, WER: 100%, TER: 60%, total WER: 26.1395%, total TER: 12.7503%, progress (thread 0): 92.0095%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_284.27_284.56, WER: 0%, TER: 0%, total WER: 26.1392%, total TER: 12.7502%, progress (thread 0): 92.0174%]
|T|: m m h m m
|P|: u m | h u m
[sample: IS1009c_H00_FIE088_381_381.29, WER: 200%, TER: 60%, total WER: 26.1411%, total TER: 12.7508%, progress (thread 0): 92.0253%]
|T|: w e | c a n
|P|:
[sample: IS1009c_H01_FIO087_755.64_755.93, WER: 100%, TER: 100%, total WER: 26.1428%, total TER: 12.752%, progress (thread 0): 92.0332%]
|T|: y e a h | b u t | w
|P|:
[sample: IS1009c_H02_FIO084_1559.51_1559.8, WER: 100%, TER: 100%, total WER: 26.1453%, total TER: 12.7541%, progress (thread 0): 92.0411%]
|T|: m m h m m
|P|: y
[sample: IS1009c_H02_FIO084_1608.9_1609.19, WER: 100%, TER: 100%, total WER: 26.1461%, total TER: 12.7551%, progress (thread 0): 92.049%]
|T|: h e l l o
|P|: y o u | k n o w
[sample: IS1009d_H01_FIO087_41.27_41.56, WER: 200%, TER: 140%, total WER: 26.1481%, total TER: 12.7566%, progress (thread 0): 92.057%]
|T|: n o
|P|: y o u | k n o w
[sample: IS1009d_H02_FIO084_952.51_952.8, WER: 200%, TER: 300%, total WER: 26.15%, total TER: 12.7579%, progress (thread 0): 92.0649%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1056.71_1057, WER: 100%, TER: 100%, total WER: 26.1509%, total TER: 12.7589%, progress (thread 0): 92.0728%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H03_FIO089_1344.32_1344.61, WER: 100%, TER: 80%, total WER: 26.1517%, total TER: 12.7597%, progress (thread 0): 92.0807%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_1389.87_1390.16, WER: 0%, TER: 0%, total WER: 26.1514%, total TER: 12.7596%, progress (thread 0): 92.0886%]
|T|: m m h m m
|P|: y e a
[sample: IS1009d_H02_FIO084_1790.06_1790.35, WER: 100%, TER: 100%, total WER: 26.1522%, total TER: 12.7606%, progress (thread 0): 92.0965%]
|T|: o k a y
|P|: i
[sample: TS3003a_H01_MTD011UID_704.99_705.28, WER: 100%, TER: 100%, total WER: 26.1531%, total TER: 12.7615%, progress (thread 0): 92.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_977.31_977.6, WER: 0%, TER: 0%, total WER: 26.1528%, total TER: 12.7613%, progress (thread 0): 92.1123%]
|T|: y o u
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1267.17_1267.46, WER: 100%, TER: 100%, total WER: 26.1536%, total TER: 12.7619%, progress (thread 0): 92.1203%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_178.78_179.07, WER: 0%, TER: 0%, total WER: 26.1533%, total TER: 12.7619%, progress (thread 0): 92.1282%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1577.63_1577.92, WER: 0%, TER: 0%, total WER: 26.153%, total TER: 12.7617%, progress (thread 0): 92.1361%]
|T|: w e l l
|P|: w e l l
[sample: TS3003b_H03_MTD012ME_1752.79_1753.08, WER: 0%, TER: 0%, total WER: 26.1527%, total TER: 12.7616%, progress (thread 0): 92.144%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_2033.23_2033.52, WER: 0%, TER: 0%, total WER: 26.1524%, total TER: 12.7615%, progress (thread 0): 92.1519%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2083.17_2083.46, WER: 0%, TER: 0%, total WER: 26.1521%, total TER: 12.7614%, progress (thread 0): 92.1598%]
|T|: y e a h
|P|: n o
[sample: TS3003b_H02_MTD0010ID_2083.37_2083.66, WER: 100%, TER: 100%, total WER: 26.153%, total TER: 12.7622%, progress (thread 0): 92.1677%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H01_MTD011UID_1289.74_1290.03, WER: 100%, TER: 40%, total WER: 26.1538%, total TER: 12.7625%, progress (thread 0): 92.1756%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1452.67_1452.96, WER: 0%, TER: 0%, total WER: 26.1535%, total TER: 12.7624%, progress (thread 0): 92.1835%]
|T|: m m h m m
|P|: h
[sample: TS3003d_H03_MTD012ME_695.88_696.17, WER: 100%, TER: 80%, total WER: 26.1543%, total TER: 12.7632%, progress (thread 0): 92.1915%]
|T|: n a y
|P|: h m
[sample: TS3003d_H01_MTD011UID_835.49_835.78, WER: 100%, TER: 100%, total WER: 26.1552%, total TER: 12.7638%, progress (thread 0): 92.1994%]
|T|: h m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_1010.19_1010.48, WER: 100%, TER: 33.3333%, total WER: 26.156%, total TER: 12.7639%, progress (thread 0): 92.2073%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H02_MTD0010ID_1075.11_1075.4, WER: 100%, TER: 100%, total WER: 26.1568%, total TER: 12.7648%, progress (thread 0): 92.2152%]
|T|: t r u e
|P|: t r u e
[sample: TS3003d_H03_MTD012ME_1129.3_1129.59, WER: 0%, TER: 0%, total WER: 26.1566%, total TER: 12.7646%, progress (thread 0): 92.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1862.62_1862.91, WER: 0%, TER: 0%, total WER: 26.1563%, total TER: 12.7645%, progress (thread 0): 92.231%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_2370.37_2370.66, WER: 100%, TER: 50%, total WER: 26.1571%, total TER: 12.7647%, progress (thread 0): 92.2389%]
|T|: h m m
|P|: h m
[sample: TS3003d_H00_MTD009PM_2517.23_2517.52, WER: 100%, TER: 33.3333%, total WER: 26.1579%, total TER: 12.7648%, progress (thread 0): 92.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2571.69_2571.98, WER: 0%, TER: 0%, total WER: 26.1576%, total TER: 12.7647%, progress (thread 0): 92.2547%]
|T|: h m m
|P|: y e h
[sample: EN2002a_H00_MEE073_218.41_218.7, WER: 100%, TER: 100%, total WER: 26.1585%, total TER: 12.7653%, progress (thread 0): 92.2627%]
|T|: i | d o n ' t | k n o w
|P|: i | d n '
[sample: EN2002a_H00_MEE073_234.63_234.92, WER: 66.6667%, TER: 58.3333%, total WER: 26.1598%, total TER: 12.7666%, progress (thread 0): 92.2706%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_526.91_527.2, WER: 100%, TER: 66.6667%, total WER: 26.1607%, total TER: 12.767%, progress (thread 0): 92.2785%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_646.99_647.28, WER: 0%, TER: 0%, total WER: 26.1604%, total TER: 12.7669%, progress (thread 0): 92.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_723.91_724.2, WER: 0%, TER: 0%, total WER: 26.1601%, total TER: 12.7667%, progress (thread 0): 92.2943%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_749.38_749.67, WER: 0%, TER: 0%, total WER: 26.1598%, total TER: 12.7666%, progress (thread 0): 92.3022%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_856.03_856.32, WER: 0%, TER: 0%, total WER: 26.1595%, total TER: 12.7665%, progress (thread 0): 92.3101%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_872.78_873.07, WER: 0%, TER: 0%, total WER: 26.1592%, total TER: 12.7664%, progress (thread 0): 92.318%]
|T|: h m m
|P|: y e h
[sample: EN2002a_H00_MEE073_1076.05_1076.34, WER: 100%, TER: 100%, total WER: 26.16%, total TER: 12.767%, progress (thread 0): 92.326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1255.44_1255.73, WER: 0%, TER: 0%, total WER: 26.1597%, total TER: 12.7669%, progress (thread 0): 92.3339%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1364.93_1365.22, WER: 0%, TER: 0%, total WER: 26.1594%, total TER: 12.7668%, progress (thread 0): 92.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1593.85_1594.14, WER: 0%, TER: 0%, total WER: 26.1591%, total TER: 12.7666%, progress (thread 0): 92.3497%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1595.68_1595.97, WER: 0%, TER: 0%, total WER: 26.1588%, total TER: 12.7665%, progress (thread 0): 92.3576%]
|T|: m m h m m
|P|: w e
[sample: EN2002a_H02_FEO072_1620.66_1620.95, WER: 100%, TER: 100%, total WER: 26.1597%, total TER: 12.7675%, progress (thread 0): 92.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1960.6_1960.89, WER: 0%, TER: 0%, total WER: 26.1594%, total TER: 12.7674%, progress (thread 0): 92.3734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2127.38_2127.67, WER: 0%, TER: 0%, total WER: 26.1591%, total TER: 12.7673%, progress (thread 0): 92.3813%]
|T|: v e r y | g o o d
|P|: w h e n
[sample: EN2002b_H02_FEO072_142.65_142.94, WER: 100%, TER: 100%, total WER: 26.1608%, total TER: 12.7691%, progress (thread 0): 92.3892%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_382.41_382.7, WER: 0%, TER: 0%, total WER: 26.1605%, total TER: 12.769%, progress (thread 0): 92.3972%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_419.68_419.97, WER: 0%, TER: 0%, total WER: 26.1602%, total TER: 12.7689%, progress (thread 0): 92.4051%]
|T|: o k a y
|P|: o k a
[sample: EN2002b_H00_FEO070_734.04_734.33, WER: 100%, TER: 25%, total WER: 26.161%, total TER: 12.769%, progress (thread 0): 92.413%]
|T|: h m m
|P|: n o
[sample: EN2002b_H03_MEE073_1150.51_1150.8, WER: 100%, TER: 100%, total WER: 26.1618%, total TER: 12.7696%, progress (thread 0): 92.4209%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_1286.29_1286.58, WER: 100%, TER: 40%, total WER: 26.1627%, total TER: 12.77%, progress (thread 0): 92.4288%]
|T|: s o
|P|: s o
[sample: EN2002b_H03_MEE073_1517.43_1517.72, WER: 0%, TER: 0%, total WER: 26.1624%, total TER: 12.7699%, progress (thread 0): 92.4367%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_61.76_62.05, WER: 0%, TER: 0%, total WER: 26.1621%, total TER: 12.7698%, progress (thread 0): 92.4446%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_697.18_697.47, WER: 100%, TER: 50%, total WER: 26.1629%, total TER: 12.7699%, progress (thread 0): 92.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1026.29_1026.58, WER: 0%, TER: 0%, total WER: 26.1626%, total TER: 12.7698%, progress (thread 0): 92.4604%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_1088_1088.29, WER: 100%, TER: 40%, total WER: 26.1634%, total TER: 12.7701%, progress (thread 0): 92.4684%]
|T|: t h a t
|P|: o
[sample: EN2002c_H03_MEE073_1302.84_1303.13, WER: 100%, TER: 100%, total WER: 26.1643%, total TER: 12.771%, progress (thread 0): 92.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1313.99_1314.28, WER: 0%, TER: 0%, total WER: 26.164%, total TER: 12.7708%, progress (thread 0): 92.4842%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_1543.66_1543.95, WER: 100%, TER: 50%, total WER: 26.1648%, total TER: 12.771%, progress (thread 0): 92.4921%]
|T|: ' c a u s e
|P|: t a u s
[sample: EN2002c_H03_MEE073_2609.79_2610.08, WER: 100%, TER: 50%, total WER: 26.1656%, total TER: 12.7715%, progress (thread 0): 92.5%]
|T|: a c t u a l l y
|P|: a c t u a l l y
[sample: EN2002d_H03_MEE073_783.19_783.48, WER: 0%, TER: 0%, total WER: 26.1653%, total TER: 12.7713%, progress (thread 0): 92.5079%]
|T|: r i g h t
|P|: u
[sample: EN2002d_H02_MEE071_833.43_833.72, WER: 100%, TER: 100%, total WER: 26.1662%, total TER: 12.7723%, progress (thread 0): 92.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1031.38_1031.67, WER: 0%, TER: 0%, total WER: 26.1659%, total TER: 12.7722%, progress (thread 0): 92.5237%]
|T|: y e a h
|P|: n o
[sample: EN2002d_H03_MEE073_1490.73_1491.02, WER: 100%, TER: 100%, total WER: 26.1667%, total TER: 12.773%, progress (thread 0): 92.5316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2069.6_2069.89, WER: 0%, TER: 0%, total WER: 26.1664%, total TER: 12.7729%, progress (thread 0): 92.5396%]
|T|: n o
|P|: n
[sample: ES2004b_H03_FEE016_602.63_602.91, WER: 100%, TER: 50%, total WER: 26.1673%, total TER: 12.7731%, progress (thread 0): 92.5475%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1146.25_1146.53, WER: 0%, TER: 0%, total WER: 26.167%, total TER: 12.773%, progress (thread 0): 92.5554%]
|T|: m m
|P|: h m
[sample: ES2004b_H03_FEE016_1316.47_1316.75, WER: 100%, TER: 50%, total WER: 26.1678%, total TER: 12.7731%, progress (thread 0): 92.5633%]
|T|: r i g h t
|P|: r i h t
[sample: ES2004b_H00_MEO015_1939.86_1940.14, WER: 100%, TER: 20%, total WER: 26.1686%, total TER: 12.7732%, progress (thread 0): 92.5712%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2216.73_2217.01, WER: 0%, TER: 0%, total WER: 26.1683%, total TER: 12.7731%, progress (thread 0): 92.5791%]
|T|: t h a n k s
|P|:
[sample: ES2004c_H01_FEE013_54.47_54.75, WER: 100%, TER: 100%, total WER: 26.1692%, total TER: 12.7743%, progress (thread 0): 92.587%]
|T|: u h
|P|: u h
[sample: ES2004c_H02_MEE014_1126.8_1127.08, WER: 0%, TER: 0%, total WER: 26.1689%, total TER: 12.7743%, progress (thread 0): 92.5949%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1180.98_1181.26, WER: 100%, TER: 50%, total WER: 26.1697%, total TER: 12.7744%, progress (thread 0): 92.6029%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1398.69_1398.97, WER: 0%, TER: 0%, total WER: 26.1694%, total TER: 12.7743%, progress (thread 0): 92.6108%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2123.64_2123.92, WER: 0%, TER: 0%, total WER: 26.1691%, total TER: 12.7742%, progress (thread 0): 92.6187%]
|T|: n o
|P|: n o
[sample: ES2004d_H02_MEE014_1050.44_1050.72, WER: 0%, TER: 0%, total WER: 26.1688%, total TER: 12.7741%, progress (thread 0): 92.6266%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_1500.31_1500.59, WER: 100%, TER: 40%, total WER: 26.1696%, total TER: 12.7745%, progress (thread 0): 92.6345%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1843.24_1843.52, WER: 0%, TER: 0%, total WER: 26.1694%, total TER: 12.7743%, progress (thread 0): 92.6424%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_2015.69_2015.97, WER: 0%, TER: 0%, total WER: 26.1691%, total TER: 12.7742%, progress (thread 0): 92.6503%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H01_FEE013_2120.6_2120.88, WER: 0%, TER: 0%, total WER: 26.1688%, total TER: 12.7741%, progress (thread 0): 92.6582%]
|T|: o k a y
|P|: o
[sample: IS1009a_H03_FIO089_188.06_188.34, WER: 100%, TER: 75%, total WER: 26.1696%, total TER: 12.7746%, progress (thread 0): 92.6661%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_793.23_793.51, WER: 100%, TER: 40%, total WER: 26.1704%, total TER: 12.775%, progress (thread 0): 92.674%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_804.79_805.07, WER: 0%, TER: 0%, total WER: 26.1701%, total TER: 12.7748%, progress (thread 0): 92.682%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_894.05_894.33, WER: 0%, TER: 0%, total WER: 26.1698%, total TER: 12.7747%, progress (thread 0): 92.6899%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1764.17_1764.45, WER: 0%, TER: 0%, total WER: 26.1695%, total TER: 12.7746%, progress (thread 0): 92.6978%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009c_H02_FIO084_724.39_724.67, WER: 100%, TER: 100%, total WER: 26.1704%, total TER: 12.7756%, progress (thread 0): 92.7057%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1140.8_1141.08, WER: 0%, TER: 0%, total WER: 26.1701%, total TER: 12.7755%, progress (thread 0): 92.7136%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1619.41_1619.69, WER: 0%, TER: 0%, total WER: 26.1698%, total TER: 12.7754%, progress (thread 0): 92.7215%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_617.29_617.57, WER: 100%, TER: 40%, total WER: 26.1706%, total TER: 12.7757%, progress (thread 0): 92.7294%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_773.28_773.56, WER: 0%, TER: 0%, total WER: 26.1703%, total TER: 12.7756%, progress (thread 0): 92.7373%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_980.1_980.38, WER: 0%, TER: 0%, total WER: 26.17%, total TER: 12.7755%, progress (thread 0): 92.7452%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1792.74_1793.02, WER: 100%, TER: 100%, total WER: 26.1709%, total TER: 12.7765%, progress (thread 0): 92.7532%]
|T|: h m m
|P|: h m
[sample: IS1009d_H02_FIO084_1830.44_1830.72, WER: 100%, TER: 33.3333%, total WER: 26.1717%, total TER: 12.7766%, progress (thread 0): 92.7611%]
|T|: m m h m m
|P|: u h | h u
[sample: IS1009d_H03_FIO089_1876.62_1876.9, WER: 200%, TER: 100%, total WER: 26.1736%, total TER: 12.7777%, progress (thread 0): 92.769%]
|T|: m o r n i n g
|P|: o n t
[sample: TS3003a_H02_MTD0010ID_16.42_16.7, WER: 100%, TER: 71.4286%, total WER: 26.1745%, total TER: 12.7786%, progress (thread 0): 92.7769%]
|T|: s u r e
|P|: s u r e
[sample: TS3003a_H03_MTD012ME_163.72_164, WER: 0%, TER: 0%, total WER: 26.1742%, total TER: 12.7785%, progress (thread 0): 92.7848%]
|T|: h m m
|P|: h m
[sample: TS3003a_H03_MTD012ME_661.22_661.5, WER: 100%, TER: 33.3333%, total WER: 26.175%, total TER: 12.7786%, progress (thread 0): 92.7927%]
|T|: h m m
|P|: h m
[sample: TS3003a_H00_MTD009PM_1235.68_1235.96, WER: 100%, TER: 33.3333%, total WER: 26.1759%, total TER: 12.7788%, progress (thread 0): 92.8006%]
|T|: y e a h
|P|: t h a
[sample: TS3003b_H00_MTD009PM_823.58_823.86, WER: 100%, TER: 75%, total WER: 26.1767%, total TER: 12.7794%, progress (thread 0): 92.8085%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1892.2_1892.48, WER: 0%, TER: 0%, total WER: 26.1764%, total TER: 12.7792%, progress (thread 0): 92.8165%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1860.06_1860.34, WER: 0%, TER: 0%, total WER: 26.1761%, total TER: 12.7791%, progress (thread 0): 92.8244%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1891.82_1892.1, WER: 100%, TER: 40%, total WER: 26.1769%, total TER: 12.7794%, progress (thread 0): 92.8323%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_267.26_267.54, WER: 0%, TER: 0%, total WER: 26.1766%, total TER: 12.7793%, progress (thread 0): 92.8402%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_365.09_365.37, WER: 0%, TER: 0%, total WER: 26.1763%, total TER: 12.7792%, progress (thread 0): 92.8481%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H00_MTD009PM_816.6_816.88, WER: 0%, TER: 0%, total WER: 26.176%, total TER: 12.7791%, progress (thread 0): 92.856%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_860.45_860.73, WER: 0%, TER: 0%, total WER: 26.1757%, total TER: 12.779%, progress (thread 0): 92.8639%]
|T|: a n d | i t
|P|: a n d | i t
[sample: TS3003d_H02_MTD0010ID_1310.28_1310.56, WER: 0%, TER: 0%, total WER: 26.1752%, total TER: 12.7788%, progress (thread 0): 92.8718%]
|T|: s h
|P|: s
[sample: TS3003d_H02_MTD0010ID_1311.62_1311.9, WER: 100%, TER: 50%, total WER: 26.176%, total TER: 12.779%, progress (thread 0): 92.8797%]
|T|: y e p
|P|: y u p
[sample: TS3003d_H02_MTD0010ID_1403.4_1403.68, WER: 100%, TER: 33.3333%, total WER: 26.1768%, total TER: 12.7791%, progress (thread 0): 92.8877%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1945.66_1945.94, WER: 0%, TER: 0%, total WER: 26.1765%, total TER: 12.779%, progress (thread 0): 92.8956%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2142.04_2142.32, WER: 0%, TER: 0%, total WER: 26.1762%, total TER: 12.7789%, progress (thread 0): 92.9035%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_49.78_50.06, WER: 100%, TER: 50%, total WER: 26.1771%, total TER: 12.7792%, progress (thread 0): 92.9114%]
|T|: y e p
|P|: y e p
[sample: EN2002a_H01_FEO070_478.66_478.94, WER: 0%, TER: 0%, total WER: 26.1768%, total TER: 12.7792%, progress (thread 0): 92.9193%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_758.4_758.68, WER: 0%, TER: 0%, total WER: 26.1765%, total TER: 12.779%, progress (thread 0): 92.9272%]
|T|: y e a h
|P|: h m
[sample: EN2002a_H01_FEO070_762.35_762.63, WER: 100%, TER: 100%, total WER: 26.1773%, total TER: 12.7799%, progress (thread 0): 92.9351%]
|T|: b u t
|P|: b u t
[sample: EN2002a_H01_FEO070_1024.58_1024.86, WER: 0%, TER: 0%, total WER: 26.177%, total TER: 12.7798%, progress (thread 0): 92.943%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1306.05_1306.33, WER: 0%, TER: 0%, total WER: 26.1767%, total TER: 12.7796%, progress (thread 0): 92.951%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1392.09_1392.37, WER: 0%, TER: 0%, total WER: 26.1764%, total TER: 12.7795%, progress (thread 0): 92.9589%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002a_H00_MEE073_1638.28_1638.56, WER: 200%, TER: 175%, total WER: 26.1784%, total TER: 12.781%, progress (thread 0): 92.9668%]
|T|: s e e
|P|: s e e
[sample: EN2002a_H01_FEO070_1702.66_1702.94, WER: 0%, TER: 0%, total WER: 26.1781%, total TER: 12.781%, progress (thread 0): 92.9747%]
|T|: y e a h
|P|: n o
[sample: EN2002a_H01_FEO070_1796.46_1796.74, WER: 100%, TER: 100%, total WER: 26.1789%, total TER: 12.7818%, progress (thread 0): 92.9826%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1831.52_1831.8, WER: 0%, TER: 0%, total WER: 26.1786%, total TER: 12.7816%, progress (thread 0): 92.9905%]
|T|: o h
|P|: u h
[sample: EN2002a_H02_FEO072_1981.37_1981.65, WER: 100%, TER: 50%, total WER: 26.1794%, total TER: 12.7818%, progress (thread 0): 92.9984%]
|T|: i | t h
|P|: i
[sample: EN2002a_H00_MEE073_2026.22_2026.5, WER: 50%, TER: 75%, total WER: 26.18%, total TER: 12.7824%, progress (thread 0): 93.0063%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H01_MEE071_16.64_16.92, WER: 0%, TER: 0%, total WER: 26.1797%, total TER: 12.7823%, progress (thread 0): 93.0142%]
|T|: m m h m m
|P|: h m
[sample: EN2002b_H03_MEE073_335.63_335.91, WER: 100%, TER: 60%, total WER: 26.1805%, total TER: 12.7828%, progress (thread 0): 93.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_356.11_356.39, WER: 0%, TER: 0%, total WER: 26.1802%, total TER: 12.7827%, progress (thread 0): 93.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_987.48_987.76, WER: 0%, TER: 0%, total WER: 26.1799%, total TER: 12.7826%, progress (thread 0): 93.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1285.46_1285.74, WER: 0%, TER: 0%, total WER: 26.1796%, total TER: 12.7825%, progress (thread 0): 93.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1450.71_1450.99, WER: 0%, TER: 0%, total WER: 26.1793%, total TER: 12.7824%, progress (thread 0): 93.0538%]
|T|: y e a h
|P|: y e a k n
[sample: EN2002b_H03_MEE073_1588.08_1588.36, WER: 100%, TER: 50%, total WER: 26.1802%, total TER: 12.7827%, progress (thread 0): 93.0617%]
|T|: y e a h
|P|: y o a h
[sample: EN2002c_H03_MEE073_264.61_264.89, WER: 100%, TER: 25%, total WER: 26.181%, total TER: 12.7828%, progress (thread 0): 93.0696%]
|T|: m m h m m
|P|: m
[sample: EN2002c_H03_MEE073_638.33_638.61, WER: 100%, TER: 80%, total WER: 26.1818%, total TER: 12.7836%, progress (thread 0): 93.0775%]
|T|: a h
|P|: u h
[sample: EN2002c_H01_FEO072_1466.83_1467.11, WER: 100%, TER: 50%, total WER: 26.1827%, total TER: 12.7838%, progress (thread 0): 93.0854%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1508.55_1508.83, WER: 100%, TER: 28.5714%, total WER: 26.1835%, total TER: 12.784%, progress (thread 0): 93.0934%]
|T|: i | k n o w
|P|: k i n d | o f
[sample: EN2002c_H03_MEE073_1627.18_1627.46, WER: 100%, TER: 83.3333%, total WER: 26.1852%, total TER: 12.785%, progress (thread 0): 93.1013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2613.98_2614.26, WER: 0%, TER: 0%, total WER: 26.1849%, total TER: 12.7849%, progress (thread 0): 93.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2738.83_2739.11, WER: 0%, TER: 0%, total WER: 26.1846%, total TER: 12.7848%, progress (thread 0): 93.1171%]
|T|: w e l l
|P|: o
[sample: EN2002c_H03_MEE073_2837.32_2837.6, WER: 100%, TER: 100%, total WER: 26.1854%, total TER: 12.7856%, progress (thread 0): 93.125%]
|T|: i | t h
|P|: i
[sample: EN2002d_H03_MEE073_209.34_209.62, WER: 50%, TER: 75%, total WER: 26.1859%, total TER: 12.7862%, progress (thread 0): 93.1329%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H03_MEE073_379.93_380.21, WER: 100%, TER: 40%, total WER: 26.1868%, total TER: 12.7865%, progress (thread 0): 93.1408%]
|T|: y e a h
|P|:
[sample: EN2002d_H03_MEE073_446.25_446.53, WER: 100%, TER: 100%, total WER: 26.1876%, total TER: 12.7873%, progress (thread 0): 93.1487%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_622.33_622.61, WER: 0%, TER: 0%, total WER: 26.1873%, total TER: 12.7872%, progress (thread 0): 93.1566%]
|T|: y e a h
|P|: h m
[sample: EN2002d_H03_MEE073_989.71_989.99, WER: 100%, TER: 100%, total WER: 26.1881%, total TER: 12.788%, progress (thread 0): 93.1646%]
|T|: h m m
|P|: h m
[sample: EN2002d_H02_MEE071_1294.26_1294.54, WER: 100%, TER: 33.3333%, total WER: 26.189%, total TER: 12.7882%, progress (thread 0): 93.1725%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1744.58_1744.86, WER: 0%, TER: 0%, total WER: 26.1887%, total TER: 12.788%, progress (thread 0): 93.1804%]
|T|: t h e
|P|: e
[sample: EN2002d_H03_MEE073_1888.86_1889.14, WER: 100%, TER: 66.6667%, total WER: 26.1895%, total TER: 12.7884%, progress (thread 0): 93.1883%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2067.26_2067.54, WER: 0%, TER: 0%, total WER: 26.1892%, total TER: 12.7883%, progress (thread 0): 93.1962%]
|T|: s u r e
|P|: t r u e
[sample: ES2004b_H00_MEO015_1306.29_1306.56, WER: 100%, TER: 75%, total WER: 26.19%, total TER: 12.7889%, progress (thread 0): 93.2041%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1834.89_1835.16, WER: 0%, TER: 0%, total WER: 26.1898%, total TER: 12.7888%, progress (thread 0): 93.212%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1954.73_1955, WER: 100%, TER: 40%, total WER: 26.1906%, total TER: 12.7891%, progress (thread 0): 93.2199%]
|T|: m m h m m
|P|: h m
[sample: ES2004c_H03_FEE016_652.96_653.23, WER: 100%, TER: 60%, total WER: 26.1914%, total TER: 12.7896%, progress (thread 0): 93.2278%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1141.18_1141.45, WER: 100%, TER: 40%, total WER: 26.1922%, total TER: 12.79%, progress (thread 0): 93.2358%]
|T|: m m
|P|: j u
[sample: ES2004c_H03_FEE016_1281.44_1281.71, WER: 100%, TER: 100%, total WER: 26.1931%, total TER: 12.7904%, progress (thread 0): 93.2437%]
|T|: o k a y
|P|: k a y
[sample: ES2004c_H00_MEO015_1403.89_1404.16, WER: 100%, TER: 25%, total WER: 26.1939%, total TER: 12.7905%, progress (thread 0): 93.2516%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1730.76_1731.03, WER: 0%, TER: 0%, total WER: 26.1936%, total TER: 12.7904%, progress (thread 0): 93.2595%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1835.22_1835.49, WER: 100%, TER: 40%, total WER: 26.1944%, total TER: 12.7907%, progress (thread 0): 93.2674%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1921.81_1922.08, WER: 0%, TER: 0%, total WER: 26.1942%, total TER: 12.7906%, progress (thread 0): 93.2753%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_2149.62_2149.89, WER: 0%, TER: 0%, total WER: 26.1939%, total TER: 12.7904%, progress (thread 0): 93.2832%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_654.95_655.22, WER: 100%, TER: 40%, total WER: 26.1947%, total TER: 12.7908%, progress (thread 0): 93.2911%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_738.4_738.67, WER: 100%, TER: 33.3333%, total WER: 26.1955%, total TER: 12.7909%, progress (thread 0): 93.299%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_793.57_793.84, WER: 0%, TER: 0%, total WER: 26.1952%, total TER: 12.7908%, progress (thread 0): 93.307%]
|T|: n o
|P|: n o
[sample: ES2004d_H03_FEE016_1418.58_1418.85, WER: 0%, TER: 0%, total WER: 26.1949%, total TER: 12.7907%, progress (thread 0): 93.3149%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1497.73_1498, WER: 0%, TER: 0%, total WER: 26.1946%, total TER: 12.7906%, progress (thread 0): 93.3228%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1559.56_1559.83, WER: 0%, TER: 0%, total WER: 26.1943%, total TER: 12.7905%, progress (thread 0): 93.3307%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2099.96_2100.23, WER: 0%, TER: 0%, total WER: 26.194%, total TER: 12.7904%, progress (thread 0): 93.3386%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_159.51_159.78, WER: 100%, TER: 40%, total WER: 26.1949%, total TER: 12.7907%, progress (thread 0): 93.3465%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_495.24_495.51, WER: 0%, TER: 0%, total WER: 26.1946%, total TER: 12.7906%, progress (thread 0): 93.3544%]
|T|: m m h m m
|P|: m h | h m
[sample: IS1009b_H02_FIO084_130.02_130.29, WER: 200%, TER: 60%, total WER: 26.1965%, total TER: 12.7911%, progress (thread 0): 93.3623%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_195.35_195.62, WER: 100%, TER: 60%, total WER: 26.1974%, total TER: 12.7917%, progress (thread 0): 93.3703%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1093_1093.27, WER: 100%, TER: 40%, total WER: 26.1982%, total TER: 12.792%, progress (thread 0): 93.3782%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H01_FIO087_323.83_324.1, WER: 0%, TER: 0%, total WER: 26.1979%, total TER: 12.7918%, progress (thread 0): 93.3861%]
|T|: m m h m m
|P|:
[sample: IS1009c_H00_FIE088_770.63_770.9, WER: 100%, TER: 100%, total WER: 26.1987%, total TER: 12.7929%, progress (thread 0): 93.394%]
|T|: m m h m m
|P|: u h | h
[sample: IS1009c_H03_FIO089_922.16_922.43, WER: 200%, TER: 80%, total WER: 26.2007%, total TER: 12.7936%, progress (thread 0): 93.4019%]
|T|: m m h m m
|P|: u h | h m
[sample: IS1009c_H03_FIO089_1095.56_1095.83, WER: 200%, TER: 80%, total WER: 26.2026%, total TER: 12.7944%, progress (thread 0): 93.4098%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1374.42_1374.69, WER: 0%, TER: 0%, total WER: 26.2024%, total TER: 12.7943%, progress (thread 0): 93.4177%]
|T|: o k a y
|P|: l i c
[sample: IS1009d_H03_FIO089_215.46_215.73, WER: 100%, TER: 100%, total WER: 26.2032%, total TER: 12.7951%, progress (thread 0): 93.4256%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_377.8_378.07, WER: 100%, TER: 100%, total WER: 26.204%, total TER: 12.7961%, progress (thread 0): 93.4335%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_381.16_381.43, WER: 100%, TER: 40%, total WER: 26.2048%, total TER: 12.7965%, progress (thread 0): 93.4415%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_938.34_938.61, WER: 100%, TER: 60%, total WER: 26.2057%, total TER: 12.797%, progress (thread 0): 93.4494%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H03_FIO089_1114.59_1114.86, WER: 100%, TER: 80%, total WER: 26.2065%, total TER: 12.7978%, progress (thread 0): 93.4573%]
|T|: o h
|P|: o h
[sample: IS1009d_H00_FIE088_1436.69_1436.96, WER: 0%, TER: 0%, total WER: 26.2062%, total TER: 12.7977%, progress (thread 0): 93.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1775.25_1775.52, WER: 0%, TER: 0%, total WER: 26.2059%, total TER: 12.7976%, progress (thread 0): 93.4731%]
|T|: o k a y
|P|: o h
[sample: IS1009d_H01_FIO087_1908.84_1909.11, WER: 100%, TER: 75%, total WER: 26.2067%, total TER: 12.7982%, progress (thread 0): 93.481%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_684.57_684.84, WER: 100%, TER: 150%, total WER: 26.2076%, total TER: 12.7988%, progress (thread 0): 93.4889%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_790.17_790.44, WER: 100%, TER: 25%, total WER: 26.2084%, total TER: 12.799%, progress (thread 0): 93.4968%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_1326.95_1327.22, WER: 0%, TER: 0%, total WER: 26.2081%, total TER: 12.7989%, progress (thread 0): 93.5047%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1909.01_1909.28, WER: 0%, TER: 0%, total WER: 26.2078%, total TER: 12.7988%, progress (thread 0): 93.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1979.53_1979.8, WER: 0%, TER: 0%, total WER: 26.2075%, total TER: 12.7987%, progress (thread 0): 93.5206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2145.84_2146.11, WER: 0%, TER: 0%, total WER: 26.2072%, total TER: 12.7985%, progress (thread 0): 93.5285%]
|T|: o h
|P|: h m
[sample: TS3003d_H01_MTD011UID_364.71_364.98, WER: 100%, TER: 100%, total WER: 26.2081%, total TER: 12.7989%, progress (thread 0): 93.5364%]
|T|: t e n
|P|: t e n
[sample: TS3003d_H02_MTD0010ID_864.45_864.72, WER: 0%, TER: 0%, total WER: 26.2078%, total TER: 12.7989%, progress (thread 0): 93.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_958.91_959.18, WER: 0%, TER: 0%, total WER: 26.2075%, total TER: 12.7987%, progress (thread 0): 93.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1378.52_1378.79, WER: 0%, TER: 0%, total WER: 26.2072%, total TER: 12.7986%, progress (thread 0): 93.5601%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_2176.17_2176.44, WER: 0%, TER: 0%, total WER: 26.2069%, total TER: 12.7986%, progress (thread 0): 93.568%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_2345.04_2345.31, WER: 100%, TER: 50%, total WER: 26.2077%, total TER: 12.7987%, progress (thread 0): 93.576%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2364.31_2364.58, WER: 0%, TER: 0%, total WER: 26.2074%, total TER: 12.7986%, progress (thread 0): 93.5839%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_2435.3_2435.57, WER: 100%, TER: 25%, total WER: 26.2082%, total TER: 12.7987%, progress (thread 0): 93.5918%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_501.72_501.99, WER: 100%, TER: 33.3333%, total WER: 26.2091%, total TER: 12.7989%, progress (thread 0): 93.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_727.19_727.46, WER: 0%, TER: 0%, total WER: 26.2088%, total TER: 12.7987%, progress (thread 0): 93.6076%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_806.36_806.63, WER: 0%, TER: 0%, total WER: 26.2085%, total TER: 12.7986%, progress (thread 0): 93.6155%]
|T|: o k a y
|P|:
[sample: EN2002a_H00_MEE073_927.86_928.13, WER: 100%, TER: 100%, total WER: 26.2093%, total TER: 12.7994%, progress (thread 0): 93.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1223.91_1224.18, WER: 0%, TER: 0%, total WER: 26.209%, total TER: 12.7993%, progress (thread 0): 93.6313%]
|T|: y e a h
|P|: y o u n w
[sample: EN2002a_H00_MEE073_1319.85_1320.12, WER: 100%, TER: 100%, total WER: 26.2099%, total TER: 12.8001%, progress (thread 0): 93.6392%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_1452_1452.27, WER: 100%, TER: 50%, total WER: 26.2107%, total TER: 12.8005%, progress (thread 0): 93.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1516.15_1516.42, WER: 0%, TER: 0%, total WER: 26.2104%, total TER: 12.8004%, progress (thread 0): 93.6551%]
|T|: y e a h
|P|: y e s
[sample: EN2002a_H03_MEE071_1533.19_1533.46, WER: 100%, TER: 50%, total WER: 26.2112%, total TER: 12.8007%, progress (thread 0): 93.663%]
|T|: y e a h
|P|: y o n
[sample: EN2002a_H00_MEE073_1556.33_1556.6, WER: 100%, TER: 75%, total WER: 26.2121%, total TER: 12.8013%, progress (thread 0): 93.6709%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1945.11_1945.38, WER: 0%, TER: 0%, total WER: 26.2118%, total TER: 12.8012%, progress (thread 0): 93.6788%]
|T|: o h
|P|: o h
[sample: EN2002a_H00_MEE073_1989_1989.27, WER: 0%, TER: 0%, total WER: 26.2115%, total TER: 12.8011%, progress (thread 0): 93.6867%]
|T|: o h
|P|: u m
[sample: EN2002a_H00_MEE073_2003.83_2004.1, WER: 100%, TER: 100%, total WER: 26.2123%, total TER: 12.8015%, progress (thread 0): 93.6946%]
|T|: n o
|P|: y o u | k n o w
[sample: EN2002a_H01_FEO070_2105.6_2105.87, WER: 200%, TER: 300%, total WER: 26.2142%, total TER: 12.8029%, progress (thread 0): 93.7025%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_355.59_355.86, WER: 0%, TER: 0%, total WER: 26.214%, total TER: 12.8028%, progress (thread 0): 93.7104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_485.61_485.88, WER: 0%, TER: 0%, total WER: 26.2137%, total TER: 12.8027%, progress (thread 0): 93.7184%]
|T|: r i g h t
|P|: g r i a t
[sample: EN2002b_H03_MEE073_1027.72_1027.99, WER: 100%, TER: 60%, total WER: 26.2145%, total TER: 12.8032%, progress (thread 0): 93.7263%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_1296.06_1296.33, WER: 0%, TER: 0%, total WER: 26.2142%, total TER: 12.8032%, progress (thread 0): 93.7342%]
|T|: s o
|P|: s
[sample: EN2002b_H01_MEE071_1333.58_1333.85, WER: 100%, TER: 50%, total WER: 26.215%, total TER: 12.8034%, progress (thread 0): 93.7421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1339.47_1339.74, WER: 0%, TER: 0%, total WER: 26.2147%, total TER: 12.8032%, progress (thread 0): 93.75%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1448.9_1449.17, WER: 0%, TER: 0%, total WER: 26.2144%, total TER: 12.8031%, progress (thread 0): 93.7579%]
|T|: a l r i g h t
|P|: a l r i g h t
[sample: EN2002c_H03_MEE073_558.45_558.72, WER: 0%, TER: 0%, total WER: 26.2141%, total TER: 12.8029%, progress (thread 0): 93.7658%]
|T|: h m m
|P|: h m
[sample: EN2002c_H01_FEO072_655.42_655.69, WER: 100%, TER: 33.3333%, total WER: 26.215%, total TER: 12.803%, progress (thread 0): 93.7737%]
|T|: c o o l
|P|: c o o l
[sample: EN2002c_H01_FEO072_778.13_778.4, WER: 0%, TER: 0%, total WER: 26.2147%, total TER: 12.8029%, progress (thread 0): 93.7816%]
|T|: o r
|P|: o l
[sample: EN2002c_H01_FEO072_798.93_799.2, WER: 100%, TER: 50%, total WER: 26.2155%, total TER: 12.8031%, progress (thread 0): 93.7896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_938.98_939.25, WER: 0%, TER: 0%, total WER: 26.2152%, total TER: 12.803%, progress (thread 0): 93.7975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1122.75_1123.02, WER: 0%, TER: 0%, total WER: 26.2149%, total TER: 12.8029%, progress (thread 0): 93.8054%]
|T|: o k a y
|P|:
[sample: EN2002c_H02_MEE071_1353.05_1353.32, WER: 100%, TER: 100%, total WER: 26.2157%, total TER: 12.8037%, progress (thread 0): 93.8133%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1574.37_1574.64, WER: 0%, TER: 0%, total WER: 26.2154%, total TER: 12.8035%, progress (thread 0): 93.8212%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_1672.35_1672.62, WER: 100%, TER: 25%, total WER: 26.2163%, total TER: 12.8036%, progress (thread 0): 93.8291%]
|T|: y e a h
|P|: n o
[sample: EN2002c_H03_MEE073_1740.26_1740.53, WER: 100%, TER: 100%, total WER: 26.2171%, total TER: 12.8045%, progress (thread 0): 93.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1980.58_1980.85, WER: 0%, TER: 0%, total WER: 26.2168%, total TER: 12.8043%, progress (thread 0): 93.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2155.14_2155.41, WER: 0%, TER: 0%, total WER: 26.2165%, total TER: 12.8042%, progress (thread 0): 93.8528%]
|T|: a l r i g h t
|P|: a h
[sample: EN2002c_H03_MEE073_2202.51_2202.78, WER: 100%, TER: 71.4286%, total WER: 26.2174%, total TER: 12.8052%, progress (thread 0): 93.8608%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2272.51_2272.78, WER: 200%, TER: 175%, total WER: 26.2193%, total TER: 12.8067%, progress (thread 0): 93.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2294.4_2294.67, WER: 0%, TER: 0%, total WER: 26.219%, total TER: 12.8066%, progress (thread 0): 93.8766%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2396.65_2396.92, WER: 0%, TER: 0%, total WER: 26.2187%, total TER: 12.8065%, progress (thread 0): 93.8845%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2568.46_2568.73, WER: 0%, TER: 0%, total WER: 26.2184%, total TER: 12.8063%, progress (thread 0): 93.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2705.6_2705.87, WER: 0%, TER: 0%, total WER: 26.2181%, total TER: 12.8062%, progress (thread 0): 93.9003%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_2790.29_2790.56, WER: 100%, TER: 50%, total WER: 26.219%, total TER: 12.8064%, progress (thread 0): 93.9082%]
|T|: i | k n o w
|P|: n
[sample: EN2002d_H03_MEE073_903.75_904.02, WER: 100%, TER: 83.3333%, total WER: 26.2206%, total TER: 12.8074%, progress (thread 0): 93.9161%]
|T|: b u t
|P|: k h a t
[sample: EN2002d_H01_FEO072_946.69_946.96, WER: 100%, TER: 100%, total WER: 26.2214%, total TER: 12.808%, progress (thread 0): 93.924%]
|T|: s o r r y
|P|: s o
[sample: EN2002d_H00_FEO070_966.35_966.62, WER: 100%, TER: 60%, total WER: 26.2223%, total TER: 12.8085%, progress (thread 0): 93.932%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1127.55_1127.82, WER: 0%, TER: 0%, total WER: 26.222%, total TER: 12.8084%, progress (thread 0): 93.9399%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1281.17_1281.44, WER: 0%, TER: 0%, total WER: 26.2217%, total TER: 12.8083%, progress (thread 0): 93.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1305.04_1305.31, WER: 0%, TER: 0%, total WER: 26.2214%, total TER: 12.8082%, progress (thread 0): 93.9557%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002d_H00_FEO070_1507.8_1508.07, WER: 100%, TER: 100%, total WER: 26.2231%, total TER: 12.8096%, progress (thread 0): 93.9636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_1832.82_1833.09, WER: 0%, TER: 0%, total WER: 26.2228%, total TER: 12.8095%, progress (thread 0): 93.9715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_2000.66_2000.93, WER: 0%, TER: 0%, total WER: 26.2225%, total TER: 12.8094%, progress (thread 0): 93.9794%]
|T|: w h a t
|P|: w e l l
[sample: EN2002d_H02_MEE071_2072.88_2073.15, WER: 100%, TER: 75%, total WER: 26.2233%, total TER: 12.8099%, progress (thread 0): 93.9873%]
|T|: y e p
|P|: y e p
[sample: EN2002d_H03_MEE073_2169.42_2169.69, WER: 0%, TER: 0%, total WER: 26.223%, total TER: 12.8099%, progress (thread 0): 93.9953%]
|T|: y e a h
|P|: y o a | h
[sample: ES2004a_H00_MEO015_17.88_18.14, WER: 200%, TER: 50%, total WER: 26.225%, total TER: 12.8102%, progress (thread 0): 94.0032%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_966.22_966.48, WER: 0%, TER: 0%, total WER: 26.2247%, total TER: 12.8101%, progress (thread 0): 94.0111%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_89.12_89.38, WER: 100%, TER: 40%, total WER: 26.2255%, total TER: 12.8104%, progress (thread 0): 94.019%]
|T|: t h e r e | w e | g o
|P|:
[sample: ES2004b_H02_MEE014_830.72_830.98, WER: 100%, TER: 100%, total WER: 26.228%, total TER: 12.8126%, progress (thread 0): 94.0269%]
|T|: h u h
|P|: a
[sample: ES2004b_H02_MEE014_1039.01_1039.27, WER: 100%, TER: 100%, total WER: 26.2288%, total TER: 12.8132%, progress (thread 0): 94.0348%]
|T|: p e n s
|P|: b e e n
[sample: ES2004b_H00_MEO015_1162.44_1162.7, WER: 100%, TER: 75%, total WER: 26.2296%, total TER: 12.8138%, progress (thread 0): 94.0427%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1848.94_1849.2, WER: 0%, TER: 0%, total WER: 26.2293%, total TER: 12.8137%, progress (thread 0): 94.0506%]
|T|: ' k a y
|P|: t o
[sample: ES2004c_H00_MEO015_188.1_188.36, WER: 100%, TER: 100%, total WER: 26.2302%, total TER: 12.8145%, progress (thread 0): 94.0585%]
|T|: y e p
|P|: y u p
[sample: ES2004c_H00_MEO015_540.57_540.83, WER: 100%, TER: 33.3333%, total WER: 26.231%, total TER: 12.8146%, progress (thread 0): 94.0665%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1192.94_1193.2, WER: 0%, TER: 0%, total WER: 26.2307%, total TER: 12.8145%, progress (thread 0): 94.0744%]
|T|: m m h m m
|P|: u h | h u m
[sample: ES2004d_H00_MEO015_39.85_40.11, WER: 200%, TER: 80%, total WER: 26.2327%, total TER: 12.8153%, progress (thread 0): 94.0823%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_129.84_130.1, WER: 0%, TER: 0%, total WER: 26.2324%, total TER: 12.8152%, progress (thread 0): 94.0902%]
|T|: o h
|P|: o m
[sample: ES2004d_H03_FEE016_1400.02_1400.28, WER: 100%, TER: 50%, total WER: 26.2332%, total TER: 12.8154%, progress (thread 0): 94.0981%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1553.35_1553.61, WER: 0%, TER: 0%, total WER: 26.2329%, total TER: 12.8152%, progress (thread 0): 94.106%]
|T|: n o
|P|: n o
[sample: ES2004d_H01_FEE013_1608.41_1608.67, WER: 0%, TER: 0%, total WER: 26.2326%, total TER: 12.8152%, progress (thread 0): 94.1139%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_1680.41_1680.67, WER: 100%, TER: 40%, total WER: 26.2334%, total TER: 12.8155%, progress (thread 0): 94.1218%]
|T|: m m h m m
|P|: h
[sample: IS1009a_H03_FIO089_195.56_195.82, WER: 100%, TER: 80%, total WER: 26.2343%, total TER: 12.8163%, progress (thread 0): 94.1297%]
|T|: o k a y
|P|: a k
[sample: IS1009b_H01_FIO087_250.63_250.89, WER: 100%, TER: 75%, total WER: 26.2351%, total TER: 12.8169%, progress (thread 0): 94.1377%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_289.01_289.27, WER: 0%, TER: 0%, total WER: 26.2348%, total TER: 12.8168%, progress (thread 0): 94.1456%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_892.83_893.09, WER: 100%, TER: 100%, total WER: 26.2356%, total TER: 12.8178%, progress (thread 0): 94.1535%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_2020.18_2020.44, WER: 0%, TER: 0%, total WER: 26.2353%, total TER: 12.8177%, progress (thread 0): 94.1614%]
|T|: m m h m m
|P|: y e | h
[sample: IS1009c_H00_FIE088_626.71_626.97, WER: 200%, TER: 100%, total WER: 26.2373%, total TER: 12.8187%, progress (thread 0): 94.1693%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_766.43_766.69, WER: 0%, TER: 0%, total WER: 26.237%, total TER: 12.8186%, progress (thread 0): 94.1772%]
|T|: m m
|P|:
[sample: IS1009d_H01_FIO087_656.85_657.11, WER: 100%, TER: 100%, total WER: 26.2378%, total TER: 12.819%, progress (thread 0): 94.1851%]
|T|: ' c a u s e
|P|:
[sample: TS3003a_H03_MTD012ME_1376.63_1376.89, WER: 100%, TER: 100%, total WER: 26.2387%, total TER: 12.8202%, progress (thread 0): 94.193%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H02_MTD0010ID_1474.04_1474.3, WER: 0%, TER: 0%, total WER: 26.2384%, total TER: 12.8201%, progress (thread 0): 94.201%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_206.59_206.85, WER: 100%, TER: 25%, total WER: 26.2392%, total TER: 12.8202%, progress (thread 0): 94.2089%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H01_MTD011UID_581.65_581.91, WER: 0%, TER: 0%, total WER: 26.2389%, total TER: 12.8201%, progress (thread 0): 94.2168%]
|T|: h m m
|P|: h m
[sample: TS3003b_H03_MTD012ME_883.3_883.56, WER: 100%, TER: 33.3333%, total WER: 26.2397%, total TER: 12.8202%, progress (thread 0): 94.2247%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1533.07_1533.33, WER: 100%, TER: 40%, total WER: 26.2406%, total TER: 12.8205%, progress (thread 0): 94.2326%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_2048.3_2048.56, WER: 0%, TER: 0%, total WER: 26.2403%, total TER: 12.8205%, progress (thread 0): 94.2405%]
|T|: m m
|P|: t e
[sample: TS3003c_H01_MTD011UID_1933.15_1933.41, WER: 100%, TER: 100%, total WER: 26.2411%, total TER: 12.8209%, progress (thread 0): 94.2484%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1980.3_1980.56, WER: 0%, TER: 0%, total WER: 26.2408%, total TER: 12.8207%, progress (thread 0): 94.2563%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_694.34_694.6, WER: 0%, TER: 0%, total WER: 26.2405%, total TER: 12.8206%, progress (thread 0): 94.2642%]
|T|: t w o
|P|: t o
[sample: TS3003d_H03_MTD012ME_1897.81_1898.07, WER: 100%, TER: 33.3333%, total WER: 26.2413%, total TER: 12.8208%, progress (thread 0): 94.2722%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2141.52_2141.78, WER: 0%, TER: 0%, total WER: 26.241%, total TER: 12.8206%, progress (thread 0): 94.2801%]
|T|: m m h m m
|P|: m h | h
[sample: EN2002a_H02_FEO072_36.34_36.6, WER: 200%, TER: 60%, total WER: 26.243%, total TER: 12.8212%, progress (thread 0): 94.288%]
|T|: y e a h
|P|: y h
[sample: EN2002a_H00_MEE073_319.23_319.49, WER: 100%, TER: 50%, total WER: 26.2438%, total TER: 12.8215%, progress (thread 0): 94.2959%]
|T|: y e a h
|P|: e h
[sample: EN2002a_H03_MEE071_671.05_671.31, WER: 100%, TER: 50%, total WER: 26.2446%, total TER: 12.8219%, progress (thread 0): 94.3038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_721.89_722.15, WER: 0%, TER: 0%, total WER: 26.2443%, total TER: 12.8218%, progress (thread 0): 94.3117%]
|T|: t r u e
|P|: t r u e
[sample: EN2002a_H00_MEE073_751.8_752.06, WER: 0%, TER: 0%, total WER: 26.2441%, total TER: 12.8217%, progress (thread 0): 94.3196%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1052.1_1052.36, WER: 0%, TER: 0%, total WER: 26.2438%, total TER: 12.8215%, progress (thread 0): 94.3275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1446.67_1446.93, WER: 0%, TER: 0%, total WER: 26.2435%, total TER: 12.8214%, progress (thread 0): 94.3354%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1777.39_1777.65, WER: 0%, TER: 0%, total WER: 26.2432%, total TER: 12.8213%, progress (thread 0): 94.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1868.15_1868.41, WER: 0%, TER: 0%, total WER: 26.2429%, total TER: 12.8212%, progress (thread 0): 94.3513%]
|T|: o k a y
|P|: g
[sample: EN2002b_H02_FEO072_93.22_93.48, WER: 100%, TER: 100%, total WER: 26.2437%, total TER: 12.822%, progress (thread 0): 94.3592%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_252.07_252.33, WER: 200%, TER: 175%, total WER: 26.2457%, total TER: 12.8235%, progress (thread 0): 94.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_460.58_460.84, WER: 0%, TER: 0%, total WER: 26.2454%, total TER: 12.8234%, progress (thread 0): 94.375%]
|T|: o h
|P|: o h
[sample: EN2002b_H03_MEE073_674.06_674.32, WER: 0%, TER: 0%, total WER: 26.2451%, total TER: 12.8233%, progress (thread 0): 94.3829%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_1026.26_1026.52, WER: 200%, TER: 175%, total WER: 26.247%, total TER: 12.8248%, progress (thread 0): 94.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1104.98_1105.24, WER: 0%, TER: 0%, total WER: 26.2467%, total TER: 12.8247%, progress (thread 0): 94.3987%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_1549.41_1549.67, WER: 100%, TER: 33.3333%, total WER: 26.2476%, total TER: 12.8249%, progress (thread 0): 94.4066%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1643.55_1643.81, WER: 0%, TER: 0%, total WER: 26.2473%, total TER: 12.8247%, progress (thread 0): 94.4146%]
|T|: r i g h t
|P|: r i
[sample: EN2002c_H02_MEE071_46.45_46.71, WER: 100%, TER: 60%, total WER: 26.2481%, total TER: 12.8253%, progress (thread 0): 94.4225%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_577.1_577.36, WER: 0%, TER: 0%, total WER: 26.2478%, total TER: 12.8252%, progress (thread 0): 94.4304%]
|T|: y e p
|P|: n o
[sample: EN2002c_H01_FEO072_600.57_600.83, WER: 100%, TER: 100%, total WER: 26.2486%, total TER: 12.8258%, progress (thread 0): 94.4383%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_775.24_775.5, WER: 0%, TER: 0%, total WER: 26.2483%, total TER: 12.8257%, progress (thread 0): 94.4462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1079.16_1079.42, WER: 0%, TER: 0%, total WER: 26.248%, total TER: 12.8256%, progress (thread 0): 94.4541%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_1105.57_1105.83, WER: 100%, TER: 40%, total WER: 26.2489%, total TER: 12.8259%, progress (thread 0): 94.462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1119.48_1119.74, WER: 0%, TER: 0%, total WER: 26.2486%, total TER: 12.8258%, progress (thread 0): 94.4699%]
|T|: m m h m m
|P|: s o
[sample: EN2002c_H03_MEE073_1983.15_1983.41, WER: 100%, TER: 100%, total WER: 26.2494%, total TER: 12.8268%, progress (thread 0): 94.4779%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1999.1_1999.36, WER: 0%, TER: 0%, total WER: 26.2491%, total TER: 12.8266%, progress (thread 0): 94.4858%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2082.11_2082.37, WER: 0%, TER: 0%, total WER: 26.2488%, total TER: 12.8265%, progress (thread 0): 94.4937%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2586.11_2586.37, WER: 0%, TER: 0%, total WER: 26.2485%, total TER: 12.8264%, progress (thread 0): 94.5016%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_347.04_347.3, WER: 0%, TER: 0%, total WER: 26.2482%, total TER: 12.8263%, progress (thread 0): 94.5095%]
|T|: w h o
|P|: h m
[sample: EN2002d_H00_FEO070_501.31_501.57, WER: 100%, TER: 66.6667%, total WER: 26.249%, total TER: 12.8267%, progress (thread 0): 94.5174%]
|T|: y e a h
|P|: y e p
[sample: EN2002d_H02_MEE071_728.46_728.72, WER: 100%, TER: 50%, total WER: 26.2499%, total TER: 12.827%, progress (thread 0): 94.5253%]
|T|: w e l l | i t ' s
|P|: j u s t
[sample: EN2002d_H03_MEE073_882.54_882.8, WER: 100%, TER: 88.8889%, total WER: 26.2515%, total TER: 12.8286%, progress (thread 0): 94.5332%]
|T|: s u p e r
|P|: s u p
[sample: EN2002d_H03_MEE073_953.8_954.06, WER: 100%, TER: 40%, total WER: 26.2524%, total TER: 12.8289%, progress (thread 0): 94.5411%]
|T|: o h | y e a h
|P|:
[sample: EN2002d_H00_FEO070_1517.63_1517.89, WER: 100%, TER: 100%, total WER: 26.254%, total TER: 12.8303%, progress (thread 0): 94.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1704.28_1704.54, WER: 0%, TER: 0%, total WER: 26.2537%, total TER: 12.8302%, progress (thread 0): 94.557%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_2135.36_2135.62, WER: 100%, TER: 33.3333%, total WER: 26.2546%, total TER: 12.8304%, progress (thread 0): 94.5649%]
|T|: m m
|P|: h m
[sample: ES2004a_H03_FEE016_666.69_666.94, WER: 100%, TER: 50%, total WER: 26.2554%, total TER: 12.8305%, progress (thread 0): 94.5728%]
|T|: y e p
|P|: y e p
[sample: ES2004b_H00_MEO015_817.97_818.22, WER: 0%, TER: 0%, total WER: 26.2551%, total TER: 12.8305%, progress (thread 0): 94.5807%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_2175.08_2175.33, WER: 100%, TER: 40%, total WER: 26.2559%, total TER: 12.8308%, progress (thread 0): 94.5886%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_620.36_620.61, WER: 100%, TER: 40%, total WER: 26.2567%, total TER: 12.8311%, progress (thread 0): 94.5965%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1866.87_1867.12, WER: 100%, TER: 100%, total WER: 26.2576%, total TER: 12.8317%, progress (thread 0): 94.6044%]
|T|: s o r r y
|P|: s o r r y
[sample: ES2004d_H01_FEE013_455.38_455.63, WER: 0%, TER: 0%, total WER: 26.2573%, total TER: 12.8315%, progress (thread 0): 94.6123%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_607.67_607.92, WER: 0%, TER: 0%, total WER: 26.257%, total TER: 12.8314%, progress (thread 0): 94.6203%]
|T|: y e s
|P|: y e a h
[sample: ES2004d_H03_FEE016_1135.45_1135.7, WER: 100%, TER: 66.6667%, total WER: 26.2578%, total TER: 12.8318%, progress (thread 0): 94.6282%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1553.38_1553.63, WER: 0%, TER: 0%, total WER: 26.2575%, total TER: 12.8317%, progress (thread 0): 94.6361%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1086.32_1086.57, WER: 100%, TER: 40%, total WER: 26.2583%, total TER: 12.832%, progress (thread 0): 94.644%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1900.42_1900.67, WER: 0%, TER: 0%, total WER: 26.2581%, total TER: 12.8319%, progress (thread 0): 94.6519%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1909.04_1909.29, WER: 0%, TER: 0%, total WER: 26.2578%, total TER: 12.8318%, progress (thread 0): 94.6598%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H00_FIE088_324.58_324.83, WER: 0%, TER: 0%, total WER: 26.2575%, total TER: 12.8316%, progress (thread 0): 94.6677%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1287.33_1287.58, WER: 0%, TER: 0%, total WER: 26.2572%, total TER: 12.8315%, progress (thread 0): 94.6756%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1781.33_1781.58, WER: 0%, TER: 0%, total WER: 26.2569%, total TER: 12.8314%, progress (thread 0): 94.6835%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_351.32_351.57, WER: 0%, TER: 0%, total WER: 26.2566%, total TER: 12.8313%, progress (thread 0): 94.6915%]
|T|: m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_427.43_427.68, WER: 100%, TER: 200%, total WER: 26.2574%, total TER: 12.8322%, progress (thread 0): 94.6994%]
|T|: m m
|P|: a
[sample: IS1009d_H02_FIO084_826.51_826.76, WER: 100%, TER: 100%, total WER: 26.2582%, total TER: 12.8326%, progress (thread 0): 94.7073%]
|T|: n o
|P|: n o
[sample: TS3003a_H03_MTD012ME_1222.74_1222.99, WER: 0%, TER: 0%, total WER: 26.2579%, total TER: 12.8325%, progress (thread 0): 94.7152%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_281.83_282.08, WER: 100%, TER: 50%, total WER: 26.2588%, total TER: 12.8327%, progress (thread 0): 94.7231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1559.52_1559.77, WER: 0%, TER: 0%, total WER: 26.2585%, total TER: 12.8326%, progress (thread 0): 94.731%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_1839.11_1839.36, WER: 0%, TER: 0%, total WER: 26.2582%, total TER: 12.8324%, progress (thread 0): 94.7389%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H02_MTD0010ID_2094.57_2094.82, WER: 100%, TER: 50%, total WER: 26.259%, total TER: 12.8328%, progress (thread 0): 94.7468%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1972.52_1972.77, WER: 100%, TER: 40%, total WER: 26.2598%, total TER: 12.8331%, progress (thread 0): 94.7548%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_303.27_303.52, WER: 100%, TER: 75%, total WER: 26.2607%, total TER: 12.8337%, progress (thread 0): 94.7627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_583.29_583.54, WER: 0%, TER: 0%, total WER: 26.2604%, total TER: 12.8336%, progress (thread 0): 94.7706%]
|T|: y e a h
|P|: y e h
[sample: TS3003d_H02_MTD0010ID_847.16_847.41, WER: 100%, TER: 25%, total WER: 26.2612%, total TER: 12.8337%, progress (thread 0): 94.7785%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1370.99_1371.24, WER: 100%, TER: 75%, total WER: 26.262%, total TER: 12.8343%, progress (thread 0): 94.7864%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1371.48_1371.73, WER: 0%, TER: 0%, total WER: 26.2617%, total TER: 12.8341%, progress (thread 0): 94.7943%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1404.32_1404.57, WER: 100%, TER: 75%, total WER: 26.2626%, total TER: 12.8347%, progress (thread 0): 94.8022%]
|T|: g o o d
|P|: c a u s e
[sample: TS3003d_H03_MTD012ME_1696.35_1696.6, WER: 100%, TER: 125%, total WER: 26.2634%, total TER: 12.8358%, progress (thread 0): 94.8101%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2064.36_2064.61, WER: 0%, TER: 0%, total WER: 26.2631%, total TER: 12.8356%, progress (thread 0): 94.818%]
|T|: y e a h
|P|:
[sample: TS3003d_H02_MTD0010ID_2111_2111.25, WER: 100%, TER: 100%, total WER: 26.2639%, total TER: 12.8365%, progress (thread 0): 94.826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2138.4_2138.65, WER: 0%, TER: 0%, total WER: 26.2636%, total TER: 12.8363%, progress (thread 0): 94.8339%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2216.27_2216.52, WER: 0%, TER: 0%, total WER: 26.2633%, total TER: 12.8362%, progress (thread 0): 94.8418%]
|T|: a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2381.92_2382.17, WER: 100%, TER: 100%, total WER: 26.2642%, total TER: 12.8366%, progress (thread 0): 94.8497%]
|T|: s o
|P|: s o
[sample: EN2002a_H00_MEE073_202.42_202.67, WER: 0%, TER: 0%, total WER: 26.2639%, total TER: 12.8366%, progress (thread 0): 94.8576%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002a_H00_MEE073_749.45_749.7, WER: 200%, TER: 175%, total WER: 26.2658%, total TER: 12.8381%, progress (thread 0): 94.8655%]
|T|: y e a h
|P|: t o
[sample: EN2002a_H00_MEE073_1642_1642.25, WER: 100%, TER: 100%, total WER: 26.2666%, total TER: 12.8389%, progress (thread 0): 94.8734%]
|T|: s o
|P|: s o
[sample: EN2002a_H02_FEO072_1664.11_1664.36, WER: 0%, TER: 0%, total WER: 26.2663%, total TER: 12.8388%, progress (thread 0): 94.8813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1964.06_1964.31, WER: 0%, TER: 0%, total WER: 26.2661%, total TER: 12.8387%, progress (thread 0): 94.8892%]
|T|: y o u ' d | b e | s
|P|: i n p
[sample: EN2002a_H00_MEE073_2096.92_2097.17, WER: 100%, TER: 100%, total WER: 26.2685%, total TER: 12.8408%, progress (thread 0): 94.8971%]
|T|: y e a h
|P|: y e p
[sample: EN2002b_H01_MEE071_93.68_93.93, WER: 100%, TER: 50%, total WER: 26.2694%, total TER: 12.8411%, progress (thread 0): 94.9051%]
|T|: r i g h t
|P|: i
[sample: EN2002b_H03_MEE073_311.61_311.86, WER: 100%, TER: 80%, total WER: 26.2702%, total TER: 12.8419%, progress (thread 0): 94.913%]
|T|: a l r i g h t
|P|: r g h t
[sample: EN2002b_H03_MEE073_1129.86_1130.11, WER: 100%, TER: 42.8571%, total WER: 26.271%, total TER: 12.8424%, progress (thread 0): 94.9209%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1222.15_1222.4, WER: 0%, TER: 0%, total WER: 26.2707%, total TER: 12.8423%, progress (thread 0): 94.9288%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1237.83_1238.08, WER: 0%, TER: 0%, total WER: 26.2704%, total TER: 12.8422%, progress (thread 0): 94.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1245.09_1245.34, WER: 0%, TER: 0%, total WER: 26.2701%, total TER: 12.842%, progress (thread 0): 94.9446%]
|T|: a n d
|P|: t h e n
[sample: EN2002b_H01_MEE071_1309.75_1310, WER: 100%, TER: 133.333%, total WER: 26.271%, total TER: 12.8429%, progress (thread 0): 94.9525%]
|T|: y e p
|P|: n o
[sample: EN2002b_H02_FEO072_1594.84_1595.09, WER: 100%, TER: 100%, total WER: 26.2718%, total TER: 12.8435%, progress (thread 0): 94.9604%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_1643.59_1643.84, WER: 200%, TER: 175%, total WER: 26.2737%, total TER: 12.845%, progress (thread 0): 94.9684%]
|T|: s u r e
|P|: s u e
[sample: EN2002c_H02_MEE071_67.06_67.31, WER: 100%, TER: 25%, total WER: 26.2746%, total TER: 12.8451%, progress (thread 0): 94.9763%]
|T|: o k a y
|P|: n
[sample: EN2002c_H03_MEE073_76.13_76.38, WER: 100%, TER: 100%, total WER: 26.2754%, total TER: 12.8459%, progress (thread 0): 94.9842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_253.63_253.88, WER: 0%, TER: 0%, total WER: 26.2751%, total TER: 12.8458%, progress (thread 0): 94.9921%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_470.85_471.1, WER: 100%, TER: 40%, total WER: 26.2759%, total TER: 12.8461%, progress (thread 0): 95%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_759.06_759.31, WER: 0%, TER: 0%, total WER: 26.2756%, total TER: 12.846%, progress (thread 0): 95.0079%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1000.82_1001.07, WER: 0%, TER: 0%, total WER: 26.2753%, total TER: 12.8459%, progress (thread 0): 95.0158%]
|T|: y e a h
|P|: y e a | h | w
[sample: EN2002c_H03_MEE073_1023.06_1023.31, WER: 300%, TER: 75%, total WER: 26.2784%, total TER: 12.8464%, progress (thread 0): 95.0237%]
|T|: m m h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1102.48_1102.73, WER: 100%, TER: 60%, total WER: 26.2793%, total TER: 12.847%, progress (thread 0): 95.0316%]
|T|: y e a h
|P|: y e s
[sample: EN2002c_H02_MEE071_1122.76_1123.01, WER: 100%, TER: 50%, total WER: 26.2801%, total TER: 12.8473%, progress (thread 0): 95.0396%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_1359.23_1359.48, WER: 100%, TER: 66.6667%, total WER: 26.2809%, total TER: 12.8477%, progress (thread 0): 95.0475%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_1508.23_1508.48, WER: 0%, TER: 0%, total WER: 26.2806%, total TER: 12.8476%, progress (thread 0): 95.0554%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2100.94_2101.19, WER: 0%, TER: 0%, total WER: 26.2803%, total TER: 12.8475%, progress (thread 0): 95.0633%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2354.17_2354.42, WER: 0%, TER: 0%, total WER: 26.28%, total TER: 12.8474%, progress (thread 0): 95.0712%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_146.37_146.62, WER: 100%, TER: 28.5714%, total WER: 26.2808%, total TER: 12.8476%, progress (thread 0): 95.0791%]
|T|: o h
|P|: o
[sample: EN2002d_H00_FEO070_286.77_287.02, WER: 100%, TER: 50%, total WER: 26.2817%, total TER: 12.8478%, progress (thread 0): 95.087%]
|T|: y e a h
|P|: y h m
[sample: EN2002d_H03_MEE073_589.91_590.16, WER: 100%, TER: 75%, total WER: 26.2825%, total TER: 12.8484%, progress (thread 0): 95.0949%]
|T|: y e a h
|P|: c a n c e
[sample: EN2002d_H03_MEE073_850.49_850.74, WER: 100%, TER: 125%, total WER: 26.2833%, total TER: 12.8494%, progress (thread 0): 95.1028%]
|T|: s o
|P|: s o
[sample: EN2002d_H02_MEE071_1251.8_1252.05, WER: 0%, TER: 0%, total WER: 26.283%, total TER: 12.8494%, progress (thread 0): 95.1108%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_1830.85_1831.1, WER: 100%, TER: 33.3333%, total WER: 26.2839%, total TER: 12.8495%, progress (thread 0): 95.1187%]
|T|: o k a y
|P|: c o n
[sample: EN2002d_H03_MEE073_1854.42_1854.67, WER: 100%, TER: 100%, total WER: 26.2847%, total TER: 12.8503%, progress (thread 0): 95.1266%]
|T|: m m
|P|: o
[sample: EN2002d_H01_FEO072_2083.17_2083.42, WER: 100%, TER: 100%, total WER: 26.2855%, total TER: 12.8508%, progress (thread 0): 95.1345%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2192.17_2192.42, WER: 0%, TER: 0%, total WER: 26.2852%, total TER: 12.8506%, progress (thread 0): 95.1424%]
|T|: s
|P|: s
[sample: ES2004a_H02_MEE014_484.48_484.72, WER: 0%, TER: 0%, total WER: 26.2849%, total TER: 12.8506%, progress (thread 0): 95.1503%]
|T|: m m
|P|:
[sample: ES2004a_H03_FEE016_775.6_775.84, WER: 100%, TER: 100%, total WER: 26.2858%, total TER: 12.851%, progress (thread 0): 95.1582%]
|T|: s o r r y
|P|: t
[sample: ES2004b_H00_MEO015_100.7_100.94, WER: 100%, TER: 100%, total WER: 26.2866%, total TER: 12.852%, progress (thread 0): 95.1661%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1311.92_1312.16, WER: 0%, TER: 0%, total WER: 26.2863%, total TER: 12.8519%, progress (thread 0): 95.174%]
|T|: m m h m m
|P|: m h u m
[sample: ES2004b_H00_MEO015_1636.65_1636.89, WER: 100%, TER: 40%, total WER: 26.2871%, total TER: 12.8522%, progress (thread 0): 95.182%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1305.92_1306.16, WER: 100%, TER: 25%, total WER: 26.2879%, total TER: 12.8523%, progress (thread 0): 95.1899%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1345.16_1345.4, WER: 100%, TER: 50%, total WER: 26.2888%, total TER: 12.8525%, progress (thread 0): 95.1978%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_1364.93_1365.17, WER: 100%, TER: 100%, total WER: 26.2896%, total TER: 12.8529%, progress (thread 0): 95.2057%]
|T|: f i n e
|P|: p o i n t
[sample: ES2004c_H00_MEO015_2156.57_2156.81, WER: 100%, TER: 75%, total WER: 26.2904%, total TER: 12.8535%, progress (thread 0): 95.2136%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_973.73_973.97, WER: 0%, TER: 0%, total WER: 26.2901%, total TER: 12.8534%, progress (thread 0): 95.2215%]
|T|: m m
|P|: h
[sample: ES2004d_H03_FEE016_1286.7_1286.94, WER: 100%, TER: 100%, total WER: 26.291%, total TER: 12.8538%, progress (thread 0): 95.2294%]
|T|: o h
|P|: o h
[sample: ES2004d_H01_FEE013_2176.67_2176.91, WER: 0%, TER: 0%, total WER: 26.2907%, total TER: 12.8537%, progress (thread 0): 95.2373%]
|T|: n o
|P|: h
[sample: IS1009a_H03_FIO089_145.55_145.79, WER: 100%, TER: 100%, total WER: 26.2915%, total TER: 12.8541%, progress (thread 0): 95.2453%]
|T|: m m h m m
|P|:
[sample: IS1009a_H00_FIE088_466.21_466.45, WER: 100%, TER: 100%, total WER: 26.2923%, total TER: 12.8551%, progress (thread 0): 95.2532%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_556.58_556.82, WER: 0%, TER: 0%, total WER: 26.292%, total TER: 12.855%, progress (thread 0): 95.2611%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1033.16_1033.4, WER: 0%, TER: 0%, total WER: 26.2917%, total TER: 12.8549%, progress (thread 0): 95.269%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_1099.19_1099.43, WER: 0%, TER: 0%, total WER: 26.2914%, total TER: 12.8548%, progress (thread 0): 95.2769%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1247.84_1248.08, WER: 100%, TER: 60%, total WER: 26.2923%, total TER: 12.8553%, progress (thread 0): 95.2848%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H03_FIO089_752.07_752.31, WER: 100%, TER: 40%, total WER: 26.2931%, total TER: 12.8557%, progress (thread 0): 95.2927%]
|T|: m m | r i g h t
|P|: r i g t
[sample: IS1009c_H00_FIE088_764.18_764.42, WER: 100%, TER: 50%, total WER: 26.2947%, total TER: 12.8563%, progress (thread 0): 95.3006%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1110.14_1110.38, WER: 100%, TER: 40%, total WER: 26.2956%, total TER: 12.8567%, progress (thread 0): 95.3085%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1123.59_1123.83, WER: 100%, TER: 40%, total WER: 26.2964%, total TER: 12.857%, progress (thread 0): 95.3165%]
|T|: h e l l o
|P|: n o
[sample: IS1009d_H02_FIO084_41.31_41.55, WER: 100%, TER: 80%, total WER: 26.2972%, total TER: 12.8578%, progress (thread 0): 95.3244%]
|T|: m m
|P|: m m
[sample: IS1009d_H03_FIO089_516.52_516.76, WER: 0%, TER: 0%, total WER: 26.2969%, total TER: 12.8577%, progress (thread 0): 95.3323%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_656.9_657.14, WER: 100%, TER: 60%, total WER: 26.2978%, total TER: 12.8583%, progress (thread 0): 95.3402%]
|T|: y e a h
|P|: y o n o
[sample: IS1009d_H02_FIO084_1011.72_1011.96, WER: 100%, TER: 75%, total WER: 26.2986%, total TER: 12.8588%, progress (thread 0): 95.3481%]
|T|: o k a y
|P|: o k a
[sample: IS1009d_H03_FIO089_1883.01_1883.25, WER: 100%, TER: 25%, total WER: 26.2994%, total TER: 12.8589%, progress (thread 0): 95.356%]
|T|: w e
|P|: w
[sample: TS3003a_H00_MTD009PM_1109.9_1110.14, WER: 100%, TER: 50%, total WER: 26.3002%, total TER: 12.8591%, progress (thread 0): 95.3639%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1420.32_1420.56, WER: 100%, TER: 50%, total WER: 26.3011%, total TER: 12.8593%, progress (thread 0): 95.3718%]
|T|: n o
|P|: n o
[sample: TS3003b_H02_MTD0010ID_1474.4_1474.64, WER: 0%, TER: 0%, total WER: 26.3008%, total TER: 12.8592%, progress (thread 0): 95.3797%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_1677.79_1678.03, WER: 0%, TER: 0%, total WER: 26.3005%, total TER: 12.8591%, progress (thread 0): 95.3877%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1975.67_1975.91, WER: 100%, TER: 100%, total WER: 26.3013%, total TER: 12.8595%, progress (thread 0): 95.3956%]
|T|: y e a h
|P|: h u m
[sample: TS3003b_H01_MTD011UID_2083.9_2084.14, WER: 100%, TER: 100%, total WER: 26.3021%, total TER: 12.8603%, progress (thread 0): 95.4035%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_1240.02_1240.26, WER: 100%, TER: 75%, total WER: 26.303%, total TER: 12.8609%, progress (thread 0): 95.4114%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1254.68_1254.92, WER: 0%, TER: 0%, total WER: 26.3027%, total TER: 12.8608%, progress (thread 0): 95.4193%]
|T|: s o
|P|: s o
[sample: TS3003c_H01_MTD011UID_1417.83_1418.07, WER: 0%, TER: 0%, total WER: 26.3024%, total TER: 12.8607%, progress (thread 0): 95.4272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1477.96_1478.2, WER: 0%, TER: 0%, total WER: 26.3021%, total TER: 12.8606%, progress (thread 0): 95.4351%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1566.64_1566.88, WER: 100%, TER: 25%, total WER: 26.3029%, total TER: 12.8607%, progress (thread 0): 95.443%]
|T|: y e a h
|P|: a h
[sample: TS3003c_H01_MTD011UID_1716.46_1716.7, WER: 100%, TER: 50%, total WER: 26.3037%, total TER: 12.8611%, progress (thread 0): 95.451%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2217.59_2217.83, WER: 0%, TER: 0%, total WER: 26.3034%, total TER: 12.861%, progress (thread 0): 95.4589%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2243.54_2243.78, WER: 0%, TER: 0%, total WER: 26.3031%, total TER: 12.8608%, progress (thread 0): 95.4668%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1049.33_1049.57, WER: 0%, TER: 0%, total WER: 26.3028%, total TER: 12.8607%, progress (thread 0): 95.4747%]
|T|: y e a h
|P|: o h
[sample: TS3003d_H01_MTD011UID_1496.68_1496.92, WER: 100%, TER: 75%, total WER: 26.3037%, total TER: 12.8613%, progress (thread 0): 95.4826%]
|T|: d | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_106.38_106.62, WER: 50%, TER: 33.3333%, total WER: 26.3042%, total TER: 12.8616%, progress (thread 0): 95.4905%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_140.88_141.12, WER: 0%, TER: 0%, total WER: 26.3039%, total TER: 12.8615%, progress (thread 0): 95.4984%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_333.3_333.54, WER: 0%, TER: 0%, total WER: 26.3036%, total TER: 12.8613%, progress (thread 0): 95.5063%]
|T|: y e a h
|P|: h m
[sample: EN2002a_H01_FEO070_398.18_398.42, WER: 100%, TER: 100%, total WER: 26.3044%, total TER: 12.8621%, progress (thread 0): 95.5142%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_481.62_481.86, WER: 100%, TER: 50%, total WER: 26.3053%, total TER: 12.8625%, progress (thread 0): 95.5222%]
|T|: y e a h
|P|: e m
[sample: EN2002a_H00_MEE073_682.97_683.21, WER: 100%, TER: 75%, total WER: 26.3061%, total TER: 12.863%, progress (thread 0): 95.5301%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H02_FEO072_1009.94_1010.18, WER: 100%, TER: 40%, total WER: 26.3069%, total TER: 12.8634%, progress (thread 0): 95.538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1041.03_1041.27, WER: 0%, TER: 0%, total WER: 26.3066%, total TER: 12.8632%, progress (thread 0): 95.5459%]
|T|: i | s e e
|P|: a s
[sample: EN2002a_H00_MEE073_1171.72_1171.96, WER: 100%, TER: 80%, total WER: 26.3083%, total TER: 12.864%, progress (thread 0): 95.5538%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002a_H00_MEE073_1209.32_1209.56, WER: 100%, TER: 85.7143%, total WER: 26.3099%, total TER: 12.8652%, progress (thread 0): 95.5617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1310.69_1310.93, WER: 0%, TER: 0%, total WER: 26.3096%, total TER: 12.8651%, progress (thread 0): 95.5696%]
|T|: o p e n
|P|:
[sample: EN2002a_H02_FEO072_1350.04_1350.28, WER: 100%, TER: 100%, total WER: 26.3105%, total TER: 12.8659%, progress (thread 0): 95.5775%]
|T|: m m
|P|: h m
[sample: EN2002a_H03_MEE071_1405.73_1405.97, WER: 100%, TER: 50%, total WER: 26.3113%, total TER: 12.8661%, progress (thread 0): 95.5854%]
|T|: r i g h t
|P|: r i g t
[sample: EN2002a_H00_MEE073_1791.17_1791.41, WER: 100%, TER: 20%, total WER: 26.3121%, total TER: 12.8662%, progress (thread 0): 95.5934%]
|T|: y e a h
|P|: t e h
[sample: EN2002a_H00_MEE073_1834.42_1834.66, WER: 100%, TER: 50%, total WER: 26.313%, total TER: 12.8665%, progress (thread 0): 95.6013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2028.12_2028.36, WER: 0%, TER: 0%, total WER: 26.3127%, total TER: 12.8664%, progress (thread 0): 95.6092%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_2040.58_2040.82, WER: 0%, TER: 0%, total WER: 26.3124%, total TER: 12.8663%, progress (thread 0): 95.6171%]
|T|: o h
|P|: o h
[sample: EN2002a_H01_FEO070_2098.91_2099.15, WER: 0%, TER: 0%, total WER: 26.3121%, total TER: 12.8662%, progress (thread 0): 95.625%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_522.48_522.72, WER: 0%, TER: 0%, total WER: 26.3118%, total TER: 12.8661%, progress (thread 0): 95.6329%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_612.23_612.47, WER: 0%, TER: 0%, total WER: 26.3115%, total TER: 12.866%, progress (thread 0): 95.6408%]
|T|: m m
|P|:
[sample: EN2002b_H02_FEO072_1475.84_1476.08, WER: 100%, TER: 100%, total WER: 26.3123%, total TER: 12.8664%, progress (thread 0): 95.6487%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_235.84_236.08, WER: 100%, TER: 33.3333%, total WER: 26.3131%, total TER: 12.8665%, progress (thread 0): 95.6566%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_571.47_571.71, WER: 0%, TER: 0%, total WER: 26.3128%, total TER: 12.8664%, progress (thread 0): 95.6646%]
|T|: h m m
|P|: y o u h
[sample: EN2002c_H03_MEE073_574.34_574.58, WER: 100%, TER: 133.333%, total WER: 26.3137%, total TER: 12.8672%, progress (thread 0): 95.6725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_598.8_599.04, WER: 0%, TER: 0%, total WER: 26.3134%, total TER: 12.8671%, progress (thread 0): 95.6804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1009.87_1010.11, WER: 0%, TER: 0%, total WER: 26.3131%, total TER: 12.867%, progress (thread 0): 95.6883%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1018.66_1018.9, WER: 0%, TER: 0%, total WER: 26.3128%, total TER: 12.8669%, progress (thread 0): 95.6962%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1281.36_1281.6, WER: 100%, TER: 33.3333%, total WER: 26.3136%, total TER: 12.867%, progress (thread 0): 95.7041%]
|T|: y e a h
|P|: y e a | k n | w
[sample: EN2002c_H03_MEE073_1396.34_1396.58, WER: 300%, TER: 125%, total WER: 26.3167%, total TER: 12.868%, progress (thread 0): 95.712%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1906.84_1907.08, WER: 0%, TER: 0%, total WER: 26.3164%, total TER: 12.8679%, progress (thread 0): 95.7199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1913.84_1914.08, WER: 0%, TER: 0%, total WER: 26.3161%, total TER: 12.8678%, progress (thread 0): 95.7279%]
|T|: ' c a u s e
|P|: a s
[sample: EN2002c_H03_MEE073_2264.19_2264.43, WER: 100%, TER: 66.6667%, total WER: 26.3169%, total TER: 12.8686%, progress (thread 0): 95.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2309.3_2309.54, WER: 0%, TER: 0%, total WER: 26.3166%, total TER: 12.8684%, progress (thread 0): 95.7437%]
|T|: r i g h t
|P|: i
[sample: EN2002c_H03_MEE073_2341.41_2341.65, WER: 100%, TER: 80%, total WER: 26.3174%, total TER: 12.8692%, progress (thread 0): 95.7516%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2370.76_2371, WER: 0%, TER: 0%, total WER: 26.3171%, total TER: 12.8691%, progress (thread 0): 95.7595%]
|T|: g o o d
|P|: g o o d
[sample: EN2002d_H01_FEO072_337.97_338.21, WER: 0%, TER: 0%, total WER: 26.3169%, total TER: 12.869%, progress (thread 0): 95.7674%]
|T|: y e a h
|P|: t o
[sample: EN2002d_H03_MEE073_390.81_391.05, WER: 100%, TER: 100%, total WER: 26.3177%, total TER: 12.8698%, progress (thread 0): 95.7753%]
|T|: r i g h t
|P|: r i g h
[sample: EN2002d_H01_FEO072_496.78_497.02, WER: 100%, TER: 20%, total WER: 26.3185%, total TER: 12.8698%, progress (thread 0): 95.7832%]
|T|: o k a y
|P|: u h
[sample: EN2002d_H00_FEO070_513.66_513.9, WER: 100%, TER: 100%, total WER: 26.3193%, total TER: 12.8707%, progress (thread 0): 95.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1190.34_1190.58, WER: 0%, TER: 0%, total WER: 26.319%, total TER: 12.8705%, progress (thread 0): 95.799%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1371.83_1372.07, WER: 0%, TER: 0%, total WER: 26.3187%, total TER: 12.8704%, progress (thread 0): 95.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1477.13_1477.37, WER: 0%, TER: 0%, total WER: 26.3184%, total TER: 12.8703%, progress (thread 0): 95.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1706.39_1706.63, WER: 0%, TER: 0%, total WER: 26.3182%, total TER: 12.8702%, progress (thread 0): 95.8228%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H03_FEE016_1717.78_1718.01, WER: 100%, TER: 200%, total WER: 26.319%, total TER: 12.8711%, progress (thread 0): 95.8307%]
|T|: m m h m m
|P|: h
[sample: ES2004b_H00_MEO015_1860.91_1861.14, WER: 100%, TER: 80%, total WER: 26.3198%, total TER: 12.8718%, progress (thread 0): 95.8386%]
|T|: t r u e
|P|: j u
[sample: ES2004b_H00_MEO015_2294.79_2295.02, WER: 100%, TER: 75%, total WER: 26.3206%, total TER: 12.8724%, progress (thread 0): 95.8465%]
|T|: y e a h
|P|: a n d
[sample: ES2004c_H00_MEO015_85.13_85.36, WER: 100%, TER: 100%, total WER: 26.3215%, total TER: 12.8732%, progress (thread 0): 95.8544%]
|T|: ' k a y
|P|: k a
[sample: ES2004c_H00_MEO015_356.94_357.17, WER: 100%, TER: 50%, total WER: 26.3223%, total TER: 12.8736%, progress (thread 0): 95.8623%]
|T|: s o
|P|: a
[sample: ES2004c_H02_MEE014_748.27_748.5, WER: 100%, TER: 100%, total WER: 26.3231%, total TER: 12.874%, progress (thread 0): 95.8702%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_1121.33_1121.56, WER: 100%, TER: 100%, total WER: 26.3239%, total TER: 12.8744%, progress (thread 0): 95.8782%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1169.98_1170.21, WER: 0%, TER: 0%, total WER: 26.3236%, total TER: 12.8743%, progress (thread 0): 95.8861%]
|T|: o k a y
|P|: o
[sample: ES2004d_H03_FEE016_711.82_712.05, WER: 100%, TER: 75%, total WER: 26.3245%, total TER: 12.8748%, progress (thread 0): 95.894%]
|T|: t w o
|P|: d o
[sample: ES2004d_H00_MEO015_732.46_732.69, WER: 100%, TER: 66.6667%, total WER: 26.3253%, total TER: 12.8752%, progress (thread 0): 95.9019%]
|T|: m m h m m
|P|: h
[sample: ES2004d_H00_MEO015_822.98_823.21, WER: 100%, TER: 80%, total WER: 26.3261%, total TER: 12.876%, progress (thread 0): 95.9098%]
|T|: ' k a y
|P|: s c h o o
[sample: ES2004d_H00_MEO015_1813.16_1813.39, WER: 100%, TER: 125%, total WER: 26.3269%, total TER: 12.877%, progress (thread 0): 95.9177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_195.04_195.27, WER: 0%, TER: 0%, total WER: 26.3267%, total TER: 12.8769%, progress (thread 0): 95.9256%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_532.46_532.69, WER: 0%, TER: 0%, total WER: 26.3264%, total TER: 12.8768%, progress (thread 0): 95.9335%]
|T|: m m
|P|: h m
[sample: IS1009b_H02_FIO084_55.21_55.44, WER: 100%, TER: 50%, total WER: 26.3272%, total TER: 12.877%, progress (thread 0): 95.9415%]
|T|: y e a h
|P|: y e
[sample: IS1009b_H02_FIO084_1919.19_1919.42, WER: 100%, TER: 50%, total WER: 26.328%, total TER: 12.8774%, progress (thread 0): 95.9494%]
|T|: m m h m m
|P|: h m
[sample: IS1009c_H00_FIE088_554.54_554.77, WER: 100%, TER: 60%, total WER: 26.3288%, total TER: 12.8779%, progress (thread 0): 95.9573%]
|T|: m m | y e s
|P|: y e a h
[sample: IS1009c_H01_FIO087_728.73_728.96, WER: 100%, TER: 83.3333%, total WER: 26.3305%, total TER: 12.8789%, progress (thread 0): 95.9652%]
|T|: y e p
|P|: y e a h
[sample: IS1009c_H03_FIO089_1639.78_1640.01, WER: 100%, TER: 66.6667%, total WER: 26.3313%, total TER: 12.8793%, progress (thread 0): 95.9731%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_747.97_748.2, WER: 100%, TER: 66.6667%, total WER: 26.3321%, total TER: 12.8796%, progress (thread 0): 95.981%]
|T|: h m m
|P|:
[sample: IS1009d_H02_FIO084_748.42_748.65, WER: 100%, TER: 100%, total WER: 26.333%, total TER: 12.8803%, progress (thread 0): 95.9889%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_758.1_758.33, WER: 100%, TER: 100%, total WER: 26.3338%, total TER: 12.8813%, progress (thread 0): 95.9968%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_1695.75_1695.98, WER: 0%, TER: 0%, total WER: 26.3335%, total TER: 12.8812%, progress (thread 0): 96.0047%]
|T|: o k a y
|P|: y e
[sample: IS1009d_H03_FIO089_1889.3_1889.53, WER: 100%, TER: 100%, total WER: 26.3343%, total TER: 12.882%, progress (thread 0): 96.0127%]
|T|: ' k a y
|P|: o k a
[sample: TS3003a_H03_MTD012ME_843.57_843.8, WER: 100%, TER: 50%, total WER: 26.3352%, total TER: 12.8823%, progress (thread 0): 96.0206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1334.2_1334.43, WER: 0%, TER: 0%, total WER: 26.3349%, total TER: 12.8822%, progress (thread 0): 96.0285%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1346.08_1346.31, WER: 0%, TER: 0%, total WER: 26.3346%, total TER: 12.8821%, progress (thread 0): 96.0364%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1078.3_1078.53, WER: 100%, TER: 50%, total WER: 26.3354%, total TER: 12.8823%, progress (thread 0): 96.0443%]
|T|: n o
|P|: n o
[sample: TS3003b_H01_MTD011UID_1716.14_1716.37, WER: 0%, TER: 0%, total WER: 26.3351%, total TER: 12.8822%, progress (thread 0): 96.0522%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_1250.59_1250.82, WER: 0%, TER: 0%, total WER: 26.3348%, total TER: 12.8822%, progress (thread 0): 96.0601%]
|T|: y e s
|P|: j u s t
[sample: TS3003c_H02_MTD0010ID_1518.39_1518.62, WER: 100%, TER: 100%, total WER: 26.3356%, total TER: 12.8828%, progress (thread 0): 96.068%]
|T|: m m
|P|: a m
[sample: TS3003c_H01_MTD011UID_1654.26_1654.49, WER: 100%, TER: 50%, total WER: 26.3365%, total TER: 12.8829%, progress (thread 0): 96.076%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_2049.46_2049.69, WER: 100%, TER: 75%, total WER: 26.3373%, total TER: 12.8835%, progress (thread 0): 96.0839%]
|T|: s o
|P|: s o
[sample: TS3003d_H01_MTD011UID_261.45_261.68, WER: 0%, TER: 0%, total WER: 26.337%, total TER: 12.8835%, progress (thread 0): 96.0918%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H02_MTD0010ID_1021.27_1021.5, WER: 0%, TER: 0%, total WER: 26.3367%, total TER: 12.8834%, progress (thread 0): 96.0997%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_1945.96_1946.19, WER: 100%, TER: 100%, total WER: 26.3375%, total TER: 12.8842%, progress (thread 0): 96.1076%]
|T|: y e a h
|P|: e a h
[sample: TS3003d_H01_MTD011UID_2064.77_2065, WER: 100%, TER: 25%, total WER: 26.3383%, total TER: 12.8843%, progress (thread 0): 96.1155%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_424.58_424.81, WER: 0%, TER: 0%, total WER: 26.338%, total TER: 12.8841%, progress (thread 0): 96.1234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_781.91_782.14, WER: 0%, TER: 0%, total WER: 26.3377%, total TER: 12.884%, progress (thread 0): 96.1313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_855.85_856.08, WER: 0%, TER: 0%, total WER: 26.3375%, total TER: 12.8839%, progress (thread 0): 96.1392%]
|T|: h m m
|P|:
[sample: EN2002a_H02_FEO072_877.24_877.47, WER: 100%, TER: 100%, total WER: 26.3383%, total TER: 12.8845%, progress (thread 0): 96.1471%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1019.4_1019.63, WER: 100%, TER: 33.3333%, total WER: 26.3391%, total TER: 12.8847%, progress (thread 0): 96.1551%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_1188.61_1188.84, WER: 100%, TER: 66.6667%, total WER: 26.3399%, total TER: 12.885%, progress (thread 0): 96.163%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1295.18_1295.41, WER: 0%, TER: 0%, total WER: 26.3396%, total TER: 12.8849%, progress (thread 0): 96.1709%]
|T|: y e a h
|P|: t o
[sample: EN2002a_H01_FEO070_1408.29_1408.52, WER: 100%, TER: 100%, total WER: 26.3405%, total TER: 12.8857%, progress (thread 0): 96.1788%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1571.85_1572.08, WER: 0%, TER: 0%, total WER: 26.3402%, total TER: 12.8857%, progress (thread 0): 96.1867%]
|T|: o h
|P|: o
[sample: EN2002b_H03_MEE073_29.06_29.29, WER: 100%, TER: 50%, total WER: 26.341%, total TER: 12.8858%, progress (thread 0): 96.1946%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_194.27_194.5, WER: 0%, TER: 0%, total WER: 26.3407%, total TER: 12.8857%, progress (thread 0): 96.2025%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_211.63_211.86, WER: 0%, TER: 0%, total WER: 26.3404%, total TER: 12.8856%, progress (thread 0): 96.2104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_493.43_493.66, WER: 0%, TER: 0%, total WER: 26.3401%, total TER: 12.8855%, progress (thread 0): 96.2184%]
|T|: h m m
|P|: h m
[sample: EN2002b_H00_FEO070_534.29_534.52, WER: 100%, TER: 33.3333%, total WER: 26.3409%, total TER: 12.8856%, progress (thread 0): 96.2263%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_672.54_672.77, WER: 100%, TER: 33.3333%, total WER: 26.3418%, total TER: 12.8858%, progress (thread 0): 96.2342%]
|T|: m m h m m
|P|: y e
[sample: EN2002b_H03_MEE073_1361.79_1362.02, WER: 100%, TER: 100%, total WER: 26.3426%, total TER: 12.8868%, progress (thread 0): 96.2421%]
|T|: y e a h
|P|: e a h
[sample: EN2002c_H03_MEE073_555.02_555.25, WER: 100%, TER: 25%, total WER: 26.3434%, total TER: 12.8869%, progress (thread 0): 96.25%]
|T|: h m m
|P|: b u h
[sample: EN2002c_H01_FEO072_699.88_700.11, WER: 100%, TER: 100%, total WER: 26.3442%, total TER: 12.8875%, progress (thread 0): 96.2579%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1900.45_1900.68, WER: 0%, TER: 0%, total WER: 26.3439%, total TER: 12.8874%, progress (thread 0): 96.2658%]
|T|: d o h
|P|: d o
[sample: EN2002c_H02_MEE071_2395.6_2395.83, WER: 100%, TER: 33.3333%, total WER: 26.3448%, total TER: 12.8875%, progress (thread 0): 96.2737%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2817.5_2817.73, WER: 0%, TER: 0%, total WER: 26.3445%, total TER: 12.8874%, progress (thread 0): 96.2816%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2846.79_2847.02, WER: 0%, TER: 0%, total WER: 26.3442%, total TER: 12.8873%, progress (thread 0): 96.2896%]
|T|: o k a y
|P|: k a
[sample: EN2002d_H03_MEE073_107.69_107.92, WER: 100%, TER: 50%, total WER: 26.345%, total TER: 12.8876%, progress (thread 0): 96.2975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_503.45_503.68, WER: 0%, TER: 0%, total WER: 26.3447%, total TER: 12.8875%, progress (thread 0): 96.3054%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_548.74_548.97, WER: 100%, TER: 40%, total WER: 26.3455%, total TER: 12.8878%, progress (thread 0): 96.3133%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_767.8_768.03, WER: 0%, TER: 0%, total WER: 26.3452%, total TER: 12.8877%, progress (thread 0): 96.3212%]
|T|: n o
|P|: n o
[sample: EN2002d_H00_FEO070_1427.88_1428.11, WER: 0%, TER: 0%, total WER: 26.3449%, total TER: 12.8876%, progress (thread 0): 96.3291%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1760.63_1760.86, WER: 100%, TER: 33.3333%, total WER: 26.3458%, total TER: 12.8878%, progress (thread 0): 96.337%]
|T|: o k a y
|P|:
[sample: EN2002d_H00_FEO070_2207.62_2207.85, WER: 100%, TER: 100%, total WER: 26.3466%, total TER: 12.8886%, progress (thread 0): 96.3449%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_1022.74_1022.96, WER: 0%, TER: 0%, total WER: 26.3463%, total TER: 12.8885%, progress (thread 0): 96.3528%]
|T|: m m
|P|: h m
[sample: ES2004b_H02_MEE014_1537.12_1537.34, WER: 100%, TER: 50%, total WER: 26.3471%, total TER: 12.8886%, progress (thread 0): 96.3608%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004c_H00_MEO015_575.7_575.92, WER: 0%, TER: 0%, total WER: 26.3468%, total TER: 12.8885%, progress (thread 0): 96.3687%]
|T|: m m h m m
|P|: y m | h
[sample: ES2004c_H03_FEE016_1311.86_1312.08, WER: 200%, TER: 80%, total WER: 26.3488%, total TER: 12.8893%, progress (thread 0): 96.3766%]
|T|: r i g h t
|P|: i
[sample: ES2004c_H00_MEO015_2116.51_2116.73, WER: 100%, TER: 80%, total WER: 26.3496%, total TER: 12.8901%, progress (thread 0): 96.3845%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_2159.26_2159.48, WER: 0%, TER: 0%, total WER: 26.3493%, total TER: 12.8899%, progress (thread 0): 96.3924%]
|T|: y e a h
|P|: e h
[sample: ES2004d_H02_MEE014_968.07_968.29, WER: 100%, TER: 50%, total WER: 26.3501%, total TER: 12.8903%, progress (thread 0): 96.4003%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1220.58_1220.8, WER: 0%, TER: 0%, total WER: 26.3498%, total TER: 12.8902%, progress (thread 0): 96.4082%]
|T|: o k a y
|P|:
[sample: ES2004d_H03_FEE016_1281.25_1281.47, WER: 100%, TER: 100%, total WER: 26.3507%, total TER: 12.891%, progress (thread 0): 96.4161%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1684.26_1684.48, WER: 0%, TER: 0%, total WER: 26.3504%, total TER: 12.8909%, progress (thread 0): 96.424%]
|T|: o k a y
|P|:
[sample: IS1009b_H03_FIO089_45.13_45.35, WER: 100%, TER: 100%, total WER: 26.3512%, total TER: 12.8917%, progress (thread 0): 96.432%]
|T|: ' k a y
|P|: c o
[sample: IS1009b_H02_FIO084_155.16_155.38, WER: 100%, TER: 100%, total WER: 26.352%, total TER: 12.8925%, progress (thread 0): 96.4399%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_404.12_404.34, WER: 100%, TER: 60%, total WER: 26.3528%, total TER: 12.893%, progress (thread 0): 96.4478%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_961.79_962.01, WER: 0%, TER: 0%, total WER: 26.3525%, total TER: 12.8929%, progress (thread 0): 96.4557%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1007.66_1007.88, WER: 0%, TER: 0%, total WER: 26.3523%, total TER: 12.8928%, progress (thread 0): 96.4636%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H00_FIE088_337.53_337.75, WER: 0%, TER: 0%, total WER: 26.352%, total TER: 12.8927%, progress (thread 0): 96.4715%]
|T|: o k a y
|P|: o
[sample: IS1009c_H01_FIO087_1263.61_1263.83, WER: 100%, TER: 75%, total WER: 26.3528%, total TER: 12.8933%, progress (thread 0): 96.4794%]
|T|: m m
|P|: e
[sample: IS1009d_H03_FIO089_590.61_590.83, WER: 100%, TER: 100%, total WER: 26.3536%, total TER: 12.8937%, progress (thread 0): 96.4873%]
|T|: m m
|P|:
[sample: IS1009d_H02_FIO084_801.03_801.25, WER: 100%, TER: 100%, total WER: 26.3544%, total TER: 12.8941%, progress (thread 0): 96.4953%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H02_FIO084_1687.52_1687.74, WER: 100%, TER: 80%, total WER: 26.3553%, total TER: 12.8948%, progress (thread 0): 96.5032%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1775.76_1775.98, WER: 0%, TER: 0%, total WER: 26.355%, total TER: 12.8947%, progress (thread 0): 96.5111%]
|T|: o k a y
|P|:
[sample: IS1009d_H03_FIO089_1846.51_1846.73, WER: 100%, TER: 100%, total WER: 26.3558%, total TER: 12.8955%, progress (thread 0): 96.519%]
|T|: m o r n i n g
|P|: m o n e y
[sample: TS3003a_H01_MTD011UID_15.34_15.56, WER: 100%, TER: 57.1429%, total WER: 26.3566%, total TER: 12.8963%, progress (thread 0): 96.5269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1316.76_1316.98, WER: 0%, TER: 0%, total WER: 26.3563%, total TER: 12.8961%, progress (thread 0): 96.5348%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1357.36_1357.58, WER: 100%, TER: 50%, total WER: 26.3571%, total TER: 12.8963%, progress (thread 0): 96.5427%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_888.56_888.78, WER: 0%, TER: 0%, total WER: 26.3568%, total TER: 12.8962%, progress (thread 0): 96.5506%]
|T|: h m m
|P|: h m
[sample: TS3003b_H03_MTD012ME_1371.93_1372.15, WER: 100%, TER: 33.3333%, total WER: 26.3577%, total TER: 12.8963%, progress (thread 0): 96.5585%]
|T|: y e a h
|P|: a n d
[sample: TS3003b_H01_MTD011UID_1600.07_1600.29, WER: 100%, TER: 100%, total WER: 26.3585%, total TER: 12.8971%, progress (thread 0): 96.5665%]
|T|: n o
|P|: n
[sample: TS3003c_H01_MTD011UID_109.75_109.97, WER: 100%, TER: 50%, total WER: 26.3593%, total TER: 12.8973%, progress (thread 0): 96.5744%]
|T|: m a y b
|P|: i
[sample: TS3003c_H00_MTD009PM_437.36_437.58, WER: 100%, TER: 100%, total WER: 26.3602%, total TER: 12.8981%, progress (thread 0): 96.5823%]
|T|: m m h m m
|P|:
[sample: TS3003c_H01_MTD011UID_1100.48_1100.7, WER: 100%, TER: 100%, total WER: 26.361%, total TER: 12.8991%, progress (thread 0): 96.5902%]
|T|: y e a h
|P|: h m
[sample: TS3003c_H01_MTD011UID_1304.78_1305, WER: 100%, TER: 100%, total WER: 26.3618%, total TER: 12.9%, progress (thread 0): 96.5981%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_2263.6_2263.82, WER: 0%, TER: 0%, total WER: 26.3615%, total TER: 12.8998%, progress (thread 0): 96.606%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_563.58_563.8, WER: 100%, TER: 75%, total WER: 26.3623%, total TER: 12.9004%, progress (thread 0): 96.6139%]
|T|: y e p
|P|: y u p
[sample: TS3003d_H02_MTD0010ID_577.11_577.33, WER: 100%, TER: 33.3333%, total WER: 26.3632%, total TER: 12.9006%, progress (thread 0): 96.6218%]
|T|: n o
|P|: u h
[sample: TS3003d_H01_MTD011UID_1458.81_1459.03, WER: 100%, TER: 100%, total WER: 26.364%, total TER: 12.901%, progress (thread 0): 96.6297%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_1674.77_1674.99, WER: 100%, TER: 50%, total WER: 26.3648%, total TER: 12.9011%, progress (thread 0): 96.6377%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1736.91_1737.13, WER: 0%, TER: 0%, total WER: 26.3645%, total TER: 12.901%, progress (thread 0): 96.6456%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_1751.36_1751.58, WER: 0%, TER: 0%, total WER: 26.3642%, total TER: 12.901%, progress (thread 0): 96.6535%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1991.39_1991.61, WER: 0%, TER: 0%, total WER: 26.3639%, total TER: 12.9008%, progress (thread 0): 96.6614%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H03_MTD012ME_1998.36_1998.58, WER: 100%, TER: 25%, total WER: 26.3647%, total TER: 12.9009%, progress (thread 0): 96.6693%]
|T|: y e a h
|P|: y o u | k n o
[sample: EN2002a_H00_MEE073_482.53_482.75, WER: 200%, TER: 150%, total WER: 26.3667%, total TER: 12.9022%, progress (thread 0): 96.6772%]
|T|: h m m
|P|: y m
[sample: EN2002a_H01_FEO070_706.29_706.51, WER: 100%, TER: 66.6667%, total WER: 26.3675%, total TER: 12.9026%, progress (thread 0): 96.6851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_805.91_806.13, WER: 0%, TER: 0%, total WER: 26.3672%, total TER: 12.9025%, progress (thread 0): 96.693%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1103.91_1104.13, WER: 0%, TER: 0%, total WER: 26.3669%, total TER: 12.9023%, progress (thread 0): 96.701%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_1216.04_1216.26, WER: 100%, TER: 66.6667%, total WER: 26.3678%, total TER: 12.9027%, progress (thread 0): 96.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1537.4_1537.62, WER: 0%, TER: 0%, total WER: 26.3675%, total TER: 12.9026%, progress (thread 0): 96.7168%]
|T|: j u s t
|P|: j u s t
[sample: EN2002a_H01_FEO070_1871.92_1872.14, WER: 0%, TER: 0%, total WER: 26.3672%, total TER: 12.9025%, progress (thread 0): 96.7247%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1963.02_1963.24, WER: 0%, TER: 0%, total WER: 26.3669%, total TER: 12.9023%, progress (thread 0): 96.7326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_639.41_639.63, WER: 0%, TER: 0%, total WER: 26.3666%, total TER: 12.9022%, progress (thread 0): 96.7405%]
|T|: r i g h t
|P|: w u t
[sample: EN2002b_H03_MEE073_1397.63_1397.85, WER: 100%, TER: 80%, total WER: 26.3674%, total TER: 12.903%, progress (thread 0): 96.7484%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_1547.56_1547.78, WER: 100%, TER: 33.3333%, total WER: 26.3682%, total TER: 12.9032%, progress (thread 0): 96.7563%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_575.92_576.14, WER: 100%, TER: 28.5714%, total WER: 26.369%, total TER: 12.9034%, progress (thread 0): 96.7642%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H02_MEE071_589.02_589.24, WER: 100%, TER: 80%, total WER: 26.3699%, total TER: 12.9042%, progress (thread 0): 96.7722%]
|T|: ' k a y
|P|: y e h
[sample: EN2002c_H03_MEE073_684.52_684.74, WER: 100%, TER: 100%, total WER: 26.3707%, total TER: 12.905%, progress (thread 0): 96.7801%]
|T|: o h | y e a h
|P|: e
[sample: EN2002c_H03_MEE073_1506.68_1506.9, WER: 100%, TER: 85.7143%, total WER: 26.3723%, total TER: 12.9062%, progress (thread 0): 96.788%]
|T|: y e a h
|P|: t o
[sample: EN2002c_H03_MEE073_1602.82_1603.04, WER: 100%, TER: 100%, total WER: 26.3732%, total TER: 12.907%, progress (thread 0): 96.7959%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1707.51_1707.73, WER: 100%, TER: 33.3333%, total WER: 26.374%, total TER: 12.9071%, progress (thread 0): 96.8038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2260.94_2261.16, WER: 0%, TER: 0%, total WER: 26.3737%, total TER: 12.907%, progress (thread 0): 96.8117%]
|T|: y e a h
|P|: e m
[sample: EN2002c_H03_MEE073_2697.66_2697.88, WER: 100%, TER: 75%, total WER: 26.3745%, total TER: 12.9076%, progress (thread 0): 96.8196%]
|T|: o k a y
|P|:
[sample: EN2002d_H03_MEE073_402.37_402.59, WER: 100%, TER: 100%, total WER: 26.3754%, total TER: 12.9084%, progress (thread 0): 96.8275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_736.63_736.85, WER: 0%, TER: 0%, total WER: 26.3751%, total TER: 12.9083%, progress (thread 0): 96.8354%]
|T|: o h
|P|: h o m
[sample: EN2002d_H00_FEO070_909.94_910.16, WER: 100%, TER: 100%, total WER: 26.3759%, total TER: 12.9087%, progress (thread 0): 96.8434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1061.24_1061.46, WER: 0%, TER: 0%, total WER: 26.3756%, total TER: 12.9086%, progress (thread 0): 96.8513%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1400.88_1401.1, WER: 0%, TER: 0%, total WER: 26.3753%, total TER: 12.9085%, progress (thread 0): 96.8592%]
|T|: b u t
|P|: b u t
[sample: EN2002d_H00_FEO070_1658_1658.22, WER: 0%, TER: 0%, total WER: 26.375%, total TER: 12.9084%, progress (thread 0): 96.8671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1732.66_1732.88, WER: 0%, TER: 0%, total WER: 26.3747%, total TER: 12.9082%, progress (thread 0): 96.875%]
|T|: m m
|P|: h m
[sample: ES2004a_H03_FEE016_622.02_622.23, WER: 100%, TER: 50%, total WER: 26.3755%, total TER: 12.9084%, progress (thread 0): 96.8829%]
|T|: y e a h
|P|: y u p
[sample: ES2004b_H00_MEO015_1323.05_1323.26, WER: 100%, TER: 75%, total WER: 26.3763%, total TER: 12.909%, progress (thread 0): 96.8908%]
|T|: m m h m m
|P|: u | h m
[sample: ES2004b_H00_MEO015_1464.74_1464.95, WER: 200%, TER: 60%, total WER: 26.3783%, total TER: 12.9095%, progress (thread 0): 96.8987%]
|T|: m m h m m
|P|: u h u
[sample: ES2004c_H00_MEO015_96.42_96.63, WER: 100%, TER: 80%, total WER: 26.3791%, total TER: 12.9103%, progress (thread 0): 96.9066%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1385.07_1385.28, WER: 0%, TER: 0%, total WER: 26.3788%, total TER: 12.9102%, progress (thread 0): 96.9146%]
|T|: h m m
|P|: h m
[sample: ES2004d_H00_MEO015_26.63_26.84, WER: 100%, TER: 33.3333%, total WER: 26.3796%, total TER: 12.9103%, progress (thread 0): 96.9225%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_627.79_628, WER: 100%, TER: 66.6667%, total WER: 26.3805%, total TER: 12.9107%, progress (thread 0): 96.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_39.06_39.27, WER: 0%, TER: 0%, total WER: 26.3802%, total TER: 12.9106%, progress (thread 0): 96.9383%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_169.39_169.6, WER: 100%, TER: 100%, total WER: 26.381%, total TER: 12.9116%, progress (thread 0): 96.9462%]
|T|: m m h m m
|P|:
[sample: IS1009b_H00_FIE088_1286.43_1286.64, WER: 100%, TER: 100%, total WER: 26.3818%, total TER: 12.9126%, progress (thread 0): 96.9541%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H00_FIE088_1289.44_1289.65, WER: 0%, TER: 0%, total WER: 26.3815%, total TER: 12.9125%, progress (thread 0): 96.962%]
|T|: m m
|P|: a n
[sample: IS1009b_H02_FIO084_1632.09_1632.3, WER: 100%, TER: 100%, total WER: 26.3824%, total TER: 12.9129%, progress (thread 0): 96.9699%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_284.08_284.29, WER: 0%, TER: 0%, total WER: 26.3821%, total TER: 12.9128%, progress (thread 0): 96.9778%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1563.53_1563.74, WER: 0%, TER: 0%, total WER: 26.3818%, total TER: 12.9126%, progress (thread 0): 96.9858%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1707.23_1707.44, WER: 0%, TER: 0%, total WER: 26.3815%, total TER: 12.9125%, progress (thread 0): 96.9937%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_741.38_741.59, WER: 0%, TER: 0%, total WER: 26.3812%, total TER: 12.9124%, progress (thread 0): 97.0016%]
|T|: y e a h
|P|:
[sample: IS1009d_H02_FIO084_1371.67_1371.88, WER: 100%, TER: 100%, total WER: 26.382%, total TER: 12.9132%, progress (thread 0): 97.0095%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1880.74_1880.95, WER: 100%, TER: 100%, total WER: 26.3828%, total TER: 12.9142%, progress (thread 0): 97.0174%]
|T|: y e a h
|P|: y e s
[sample: TS3003a_H01_MTD011UID_1295.8_1296.01, WER: 100%, TER: 50%, total WER: 26.3836%, total TER: 12.9146%, progress (thread 0): 97.0253%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_901.66_901.87, WER: 0%, TER: 0%, total WER: 26.3834%, total TER: 12.9145%, progress (thread 0): 97.0332%]
|T|: y e a h
|P|: h u h
[sample: TS3003b_H01_MTD011UID_1547.15_1547.36, WER: 100%, TER: 75%, total WER: 26.3842%, total TER: 12.915%, progress (thread 0): 97.0411%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1091.82_1092.03, WER: 0%, TER: 0%, total WER: 26.3839%, total TER: 12.9149%, progress (thread 0): 97.049%]
|T|: y e a h
|P|: h u h
[sample: TS3003c_H01_MTD011UID_1210.7_1210.91, WER: 100%, TER: 75%, total WER: 26.3847%, total TER: 12.9155%, progress (thread 0): 97.057%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_530.79_531, WER: 100%, TER: 75%, total WER: 26.3855%, total TER: 12.9161%, progress (thread 0): 97.0649%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1465.64_1465.85, WER: 0%, TER: 0%, total WER: 26.3852%, total TER: 12.916%, progress (thread 0): 97.0728%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1695.15_1695.36, WER: 0%, TER: 0%, total WER: 26.3849%, total TER: 12.9158%, progress (thread 0): 97.0807%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1711.66_1711.87, WER: 0%, TER: 0%, total WER: 26.3846%, total TER: 12.9157%, progress (thread 0): 97.0886%]
|T|: y e a h
|P|: y e p
[sample: TS3003d_H00_MTD009PM_1789.64_1789.85, WER: 100%, TER: 50%, total WER: 26.3855%, total TER: 12.9161%, progress (thread 0): 97.0965%]
|T|: n o
|P|: n o
[sample: TS3003d_H03_MTD012ME_2014.88_2015.09, WER: 0%, TER: 0%, total WER: 26.3852%, total TER: 12.916%, progress (thread 0): 97.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2064.37_2064.58, WER: 0%, TER: 0%, total WER: 26.3849%, total TER: 12.9159%, progress (thread 0): 97.1123%]
|T|: y e a h
|P|: e h
[sample: TS3003d_H01_MTD011UID_2107.64_2107.85, WER: 100%, TER: 50%, total WER: 26.3857%, total TER: 12.9162%, progress (thread 0): 97.1203%]
|T|: y e a h
|P|: h m
[sample: TS3003d_H01_MTD011UID_2462.55_2462.76, WER: 100%, TER: 100%, total WER: 26.3865%, total TER: 12.917%, progress (thread 0): 97.1282%]
|T|: s o r r y
|P|: s o r r
[sample: EN2002a_H02_FEO072_377.93_378.14, WER: 100%, TER: 20%, total WER: 26.3873%, total TER: 12.9171%, progress (thread 0): 97.1361%]
|T|: o h
|P|:
[sample: EN2002a_H03_MEE071_483.42_483.63, WER: 100%, TER: 100%, total WER: 26.3882%, total TER: 12.9175%, progress (thread 0): 97.144%]
|T|: y e a h
|P|: y a h
[sample: EN2002a_H03_MEE071_648.41_648.62, WER: 100%, TER: 25%, total WER: 26.389%, total TER: 12.9176%, progress (thread 0): 97.1519%]
|T|: y e a h
|P|: c o m
[sample: EN2002a_H01_FEO070_1319.66_1319.87, WER: 100%, TER: 100%, total WER: 26.3898%, total TER: 12.9184%, progress (thread 0): 97.1598%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1429.36_1429.57, WER: 0%, TER: 0%, total WER: 26.3895%, total TER: 12.9183%, progress (thread 0): 97.1677%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1896.08_1896.29, WER: 0%, TER: 0%, total WER: 26.3892%, total TER: 12.9182%, progress (thread 0): 97.1756%]
|T|: y e a h
|P|: y e
[sample: EN2002a_H03_MEE071_2122.73_2122.94, WER: 100%, TER: 50%, total WER: 26.3901%, total TER: 12.9185%, progress (thread 0): 97.1835%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_266.76_266.97, WER: 0%, TER: 0%, total WER: 26.3898%, total TER: 12.9184%, progress (thread 0): 97.1915%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_291.79_292, WER: 0%, TER: 0%, total WER: 26.3895%, total TER: 12.9183%, progress (thread 0): 97.1994%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1335.23_1335.44, WER: 0%, TER: 0%, total WER: 26.3892%, total TER: 12.9182%, progress (thread 0): 97.2073%]
|T|: ' k a y
|P|: k u
[sample: EN2002c_H03_MEE073_21.49_21.7, WER: 100%, TER: 75%, total WER: 26.39%, total TER: 12.9188%, progress (thread 0): 97.2152%]
|T|: o k a y
|P|:
[sample: EN2002c_H03_MEE073_183.87_184.08, WER: 100%, TER: 100%, total WER: 26.3908%, total TER: 12.9196%, progress (thread 0): 97.2231%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1748.27_1748.48, WER: 0%, TER: 0%, total WER: 26.3905%, total TER: 12.9194%, progress (thread 0): 97.231%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002c_H03_MEE073_1810.36_1810.57, WER: 100%, TER: 85.7143%, total WER: 26.3922%, total TER: 12.9206%, progress (thread 0): 97.2389%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1861.15_1861.36, WER: 0%, TER: 0%, total WER: 26.3919%, total TER: 12.9205%, progress (thread 0): 97.2468%]
|T|: y e a h
|P|: y o u | k n
[sample: EN2002c_H03_MEE073_2180.5_2180.71, WER: 200%, TER: 125%, total WER: 26.3938%, total TER: 12.9215%, progress (thread 0): 97.2547%]
|T|: t r u e
|P|: t r u e
[sample: EN2002c_H03_MEE073_2453.86_2454.07, WER: 0%, TER: 0%, total WER: 26.3935%, total TER: 12.9214%, progress (thread 0): 97.2627%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2819.2_2819.41, WER: 0%, TER: 0%, total WER: 26.3932%, total TER: 12.9213%, progress (thread 0): 97.2706%]
|T|: m m h m m
|P|: u h m
[sample: EN2002d_H01_FEO072_164.51_164.72, WER: 100%, TER: 60%, total WER: 26.3941%, total TER: 12.9218%, progress (thread 0): 97.2785%]
|T|: r i g h t
|P|: t o
[sample: EN2002d_H03_MEE073_338.56_338.77, WER: 100%, TER: 100%, total WER: 26.3949%, total TER: 12.9229%, progress (thread 0): 97.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_647.49_647.7, WER: 0%, TER: 0%, total WER: 26.3946%, total TER: 12.9227%, progress (thread 0): 97.2943%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_782.98_783.19, WER: 100%, TER: 100%, total WER: 26.3954%, total TER: 12.9231%, progress (thread 0): 97.3022%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_998.01_998.22, WER: 0%, TER: 0%, total WER: 26.3951%, total TER: 12.923%, progress (thread 0): 97.3101%]
|T|: o h | y e a h
|P|: c
[sample: EN2002d_H00_FEO070_1377.89_1378.1, WER: 100%, TER: 100%, total WER: 26.3968%, total TER: 12.9244%, progress (thread 0): 97.318%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1391.57_1391.78, WER: 0%, TER: 0%, total WER: 26.3965%, total TER: 12.9243%, progress (thread 0): 97.326%]
|T|: o h | y e a h
|P|: y e
[sample: EN2002d_H03_MEE073_1912.21_1912.42, WER: 100%, TER: 71.4286%, total WER: 26.3981%, total TER: 12.9252%, progress (thread 0): 97.3339%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_2147.17_2147.38, WER: 100%, TER: 33.3333%, total WER: 26.3989%, total TER: 12.9254%, progress (thread 0): 97.3418%]
|T|: o k a y
|P|: j
[sample: ES2004b_H00_MEO015_338.55_338.75, WER: 100%, TER: 100%, total WER: 26.3998%, total TER: 12.9262%, progress (thread 0): 97.3497%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1514.44_1514.64, WER: 0%, TER: 0%, total WER: 26.3995%, total TER: 12.9261%, progress (thread 0): 97.3576%]
|T|: o o p s
|P|:
[sample: ES2004c_H00_MEO015_54.23_54.43, WER: 100%, TER: 100%, total WER: 26.4003%, total TER: 12.9269%, progress (thread 0): 97.3655%]
|T|: m m h m m
|P|: h
[sample: ES2004c_H00_MEO015_1395.98_1396.18, WER: 100%, TER: 80%, total WER: 26.4011%, total TER: 12.9277%, progress (thread 0): 97.3734%]
|T|: ' k a y
|P|: t
[sample: ES2004d_H00_MEO015_562.82_563.02, WER: 100%, TER: 100%, total WER: 26.4019%, total TER: 12.9285%, progress (thread 0): 97.3813%]
|T|: y e p
|P|: y e a h
[sample: ES2004d_H02_MEE014_922.35_922.55, WER: 100%, TER: 66.6667%, total WER: 26.4028%, total TER: 12.9289%, progress (thread 0): 97.3892%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1214.04_1214.24, WER: 0%, TER: 0%, total WER: 26.4025%, total TER: 12.9287%, progress (thread 0): 97.3972%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1952.42_1952.62, WER: 0%, TER: 0%, total WER: 26.4022%, total TER: 12.9286%, progress (thread 0): 97.4051%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_784.26_784.46, WER: 0%, TER: 0%, total WER: 26.4019%, total TER: 12.9285%, progress (thread 0): 97.413%]
|T|: ' k a y
|P|: t o
[sample: IS1009a_H03_FIO089_794.48_794.68, WER: 100%, TER: 100%, total WER: 26.4027%, total TER: 12.9293%, progress (thread 0): 97.4209%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H02_FIO084_179.12_179.32, WER: 100%, TER: 200%, total WER: 26.4035%, total TER: 12.9302%, progress (thread 0): 97.4288%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1111.83_1112.03, WER: 0%, TER: 0%, total WER: 26.4032%, total TER: 12.9301%, progress (thread 0): 97.4367%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_2011.99_2012.19, WER: 100%, TER: 100%, total WER: 26.404%, total TER: 12.9311%, progress (thread 0): 97.4446%]
|T|: y e a h
|P|: y e a
[sample: IS1009c_H03_FIO089_285.99_286.19, WER: 100%, TER: 25%, total WER: 26.4049%, total TER: 12.9312%, progress (thread 0): 97.4525%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1689.26_1689.46, WER: 0%, TER: 0%, total WER: 26.4046%, total TER: 12.9311%, progress (thread 0): 97.4604%]
|T|: m m
|P|:
[sample: IS1009d_H01_FIO087_964.59_964.79, WER: 100%, TER: 100%, total WER: 26.4054%, total TER: 12.9315%, progress (thread 0): 97.4684%]
|T|: y e a h
|P|: a n d
[sample: TS3003a_H01_MTD011UID_1286.22_1286.42, WER: 100%, TER: 100%, total WER: 26.4062%, total TER: 12.9323%, progress (thread 0): 97.4763%]
|T|: m m
|P|: y
[sample: TS3003a_H01_MTD011UID_1431.86_1432.06, WER: 100%, TER: 100%, total WER: 26.407%, total TER: 12.9327%, progress (thread 0): 97.4842%]
|T|: ' k a y
|P|: i n g
[sample: TS3003b_H03_MTD012ME_550.33_550.53, WER: 100%, TER: 100%, total WER: 26.4079%, total TER: 12.9335%, progress (thread 0): 97.4921%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_576.96_577.16, WER: 100%, TER: 50%, total WER: 26.4087%, total TER: 12.9337%, progress (thread 0): 97.5%]
|T|: y e a h
|P|: u h
[sample: TS3003b_H01_MTD011UID_1340.71_1340.91, WER: 100%, TER: 75%, total WER: 26.4095%, total TER: 12.9342%, progress (thread 0): 97.5079%]
|T|: m m
|P|: y e
[sample: TS3003b_H01_MTD011UID_1635.98_1636.18, WER: 100%, TER: 100%, total WER: 26.4103%, total TER: 12.9346%, progress (thread 0): 97.5158%]
|T|: m m
|P|: h m
[sample: TS3003c_H03_MTD012ME_1936.99_1937.19, WER: 100%, TER: 50%, total WER: 26.4112%, total TER: 12.9348%, progress (thread 0): 97.5237%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_214.36_214.56, WER: 0%, TER: 0%, total WER: 26.4109%, total TER: 12.9347%, progress (thread 0): 97.5316%]
|T|: y e p
|P|: y e
[sample: TS3003d_H00_MTD009PM_418.75_418.95, WER: 100%, TER: 33.3333%, total WER: 26.4117%, total TER: 12.9348%, progress (thread 0): 97.5396%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1236.15_1236.35, WER: 0%, TER: 0%, total WER: 26.4114%, total TER: 12.9347%, progress (thread 0): 97.5475%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1334.1_1334.3, WER: 0%, TER: 0%, total WER: 26.4111%, total TER: 12.9346%, progress (thread 0): 97.5554%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1736.65_1736.85, WER: 0%, TER: 0%, total WER: 26.4108%, total TER: 12.9345%, progress (thread 0): 97.5633%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1862.16_1862.36, WER: 0%, TER: 0%, total WER: 26.4105%, total TER: 12.9344%, progress (thread 0): 97.5712%]
|T|: h m m
|P|: w h e n
[sample: TS3003d_H01_MTD011UID_2423.71_2423.91, WER: 100%, TER: 100%, total WER: 26.4113%, total TER: 12.935%, progress (thread 0): 97.5791%]
|T|: y e a h
|P|: h
[sample: EN2002a_H00_MEE073_6.65_6.85, WER: 100%, TER: 75%, total WER: 26.4122%, total TER: 12.9355%, progress (thread 0): 97.587%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_207.6_207.8, WER: 0%, TER: 0%, total WER: 26.4119%, total TER: 12.9354%, progress (thread 0): 97.5949%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_325.97_326.17, WER: 100%, TER: 33.3333%, total WER: 26.4127%, total TER: 12.9356%, progress (thread 0): 97.6029%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_517.59_517.79, WER: 0%, TER: 0%, total WER: 26.4124%, total TER: 12.9354%, progress (thread 0): 97.6108%]
|T|: m m h m m
|P|: e h
[sample: EN2002a_H00_MEE073_625.1_625.3, WER: 100%, TER: 80%, total WER: 26.4132%, total TER: 12.9362%, progress (thread 0): 97.6187%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1082.64_1082.84, WER: 0%, TER: 0%, total WER: 26.4129%, total TER: 12.9361%, progress (thread 0): 97.6266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1296.98_1297.18, WER: 0%, TER: 0%, total WER: 26.4126%, total TER: 12.936%, progress (thread 0): 97.6345%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1544.23_1544.43, WER: 100%, TER: 33.3333%, total WER: 26.4134%, total TER: 12.9361%, progress (thread 0): 97.6424%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_1988.65_1988.85, WER: 0%, TER: 0%, total WER: 26.4132%, total TER: 12.936%, progress (thread 0): 97.6503%]
|T|: y e p
|P|: y e p
[sample: EN2002b_H00_FEO070_58.73_58.93, WER: 0%, TER: 0%, total WER: 26.4129%, total TER: 12.9359%, progress (thread 0): 97.6582%]
|T|: d o | w e | d
|P|: d w e
[sample: EN2002b_H01_MEE071_223.55_223.75, WER: 100%, TER: 57.1429%, total WER: 26.4153%, total TER: 12.9366%, progress (thread 0): 97.6661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_233.29_233.49, WER: 0%, TER: 0%, total WER: 26.415%, total TER: 12.9365%, progress (thread 0): 97.674%]
|T|: h m m
|P|: y e h
[sample: EN2002b_H03_MEE073_259.41_259.61, WER: 100%, TER: 100%, total WER: 26.4159%, total TER: 12.9371%, progress (thread 0): 97.682%]
|T|: y e a h
|P|: t o
[sample: EN2002b_H03_MEE073_498.07_498.27, WER: 100%, TER: 100%, total WER: 26.4167%, total TER: 12.9379%, progress (thread 0): 97.6899%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1591.2_1591.4, WER: 0%, TER: 0%, total WER: 26.4164%, total TER: 12.9378%, progress (thread 0): 97.6978%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H00_FEO070_1723.38_1723.58, WER: 0%, TER: 0%, total WER: 26.4161%, total TER: 12.9377%, progress (thread 0): 97.7057%]
|T|: o k a y
|P|: g o
[sample: EN2002c_H03_MEE073_241.42_241.62, WER: 100%, TER: 100%, total WER: 26.4169%, total TER: 12.9385%, progress (thread 0): 97.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_404.9_405.1, WER: 0%, TER: 0%, total WER: 26.4166%, total TER: 12.9384%, progress (thread 0): 97.7215%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_817.73_817.93, WER: 100%, TER: 33.3333%, total WER: 26.4174%, total TER: 12.9385%, progress (thread 0): 97.7294%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1319.11_1319.31, WER: 0%, TER: 0%, total WER: 26.4171%, total TER: 12.9384%, progress (thread 0): 97.7373%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1392.95_1393.15, WER: 0%, TER: 0%, total WER: 26.4168%, total TER: 12.9383%, progress (thread 0): 97.7453%]
|T|: r i g h t
|P|: f r i r t
[sample: EN2002c_H03_MEE073_1395.54_1395.74, WER: 100%, TER: 60%, total WER: 26.4177%, total TER: 12.9388%, progress (thread 0): 97.7532%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1637.64_1637.84, WER: 0%, TER: 0%, total WER: 26.4174%, total TER: 12.9387%, progress (thread 0): 97.7611%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1911.7_1911.9, WER: 0%, TER: 0%, total WER: 26.4171%, total TER: 12.9385%, progress (thread 0): 97.769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1939.05_1939.25, WER: 0%, TER: 0%, total WER: 26.4168%, total TER: 12.9384%, progress (thread 0): 97.7769%]
|T|: y e a h
|P|: t o
[sample: EN2002d_H03_MEE073_327.41_327.61, WER: 100%, TER: 100%, total WER: 26.4176%, total TER: 12.9392%, progress (thread 0): 97.7848%]
|T|: y e a h
|P|: s h n g
[sample: EN2002d_H03_MEE073_967.12_967.32, WER: 100%, TER: 100%, total WER: 26.4184%, total TER: 12.94%, progress (thread 0): 97.7927%]
|T|: y e p
|P|: y u p
[sample: ES2004a_H03_FEE016_1048.29_1048.48, WER: 100%, TER: 33.3333%, total WER: 26.4193%, total TER: 12.9401%, progress (thread 0): 97.8006%]
|T|: o o p s
|P|:
[sample: ES2004b_H00_MEO015_572.79_572.98, WER: 100%, TER: 100%, total WER: 26.4201%, total TER: 12.941%, progress (thread 0): 97.8085%]
|T|: a l r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1308.81_1309, WER: 100%, TER: 28.5714%, total WER: 26.4209%, total TER: 12.9412%, progress (thread 0): 97.8165%]
|T|: h u h
|P|: h
[sample: ES2004b_H02_MEE014_1454.52_1454.71, WER: 100%, TER: 66.6667%, total WER: 26.4217%, total TER: 12.9416%, progress (thread 0): 97.8244%]
|T|: m m
|P|: y o u | k
[sample: ES2004b_H03_FEE016_1635.41_1635.6, WER: 200%, TER: 250%, total WER: 26.4237%, total TER: 12.9427%, progress (thread 0): 97.8323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1436.81_1437, WER: 0%, TER: 0%, total WER: 26.4234%, total TER: 12.9426%, progress (thread 0): 97.8402%]
|T|: t h i n g
|P|: t h i n g
[sample: ES2004d_H02_MEE014_2209.98_2210.17, WER: 0%, TER: 0%, total WER: 26.4231%, total TER: 12.9424%, progress (thread 0): 97.8481%]
|T|: y e a h
|P|: t h e r
[sample: IS1009a_H02_FIO084_805.49_805.68, WER: 100%, TER: 100%, total WER: 26.4239%, total TER: 12.9432%, progress (thread 0): 97.856%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_690.57_690.76, WER: 0%, TER: 0%, total WER: 26.4236%, total TER: 12.9431%, progress (thread 0): 97.8639%]
|T|: m m h m m
|P|:
[sample: IS1009c_H00_FIE088_808.35_808.54, WER: 100%, TER: 100%, total WER: 26.4244%, total TER: 12.9441%, progress (thread 0): 97.8718%]
|T|: b a t t e r y
|P|: t h a t
[sample: IS1009c_H01_FIO087_1631.84_1632.03, WER: 100%, TER: 85.7143%, total WER: 26.4252%, total TER: 12.9453%, progress (thread 0): 97.8798%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1314.07_1314.26, WER: 0%, TER: 0%, total WER: 26.425%, total TER: 12.9452%, progress (thread 0): 97.8877%]
|T|: ' k a y
|P|: k a n
[sample: TS3003a_H03_MTD012ME_506.74_506.93, WER: 100%, TER: 50%, total WER: 26.4258%, total TER: 12.9455%, progress (thread 0): 97.8956%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1214.55_1214.74, WER: 100%, TER: 50%, total WER: 26.4266%, total TER: 12.9457%, progress (thread 0): 97.9035%]
|T|: ' k a y
|P|: g a y
[sample: TS3003a_H01_MTD011UID_1401.33_1401.52, WER: 100%, TER: 50%, total WER: 26.4274%, total TER: 12.946%, progress (thread 0): 97.9114%]
|T|: y e a h
|P|: n o
[sample: TS3003b_H01_MTD011UID_2147.67_2147.86, WER: 100%, TER: 100%, total WER: 26.4282%, total TER: 12.9468%, progress (thread 0): 97.9193%]
|T|: y e a h
|P|: i
[sample: TS3003c_H01_MTD011UID_1090.92_1091.11, WER: 100%, TER: 100%, total WER: 26.4291%, total TER: 12.9476%, progress (thread 0): 97.9272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1632.95_1633.14, WER: 0%, TER: 0%, total WER: 26.4288%, total TER: 12.9475%, progress (thread 0): 97.9351%]
|T|: y e a h
|P|: a n
[sample: TS3003d_H01_MTD011UID_375.66_375.85, WER: 100%, TER: 75%, total WER: 26.4296%, total TER: 12.9481%, progress (thread 0): 97.943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_507.6_507.79, WER: 0%, TER: 0%, total WER: 26.4293%, total TER: 12.948%, progress (thread 0): 97.951%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_816.71_816.9, WER: 0%, TER: 0%, total WER: 26.429%, total TER: 12.9479%, progress (thread 0): 97.9589%]
|T|: m m
|P|:
[sample: TS3003d_H01_MTD011UID_966.74_966.93, WER: 100%, TER: 100%, total WER: 26.4298%, total TER: 12.9483%, progress (thread 0): 97.9668%]
|T|: m m
|P|: h m
[sample: TS3003d_H00_MTD009PM_1884.46_1884.65, WER: 100%, TER: 50%, total WER: 26.4306%, total TER: 12.9484%, progress (thread 0): 97.9747%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1975.04_1975.23, WER: 100%, TER: 66.6667%, total WER: 26.4315%, total TER: 12.9488%, progress (thread 0): 97.9826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1998.6_1998.79, WER: 0%, TER: 0%, total WER: 26.4312%, total TER: 12.9487%, progress (thread 0): 97.9905%]
|T|: y e p
|P|: y u p
[sample: EN2002a_H00_MEE073_399.03_399.22, WER: 100%, TER: 33.3333%, total WER: 26.432%, total TER: 12.9488%, progress (thread 0): 97.9984%]
|T|: y e a h
|P|: r i g h t
[sample: EN2002a_H03_MEE071_741.39_741.58, WER: 100%, TER: 100%, total WER: 26.4328%, total TER: 12.9496%, progress (thread 0): 98.0063%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1037.67_1037.86, WER: 0%, TER: 0%, total WER: 26.4325%, total TER: 12.9495%, progress (thread 0): 98.0142%]
|T|: w h
|P|:
[sample: EN2002a_H03_MEE071_1305.67_1305.86, WER: 100%, TER: 100%, total WER: 26.4333%, total TER: 12.9499%, progress (thread 0): 98.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1781.11_1781.3, WER: 0%, TER: 0%, total WER: 26.4331%, total TER: 12.9498%, progress (thread 0): 98.0301%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H02_FEO072_1823.51_1823.7, WER: 100%, TER: 50%, total WER: 26.4339%, total TER: 12.9501%, progress (thread 0): 98.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1836.03_1836.22, WER: 0%, TER: 0%, total WER: 26.4336%, total TER: 12.95%, progress (thread 0): 98.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1886.9_1887.09, WER: 0%, TER: 0%, total WER: 26.4333%, total TER: 12.9499%, progress (thread 0): 98.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1917.91_1918.1, WER: 0%, TER: 0%, total WER: 26.433%, total TER: 12.9498%, progress (thread 0): 98.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_142.44_142.63, WER: 0%, TER: 0%, total WER: 26.4327%, total TER: 12.9497%, progress (thread 0): 98.0696%]
|T|: u h h u h
|P|: w
[sample: EN2002c_H03_MEE073_1527.18_1527.37, WER: 100%, TER: 100%, total WER: 26.4335%, total TER: 12.9507%, progress (thread 0): 98.0775%]
|T|: h m m
|P|: y e a
[sample: EN2002c_H03_MEE073_1630_1630.19, WER: 100%, TER: 100%, total WER: 26.4343%, total TER: 12.9513%, progress (thread 0): 98.0854%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2672.39_2672.58, WER: 100%, TER: 33.3333%, total WER: 26.4352%, total TER: 12.9514%, progress (thread 0): 98.0934%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H03_MEE073_2679.33_2679.52, WER: 100%, TER: 80%, total WER: 26.436%, total TER: 12.9522%, progress (thread 0): 98.1013%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_647.1_647.29, WER: 0%, TER: 0%, total WER: 26.4357%, total TER: 12.9521%, progress (thread 0): 98.1092%]
|T|: y e a h
|P|: t h
[sample: EN2002d_H03_MEE073_649.08_649.27, WER: 100%, TER: 75%, total WER: 26.4365%, total TER: 12.9526%, progress (thread 0): 98.1171%]
|T|: o k
|P|: i ' m
[sample: EN2002d_H03_MEE073_909.71_909.9, WER: 100%, TER: 150%, total WER: 26.4373%, total TER: 12.9533%, progress (thread 0): 98.125%]
|T|: s u r e
|P|: t r e
[sample: EN2002d_H03_MEE073_1568.39_1568.58, WER: 100%, TER: 50%, total WER: 26.4382%, total TER: 12.9536%, progress (thread 0): 98.1329%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_1708.21_1708.4, WER: 100%, TER: 66.6667%, total WER: 26.439%, total TER: 12.954%, progress (thread 0): 98.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2037.78_2037.97, WER: 0%, TER: 0%, total WER: 26.4387%, total TER: 12.9539%, progress (thread 0): 98.1487%]
|T|: s o
|P|: s h
[sample: ES2004a_H03_FEE016_605.84_606.02, WER: 100%, TER: 50%, total WER: 26.4395%, total TER: 12.954%, progress (thread 0): 98.1566%]
|T|: ' k a y
|P|:
[sample: ES2004b_H00_MEO015_1027.78_1027.96, WER: 100%, TER: 100%, total WER: 26.4403%, total TER: 12.9549%, progress (thread 0): 98.1646%]
|T|: m m h m m
|P|:
[sample: ES2004c_H03_FEE016_1360.96_1361.14, WER: 100%, TER: 100%, total WER: 26.4412%, total TER: 12.9559%, progress (thread 0): 98.1725%]
|T|: m m
|P|: h m
[sample: ES2004d_H02_MEE014_2221.15_2221.33, WER: 100%, TER: 50%, total WER: 26.442%, total TER: 12.956%, progress (thread 0): 98.1804%]
|T|: m m h m m
|P|: a
[sample: IS1009b_H02_FIO084_361.5_361.68, WER: 100%, TER: 100%, total WER: 26.4428%, total TER: 12.957%, progress (thread 0): 98.1883%]
|T|: u m
|P|: i | m a
[sample: IS1009c_H03_FIO089_1368.34_1368.52, WER: 200%, TER: 150%, total WER: 26.4447%, total TER: 12.9577%, progress (thread 0): 98.1962%]
|T|: a n d
|P|: a n d
[sample: IS1009c_H01_FIO087_1687.91_1688.09, WER: 0%, TER: 0%, total WER: 26.4444%, total TER: 12.9576%, progress (thread 0): 98.2041%]
|T|: m m
|P|: h m
[sample: IS1009d_H00_FIE088_1039.16_1039.34, WER: 100%, TER: 50%, total WER: 26.4453%, total TER: 12.9578%, progress (thread 0): 98.212%]
|T|: y e s
|P|: i t ' s
[sample: IS1009d_H01_FIO087_1532.02_1532.2, WER: 100%, TER: 100%, total WER: 26.4461%, total TER: 12.9584%, progress (thread 0): 98.2199%]
|T|: n o
|P|: y o u | k
[sample: TS3003a_H00_MTD009PM_1017.51_1017.69, WER: 200%, TER: 200%, total WER: 26.448%, total TER: 12.9592%, progress (thread 0): 98.2278%]
|T|: y e a h
|P|: y e a
[sample: TS3003b_H00_MTD009PM_583.19_583.37, WER: 100%, TER: 25%, total WER: 26.4488%, total TER: 12.9594%, progress (thread 0): 98.2358%]
|T|: m m
|P|: h h
[sample: TS3003b_H01_MTD011UID_1188.77_1188.95, WER: 100%, TER: 100%, total WER: 26.4497%, total TER: 12.9598%, progress (thread 0): 98.2437%]
|T|: y e a h
|P|: y o
[sample: TS3003b_H02_MTD0010ID_1801.24_1801.42, WER: 100%, TER: 75%, total WER: 26.4505%, total TER: 12.9603%, progress (thread 0): 98.2516%]
|T|: m m
|P|:
[sample: TS3003b_H01_MTD011UID_1801.82_1802, WER: 100%, TER: 100%, total WER: 26.4513%, total TER: 12.9607%, progress (thread 0): 98.2595%]
|T|: h m m
|P|: r i g h t
[sample: TS3003d_H01_MTD011UID_1131_1131.18, WER: 100%, TER: 166.667%, total WER: 26.4521%, total TER: 12.9618%, progress (thread 0): 98.2674%]
|T|: a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_2394.1_2394.28, WER: 100%, TER: 50%, total WER: 26.453%, total TER: 12.962%, progress (thread 0): 98.2753%]
|T|: n o
|P|: h m
[sample: EN2002a_H00_MEE073_23.11_23.29, WER: 100%, TER: 100%, total WER: 26.4538%, total TER: 12.9624%, progress (thread 0): 98.2832%]
|T|: y e a h
|P|: y o
[sample: EN2002a_H01_FEO070_32.53_32.71, WER: 100%, TER: 75%, total WER: 26.4546%, total TER: 12.963%, progress (thread 0): 98.2911%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_106.69_106.87, WER: 100%, TER: 33.3333%, total WER: 26.4554%, total TER: 12.9631%, progress (thread 0): 98.299%]
|T|: y e a h
|P|: a n
[sample: EN2002a_H00_MEE073_1441.15_1441.33, WER: 100%, TER: 75%, total WER: 26.4563%, total TER: 12.9637%, progress (thread 0): 98.307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1717.2_1717.38, WER: 0%, TER: 0%, total WER: 26.456%, total TER: 12.9636%, progress (thread 0): 98.3149%]
|T|: o k a y
|P|:
[sample: EN2002b_H03_MEE073_530.52_530.7, WER: 100%, TER: 100%, total WER: 26.4568%, total TER: 12.9644%, progress (thread 0): 98.3228%]
|T|: i s | i t
|P|: i s
[sample: EN2002b_H01_MEE071_1142.07_1142.25, WER: 50%, TER: 60%, total WER: 26.4573%, total TER: 12.9649%, progress (thread 0): 98.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1339.32_1339.5, WER: 0%, TER: 0%, total WER: 26.457%, total TER: 12.9648%, progress (thread 0): 98.3386%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_186.7_186.88, WER: 0%, TER: 0%, total WER: 26.4567%, total TER: 12.9647%, progress (thread 0): 98.3465%]
|T|: a l r i g h t
|P|: r
[sample: EN2002c_H03_MEE073_536.86_537.04, WER: 100%, TER: 85.7143%, total WER: 26.4575%, total TER: 12.9658%, progress (thread 0): 98.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1667.09_1667.27, WER: 0%, TER: 0%, total WER: 26.4572%, total TER: 12.9657%, progress (thread 0): 98.3623%]
|T|: h m m
|P|:
[sample: EN2002d_H01_FEO072_363.49_363.67, WER: 100%, TER: 100%, total WER: 26.4581%, total TER: 12.9663%, progress (thread 0): 98.3703%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_500.44_500.62, WER: 0%, TER: 0%, total WER: 26.4578%, total TER: 12.9662%, progress (thread 0): 98.3782%]
|T|: w h a t
|P|: w h i t
[sample: EN2002d_H00_FEO070_508.48_508.66, WER: 100%, TER: 25%, total WER: 26.4586%, total TER: 12.9663%, progress (thread 0): 98.3861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_512.59_512.77, WER: 0%, TER: 0%, total WER: 26.4583%, total TER: 12.9662%, progress (thread 0): 98.394%]
|T|: y e a h
|P|: i t s
[sample: EN2002d_H02_MEE071_518.66_518.84, WER: 100%, TER: 100%, total WER: 26.4591%, total TER: 12.967%, progress (thread 0): 98.4019%]
|T|: y e a h
|P|: h u h
[sample: EN2002d_H00_FEO070_882.18_882.36, WER: 100%, TER: 75%, total WER: 26.4599%, total TER: 12.9676%, progress (thread 0): 98.4098%]
|T|: m m
|P|:
[sample: EN2002d_H02_MEE071_1451.31_1451.49, WER: 100%, TER: 100%, total WER: 26.4608%, total TER: 12.968%, progress (thread 0): 98.4177%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1581.55_1581.73, WER: 0%, TER: 0%, total WER: 26.4605%, total TER: 12.9678%, progress (thread 0): 98.4256%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_2062.62_2062.8, WER: 0%, TER: 0%, total WER: 26.4602%, total TER: 12.9677%, progress (thread 0): 98.4335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2166.41_2166.59, WER: 0%, TER: 0%, total WER: 26.4599%, total TER: 12.9676%, progress (thread 0): 98.4415%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_2172.96_2173.14, WER: 0%, TER: 0%, total WER: 26.4596%, total TER: 12.9674%, progress (thread 0): 98.4494%]
|T|: a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_843.75_843.92, WER: 100%, TER: 100%, total WER: 26.4604%, total TER: 12.9678%, progress (thread 0): 98.4573%]
|T|: y e p
|P|: y e a h
[sample: ES2004c_H00_MEO015_2220.58_2220.75, WER: 100%, TER: 66.6667%, total WER: 26.4612%, total TER: 12.9682%, progress (thread 0): 98.4652%]
|T|: y e a h
|P|:
[sample: IS1009b_H02_FIO084_381.27_381.44, WER: 100%, TER: 100%, total WER: 26.462%, total TER: 12.969%, progress (thread 0): 98.4731%]
|T|: h i
|P|: i n
[sample: IS1009c_H02_FIO084_43.25_43.42, WER: 100%, TER: 100%, total WER: 26.4629%, total TER: 12.9694%, progress (thread 0): 98.481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_347.27_347.44, WER: 0%, TER: 0%, total WER: 26.4626%, total TER: 12.9693%, progress (thread 0): 98.4889%]
|T|: m m
|P|: h
[sample: IS1009d_H02_FIO084_784.48_784.65, WER: 100%, TER: 100%, total WER: 26.4634%, total TER: 12.9697%, progress (thread 0): 98.4968%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_182.69_182.86, WER: 100%, TER: 66.6667%, total WER: 26.4642%, total TER: 12.9701%, progress (thread 0): 98.5047%]
|T|: h m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_748.2_748.37, WER: 100%, TER: 33.3333%, total WER: 26.465%, total TER: 12.9702%, progress (thread 0): 98.5127%]
|T|: y e a h
|P|: a n d
[sample: TS3003a_H01_MTD011UID_1432.6_1432.77, WER: 100%, TER: 100%, total WER: 26.4659%, total TER: 12.971%, progress (thread 0): 98.5206%]
|T|: n o
|P|: t h e t
[sample: TS3003a_H00_MTD009PM_1442.45_1442.62, WER: 100%, TER: 200%, total WER: 26.4667%, total TER: 12.9719%, progress (thread 0): 98.5285%]
|T|: y e a h
|P|:
[sample: TS3003b_H01_MTD011UID_1584.22_1584.39, WER: 100%, TER: 100%, total WER: 26.4675%, total TER: 12.9727%, progress (thread 0): 98.5364%]
|T|: n o
|P|: u h
[sample: TS3003b_H01_MTD011UID_1779.4_1779.57, WER: 100%, TER: 100%, total WER: 26.4683%, total TER: 12.9731%, progress (thread 0): 98.5443%]
|T|: y e a h
|P|: o p
[sample: TS3003b_H03_MTD012ME_2009.37_2009.54, WER: 100%, TER: 100%, total WER: 26.4691%, total TER: 12.9739%, progress (thread 0): 98.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1183.13_1183.3, WER: 0%, TER: 0%, total WER: 26.4688%, total TER: 12.9738%, progress (thread 0): 98.5601%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2272.21_2272.38, WER: 0%, TER: 0%, total WER: 26.4686%, total TER: 12.9737%, progress (thread 0): 98.568%]
|T|: y e a h
|P|: e a h
[sample: TS3003d_H01_MTD011UID_306.52_306.69, WER: 100%, TER: 25%, total WER: 26.4694%, total TER: 12.9738%, progress (thread 0): 98.576%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_1283.14_1283.31, WER: 100%, TER: 100%, total WER: 26.4702%, total TER: 12.9746%, progress (thread 0): 98.5839%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_549.83_550, WER: 100%, TER: 100%, total WER: 26.471%, total TER: 12.9752%, progress (thread 0): 98.5918%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_1153.54_1153.71, WER: 100%, TER: 100%, total WER: 26.4718%, total TER: 12.9758%, progress (thread 0): 98.5997%]
|T|: y e a h
|P|:
[sample: EN2002a_H00_MEE073_1180.31_1180.48, WER: 100%, TER: 100%, total WER: 26.4727%, total TER: 12.9766%, progress (thread 0): 98.6076%]
|T|: h m m
|P|: h m
[sample: EN2002a_H02_FEO072_2053.69_2053.86, WER: 100%, TER: 33.3333%, total WER: 26.4735%, total TER: 12.9768%, progress (thread 0): 98.6155%]
|T|: y e a h
|P|: j u s
[sample: EN2002a_H01_FEO070_2086.54_2086.71, WER: 100%, TER: 100%, total WER: 26.4743%, total TER: 12.9776%, progress (thread 0): 98.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_423.51_423.68, WER: 0%, TER: 0%, total WER: 26.474%, total TER: 12.9774%, progress (thread 0): 98.6313%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1344.27_1344.44, WER: 100%, TER: 100%, total WER: 26.4748%, total TER: 12.9783%, progress (thread 0): 98.6392%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1400.92_1401.09, WER: 0%, TER: 0%, total WER: 26.4745%, total TER: 12.9781%, progress (thread 0): 98.6472%]
|T|: y e a h
|P|: e s t
[sample: EN2002b_H01_MEE071_1518.9_1519.07, WER: 100%, TER: 75%, total WER: 26.4754%, total TER: 12.9787%, progress (thread 0): 98.6551%]
|T|: y e a h
|P|: i n
[sample: EN2002c_H03_MEE073_55.6_55.77, WER: 100%, TER: 100%, total WER: 26.4762%, total TER: 12.9795%, progress (thread 0): 98.663%]
|T|: y e a h
|P|:
[sample: EN2002c_H03_MEE073_711.52_711.69, WER: 100%, TER: 100%, total WER: 26.477%, total TER: 12.9803%, progress (thread 0): 98.6709%]
|T|: b u t
|P|: t a e
[sample: EN2002d_H02_MEE071_431.16_431.33, WER: 100%, TER: 100%, total WER: 26.4778%, total TER: 12.9809%, progress (thread 0): 98.6788%]
|T|: y e a h
|P|: i s
[sample: EN2002d_H02_MEE071_649.62_649.79, WER: 100%, TER: 100%, total WER: 26.4786%, total TER: 12.9817%, progress (thread 0): 98.6867%]
|T|: y e a h
|P|: h
[sample: EN2002d_H03_MEE073_831.51_831.68, WER: 100%, TER: 75%, total WER: 26.4795%, total TER: 12.9823%, progress (thread 0): 98.6946%]
|T|: s o
|P|:
[sample: EN2002d_H03_MEE073_1058.82_1058.99, WER: 100%, TER: 100%, total WER: 26.4803%, total TER: 12.9827%, progress (thread 0): 98.7025%]
|T|: o h
|P|: i
[sample: EN2002d_H00_FEO070_1459.48_1459.65, WER: 100%, TER: 100%, total WER: 26.4811%, total TER: 12.9831%, progress (thread 0): 98.7104%]
|T|: r i g h t
|P|: r i h t
[sample: EN2002d_H03_MEE073_1858.1_1858.27, WER: 100%, TER: 20%, total WER: 26.4819%, total TER: 12.9832%, progress (thread 0): 98.7184%]
|T|: i | m e a n
|P|: i | m n
[sample: ES2004c_H03_FEE016_691.32_691.48, WER: 50%, TER: 33.3333%, total WER: 26.4825%, total TER: 12.9835%, progress (thread 0): 98.7263%]
|T|: o k a y
|P|:
[sample: ES2004c_H03_FEE016_1560.36_1560.52, WER: 100%, TER: 100%, total WER: 26.4833%, total TER: 12.9843%, progress (thread 0): 98.7342%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H00_FIE088_757.84_758, WER: 0%, TER: 0%, total WER: 26.483%, total TER: 12.9842%, progress (thread 0): 98.7421%]
|T|: n o
|P|: n o
[sample: IS1009d_H01_FIO087_584.12_584.28, WER: 0%, TER: 0%, total WER: 26.4827%, total TER: 12.9841%, progress (thread 0): 98.75%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_740.48_740.64, WER: 0%, TER: 0%, total WER: 26.4824%, total TER: 12.984%, progress (thread 0): 98.7579%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_826.94_827.1, WER: 0%, TER: 0%, total WER: 26.4821%, total TER: 12.9839%, progress (thread 0): 98.7658%]
|T|: n o
|P|: n o
[sample: IS1009d_H03_FIO089_862.59_862.75, WER: 0%, TER: 0%, total WER: 26.4818%, total TER: 12.9838%, progress (thread 0): 98.7737%]
|T|: y e a h
|P|: y e a
[sample: IS1009d_H02_FIO084_1025.48_1025.64, WER: 100%, TER: 25%, total WER: 26.4826%, total TER: 12.9839%, progress (thread 0): 98.7816%]
|T|: y e a h
|P|: a n
[sample: TS3003a_H01_MTD011UID_580.47_580.63, WER: 100%, TER: 75%, total WER: 26.4834%, total TER: 12.9845%, progress (thread 0): 98.7896%]
|T|: u h
|P|: r i h
[sample: TS3003a_H00_MTD009PM_617.55_617.71, WER: 100%, TER: 100%, total WER: 26.4843%, total TER: 12.9849%, progress (thread 0): 98.7975%]
|T|: u h
|P|: e a h
[sample: TS3003a_H00_MTD009PM_1421.57_1421.73, WER: 100%, TER: 100%, total WER: 26.4851%, total TER: 12.9853%, progress (thread 0): 98.8054%]
|T|: h m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1789.09_1789.25, WER: 100%, TER: 33.3333%, total WER: 26.4859%, total TER: 12.9855%, progress (thread 0): 98.8133%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2009.54_2009.7, WER: 0%, TER: 0%, total WER: 26.4856%, total TER: 12.9853%, progress (thread 0): 98.8212%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1865.26_1865.42, WER: 100%, TER: 66.6667%, total WER: 26.4864%, total TER: 12.9857%, progress (thread 0): 98.8291%]
|T|: o h
|P|: t o
[sample: TS3003d_H00_MTD009PM_2587.23_2587.39, WER: 100%, TER: 100%, total WER: 26.4872%, total TER: 12.9861%, progress (thread 0): 98.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_376.27_376.43, WER: 0%, TER: 0%, total WER: 26.487%, total TER: 12.986%, progress (thread 0): 98.8449%]
|T|: y e a h
|P|: s i n e
[sample: EN2002a_H00_MEE073_720.58_720.74, WER: 100%, TER: 100%, total WER: 26.4878%, total TER: 12.9868%, progress (thread 0): 98.8529%]
|T|: y e a h
|P|: b u t
[sample: EN2002a_H00_MEE073_737.85_738.01, WER: 100%, TER: 100%, total WER: 26.4886%, total TER: 12.9876%, progress (thread 0): 98.8608%]
|T|: y e a h
|P|: y o
[sample: EN2002a_H00_MEE073_844.91_845.07, WER: 100%, TER: 75%, total WER: 26.4894%, total TER: 12.9882%, progress (thread 0): 98.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1041.33_1041.49, WER: 0%, TER: 0%, total WER: 26.4891%, total TER: 12.9881%, progress (thread 0): 98.8766%]
|T|: y e a h
|P|: b u t
[sample: EN2002a_H00_MEE073_1050.03_1050.19, WER: 100%, TER: 100%, total WER: 26.4899%, total TER: 12.9889%, progress (thread 0): 98.8845%]
|T|: w h y
|P|: w
[sample: EN2002a_H00_MEE073_1237.91_1238.07, WER: 100%, TER: 66.6667%, total WER: 26.4908%, total TER: 12.9893%, progress (thread 0): 98.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1596.11_1596.27, WER: 0%, TER: 0%, total WER: 26.4905%, total TER: 12.9891%, progress (thread 0): 98.9003%]
|T|: s o r r y
|P|: t u e
[sample: EN2002a_H00_MEE073_1813.61_1813.77, WER: 100%, TER: 100%, total WER: 26.4913%, total TER: 12.9901%, progress (thread 0): 98.9082%]
|T|: s u r e
|P|:
[sample: EN2002a_H00_MEE073_1881.96_1882.12, WER: 100%, TER: 100%, total WER: 26.4921%, total TER: 12.991%, progress (thread 0): 98.9161%]
|T|: y e a h
|P|: y o u
[sample: EN2002b_H02_FEO072_39.76_39.92, WER: 100%, TER: 75%, total WER: 26.4929%, total TER: 12.9915%, progress (thread 0): 98.924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_317.94_318.1, WER: 0%, TER: 0%, total WER: 26.4926%, total TER: 12.9914%, progress (thread 0): 98.932%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1075.72_1075.88, WER: 0%, TER: 0%, total WER: 26.4923%, total TER: 12.9913%, progress (thread 0): 98.9399%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1155.04_1155.2, WER: 100%, TER: 100%, total WER: 26.4932%, total TER: 12.9921%, progress (thread 0): 98.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_713.06_713.22, WER: 0%, TER: 0%, total WER: 26.4929%, total TER: 12.9919%, progress (thread 0): 98.9557%]
|T|: o h
|P|:
[sample: EN2002c_H03_MEE073_1210.59_1210.75, WER: 100%, TER: 100%, total WER: 26.4937%, total TER: 12.9924%, progress (thread 0): 98.9636%]
|T|: r i g h t
|P|: o r
[sample: EN2002c_H03_MEE073_1484.69_1484.85, WER: 100%, TER: 100%, total WER: 26.4945%, total TER: 12.9934%, progress (thread 0): 98.9715%]
|T|: y e a h
|P|: e
[sample: EN2002c_H03_MEE073_2554.98_2555.14, WER: 100%, TER: 75%, total WER: 26.4953%, total TER: 12.9939%, progress (thread 0): 98.9794%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2776.86_2777.02, WER: 100%, TER: 33.3333%, total WER: 26.4961%, total TER: 12.9941%, progress (thread 0): 98.9873%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_645.29_645.45, WER: 100%, TER: 100%, total WER: 26.497%, total TER: 12.9945%, progress (thread 0): 98.9952%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1397.11_1397.27, WER: 0%, TER: 0%, total WER: 26.4967%, total TER: 12.9944%, progress (thread 0): 99.0032%]
|T|: o o p s
|P|:
[sample: ES2004a_H00_MEO015_188.28_188.43, WER: 100%, TER: 100%, total WER: 26.4975%, total TER: 12.9952%, progress (thread 0): 99.0111%]
|T|: o o h
|P|: i t
[sample: ES2004b_H02_MEE014_534.45_534.6, WER: 100%, TER: 100%, total WER: 26.4983%, total TER: 12.9958%, progress (thread 0): 99.019%]
|T|: ' k a y
|P|:
[sample: ES2004c_H01_FEE013_472.18_472.33, WER: 100%, TER: 100%, total WER: 26.4991%, total TER: 12.9966%, progress (thread 0): 99.0269%]
|T|: y e s
|P|:
[sample: IS1009a_H01_FIO087_693.82_693.97, WER: 100%, TER: 100%, total WER: 26.5%, total TER: 12.9972%, progress (thread 0): 99.0348%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1647.59_1647.74, WER: 0%, TER: 0%, total WER: 26.4997%, total TER: 12.9971%, progress (thread 0): 99.0427%]
|T|: y e a h
|P|: s a m e
[sample: IS1009d_H02_FIO084_1392_1392.15, WER: 100%, TER: 100%, total WER: 26.5005%, total TER: 12.9979%, progress (thread 0): 99.0506%]
|T|: t w o
|P|: d o
[sample: IS1009d_H01_FIO087_1586.28_1586.43, WER: 100%, TER: 66.6667%, total WER: 26.5013%, total TER: 12.9983%, progress (thread 0): 99.0585%]
|T|: n o
|P|:
[sample: IS1009d_H01_FIO087_1824.98_1825.13, WER: 100%, TER: 100%, total WER: 26.5021%, total TER: 12.9987%, progress (thread 0): 99.0665%]
|T|: y e a h
|P|: n
[sample: IS1009d_H02_FIO084_1883.61_1883.76, WER: 100%, TER: 100%, total WER: 26.5029%, total TER: 12.9995%, progress (thread 0): 99.0744%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_567.51_567.66, WER: 0%, TER: 0%, total WER: 26.5026%, total TER: 12.9993%, progress (thread 0): 99.0823%]
|T|: y e a h
|P|:
[sample: TS3003c_H02_MTD0010ID_2049.54_2049.69, WER: 100%, TER: 100%, total WER: 26.5035%, total TER: 13.0002%, progress (thread 0): 99.0902%]
|T|: m m
|P|: m
[sample: TS3003d_H01_MTD011UID_2041.85_2042, WER: 100%, TER: 50%, total WER: 26.5043%, total TER: 13.0003%, progress (thread 0): 99.0981%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_639.89_640.04, WER: 100%, TER: 66.6667%, total WER: 26.5051%, total TER: 13.0007%, progress (thread 0): 99.106%]
|T|: y e a h
|P|: a n
[sample: EN2002a_H00_MEE073_967.58_967.73, WER: 100%, TER: 75%, total WER: 26.5059%, total TER: 13.0013%, progress (thread 0): 99.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1030.96_1031.11, WER: 0%, TER: 0%, total WER: 26.5056%, total TER: 13.0012%, progress (thread 0): 99.1218%]
|T|: y e a h
|P|: h o
[sample: EN2002a_H01_FEO070_1104.83_1104.98, WER: 100%, TER: 100%, total WER: 26.5065%, total TER: 13.002%, progress (thread 0): 99.1297%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1527.69_1527.84, WER: 100%, TER: 33.3333%, total WER: 26.5073%, total TER: 13.0021%, progress (thread 0): 99.1377%]
|T|: y e a h
|P|:
[sample: EN2002a_H00_MEE073_1539.77_1539.92, WER: 100%, TER: 100%, total WER: 26.5081%, total TER: 13.0029%, progress (thread 0): 99.1456%]
|T|: y e a h
|P|: r e
[sample: EN2002a_H00_MEE073_1744.01_1744.16, WER: 100%, TER: 75%, total WER: 26.5089%, total TER: 13.0035%, progress (thread 0): 99.1535%]
|T|: y e a h
|P|: y o a
[sample: EN2002a_H01_FEO070_1785.21_1785.36, WER: 100%, TER: 50%, total WER: 26.5097%, total TER: 13.0038%, progress (thread 0): 99.1614%]
|T|: y e p
|P|: y e p
[sample: EN2002b_H03_MEE073_382.32_382.47, WER: 0%, TER: 0%, total WER: 26.5094%, total TER: 13.0037%, progress (thread 0): 99.1693%]
|T|: u h
|P|: y e a h
[sample: EN2002b_H02_FEO072_424.35_424.5, WER: 100%, TER: 150%, total WER: 26.5103%, total TER: 13.0044%, progress (thread 0): 99.1772%]
|T|: y e a h
|P|:
[sample: EN2002b_H00_FEO070_496.03_496.18, WER: 100%, TER: 100%, total WER: 26.5111%, total TER: 13.0052%, progress (thread 0): 99.1851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_897.7_897.85, WER: 0%, TER: 0%, total WER: 26.5108%, total TER: 13.0051%, progress (thread 0): 99.193%]
|T|: y e a h
|P|: t h
[sample: EN2002c_H03_MEE073_2198.65_2198.8, WER: 100%, TER: 75%, total WER: 26.5116%, total TER: 13.0056%, progress (thread 0): 99.201%]
|T|: y e a h
|P|: i t
[sample: EN2002d_H00_FEO070_1290.13_1290.28, WER: 100%, TER: 100%, total WER: 26.5124%, total TER: 13.0065%, progress (thread 0): 99.2089%]
|T|: n o
|P|:
[sample: EN2002d_H03_MEE073_1424.03_1424.18, WER: 100%, TER: 100%, total WER: 26.5132%, total TER: 13.0069%, progress (thread 0): 99.2168%]
|T|: r i g h t
|P|:
[sample: EN2002d_H03_MEE073_1909.61_1909.76, WER: 100%, TER: 100%, total WER: 26.5141%, total TER: 13.0079%, progress (thread 0): 99.2247%]
|T|: o k a y
|P|:
[sample: EN2002d_H03_MEE073_1985.35_1985.5, WER: 100%, TER: 100%, total WER: 26.5149%, total TER: 13.0087%, progress (thread 0): 99.2326%]
|T|: i | t h
|P|: i
[sample: ES2004b_H01_FEE013_2305.5_2305.64, WER: 50%, TER: 75%, total WER: 26.5154%, total TER: 13.0093%, progress (thread 0): 99.2405%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_777.13_777.27, WER: 100%, TER: 100%, total WER: 26.5162%, total TER: 13.0097%, progress (thread 0): 99.2484%]
|T|: y e a h
|P|:
[sample: IS1009a_H02_FIO084_56.9_57.04, WER: 100%, TER: 100%, total WER: 26.5171%, total TER: 13.0105%, progress (thread 0): 99.2563%]
|T|: y e s
|P|: y e a
[sample: IS1009d_H01_FIO087_645.27_645.41, WER: 100%, TER: 33.3333%, total WER: 26.5179%, total TER: 13.0106%, progress (thread 0): 99.2642%]
|T|: m m h m m
|P|:
[sample: IS1009d_H00_FIE088_770.97_771.11, WER: 100%, TER: 100%, total WER: 26.5187%, total TER: 13.0116%, progress (thread 0): 99.2721%]
|T|: h m m
|P|:
[sample: TS3003a_H01_MTD011UID_189.69_189.83, WER: 100%, TER: 100%, total WER: 26.5195%, total TER: 13.0122%, progress (thread 0): 99.2801%]
|T|: m m
|P|:
[sample: TS3003a_H01_MTD011UID_903.08_903.22, WER: 100%, TER: 100%, total WER: 26.5203%, total TER: 13.0126%, progress (thread 0): 99.288%]
|T|: m m
|P|:
[sample: TS3003b_H01_MTD011UID_2047.35_2047.49, WER: 100%, TER: 100%, total WER: 26.5212%, total TER: 13.013%, progress (thread 0): 99.2959%]
|T|: ' k a y
|P|:
[sample: TS3003b_H03_MTD012ME_2140.48_2140.62, WER: 100%, TER: 100%, total WER: 26.522%, total TER: 13.0138%, progress (thread 0): 99.3038%]
|T|: y e a h
|P|:
[sample: TS3003c_H03_MTD012ME_1869.23_1869.37, WER: 100%, TER: 100%, total WER: 26.5228%, total TER: 13.0147%, progress (thread 0): 99.3117%]
|T|: y e a h
|P|:
[sample: TS3003d_H00_MTD009PM_1390.61_1390.75, WER: 100%, TER: 100%, total WER: 26.5236%, total TER: 13.0155%, progress (thread 0): 99.3196%]
|T|: y e a h
|P|:
[sample: TS3003d_H00_MTD009PM_2021.71_2021.85, WER: 100%, TER: 100%, total WER: 26.5244%, total TER: 13.0163%, progress (thread 0): 99.3275%]
|T|: r i g h t
|P|: i k
[sample: EN2002a_H00_MEE073_1184.15_1184.29, WER: 100%, TER: 80%, total WER: 26.5253%, total TER: 13.017%, progress (thread 0): 99.3354%]
|T|: h m m
|P|: i h m
[sample: EN2002a_H00_MEE073_1193.94_1194.08, WER: 100%, TER: 66.6667%, total WER: 26.5261%, total TER: 13.0174%, progress (thread 0): 99.3434%]
|T|: y e p
|P|: y e h
[sample: EN2002a_H00_MEE073_2048.07_2048.21, WER: 100%, TER: 33.3333%, total WER: 26.5269%, total TER: 13.0176%, progress (thread 0): 99.3513%]
|T|: ' k a y
|P|:
[sample: EN2002c_H03_MEE073_103.84_103.98, WER: 100%, TER: 100%, total WER: 26.5277%, total TER: 13.0184%, progress (thread 0): 99.3592%]
|T|: y e a h
|P|: g e
[sample: EN2002c_H02_MEE071_531.32_531.46, WER: 100%, TER: 75%, total WER: 26.5285%, total TER: 13.0189%, progress (thread 0): 99.3671%]
|T|: y e a h
|P|: s o
[sample: EN2002c_H03_MEE073_667.22_667.36, WER: 100%, TER: 100%, total WER: 26.5294%, total TER: 13.0198%, progress (thread 0): 99.375%]
|T|: y e a h
|P|: y e
[sample: EN2002c_H02_MEE071_1476.08_1476.22, WER: 100%, TER: 50%, total WER: 26.5302%, total TER: 13.0201%, progress (thread 0): 99.3829%]
|T|: m m
|P|:
[sample: EN2002c_H03_MEE073_2096.91_2097.05, WER: 100%, TER: 100%, total WER: 26.531%, total TER: 13.0205%, progress (thread 0): 99.3908%]
|T|: o k a y
|P|: e
[sample: EN2002d_H00_FEO070_1251.9_1252.04, WER: 100%, TER: 100%, total WER: 26.5318%, total TER: 13.0213%, progress (thread 0): 99.3987%]
|T|: m m
|P|: y e a
[sample: ES2004c_H03_FEE016_98.28_98.41, WER: 100%, TER: 150%, total WER: 26.5326%, total TER: 13.0219%, progress (thread 0): 99.4066%]
|T|: s
|P|:
[sample: ES2004d_H03_FEE016_1370.74_1370.87, WER: 100%, TER: 100%, total WER: 26.5335%, total TER: 13.0221%, progress (thread 0): 99.4146%]
|T|: o r | a | b
|P|:
[sample: IS1009a_H01_FIO087_573.12_573.25, WER: 100%, TER: 100%, total WER: 26.5359%, total TER: 13.0234%, progress (thread 0): 99.4225%]
|T|: h m m
|P|: y e
[sample: IS1009c_H02_FIO084_144.08_144.21, WER: 100%, TER: 100%, total WER: 26.5367%, total TER: 13.024%, progress (thread 0): 99.4304%]
|T|: o k a y
|P|: j u s
[sample: IS1009d_H00_FIE088_604.16_604.29, WER: 100%, TER: 100%, total WER: 26.5376%, total TER: 13.0248%, progress (thread 0): 99.4383%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_942.55_942.68, WER: 100%, TER: 100%, total WER: 26.5384%, total TER: 13.0254%, progress (thread 0): 99.4462%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_392.63_392.76, WER: 100%, TER: 66.6667%, total WER: 26.5392%, total TER: 13.0258%, progress (thread 0): 99.4541%]
|T|: h u h
|P|:
[sample: TS3003a_H01_MTD011UID_1266.08_1266.21, WER: 100%, TER: 100%, total WER: 26.54%, total TER: 13.0264%, progress (thread 0): 99.462%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1335.11_1335.24, WER: 100%, TER: 100%, total WER: 26.5408%, total TER: 13.0268%, progress (thread 0): 99.4699%]
|T|: y e p
|P|: y h a
[sample: TS3003a_H01_MTD011UID_1475.42_1475.55, WER: 100%, TER: 66.6667%, total WER: 26.5417%, total TER: 13.0271%, progress (thread 0): 99.4778%]
|T|: y e a h
|P|:
[sample: TS3003b_H01_MTD011UID_1528.52_1528.65, WER: 100%, TER: 100%, total WER: 26.5425%, total TER: 13.0279%, progress (thread 0): 99.4858%]
|T|: m m
|P|:
[sample: TS3003c_H01_MTD011UID_1173.23_1173.36, WER: 100%, TER: 100%, total WER: 26.5433%, total TER: 13.0284%, progress (thread 0): 99.4937%]
|T|: y e a h
|P|: t h
[sample: TS3003d_H00_MTD009PM_1693.45_1693.58, WER: 100%, TER: 75%, total WER: 26.5441%, total TER: 13.0289%, progress (thread 0): 99.5016%]
|T|: ' k a y
|P|:
[sample: EN2002a_H00_MEE073_32.58_32.71, WER: 100%, TER: 100%, total WER: 26.5449%, total TER: 13.0297%, progress (thread 0): 99.5095%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_984.87_985, WER: 100%, TER: 100%, total WER: 26.5458%, total TER: 13.0303%, progress (thread 0): 99.5174%]
|T|: w e l l | f
|P|: b u h
[sample: EN2002a_H00_MEE073_1458.73_1458.86, WER: 100%, TER: 100%, total WER: 26.5474%, total TER: 13.0316%, progress (thread 0): 99.5253%]
|T|: y e p
|P|: y e a
[sample: EN2002b_H01_MEE071_493.19_493.32, WER: 100%, TER: 33.3333%, total WER: 26.5482%, total TER: 13.0317%, progress (thread 0): 99.5332%]
|T|: y e a h
|P|:
[sample: EN2002c_H03_MEE073_665.01_665.14, WER: 100%, TER: 100%, total WER: 26.549%, total TER: 13.0325%, progress (thread 0): 99.5411%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_135.16_135.29, WER: 100%, TER: 100%, total WER: 26.5499%, total TER: 13.0329%, progress (thread 0): 99.549%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H01_FEO072_726.53_726.66, WER: 100%, TER: 25%, total WER: 26.5507%, total TER: 13.033%, progress (thread 0): 99.557%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H01_FEO072_794.79_794.92, WER: 100%, TER: 25%, total WER: 26.5515%, total TER: 13.0331%, progress (thread 0): 99.5649%]
|T|: y e a h
|P|: g o
[sample: EN2002d_H00_FEO070_1595.77_1595.9, WER: 100%, TER: 100%, total WER: 26.5523%, total TER: 13.0339%, progress (thread 0): 99.5728%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_1641.64_1641.77, WER: 100%, TER: 100%, total WER: 26.5531%, total TER: 13.0343%, progress (thread 0): 99.5807%]
|T|: y e a h
|P|:
[sample: ES2004b_H00_MEO015_922.62_922.74, WER: 100%, TER: 100%, total WER: 26.554%, total TER: 13.0351%, progress (thread 0): 99.5886%]
|T|: o k a y
|P|: j u
[sample: ES2004b_H00_MEO015_1977.87_1977.99, WER: 100%, TER: 100%, total WER: 26.5548%, total TER: 13.036%, progress (thread 0): 99.5965%]
|T|: y e a h
|P|: i
[sample: IS1009b_H02_FIO084_173.86_173.98, WER: 100%, TER: 100%, total WER: 26.5556%, total TER: 13.0368%, progress (thread 0): 99.6044%]
|T|: h m m
|P|:
[sample: IS1009c_H02_FIO084_1703.14_1703.26, WER: 100%, TER: 100%, total WER: 26.5564%, total TER: 13.0374%, progress (thread 0): 99.6123%]
|T|: m m | m m
|P|:
[sample: IS1009d_H03_FIO089_870.76_870.88, WER: 100%, TER: 100%, total WER: 26.5581%, total TER: 13.0384%, progress (thread 0): 99.6203%]
|T|: y e p
|P|: i f
[sample: TS3003a_H01_MTD011UID_257.2_257.32, WER: 100%, TER: 100%, total WER: 26.5589%, total TER: 13.039%, progress (thread 0): 99.6282%]
|T|: y e a h
|P|: j u s
[sample: EN2002a_H03_MEE071_170.81_170.93, WER: 100%, TER: 100%, total WER: 26.5597%, total TER: 13.0398%, progress (thread 0): 99.6361%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1468.26_1468.38, WER: 100%, TER: 33.3333%, total WER: 26.5605%, total TER: 13.0399%, progress (thread 0): 99.644%]
|T|: n o
|P|: n h
[sample: EN2002b_H03_MEE073_4.83_4.95, WER: 100%, TER: 50%, total WER: 26.5613%, total TER: 13.0401%, progress (thread 0): 99.6519%]
|T|: a l t e r
|P|: u
[sample: EN2002b_H03_MEE073_170.46_170.58, WER: 100%, TER: 100%, total WER: 26.5622%, total TER: 13.0411%, progress (thread 0): 99.6598%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1641.92_1642.04, WER: 100%, TER: 100%, total WER: 26.563%, total TER: 13.0419%, progress (thread 0): 99.6677%]
|T|: y e a h
|P|: y e
[sample: EN2002d_H03_MEE073_806.78_806.9, WER: 100%, TER: 50%, total WER: 26.5638%, total TER: 13.0423%, progress (thread 0): 99.6756%]
|T|: o k a y
|P|:
[sample: ES2004a_H00_MEO015_71.95_72.06, WER: 100%, TER: 100%, total WER: 26.5646%, total TER: 13.0431%, progress (thread 0): 99.6835%]
|T|: o
|P|:
[sample: ES2004a_H03_FEE016_403.24_403.35, WER: 100%, TER: 100%, total WER: 26.5654%, total TER: 13.0433%, progress (thread 0): 99.6915%]
|T|: ' k a y
|P|:
[sample: ES2004a_H03_FEE016_1042.19_1042.3, WER: 100%, TER: 100%, total WER: 26.5662%, total TER: 13.0441%, progress (thread 0): 99.6994%]
|T|: m m
|P|:
[sample: IS1009c_H02_FIO084_620.84_620.95, WER: 100%, TER: 100%, total WER: 26.5671%, total TER: 13.0445%, progress (thread 0): 99.7073%]
|T|: y e a h
|P|:
[sample: TS3003a_H01_MTD011UID_436.27_436.38, WER: 100%, TER: 100%, total WER: 26.5679%, total TER: 13.0453%, progress (thread 0): 99.7152%]
|T|: y e a h
|P|: o
[sample: EN2002a_H00_MEE073_669.47_669.58, WER: 100%, TER: 100%, total WER: 26.5687%, total TER: 13.0461%, progress (thread 0): 99.7231%]
|T|: y e a h
|P|:
[sample: EN2002a_H01_FEO070_1169.61_1169.72, WER: 100%, TER: 100%, total WER: 26.5695%, total TER: 13.0469%, progress (thread 0): 99.731%]
|T|: n o
|P|:
[sample: EN2002b_H02_FEO072_4.48_4.59, WER: 100%, TER: 100%, total WER: 26.5703%, total TER: 13.0473%, progress (thread 0): 99.7389%]
|T|: y e p
|P|:
[sample: EN2002b_H03_MEE073_307.97_308.08, WER: 100%, TER: 100%, total WER: 26.5712%, total TER: 13.0479%, progress (thread 0): 99.7468%]
|T|: h m m
|P|:
[sample: EN2002b_H03_MEE073_1578.82_1578.93, WER: 100%, TER: 100%, total WER: 26.572%, total TER: 13.0485%, progress (thread 0): 99.7547%]
|T|: h m m
|P|:
[sample: EN2002c_H03_MEE073_1446.36_1446.47, WER: 100%, TER: 100%, total WER: 26.5728%, total TER: 13.0491%, progress (thread 0): 99.7627%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_118.11_118.22, WER: 100%, TER: 100%, total WER: 26.5736%, total TER: 13.0495%, progress (thread 0): 99.7706%]
|T|: s o
|P|:
[sample: EN2002d_H02_MEE071_2107.05_2107.16, WER: 100%, TER: 100%, total WER: 26.5744%, total TER: 13.0499%, progress (thread 0): 99.7785%]
|T|: t
|P|:
[sample: ES2004c_H02_MEE014_1184.81_1184.91, WER: 100%, TER: 100%, total WER: 26.5753%, total TER: 13.0501%, progress (thread 0): 99.7864%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_1744.56_1744.66, WER: 100%, TER: 100%, total WER: 26.5761%, total TER: 13.0507%, progress (thread 0): 99.7943%]
|T|: o h
|P|:
[sample: TS3003c_H02_MTD0010ID_1023.66_1023.76, WER: 100%, TER: 100%, total WER: 26.5769%, total TER: 13.0512%, progress (thread 0): 99.8022%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_2013_2013.1, WER: 100%, TER: 100%, total WER: 26.5777%, total TER: 13.0518%, progress (thread 0): 99.8101%]
|T|: y e a h
|P|:
[sample: EN2002b_H00_FEO070_43.63_43.73, WER: 100%, TER: 100%, total WER: 26.5785%, total TER: 13.0526%, progress (thread 0): 99.818%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_293.52_293.62, WER: 100%, TER: 100%, total WER: 26.5794%, total TER: 13.0534%, progress (thread 0): 99.826%]
|T|: y e p
|P|: e
[sample: EN2002c_H03_MEE073_763_763.1, WER: 100%, TER: 66.6667%, total WER: 26.5802%, total TER: 13.0537%, progress (thread 0): 99.8339%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_2099.51_2099.61, WER: 100%, TER: 100%, total WER: 26.581%, total TER: 13.0541%, progress (thread 0): 99.8418%]
|T|: ' k a y
|P|:
[sample: ES2004a_H00_MEO015_168.79_168.88, WER: 100%, TER: 100%, total WER: 26.5818%, total TER: 13.055%, progress (thread 0): 99.8497%]
|T|: y e p
|P|:
[sample: ES2004c_H00_MEO015_360.73_360.82, WER: 100%, TER: 100%, total WER: 26.5826%, total TER: 13.0556%, progress (thread 0): 99.8576%]
|T|: m m h m m
|P|:
[sample: ES2004c_H03_FEE016_1132.2_1132.29, WER: 100%, TER: 100%, total WER: 26.5835%, total TER: 13.0566%, progress (thread 0): 99.8655%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1651.84_1651.93, WER: 100%, TER: 100%, total WER: 26.5843%, total TER: 13.0572%, progress (thread 0): 99.8734%]
|T|: m m h m m
|P|:
[sample: IS1009c_H02_FIO084_1753.49_1753.58, WER: 100%, TER: 100%, total WER: 26.5851%, total TER: 13.0582%, progress (thread 0): 99.8813%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_749.88_749.97, WER: 100%, TER: 100%, total WER: 26.5859%, total TER: 13.0588%, progress (thread 0): 99.8892%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1718.32_1718.41, WER: 100%, TER: 100%, total WER: 26.5867%, total TER: 13.0598%, progress (thread 0): 99.8972%]
|T|: w h a t
|P|:
[sample: TS3003d_H00_MTD009PM_1597.33_1597.42, WER: 100%, TER: 100%, total WER: 26.5875%, total TER: 13.0606%, progress (thread 0): 99.9051%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_887.36_887.45, WER: 100%, TER: 100%, total WER: 26.5884%, total TER: 13.0612%, progress (thread 0): 99.913%]
|T|: d a m n
|P|:
[sample: EN2002a_H00_MEE073_1500.91_1501, WER: 100%, TER: 100%, total WER: 26.5892%, total TER: 13.062%, progress (thread 0): 99.9209%]
|T|: h m m
|P|:
[sample: EN2002c_H03_MEE073_429.63_429.72, WER: 100%, TER: 100%, total WER: 26.59%, total TER: 13.0626%, progress (thread 0): 99.9288%]
|T|: y e a h
|P|: a
[sample: EN2002c_H02_MEE071_2578.94_2579.03, WER: 100%, TER: 75%, total WER: 26.5908%, total TER: 13.0632%, progress (thread 0): 99.9367%]
|T|: d a m n
|P|:
[sample: EN2002d_H03_MEE073_999.94_1000.03, WER: 100%, TER: 100%, total WER: 26.5916%, total TER: 13.064%, progress (thread 0): 99.9446%]
|T|: m m
|P|:
[sample: ES2004a_H00_MEO015_252.48_252.56, WER: 100%, TER: 100%, total WER: 26.5925%, total TER: 13.0644%, progress (thread 0): 99.9525%]
|T|: m m
|P|:
[sample: ES2004c_H00_MEO015_1885.03_1885.11, WER: 100%, TER: 100%, total WER: 26.5933%, total TER: 13.0648%, progress (thread 0): 99.9604%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1313.86_1313.94, WER: 100%, TER: 100%, total WER: 26.5941%, total TER: 13.0652%, progress (thread 0): 99.9684%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_185.9_185.98, WER: 100%, TER: 100%, total WER: 26.5949%, total TER: 13.0658%, progress (thread 0): 99.9763%]
|T|: g
|P|:
[sample: EN2002d_H03_MEE073_241.62_241.69, WER: 100%, TER: 100%, total WER: 26.5957%, total TER: 13.066%, progress (thread 0): 99.9842%]
|T|: h m m
|P|:
[sample: IS1009a_H02_FIO084_490.4_490.46, WER: 100%, TER: 100%, total WER: 26.5966%, total TER: 13.0666%, progress (thread 0): 99.9921%]
|T|: ' k a y
|P|:
[sample: EN2002b_H03_MEE073_613.94_614, WER: 100%, TER: 100%, total WER: 26.5974%, total TER: 13.0674%, progress (thread 0): 100%]
I1224 05:05:23.029392 9187 Test.cpp:418] ------
I1224 05:05:23.029417 9187 Test.cpp:419] [Test ami_limited_supervision/test.lst (12640 samples) in 363.179s (actual decoding time 0.0287s/sample) -- WER: 26.5974%, TER: 13.0674%]
###Markdown
We can see that the viterbi WER is 26.6% before finetuning. Step 3: Run FinetuningNow, let's run finetuning with the AMI Corpus to see if we can improve the WER. Important parameters for `fl_asr_finetune_ctc`:`--train`, `--valid` - list files for training and validation sets respectively. Use comma to separate multiple files`--datadir` - [optional] base path to be used for `--train`, `--valid` flags`--lr` - learning rate for SGD`--momentum` - SGD momentum `--lr_decay` - epoch at which learning decay starts `--lr_decay_step` - learning rate halves after this epoch interval starting from epoch given by `lr_decay` `--arch` - architecture file. Tune droupout if necessary. `--tokens` - tokens file `--batchsize` - batchsize per process`--lexicon` - lexicon file `--rundir` - path to store checkpoint logs`--reportiters` - Number of updates after which we will run evaluation on validation data and save model, if 0 we only do this at end of each epoch>Amount of train data | Config to use >---|---|> 10 min| --train train_10min_0.lst> 1 hr| --train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst> 10 hr| --train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lstLet's run finetuning with 10hr AMI data (**~7min** for 1000 updates with evaluation on dev set)
###Code
! ./flashlight/build/bin/asr/fl_asr_tutorial_finetune_ctc model.bin \
--datadir ami_limited_supervision \
--train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lst \
--valid dev:dev.lst \
--arch arch.txt \
--tokens tokens.txt \
--lexicon lexicon.txt \
--rundir checkpoint \
--lr 0.025 \
--netoptim sgd \
--momentum 0.8 \
--reportiters 1000 \
--lr_decay 100 \
--lr_decay_step 50 \
--iter 25000 \
--batchsize 4 \
--warmup 0
###Output
I1224 06:39:48.599629 11517 FinetuneCTC.cpp:76] Parsing command line flags
Initialized NCCL 2.7.8 successfully!
I1224 06:39:49.002488 11517 FinetuneCTC.cpp:106] Gflags after parsing
--flagfile=; --fromenv=; --tryfromenv=; --undefok=; --tab_completion_columns=80; --tab_completion_word=; --help=false; --helpfull=false; --helpmatch=; --helpon=; --helppackage=false; --helpshort=false; --helpxml=false; --version=false; --adambeta1=0.94999999999999996; --adambeta2=0.98999999999999999; --am=; --am_decoder_tr_dropout=0.20000000000000001; --am_decoder_tr_layerdrop=0.20000000000000001; --am_decoder_tr_layers=6; --arch=arch.txt; --attention=keyvalue; --attentionthreshold=2147483647; --attnWindow=softPretrain; --attnconvchannel=0; --attnconvkernel=0; --attndim=0; --batching_max_duration=0; --batching_strategy=none; --batchsize=4; --beamsize=2500; --beamsizetoken=250000; --beamthreshold=25; --channels=1; --criterion=ctc; --critoptim=adagrad; --datadir=ami_limited_supervision; --decoderattnround=1; --decoderdropout=0; --decoderrnnlayer=1; --decodertype=wrd; --devwin=0; --emission_dir=; --emission_queue_size=3000; --enable_distributed=true; --encoderdim=256; --eosscore=0; --eostoken=false; --everstoredb=false; --fftcachesize=1; --filterbanks=80; --fl_amp_max_scale_factor=32000; --fl_amp_scale_factor=4096; --fl_amp_scale_factor_update_interval=2000; --fl_amp_use_mixed_precision=false; --fl_benchmark_mode=true; --fl_log_level=; --fl_optim_mode=; --fl_vlog_level=0; --flagsfile=; --framesizems=25; --framestridems=10; --gamma=1; --gumbeltemperature=1; --highfreqfilterbank=-1; --inputfeeding=false; --isbeamdump=false; --iter=25000; --itersave=false; --labelsmooth=0.050000000000000003; --leftWindowSize=50; --lexicon=lexicon.txt; --linlr=-1; --linlrcrit=-1; --linseg=0; --lm=; --lm_memory=5000; --lm_vocab=; --lmtype=kenlm; --lmweight=0; --lmweight_high=4; --lmweight_low=0; --lmweight_step=0.20000000000000001; --localnrmlleftctx=0; --localnrmlrightctx=0; --logadd=false; --lowfreqfilterbank=0; --lr=0.025000000000000001; --lr_decay=100; --lr_decay_step=50; --lrcosine=false; --lrcrit=0.02; --max_devices_per_node=8; --maxdecoderoutputlen=400; --maxgradnorm=0.10000000000000001; --maxload=-1; --maxrate=10; --maxsil=50; --maxword=-1; --melfloor=1; --mfcc=false; --mfcccoeffs=13; --mfsc=true; --minrate=3; --minsil=0; --momentum=0.80000000000000004; --netoptim=sgd; --nthread=6; --nthread_decoder=1; --nthread_decoder_am_forward=1; --numattnhead=8; --onorm=target; --optimepsilon=1e-08; --optimrho=0.90000000000000002; --pctteacherforcing=99; --pcttraineval=1; --pow=false; --pretrainWindow=0; --replabel=0; --reportiters=1000; --rightWindowSize=50; --rndv_filepath=; --rundir=checkpoint; --samplerate=16000; --sampletarget=0.01; --samplingstrategy=rand; --saug_fmaskf=30; --saug_fmaskn=2; --saug_start_update=24000; --saug_tmaskn=10; --saug_tmaskp=0.050000000000000003; --saug_tmaskt=30; --sclite=; --seed=0; --sfx_config=; --show=false; --showletters=false; --silscore=0; --smearing=none; --smoothingtemperature=1; --softwoffset=10; --softwrate=5; --softwstd=4; --sqnorm=true; --stepsize=9223372036854775807; --surround=; --test=; --tokens=tokens.txt; --train=train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lst; --trainWithWindow=true; --transdiag=0; --unkscore=-inf; --use_memcache=false; --uselexicon=true; --usewordpiece=false; --valid=dev:dev.lst; --validbatchsize=-1; --warmup=0; --weightdecay=0; --wordscore=0; --wordseparator=|; --world_rank=0; --world_size=1; --alsologtoemail=; --alsologtostderr=false; --colorlogtostderr=false; --drop_log_memory=true; --log_backtrace_at=; --log_dir=; --log_link=; --log_prefix=true; --logbuflevel=0; --logbufsecs=30; --logemaillevel=999; --logfile_mode=436; --logmailer=/bin/mail; --logtostderr=true; --max_log_size=1800; --minloglevel=0; --stderrthreshold=2; --stop_logging_if_full_disk=false; --symbolize_stacktrace=true; --v=0; --vmodule=;
I1224 06:39:49.002910 11517 FinetuneCTC.cpp:107] Experiment path: checkpoint
I1224 06:39:49.002919 11517 FinetuneCTC.cpp:108] Experiment runidx: 1
I1224 06:39:49.003252 11517 FinetuneCTC.cpp:153] Number of classes (network): 29
I1224 06:39:49.248888 11517 FinetuneCTC.cpp:160] Number of words: 200001
I1224 06:39:50.344347 11517 FinetuneCTC.cpp:248] Loading architecture file from arch.txt
I1224 06:39:50.868463 11517 FinetuneCTC.cpp:277] [Network] Sequential [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> (19) -> (20) -> (21) -> (22) -> (23) -> (24) -> (25) -> (26) -> (27) -> (28) -> (29) -> (30) -> (31) -> (32) -> (33) -> (34) -> (35) -> (36) -> (37) -> (38) -> (39) -> (40) -> (41) -> (42) -> output]
(0): View (-1 1 80 0)
(1): LayerNorm ( axis : { 0 1 2 } , size : -1)
(2): Conv2D (80->768, 7x1, 3,1, SAME,0, 1, 1) (with bias)
(3): GatedLinearUnit (2)
(4): Dropout (0.050000)
(5): Reorder (2,0,3,1)
(6): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(7): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(8): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(9): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(10): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(11): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(12): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(13): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(14): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(15): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(16): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(17): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(18): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(19): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(20): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(21): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(22): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(23): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(24): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(25): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(26): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(27): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(28): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(29): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(30): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(31): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(32): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(33): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(34): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(35): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(36): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(37): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(38): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(39): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(40): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(41): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(42): Linear (384->29) (with bias)
I1224 06:39:50.868662 11517 FinetuneCTC.cpp:278] [Network Params: 70498735]
I1224 06:39:50.868705 11517 FinetuneCTC.cpp:283] [Criterion] ConnectionistTemporalClassificationCriterion
I1224 06:39:51.004284 11517 FinetuneCTC.cpp:287] [Network Optimizer] SGD (momentum=0.8)
I1224 06:39:51.005266 11517 FinetuneCTC.cpp:547] Shuffling trainset
I1224 06:39:51.005805 11517 FinetuneCTC.cpp:554] Epoch 1 started!
I1224 06:46:52.988443 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 1000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:32 | bch(ms): 212.42 | smp(ms): 3.21 | fwd(ms): 82.29 | crit-fwd(ms): 2.90 | bwd(ms): 94.35 | optim(ms): 31.26 | loss: 2.78453 | train-TER: 9.68 | train-WER: 21.00 | dev-loss: 2.59365 | dev-TER: 10.09 | dev-WER: 19.50 | avg-isz: 287 | avg-tsz: 040 | max-tsz: 339 | avr-batchsz: 4.00 | hrs: 3.20 | thrpt(sec/sec): 54.18 | timestamp: 2020-12-24 06:46:52
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 06:53:31.970283 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 2000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:06 | bch(ms): 186.76 | smp(ms): 0.04 | fwd(ms): 69.67 | crit-fwd(ms): 2.02 | bwd(ms): 86.59 | optim(ms): 30.22 | loss: 2.63802 | train-TER: 9.86 | train-WER: 22.43 | dev-loss: 2.54714 | dev-TER: 9.84 | dev-WER: 18.86 | avg-isz: 259 | avg-tsz: 036 | max-tsz: 345 | avr-batchsz: 4.00 | hrs: 2.88 | thrpt(sec/sec): 55.57 | timestamp: 2020-12-24 06:53:31
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 07:00:07.246326 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 3000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:02 | bch(ms): 182.40 | smp(ms): 0.03 | fwd(ms): 67.86 | crit-fwd(ms): 1.92 | bwd(ms): 84.27 | optim(ms): 30.00 | loss: 2.57714 | train-TER: 12.22 | train-WER: 24.38 | dev-loss: 2.45296 | dev-TER: 9.55 | dev-WER: 18.37 | avg-isz: 248 | avg-tsz: 035 | max-tsz: 257 | avr-batchsz: 4.00 | hrs: 2.76 | thrpt(sec/sec): 54.53 | timestamp: 2020-12-24 07:00:07
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 07:01:30.886020 11517 FinetuneCTC.cpp:547] Shuffling trainset
I1224 07:01:30.886448 11517 FinetuneCTC.cpp:554] Epoch 2 started!
[5e8e495af856:11519] *** Process received signal ***
[5e8e495af856:11519] Signal: Segmentation fault (11)
[5e8e495af856:11519] Signal code: Address not mapped (1)
[5e8e495af856:11519] Failing at address: 0x7f848b62120d
[5e8e495af856:11519] [ 0] /lib/x86_64-linux-gnu/libpthread.so.0(+0x12980)[0x7f848e2cd980]
[5e8e495af856:11519] [ 1] /lib/x86_64-linux-gnu/libc.so.6(getenv+0xa5)[0x7f848df0c8a5]
[5e8e495af856:11519] [ 2] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(_ZN13TCMallocGuardD1Ev+0x34)[0x7f848e777e44]
[5e8e495af856:11519] [ 3] /lib/x86_64-linux-gnu/libc.so.6(__cxa_finalize+0xf5)[0x7f848df0d735]
[5e8e495af856:11519] [ 4] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(+0x13cb3)[0x7f848e775cb3]
[5e8e495af856:11519] *** End of error message ***
^C
###Markdown
Step 4: Run Decoding Viterbi decoding
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am checkpoint/001_model_dev.bin --datadir '' --emission_dir '' --uselexicon false \
--test ami_limited_supervision/test.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1046.72_1047.07, WER: 0%, TER: 0%, total WER: 18.9985%, total TER: 8.47729%, progress (thread 0): 86.8275%]
|T|: m m
|P|: m m
[sample: ES2004c_H01_FEE013_1294.34_1294.69, WER: 0%, TER: 0%, total WER: 18.9983%, total TER: 8.47725%, progress (thread 0): 86.8354%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1302.15_1302.5, WER: 100%, TER: 0%, total WER: 18.9992%, total TER: 8.47715%, progress (thread 0): 86.8434%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1515.72_1516.07, WER: 0%, TER: 0%, total WER: 18.999%, total TER: 8.47707%, progress (thread 0): 86.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1690.13_1690.48, WER: 0%, TER: 0%, total WER: 18.9988%, total TER: 8.47699%, progress (thread 0): 86.8592%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2078.48_2078.83, WER: 100%, TER: 0%, total WER: 18.9997%, total TER: 8.47689%, progress (thread 0): 86.8671%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H02_MEE014_2291.54_2291.89, WER: 0%, TER: 0%, total WER: 18.9995%, total TER: 8.47681%, progress (thread 0): 86.875%]
|T|: o h
|P|: u m
[sample: ES2004d_H00_MEO015_127.17_127.52, WER: 100%, TER: 100%, total WER: 19.0004%, total TER: 8.47725%, progress (thread 0): 86.8829%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_561.86_562.21, WER: 0%, TER: 0%, total WER: 19.0002%, total TER: 8.47717%, progress (thread 0): 86.8908%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004d_H00_MEO015_640.16_640.51, WER: 100%, TER: 25%, total WER: 19.0011%, total TER: 8.47732%, progress (thread 0): 86.8987%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_821.44_821.79, WER: 0%, TER: 0%, total WER: 19.0009%, total TER: 8.47722%, progress (thread 0): 86.9066%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H01_FEE013_822.51_822.86, WER: 100%, TER: 0%, total WER: 19.0019%, total TER: 8.47712%, progress (thread 0): 86.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1778.02_1778.37, WER: 0%, TER: 0%, total WER: 19.0016%, total TER: 8.47704%, progress (thread 0): 86.9225%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_76.08_76.43, WER: 100%, TER: 0%, total WER: 19.0026%, total TER: 8.47694%, progress (thread 0): 86.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_431.19_431.54, WER: 0%, TER: 0%, total WER: 19.0023%, total TER: 8.47686%, progress (thread 0): 86.9383%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_492.57_492.92, WER: 100%, TER: 66.6667%, total WER: 19.0033%, total TER: 8.47727%, progress (thread 0): 86.9462%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_232.67_233.02, WER: 0%, TER: 0%, total WER: 19.003%, total TER: 8.47719%, progress (thread 0): 86.9541%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_361.11_361.46, WER: 0%, TER: 0%, total WER: 19.0028%, total TER: 8.47711%, progress (thread 0): 86.962%]
|T|: y e p
|P|: y e a h
[sample: IS1009b_H00_FIE088_723.57_723.92, WER: 100%, TER: 66.6667%, total WER: 19.0038%, total TER: 8.47752%, progress (thread 0): 86.9699%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_993.71_994.06, WER: 100%, TER: 60%, total WER: 19.0047%, total TER: 8.47813%, progress (thread 0): 86.9778%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1007.39_1007.74, WER: 0%, TER: 0%, total WER: 19.0045%, total TER: 8.47807%, progress (thread 0): 86.9858%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1079.18_1079.53, WER: 100%, TER: 0%, total WER: 19.0054%, total TER: 8.47797%, progress (thread 0): 86.9937%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1082.71_1083.06, WER: 100%, TER: 0%, total WER: 19.0063%, total TER: 8.47787%, progress (thread 0): 87.0016%]
|T|: t h i s | o n e
|P|: t h i s | o n e
[sample: IS1009b_H00_FIE088_1179.44_1179.79, WER: 0%, TER: 0%, total WER: 19.0059%, total TER: 8.47771%, progress (thread 0): 87.0095%]
|T|: y o u ' r e | t h r e e
|P|: y o u | t h r e e
[sample: IS1009b_H00_FIE088_1203.06_1203.41, WER: 50%, TER: 25%, total WER: 19.0066%, total TER: 8.47818%, progress (thread 0): 87.0174%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1699.07_1699.42, WER: 0%, TER: 0%, total WER: 19.0064%, total TER: 8.4781%, progress (thread 0): 87.0253%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1821.76_1822.11, WER: 0%, TER: 0%, total WER: 19.0061%, total TER: 8.47802%, progress (thread 0): 87.0332%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_527.63_527.98, WER: 100%, TER: 0%, total WER: 19.0071%, total TER: 8.47792%, progress (thread 0): 87.0411%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_546.82_547.17, WER: 100%, TER: 0%, total WER: 19.008%, total TER: 8.47782%, progress (thread 0): 87.049%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_609.9_610.25, WER: 100%, TER: 0%, total WER: 19.0089%, total TER: 8.47772%, progress (thread 0): 87.057%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_688.49_688.84, WER: 100%, TER: 20%, total WER: 19.0098%, total TER: 8.47786%, progress (thread 0): 87.0649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1303.58_1303.93, WER: 0%, TER: 0%, total WER: 19.0096%, total TER: 8.47778%, progress (thread 0): 87.0728%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H01_FIO087_1514.93_1515.28, WER: 100%, TER: 0%, total WER: 19.0105%, total TER: 8.47768%, progress (thread 0): 87.0807%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1737.76_1738.11, WER: 100%, TER: 0%, total WER: 19.0115%, total TER: 8.47758%, progress (thread 0): 87.0886%]
|T|: a h
|P|: m m
[sample: IS1009d_H02_FIO084_734.11_734.46, WER: 100%, TER: 100%, total WER: 19.0124%, total TER: 8.47801%, progress (thread 0): 87.0965%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_742.93_743.28, WER: 0%, TER: 0%, total WER: 19.0122%, total TER: 8.47795%, progress (thread 0): 87.1044%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_743.52_743.87, WER: 0%, TER: 0%, total WER: 19.0119%, total TER: 8.47787%, progress (thread 0): 87.1123%]
|T|: y e a h
|P|: w h a t
[sample: IS1009d_H02_FIO084_953.71_954.06, WER: 100%, TER: 75%, total WER: 19.0129%, total TER: 8.4785%, progress (thread 0): 87.1203%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1243.4_1243.75, WER: 0%, TER: 0%, total WER: 19.0126%, total TER: 8.47842%, progress (thread 0): 87.1282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1323.32_1323.67, WER: 0%, TER: 0%, total WER: 19.0124%, total TER: 8.47834%, progress (thread 0): 87.1361%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_1417.8_1418.15, WER: 100%, TER: 0%, total WER: 19.0133%, total TER: 8.47824%, progress (thread 0): 87.144%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1674.44_1674.79, WER: 100%, TER: 0%, total WER: 19.0143%, total TER: 8.47814%, progress (thread 0): 87.1519%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_1745.56_1745.91, WER: 100%, TER: 40%, total WER: 19.0152%, total TER: 8.47851%, progress (thread 0): 87.1598%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_506.21_506.56, WER: 0%, TER: 0%, total WER: 19.015%, total TER: 8.47843%, progress (thread 0): 87.1677%]
|T|: t h e | p e n
|P|: t e
[sample: TS3003a_H02_MTD0010ID_1074.9_1075.25, WER: 100%, TER: 71.4286%, total WER: 19.0168%, total TER: 8.47947%, progress (thread 0): 87.1756%]
|T|: r i g h t
|P|: r i g h t
[sample: TS3003a_H03_MTD012ME_1219.55_1219.9, WER: 0%, TER: 0%, total WER: 19.0166%, total TER: 8.47937%, progress (thread 0): 87.1835%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003a_H01_MTD011UID_1371.45_1371.8, WER: 100%, TER: 20%, total WER: 19.0175%, total TER: 8.4795%, progress (thread 0): 87.1915%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_1453.73_1454.08, WER: 0%, TER: 0%, total WER: 19.0173%, total TER: 8.47942%, progress (thread 0): 87.1994%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1295.49_1295.84, WER: 0%, TER: 0%, total WER: 19.0171%, total TER: 8.47938%, progress (thread 0): 87.2073%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1351.56_1351.91, WER: 0%, TER: 0%, total WER: 19.0169%, total TER: 8.47934%, progress (thread 0): 87.2152%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_180.89_181.24, WER: 0%, TER: 0%, total WER: 19.0167%, total TER: 8.47926%, progress (thread 0): 87.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1380.5_1380.85, WER: 0%, TER: 0%, total WER: 19.0164%, total TER: 8.47918%, progress (thread 0): 87.231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1531.23_1531.58, WER: 0%, TER: 0%, total WER: 19.0162%, total TER: 8.4791%, progress (thread 0): 87.2389%]
|T|: t h a t ' s | g o o d
|P|: t h a t ' s g o
[sample: TS3003c_H03_MTD012ME_1567.88_1568.23, WER: 100%, TER: 27.2727%, total WER: 19.0181%, total TER: 8.47959%, progress (thread 0): 87.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1713.34_1713.69, WER: 0%, TER: 0%, total WER: 19.0178%, total TER: 8.47951%, progress (thread 0): 87.2547%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_2280.01_2280.36, WER: 0%, TER: 0%, total WER: 19.0176%, total TER: 8.47943%, progress (thread 0): 87.2627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_306.6_306.95, WER: 0%, TER: 0%, total WER: 19.0174%, total TER: 8.47935%, progress (thread 0): 87.2706%]
|T|: n o
|P|: n o
[sample: TS3003d_H00_MTD009PM_819.47_819.82, WER: 0%, TER: 0%, total WER: 19.0172%, total TER: 8.47931%, progress (thread 0): 87.2785%]
|T|: a l r i g h t
|P|: i
[sample: TS3003d_H00_MTD009PM_965.64_965.99, WER: 100%, TER: 85.7143%, total WER: 19.0181%, total TER: 8.48058%, progress (thread 0): 87.2864%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1373.9_1374.25, WER: 100%, TER: 66.6667%, total WER: 19.019%, total TER: 8.48099%, progress (thread 0): 87.2943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1847.37_1847.72, WER: 0%, TER: 0%, total WER: 19.0188%, total TER: 8.48091%, progress (thread 0): 87.3022%]
|T|: n i n e
|P|: n i n e
[sample: TS3003d_H03_MTD012ME_1899.7_1900.05, WER: 0%, TER: 0%, total WER: 19.0186%, total TER: 8.48083%, progress (thread 0): 87.3101%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H01_MTD011UID_2218.91_2219.26, WER: 0%, TER: 0%, total WER: 19.0184%, total TER: 8.48075%, progress (thread 0): 87.318%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_142.16_142.51, WER: 0%, TER: 0%, total WER: 19.0182%, total TER: 8.48065%, progress (thread 0): 87.326%]
|T|: h m m
|P|: m m m m
[sample: EN2002a_H00_MEE073_203.92_204.27, WER: 100%, TER: 66.6667%, total WER: 19.0191%, total TER: 8.48107%, progress (thread 0): 87.3339%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_220.21_220.56, WER: 0%, TER: 0%, total WER: 19.0189%, total TER: 8.48099%, progress (thread 0): 87.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1393.11_1393.46, WER: 0%, TER: 0%, total WER: 19.0187%, total TER: 8.48091%, progress (thread 0): 87.3497%]
|T|: n o
|P|: n o
[sample: EN2002a_H02_FEO072_1494.01_1494.36, WER: 0%, TER: 0%, total WER: 19.0184%, total TER: 8.48087%, progress (thread 0): 87.3576%]
|T|: n o
|P|: y e a h
[sample: EN2002a_H01_FEO070_1616.41_1616.76, WER: 100%, TER: 200%, total WER: 19.0194%, total TER: 8.48177%, progress (thread 0): 87.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2062.28_2062.63, WER: 0%, TER: 0%, total WER: 19.0192%, total TER: 8.48169%, progress (thread 0): 87.3734%]
|T|: t h e n | u m
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_306.19_306.54, WER: 100%, TER: 114.286%, total WER: 19.021%, total TER: 8.48343%, progress (thread 0): 87.3813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_679.83_680.18, WER: 0%, TER: 0%, total WER: 19.0208%, total TER: 8.48335%, progress (thread 0): 87.3892%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1132.98_1133.33, WER: 0%, TER: 0%, total WER: 19.0206%, total TER: 8.48327%, progress (thread 0): 87.3972%]
|T|: s h o u l d n ' t | n o
|P|: s h o u d n ' n
[sample: EN2002b_H01_MEE071_1400.16_1400.51, WER: 100%, TER: 33.3333%, total WER: 19.0224%, total TER: 8.48398%, progress (thread 0): 87.4051%]
|T|: n o
|P|: y e a h
[sample: EN2002b_H02_FEO072_1650.79_1651.14, WER: 100%, TER: 200%, total WER: 19.0233%, total TER: 8.48488%, progress (thread 0): 87.413%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_550.49_550.84, WER: 0%, TER: 0%, total WER: 19.0231%, total TER: 8.4848%, progress (thread 0): 87.4209%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_586.84_587.19, WER: 0%, TER: 0%, total WER: 19.0229%, total TER: 8.48472%, progress (thread 0): 87.4288%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_725.66_726.01, WER: 0%, TER: 0%, total WER: 19.0227%, total TER: 8.48468%, progress (thread 0): 87.4367%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_743.92_744.27, WER: 0%, TER: 0%, total WER: 19.0225%, total TER: 8.48464%, progress (thread 0): 87.4446%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_975_975.35, WER: 0%, TER: 0%, total WER: 19.0222%, total TER: 8.48456%, progress (thread 0): 87.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1520.32_1520.67, WER: 0%, TER: 0%, total WER: 19.022%, total TER: 8.48448%, progress (thread 0): 87.4604%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_1667.21_1667.56, WER: 0%, TER: 0%, total WER: 19.0218%, total TER: 8.48444%, progress (thread 0): 87.4684%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_2075.98_2076.33, WER: 0%, TER: 0%, total WER: 19.0216%, total TER: 8.4844%, progress (thread 0): 87.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2182.63_2182.98, WER: 0%, TER: 0%, total WER: 19.0214%, total TER: 8.48432%, progress (thread 0): 87.4842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_352.2_352.55, WER: 0%, TER: 0%, total WER: 19.0212%, total TER: 8.48424%, progress (thread 0): 87.4921%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002d_H03_MEE073_387.18_387.53, WER: 0%, TER: 0%, total WER: 19.0207%, total TER: 8.4841%, progress (thread 0): 87.5%]
|T|: m m
|P|: m m
[sample: EN2002d_H01_FEO072_831.78_832.13, WER: 0%, TER: 0%, total WER: 19.0205%, total TER: 8.48406%, progress (thread 0): 87.5079%]
|T|: n o
|P|: n o
[sample: EN2002d_H03_MEE073_909.36_909.71, WER: 0%, TER: 0%, total WER: 19.0203%, total TER: 8.48402%, progress (thread 0): 87.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1113.08_1113.43, WER: 0%, TER: 0%, total WER: 19.0201%, total TER: 8.48394%, progress (thread 0): 87.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1233.84_1234.19, WER: 0%, TER: 0%, total WER: 19.0199%, total TER: 8.48386%, progress (thread 0): 87.5316%]
|T|: i | d o n ' t | k n o w
|P|: i d
[sample: EN2002d_H01_FEO072_1245.04_1245.39, WER: 100%, TER: 83.3333%, total WER: 19.0226%, total TER: 8.48597%, progress (thread 0): 87.5396%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1798.12_1798.47, WER: 0%, TER: 0%, total WER: 19.0224%, total TER: 8.48589%, progress (thread 0): 87.5475%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1926.51_1926.86, WER: 0%, TER: 0%, total WER: 19.0222%, total TER: 8.48581%, progress (thread 0): 87.5554%]
|T|: u h h u h
|P|: u m
[sample: EN2002d_H00_FEO070_2027.85_2028.2, WER: 100%, TER: 80%, total WER: 19.0231%, total TER: 8.48666%, progress (thread 0): 87.5633%]
|T|: o h | s o r r y
|P|: o k a y
[sample: ES2004a_H00_MEO015_255.91_256.25, WER: 100%, TER: 75%, total WER: 19.025%, total TER: 8.48791%, progress (thread 0): 87.5712%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H01_FEE013_545.3_545.64, WER: 100%, TER: 0%, total WER: 19.0259%, total TER: 8.48781%, progress (thread 0): 87.5791%]
|T|: r e a l l y
|P|: r e a l l y
[sample: ES2004a_H01_FEE013_603.88_604.22, WER: 0%, TER: 0%, total WER: 19.0257%, total TER: 8.48769%, progress (thread 0): 87.587%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_635.54_635.88, WER: 100%, TER: 0%, total WER: 19.0266%, total TER: 8.48759%, progress (thread 0): 87.5949%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H03_FEE016_811.15_811.49, WER: 0%, TER: 0%, total WER: 19.0264%, total TER: 8.48751%, progress (thread 0): 87.6028%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_954.93_955.27, WER: 100%, TER: 0%, total WER: 19.0273%, total TER: 8.48741%, progress (thread 0): 87.6108%]
|T|: m m h m m
|P|: m m
[sample: ES2004b_H03_FEE016_1445.71_1446.05, WER: 100%, TER: 60%, total WER: 19.0282%, total TER: 8.48802%, progress (thread 0): 87.6187%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1995.5_1995.84, WER: 100%, TER: 0%, total WER: 19.0291%, total TER: 8.48792%, progress (thread 0): 87.6266%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H03_FEE016_22.85_23.19, WER: 100%, TER: 25%, total WER: 19.03%, total TER: 8.48807%, progress (thread 0): 87.6345%]
|T|: t h a n k | y o u
|P|: o k a y
[sample: ES2004c_H03_FEE016_201.1_201.44, WER: 100%, TER: 77.7778%, total WER: 19.0319%, total TER: 8.48954%, progress (thread 0): 87.6424%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_451.06_451.4, WER: 100%, TER: 25%, total WER: 19.0328%, total TER: 8.4897%, progress (thread 0): 87.6503%]
|T|: n o
|P|: n o t
[sample: ES2004c_H02_MEE014_535.19_535.53, WER: 100%, TER: 50%, total WER: 19.0337%, total TER: 8.48989%, progress (thread 0): 87.6582%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1178.32_1178.66, WER: 0%, TER: 0%, total WER: 19.0335%, total TER: 8.48981%, progress (thread 0): 87.6661%]
|T|: h m m
|P|: m m
[sample: ES2004c_H02_MEE014_1181.26_1181.6, WER: 100%, TER: 33.3333%, total WER: 19.0344%, total TER: 8.48999%, progress (thread 0): 87.674%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1478.47_1478.81, WER: 0%, TER: 0%, total WER: 19.0342%, total TER: 8.48995%, progress (thread 0): 87.682%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1640.61_1640.95, WER: 100%, TER: 0%, total WER: 19.0351%, total TER: 8.48985%, progress (thread 0): 87.6899%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2025.11_2025.45, WER: 100%, TER: 0%, total WER: 19.036%, total TER: 8.48975%, progress (thread 0): 87.6978%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2072.31_2072.65, WER: 100%, TER: 0%, total WER: 19.037%, total TER: 8.48965%, progress (thread 0): 87.7057%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_100.76_101.1, WER: 0%, TER: 0%, total WER: 19.0367%, total TER: 8.48957%, progress (thread 0): 87.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H01_FEE013_139.4_139.74, WER: 0%, TER: 0%, total WER: 19.0365%, total TER: 8.48947%, progress (thread 0): 87.7215%]
|T|: y e p
|P|: y e p
[sample: ES2004d_H00_MEO015_155.57_155.91, WER: 0%, TER: 0%, total WER: 19.0363%, total TER: 8.48941%, progress (thread 0): 87.7294%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_178.12_178.46, WER: 0%, TER: 0%, total WER: 19.0361%, total TER: 8.48933%, progress (thread 0): 87.7373%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_405.44_405.78, WER: 0%, TER: 0%, total WER: 19.0359%, total TER: 8.48925%, progress (thread 0): 87.7453%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H00_MEO015_548.5_548.84, WER: 100%, TER: 0%, total WER: 19.0368%, total TER: 8.48915%, progress (thread 0): 87.7532%]
|T|: t h r e e
|P|: t h r e e
[sample: ES2004d_H02_MEE014_646.42_646.76, WER: 0%, TER: 0%, total WER: 19.0366%, total TER: 8.48905%, progress (thread 0): 87.7611%]
|T|: f o u r
|P|: f o u r
[sample: ES2004d_H01_FEE013_911.53_911.87, WER: 0%, TER: 0%, total WER: 19.0364%, total TER: 8.48897%, progress (thread 0): 87.769%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1135.09_1135.43, WER: 0%, TER: 0%, total WER: 19.0362%, total TER: 8.48889%, progress (thread 0): 87.7769%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_1953.89_1954.23, WER: 0%, TER: 0%, total WER: 19.0359%, total TER: 8.48885%, progress (thread 0): 87.7848%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_792.79_793.13, WER: 0%, TER: 0%, total WER: 19.0357%, total TER: 8.48879%, progress (thread 0): 87.7927%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_43.16_43.5, WER: 100%, TER: 0%, total WER: 19.0366%, total TER: 8.48869%, progress (thread 0): 87.8006%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_260.98_261.32, WER: 0%, TER: 0%, total WER: 19.0364%, total TER: 8.48861%, progress (thread 0): 87.8085%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_425.4_425.74, WER: 0%, TER: 0%, total WER: 19.0362%, total TER: 8.48853%, progress (thread 0): 87.8165%]
|T|: y e p
|P|: y o h
[sample: IS1009b_H00_FIE088_598.28_598.62, WER: 100%, TER: 66.6667%, total WER: 19.0371%, total TER: 8.48894%, progress (thread 0): 87.8244%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_613.29_613.63, WER: 0%, TER: 0%, total WER: 19.0369%, total TER: 8.48886%, progress (thread 0): 87.8323%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1002.09_1002.43, WER: 0%, TER: 0%, total WER: 19.0367%, total TER: 8.4888%, progress (thread 0): 87.8402%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H00_FIE088_1223.66_1224, WER: 100%, TER: 20%, total WER: 19.0376%, total TER: 8.48894%, progress (thread 0): 87.8481%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1225.44_1225.78, WER: 100%, TER: 0%, total WER: 19.0385%, total TER: 8.48884%, progress (thread 0): 87.856%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H03_FIO089_1734.63_1734.97, WER: 100%, TER: 200%, total WER: 19.0395%, total TER: 8.48974%, progress (thread 0): 87.8639%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1834.47_1834.81, WER: 0%, TER: 0%, total WER: 19.0392%, total TER: 8.48966%, progress (thread 0): 87.8718%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_1882.63_1882.97, WER: 0%, TER: 0%, total WER: 19.039%, total TER: 8.48958%, progress (thread 0): 87.8797%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H01_FIO087_1960.82_1961.16, WER: 100%, TER: 0%, total WER: 19.0399%, total TER: 8.48948%, progress (thread 0): 87.8877%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_535.66_536, WER: 100%, TER: 0%, total WER: 19.0409%, total TER: 8.48938%, progress (thread 0): 87.8956%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_575.77_576.11, WER: 100%, TER: 0%, total WER: 19.0418%, total TER: 8.48928%, progress (thread 0): 87.9035%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_950.04_950.38, WER: 100%, TER: 0%, total WER: 19.0427%, total TER: 8.48918%, progress (thread 0): 87.9114%]
|T|: i t | w o r k s
|P|: b u t
[sample: IS1009c_H01_FIO087_1083.82_1084.16, WER: 100%, TER: 100%, total WER: 19.0445%, total TER: 8.4909%, progress (thread 0): 87.9193%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_1109.75_1110.09, WER: 100%, TER: 0%, total WER: 19.0455%, total TER: 8.4908%, progress (thread 0): 87.9272%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1603.38_1603.72, WER: 100%, TER: 0%, total WER: 19.0464%, total TER: 8.4907%, progress (thread 0): 87.9351%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1613.06_1613.4, WER: 100%, TER: 0%, total WER: 19.0473%, total TER: 8.4906%, progress (thread 0): 87.943%]
|T|: y e s
|P|: y e a s
[sample: IS1009c_H01_FIO087_1633.76_1634.1, WER: 100%, TER: 33.3333%, total WER: 19.0482%, total TER: 8.49078%, progress (thread 0): 87.951%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_434.93_435.27, WER: 100%, TER: 0%, total WER: 19.0491%, total TER: 8.49068%, progress (thread 0): 87.9589%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_637.66_638, WER: 100%, TER: 40%, total WER: 19.0501%, total TER: 8.49105%, progress (thread 0): 87.9668%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_649.7_650.04, WER: 0%, TER: 0%, total WER: 19.0498%, total TER: 8.49095%, progress (thread 0): 87.9747%]
|T|: h m m
|P|: m h t
[sample: IS1009d_H02_FIO084_674.88_675.22, WER: 100%, TER: 100%, total WER: 19.0508%, total TER: 8.49159%, progress (thread 0): 87.9826%]
|T|: o o p s
|P|: u p s
[sample: IS1009d_H00_FIE088_1068.99_1069.33, WER: 100%, TER: 50%, total WER: 19.0517%, total TER: 8.49199%, progress (thread 0): 87.9905%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H01_FIO087_1509.75_1510.09, WER: 0%, TER: 0%, total WER: 19.0515%, total TER: 8.49193%, progress (thread 0): 87.9984%]
|T|: t h a n k | y o u
|P|: t h a n k | y o u
[sample: TS3003a_H03_MTD012ME_48.1_48.44, WER: 0%, TER: 0%, total WER: 19.051%, total TER: 8.49175%, progress (thread 0): 88.0063%]
|T|: g u e s s
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_983.96_984.3, WER: 100%, TER: 80%, total WER: 19.0519%, total TER: 8.49259%, progress (thread 0): 88.0142%]
|T|: t h e | p e n
|P|: m m h
[sample: TS3003a_H02_MTD0010ID_1090.49_1090.83, WER: 100%, TER: 100%, total WER: 19.0538%, total TER: 8.49409%, progress (thread 0): 88.0222%]
|T|: t h i n g
|P|: t h i n g
[sample: TS3003a_H00_MTD009PM_1320.19_1320.53, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49399%, progress (thread 0): 88.0301%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_132.84_133.18, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49395%, progress (thread 0): 88.038%]
|T|: r i g h t
|P|: b u h
[sample: TS3003b_H01_MTD011UID_2086.12_2086.46, WER: 100%, TER: 80%, total WER: 19.0543%, total TER: 8.4948%, progress (thread 0): 88.0459%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2089.85_2090.19, WER: 0%, TER: 0%, total WER: 19.0541%, total TER: 8.49472%, progress (thread 0): 88.0538%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_982.32_982.66, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49464%, progress (thread 0): 88.0617%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_1007.54_1007.88, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49456%, progress (thread 0): 88.0696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1240.35_1240.69, WER: 0%, TER: 0%, total WER: 19.0534%, total TER: 8.49448%, progress (thread 0): 88.0775%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1413.87_1414.21, WER: 0%, TER: 0%, total WER: 19.0532%, total TER: 8.4944%, progress (thread 0): 88.0854%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1849.58_1849.92, WER: 0%, TER: 0%, total WER: 19.053%, total TER: 8.49432%, progress (thread 0): 88.0934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_595.75_596.09, WER: 0%, TER: 0%, total WER: 19.0528%, total TER: 8.49424%, progress (thread 0): 88.1013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_921.62_921.96, WER: 0%, TER: 0%, total WER: 19.0525%, total TER: 8.49416%, progress (thread 0): 88.1092%]
|T|: u h | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1308.11_1308.45, WER: 50%, TER: 42.8571%, total WER: 19.0532%, total TER: 8.49472%, progress (thread 0): 88.1171%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1388.52_1388.86, WER: 0%, TER: 0%, total WER: 19.053%, total TER: 8.49464%, progress (thread 0): 88.125%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1802.71_1803.05, WER: 0%, TER: 0%, total WER: 19.0528%, total TER: 8.49456%, progress (thread 0): 88.1329%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1835.63_1835.97, WER: 100%, TER: 25%, total WER: 19.0537%, total TER: 8.49472%, progress (thread 0): 88.1408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2091.81_2092.15, WER: 0%, TER: 0%, total WER: 19.0535%, total TER: 8.49464%, progress (thread 0): 88.1487%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2217.62_2217.96, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49456%, progress (thread 0): 88.1566%]
|T|: m m | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2323.41_2323.75, WER: 50%, TER: 42.8571%, total WER: 19.054%, total TER: 8.49512%, progress (thread 0): 88.1646%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2420.39_2420.73, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49504%, progress (thread 0): 88.1725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_406.15_406.49, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49496%, progress (thread 0): 88.1804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_601.54_601.88, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49488%, progress (thread 0): 88.1883%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H02_FEO072_713.14_713.48, WER: 0%, TER: 0%, total WER: 19.0531%, total TER: 8.49482%, progress (thread 0): 88.1962%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_859.83_860.17, WER: 100%, TER: 66.6667%, total WER: 19.0541%, total TER: 8.49524%, progress (thread 0): 88.2041%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002a_H01_FEO070_920.16_920.5, WER: 100%, TER: 28.5714%, total WER: 19.055%, total TER: 8.49557%, progress (thread 0): 88.212%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1586.73_1587.07, WER: 0%, TER: 0%, total WER: 19.0548%, total TER: 8.49549%, progress (thread 0): 88.2199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1927.75_1928.09, WER: 0%, TER: 0%, total WER: 19.0545%, total TER: 8.49541%, progress (thread 0): 88.2279%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1929.86_1930.2, WER: 0%, TER: 0%, total WER: 19.0543%, total TER: 8.49533%, progress (thread 0): 88.2358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2048.6_2048.94, WER: 0%, TER: 0%, total WER: 19.0541%, total TER: 8.49525%, progress (thread 0): 88.2437%]
|T|: s o | i t ' s
|P|: t m
[sample: EN2002a_H03_MEE071_2098.66_2099, WER: 100%, TER: 85.7143%, total WER: 19.0559%, total TER: 8.49652%, progress (thread 0): 88.2516%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_2129.89_2130.23, WER: 0%, TER: 0%, total WER: 19.0557%, total TER: 8.49644%, progress (thread 0): 88.2595%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_144.51_144.85, WER: 0%, TER: 0%, total WER: 19.0555%, total TER: 8.49636%, progress (thread 0): 88.2674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_190.18_190.52, WER: 0%, TER: 0%, total WER: 19.0553%, total TER: 8.49628%, progress (thread 0): 88.2753%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_302.27_302.61, WER: 100%, TER: 66.6667%, total WER: 19.0562%, total TER: 8.49669%, progress (thread 0): 88.2832%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_309.52_309.86, WER: 0%, TER: 0%, total WER: 19.056%, total TER: 8.49661%, progress (thread 0): 88.2911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_363.35_363.69, WER: 0%, TER: 0%, total WER: 19.0558%, total TER: 8.49653%, progress (thread 0): 88.299%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_409.14_409.48, WER: 0%, TER: 0%, total WER: 19.0556%, total TER: 8.49645%, progress (thread 0): 88.307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_736.4_736.74, WER: 0%, TER: 0%, total WER: 19.0553%, total TER: 8.49637%, progress (thread 0): 88.3149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_935.86_936.2, WER: 0%, TER: 0%, total WER: 19.0551%, total TER: 8.49629%, progress (thread 0): 88.3228%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002b_H03_MEE073_1252.52_1252.86, WER: 0%, TER: 0%, total WER: 19.0547%, total TER: 8.49615%, progress (thread 0): 88.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1563.14_1563.48, WER: 0%, TER: 0%, total WER: 19.0545%, total TER: 8.49607%, progress (thread 0): 88.3386%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1626.33_1626.67, WER: 0%, TER: 0%, total WER: 19.0543%, total TER: 8.49599%, progress (thread 0): 88.3465%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H01_FEO072_67.59_67.93, WER: 0%, TER: 0%, total WER: 19.054%, total TER: 8.49591%, progress (thread 0): 88.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_273.14_273.48, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49583%, progress (thread 0): 88.3623%]
|T|: m m h m m
|P|: m m m m
[sample: EN2002c_H03_MEE073_444.82_445.16, WER: 100%, TER: 20%, total WER: 19.0548%, total TER: 8.49596%, progress (thread 0): 88.3703%]
|T|: o r
|P|: m h
[sample: EN2002c_H01_FEO072_486.4_486.74, WER: 100%, TER: 100%, total WER: 19.0557%, total TER: 8.4964%, progress (thread 0): 88.3782%]
|T|: o h | w e l l
|P|: o h
[sample: EN2002c_H03_MEE073_497.23_497.57, WER: 50%, TER: 71.4286%, total WER: 19.0564%, total TER: 8.49743%, progress (thread 0): 88.3861%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002c_H01_FEO072_721.91_722.25, WER: 200%, TER: 14.2857%, total WER: 19.0584%, total TER: 8.49753%, progress (thread 0): 88.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1305.34_1305.68, WER: 0%, TER: 0%, total WER: 19.0582%, total TER: 8.49745%, progress (thread 0): 88.4019%]
|T|: o h
|P|: o h
[sample: EN2002c_H01_FEO072_1925.9_1926.24, WER: 0%, TER: 0%, total WER: 19.058%, total TER: 8.49741%, progress (thread 0): 88.4098%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2040.49_2040.83, WER: 0%, TER: 0%, total WER: 19.0578%, total TER: 8.49733%, progress (thread 0): 88.4177%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002c_H03_MEE073_2245.81_2246.15, WER: 100%, TER: 20%, total WER: 19.0587%, total TER: 8.49746%, progress (thread 0): 88.4256%]
|T|: y e a h
|P|: r e a h
[sample: EN2002d_H03_MEE073_344.02_344.36, WER: 100%, TER: 25%, total WER: 19.0596%, total TER: 8.49762%, progress (thread 0): 88.4335%]
|T|: w h a t | w a s | i t
|P|: w h a | w a s | i t
[sample: EN2002d_H03_MEE073_508.22_508.56, WER: 33.3333%, TER: 9.09091%, total WER: 19.0601%, total TER: 8.49763%, progress (thread 0): 88.4415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_688.4_688.74, WER: 0%, TER: 0%, total WER: 19.0599%, total TER: 8.49755%, progress (thread 0): 88.4494%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_715.16_715.5, WER: 0%, TER: 0%, total WER: 19.0597%, total TER: 8.49747%, progress (thread 0): 88.4573%]
|T|: u h
|P|: m m
[sample: EN2002d_H03_MEE073_1125.77_1126.11, WER: 100%, TER: 100%, total WER: 19.0606%, total TER: 8.4979%, progress (thread 0): 88.4652%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1325.04_1325.38, WER: 100%, TER: 33.3333%, total WER: 19.0615%, total TER: 8.49808%, progress (thread 0): 88.4731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1423.14_1423.48, WER: 0%, TER: 0%, total WER: 19.0613%, total TER: 8.498%, progress (thread 0): 88.481%]
|T|: n o | w e | p
|P|: n o | w e
[sample: EN2002d_H00_FEO070_1437.94_1438.28, WER: 33.3333%, TER: 28.5714%, total WER: 19.0618%, total TER: 8.49833%, progress (thread 0): 88.4889%]
|T|: w i t h
|P|: w i t h
[sample: EN2002d_H02_MEE071_2073.15_2073.49, WER: 0%, TER: 0%, total WER: 19.0616%, total TER: 8.49825%, progress (thread 0): 88.4968%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_258.31_258.64, WER: 100%, TER: 0%, total WER: 19.0625%, total TER: 8.49815%, progress (thread 0): 88.5048%]
|T|: m m | y e a h
|P|: m y e a h
[sample: ES2004a_H00_MEO015_1048.05_1048.38, WER: 100%, TER: 28.5714%, total WER: 19.0643%, total TER: 8.49848%, progress (thread 0): 88.5127%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_736.03_736.36, WER: 0%, TER: 0%, total WER: 19.0641%, total TER: 8.4984%, progress (thread 0): 88.5206%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1313.86_1314.19, WER: 0%, TER: 0%, total WER: 19.0639%, total TER: 8.49832%, progress (thread 0): 88.5285%]
|T|: m m
|P|: m m
[sample: ES2004b_H01_FEE013_1711.46_1711.79, WER: 0%, TER: 0%, total WER: 19.0637%, total TER: 8.49828%, progress (thread 0): 88.5364%]
|T|: u h h u h
|P|: m h | h u h
[sample: ES2004b_H03_FEE016_2208.79_2209.12, WER: 200%, TER: 40%, total WER: 19.0657%, total TER: 8.49865%, progress (thread 0): 88.5443%]
|T|: o k a y
|P|: o k a y
[sample: ES2004b_H03_FEE016_2308.52_2308.85, WER: 0%, TER: 0%, total WER: 19.0655%, total TER: 8.49857%, progress (thread 0): 88.5522%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_172.32_172.65, WER: 0%, TER: 0%, total WER: 19.0653%, total TER: 8.49849%, progress (thread 0): 88.5601%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_560.34_560.67, WER: 0%, TER: 0%, total WER: 19.0651%, total TER: 8.49845%, progress (thread 0): 88.568%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H01_FEE013_873.37_873.7, WER: 100%, TER: 0%, total WER: 19.066%, total TER: 8.49835%, progress (thread 0): 88.576%]
|T|: w e l l
|P|: w e l l
[sample: ES2004c_H02_MEE014_925.27_925.6, WER: 0%, TER: 0%, total WER: 19.0658%, total TER: 8.49827%, progress (thread 0): 88.5839%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1268.11_1268.44, WER: 0%, TER: 0%, total WER: 19.0656%, total TER: 8.49819%, progress (thread 0): 88.5918%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1965.56_1965.89, WER: 100%, TER: 0%, total WER: 19.0665%, total TER: 8.49809%, progress (thread 0): 88.5997%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H01_FEE013_607.94_608.27, WER: 100%, TER: 0%, total WER: 19.0674%, total TER: 8.49799%, progress (thread 0): 88.6076%]
|T|: t w o
|P|: t w o
[sample: ES2004d_H02_MEE014_753.08_753.41, WER: 0%, TER: 0%, total WER: 19.0672%, total TER: 8.49793%, progress (thread 0): 88.6155%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H03_FEE016_876.07_876.4, WER: 100%, TER: 0%, total WER: 19.0681%, total TER: 8.49783%, progress (thread 0): 88.6234%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H00_MEO015_1304.99_1305.32, WER: 0%, TER: 0%, total WER: 19.0679%, total TER: 8.49777%, progress (thread 0): 88.6313%]
|T|: s u r e
|P|: s u r e
[sample: ES2004d_H03_FEE016_1499.95_1500.28, WER: 0%, TER: 0%, total WER: 19.0677%, total TER: 8.49769%, progress (thread 0): 88.6392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1845.84_1846.17, WER: 0%, TER: 0%, total WER: 19.0674%, total TER: 8.49761%, progress (thread 0): 88.6471%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1856.75_1857.08, WER: 0%, TER: 0%, total WER: 19.0672%, total TER: 8.49753%, progress (thread 0): 88.6551%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1984.68_1985.01, WER: 0%, TER: 0%, total WER: 19.067%, total TER: 8.49745%, progress (thread 0): 88.663%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_2126.04_2126.37, WER: 0%, TER: 0%, total WER: 19.0668%, total TER: 8.49741%, progress (thread 0): 88.6709%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_55.7_56.03, WER: 0%, TER: 0%, total WER: 19.0666%, total TER: 8.49733%, progress (thread 0): 88.6788%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_226.94_227.27, WER: 100%, TER: 0%, total WER: 19.0675%, total TER: 8.49723%, progress (thread 0): 88.6867%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_318.12_318.45, WER: 0%, TER: 0%, total WER: 19.0673%, total TER: 8.49715%, progress (thread 0): 88.6946%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_652.91_653.24, WER: 100%, TER: 0%, total WER: 19.0682%, total TER: 8.49705%, progress (thread 0): 88.7025%]
|T|: t h e n
|P|: a n d | t h e n
[sample: IS1009a_H00_FIE088_714.21_714.54, WER: 100%, TER: 100%, total WER: 19.0691%, total TER: 8.49791%, progress (thread 0): 88.7104%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_741.37_741.7, WER: 100%, TER: 0%, total WER: 19.07%, total TER: 8.49781%, progress (thread 0): 88.7184%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_790.95_791.28, WER: 100%, TER: 0%, total WER: 19.0709%, total TER: 8.49771%, progress (thread 0): 88.7263%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_787.47_787.8, WER: 100%, TER: 0%, total WER: 19.0719%, total TER: 8.49761%, progress (thread 0): 88.7342%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1063.18_1063.51, WER: 100%, TER: 0%, total WER: 19.0728%, total TER: 8.49751%, progress (thread 0): 88.7421%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1809.47_1809.8, WER: 0%, TER: 0%, total WER: 19.0726%, total TER: 8.49743%, progress (thread 0): 88.75%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1882.22_1882.55, WER: 100%, TER: 0%, total WER: 19.0735%, total TER: 8.49733%, progress (thread 0): 88.7579%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_408.02_408.35, WER: 100%, TER: 0%, total WER: 19.0744%, total TER: 8.49723%, progress (thread 0): 88.7658%]
|T|: m m h m m
|P|: m m
[sample: IS1009c_H00_FIE088_430.74_431.07, WER: 100%, TER: 60%, total WER: 19.0753%, total TER: 8.49784%, progress (thread 0): 88.7737%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1552.21_1552.54, WER: 0%, TER: 0%, total WER: 19.0751%, total TER: 8.49776%, progress (thread 0): 88.7816%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_383.89_384.22, WER: 100%, TER: 0%, total WER: 19.076%, total TER: 8.49766%, progress (thread 0): 88.7896%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_388.95_389.28, WER: 100%, TER: 0%, total WER: 19.0769%, total TER: 8.49756%, progress (thread 0): 88.7975%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_483.18_483.51, WER: 100%, TER: 0%, total WER: 19.0779%, total TER: 8.49746%, progress (thread 0): 88.8054%]
|T|: t h a t ' s | r i g h t
|P|: t h a t ' s | r g h t
[sample: IS1009d_H00_FIE088_757.72_758.05, WER: 50%, TER: 8.33333%, total WER: 19.0786%, total TER: 8.49745%, progress (thread 0): 88.8133%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H01_FIO087_784.18_784.51, WER: 100%, TER: 0%, total WER: 19.0795%, total TER: 8.49735%, progress (thread 0): 88.8212%]
|T|: y e a h
|P|: m m
[sample: IS1009d_H02_FIO084_975.71_976.04, WER: 100%, TER: 100%, total WER: 19.0804%, total TER: 8.49822%, progress (thread 0): 88.8291%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_1183.1_1183.43, WER: 0%, TER: 0%, total WER: 19.0802%, total TER: 8.49818%, progress (thread 0): 88.837%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H02_FIO084_1185.85_1186.18, WER: 100%, TER: 0%, total WER: 19.0811%, total TER: 8.49808%, progress (thread 0): 88.8449%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_1310.32_1310.65, WER: 100%, TER: 40%, total WER: 19.082%, total TER: 8.49845%, progress (thread 0): 88.8528%]
|T|: u h
|P|: u m
[sample: TS3003a_H01_MTD011UID_915.22_915.55, WER: 100%, TER: 50%, total WER: 19.0829%, total TER: 8.49864%, progress (thread 0): 88.8608%]
|T|: o r | w h o
|P|: o h | w h o
[sample: TS3003a_H01_MTD011UID_975.03_975.36, WER: 50%, TER: 16.6667%, total WER: 19.0836%, total TER: 8.49876%, progress (thread 0): 88.8687%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1261.99_1262.32, WER: 0%, TER: 0%, total WER: 19.0834%, total TER: 8.49872%, progress (thread 0): 88.8766%]
|T|: s o
|P|: s o
[sample: TS3003b_H03_MTD012ME_280.47_280.8, WER: 0%, TER: 0%, total WER: 19.0832%, total TER: 8.49868%, progress (thread 0): 88.8845%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_842.62_842.95, WER: 0%, TER: 0%, total WER: 19.083%, total TER: 8.4986%, progress (thread 0): 88.8924%]
|T|: o k a y
|P|: h m h
[sample: TS3003b_H00_MTD009PM_923.51_923.84, WER: 100%, TER: 100%, total WER: 19.0839%, total TER: 8.49946%, progress (thread 0): 88.9003%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1173.66_1173.99, WER: 0%, TER: 0%, total WER: 19.0837%, total TER: 8.49938%, progress (thread 0): 88.9082%]
|T|: n a h
|P|: n e a h
[sample: TS3003c_H01_MTD011UID_1009.07_1009.4, WER: 100%, TER: 33.3333%, total WER: 19.0846%, total TER: 8.49955%, progress (thread 0): 88.9161%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1321.92_1322.25, WER: 100%, TER: 25%, total WER: 19.0855%, total TER: 8.49971%, progress (thread 0): 88.924%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1395.11_1395.44, WER: 0%, TER: 0%, total WER: 19.0853%, total TER: 8.49967%, progress (thread 0): 88.932%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1507.79_1508.12, WER: 0%, TER: 0%, total WER: 19.0851%, total TER: 8.49959%, progress (thread 0): 88.9399%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_1540.01_1540.34, WER: 100%, TER: 0%, total WER: 19.086%, total TER: 8.49949%, progress (thread 0): 88.9478%]
|T|: y e p
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1653.43_1653.76, WER: 100%, TER: 66.6667%, total WER: 19.0869%, total TER: 8.4999%, progress (thread 0): 88.9557%]
|T|: u h
|P|: m h m m
[sample: TS3003c_H01_MTD011UID_1692.63_1692.96, WER: 100%, TER: 150%, total WER: 19.0878%, total TER: 8.50056%, progress (thread 0): 88.9636%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2074.03_2074.36, WER: 0%, TER: 0%, total WER: 19.0876%, total TER: 8.50048%, progress (thread 0): 88.9715%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2241.48_2241.81, WER: 0%, TER: 0%, total WER: 19.0874%, total TER: 8.5004%, progress (thread 0): 88.9794%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_693.96_694.29, WER: 0%, TER: 0%, total WER: 19.0872%, total TER: 8.50032%, progress (thread 0): 88.9873%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_872.34_872.67, WER: 0%, TER: 0%, total WER: 19.087%, total TER: 8.50024%, progress (thread 0): 88.9953%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_889.13_889.46, WER: 0%, TER: 0%, total WER: 19.0868%, total TER: 8.50016%, progress (thread 0): 89.0032%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_1661.82_1662.15, WER: 0%, TER: 0%, total WER: 19.0865%, total TER: 8.50008%, progress (thread 0): 89.0111%]
|T|: y e a h
|P|: n a h
[sample: TS3003d_H01_MTD011UID_2072.21_2072.54, WER: 100%, TER: 50%, total WER: 19.0875%, total TER: 8.50047%, progress (thread 0): 89.019%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_2085.82_2086.15, WER: 100%, TER: 0%, total WER: 19.0884%, total TER: 8.50037%, progress (thread 0): 89.0269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2164.01_2164.34, WER: 0%, TER: 0%, total WER: 19.0882%, total TER: 8.50029%, progress (thread 0): 89.0348%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2179.8_2180.13, WER: 0%, TER: 0%, total WER: 19.0879%, total TER: 8.50021%, progress (thread 0): 89.0427%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_2461.76_2462.09, WER: 100%, TER: 0%, total WER: 19.0889%, total TER: 8.50011%, progress (thread 0): 89.0506%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_299.48_299.81, WER: 0%, TER: 0%, total WER: 19.0886%, total TER: 8.50003%, progress (thread 0): 89.0585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_359.5_359.83, WER: 0%, TER: 0%, total WER: 19.0884%, total TER: 8.49995%, progress (thread 0): 89.0665%]
|T|: w a i t
|P|: w a i t
[sample: EN2002a_H02_FEO072_387.53_387.86, WER: 0%, TER: 0%, total WER: 19.0882%, total TER: 8.49987%, progress (thread 0): 89.0744%]
|T|: y e a h | y e a h
|P|: e h | y e a h
[sample: EN2002a_H03_MEE071_593.65_593.98, WER: 50%, TER: 22.2222%, total WER: 19.0889%, total TER: 8.50016%, progress (thread 0): 89.0823%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_594.32_594.65, WER: 0%, TER: 0%, total WER: 19.0887%, total TER: 8.50008%, progress (thread 0): 89.0902%]
|T|: y e a h
|P|: n e a h
[sample: EN2002a_H01_FEO070_690.97_691.3, WER: 100%, TER: 25%, total WER: 19.0896%, total TER: 8.50024%, progress (thread 0): 89.0981%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_1018.98_1019.31, WER: 0%, TER: 0%, total WER: 19.0894%, total TER: 8.50016%, progress (thread 0): 89.106%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1028.68_1029.01, WER: 0%, TER: 0%, total WER: 19.0892%, total TER: 8.50008%, progress (thread 0): 89.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1142.23_1142.56, WER: 0%, TER: 0%, total WER: 19.089%, total TER: 8.5%, progress (thread 0): 89.1218%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1361.22_1361.55, WER: 0%, TER: 0%, total WER: 19.0887%, total TER: 8.49992%, progress (thread 0): 89.1297%]
|T|: r i g h t
|P|: i h
[sample: EN2002a_H03_MEE071_1383.46_1383.79, WER: 100%, TER: 60%, total WER: 19.0897%, total TER: 8.50053%, progress (thread 0): 89.1377%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1864.48_1864.81, WER: 0%, TER: 0%, total WER: 19.0894%, total TER: 8.50045%, progress (thread 0): 89.1456%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1964.31_1964.64, WER: 0%, TER: 0%, total WER: 19.0892%, total TER: 8.50037%, progress (thread 0): 89.1535%]
|T|: u m
|P|: m m
[sample: EN2002b_H03_MEE073_139.44_139.77, WER: 100%, TER: 50%, total WER: 19.0901%, total TER: 8.50056%, progress (thread 0): 89.1614%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_417.94_418.27, WER: 0%, TER: 0%, total WER: 19.0899%, total TER: 8.50048%, progress (thread 0): 89.1693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_420.46_420.79, WER: 0%, TER: 0%, total WER: 19.0897%, total TER: 8.5004%, progress (thread 0): 89.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_705.52_705.85, WER: 0%, TER: 0%, total WER: 19.0895%, total TER: 8.50032%, progress (thread 0): 89.1851%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_731.78_732.11, WER: 0%, TER: 0%, total WER: 19.0893%, total TER: 8.50024%, progress (thread 0): 89.193%]
|T|: y e a h | t
|P|: y e a h
[sample: EN2002b_H02_FEO072_1235.77_1236.1, WER: 50%, TER: 33.3333%, total WER: 19.09%, total TER: 8.50059%, progress (thread 0): 89.201%]
|T|: h m m
|P|: m m m
[sample: EN2002b_H03_MEE073_1236.72_1237.05, WER: 100%, TER: 33.3333%, total WER: 19.0909%, total TER: 8.50077%, progress (thread 0): 89.2089%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_1256.5_1256.83, WER: 0%, TER: 0%, total WER: 19.0907%, total TER: 8.50069%, progress (thread 0): 89.2168%]
|T|: h m m
|P|: m m m
[sample: EN2002b_H02_FEO072_1475.51_1475.84, WER: 100%, TER: 33.3333%, total WER: 19.0916%, total TER: 8.50086%, progress (thread 0): 89.2247%]
|T|: o h | r i g h t
|P|: a l | r i g h t
[sample: EN2002b_H02_FEO072_1611.8_1612.13, WER: 50%, TER: 25%, total WER: 19.0923%, total TER: 8.50117%, progress (thread 0): 89.2326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_93.86_94.19, WER: 0%, TER: 0%, total WER: 19.0921%, total TER: 8.50109%, progress (thread 0): 89.2405%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_245.2_245.53, WER: 0%, TER: 0%, total WER: 19.0919%, total TER: 8.50101%, progress (thread 0): 89.2484%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H01_FEO072_447.06_447.39, WER: 100%, TER: 0%, total WER: 19.0928%, total TER: 8.50091%, progress (thread 0): 89.2563%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002c_H03_MEE073_823.26_823.59, WER: 0%, TER: 0%, total WER: 19.0924%, total TER: 8.50077%, progress (thread 0): 89.2642%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_850.62_850.95, WER: 0%, TER: 0%, total WER: 19.0921%, total TER: 8.50069%, progress (thread 0): 89.2722%]
|T|: t h a t ' s | a
|P|: o s a y
[sample: EN2002c_H03_MEE073_903.73_904.06, WER: 100%, TER: 87.5%, total WER: 19.094%, total TER: 8.50218%, progress (thread 0): 89.2801%]
|T|: o h
|P|: o h
[sample: EN2002c_H03_MEE073_962_962.33, WER: 0%, TER: 0%, total WER: 19.0938%, total TER: 8.50214%, progress (thread 0): 89.288%]
|T|: y e a h | r i g h t
|P|: y e a h | r g h t
[sample: EN2002c_H03_MEE073_1287.41_1287.74, WER: 50%, TER: 10%, total WER: 19.0945%, total TER: 8.50217%, progress (thread 0): 89.2959%]
|T|: t h a t ' s | t r u e
|P|: t h a t ' s | t r e
[sample: EN2002c_H03_MEE073_1299.73_1300.06, WER: 50%, TER: 9.09091%, total WER: 19.0952%, total TER: 8.50219%, progress (thread 0): 89.3038%]
|T|: y o u | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2044.96_2045.29, WER: 0%, TER: 0%, total WER: 19.0947%, total TER: 8.50203%, progress (thread 0): 89.3117%]
|T|: m m
|P|: m m
[sample: EN2002c_H02_MEE071_2072_2072.33, WER: 0%, TER: 0%, total WER: 19.0945%, total TER: 8.50199%, progress (thread 0): 89.3196%]
|T|: o o h
|P|: o o h
[sample: EN2002c_H01_FEO072_2817.88_2818.21, WER: 0%, TER: 0%, total WER: 19.0943%, total TER: 8.50193%, progress (thread 0): 89.3275%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002d_H01_FEO072_419.86_420.19, WER: 200%, TER: 14.2857%, total WER: 19.0963%, total TER: 8.50202%, progress (thread 0): 89.3354%]
|T|: m m
|P|: u m
[sample: EN2002d_H03_MEE073_488.3_488.63, WER: 100%, TER: 50%, total WER: 19.0973%, total TER: 8.50222%, progress (thread 0): 89.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1089.65_1089.98, WER: 0%, TER: 0%, total WER: 19.097%, total TER: 8.50214%, progress (thread 0): 89.3513%]
|T|: w e l l
|P|: n o
[sample: EN2002d_H03_MEE073_1132.11_1132.44, WER: 100%, TER: 100%, total WER: 19.098%, total TER: 8.503%, progress (thread 0): 89.3592%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1131.27_1131.6, WER: 100%, TER: 33.3333%, total WER: 19.0989%, total TER: 8.50317%, progress (thread 0): 89.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1257.89_1258.22, WER: 0%, TER: 0%, total WER: 19.0987%, total TER: 8.50309%, progress (thread 0): 89.375%]
|T|: r e a l l y
|P|: r e a l l y
[sample: EN2002d_H01_FEO072_1345.97_1346.3, WER: 0%, TER: 0%, total WER: 19.0984%, total TER: 8.50297%, progress (thread 0): 89.3829%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1429.74_1430.07, WER: 100%, TER: 33.3333%, total WER: 19.0994%, total TER: 8.50315%, progress (thread 0): 89.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1957.8_1958.13, WER: 0%, TER: 0%, total WER: 19.0991%, total TER: 8.50307%, progress (thread 0): 89.3987%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_863.21_863.53, WER: 100%, TER: 0%, total WER: 19.1001%, total TER: 8.50297%, progress (thread 0): 89.4066%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_895.43_895.75, WER: 100%, TER: 0%, total WER: 19.101%, total TER: 8.50287%, progress (thread 0): 89.4146%]
|T|: m m
|P|: m m
[sample: ES2004b_H01_FEE013_1140.58_1140.9, WER: 0%, TER: 0%, total WER: 19.1008%, total TER: 8.50283%, progress (thread 0): 89.4225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1182.75_1183.07, WER: 0%, TER: 0%, total WER: 19.1005%, total TER: 8.50275%, progress (thread 0): 89.4304%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1471.32_1471.64, WER: 100%, TER: 0%, total WER: 19.1015%, total TER: 8.50265%, progress (thread 0): 89.4383%]
|T|: s
|P|: m m
[sample: ES2004b_H01_FEE013_1922.51_1922.83, WER: 100%, TER: 200%, total WER: 19.1024%, total TER: 8.5031%, progress (thread 0): 89.4462%]
|T|: m m h m m
|P|: m m
[sample: ES2004b_H03_FEE016_1953.34_1953.66, WER: 100%, TER: 60%, total WER: 19.1033%, total TER: 8.5037%, progress (thread 0): 89.4541%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1966.24_1966.56, WER: 0%, TER: 0%, total WER: 19.1031%, total TER: 8.50362%, progress (thread 0): 89.462%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_806.95_807.27, WER: 0%, TER: 0%, total WER: 19.1029%, total TER: 8.50354%, progress (thread 0): 89.4699%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H03_FEE016_1066.11_1066.43, WER: 0%, TER: 0%, total WER: 19.1026%, total TER: 8.50346%, progress (thread 0): 89.4779%]
|T|: m m
|P|: m m
[sample: ES2004c_H00_MEO015_1890.24_1890.56, WER: 0%, TER: 0%, total WER: 19.1024%, total TER: 8.50342%, progress (thread 0): 89.4858%]
|T|: y e a h
|P|: y e a p
[sample: ES2004d_H00_MEO015_341.64_341.96, WER: 100%, TER: 25%, total WER: 19.1033%, total TER: 8.50358%, progress (thread 0): 89.4937%]
|T|: n o
|P|: n o
[sample: ES2004d_H00_MEO015_400.96_401.28, WER: 0%, TER: 0%, total WER: 19.1031%, total TER: 8.50354%, progress (thread 0): 89.5016%]
|T|: o o p s
|P|: i p s
[sample: ES2004d_H03_FEE016_430.55_430.87, WER: 100%, TER: 50%, total WER: 19.104%, total TER: 8.50393%, progress (thread 0): 89.5095%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_774.15_774.47, WER: 0%, TER: 0%, total WER: 19.1038%, total TER: 8.50385%, progress (thread 0): 89.5174%]
|T|: o n e
|P|: w e l l
[sample: ES2004d_H02_MEE014_785.53_785.85, WER: 100%, TER: 133.333%, total WER: 19.1047%, total TER: 8.50473%, progress (thread 0): 89.5253%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_840.47_840.79, WER: 0%, TER: 0%, total WER: 19.1045%, total TER: 8.50467%, progress (thread 0): 89.5332%]
|T|: m m h m m
|P|: m m m m
[sample: ES2004d_H03_FEE016_1930.95_1931.27, WER: 100%, TER: 20%, total WER: 19.1054%, total TER: 8.5048%, progress (thread 0): 89.5411%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2146.44_2146.76, WER: 0%, TER: 0%, total WER: 19.1052%, total TER: 8.50472%, progress (thread 0): 89.549%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_290.82_291.14, WER: 100%, TER: 0%, total WER: 19.1061%, total TER: 8.50462%, progress (thread 0): 89.557%]
|T|: o o p s
|P|: s o
[sample: IS1009a_H03_FIO089_420.14_420.46, WER: 100%, TER: 75%, total WER: 19.1071%, total TER: 8.50525%, progress (thread 0): 89.5649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H01_FIO087_494.72_495.04, WER: 0%, TER: 0%, total WER: 19.1068%, total TER: 8.50517%, progress (thread 0): 89.5728%]
|T|: y e a h
|P|: h
[sample: IS1009a_H02_FIO084_577.05_577.37, WER: 100%, TER: 75%, total WER: 19.1078%, total TER: 8.50579%, progress (thread 0): 89.5807%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H02_FIO084_641.8_642.12, WER: 100%, TER: 20%, total WER: 19.1087%, total TER: 8.50593%, progress (thread 0): 89.5886%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_197.33_197.65, WER: 0%, TER: 0%, total WER: 19.1085%, total TER: 8.50585%, progress (thread 0): 89.5965%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_595.82_596.14, WER: 100%, TER: 60%, total WER: 19.1094%, total TER: 8.50645%, progress (thread 0): 89.6044%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1073.83_1074.15, WER: 100%, TER: 0%, total WER: 19.1103%, total TER: 8.50635%, progress (thread 0): 89.6123%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1139.48_1139.8, WER: 100%, TER: 0%, total WER: 19.1112%, total TER: 8.50625%, progress (thread 0): 89.6202%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1268.25_1268.57, WER: 100%, TER: 0%, total WER: 19.1121%, total TER: 8.50615%, progress (thread 0): 89.6282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1877.89_1878.21, WER: 0%, TER: 0%, total WER: 19.1119%, total TER: 8.50607%, progress (thread 0): 89.6361%]
|T|: m m h m m
|P|: m e n
[sample: IS1009b_H03_FIO089_1894.73_1895.05, WER: 100%, TER: 80%, total WER: 19.1128%, total TER: 8.50691%, progress (thread 0): 89.644%]
|T|: h i
|P|: k a y
[sample: IS1009c_H01_FIO087_43.77_44.09, WER: 100%, TER: 150%, total WER: 19.1137%, total TER: 8.50758%, progress (thread 0): 89.6519%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_405.29_405.61, WER: 100%, TER: 0%, total WER: 19.1146%, total TER: 8.50748%, progress (thread 0): 89.6598%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_524.19_524.51, WER: 100%, TER: 0%, total WER: 19.1156%, total TER: 8.50738%, progress (thread 0): 89.6677%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_1640_1640.32, WER: 0%, TER: 0%, total WER: 19.1153%, total TER: 8.50732%, progress (thread 0): 89.6756%]
|T|: m e n u
|P|: m e n u
[sample: IS1009d_H00_FIE088_523.67_523.99, WER: 0%, TER: 0%, total WER: 19.1151%, total TER: 8.50724%, progress (thread 0): 89.6835%]
|T|: p a r d o n | m e
|P|: b u d d o n
[sample: IS1009d_H01_FIO087_699.16_699.48, WER: 100%, TER: 66.6667%, total WER: 19.117%, total TER: 8.50847%, progress (thread 0): 89.6915%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_799.6_799.92, WER: 100%, TER: 60%, total WER: 19.1179%, total TER: 8.50907%, progress (thread 0): 89.6994%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1056.69_1057.01, WER: 0%, TER: 0%, total WER: 19.1177%, total TER: 8.50899%, progress (thread 0): 89.7073%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1722.84_1723.16, WER: 100%, TER: 0%, total WER: 19.1186%, total TER: 8.50889%, progress (thread 0): 89.7152%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1741.45_1741.77, WER: 100%, TER: 0%, total WER: 19.1195%, total TER: 8.50879%, progress (thread 0): 89.7231%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1791.05_1791.37, WER: 0%, TER: 0%, total WER: 19.1193%, total TER: 8.50871%, progress (thread 0): 89.731%]
|T|: y e a h
|P|: m m
[sample: TS3003b_H01_MTD011UID_1651.31_1651.63, WER: 100%, TER: 100%, total WER: 19.1202%, total TER: 8.50957%, progress (thread 0): 89.7389%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_1722.53_1722.85, WER: 100%, TER: 25%, total WER: 19.1211%, total TER: 8.50973%, progress (thread 0): 89.7468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2090.25_2090.57, WER: 0%, TER: 0%, total WER: 19.1209%, total TER: 8.50965%, progress (thread 0): 89.7547%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_2074.3_2074.62, WER: 100%, TER: 0%, total WER: 19.1218%, total TER: 8.50955%, progress (thread 0): 89.7627%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_2134.48_2134.8, WER: 100%, TER: 0%, total WER: 19.1227%, total TER: 8.50945%, progress (thread 0): 89.7706%]
|T|: n a y
|P|: n o
[sample: TS3003d_H01_MTD011UID_417.84_418.16, WER: 100%, TER: 66.6667%, total WER: 19.1236%, total TER: 8.50986%, progress (thread 0): 89.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_694.59_694.91, WER: 0%, TER: 0%, total WER: 19.1234%, total TER: 8.50978%, progress (thread 0): 89.7864%]
|T|: o r | b
|P|: o r
[sample: TS3003d_H03_MTD012ME_896.59_896.91, WER: 50%, TER: 50%, total WER: 19.1241%, total TER: 8.51017%, progress (thread 0): 89.7943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1403.4_1403.72, WER: 0%, TER: 0%, total WER: 19.1239%, total TER: 8.51009%, progress (thread 0): 89.8022%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1492.24_1492.56, WER: 0%, TER: 0%, total WER: 19.1237%, total TER: 8.51001%, progress (thread 0): 89.8101%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_2024.39_2024.71, WER: 100%, TER: 100%, total WER: 19.1246%, total TER: 8.51087%, progress (thread 0): 89.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2381.84_2382.16, WER: 0%, TER: 0%, total WER: 19.1244%, total TER: 8.51079%, progress (thread 0): 89.826%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_68.23_68.55, WER: 0%, TER: 0%, total WER: 19.1242%, total TER: 8.51069%, progress (thread 0): 89.8339%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_359.54_359.86, WER: 50%, TER: 42.8571%, total WER: 19.1249%, total TER: 8.51125%, progress (thread 0): 89.8418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_496.88_497.2, WER: 0%, TER: 0%, total WER: 19.1247%, total TER: 8.51117%, progress (thread 0): 89.8497%]
|T|: h m m
|P|: n m m
[sample: EN2002a_H02_FEO072_504.18_504.5, WER: 100%, TER: 33.3333%, total WER: 19.1256%, total TER: 8.51135%, progress (thread 0): 89.8576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_567.01_567.33, WER: 0%, TER: 0%, total WER: 19.1254%, total TER: 8.51131%, progress (thread 0): 89.8655%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_604.34_604.66, WER: 100%, TER: 0%, total WER: 19.1263%, total TER: 8.51121%, progress (thread 0): 89.8734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_729.25_729.57, WER: 0%, TER: 0%, total WER: 19.1261%, total TER: 8.51113%, progress (thread 0): 89.8813%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_800.36_800.68, WER: 0%, TER: 0%, total WER: 19.1258%, total TER: 8.51103%, progress (thread 0): 89.8892%]
|T|: o h
|P|: n o
[sample: EN2002a_H02_FEO072_856.5_856.82, WER: 100%, TER: 100%, total WER: 19.1268%, total TER: 8.51146%, progress (thread 0): 89.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1050.6_1050.92, WER: 0%, TER: 0%, total WER: 19.1265%, total TER: 8.51138%, progress (thread 0): 89.9051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1101.18_1101.5, WER: 0%, TER: 0%, total WER: 19.1263%, total TER: 8.5113%, progress (thread 0): 89.913%]
|T|: o h | n o
|P|: o o
[sample: EN2002a_H01_FEO070_1448.67_1448.99, WER: 100%, TER: 60%, total WER: 19.1282%, total TER: 8.5119%, progress (thread 0): 89.9209%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1448.83_1449.15, WER: 100%, TER: 33.3333%, total WER: 19.1291%, total TER: 8.51208%, progress (thread 0): 89.9288%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1537.39_1537.71, WER: 0%, TER: 0%, total WER: 19.1289%, total TER: 8.512%, progress (thread 0): 89.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1862.6_1862.92, WER: 0%, TER: 0%, total WER: 19.1286%, total TER: 8.51192%, progress (thread 0): 89.9446%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_283.05_283.37, WER: 100%, TER: 100%, total WER: 19.1295%, total TER: 8.51278%, progress (thread 0): 89.9525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_363.29_363.61, WER: 0%, TER: 0%, total WER: 19.1293%, total TER: 8.5127%, progress (thread 0): 89.9604%]
|T|: o h | y e a h
|P|: m e a h
[sample: EN2002b_H03_MEE073_679.41_679.73, WER: 100%, TER: 57.1429%, total WER: 19.1312%, total TER: 8.5135%, progress (thread 0): 89.9684%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_922.82_923.14, WER: 0%, TER: 0%, total WER: 19.1309%, total TER: 8.51342%, progress (thread 0): 89.9763%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H02_FEO072_1100.2_1100.52, WER: 100%, TER: 0%, total WER: 19.1319%, total TER: 8.51332%, progress (thread 0): 89.9842%]
|T|: g o o d | p o i n t
|P|: t o e | p o i n t
[sample: EN2002b_H03_MEE073_1217.83_1218.15, WER: 50%, TER: 30%, total WER: 19.1326%, total TER: 8.51382%, progress (thread 0): 89.9921%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1261.76_1262.08, WER: 0%, TER: 0%, total WER: 19.1323%, total TER: 8.51374%, progress (thread 0): 90%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1598.38_1598.7, WER: 100%, TER: 33.3333%, total WER: 19.1333%, total TER: 8.51392%, progress (thread 0): 90.0079%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1611.81_1612.13, WER: 50%, TER: 55.5556%, total WER: 19.134%, total TER: 8.51491%, progress (thread 0): 90.0158%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1705.25_1705.57, WER: 0%, TER: 0%, total WER: 19.1337%, total TER: 8.51483%, progress (thread 0): 90.0237%]
|T|: g o o d
|P|: o k a y
[sample: EN2002c_H01_FEO072_489.73_490.05, WER: 100%, TER: 100%, total WER: 19.1347%, total TER: 8.51569%, progress (thread 0): 90.0316%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_653.98_654.3, WER: 0%, TER: 0%, total WER: 19.1344%, total TER: 8.51565%, progress (thread 0): 90.0396%]
|T|: y e a h
|P|: e h
[sample: EN2002c_H01_FEO072_1352.96_1353.28, WER: 100%, TER: 50%, total WER: 19.1354%, total TER: 8.51604%, progress (thread 0): 90.0475%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_2296.14_2296.46, WER: 0%, TER: 0%, total WER: 19.1351%, total TER: 8.516%, progress (thread 0): 90.0554%]
|T|: i | t h i n k
|P|: i | t h i n k
[sample: EN2002c_H01_FEO072_2336.35_2336.67, WER: 0%, TER: 0%, total WER: 19.1347%, total TER: 8.51586%, progress (thread 0): 90.0633%]
|T|: o h
|P|: e h
[sample: EN2002c_H01_FEO072_2368.69_2369.01, WER: 100%, TER: 50%, total WER: 19.1356%, total TER: 8.51605%, progress (thread 0): 90.0712%]
|T|: w h a t
|P|: w e l l
[sample: EN2002c_H01_FEO072_2484.6_2484.92, WER: 100%, TER: 75%, total WER: 19.1365%, total TER: 8.51668%, progress (thread 0): 90.0791%]
|T|: h m m
|P|: m m m
[sample: EN2002c_H03_MEE073_2800.26_2800.58, WER: 100%, TER: 33.3333%, total WER: 19.1375%, total TER: 8.51685%, progress (thread 0): 90.087%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_2829.54_2829.86, WER: 0%, TER: 0%, total WER: 19.1372%, total TER: 8.51677%, progress (thread 0): 90.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_347.79_348.11, WER: 0%, TER: 0%, total WER: 19.137%, total TER: 8.51669%, progress (thread 0): 90.1028%]
|T|: i t ' s | g o o d
|P|: t h a ' s | g o
[sample: EN2002d_H00_FEO070_420.4_420.72, WER: 100%, TER: 55.5556%, total WER: 19.1388%, total TER: 8.51769%, progress (thread 0): 90.1108%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002d_H01_FEO072_574.1_574.42, WER: 100%, TER: 20%, total WER: 19.1398%, total TER: 8.51782%, progress (thread 0): 90.1187%]
|T|: b u t
|P|: b u t
[sample: EN2002d_H01_FEO072_590.21_590.53, WER: 0%, TER: 0%, total WER: 19.1395%, total TER: 8.51776%, progress (thread 0): 90.1266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_769.35_769.67, WER: 0%, TER: 0%, total WER: 19.1393%, total TER: 8.51768%, progress (thread 0): 90.1345%]
|T|: o h
|P|: o h
[sample: EN2002d_H03_MEE073_939.78_940.1, WER: 0%, TER: 0%, total WER: 19.1391%, total TER: 8.51764%, progress (thread 0): 90.1424%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_954.69_955.01, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.51756%, progress (thread 0): 90.1503%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1189.57_1189.89, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51748%, progress (thread 0): 90.1582%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1203.2_1203.52, WER: 0%, TER: 0%, total WER: 19.1385%, total TER: 8.5174%, progress (thread 0): 90.1661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1255.64_1255.96, WER: 0%, TER: 0%, total WER: 19.1382%, total TER: 8.51732%, progress (thread 0): 90.174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1754.3_1754.62, WER: 0%, TER: 0%, total WER: 19.138%, total TER: 8.51724%, progress (thread 0): 90.182%]
|T|: s o
|P|: s o
[sample: EN2002d_H01_FEO072_1974.1_1974.42, WER: 0%, TER: 0%, total WER: 19.1378%, total TER: 8.5172%, progress (thread 0): 90.1899%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_2190.35_2190.67, WER: 0%, TER: 0%, total WER: 19.1376%, total TER: 8.51716%, progress (thread 0): 90.1978%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_582.6_582.91, WER: 0%, TER: 0%, total WER: 19.1374%, total TER: 8.51708%, progress (thread 0): 90.2057%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_687.25_687.56, WER: 100%, TER: 0%, total WER: 19.1383%, total TER: 8.51698%, progress (thread 0): 90.2136%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_688.13_688.44, WER: 0%, TER: 0%, total WER: 19.1381%, total TER: 8.5169%, progress (thread 0): 90.2215%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_777.34_777.65, WER: 100%, TER: 0%, total WER: 19.139%, total TER: 8.5168%, progress (thread 0): 90.2294%]
|T|: y
|P|: y
[sample: ES2004a_H02_MEE014_949.9_950.21, WER: 0%, TER: 0%, total WER: 19.1388%, total TER: 8.51678%, progress (thread 0): 90.2373%]
|T|: n o
|P|: n o
[sample: ES2004b_H03_FEE016_607.28_607.59, WER: 0%, TER: 0%, total WER: 19.1386%, total TER: 8.51674%, progress (thread 0): 90.2453%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1612.5_1612.81, WER: 100%, TER: 0%, total WER: 19.1395%, total TER: 8.51664%, progress (thread 0): 90.2532%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_2237.11_2237.42, WER: 0%, TER: 0%, total WER: 19.1393%, total TER: 8.51656%, progress (thread 0): 90.2611%]
|T|: b u t
|P|: b u t
[sample: ES2004c_H02_MEE014_571.73_572.04, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.5165%, progress (thread 0): 90.269%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1321.04_1321.35, WER: 0%, TER: 0%, total WER: 19.1388%, total TER: 8.51642%, progress (thread 0): 90.2769%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_1582.98_1583.29, WER: 0%, TER: 0%, total WER: 19.1386%, total TER: 8.51634%, progress (thread 0): 90.2848%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2288.73_2289.04, WER: 0%, TER: 0%, total WER: 19.1384%, total TER: 8.51626%, progress (thread 0): 90.2927%]
|T|: m m
|P|: h m m
[sample: ES2004d_H00_MEO015_846.15_846.46, WER: 100%, TER: 50%, total WER: 19.1393%, total TER: 8.51646%, progress (thread 0): 90.3006%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1669.25_1669.56, WER: 0%, TER: 0%, total WER: 19.1391%, total TER: 8.51638%, progress (thread 0): 90.3085%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1669.66_1669.97, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.5163%, progress (thread 0): 90.3165%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1825.79_1826.1, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51622%, progress (thread 0): 90.3244%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1901.99_1902.3, WER: 0%, TER: 0%, total WER: 19.1384%, total TER: 8.51614%, progress (thread 0): 90.3323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1967.13_1967.44, WER: 0%, TER: 0%, total WER: 19.1382%, total TER: 8.51606%, progress (thread 0): 90.3402%]
|T|: w e l l
|P|: o h
[sample: ES2004d_H02_MEE014_2085.81_2086.12, WER: 100%, TER: 100%, total WER: 19.1391%, total TER: 8.51692%, progress (thread 0): 90.3481%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2220.03_2220.34, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.51684%, progress (thread 0): 90.356%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_90.21_90.52, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51676%, progress (thread 0): 90.3639%]
|T|: u h
|P|: m m
[sample: IS1009a_H02_FIO084_451.06_451.37, WER: 100%, TER: 100%, total WER: 19.1396%, total TER: 8.51719%, progress (thread 0): 90.3718%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_702.97_703.28, WER: 0%, TER: 0%, total WER: 19.1394%, total TER: 8.51711%, progress (thread 0): 90.3797%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_795.57_795.88, WER: 0%, TER: 0%, total WER: 19.1392%, total TER: 8.51703%, progress (thread 0): 90.3877%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_218.1_218.41, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.51699%, progress (thread 0): 90.3956%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_616.32_616.63, WER: 100%, TER: 0%, total WER: 19.1399%, total TER: 8.51689%, progress (thread 0): 90.4035%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1692.92_1693.23, WER: 0%, TER: 0%, total WER: 19.1397%, total TER: 8.51681%, progress (thread 0): 90.4114%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1832.99_1833.3, WER: 0%, TER: 0%, total WER: 19.1395%, total TER: 8.51673%, progress (thread 0): 90.4193%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1844.23_1844.54, WER: 0%, TER: 0%, total WER: 19.1392%, total TER: 8.51667%, progress (thread 0): 90.4272%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H03_FIO089_1904.39_1904.7, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.51657%, progress (thread 0): 90.4351%]
|T|: m m h m m
|P|: m e m
[sample: IS1009c_H00_FIE088_1140.15_1140.46, WER: 100%, TER: 60%, total WER: 19.1399%, total TER: 8.51717%, progress (thread 0): 90.443%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_1163.03_1163.34, WER: 100%, TER: 0%, total WER: 19.1409%, total TER: 8.51707%, progress (thread 0): 90.451%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_1237.53_1237.84, WER: 100%, TER: 0%, total WER: 19.1418%, total TER: 8.51697%, progress (thread 0): 90.4589%]
|T|: ' k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1429.75_1430.06, WER: 100%, TER: 25%, total WER: 19.1427%, total TER: 8.51712%, progress (thread 0): 90.4668%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1554.5_1554.81, WER: 100%, TER: 0%, total WER: 19.1436%, total TER: 8.51702%, progress (thread 0): 90.4747%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H01_FIO087_1580.06_1580.37, WER: 100%, TER: 0%, total WER: 19.1445%, total TER: 8.51692%, progress (thread 0): 90.4826%]
|T|: o h
|P|: o h
[sample: IS1009c_H00_FIE088_1694.98_1695.29, WER: 0%, TER: 0%, total WER: 19.1443%, total TER: 8.51688%, progress (thread 0): 90.4905%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_363.39_363.7, WER: 100%, TER: 0%, total WER: 19.1452%, total TER: 8.51678%, progress (thread 0): 90.4984%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_894.86_895.17, WER: 0%, TER: 0%, total WER: 19.145%, total TER: 8.51674%, progress (thread 0): 90.5063%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_903.74_904.05, WER: 0%, TER: 0%, total WER: 19.1448%, total TER: 8.51666%, progress (thread 0): 90.5142%]
|T|: n o
|P|: n o
[sample: IS1009d_H00_FIE088_1011.62_1011.93, WER: 0%, TER: 0%, total WER: 19.1446%, total TER: 8.51662%, progress (thread 0): 90.5222%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1012.22_1012.53, WER: 100%, TER: 0%, total WER: 19.1455%, total TER: 8.51652%, progress (thread 0): 90.5301%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1105.97_1106.28, WER: 100%, TER: 0%, total WER: 19.1464%, total TER: 8.51642%, progress (thread 0): 90.538%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1171.27_1171.58, WER: 0%, TER: 0%, total WER: 19.1462%, total TER: 8.51634%, progress (thread 0): 90.5459%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1243.74_1244.05, WER: 100%, TER: 66.6667%, total WER: 19.1471%, total TER: 8.51675%, progress (thread 0): 90.5538%]
|T|: w h y
|P|: w o w
[sample: IS1009d_H00_FIE088_1442.55_1442.86, WER: 100%, TER: 66.6667%, total WER: 19.148%, total TER: 8.51716%, progress (thread 0): 90.5617%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1681.21_1681.52, WER: 0%, TER: 0%, total WER: 19.1478%, total TER: 8.51708%, progress (thread 0): 90.5696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_216.48_216.79, WER: 0%, TER: 0%, total WER: 19.1476%, total TER: 8.517%, progress (thread 0): 90.5775%]
|T|: m e a t
|P|: n e a t
[sample: TS3003a_H03_MTD012ME_595.66_595.97, WER: 100%, TER: 25%, total WER: 19.1485%, total TER: 8.51716%, progress (thread 0): 90.5854%]
|T|: s o
|P|: s o
[sample: TS3003a_H03_MTD012ME_884.45_884.76, WER: 0%, TER: 0%, total WER: 19.1483%, total TER: 8.51712%, progress (thread 0): 90.5934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1418.64_1418.95, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51704%, progress (thread 0): 90.6013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_71.8_72.11, WER: 0%, TER: 0%, total WER: 19.1478%, total TER: 8.51696%, progress (thread 0): 90.6092%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_538.03_538.34, WER: 0%, TER: 0%, total WER: 19.1476%, total TER: 8.51688%, progress (thread 0): 90.6171%]
|T|: m m
|P|: m h
[sample: TS3003b_H01_MTD011UID_1469.03_1469.34, WER: 100%, TER: 50%, total WER: 19.1485%, total TER: 8.51707%, progress (thread 0): 90.625%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1715.8_1716.11, WER: 0%, TER: 0%, total WER: 19.1483%, total TER: 8.51703%, progress (thread 0): 90.6329%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1822.91_1823.22, WER: 0%, TER: 0%, total WER: 19.1481%, total TER: 8.51695%, progress (thread 0): 90.6408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_794.24_794.55, WER: 0%, TER: 0%, total WER: 19.1479%, total TER: 8.51687%, progress (thread 0): 90.6487%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_934.63_934.94, WER: 100%, TER: 25%, total WER: 19.1488%, total TER: 8.51703%, progress (thread 0): 90.6566%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1136.65_1136.96, WER: 0%, TER: 0%, total WER: 19.1486%, total TER: 8.51699%, progress (thread 0): 90.6646%]
|T|: y e a h
|P|: y e m
[sample: TS3003c_H01_MTD011UID_1444.43_1444.74, WER: 100%, TER: 50%, total WER: 19.1495%, total TER: 8.51738%, progress (thread 0): 90.6725%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1813.2_1813.51, WER: 0%, TER: 0%, total WER: 19.1493%, total TER: 8.5173%, progress (thread 0): 90.6804%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_255.36_255.67, WER: 0%, TER: 0%, total WER: 19.1491%, total TER: 8.51722%, progress (thread 0): 90.6883%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_453.54_453.85, WER: 0%, TER: 0%, total WER: 19.1488%, total TER: 8.51718%, progress (thread 0): 90.6962%]
|T|: y e p
|P|: o h
[sample: TS3003d_H02_MTD0010ID_562.3_562.61, WER: 100%, TER: 100%, total WER: 19.1498%, total TER: 8.51782%, progress (thread 0): 90.7041%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1317.08_1317.39, WER: 0%, TER: 0%, total WER: 19.1495%, total TER: 8.51774%, progress (thread 0): 90.712%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_1546.63_1546.94, WER: 100%, TER: 0%, total WER: 19.1505%, total TER: 8.51764%, progress (thread 0): 90.7199%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2324.36_2324.67, WER: 0%, TER: 0%, total WER: 19.1502%, total TER: 8.51756%, progress (thread 0): 90.7278%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2373.13_2373.44, WER: 0%, TER: 0%, total WER: 19.15%, total TER: 8.51748%, progress (thread 0): 90.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_595.97_596.28, WER: 0%, TER: 0%, total WER: 19.1498%, total TER: 8.5174%, progress (thread 0): 90.7437%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_694.14_694.45, WER: 0%, TER: 0%, total WER: 19.1496%, total TER: 8.51732%, progress (thread 0): 90.7516%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_704.35_704.66, WER: 0%, TER: 0%, total WER: 19.1494%, total TER: 8.51724%, progress (thread 0): 90.7595%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_910.51_910.82, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51716%, progress (thread 0): 90.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1104.75_1105.06, WER: 0%, TER: 0%, total WER: 19.1489%, total TER: 8.51708%, progress (thread 0): 90.7753%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1619.48_1619.79, WER: 100%, TER: 33.3333%, total WER: 19.1499%, total TER: 8.51726%, progress (thread 0): 90.7832%]
|T|: o h
|P|: m m
[sample: EN2002a_H02_FEO072_1674.71_1675.02, WER: 100%, TER: 100%, total WER: 19.1508%, total TER: 8.51769%, progress (thread 0): 90.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1973.2_1973.51, WER: 0%, TER: 0%, total WER: 19.1505%, total TER: 8.51761%, progress (thread 0): 90.799%]
|T|: o h | r i g h t
|P|: o h | r i g h t
[sample: EN2002a_H03_MEE071_1971.01_1971.32, WER: 0%, TER: 0%, total WER: 19.1501%, total TER: 8.51745%, progress (thread 0): 90.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_346.95_347.26, WER: 0%, TER: 0%, total WER: 19.1499%, total TER: 8.51737%, progress (thread 0): 90.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_372.58_372.89, WER: 0%, TER: 0%, total WER: 19.1497%, total TER: 8.51729%, progress (thread 0): 90.8228%]
|T|: m m
|P|: m m
[sample: EN2002b_H03_MEE073_565.42_565.73, WER: 0%, TER: 0%, total WER: 19.1495%, total TER: 8.51725%, progress (thread 0): 90.8307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_729.79_730.1, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51717%, progress (thread 0): 90.8386%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1214.71_1215.02, WER: 0%, TER: 0%, total WER: 19.149%, total TER: 8.51709%, progress (thread 0): 90.8465%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1323.81_1324.12, WER: 0%, TER: 0%, total WER: 19.1488%, total TER: 8.51701%, progress (thread 0): 90.8544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1461.61_1461.92, WER: 0%, TER: 0%, total WER: 19.1486%, total TER: 8.51693%, progress (thread 0): 90.8623%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1750.66_1750.97, WER: 0%, TER: 0%, total WER: 19.1484%, total TER: 8.51685%, progress (thread 0): 90.8703%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_441.29_441.6, WER: 0%, TER: 0%, total WER: 19.1482%, total TER: 8.51681%, progress (thread 0): 90.8782%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_543.24_543.55, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51677%, progress (thread 0): 90.8861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_543.24_543.55, WER: 0%, TER: 0%, total WER: 19.1477%, total TER: 8.51669%, progress (thread 0): 90.894%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_982.53_982.84, WER: 100%, TER: 33.3333%, total WER: 19.1486%, total TER: 8.51686%, progress (thread 0): 90.9019%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1524.82_1525.13, WER: 0%, TER: 0%, total WER: 19.1484%, total TER: 8.51678%, progress (thread 0): 90.9098%]
|T|: u m
|P|: u m
[sample: EN2002c_H03_MEE073_1904.01_1904.32, WER: 0%, TER: 0%, total WER: 19.1482%, total TER: 8.51674%, progress (thread 0): 90.9177%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1958.34_1958.65, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51666%, progress (thread 0): 90.9256%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_2240.67_2240.98, WER: 100%, TER: 0%, total WER: 19.1489%, total TER: 8.51656%, progress (thread 0): 90.9335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2358.92_2359.23, WER: 0%, TER: 0%, total WER: 19.1487%, total TER: 8.51648%, progress (thread 0): 90.9415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2477.51_2477.82, WER: 0%, TER: 0%, total WER: 19.1485%, total TER: 8.5164%, progress (thread 0): 90.9494%]
|T|: n o
|P|: n i c
[sample: EN2002c_H02_MEE071_2608.15_2608.46, WER: 100%, TER: 100%, total WER: 19.1494%, total TER: 8.51683%, progress (thread 0): 90.9573%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2724.28_2724.59, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51675%, progress (thread 0): 90.9652%]
|T|: t i c k
|P|: t i c k
[sample: EN2002c_H01_FEO072_2878.5_2878.81, WER: 0%, TER: 0%, total WER: 19.149%, total TER: 8.51667%, progress (thread 0): 90.9731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_391.81_392.12, WER: 0%, TER: 0%, total WER: 19.1487%, total TER: 8.51659%, progress (thread 0): 90.981%]
|T|: y e a h | o k a y
|P|: o k a y
[sample: EN2002d_H00_FEO070_1379.88_1380.19, WER: 50%, TER: 55.5556%, total WER: 19.1494%, total TER: 8.51758%, progress (thread 0): 90.9889%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H01_FEO072_1512.76_1513.07, WER: 100%, TER: 0%, total WER: 19.1504%, total TER: 8.51748%, progress (thread 0): 90.9968%]
|T|: s o
|P|: y e a h
[sample: EN2002d_H00_FEO070_1542.52_1542.83, WER: 100%, TER: 200%, total WER: 19.1513%, total TER: 8.51838%, progress (thread 0): 91.0047%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1546.76_1547.07, WER: 0%, TER: 0%, total WER: 19.1511%, total TER: 8.5183%, progress (thread 0): 91.0127%]
|T|: s o
|P|: s o
[sample: EN2002d_H03_MEE073_1822.41_1822.72, WER: 0%, TER: 0%, total WER: 19.1508%, total TER: 8.51826%, progress (thread 0): 91.0206%]
|T|: m m
|P|: m m
[sample: EN2002d_H00_FEO070_1872.39_1872.7, WER: 0%, TER: 0%, total WER: 19.1506%, total TER: 8.51822%, progress (thread 0): 91.0285%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2114.3_2114.61, WER: 0%, TER: 0%, total WER: 19.1504%, total TER: 8.51814%, progress (thread 0): 91.0364%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_819.96_820.26, WER: 0%, TER: 0%, total WER: 19.1502%, total TER: 8.51806%, progress (thread 0): 91.0443%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1232.15_1232.45, WER: 0%, TER: 0%, total WER: 19.15%, total TER: 8.51798%, progress (thread 0): 91.0522%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1572.74_1573.04, WER: 100%, TER: 0%, total WER: 19.1509%, total TER: 8.51788%, progress (thread 0): 91.0601%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1784.08_1784.38, WER: 100%, TER: 0%, total WER: 19.1518%, total TER: 8.51778%, progress (thread 0): 91.068%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_2252.76_2253.06, WER: 0%, TER: 0%, total WER: 19.1516%, total TER: 8.5177%, progress (thread 0): 91.076%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_849.84_850.14, WER: 0%, TER: 0%, total WER: 19.1514%, total TER: 8.51762%, progress (thread 0): 91.0839%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1555.92_1556.22, WER: 0%, TER: 0%, total WER: 19.1511%, total TER: 8.51754%, progress (thread 0): 91.0918%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H00_MEO015_1640.82_1641.12, WER: 100%, TER: 0%, total WER: 19.1521%, total TER: 8.51744%, progress (thread 0): 91.0997%]
|T|: ' k a y
|P|: k e y
[sample: ES2004d_H00_MEO015_271.77_272.07, WER: 100%, TER: 50%, total WER: 19.153%, total TER: 8.51783%, progress (thread 0): 91.1076%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_639.49_639.79, WER: 0%, TER: 0%, total WER: 19.1528%, total TER: 8.51775%, progress (thread 0): 91.1155%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1390.02_1390.32, WER: 0%, TER: 0%, total WER: 19.1525%, total TER: 8.51767%, progress (thread 0): 91.1234%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1711.3_1711.6, WER: 0%, TER: 0%, total WER: 19.1523%, total TER: 8.51759%, progress (thread 0): 91.1313%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_2002_2002.3, WER: 0%, TER: 0%, total WER: 19.1521%, total TER: 8.51755%, progress (thread 0): 91.1392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2096.66_2096.96, WER: 0%, TER: 0%, total WER: 19.1519%, total TER: 8.51747%, progress (thread 0): 91.1472%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_617.96_618.26, WER: 100%, TER: 0%, total WER: 19.1528%, total TER: 8.51737%, progress (thread 0): 91.1551%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H01_FIO087_782.9_783.2, WER: 0%, TER: 0%, total WER: 19.1526%, total TER: 8.51729%, progress (thread 0): 91.163%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_182.06_182.36, WER: 0%, TER: 0%, total WER: 19.1524%, total TER: 8.51721%, progress (thread 0): 91.1709%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_574.49_574.79, WER: 100%, TER: 0%, total WER: 19.1533%, total TER: 8.51711%, progress (thread 0): 91.1788%]
|T|: m m h m m
|P|: m m m
[sample: IS1009b_H03_FIO089_707.08_707.38, WER: 100%, TER: 40%, total WER: 19.1542%, total TER: 8.51748%, progress (thread 0): 91.1867%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1025.61_1025.91, WER: 0%, TER: 0%, total WER: 19.154%, total TER: 8.5174%, progress (thread 0): 91.1946%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_1366.95_1367.25, WER: 0%, TER: 0%, total WER: 19.1538%, total TER: 8.51736%, progress (thread 0): 91.2025%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1371.17_1371.47, WER: 100%, TER: 0%, total WER: 19.1547%, total TER: 8.51726%, progress (thread 0): 91.2104%]
|T|: h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1604.76_1605.06, WER: 100%, TER: 33.3333%, total WER: 19.1556%, total TER: 8.51744%, progress (thread 0): 91.2184%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1617.66_1617.96, WER: 0%, TER: 0%, total WER: 19.1554%, total TER: 8.51736%, progress (thread 0): 91.2263%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_335.7_336, WER: 0%, TER: 0%, total WER: 19.1552%, total TER: 8.51728%, progress (thread 0): 91.2342%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_1106.15_1106.45, WER: 100%, TER: 0%, total WER: 19.1561%, total TER: 8.51718%, progress (thread 0): 91.2421%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1237.83_1238.13, WER: 0%, TER: 0%, total WER: 19.1559%, total TER: 8.51712%, progress (thread 0): 91.25%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1259.97_1260.27, WER: 0%, TER: 0%, total WER: 19.1556%, total TER: 8.51704%, progress (thread 0): 91.2579%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1297.17_1297.47, WER: 0%, TER: 0%, total WER: 19.1554%, total TER: 8.51696%, progress (thread 0): 91.2658%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_607.11_607.41, WER: 100%, TER: 0%, total WER: 19.1563%, total TER: 8.51686%, progress (thread 0): 91.2737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_769.42_769.72, WER: 0%, TER: 0%, total WER: 19.1561%, total TER: 8.51678%, progress (thread 0): 91.2816%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1062.93_1063.23, WER: 0%, TER: 0%, total WER: 19.1559%, total TER: 8.5167%, progress (thread 0): 91.2896%]
|T|: o n e
|P|: b u t
[sample: IS1009d_H01_FIO087_1482_1482.3, WER: 100%, TER: 100%, total WER: 19.1568%, total TER: 8.51734%, progress (thread 0): 91.2975%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H00_FIE088_1528.1_1528.4, WER: 0%, TER: 0%, total WER: 19.1566%, total TER: 8.51728%, progress (thread 0): 91.3054%]
|T|: y e a h
|P|: i
[sample: IS1009d_H02_FIO084_1824.41_1824.71, WER: 100%, TER: 100%, total WER: 19.1575%, total TER: 8.51814%, progress (thread 0): 91.3133%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1853.09_1853.39, WER: 0%, TER: 0%, total WER: 19.1573%, total TER: 8.51806%, progress (thread 0): 91.3212%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_238.31_238.61, WER: 0%, TER: 0%, total WER: 19.1571%, total TER: 8.51798%, progress (thread 0): 91.3291%]
|T|: s o
|P|: s o
[sample: TS3003a_H02_MTD0010ID_1080.77_1081.07, WER: 0%, TER: 0%, total WER: 19.1569%, total TER: 8.51794%, progress (thread 0): 91.337%]
|T|: h m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1308.88_1309.18, WER: 100%, TER: 33.3333%, total WER: 19.1578%, total TER: 8.51812%, progress (thread 0): 91.3449%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_1423.96_1424.26, WER: 0%, TER: 0%, total WER: 19.1576%, total TER: 8.51806%, progress (thread 0): 91.3529%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1463.25_1463.55, WER: 0%, TER: 0%, total WER: 19.1573%, total TER: 8.51798%, progress (thread 0): 91.3608%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_1535.95_1536.25, WER: 0%, TER: 0%, total WER: 19.1571%, total TER: 8.51792%, progress (thread 0): 91.3687%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003b_H03_MTD012ME_1635.81_1636.11, WER: 100%, TER: 0%, total WER: 19.158%, total TER: 8.51782%, progress (thread 0): 91.3766%]
|T|: h m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1834.41_1834.71, WER: 100%, TER: 50%, total WER: 19.159%, total TER: 8.51801%, progress (thread 0): 91.3845%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1871.5_1871.8, WER: 0%, TER: 0%, total WER: 19.1587%, total TER: 8.51797%, progress (thread 0): 91.3924%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2279.98_2280.28, WER: 0%, TER: 0%, total WER: 19.1585%, total TER: 8.51789%, progress (thread 0): 91.4003%]
|T|: s
|P|: y m
[sample: TS3003d_H01_MTD011UID_856.51_856.81, WER: 100%, TER: 200%, total WER: 19.1594%, total TER: 8.51834%, progress (thread 0): 91.4082%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1057.72_1058.02, WER: 0%, TER: 0%, total WER: 19.1592%, total TER: 8.51826%, progress (thread 0): 91.4161%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1709.82_1710.12, WER: 0%, TER: 0%, total WER: 19.159%, total TER: 8.51818%, progress (thread 0): 91.424%]
|T|: y e s
|P|: y e s
[sample: TS3003d_H02_MTD0010ID_1732.89_1733.19, WER: 0%, TER: 0%, total WER: 19.1588%, total TER: 8.51812%, progress (thread 0): 91.432%]
|T|: y e a h
|P|: y m a m
[sample: TS3003d_H00_MTD009PM_1821.73_1822.03, WER: 100%, TER: 50%, total WER: 19.1597%, total TER: 8.51851%, progress (thread 0): 91.4399%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2250.87_2251.17, WER: 0%, TER: 0%, total WER: 19.1595%, total TER: 8.51843%, progress (thread 0): 91.4478%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2482.46_2482.76, WER: 0%, TER: 0%, total WER: 19.1593%, total TER: 8.51835%, progress (thread 0): 91.4557%]
|T|: r i g h t
|P|: y o u r e r i g h t
[sample: EN2002a_H03_MEE071_11.83_12.13, WER: 100%, TER: 100%, total WER: 19.1602%, total TER: 8.51942%, progress (thread 0): 91.4636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_48.72_49.02, WER: 0%, TER: 0%, total WER: 19.16%, total TER: 8.51934%, progress (thread 0): 91.4715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_203.25_203.55, WER: 0%, TER: 0%, total WER: 19.1597%, total TER: 8.51926%, progress (thread 0): 91.4794%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_261.14_261.44, WER: 100%, TER: 33.3333%, total WER: 19.1607%, total TER: 8.51944%, progress (thread 0): 91.4873%]
|T|: w h y | n o t
|P|: w h y | d o n ' t
[sample: EN2002a_H03_MEE071_375.98_376.28, WER: 50%, TER: 42.8571%, total WER: 19.1614%, total TER: 8.52%, progress (thread 0): 91.4953%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_469.41_469.71, WER: 0%, TER: 0%, total WER: 19.1611%, total TER: 8.51992%, progress (thread 0): 91.5032%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_486.08_486.38, WER: 0%, TER: 0%, total WER: 19.1609%, total TER: 8.51984%, progress (thread 0): 91.5111%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_508.71_509.01, WER: 0%, TER: 0%, total WER: 19.1607%, total TER: 8.51976%, progress (thread 0): 91.519%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_521.78_522.08, WER: 0%, TER: 0%, total WER: 19.1605%, total TER: 8.51968%, progress (thread 0): 91.5269%]
|T|: s o
|P|: s o
[sample: EN2002a_H03_MEE071_738.15_738.45, WER: 0%, TER: 0%, total WER: 19.1603%, total TER: 8.51964%, progress (thread 0): 91.5348%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_747.85_748.15, WER: 100%, TER: 0%, total WER: 19.1612%, total TER: 8.51954%, progress (thread 0): 91.5427%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_795.73_796.03, WER: 100%, TER: 33.3333%, total WER: 19.1621%, total TER: 8.51971%, progress (thread 0): 91.5506%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_877.67_877.97, WER: 100%, TER: 0%, total WER: 19.163%, total TER: 8.51961%, progress (thread 0): 91.5585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_918.33_918.63, WER: 0%, TER: 0%, total WER: 19.1628%, total TER: 8.51954%, progress (thread 0): 91.5665%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1889.11_1889.41, WER: 0%, TER: 0%, total WER: 19.1626%, total TER: 8.51946%, progress (thread 0): 91.5744%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H02_FEO072_2116.83_2117.13, WER: 0%, TER: 0%, total WER: 19.1624%, total TER: 8.51936%, progress (thread 0): 91.5823%]
|T|: o h | y e a h
|P|: h m h
[sample: EN2002b_H03_MEE073_211.33_211.63, WER: 100%, TER: 71.4286%, total WER: 19.1642%, total TER: 8.52039%, progress (thread 0): 91.5902%]
|T|: w e ' l l | s e e
|P|: w e l l | s e
[sample: EN2002b_H00_FEO070_255.08_255.38, WER: 100%, TER: 22.2222%, total WER: 19.166%, total TER: 8.52068%, progress (thread 0): 91.5981%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_368.42_368.72, WER: 0%, TER: 0%, total WER: 19.1658%, total TER: 8.5206%, progress (thread 0): 91.606%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H03_MEE073_391.92_392.22, WER: 100%, TER: 0%, total WER: 19.1667%, total TER: 8.5205%, progress (thread 0): 91.6139%]
|T|: m m
|P|: m m
[sample: EN2002b_H03_MEE073_526.11_526.41, WER: 0%, TER: 0%, total WER: 19.1665%, total TER: 8.52046%, progress (thread 0): 91.6218%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002b_H02_FEO072_627.98_628.28, WER: 200%, TER: 14.2857%, total WER: 19.1685%, total TER: 8.52055%, progress (thread 0): 91.6298%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1551.52_1551.82, WER: 0%, TER: 0%, total WER: 19.1683%, total TER: 8.52047%, progress (thread 0): 91.6377%]
|T|: b u t
|P|: b h u t
[sample: EN2002b_H01_MEE071_1611.31_1611.61, WER: 100%, TER: 33.3333%, total WER: 19.1692%, total TER: 8.52065%, progress (thread 0): 91.6456%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1729.42_1729.72, WER: 0%, TER: 0%, total WER: 19.169%, total TER: 8.52057%, progress (thread 0): 91.6535%]
|T|: i | t h i n k
|P|: i
[sample: EN2002c_H01_FEO072_232.37_232.67, WER: 50%, TER: 85.7143%, total WER: 19.1697%, total TER: 8.52183%, progress (thread 0): 91.6614%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_355.18_355.48, WER: 0%, TER: 0%, total WER: 19.1695%, total TER: 8.52175%, progress (thread 0): 91.6693%]
|T|: n o | n o
|P|: m m
[sample: EN2002c_H03_MEE073_543.55_543.85, WER: 100%, TER: 100%, total WER: 19.1713%, total TER: 8.52282%, progress (thread 0): 91.6772%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_700.09_700.39, WER: 100%, TER: 0%, total WER: 19.1722%, total TER: 8.52272%, progress (thread 0): 91.6851%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H02_MEE071_1131.17_1131.47, WER: 100%, TER: 66.6667%, total WER: 19.1731%, total TER: 8.52313%, progress (thread 0): 91.693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1143.34_1143.64, WER: 0%, TER: 0%, total WER: 19.1729%, total TER: 8.52305%, progress (thread 0): 91.701%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_1321.8_1322.1, WER: 0%, TER: 0%, total WER: 19.1727%, total TER: 8.52301%, progress (thread 0): 91.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1600.62_1600.92, WER: 0%, TER: 0%, total WER: 19.1725%, total TER: 8.52293%, progress (thread 0): 91.7168%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2301.16_2301.46, WER: 0%, TER: 0%, total WER: 19.1723%, total TER: 8.52285%, progress (thread 0): 91.7247%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2443.4_2443.7, WER: 0%, TER: 0%, total WER: 19.1721%, total TER: 8.52277%, progress (thread 0): 91.7326%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H02_MEE071_2847.83_2848.13, WER: 100%, TER: 0%, total WER: 19.173%, total TER: 8.52267%, progress (thread 0): 91.7405%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_673.96_674.26, WER: 0%, TER: 0%, total WER: 19.1728%, total TER: 8.52259%, progress (thread 0): 91.7484%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_834.35_834.65, WER: 0%, TER: 0%, total WER: 19.1725%, total TER: 8.52251%, progress (thread 0): 91.7563%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_925.24_925.54, WER: 0%, TER: 0%, total WER: 19.1723%, total TER: 8.52243%, progress (thread 0): 91.7642%]
|T|: u m
|P|: u m
[sample: EN2002d_H01_FEO072_1191.17_1191.47, WER: 0%, TER: 0%, total WER: 19.1721%, total TER: 8.52239%, progress (thread 0): 91.7721%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1205.4_1205.7, WER: 0%, TER: 0%, total WER: 19.1719%, total TER: 8.52229%, progress (thread 0): 91.7801%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H03_MEE073_1221.17_1221.47, WER: 100%, TER: 0%, total WER: 19.1728%, total TER: 8.52219%, progress (thread 0): 91.788%]
|T|: y e a h
|P|: m m
[sample: EN2002d_H00_FEO070_1249.7_1250, WER: 100%, TER: 100%, total WER: 19.1737%, total TER: 8.52305%, progress (thread 0): 91.7959%]
|T|: s u r e
|P|: s u r e
[sample: EN2002d_H03_MEE073_1672.53_1672.83, WER: 0%, TER: 0%, total WER: 19.1735%, total TER: 8.52297%, progress (thread 0): 91.8038%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_2192.66_2192.96, WER: 0%, TER: 0%, total WER: 19.1733%, total TER: 8.52293%, progress (thread 0): 91.8117%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_747.66_747.95, WER: 0%, TER: 0%, total WER: 19.1731%, total TER: 8.52285%, progress (thread 0): 91.8196%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004a_H00_MEO015_784.99_785.28, WER: 100%, TER: 20%, total WER: 19.174%, total TER: 8.52299%, progress (thread 0): 91.8275%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_826.06_826.35, WER: 0%, TER: 0%, total WER: 19.1738%, total TER: 8.52289%, progress (thread 0): 91.8354%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2019.84_2020.13, WER: 0%, TER: 0%, total WER: 19.1736%, total TER: 8.52281%, progress (thread 0): 91.8434%]
|T|: o k a y
|P|: m m
[sample: ES2004b_H02_MEE014_2295.26_2295.55, WER: 100%, TER: 100%, total WER: 19.1745%, total TER: 8.52366%, progress (thread 0): 91.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_316.02_316.31, WER: 0%, TER: 0%, total WER: 19.1742%, total TER: 8.52358%, progress (thread 0): 91.8592%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004c_H00_MEO015_1261.19_1261.48, WER: 200%, TER: 20%, total WER: 19.1763%, total TER: 8.52372%, progress (thread 0): 91.8671%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H01_FEE013_1987.8_1988.09, WER: 100%, TER: 0%, total WER: 19.1772%, total TER: 8.52362%, progress (thread 0): 91.875%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_2244.26_2244.55, WER: 0%, TER: 0%, total WER: 19.177%, total TER: 8.52354%, progress (thread 0): 91.8829%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_211.03_211.32, WER: 0%, TER: 0%, total WER: 19.1768%, total TER: 8.52344%, progress (thread 0): 91.8908%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H03_FEE016_810.67_810.96, WER: 0%, TER: 0%, total WER: 19.1766%, total TER: 8.52334%, progress (thread 0): 91.8987%]
|T|: m m
|P|: m m
[sample: ES2004d_H03_FEE016_1204.42_1204.71, WER: 0%, TER: 0%, total WER: 19.1763%, total TER: 8.5233%, progress (thread 0): 91.9066%]
|T|: u m
|P|: u m
[sample: ES2004d_H01_FEE013_1227.88_1228.17, WER: 0%, TER: 0%, total WER: 19.1761%, total TER: 8.52326%, progress (thread 0): 91.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1634.52_1634.81, WER: 0%, TER: 0%, total WER: 19.1759%, total TER: 8.52318%, progress (thread 0): 91.9225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1787.34_1787.63, WER: 0%, TER: 0%, total WER: 19.1757%, total TER: 8.5231%, progress (thread 0): 91.9304%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1964.63_1964.92, WER: 0%, TER: 0%, total WER: 19.1755%, total TER: 8.52302%, progress (thread 0): 91.9383%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_118.76_119.05, WER: 0%, TER: 0%, total WER: 19.1753%, total TER: 8.52294%, progress (thread 0): 91.9462%]
|T|: h m m
|P|: m m
[sample: IS1009a_H02_FIO084_803.39_803.68, WER: 100%, TER: 33.3333%, total WER: 19.1762%, total TER: 8.52311%, progress (thread 0): 91.9541%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_708.64_708.93, WER: 100%, TER: 0%, total WER: 19.1771%, total TER: 8.52301%, progress (thread 0): 91.962%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_998.44_998.73, WER: 0%, TER: 0%, total WER: 19.1769%, total TER: 8.52293%, progress (thread 0): 91.9699%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1311.53_1311.82, WER: 100%, TER: 0%, total WER: 19.1778%, total TER: 8.52284%, progress (thread 0): 91.9778%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1356.24_1356.53, WER: 100%, TER: 60%, total WER: 19.1787%, total TER: 8.52344%, progress (thread 0): 91.9858%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1658.74_1659.03, WER: 100%, TER: 60%, total WER: 19.1796%, total TER: 8.52404%, progress (thread 0): 91.9937%]
|T|: h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1899.66_1899.95, WER: 100%, TER: 33.3333%, total WER: 19.1805%, total TER: 8.52422%, progress (thread 0): 92.0016%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H03_FIO089_1999.16_1999.45, WER: 100%, TER: 20%, total WER: 19.1814%, total TER: 8.52435%, progress (thread 0): 92.0095%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_284.27_284.56, WER: 0%, TER: 0%, total WER: 19.1812%, total TER: 8.52429%, progress (thread 0): 92.0174%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_381_381.29, WER: 100%, TER: 20%, total WER: 19.1821%, total TER: 8.52442%, progress (thread 0): 92.0253%]
|T|: w e | c a n
|P|: w e g e t
[sample: IS1009c_H01_FIO087_755.64_755.93, WER: 100%, TER: 66.6667%, total WER: 19.1839%, total TER: 8.52524%, progress (thread 0): 92.0332%]
|T|: y e a h | b u t | w
|P|: y e a h
[sample: IS1009c_H02_FIO084_1559.51_1559.8, WER: 66.6667%, TER: 60%, total WER: 19.1855%, total TER: 8.52645%, progress (thread 0): 92.0411%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H02_FIO084_1608.9_1609.19, WER: 100%, TER: 20%, total WER: 19.1865%, total TER: 8.52658%, progress (thread 0): 92.049%]
|T|: h e l l o
|P|: o
[sample: IS1009d_H01_FIO087_41.27_41.56, WER: 100%, TER: 80%, total WER: 19.1874%, total TER: 8.52742%, progress (thread 0): 92.057%]
|T|: n o
|P|: n
[sample: IS1009d_H02_FIO084_952.51_952.8, WER: 100%, TER: 50%, total WER: 19.1883%, total TER: 8.52761%, progress (thread 0): 92.0649%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1056.71_1057, WER: 100%, TER: 0%, total WER: 19.1892%, total TER: 8.52751%, progress (thread 0): 92.0728%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1344.32_1344.61, WER: 100%, TER: 0%, total WER: 19.1901%, total TER: 8.52741%, progress (thread 0): 92.0807%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_1389.87_1390.16, WER: 0%, TER: 0%, total WER: 19.1899%, total TER: 8.52733%, progress (thread 0): 92.0886%]
|T|: m m h m m
|P|: y e a m
[sample: IS1009d_H02_FIO084_1790.06_1790.35, WER: 100%, TER: 80%, total WER: 19.1908%, total TER: 8.52817%, progress (thread 0): 92.0965%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_704.99_705.28, WER: 0%, TER: 0%, total WER: 19.1906%, total TER: 8.52809%, progress (thread 0): 92.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_977.31_977.6, WER: 0%, TER: 0%, total WER: 19.1904%, total TER: 8.52801%, progress (thread 0): 92.1123%]
|T|: y o u
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1267.17_1267.46, WER: 100%, TER: 100%, total WER: 19.1913%, total TER: 8.52865%, progress (thread 0): 92.1203%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_178.78_179.07, WER: 0%, TER: 0%, total WER: 19.1911%, total TER: 8.52859%, progress (thread 0): 92.1282%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1577.63_1577.92, WER: 0%, TER: 0%, total WER: 19.1908%, total TER: 8.52851%, progress (thread 0): 92.1361%]
|T|: w e l l
|P|: w e l l
[sample: TS3003b_H03_MTD012ME_1752.79_1753.08, WER: 0%, TER: 0%, total WER: 19.1906%, total TER: 8.52843%, progress (thread 0): 92.144%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_2033.23_2033.52, WER: 0%, TER: 0%, total WER: 19.1904%, total TER: 8.52835%, progress (thread 0): 92.1519%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2083.17_2083.46, WER: 0%, TER: 0%, total WER: 19.1902%, total TER: 8.52827%, progress (thread 0): 92.1598%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_2083.37_2083.66, WER: 0%, TER: 0%, total WER: 19.19%, total TER: 8.52819%, progress (thread 0): 92.1677%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H01_MTD011UID_1289.74_1290.03, WER: 100%, TER: 0%, total WER: 19.1909%, total TER: 8.52809%, progress (thread 0): 92.1756%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1452.67_1452.96, WER: 0%, TER: 0%, total WER: 19.1907%, total TER: 8.52801%, progress (thread 0): 92.1835%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003d_H03_MTD012ME_695.88_696.17, WER: 100%, TER: 20%, total WER: 19.1916%, total TER: 8.52815%, progress (thread 0): 92.1915%]
|T|: n a y
|P|: n m h
[sample: TS3003d_H01_MTD011UID_835.49_835.78, WER: 100%, TER: 66.6667%, total WER: 19.1925%, total TER: 8.52856%, progress (thread 0): 92.1994%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1010.19_1010.48, WER: 100%, TER: 33.3333%, total WER: 19.1934%, total TER: 8.52873%, progress (thread 0): 92.2073%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H02_MTD0010ID_1075.11_1075.4, WER: 100%, TER: 75%, total WER: 19.1943%, total TER: 8.52935%, progress (thread 0): 92.2152%]
|T|: t r u e
|P|: t r u e
[sample: TS3003d_H03_MTD012ME_1129.3_1129.59, WER: 0%, TER: 0%, total WER: 19.1941%, total TER: 8.52927%, progress (thread 0): 92.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1862.62_1862.91, WER: 0%, TER: 0%, total WER: 19.1939%, total TER: 8.52919%, progress (thread 0): 92.231%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2370.37_2370.66, WER: 0%, TER: 0%, total WER: 19.1937%, total TER: 8.52915%, progress (thread 0): 92.2389%]
|T|: h m m
|P|: m m
[sample: TS3003d_H00_MTD009PM_2517.23_2517.52, WER: 100%, TER: 33.3333%, total WER: 19.1946%, total TER: 8.52933%, progress (thread 0): 92.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2571.69_2571.98, WER: 0%, TER: 0%, total WER: 19.1944%, total TER: 8.52925%, progress (thread 0): 92.2547%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_218.41_218.7, WER: 100%, TER: 33.3333%, total WER: 19.1953%, total TER: 8.52942%, progress (thread 0): 92.2627%]
|T|: i | d o n ' t | k n o w
|P|: i | d o n t
[sample: EN2002a_H00_MEE073_234.63_234.92, WER: 66.6667%, TER: 50%, total WER: 19.1969%, total TER: 8.53059%, progress (thread 0): 92.2706%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_526.91_527.2, WER: 100%, TER: 66.6667%, total WER: 19.1978%, total TER: 8.531%, progress (thread 0): 92.2785%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_646.99_647.28, WER: 0%, TER: 0%, total WER: 19.1976%, total TER: 8.53092%, progress (thread 0): 92.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_723.91_724.2, WER: 0%, TER: 0%, total WER: 19.1974%, total TER: 8.53084%, progress (thread 0): 92.2943%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_749.38_749.67, WER: 0%, TER: 0%, total WER: 19.1971%, total TER: 8.53076%, progress (thread 0): 92.3022%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_856.03_856.32, WER: 0%, TER: 0%, total WER: 19.1969%, total TER: 8.53068%, progress (thread 0): 92.3101%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_872.78_873.07, WER: 0%, TER: 0%, total WER: 19.1967%, total TER: 8.5306%, progress (thread 0): 92.318%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1076.05_1076.34, WER: 100%, TER: 33.3333%, total WER: 19.1976%, total TER: 8.53077%, progress (thread 0): 92.326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1255.44_1255.73, WER: 0%, TER: 0%, total WER: 19.1974%, total TER: 8.53069%, progress (thread 0): 92.3339%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1364.93_1365.22, WER: 0%, TER: 0%, total WER: 19.1972%, total TER: 8.53061%, progress (thread 0): 92.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1593.85_1594.14, WER: 0%, TER: 0%, total WER: 19.197%, total TER: 8.53053%, progress (thread 0): 92.3497%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1595.68_1595.97, WER: 0%, TER: 0%, total WER: 19.1968%, total TER: 8.53045%, progress (thread 0): 92.3576%]
|T|: m m h m m
|P|: m m
[sample: EN2002a_H02_FEO072_1620.66_1620.95, WER: 100%, TER: 60%, total WER: 19.1977%, total TER: 8.53105%, progress (thread 0): 92.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1960.6_1960.89, WER: 0%, TER: 0%, total WER: 19.1975%, total TER: 8.53097%, progress (thread 0): 92.3734%]
|T|: y e a h
|P|: o m
[sample: EN2002a_H00_MEE073_2127.38_2127.67, WER: 100%, TER: 100%, total WER: 19.1984%, total TER: 8.53183%, progress (thread 0): 92.3813%]
|T|: v e r y | g o o d
|P|: m o n
[sample: EN2002b_H02_FEO072_142.65_142.94, WER: 100%, TER: 88.8889%, total WER: 19.2002%, total TER: 8.53352%, progress (thread 0): 92.3892%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_382.41_382.7, WER: 0%, TER: 0%, total WER: 19.2%, total TER: 8.53344%, progress (thread 0): 92.3972%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_419.68_419.97, WER: 0%, TER: 0%, total WER: 19.1998%, total TER: 8.53336%, progress (thread 0): 92.4051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_734.04_734.33, WER: 0%, TER: 0%, total WER: 19.1995%, total TER: 8.53328%, progress (thread 0): 92.413%]
|T|: h m m
|P|: n o
[sample: EN2002b_H03_MEE073_1150.51_1150.8, WER: 100%, TER: 100%, total WER: 19.2005%, total TER: 8.53393%, progress (thread 0): 92.4209%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H03_MEE073_1286.29_1286.58, WER: 100%, TER: 0%, total WER: 19.2014%, total TER: 8.53383%, progress (thread 0): 92.4288%]
|T|: s o
|P|: s o
[sample: EN2002b_H03_MEE073_1517.43_1517.72, WER: 0%, TER: 0%, total WER: 19.2011%, total TER: 8.53379%, progress (thread 0): 92.4367%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_61.76_62.05, WER: 0%, TER: 0%, total WER: 19.2009%, total TER: 8.53371%, progress (thread 0): 92.4446%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_697.18_697.47, WER: 0%, TER: 0%, total WER: 19.2007%, total TER: 8.53367%, progress (thread 0): 92.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1026.29_1026.58, WER: 0%, TER: 0%, total WER: 19.2005%, total TER: 8.53359%, progress (thread 0): 92.4604%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_1088_1088.29, WER: 100%, TER: 0%, total WER: 19.2014%, total TER: 8.53349%, progress (thread 0): 92.4684%]
|T|: t h a t
|P|: o k y
[sample: EN2002c_H03_MEE073_1302.84_1303.13, WER: 100%, TER: 100%, total WER: 19.2023%, total TER: 8.53434%, progress (thread 0): 92.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1313.99_1314.28, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53426%, progress (thread 0): 92.4842%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_1543.66_1543.95, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53422%, progress (thread 0): 92.4921%]
|T|: ' c a u s e
|P|: t h e s
[sample: EN2002c_H03_MEE073_2609.79_2610.08, WER: 100%, TER: 83.3333%, total WER: 19.2028%, total TER: 8.53527%, progress (thread 0): 92.5%]
|T|: a c t u a l l y
|P|: a c t u a l l y
[sample: EN2002d_H03_MEE073_783.19_783.48, WER: 0%, TER: 0%, total WER: 19.2026%, total TER: 8.53512%, progress (thread 0): 92.5079%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H02_MEE071_833.43_833.72, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53502%, progress (thread 0): 92.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1031.38_1031.67, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53494%, progress (thread 0): 92.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1490.73_1491.02, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53486%, progress (thread 0): 92.5316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2069.6_2069.89, WER: 0%, TER: 0%, total WER: 19.2017%, total TER: 8.53478%, progress (thread 0): 92.5396%]
|T|: n o
|P|: n m m
[sample: ES2004b_H03_FEE016_602.63_602.91, WER: 100%, TER: 100%, total WER: 19.2026%, total TER: 8.5352%, progress (thread 0): 92.5475%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1146.25_1146.53, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53512%, progress (thread 0): 92.5554%]
|T|: m m
|P|: m m
[sample: ES2004b_H03_FEE016_1316.47_1316.75, WER: 0%, TER: 0%, total WER: 19.2022%, total TER: 8.53508%, progress (thread 0): 92.5633%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1939.86_1940.14, WER: 0%, TER: 0%, total WER: 19.202%, total TER: 8.53498%, progress (thread 0): 92.5712%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2216.73_2217.01, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.5349%, progress (thread 0): 92.5791%]
|T|: t h a n k s
|P|: m
[sample: ES2004c_H01_FEE013_54.47_54.75, WER: 100%, TER: 100%, total WER: 19.2027%, total TER: 8.53619%, progress (thread 0): 92.587%]
|T|: u h
|P|: u h
[sample: ES2004c_H02_MEE014_1126.8_1127.08, WER: 0%, TER: 0%, total WER: 19.2025%, total TER: 8.53615%, progress (thread 0): 92.5949%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1180.98_1181.26, WER: 0%, TER: 0%, total WER: 19.2022%, total TER: 8.53611%, progress (thread 0): 92.6029%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1398.69_1398.97, WER: 0%, TER: 0%, total WER: 19.202%, total TER: 8.53603%, progress (thread 0): 92.6108%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2123.64_2123.92, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.53595%, progress (thread 0): 92.6187%]
|T|: n o
|P|: n o
[sample: ES2004d_H02_MEE014_1050.44_1050.72, WER: 0%, TER: 0%, total WER: 19.2016%, total TER: 8.53591%, progress (thread 0): 92.6266%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H00_MEO015_1500.31_1500.59, WER: 100%, TER: 0%, total WER: 19.2025%, total TER: 8.53581%, progress (thread 0): 92.6345%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1843.24_1843.52, WER: 0%, TER: 0%, total WER: 19.2023%, total TER: 8.53573%, progress (thread 0): 92.6424%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_2015.69_2015.97, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53563%, progress (thread 0): 92.6503%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H01_FEE013_2120.6_2120.88, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.53555%, progress (thread 0): 92.6582%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_188.06_188.34, WER: 0%, TER: 0%, total WER: 19.2016%, total TER: 8.53547%, progress (thread 0): 92.6661%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_793.23_793.51, WER: 100%, TER: 0%, total WER: 19.2025%, total TER: 8.53537%, progress (thread 0): 92.674%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_804.79_805.07, WER: 0%, TER: 0%, total WER: 19.2023%, total TER: 8.53529%, progress (thread 0): 92.682%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_894.05_894.33, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53521%, progress (thread 0): 92.6899%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1764.17_1764.45, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53513%, progress (thread 0): 92.6978%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009c_H02_FIO084_724.39_724.67, WER: 100%, TER: 100%, total WER: 19.2028%, total TER: 8.5362%, progress (thread 0): 92.7057%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1140.8_1141.08, WER: 0%, TER: 0%, total WER: 19.2026%, total TER: 8.53614%, progress (thread 0): 92.7136%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1619.41_1619.69, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53606%, progress (thread 0): 92.7215%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_617.29_617.57, WER: 100%, TER: 0%, total WER: 19.2033%, total TER: 8.53596%, progress (thread 0): 92.7294%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_773.28_773.56, WER: 0%, TER: 0%, total WER: 19.2031%, total TER: 8.53586%, progress (thread 0): 92.7373%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_980.1_980.38, WER: 0%, TER: 0%, total WER: 19.2028%, total TER: 8.53578%, progress (thread 0): 92.7452%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1792.74_1793.02, WER: 100%, TER: 20%, total WER: 19.2038%, total TER: 8.53591%, progress (thread 0): 92.7532%]
|T|: h m m
|P|: m m
[sample: IS1009d_H02_FIO084_1830.44_1830.72, WER: 100%, TER: 33.3333%, total WER: 19.2047%, total TER: 8.53609%, progress (thread 0): 92.7611%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1876.62_1876.9, WER: 100%, TER: 20%, total WER: 19.2056%, total TER: 8.53622%, progress (thread 0): 92.769%]
|T|: m o r n i n g
|P|: o
[sample: TS3003a_H02_MTD0010ID_16.42_16.7, WER: 100%, TER: 85.7143%, total WER: 19.2065%, total TER: 8.53749%, progress (thread 0): 92.7769%]
|T|: s u r e
|P|: s u r e
[sample: TS3003a_H03_MTD012ME_163.72_164, WER: 0%, TER: 0%, total WER: 19.2063%, total TER: 8.53741%, progress (thread 0): 92.7848%]
|T|: h m m
|P|: h m m
[sample: TS3003a_H03_MTD012ME_661.22_661.5, WER: 0%, TER: 0%, total WER: 19.2061%, total TER: 8.53735%, progress (thread 0): 92.7927%]
|T|: h m m
|P|: m m
[sample: TS3003a_H00_MTD009PM_1235.68_1235.96, WER: 100%, TER: 33.3333%, total WER: 19.207%, total TER: 8.53752%, progress (thread 0): 92.8006%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_823.58_823.86, WER: 0%, TER: 0%, total WER: 19.2068%, total TER: 8.53744%, progress (thread 0): 92.8085%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1892.2_1892.48, WER: 0%, TER: 0%, total WER: 19.2065%, total TER: 8.53736%, progress (thread 0): 92.8165%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1860.06_1860.34, WER: 0%, TER: 0%, total WER: 19.2063%, total TER: 8.53728%, progress (thread 0): 92.8244%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_1891.82_1892.1, WER: 100%, TER: 0%, total WER: 19.2072%, total TER: 8.53718%, progress (thread 0): 92.8323%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_267.26_267.54, WER: 0%, TER: 0%, total WER: 19.207%, total TER: 8.5371%, progress (thread 0): 92.8402%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_365.09_365.37, WER: 0%, TER: 0%, total WER: 19.2068%, total TER: 8.53702%, progress (thread 0): 92.8481%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_816.6_816.88, WER: 100%, TER: 66.6667%, total WER: 19.2077%, total TER: 8.53743%, progress (thread 0): 92.856%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_860.45_860.73, WER: 0%, TER: 0%, total WER: 19.2075%, total TER: 8.53735%, progress (thread 0): 92.8639%]
|T|: a n d | i t
|P|: a n d | i t
[sample: TS3003d_H02_MTD0010ID_1310.28_1310.56, WER: 0%, TER: 0%, total WER: 19.2071%, total TER: 8.53723%, progress (thread 0): 92.8718%]
|T|: s h
|P|: s
[sample: TS3003d_H02_MTD0010ID_1311.62_1311.9, WER: 100%, TER: 50%, total WER: 19.208%, total TER: 8.53742%, progress (thread 0): 92.8797%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1403.4_1403.68, WER: 100%, TER: 66.6667%, total WER: 19.2089%, total TER: 8.53783%, progress (thread 0): 92.8877%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1945.66_1945.94, WER: 0%, TER: 0%, total WER: 19.2087%, total TER: 8.53775%, progress (thread 0): 92.8956%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2142.04_2142.32, WER: 0%, TER: 0%, total WER: 19.2084%, total TER: 8.53767%, progress (thread 0): 92.9035%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_49.78_50.06, WER: 0%, TER: 0%, total WER: 19.2082%, total TER: 8.53759%, progress (thread 0): 92.9114%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_478.66_478.94, WER: 100%, TER: 66.6667%, total WER: 19.2091%, total TER: 8.538%, progress (thread 0): 92.9193%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_758.4_758.68, WER: 0%, TER: 0%, total WER: 19.2089%, total TER: 8.53792%, progress (thread 0): 92.9272%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H01_FEO070_762.35_762.63, WER: 100%, TER: 100%, total WER: 19.2098%, total TER: 8.53878%, progress (thread 0): 92.9351%]
|T|: b u t
|P|: b u t
[sample: EN2002a_H01_FEO070_1024.58_1024.86, WER: 0%, TER: 0%, total WER: 19.2096%, total TER: 8.53872%, progress (thread 0): 92.943%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1306.05_1306.33, WER: 0%, TER: 0%, total WER: 19.2094%, total TER: 8.53864%, progress (thread 0): 92.951%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1392.09_1392.37, WER: 0%, TER: 0%, total WER: 19.2092%, total TER: 8.53856%, progress (thread 0): 92.9589%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1638.28_1638.56, WER: 0%, TER: 0%, total WER: 19.209%, total TER: 8.53848%, progress (thread 0): 92.9668%]
|T|: s e e
|P|: s e e
[sample: EN2002a_H01_FEO070_1702.66_1702.94, WER: 0%, TER: 0%, total WER: 19.2088%, total TER: 8.53842%, progress (thread 0): 92.9747%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1796.46_1796.74, WER: 0%, TER: 0%, total WER: 19.2085%, total TER: 8.53834%, progress (thread 0): 92.9826%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1831.52_1831.8, WER: 0%, TER: 0%, total WER: 19.2083%, total TER: 8.53826%, progress (thread 0): 92.9905%]
|T|: o h
|P|: a h
[sample: EN2002a_H02_FEO072_1981.37_1981.65, WER: 100%, TER: 50%, total WER: 19.2092%, total TER: 8.53845%, progress (thread 0): 92.9984%]
|T|: i | t h
|P|: i
[sample: EN2002a_H00_MEE073_2026.22_2026.5, WER: 50%, TER: 75%, total WER: 19.2099%, total TER: 8.53907%, progress (thread 0): 93.0063%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H01_MEE071_16.64_16.92, WER: 0%, TER: 0%, total WER: 19.2097%, total TER: 8.53899%, progress (thread 0): 93.0142%]
|T|: m m h m m
|P|: m m
[sample: EN2002b_H03_MEE073_335.63_335.91, WER: 100%, TER: 60%, total WER: 19.2106%, total TER: 8.5396%, progress (thread 0): 93.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_356.11_356.39, WER: 0%, TER: 0%, total WER: 19.2104%, total TER: 8.53952%, progress (thread 0): 93.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_987.48_987.76, WER: 0%, TER: 0%, total WER: 19.2102%, total TER: 8.53944%, progress (thread 0): 93.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1285.46_1285.74, WER: 0%, TER: 0%, total WER: 19.21%, total TER: 8.53936%, progress (thread 0): 93.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1450.71_1450.99, WER: 0%, TER: 0%, total WER: 19.2098%, total TER: 8.53928%, progress (thread 0): 93.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1588.08_1588.36, WER: 0%, TER: 0%, total WER: 19.2095%, total TER: 8.5392%, progress (thread 0): 93.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_264.61_264.89, WER: 0%, TER: 0%, total WER: 19.2093%, total TER: 8.53912%, progress (thread 0): 93.0696%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_638.33_638.61, WER: 100%, TER: 0%, total WER: 19.2102%, total TER: 8.53902%, progress (thread 0): 93.0775%]
|T|: a h
|P|: o h
[sample: EN2002c_H01_FEO072_1466.83_1467.11, WER: 100%, TER: 50%, total WER: 19.2111%, total TER: 8.53921%, progress (thread 0): 93.0854%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1508.55_1508.83, WER: 100%, TER: 28.5714%, total WER: 19.2121%, total TER: 8.53954%, progress (thread 0): 93.0934%]
|T|: i | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_1627.18_1627.46, WER: 50%, TER: 50%, total WER: 19.2127%, total TER: 8.54012%, progress (thread 0): 93.1013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2613.98_2614.26, WER: 0%, TER: 0%, total WER: 19.2125%, total TER: 8.54004%, progress (thread 0): 93.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2738.83_2739.11, WER: 0%, TER: 0%, total WER: 19.2123%, total TER: 8.53996%, progress (thread 0): 93.1171%]
|T|: w e l l
|P|: m m
[sample: EN2002c_H03_MEE073_2837.32_2837.6, WER: 100%, TER: 100%, total WER: 19.2132%, total TER: 8.54082%, progress (thread 0): 93.125%]
|T|: i | t h
|P|: i
[sample: EN2002d_H03_MEE073_209.34_209.62, WER: 50%, TER: 75%, total WER: 19.2139%, total TER: 8.54144%, progress (thread 0): 93.1329%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H03_MEE073_379.93_380.21, WER: 100%, TER: 0%, total WER: 19.2148%, total TER: 8.54134%, progress (thread 0): 93.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_446.25_446.53, WER: 0%, TER: 0%, total WER: 19.2146%, total TER: 8.54126%, progress (thread 0): 93.1487%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_622.33_622.61, WER: 0%, TER: 0%, total WER: 19.2144%, total TER: 8.54118%, progress (thread 0): 93.1566%]
|T|: y e a h
|P|: y m a m
[sample: EN2002d_H03_MEE073_989.71_989.99, WER: 100%, TER: 50%, total WER: 19.2153%, total TER: 8.54157%, progress (thread 0): 93.1646%]
|T|: h m m
|P|: m m
[sample: EN2002d_H02_MEE071_1294.26_1294.54, WER: 100%, TER: 33.3333%, total WER: 19.2162%, total TER: 8.54174%, progress (thread 0): 93.1725%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1744.58_1744.86, WER: 0%, TER: 0%, total WER: 19.216%, total TER: 8.54166%, progress (thread 0): 93.1804%]
|T|: t h e
|P|: y m a h
[sample: EN2002d_H03_MEE073_1888.86_1889.14, WER: 100%, TER: 133.333%, total WER: 19.2169%, total TER: 8.54254%, progress (thread 0): 93.1883%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2067.26_2067.54, WER: 0%, TER: 0%, total WER: 19.2167%, total TER: 8.54246%, progress (thread 0): 93.1962%]
|T|: s u r e
|P|: s r u e
[sample: ES2004b_H00_MEO015_1306.29_1306.56, WER: 100%, TER: 50%, total WER: 19.2176%, total TER: 8.54284%, progress (thread 0): 93.2041%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1834.89_1835.16, WER: 0%, TER: 0%, total WER: 19.2174%, total TER: 8.54276%, progress (thread 0): 93.212%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1954.73_1955, WER: 100%, TER: 0%, total WER: 19.2183%, total TER: 8.54266%, progress (thread 0): 93.2199%]
|T|: m m h m m
|P|: m m m
[sample: ES2004c_H03_FEE016_652.96_653.23, WER: 100%, TER: 40%, total WER: 19.2192%, total TER: 8.54303%, progress (thread 0): 93.2278%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004c_H00_MEO015_1141.18_1141.45, WER: 100%, TER: 20%, total WER: 19.2201%, total TER: 8.54317%, progress (thread 0): 93.2358%]
|T|: m m
|P|: s o
[sample: ES2004c_H03_FEE016_1281.44_1281.71, WER: 100%, TER: 100%, total WER: 19.221%, total TER: 8.54359%, progress (thread 0): 93.2437%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1403.89_1404.16, WER: 0%, TER: 0%, total WER: 19.2208%, total TER: 8.54351%, progress (thread 0): 93.2516%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1730.76_1731.03, WER: 0%, TER: 0%, total WER: 19.2206%, total TER: 8.54343%, progress (thread 0): 93.2595%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H00_MEO015_1835.22_1835.49, WER: 100%, TER: 0%, total WER: 19.2215%, total TER: 8.54333%, progress (thread 0): 93.2674%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1921.81_1922.08, WER: 0%, TER: 0%, total WER: 19.2213%, total TER: 8.54325%, progress (thread 0): 93.2753%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_2149.62_2149.89, WER: 0%, TER: 0%, total WER: 19.2211%, total TER: 8.54317%, progress (thread 0): 93.2832%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_654.95_655.22, WER: 100%, TER: 20%, total WER: 19.222%, total TER: 8.54331%, progress (thread 0): 93.2911%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_738.4_738.67, WER: 100%, TER: 33.3333%, total WER: 19.2229%, total TER: 8.54348%, progress (thread 0): 93.299%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_793.57_793.84, WER: 0%, TER: 0%, total WER: 19.2227%, total TER: 8.5434%, progress (thread 0): 93.307%]
|T|: n o
|P|: n o
[sample: ES2004d_H03_FEE016_1418.58_1418.85, WER: 0%, TER: 0%, total WER: 19.2225%, total TER: 8.54336%, progress (thread 0): 93.3149%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1497.73_1498, WER: 0%, TER: 0%, total WER: 19.2222%, total TER: 8.54328%, progress (thread 0): 93.3228%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1559.56_1559.83, WER: 0%, TER: 0%, total WER: 19.222%, total TER: 8.5432%, progress (thread 0): 93.3307%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2099.96_2100.23, WER: 0%, TER: 0%, total WER: 19.2218%, total TER: 8.54312%, progress (thread 0): 93.3386%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_159.51_159.78, WER: 100%, TER: 0%, total WER: 19.2227%, total TER: 8.54302%, progress (thread 0): 93.3465%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_495.24_495.51, WER: 0%, TER: 0%, total WER: 19.2225%, total TER: 8.54294%, progress (thread 0): 93.3544%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H02_FIO084_130.02_130.29, WER: 100%, TER: 20%, total WER: 19.2234%, total TER: 8.54308%, progress (thread 0): 93.3623%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_195.35_195.62, WER: 100%, TER: 60%, total WER: 19.2243%, total TER: 8.54368%, progress (thread 0): 93.3703%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H00_FIE088_1093_1093.27, WER: 100%, TER: 20%, total WER: 19.2252%, total TER: 8.54381%, progress (thread 0): 93.3782%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H01_FIO087_323.83_324.1, WER: 0%, TER: 0%, total WER: 19.225%, total TER: 8.54371%, progress (thread 0): 93.3861%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_770.63_770.9, WER: 100%, TER: 0%, total WER: 19.2259%, total TER: 8.54361%, progress (thread 0): 93.394%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_922.16_922.43, WER: 100%, TER: 20%, total WER: 19.2268%, total TER: 8.54375%, progress (thread 0): 93.4019%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_1095.56_1095.83, WER: 100%, TER: 20%, total WER: 19.2277%, total TER: 8.54388%, progress (thread 0): 93.4098%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1374.42_1374.69, WER: 0%, TER: 0%, total WER: 19.2275%, total TER: 8.5438%, progress (thread 0): 93.4177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_215.46_215.73, WER: 0%, TER: 0%, total WER: 19.2273%, total TER: 8.54372%, progress (thread 0): 93.4256%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_377.8_378.07, WER: 100%, TER: 0%, total WER: 19.2282%, total TER: 8.54362%, progress (thread 0): 93.4335%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_381.16_381.43, WER: 100%, TER: 0%, total WER: 19.2291%, total TER: 8.54352%, progress (thread 0): 93.4415%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H02_FIO084_938.34_938.61, WER: 100%, TER: 20%, total WER: 19.23%, total TER: 8.54365%, progress (thread 0): 93.4494%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1114.59_1114.86, WER: 100%, TER: 20%, total WER: 19.2309%, total TER: 8.54379%, progress (thread 0): 93.4573%]
|T|: o h
|P|: o h
[sample: IS1009d_H00_FIE088_1436.69_1436.96, WER: 0%, TER: 0%, total WER: 19.2307%, total TER: 8.54375%, progress (thread 0): 93.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1775.25_1775.52, WER: 0%, TER: 0%, total WER: 19.2305%, total TER: 8.54367%, progress (thread 0): 93.4731%]
|T|: o k a y
|P|: o h
[sample: IS1009d_H01_FIO087_1908.84_1909.11, WER: 100%, TER: 75%, total WER: 19.2314%, total TER: 8.54429%, progress (thread 0): 93.481%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_684.57_684.84, WER: 100%, TER: 150%, total WER: 19.2323%, total TER: 8.54495%, progress (thread 0): 93.4889%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_790.17_790.44, WER: 100%, TER: 25%, total WER: 19.2332%, total TER: 8.54511%, progress (thread 0): 93.4968%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_1326.95_1327.22, WER: 0%, TER: 0%, total WER: 19.233%, total TER: 8.54507%, progress (thread 0): 93.5047%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1909.01_1909.28, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54499%, progress (thread 0): 93.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1979.53_1979.8, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54491%, progress (thread 0): 93.5206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2145.84_2146.11, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54483%, progress (thread 0): 93.5285%]
|T|: o h
|P|: m m
[sample: TS3003d_H01_MTD011UID_364.71_364.98, WER: 100%, TER: 100%, total WER: 19.2333%, total TER: 8.54525%, progress (thread 0): 93.5364%]
|T|: t e n
|P|: t e n
[sample: TS3003d_H02_MTD0010ID_864.45_864.72, WER: 0%, TER: 0%, total WER: 19.2331%, total TER: 8.54519%, progress (thread 0): 93.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_958.91_959.18, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54511%, progress (thread 0): 93.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1378.52_1378.79, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54503%, progress (thread 0): 93.5601%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_2176.17_2176.44, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54499%, progress (thread 0): 93.568%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2345.04_2345.31, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54495%, progress (thread 0): 93.576%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2364.31_2364.58, WER: 0%, TER: 0%, total WER: 19.232%, total TER: 8.54487%, progress (thread 0): 93.5839%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_2435.3_2435.57, WER: 100%, TER: 25%, total WER: 19.2329%, total TER: 8.54503%, progress (thread 0): 93.5918%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_501.72_501.99, WER: 100%, TER: 33.3333%, total WER: 19.2338%, total TER: 8.5452%, progress (thread 0): 93.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_727.19_727.46, WER: 0%, TER: 0%, total WER: 19.2336%, total TER: 8.54512%, progress (thread 0): 93.6076%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_806.36_806.63, WER: 0%, TER: 0%, total WER: 19.2334%, total TER: 8.54504%, progress (thread 0): 93.6155%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_927.86_928.13, WER: 0%, TER: 0%, total WER: 19.2332%, total TER: 8.54496%, progress (thread 0): 93.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1223.91_1224.18, WER: 0%, TER: 0%, total WER: 19.2329%, total TER: 8.54488%, progress (thread 0): 93.6313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1319.85_1320.12, WER: 0%, TER: 0%, total WER: 19.2327%, total TER: 8.5448%, progress (thread 0): 93.6392%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1452_1452.27, WER: 0%, TER: 0%, total WER: 19.2325%, total TER: 8.54472%, progress (thread 0): 93.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1516.15_1516.42, WER: 0%, TER: 0%, total WER: 19.2323%, total TER: 8.54464%, progress (thread 0): 93.6551%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1533.19_1533.46, WER: 0%, TER: 0%, total WER: 19.2321%, total TER: 8.54456%, progress (thread 0): 93.663%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1556.33_1556.6, WER: 0%, TER: 0%, total WER: 19.2319%, total TER: 8.54448%, progress (thread 0): 93.6709%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1945.11_1945.38, WER: 0%, TER: 0%, total WER: 19.2316%, total TER: 8.5444%, progress (thread 0): 93.6788%]
|T|: o h
|P|: o h
[sample: EN2002a_H00_MEE073_1989_1989.27, WER: 0%, TER: 0%, total WER: 19.2314%, total TER: 8.54436%, progress (thread 0): 93.6867%]
|T|: o h
|P|: u m
[sample: EN2002a_H00_MEE073_2003.83_2004.1, WER: 100%, TER: 100%, total WER: 19.2323%, total TER: 8.54479%, progress (thread 0): 93.6946%]
|T|: n o
|P|: n
[sample: EN2002a_H01_FEO070_2105.6_2105.87, WER: 100%, TER: 50%, total WER: 19.2332%, total TER: 8.54498%, progress (thread 0): 93.7025%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_355.59_355.86, WER: 0%, TER: 0%, total WER: 19.233%, total TER: 8.54494%, progress (thread 0): 93.7104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_485.61_485.88, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54486%, progress (thread 0): 93.7184%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1027.72_1027.99, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54476%, progress (thread 0): 93.7263%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_1296.06_1296.33, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54472%, progress (thread 0): 93.7342%]
|T|: s o
|P|: s o
[sample: EN2002b_H01_MEE071_1333.58_1333.85, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54468%, progress (thread 0): 93.7421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1339.47_1339.74, WER: 0%, TER: 0%, total WER: 19.2319%, total TER: 8.5446%, progress (thread 0): 93.75%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1448.9_1449.17, WER: 0%, TER: 0%, total WER: 19.2317%, total TER: 8.54452%, progress (thread 0): 93.7579%]
|T|: a l r i g h t
|P|: a l r i g h t
[sample: EN2002c_H03_MEE073_558.45_558.72, WER: 0%, TER: 0%, total WER: 19.2315%, total TER: 8.54439%, progress (thread 0): 93.7658%]
|T|: h m m
|P|: m m
[sample: EN2002c_H01_FEO072_655.42_655.69, WER: 100%, TER: 33.3333%, total WER: 19.2324%, total TER: 8.54456%, progress (thread 0): 93.7737%]
|T|: c o o l
|P|: c o o l
[sample: EN2002c_H01_FEO072_778.13_778.4, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54448%, progress (thread 0): 93.7816%]
|T|: o r
|P|: o h
[sample: EN2002c_H01_FEO072_798.93_799.2, WER: 100%, TER: 50%, total WER: 19.2331%, total TER: 8.54467%, progress (thread 0): 93.7896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_938.98_939.25, WER: 0%, TER: 0%, total WER: 19.2329%, total TER: 8.54459%, progress (thread 0): 93.7975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1122.75_1123.02, WER: 0%, TER: 0%, total WER: 19.2327%, total TER: 8.54451%, progress (thread 0): 93.8054%]
|T|: o k a y
|P|: m h m m
[sample: EN2002c_H02_MEE071_1353.05_1353.32, WER: 100%, TER: 100%, total WER: 19.2336%, total TER: 8.54537%, progress (thread 0): 93.8133%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1574.37_1574.64, WER: 0%, TER: 0%, total WER: 19.2334%, total TER: 8.54527%, progress (thread 0): 93.8212%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_1672.35_1672.62, WER: 100%, TER: 25%, total WER: 19.2343%, total TER: 8.54542%, progress (thread 0): 93.8291%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1740.26_1740.53, WER: 0%, TER: 0%, total WER: 19.2341%, total TER: 8.54534%, progress (thread 0): 93.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1980.58_1980.85, WER: 0%, TER: 0%, total WER: 19.2338%, total TER: 8.54526%, progress (thread 0): 93.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2155.14_2155.41, WER: 0%, TER: 0%, total WER: 19.2336%, total TER: 8.54518%, progress (thread 0): 93.8528%]
|T|: a l r i g h t
|P|: o h r i
[sample: EN2002c_H03_MEE073_2202.51_2202.78, WER: 100%, TER: 71.4286%, total WER: 19.2345%, total TER: 8.54621%, progress (thread 0): 93.8608%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2272.51_2272.78, WER: 0%, TER: 0%, total WER: 19.2343%, total TER: 8.54613%, progress (thread 0): 93.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2294.4_2294.67, WER: 0%, TER: 0%, total WER: 19.2341%, total TER: 8.54605%, progress (thread 0): 93.8766%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2396.65_2396.92, WER: 0%, TER: 0%, total WER: 19.2339%, total TER: 8.54597%, progress (thread 0): 93.8845%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2568.46_2568.73, WER: 0%, TER: 0%, total WER: 19.2337%, total TER: 8.54589%, progress (thread 0): 93.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2705.6_2705.87, WER: 0%, TER: 0%, total WER: 19.2335%, total TER: 8.54581%, progress (thread 0): 93.9003%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_2790.29_2790.56, WER: 0%, TER: 0%, total WER: 19.2332%, total TER: 8.54577%, progress (thread 0): 93.9082%]
|T|: i | k n o w
|P|: y o u | k n o w
[sample: EN2002d_H03_MEE073_903.75_904.02, WER: 50%, TER: 50%, total WER: 19.2339%, total TER: 8.54635%, progress (thread 0): 93.9161%]
|T|: b u t
|P|: w h l
[sample: EN2002d_H01_FEO072_946.69_946.96, WER: 100%, TER: 100%, total WER: 19.2348%, total TER: 8.54699%, progress (thread 0): 93.924%]
|T|: s o r r y
|P|: s o r r y
[sample: EN2002d_H00_FEO070_966.35_966.62, WER: 0%, TER: 0%, total WER: 19.2346%, total TER: 8.54689%, progress (thread 0): 93.932%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1127.55_1127.82, WER: 0%, TER: 0%, total WER: 19.2344%, total TER: 8.54681%, progress (thread 0): 93.9399%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1281.17_1281.44, WER: 0%, TER: 0%, total WER: 19.2342%, total TER: 8.54673%, progress (thread 0): 93.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1305.04_1305.31, WER: 0%, TER: 0%, total WER: 19.234%, total TER: 8.54665%, progress (thread 0): 93.9557%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002d_H00_FEO070_1507.8_1508.07, WER: 100%, TER: 100%, total WER: 19.2358%, total TER: 8.54815%, progress (thread 0): 93.9636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_1832.82_1833.09, WER: 0%, TER: 0%, total WER: 19.2356%, total TER: 8.54807%, progress (thread 0): 93.9715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_2000.66_2000.93, WER: 0%, TER: 0%, total WER: 19.2354%, total TER: 8.54799%, progress (thread 0): 93.9794%]
|T|: w h a t
|P|: w e a l
[sample: EN2002d_H02_MEE071_2072.88_2073.15, WER: 100%, TER: 50%, total WER: 19.2363%, total TER: 8.54838%, progress (thread 0): 93.9873%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_2169.42_2169.69, WER: 100%, TER: 66.6667%, total WER: 19.2372%, total TER: 8.54879%, progress (thread 0): 93.9953%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_17.88_18.14, WER: 0%, TER: 0%, total WER: 19.237%, total TER: 8.54871%, progress (thread 0): 94.0032%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_966.22_966.48, WER: 0%, TER: 0%, total WER: 19.2367%, total TER: 8.54861%, progress (thread 0): 94.0111%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_89.12_89.38, WER: 100%, TER: 20%, total WER: 19.2376%, total TER: 8.54874%, progress (thread 0): 94.019%]
|T|: t h e r e | w e | g o
|P|: o k a y
[sample: ES2004b_H02_MEE014_830.72_830.98, WER: 100%, TER: 100%, total WER: 19.2404%, total TER: 8.55109%, progress (thread 0): 94.0269%]
|T|: h u h
|P|: m m
[sample: ES2004b_H02_MEE014_1039.01_1039.27, WER: 100%, TER: 100%, total WER: 19.2413%, total TER: 8.55173%, progress (thread 0): 94.0348%]
|T|: p e n s
|P|: i n
[sample: ES2004b_H00_MEO015_1162.44_1162.7, WER: 100%, TER: 75%, total WER: 19.2422%, total TER: 8.55235%, progress (thread 0): 94.0427%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1848.94_1849.2, WER: 0%, TER: 0%, total WER: 19.242%, total TER: 8.55227%, progress (thread 0): 94.0506%]
|T|: ' k a y
|P|: k a y
[sample: ES2004c_H00_MEO015_188.1_188.36, WER: 100%, TER: 25%, total WER: 19.2429%, total TER: 8.55243%, progress (thread 0): 94.0585%]
|T|: y e p
|P|: y e p
[sample: ES2004c_H00_MEO015_540.57_540.83, WER: 0%, TER: 0%, total WER: 19.2427%, total TER: 8.55237%, progress (thread 0): 94.0665%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1192.94_1193.2, WER: 0%, TER: 0%, total WER: 19.2425%, total TER: 8.55229%, progress (thread 0): 94.0744%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_39.85_40.11, WER: 100%, TER: 20%, total WER: 19.2434%, total TER: 8.55242%, progress (thread 0): 94.0823%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_129.84_130.1, WER: 0%, TER: 0%, total WER: 19.2431%, total TER: 8.55234%, progress (thread 0): 94.0902%]
|T|: o h
|P|: m h
[sample: ES2004d_H03_FEE016_1400.02_1400.28, WER: 100%, TER: 50%, total WER: 19.2441%, total TER: 8.55253%, progress (thread 0): 94.0981%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1553.35_1553.61, WER: 0%, TER: 0%, total WER: 19.2438%, total TER: 8.55245%, progress (thread 0): 94.106%]
|T|: n o
|P|: y e a h
[sample: ES2004d_H01_FEE013_1608.41_1608.67, WER: 100%, TER: 200%, total WER: 19.2447%, total TER: 8.55335%, progress (thread 0): 94.1139%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_1680.41_1680.67, WER: 100%, TER: 20%, total WER: 19.2457%, total TER: 8.55348%, progress (thread 0): 94.1218%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H03_FIO089_195.56_195.82, WER: 100%, TER: 20%, total WER: 19.2466%, total TER: 8.55362%, progress (thread 0): 94.1297%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_250.63_250.89, WER: 0%, TER: 0%, total WER: 19.2463%, total TER: 8.55354%, progress (thread 0): 94.1377%]
|T|: y e s
|P|: y e a h
[sample: IS1009b_H01_FIO087_289.01_289.27, WER: 100%, TER: 66.6667%, total WER: 19.2473%, total TER: 8.55394%, progress (thread 0): 94.1456%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_892.83_893.09, WER: 100%, TER: 60%, total WER: 19.2482%, total TER: 8.55455%, progress (thread 0): 94.1535%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_2020.18_2020.44, WER: 0%, TER: 0%, total WER: 19.2479%, total TER: 8.55447%, progress (thread 0): 94.1614%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_626.71_626.97, WER: 100%, TER: 40%, total WER: 19.2489%, total TER: 8.55483%, progress (thread 0): 94.1693%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_766.43_766.69, WER: 0%, TER: 0%, total WER: 19.2486%, total TER: 8.55475%, progress (thread 0): 94.1772%]
|T|: m m
|P|: y m m
[sample: IS1009d_H01_FIO087_656.85_657.11, WER: 100%, TER: 50%, total WER: 19.2495%, total TER: 8.55495%, progress (thread 0): 94.1851%]
|T|: ' c a u s e
|P|: s
[sample: TS3003a_H03_MTD012ME_1376.63_1376.89, WER: 100%, TER: 83.3333%, total WER: 19.2505%, total TER: 8.55599%, progress (thread 0): 94.193%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H02_MTD0010ID_1474.04_1474.3, WER: 0%, TER: 0%, total WER: 19.2502%, total TER: 8.55591%, progress (thread 0): 94.201%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_206.59_206.85, WER: 100%, TER: 25%, total WER: 19.2511%, total TER: 8.55607%, progress (thread 0): 94.2089%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H01_MTD011UID_581.65_581.91, WER: 0%, TER: 0%, total WER: 19.2509%, total TER: 8.55599%, progress (thread 0): 94.2168%]
|T|: h m m
|P|: m m
[sample: TS3003b_H03_MTD012ME_883.3_883.56, WER: 100%, TER: 33.3333%, total WER: 19.2518%, total TER: 8.55616%, progress (thread 0): 94.2247%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1533.07_1533.33, WER: 100%, TER: 40%, total WER: 19.2527%, total TER: 8.55653%, progress (thread 0): 94.2326%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_2048.3_2048.56, WER: 0%, TER: 0%, total WER: 19.2525%, total TER: 8.55647%, progress (thread 0): 94.2405%]
|T|: m m
|P|: h m m
[sample: TS3003c_H01_MTD011UID_1933.15_1933.41, WER: 100%, TER: 50%, total WER: 19.2534%, total TER: 8.55666%, progress (thread 0): 94.2484%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1980.3_1980.56, WER: 0%, TER: 0%, total WER: 19.2532%, total TER: 8.55658%, progress (thread 0): 94.2563%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_694.34_694.6, WER: 0%, TER: 0%, total WER: 19.253%, total TER: 8.5565%, progress (thread 0): 94.2642%]
|T|: t w o
|P|: t w o
[sample: TS3003d_H03_MTD012ME_1897.81_1898.07, WER: 0%, TER: 0%, total WER: 19.2528%, total TER: 8.55644%, progress (thread 0): 94.2722%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2141.52_2141.78, WER: 0%, TER: 0%, total WER: 19.2526%, total TER: 8.55636%, progress (thread 0): 94.2801%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002a_H02_FEO072_36.34_36.6, WER: 100%, TER: 20%, total WER: 19.2535%, total TER: 8.5565%, progress (thread 0): 94.288%]
|T|: y e a h
|P|: y m a m
[sample: EN2002a_H00_MEE073_319.23_319.49, WER: 100%, TER: 50%, total WER: 19.2544%, total TER: 8.55688%, progress (thread 0): 94.2959%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_671.05_671.31, WER: 0%, TER: 0%, total WER: 19.2542%, total TER: 8.5568%, progress (thread 0): 94.3038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_721.89_722.15, WER: 0%, TER: 0%, total WER: 19.254%, total TER: 8.55672%, progress (thread 0): 94.3117%]
|T|: t r u e
|P|: t r u e
[sample: EN2002a_H00_MEE073_751.8_752.06, WER: 0%, TER: 0%, total WER: 19.2537%, total TER: 8.55664%, progress (thread 0): 94.3196%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1052.1_1052.36, WER: 0%, TER: 0%, total WER: 19.2535%, total TER: 8.55656%, progress (thread 0): 94.3275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1446.67_1446.93, WER: 0%, TER: 0%, total WER: 19.2533%, total TER: 8.55648%, progress (thread 0): 94.3354%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1777.39_1777.65, WER: 0%, TER: 0%, total WER: 19.2531%, total TER: 8.5564%, progress (thread 0): 94.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1868.15_1868.41, WER: 0%, TER: 0%, total WER: 19.2529%, total TER: 8.55632%, progress (thread 0): 94.3513%]
|T|: o k a y
|P|: o g a y
[sample: EN2002b_H02_FEO072_93.22_93.48, WER: 100%, TER: 25%, total WER: 19.2538%, total TER: 8.55648%, progress (thread 0): 94.3592%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_252.07_252.33, WER: 0%, TER: 0%, total WER: 19.2536%, total TER: 8.5564%, progress (thread 0): 94.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_460.58_460.84, WER: 0%, TER: 0%, total WER: 19.2533%, total TER: 8.55632%, progress (thread 0): 94.375%]
|T|: o h
|P|: o h
[sample: EN2002b_H03_MEE073_674.06_674.32, WER: 0%, TER: 0%, total WER: 19.2531%, total TER: 8.55628%, progress (thread 0): 94.3829%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1026.26_1026.52, WER: 0%, TER: 0%, total WER: 19.2529%, total TER: 8.5562%, progress (thread 0): 94.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1104.98_1105.24, WER: 0%, TER: 0%, total WER: 19.2527%, total TER: 8.55612%, progress (thread 0): 94.3987%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1549.41_1549.67, WER: 100%, TER: 33.3333%, total WER: 19.2536%, total TER: 8.55629%, progress (thread 0): 94.4066%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1643.55_1643.81, WER: 0%, TER: 0%, total WER: 19.2534%, total TER: 8.55621%, progress (thread 0): 94.4146%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H02_MEE071_46.45_46.71, WER: 0%, TER: 0%, total WER: 19.2532%, total TER: 8.55611%, progress (thread 0): 94.4225%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_577.1_577.36, WER: 0%, TER: 0%, total WER: 19.253%, total TER: 8.55605%, progress (thread 0): 94.4304%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_600.57_600.83, WER: 100%, TER: 66.6667%, total WER: 19.2539%, total TER: 8.55646%, progress (thread 0): 94.4383%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_775.24_775.5, WER: 0%, TER: 0%, total WER: 19.2536%, total TER: 8.55638%, progress (thread 0): 94.4462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1079.16_1079.42, WER: 0%, TER: 0%, total WER: 19.2534%, total TER: 8.5563%, progress (thread 0): 94.4541%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002c_H03_MEE073_1105.57_1105.83, WER: 100%, TER: 20%, total WER: 19.2543%, total TER: 8.55643%, progress (thread 0): 94.462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1119.48_1119.74, WER: 0%, TER: 0%, total WER: 19.2541%, total TER: 8.55635%, progress (thread 0): 94.4699%]
|T|: m m h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1983.15_1983.41, WER: 100%, TER: 60%, total WER: 19.255%, total TER: 8.55695%, progress (thread 0): 94.4779%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1999.1_1999.36, WER: 0%, TER: 0%, total WER: 19.2548%, total TER: 8.55685%, progress (thread 0): 94.4858%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2082.11_2082.37, WER: 0%, TER: 0%, total WER: 19.2546%, total TER: 8.55677%, progress (thread 0): 94.4937%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2586.11_2586.37, WER: 0%, TER: 0%, total WER: 19.2544%, total TER: 8.55669%, progress (thread 0): 94.5016%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_347.04_347.3, WER: 0%, TER: 0%, total WER: 19.2542%, total TER: 8.55661%, progress (thread 0): 94.5095%]
|T|: w h o
|P|: h m m
[sample: EN2002d_H00_FEO070_501.31_501.57, WER: 100%, TER: 100%, total WER: 19.2551%, total TER: 8.55726%, progress (thread 0): 94.5174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_728.46_728.72, WER: 0%, TER: 0%, total WER: 19.2549%, total TER: 8.55718%, progress (thread 0): 94.5253%]
|T|: w e l l | i t ' s
|P|: j u s t
[sample: EN2002d_H03_MEE073_882.54_882.8, WER: 100%, TER: 88.8889%, total WER: 19.2567%, total TER: 8.55886%, progress (thread 0): 94.5332%]
|T|: s u p e r
|P|: s u p e
[sample: EN2002d_H03_MEE073_953.8_954.06, WER: 100%, TER: 20%, total WER: 19.2576%, total TER: 8.559%, progress (thread 0): 94.5411%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1517.63_1517.89, WER: 50%, TER: 42.8571%, total WER: 19.2583%, total TER: 8.55956%, progress (thread 0): 94.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1704.28_1704.54, WER: 0%, TER: 0%, total WER: 19.2581%, total TER: 8.55948%, progress (thread 0): 94.557%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_2135.36_2135.62, WER: 100%, TER: 33.3333%, total WER: 19.259%, total TER: 8.55965%, progress (thread 0): 94.5649%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_666.69_666.94, WER: 0%, TER: 0%, total WER: 19.2587%, total TER: 8.55961%, progress (thread 0): 94.5728%]
|T|: y e p
|P|: y e a h
[sample: ES2004b_H00_MEO015_817.97_818.22, WER: 100%, TER: 66.6667%, total WER: 19.2597%, total TER: 8.56002%, progress (thread 0): 94.5807%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_2175.08_2175.33, WER: 100%, TER: 20%, total WER: 19.2606%, total TER: 8.56015%, progress (thread 0): 94.5886%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004c_H00_MEO015_620.36_620.61, WER: 100%, TER: 20%, total WER: 19.2615%, total TER: 8.56029%, progress (thread 0): 94.5965%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1866.87_1867.12, WER: 100%, TER: 100%, total WER: 19.2624%, total TER: 8.56093%, progress (thread 0): 94.6044%]
|T|: s o r r y
|P|: s o r r y
[sample: ES2004d_H01_FEE013_455.38_455.63, WER: 0%, TER: 0%, total WER: 19.2622%, total TER: 8.56083%, progress (thread 0): 94.6123%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_607.67_607.92, WER: 0%, TER: 0%, total WER: 19.2619%, total TER: 8.56075%, progress (thread 0): 94.6203%]
|T|: y e s
|P|: y e a h
[sample: ES2004d_H03_FEE016_1135.45_1135.7, WER: 100%, TER: 66.6667%, total WER: 19.2628%, total TER: 8.56115%, progress (thread 0): 94.6282%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1553.38_1553.63, WER: 0%, TER: 0%, total WER: 19.2626%, total TER: 8.56107%, progress (thread 0): 94.6361%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H03_FIO089_1086.32_1086.57, WER: 100%, TER: 20%, total WER: 19.2635%, total TER: 8.56121%, progress (thread 0): 94.644%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1900.42_1900.67, WER: 0%, TER: 0%, total WER: 19.2633%, total TER: 8.56113%, progress (thread 0): 94.6519%]
|T|: y e s
|P|: y e a h
[sample: IS1009b_H01_FIO087_1909.04_1909.29, WER: 100%, TER: 66.6667%, total WER: 19.2642%, total TER: 8.56153%, progress (thread 0): 94.6598%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H00_FIE088_324.58_324.83, WER: 0%, TER: 0%, total WER: 19.264%, total TER: 8.56143%, progress (thread 0): 94.6677%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1287.33_1287.58, WER: 0%, TER: 0%, total WER: 19.2638%, total TER: 8.56135%, progress (thread 0): 94.6756%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1781.33_1781.58, WER: 0%, TER: 0%, total WER: 19.2636%, total TER: 8.56127%, progress (thread 0): 94.6835%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_351.32_351.57, WER: 0%, TER: 0%, total WER: 19.2634%, total TER: 8.56119%, progress (thread 0): 94.6915%]
|T|: m m
|P|: m m m
[sample: IS1009d_H03_FIO089_427.43_427.68, WER: 100%, TER: 50%, total WER: 19.2643%, total TER: 8.56139%, progress (thread 0): 94.6994%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_826.51_826.76, WER: 0%, TER: 0%, total WER: 19.2641%, total TER: 8.56135%, progress (thread 0): 94.7073%]
|T|: n o
|P|: n o
[sample: TS3003a_H03_MTD012ME_1222.74_1222.99, WER: 0%, TER: 0%, total WER: 19.2638%, total TER: 8.56131%, progress (thread 0): 94.7152%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_281.83_282.08, WER: 0%, TER: 0%, total WER: 19.2636%, total TER: 8.56127%, progress (thread 0): 94.7231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1559.52_1559.77, WER: 0%, TER: 0%, total WER: 19.2634%, total TER: 8.56119%, progress (thread 0): 94.731%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_1839.11_1839.36, WER: 0%, TER: 0%, total WER: 19.2632%, total TER: 8.56111%, progress (thread 0): 94.7389%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H02_MTD0010ID_2094.57_2094.82, WER: 100%, TER: 50%, total WER: 19.2641%, total TER: 8.5615%, progress (thread 0): 94.7468%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003c_H03_MTD012ME_1972.52_1972.77, WER: 100%, TER: 20%, total WER: 19.265%, total TER: 8.56163%, progress (thread 0): 94.7548%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_303.27_303.52, WER: 100%, TER: 75%, total WER: 19.2659%, total TER: 8.56225%, progress (thread 0): 94.7627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_583.29_583.54, WER: 0%, TER: 0%, total WER: 19.2657%, total TER: 8.56217%, progress (thread 0): 94.7706%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_847.16_847.41, WER: 0%, TER: 0%, total WER: 19.2655%, total TER: 8.56209%, progress (thread 0): 94.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1370.99_1371.24, WER: 0%, TER: 0%, total WER: 19.2653%, total TER: 8.56201%, progress (thread 0): 94.7864%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1371.48_1371.73, WER: 0%, TER: 0%, total WER: 19.265%, total TER: 8.56193%, progress (thread 0): 94.7943%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_1404.32_1404.57, WER: 100%, TER: 75%, total WER: 19.266%, total TER: 8.56255%, progress (thread 0): 94.8022%]
|T|: g o o d
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1696.35_1696.6, WER: 100%, TER: 100%, total WER: 19.2669%, total TER: 8.5634%, progress (thread 0): 94.8101%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2064.36_2064.61, WER: 0%, TER: 0%, total WER: 19.2666%, total TER: 8.56332%, progress (thread 0): 94.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_2111_2111.25, WER: 0%, TER: 0%, total WER: 19.2664%, total TER: 8.56324%, progress (thread 0): 94.826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2138.4_2138.65, WER: 0%, TER: 0%, total WER: 19.2662%, total TER: 8.56316%, progress (thread 0): 94.8339%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2216.27_2216.52, WER: 0%, TER: 0%, total WER: 19.266%, total TER: 8.56308%, progress (thread 0): 94.8418%]
|T|: a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2381.92_2382.17, WER: 100%, TER: 100%, total WER: 19.2669%, total TER: 8.56351%, progress (thread 0): 94.8497%]
|T|: s o
|P|: s o
[sample: EN2002a_H00_MEE073_202.42_202.67, WER: 0%, TER: 0%, total WER: 19.2667%, total TER: 8.56347%, progress (thread 0): 94.8576%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_749.45_749.7, WER: 0%, TER: 0%, total WER: 19.2665%, total TER: 8.56339%, progress (thread 0): 94.8655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1642_1642.25, WER: 0%, TER: 0%, total WER: 19.2663%, total TER: 8.56331%, progress (thread 0): 94.8734%]
|T|: s o
|P|: s o
[sample: EN2002a_H02_FEO072_1664.11_1664.36, WER: 0%, TER: 0%, total WER: 19.266%, total TER: 8.56327%, progress (thread 0): 94.8813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1964.06_1964.31, WER: 0%, TER: 0%, total WER: 19.2658%, total TER: 8.56319%, progress (thread 0): 94.8892%]
|T|: y o u ' d | b e | s
|P|: c i m p e
[sample: EN2002a_H00_MEE073_2096.92_2097.17, WER: 100%, TER: 90%, total WER: 19.2685%, total TER: 8.56509%, progress (thread 0): 94.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_93.68_93.93, WER: 0%, TER: 0%, total WER: 19.2683%, total TER: 8.56501%, progress (thread 0): 94.9051%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_311.61_311.86, WER: 0%, TER: 0%, total WER: 19.2681%, total TER: 8.56491%, progress (thread 0): 94.913%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1129.86_1130.11, WER: 100%, TER: 28.5714%, total WER: 19.269%, total TER: 8.56524%, progress (thread 0): 94.9209%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1222.15_1222.4, WER: 0%, TER: 0%, total WER: 19.2688%, total TER: 8.56518%, progress (thread 0): 94.9288%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1237.83_1238.08, WER: 0%, TER: 0%, total WER: 19.2686%, total TER: 8.5651%, progress (thread 0): 94.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1245.09_1245.34, WER: 0%, TER: 0%, total WER: 19.2684%, total TER: 8.56502%, progress (thread 0): 94.9446%]
|T|: a n d
|P|: t h e n
[sample: EN2002b_H01_MEE071_1309.75_1310, WER: 100%, TER: 133.333%, total WER: 19.2693%, total TER: 8.56589%, progress (thread 0): 94.9525%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H02_FEO072_1594.84_1595.09, WER: 100%, TER: 66.6667%, total WER: 19.2702%, total TER: 8.5663%, progress (thread 0): 94.9604%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1643.59_1643.84, WER: 0%, TER: 0%, total WER: 19.27%, total TER: 8.56622%, progress (thread 0): 94.9684%]
|T|: s u r e
|P|: s u r e
[sample: EN2002c_H02_MEE071_67.06_67.31, WER: 0%, TER: 0%, total WER: 19.2697%, total TER: 8.56614%, progress (thread 0): 94.9763%]
|T|: o k a y
|P|: m m
[sample: EN2002c_H03_MEE073_76.13_76.38, WER: 100%, TER: 100%, total WER: 19.2707%, total TER: 8.56699%, progress (thread 0): 94.9842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_253.63_253.88, WER: 0%, TER: 0%, total WER: 19.2704%, total TER: 8.56691%, progress (thread 0): 94.9921%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_470.85_471.1, WER: 100%, TER: 0%, total WER: 19.2713%, total TER: 8.56681%, progress (thread 0): 95%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_759.06_759.31, WER: 0%, TER: 0%, total WER: 19.2711%, total TER: 8.56671%, progress (thread 0): 95.0079%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1000.82_1001.07, WER: 0%, TER: 0%, total WER: 19.2709%, total TER: 8.56663%, progress (thread 0): 95.0158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1023.06_1023.31, WER: 0%, TER: 0%, total WER: 19.2707%, total TER: 8.56655%, progress (thread 0): 95.0237%]
|T|: m m h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1102.48_1102.73, WER: 100%, TER: 60%, total WER: 19.2716%, total TER: 8.56715%, progress (thread 0): 95.0316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1122.76_1123.01, WER: 0%, TER: 0%, total WER: 19.2714%, total TER: 8.56707%, progress (thread 0): 95.0396%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_1359.23_1359.48, WER: 100%, TER: 66.6667%, total WER: 19.2723%, total TER: 8.56748%, progress (thread 0): 95.0475%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_1508.23_1508.48, WER: 0%, TER: 0%, total WER: 19.2721%, total TER: 8.56742%, progress (thread 0): 95.0554%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2100.94_2101.19, WER: 0%, TER: 0%, total WER: 19.2719%, total TER: 8.56734%, progress (thread 0): 95.0633%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2354.17_2354.42, WER: 0%, TER: 0%, total WER: 19.2716%, total TER: 8.56726%, progress (thread 0): 95.0712%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_146.37_146.62, WER: 100%, TER: 28.5714%, total WER: 19.2725%, total TER: 8.56759%, progress (thread 0): 95.0791%]
|T|: o h
|P|: m m
[sample: EN2002d_H00_FEO070_286.77_287.02, WER: 100%, TER: 100%, total WER: 19.2735%, total TER: 8.56801%, progress (thread 0): 95.087%]
|T|: y e a h
|P|: m m
[sample: EN2002d_H03_MEE073_589.91_590.16, WER: 100%, TER: 100%, total WER: 19.2744%, total TER: 8.56887%, progress (thread 0): 95.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_850.49_850.74, WER: 0%, TER: 0%, total WER: 19.2741%, total TER: 8.56879%, progress (thread 0): 95.1028%]
|T|: s o
|P|: h m h
[sample: EN2002d_H02_MEE071_1251.8_1252.05, WER: 100%, TER: 150%, total WER: 19.2751%, total TER: 8.56945%, progress (thread 0): 95.1108%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_1830.85_1831.1, WER: 100%, TER: 33.3333%, total WER: 19.276%, total TER: 8.56962%, progress (thread 0): 95.1187%]
|T|: o k a y
|P|: c o y
[sample: EN2002d_H03_MEE073_1854.42_1854.67, WER: 100%, TER: 75%, total WER: 19.2769%, total TER: 8.57024%, progress (thread 0): 95.1266%]
|T|: m m
|P|: h m m
[sample: EN2002d_H01_FEO072_2083.17_2083.42, WER: 100%, TER: 50%, total WER: 19.2778%, total TER: 8.57044%, progress (thread 0): 95.1345%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2192.17_2192.42, WER: 0%, TER: 0%, total WER: 19.2776%, total TER: 8.57036%, progress (thread 0): 95.1424%]
|T|: s
|P|: s
[sample: ES2004a_H02_MEE014_484.48_484.72, WER: 0%, TER: 0%, total WER: 19.2773%, total TER: 8.57033%, progress (thread 0): 95.1503%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_775.6_775.84, WER: 0%, TER: 0%, total WER: 19.2771%, total TER: 8.57029%, progress (thread 0): 95.1582%]
|T|: s o r r y
|P|: m m
[sample: ES2004b_H00_MEO015_100.7_100.94, WER: 100%, TER: 100%, total WER: 19.278%, total TER: 8.57136%, progress (thread 0): 95.1661%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1311.92_1312.16, WER: 0%, TER: 0%, total WER: 19.2778%, total TER: 8.57126%, progress (thread 0): 95.174%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_1636.65_1636.89, WER: 100%, TER: 20%, total WER: 19.2787%, total TER: 8.5714%, progress (thread 0): 95.182%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1305.92_1306.16, WER: 100%, TER: 25%, total WER: 19.2796%, total TER: 8.57155%, progress (thread 0): 95.1899%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1345.16_1345.4, WER: 0%, TER: 0%, total WER: 19.2794%, total TER: 8.57151%, progress (thread 0): 95.1978%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1364.93_1365.17, WER: 0%, TER: 0%, total WER: 19.2792%, total TER: 8.57147%, progress (thread 0): 95.2057%]
|T|: f i n e
|P|: i n y
[sample: ES2004c_H00_MEO015_2156.57_2156.81, WER: 100%, TER: 50%, total WER: 19.2801%, total TER: 8.57186%, progress (thread 0): 95.2136%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_973.73_973.97, WER: 0%, TER: 0%, total WER: 19.2799%, total TER: 8.5718%, progress (thread 0): 95.2215%]
|T|: m m
|P|: m m
[sample: ES2004d_H03_FEE016_1286.7_1286.94, WER: 0%, TER: 0%, total WER: 19.2797%, total TER: 8.57176%, progress (thread 0): 95.2294%]
|T|: o h
|P|: o h
[sample: ES2004d_H01_FEE013_2176.67_2176.91, WER: 0%, TER: 0%, total WER: 19.2794%, total TER: 8.57172%, progress (thread 0): 95.2373%]
|T|: n o
|P|: m m
[sample: IS1009a_H03_FIO089_145.55_145.79, WER: 100%, TER: 100%, total WER: 19.2804%, total TER: 8.57214%, progress (thread 0): 95.2453%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H00_FIE088_466.21_466.45, WER: 100%, TER: 20%, total WER: 19.2813%, total TER: 8.57228%, progress (thread 0): 95.2532%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_556.58_556.82, WER: 0%, TER: 0%, total WER: 19.281%, total TER: 8.5722%, progress (thread 0): 95.2611%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1033.16_1033.4, WER: 0%, TER: 0%, total WER: 19.2808%, total TER: 8.57212%, progress (thread 0): 95.269%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_1099.19_1099.43, WER: 0%, TER: 0%, total WER: 19.2806%, total TER: 8.57204%, progress (thread 0): 95.2769%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1247.84_1248.08, WER: 100%, TER: 0%, total WER: 19.2815%, total TER: 8.57194%, progress (thread 0): 95.2848%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_752.07_752.31, WER: 100%, TER: 20%, total WER: 19.2824%, total TER: 8.57207%, progress (thread 0): 95.2927%]
|T|: m m | r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_764.18_764.42, WER: 50%, TER: 37.5%, total WER: 19.2831%, total TER: 8.57261%, progress (thread 0): 95.3006%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1110.14_1110.38, WER: 100%, TER: 40%, total WER: 19.284%, total TER: 8.57298%, progress (thread 0): 95.3085%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_1123.59_1123.83, WER: 100%, TER: 20%, total WER: 19.2849%, total TER: 8.57311%, progress (thread 0): 95.3165%]
|T|: h e l l o
|P|: n o
[sample: IS1009d_H02_FIO084_41.31_41.55, WER: 100%, TER: 80%, total WER: 19.2858%, total TER: 8.57394%, progress (thread 0): 95.3244%]
|T|: m m
|P|: m m
[sample: IS1009d_H03_FIO089_516.52_516.76, WER: 0%, TER: 0%, total WER: 19.2856%, total TER: 8.5739%, progress (thread 0): 95.3323%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_656.9_657.14, WER: 100%, TER: 60%, total WER: 19.2865%, total TER: 8.5745%, progress (thread 0): 95.3402%]
|T|: y e a h
|P|: n o
[sample: IS1009d_H02_FIO084_1011.72_1011.96, WER: 100%, TER: 100%, total WER: 19.2874%, total TER: 8.57536%, progress (thread 0): 95.3481%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_1883.01_1883.25, WER: 0%, TER: 0%, total WER: 19.2872%, total TER: 8.57528%, progress (thread 0): 95.356%]
|T|: w e
|P|: w e
[sample: TS3003a_H00_MTD009PM_1109.9_1110.14, WER: 0%, TER: 0%, total WER: 19.287%, total TER: 8.57524%, progress (thread 0): 95.3639%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1420.32_1420.56, WER: 0%, TER: 0%, total WER: 19.2868%, total TER: 8.5752%, progress (thread 0): 95.3718%]
|T|: n o
|P|: n o
[sample: TS3003b_H02_MTD0010ID_1474.4_1474.64, WER: 0%, TER: 0%, total WER: 19.2866%, total TER: 8.57516%, progress (thread 0): 95.3797%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_1677.79_1678.03, WER: 0%, TER: 0%, total WER: 19.2863%, total TER: 8.57508%, progress (thread 0): 95.3877%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1975.67_1975.91, WER: 100%, TER: 100%, total WER: 19.2873%, total TER: 8.5755%, progress (thread 0): 95.3956%]
|T|: y e a h
|P|: h m m
[sample: TS3003b_H01_MTD011UID_2083.9_2084.14, WER: 100%, TER: 100%, total WER: 19.2882%, total TER: 8.57635%, progress (thread 0): 95.4035%]
|T|: y e a h
|P|: a h
[sample: TS3003c_H01_MTD011UID_1240.02_1240.26, WER: 100%, TER: 50%, total WER: 19.2891%, total TER: 8.57674%, progress (thread 0): 95.4114%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1254.68_1254.92, WER: 0%, TER: 0%, total WER: 19.2888%, total TER: 8.57666%, progress (thread 0): 95.4193%]
|T|: s o
|P|: s o
[sample: TS3003c_H01_MTD011UID_1417.83_1418.07, WER: 0%, TER: 0%, total WER: 19.2886%, total TER: 8.57662%, progress (thread 0): 95.4272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1477.96_1478.2, WER: 0%, TER: 0%, total WER: 19.2884%, total TER: 8.57654%, progress (thread 0): 95.4351%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1566.64_1566.88, WER: 100%, TER: 25%, total WER: 19.2893%, total TER: 8.57669%, progress (thread 0): 95.443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1716.46_1716.7, WER: 0%, TER: 0%, total WER: 19.2891%, total TER: 8.57661%, progress (thread 0): 95.451%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2217.59_2217.83, WER: 0%, TER: 0%, total WER: 19.2889%, total TER: 8.57653%, progress (thread 0): 95.4589%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2243.54_2243.78, WER: 0%, TER: 0%, total WER: 19.2887%, total TER: 8.57645%, progress (thread 0): 95.4668%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1049.33_1049.57, WER: 0%, TER: 0%, total WER: 19.2885%, total TER: 8.57637%, progress (thread 0): 95.4747%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1496.68_1496.92, WER: 100%, TER: 75%, total WER: 19.2894%, total TER: 8.57699%, progress (thread 0): 95.4826%]
|T|: d | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_106.38_106.62, WER: 50%, TER: 33.3333%, total WER: 19.2901%, total TER: 8.57734%, progress (thread 0): 95.4905%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_140.88_141.12, WER: 0%, TER: 0%, total WER: 19.2898%, total TER: 8.57726%, progress (thread 0): 95.4984%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_333.3_333.54, WER: 0%, TER: 0%, total WER: 19.2896%, total TER: 8.57716%, progress (thread 0): 95.5063%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H01_FEO070_398.18_398.42, WER: 100%, TER: 100%, total WER: 19.2905%, total TER: 8.57801%, progress (thread 0): 95.5142%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_481.62_481.86, WER: 0%, TER: 0%, total WER: 19.2903%, total TER: 8.57793%, progress (thread 0): 95.5222%]
|T|: y e a h
|P|: y e a m
[sample: EN2002a_H00_MEE073_682.97_683.21, WER: 100%, TER: 25%, total WER: 19.2912%, total TER: 8.57809%, progress (thread 0): 95.5301%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002a_H02_FEO072_1009.94_1010.18, WER: 100%, TER: 20%, total WER: 19.2921%, total TER: 8.57822%, progress (thread 0): 95.538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1041.03_1041.27, WER: 0%, TER: 0%, total WER: 19.2919%, total TER: 8.57814%, progress (thread 0): 95.5459%]
|T|: i | s e e
|P|: a t ' s
[sample: EN2002a_H00_MEE073_1171.72_1171.96, WER: 100%, TER: 100%, total WER: 19.2937%, total TER: 8.57921%, progress (thread 0): 95.5538%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002a_H00_MEE073_1209.32_1209.56, WER: 100%, TER: 85.7143%, total WER: 19.2955%, total TER: 8.58047%, progress (thread 0): 95.5617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1310.69_1310.93, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.58039%, progress (thread 0): 95.5696%]
|T|: o p e n
|P|: o p a y
[sample: EN2002a_H02_FEO072_1350.04_1350.28, WER: 100%, TER: 50%, total WER: 19.2962%, total TER: 8.58077%, progress (thread 0): 95.5775%]
|T|: m m
|P|: m m
[sample: EN2002a_H03_MEE071_1405.73_1405.97, WER: 0%, TER: 0%, total WER: 19.296%, total TER: 8.58073%, progress (thread 0): 95.5854%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1791.17_1791.41, WER: 0%, TER: 0%, total WER: 19.2958%, total TER: 8.58063%, progress (thread 0): 95.5934%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1834.42_1834.66, WER: 0%, TER: 0%, total WER: 19.2956%, total TER: 8.58055%, progress (thread 0): 95.6013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2028.12_2028.36, WER: 0%, TER: 0%, total WER: 19.2954%, total TER: 8.58047%, progress (thread 0): 95.6092%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_2040.58_2040.82, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.58039%, progress (thread 0): 95.6171%]
|T|: o h
|P|: o h
[sample: EN2002a_H01_FEO070_2098.91_2099.15, WER: 0%, TER: 0%, total WER: 19.2949%, total TER: 8.58035%, progress (thread 0): 95.625%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_522.48_522.72, WER: 0%, TER: 0%, total WER: 19.2947%, total TER: 8.58027%, progress (thread 0): 95.6329%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_612.23_612.47, WER: 0%, TER: 0%, total WER: 19.2945%, total TER: 8.58019%, progress (thread 0): 95.6408%]
|T|: m m
|P|: m m
[sample: EN2002b_H02_FEO072_1475.84_1476.08, WER: 0%, TER: 0%, total WER: 19.2943%, total TER: 8.58015%, progress (thread 0): 95.6487%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_235.84_236.08, WER: 100%, TER: 33.3333%, total WER: 19.2952%, total TER: 8.58033%, progress (thread 0): 95.6566%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_571.47_571.71, WER: 0%, TER: 0%, total WER: 19.295%, total TER: 8.58025%, progress (thread 0): 95.6646%]
|T|: h m m
|P|: y e a h
[sample: EN2002c_H03_MEE073_574.34_574.58, WER: 100%, TER: 133.333%, total WER: 19.2959%, total TER: 8.58112%, progress (thread 0): 95.6725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_598.8_599.04, WER: 0%, TER: 0%, total WER: 19.2956%, total TER: 8.58104%, progress (thread 0): 95.6804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1009.87_1010.11, WER: 0%, TER: 0%, total WER: 19.2954%, total TER: 8.58096%, progress (thread 0): 95.6883%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1018.66_1018.9, WER: 0%, TER: 0%, total WER: 19.2952%, total TER: 8.58086%, progress (thread 0): 95.6962%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1281.36_1281.6, WER: 100%, TER: 33.3333%, total WER: 19.2961%, total TER: 8.58103%, progress (thread 0): 95.7041%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1396.34_1396.58, WER: 0%, TER: 0%, total WER: 19.2959%, total TER: 8.58095%, progress (thread 0): 95.712%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1906.84_1907.08, WER: 0%, TER: 0%, total WER: 19.2957%, total TER: 8.58087%, progress (thread 0): 95.7199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1913.84_1914.08, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58079%, progress (thread 0): 95.7279%]
|T|: ' c a u s e
|P|: t h e ' s
[sample: EN2002c_H03_MEE073_2264.19_2264.43, WER: 100%, TER: 83.3333%, total WER: 19.2964%, total TER: 8.58184%, progress (thread 0): 95.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2309.3_2309.54, WER: 0%, TER: 0%, total WER: 19.2962%, total TER: 8.58176%, progress (thread 0): 95.7437%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2341.41_2341.65, WER: 0%, TER: 0%, total WER: 19.2959%, total TER: 8.58166%, progress (thread 0): 95.7516%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2370.76_2371, WER: 0%, TER: 0%, total WER: 19.2957%, total TER: 8.58156%, progress (thread 0): 95.7595%]
|T|: g o o d
|P|: g o o d
[sample: EN2002d_H01_FEO072_337.97_338.21, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58148%, progress (thread 0): 95.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_390.81_391.05, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.5814%, progress (thread 0): 95.7753%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_496.78_497.02, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.5813%, progress (thread 0): 95.7832%]
|T|: o k a y
|P|: h u h
[sample: EN2002d_H00_FEO070_513.66_513.9, WER: 100%, TER: 100%, total WER: 19.296%, total TER: 8.58215%, progress (thread 0): 95.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1190.34_1190.58, WER: 0%, TER: 0%, total WER: 19.2958%, total TER: 8.58207%, progress (thread 0): 95.799%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1371.83_1372.07, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58199%, progress (thread 0): 95.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1477.13_1477.37, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.58191%, progress (thread 0): 95.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1706.39_1706.63, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.58183%, progress (thread 0): 95.8228%]
|T|: m m
|P|: m m
[sample: ES2004b_H03_FEE016_1717.78_1718.01, WER: 0%, TER: 0%, total WER: 19.2949%, total TER: 8.58179%, progress (thread 0): 95.8307%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1860.91_1861.14, WER: 100%, TER: 40%, total WER: 19.2958%, total TER: 8.58216%, progress (thread 0): 95.8386%]
|T|: t r u e
|P|: s u e
[sample: ES2004b_H00_MEO015_2294.79_2295.02, WER: 100%, TER: 50%, total WER: 19.2967%, total TER: 8.58254%, progress (thread 0): 95.8465%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_85.13_85.36, WER: 0%, TER: 0%, total WER: 19.2965%, total TER: 8.58246%, progress (thread 0): 95.8544%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_356.94_357.17, WER: 100%, TER: 25%, total WER: 19.2974%, total TER: 8.58262%, progress (thread 0): 95.8623%]
|T|: s o
|P|: s a e
[sample: ES2004c_H02_MEE014_748.27_748.5, WER: 100%, TER: 100%, total WER: 19.2983%, total TER: 8.58304%, progress (thread 0): 95.8702%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1121.33_1121.56, WER: 0%, TER: 0%, total WER: 19.2981%, total TER: 8.583%, progress (thread 0): 95.8782%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1169.98_1170.21, WER: 0%, TER: 0%, total WER: 19.2979%, total TER: 8.58292%, progress (thread 0): 95.8861%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H03_FEE016_711.82_712.05, WER: 0%, TER: 0%, total WER: 19.2977%, total TER: 8.58284%, progress (thread 0): 95.894%]
|T|: t w o
|P|: t o
[sample: ES2004d_H00_MEO015_732.46_732.69, WER: 100%, TER: 33.3333%, total WER: 19.2986%, total TER: 8.58301%, progress (thread 0): 95.9019%]
|T|: m m h m m
|P|: m e h m
[sample: ES2004d_H00_MEO015_822.98_823.21, WER: 100%, TER: 40%, total WER: 19.2995%, total TER: 8.58338%, progress (thread 0): 95.9098%]
|T|: ' k a y
|P|: s o
[sample: ES2004d_H00_MEO015_1813.16_1813.39, WER: 100%, TER: 100%, total WER: 19.3004%, total TER: 8.58423%, progress (thread 0): 95.9177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_195.04_195.27, WER: 0%, TER: 0%, total WER: 19.3002%, total TER: 8.58415%, progress (thread 0): 95.9256%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_532.46_532.69, WER: 100%, TER: 66.6667%, total WER: 19.3011%, total TER: 8.58456%, progress (thread 0): 95.9335%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_55.21_55.44, WER: 0%, TER: 0%, total WER: 19.3008%, total TER: 8.58452%, progress (thread 0): 95.9415%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1919.19_1919.42, WER: 0%, TER: 0%, total WER: 19.3006%, total TER: 8.58444%, progress (thread 0): 95.9494%]
|T|: m m h m m
|P|: m m
[sample: IS1009c_H00_FIE088_554.54_554.77, WER: 100%, TER: 60%, total WER: 19.3015%, total TER: 8.58504%, progress (thread 0): 95.9573%]
|T|: m m | y e s
|P|: y e a h
[sample: IS1009c_H01_FIO087_728.73_728.96, WER: 100%, TER: 83.3333%, total WER: 19.3033%, total TER: 8.58608%, progress (thread 0): 95.9652%]
|T|: y e p
|P|: y e a h
[sample: IS1009c_H03_FIO089_1639.78_1640.01, WER: 100%, TER: 66.6667%, total WER: 19.3042%, total TER: 8.58649%, progress (thread 0): 95.9731%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_747.97_748.2, WER: 100%, TER: 66.6667%, total WER: 19.3052%, total TER: 8.5869%, progress (thread 0): 95.981%]
|T|: h m m
|P|: m m
[sample: IS1009d_H02_FIO084_748.42_748.65, WER: 100%, TER: 33.3333%, total WER: 19.3061%, total TER: 8.58707%, progress (thread 0): 95.9889%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_758.1_758.33, WER: 100%, TER: 100%, total WER: 19.307%, total TER: 8.58814%, progress (thread 0): 95.9968%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1695.75_1695.98, WER: 100%, TER: 66.6667%, total WER: 19.3079%, total TER: 8.58854%, progress (thread 0): 96.0047%]
|T|: o k a y
|P|: y e a h
[sample: IS1009d_H03_FIO089_1889.3_1889.53, WER: 100%, TER: 75%, total WER: 19.3088%, total TER: 8.58916%, progress (thread 0): 96.0127%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_843.57_843.8, WER: 100%, TER: 25%, total WER: 19.3097%, total TER: 8.58931%, progress (thread 0): 96.0206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1334.2_1334.43, WER: 0%, TER: 0%, total WER: 19.3095%, total TER: 8.58923%, progress (thread 0): 96.0285%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1346.08_1346.31, WER: 0%, TER: 0%, total WER: 19.3092%, total TER: 8.58915%, progress (thread 0): 96.0364%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1078.3_1078.53, WER: 0%, TER: 0%, total WER: 19.309%, total TER: 8.58911%, progress (thread 0): 96.0443%]
|T|: n o
|P|: n o
[sample: TS3003b_H01_MTD011UID_1716.14_1716.37, WER: 0%, TER: 0%, total WER: 19.3088%, total TER: 8.58907%, progress (thread 0): 96.0522%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_1250.59_1250.82, WER: 0%, TER: 0%, total WER: 19.3086%, total TER: 8.58903%, progress (thread 0): 96.0601%]
|T|: y e s
|P|: j u s t
[sample: TS3003c_H02_MTD0010ID_1518.39_1518.62, WER: 100%, TER: 100%, total WER: 19.3095%, total TER: 8.58967%, progress (thread 0): 96.068%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1654.26_1654.49, WER: 0%, TER: 0%, total WER: 19.3093%, total TER: 8.58963%, progress (thread 0): 96.076%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_2049.46_2049.69, WER: 100%, TER: 75%, total WER: 19.3102%, total TER: 8.59025%, progress (thread 0): 96.0839%]
|T|: s o
|P|: s o
[sample: TS3003d_H01_MTD011UID_261.45_261.68, WER: 0%, TER: 0%, total WER: 19.31%, total TER: 8.59021%, progress (thread 0): 96.0918%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1021.27_1021.5, WER: 100%, TER: 66.6667%, total WER: 19.3109%, total TER: 8.59062%, progress (thread 0): 96.0997%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1945.96_1946.19, WER: 100%, TER: 75%, total WER: 19.3118%, total TER: 8.59124%, progress (thread 0): 96.1076%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2064.77_2065, WER: 0%, TER: 0%, total WER: 19.3116%, total TER: 8.59116%, progress (thread 0): 96.1155%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_424.58_424.81, WER: 0%, TER: 0%, total WER: 19.3114%, total TER: 8.59106%, progress (thread 0): 96.1234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_781.91_782.14, WER: 0%, TER: 0%, total WER: 19.3111%, total TER: 8.59098%, progress (thread 0): 96.1313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_855.85_856.08, WER: 0%, TER: 0%, total WER: 19.3109%, total TER: 8.5909%, progress (thread 0): 96.1392%]
|T|: h m m
|P|: m m
[sample: EN2002a_H02_FEO072_877.24_877.47, WER: 100%, TER: 33.3333%, total WER: 19.3118%, total TER: 8.59107%, progress (thread 0): 96.1471%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H00_MEE073_1019.4_1019.63, WER: 0%, TER: 0%, total WER: 19.3116%, total TER: 8.59101%, progress (thread 0): 96.1551%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_1188.61_1188.84, WER: 100%, TER: 66.6667%, total WER: 19.3125%, total TER: 8.59142%, progress (thread 0): 96.163%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1295.18_1295.41, WER: 0%, TER: 0%, total WER: 19.3123%, total TER: 8.59134%, progress (thread 0): 96.1709%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1408.29_1408.52, WER: 0%, TER: 0%, total WER: 19.3121%, total TER: 8.59126%, progress (thread 0): 96.1788%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1571.85_1572.08, WER: 0%, TER: 0%, total WER: 19.3119%, total TER: 8.59122%, progress (thread 0): 96.1867%]
|T|: o h
|P|: o
[sample: EN2002b_H03_MEE073_29.06_29.29, WER: 100%, TER: 50%, total WER: 19.3128%, total TER: 8.59141%, progress (thread 0): 96.1946%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_194.27_194.5, WER: 0%, TER: 0%, total WER: 19.3125%, total TER: 8.59133%, progress (thread 0): 96.2025%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_211.63_211.86, WER: 0%, TER: 0%, total WER: 19.3123%, total TER: 8.59125%, progress (thread 0): 96.2104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_493.43_493.66, WER: 0%, TER: 0%, total WER: 19.3121%, total TER: 8.59117%, progress (thread 0): 96.2184%]
|T|: h m m
|P|: m m
[sample: EN2002b_H00_FEO070_534.29_534.52, WER: 100%, TER: 33.3333%, total WER: 19.313%, total TER: 8.59134%, progress (thread 0): 96.2263%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_672.54_672.77, WER: 100%, TER: 33.3333%, total WER: 19.3139%, total TER: 8.59152%, progress (thread 0): 96.2342%]
|T|: m m h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1361.79_1362.02, WER: 100%, TER: 60%, total WER: 19.3148%, total TER: 8.59211%, progress (thread 0): 96.2421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_555.02_555.25, WER: 0%, TER: 0%, total WER: 19.3146%, total TER: 8.59203%, progress (thread 0): 96.25%]
|T|: h m m
|P|: h m m
[sample: EN2002c_H01_FEO072_699.88_700.11, WER: 0%, TER: 0%, total WER: 19.3144%, total TER: 8.59197%, progress (thread 0): 96.2579%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1900.45_1900.68, WER: 0%, TER: 0%, total WER: 19.3142%, total TER: 8.59189%, progress (thread 0): 96.2658%]
|T|: d o h
|P|: s o
[sample: EN2002c_H02_MEE071_2395.6_2395.83, WER: 100%, TER: 66.6667%, total WER: 19.3151%, total TER: 8.5923%, progress (thread 0): 96.2737%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2817.5_2817.73, WER: 0%, TER: 0%, total WER: 19.3149%, total TER: 8.59222%, progress (thread 0): 96.2816%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2846.79_2847.02, WER: 0%, TER: 0%, total WER: 19.3147%, total TER: 8.59214%, progress (thread 0): 96.2896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_107.69_107.92, WER: 0%, TER: 0%, total WER: 19.3144%, total TER: 8.59206%, progress (thread 0): 96.2975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_503.45_503.68, WER: 0%, TER: 0%, total WER: 19.3142%, total TER: 8.59198%, progress (thread 0): 96.3054%]
|T|: m m h m m
|P|: m m
[sample: EN2002d_H01_FEO072_548.74_548.97, WER: 100%, TER: 60%, total WER: 19.3151%, total TER: 8.59258%, progress (thread 0): 96.3133%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_767.8_768.03, WER: 0%, TER: 0%, total WER: 19.3149%, total TER: 8.5925%, progress (thread 0): 96.3212%]
|T|: n o
|P|: n o
[sample: EN2002d_H00_FEO070_1427.88_1428.11, WER: 0%, TER: 0%, total WER: 19.3147%, total TER: 8.59246%, progress (thread 0): 96.3291%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1760.63_1760.86, WER: 100%, TER: 33.3333%, total WER: 19.3156%, total TER: 8.59263%, progress (thread 0): 96.337%]
|T|: o k a y
|P|: y e h
[sample: EN2002d_H00_FEO070_2207.62_2207.85, WER: 100%, TER: 100%, total WER: 19.3165%, total TER: 8.59348%, progress (thread 0): 96.3449%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_1022.74_1022.96, WER: 0%, TER: 0%, total WER: 19.3163%, total TER: 8.5934%, progress (thread 0): 96.3528%]
|T|: m m
|P|: m m
[sample: ES2004b_H02_MEE014_1537.12_1537.34, WER: 0%, TER: 0%, total WER: 19.3161%, total TER: 8.59336%, progress (thread 0): 96.3608%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004c_H00_MEO015_575.7_575.92, WER: 0%, TER: 0%, total WER: 19.3159%, total TER: 8.59326%, progress (thread 0): 96.3687%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1311.86_1312.08, WER: 100%, TER: 40%, total WER: 19.3168%, total TER: 8.59363%, progress (thread 0): 96.3766%]
|T|: r i g h t
|P|: i k
[sample: ES2004c_H00_MEO015_2116.51_2116.73, WER: 100%, TER: 80%, total WER: 19.3177%, total TER: 8.59446%, progress (thread 0): 96.3845%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_2159.26_2159.48, WER: 0%, TER: 0%, total WER: 19.3174%, total TER: 8.59438%, progress (thread 0): 96.3924%]
|T|: y e a h
|P|: y e m
[sample: ES2004d_H02_MEE014_968.07_968.29, WER: 100%, TER: 50%, total WER: 19.3183%, total TER: 8.59477%, progress (thread 0): 96.4003%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1220.58_1220.8, WER: 0%, TER: 0%, total WER: 19.3181%, total TER: 8.59469%, progress (thread 0): 96.4082%]
|T|: o k a y
|P|: y o u t
[sample: ES2004d_H03_FEE016_1281.25_1281.47, WER: 100%, TER: 100%, total WER: 19.319%, total TER: 8.59554%, progress (thread 0): 96.4161%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1684.26_1684.48, WER: 0%, TER: 0%, total WER: 19.3188%, total TER: 8.59546%, progress (thread 0): 96.424%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_45.13_45.35, WER: 0%, TER: 0%, total WER: 19.3186%, total TER: 8.59538%, progress (thread 0): 96.432%]
|T|: ' k a y
|P|: m m
[sample: IS1009b_H02_FIO084_155.16_155.38, WER: 100%, TER: 100%, total WER: 19.3195%, total TER: 8.59623%, progress (thread 0): 96.4399%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_404.12_404.34, WER: 100%, TER: 60%, total WER: 19.3204%, total TER: 8.59683%, progress (thread 0): 96.4478%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_961.79_962.01, WER: 0%, TER: 0%, total WER: 19.3202%, total TER: 8.59675%, progress (thread 0): 96.4557%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1007.66_1007.88, WER: 0%, TER: 0%, total WER: 19.32%, total TER: 8.59667%, progress (thread 0): 96.4636%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H00_FIE088_337.53_337.75, WER: 0%, TER: 0%, total WER: 19.3198%, total TER: 8.59659%, progress (thread 0): 96.4715%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1263.61_1263.83, WER: 0%, TER: 0%, total WER: 19.3195%, total TER: 8.59651%, progress (thread 0): 96.4794%]
|T|: m m
|P|: m m m
[sample: IS1009d_H03_FIO089_590.61_590.83, WER: 100%, TER: 50%, total WER: 19.3204%, total TER: 8.5967%, progress (thread 0): 96.4873%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_801.03_801.25, WER: 0%, TER: 0%, total WER: 19.3202%, total TER: 8.59666%, progress (thread 0): 96.4953%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_1687.52_1687.74, WER: 100%, TER: 60%, total WER: 19.3211%, total TER: 8.59726%, progress (thread 0): 96.5032%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1775.76_1775.98, WER: 0%, TER: 0%, total WER: 19.3209%, total TER: 8.59718%, progress (thread 0): 96.5111%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_1846.51_1846.73, WER: 0%, TER: 0%, total WER: 19.3207%, total TER: 8.5971%, progress (thread 0): 96.519%]
|T|: m o r n i n g
|P|: m o n e y
[sample: TS3003a_H01_MTD011UID_15.34_15.56, WER: 100%, TER: 57.1429%, total WER: 19.3216%, total TER: 8.59789%, progress (thread 0): 96.5269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1316.76_1316.98, WER: 0%, TER: 0%, total WER: 19.3214%, total TER: 8.59781%, progress (thread 0): 96.5348%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1357.36_1357.58, WER: 0%, TER: 0%, total WER: 19.3212%, total TER: 8.59777%, progress (thread 0): 96.5427%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_888.56_888.78, WER: 0%, TER: 0%, total WER: 19.321%, total TER: 8.59769%, progress (thread 0): 96.5506%]
|T|: h m m
|P|: m m
[sample: TS3003b_H03_MTD012ME_1371.93_1372.15, WER: 100%, TER: 33.3333%, total WER: 19.3219%, total TER: 8.59787%, progress (thread 0): 96.5585%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1600.07_1600.29, WER: 0%, TER: 0%, total WER: 19.3216%, total TER: 8.59779%, progress (thread 0): 96.5665%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_109.75_109.97, WER: 0%, TER: 0%, total WER: 19.3214%, total TER: 8.59775%, progress (thread 0): 96.5744%]
|T|: m a y b
|P|: i
[sample: TS3003c_H00_MTD009PM_437.36_437.58, WER: 100%, TER: 100%, total WER: 19.3223%, total TER: 8.5986%, progress (thread 0): 96.5823%]
|T|: m m h m m
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_1100.48_1100.7, WER: 100%, TER: 100%, total WER: 19.3232%, total TER: 8.59966%, progress (thread 0): 96.5902%]
|T|: y e a h
|P|: m m
[sample: TS3003c_H01_MTD011UID_1304.78_1305, WER: 100%, TER: 100%, total WER: 19.3241%, total TER: 8.60051%, progress (thread 0): 96.5981%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_2263.6_2263.82, WER: 0%, TER: 0%, total WER: 19.3239%, total TER: 8.60043%, progress (thread 0): 96.606%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_563.58_563.8, WER: 0%, TER: 0%, total WER: 19.3237%, total TER: 8.60035%, progress (thread 0): 96.6139%]
|T|: y e p
|P|: y e a p
[sample: TS3003d_H02_MTD0010ID_577.11_577.33, WER: 100%, TER: 33.3333%, total WER: 19.3246%, total TER: 8.60053%, progress (thread 0): 96.6218%]
|T|: n o
|P|: o h
[sample: TS3003d_H01_MTD011UID_1458.81_1459.03, WER: 100%, TER: 100%, total WER: 19.3255%, total TER: 8.60095%, progress (thread 0): 96.6297%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1674.77_1674.99, WER: 0%, TER: 0%, total WER: 19.3253%, total TER: 8.60091%, progress (thread 0): 96.6377%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1736.91_1737.13, WER: 0%, TER: 0%, total WER: 19.3251%, total TER: 8.60083%, progress (thread 0): 96.6456%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_1751.36_1751.58, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60079%, progress (thread 0): 96.6535%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1991.39_1991.61, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60071%, progress (thread 0): 96.6614%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1998.36_1998.58, WER: 0%, TER: 0%, total WER: 19.3244%, total TER: 8.60063%, progress (thread 0): 96.6693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_482.53_482.75, WER: 0%, TER: 0%, total WER: 19.3242%, total TER: 8.60055%, progress (thread 0): 96.6772%]
|T|: h m m
|P|: m m
[sample: EN2002a_H01_FEO070_706.29_706.51, WER: 100%, TER: 33.3333%, total WER: 19.3251%, total TER: 8.60073%, progress (thread 0): 96.6851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_805.91_806.13, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60064%, progress (thread 0): 96.693%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1103.91_1104.13, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60054%, progress (thread 0): 96.701%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_1216.04_1216.26, WER: 100%, TER: 66.6667%, total WER: 19.3256%, total TER: 8.60095%, progress (thread 0): 96.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1537.4_1537.62, WER: 0%, TER: 0%, total WER: 19.3254%, total TER: 8.60087%, progress (thread 0): 96.7168%]
|T|: j u s t
|P|: j u s t
[sample: EN2002a_H01_FEO070_1871.92_1872.14, WER: 0%, TER: 0%, total WER: 19.3252%, total TER: 8.60079%, progress (thread 0): 96.7247%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1963.02_1963.24, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60071%, progress (thread 0): 96.7326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_639.41_639.63, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60063%, progress (thread 0): 96.7405%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1397.63_1397.85, WER: 0%, TER: 0%, total WER: 19.3245%, total TER: 8.60053%, progress (thread 0): 96.7484%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1547.56_1547.78, WER: 100%, TER: 33.3333%, total WER: 19.3254%, total TER: 8.6007%, progress (thread 0): 96.7563%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_575.92_576.14, WER: 100%, TER: 28.5714%, total WER: 19.3263%, total TER: 8.60103%, progress (thread 0): 96.7642%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H02_MEE071_589.02_589.24, WER: 100%, TER: 80%, total WER: 19.3272%, total TER: 8.60186%, progress (thread 0): 96.7722%]
|T|: ' k a y
|P|: y e a h
[sample: EN2002c_H03_MEE073_684.52_684.74, WER: 100%, TER: 75%, total WER: 19.3281%, total TER: 8.60248%, progress (thread 0): 96.7801%]
|T|: o h | y e a h
|P|: m h
[sample: EN2002c_H03_MEE073_1506.68_1506.9, WER: 100%, TER: 85.7143%, total WER: 19.3299%, total TER: 8.60374%, progress (thread 0): 96.788%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1602.82_1603.04, WER: 0%, TER: 0%, total WER: 19.3297%, total TER: 8.60366%, progress (thread 0): 96.7959%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1707.51_1707.73, WER: 100%, TER: 33.3333%, total WER: 19.3306%, total TER: 8.60383%, progress (thread 0): 96.8038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2260.94_2261.16, WER: 0%, TER: 0%, total WER: 19.3304%, total TER: 8.60375%, progress (thread 0): 96.8117%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2697.66_2697.88, WER: 0%, TER: 0%, total WER: 19.3302%, total TER: 8.60367%, progress (thread 0): 96.8196%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_402.37_402.59, WER: 0%, TER: 0%, total WER: 19.33%, total TER: 8.60359%, progress (thread 0): 96.8275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_736.63_736.85, WER: 0%, TER: 0%, total WER: 19.3298%, total TER: 8.60351%, progress (thread 0): 96.8354%]
|T|: o h
|P|: h u h
[sample: EN2002d_H00_FEO070_909.94_910.16, WER: 100%, TER: 100%, total WER: 19.3307%, total TER: 8.60393%, progress (thread 0): 96.8434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1061.24_1061.46, WER: 0%, TER: 0%, total WER: 19.3304%, total TER: 8.60385%, progress (thread 0): 96.8513%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1400.88_1401.1, WER: 0%, TER: 0%, total WER: 19.3302%, total TER: 8.60377%, progress (thread 0): 96.8592%]
|T|: b u t
|P|: b u h
[sample: EN2002d_H00_FEO070_1658_1658.22, WER: 100%, TER: 33.3333%, total WER: 19.3311%, total TER: 8.60395%, progress (thread 0): 96.8671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1732.66_1732.88, WER: 0%, TER: 0%, total WER: 19.3309%, total TER: 8.60387%, progress (thread 0): 96.875%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_622.02_622.23, WER: 0%, TER: 0%, total WER: 19.3307%, total TER: 8.60383%, progress (thread 0): 96.8829%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_1323.05_1323.26, WER: 0%, TER: 0%, total WER: 19.3305%, total TER: 8.60375%, progress (thread 0): 96.8908%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1464.74_1464.95, WER: 100%, TER: 40%, total WER: 19.3314%, total TER: 8.60411%, progress (thread 0): 96.8987%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_96.42_96.63, WER: 100%, TER: 40%, total WER: 19.3323%, total TER: 8.60448%, progress (thread 0): 96.9066%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1385.07_1385.28, WER: 0%, TER: 0%, total WER: 19.3321%, total TER: 8.6044%, progress (thread 0): 96.9146%]
|T|: h m m
|P|: m m
[sample: ES2004d_H00_MEO015_26.63_26.84, WER: 100%, TER: 33.3333%, total WER: 19.333%, total TER: 8.60457%, progress (thread 0): 96.9225%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_627.79_628, WER: 100%, TER: 66.6667%, total WER: 19.3339%, total TER: 8.60498%, progress (thread 0): 96.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_39.06_39.27, WER: 0%, TER: 0%, total WER: 19.3337%, total TER: 8.6049%, progress (thread 0): 96.9383%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_169.39_169.6, WER: 100%, TER: 60%, total WER: 19.3346%, total TER: 8.60549%, progress (thread 0): 96.9462%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1286.43_1286.64, WER: 100%, TER: 60%, total WER: 19.3355%, total TER: 8.60609%, progress (thread 0): 96.9541%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H00_FIE088_1289.44_1289.65, WER: 0%, TER: 0%, total WER: 19.3353%, total TER: 8.60599%, progress (thread 0): 96.962%]
|T|: m m
|P|: o n
[sample: IS1009b_H02_FIO084_1632.09_1632.3, WER: 100%, TER: 100%, total WER: 19.3362%, total TER: 8.60642%, progress (thread 0): 96.9699%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_284.08_284.29, WER: 0%, TER: 0%, total WER: 19.3359%, total TER: 8.60634%, progress (thread 0): 96.9778%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1563.53_1563.74, WER: 0%, TER: 0%, total WER: 19.3357%, total TER: 8.60626%, progress (thread 0): 96.9858%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1707.23_1707.44, WER: 0%, TER: 0%, total WER: 19.3355%, total TER: 8.60618%, progress (thread 0): 96.9937%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_741.38_741.59, WER: 0%, TER: 0%, total WER: 19.3353%, total TER: 8.6061%, progress (thread 0): 97.0016%]
|T|: y e a h
|P|:
[sample: IS1009d_H02_FIO084_1371.67_1371.88, WER: 100%, TER: 100%, total WER: 19.3362%, total TER: 8.60695%, progress (thread 0): 97.0095%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1880.74_1880.95, WER: 100%, TER: 40%, total WER: 19.3371%, total TER: 8.60731%, progress (thread 0): 97.0174%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1295.8_1296.01, WER: 0%, TER: 0%, total WER: 19.3369%, total TER: 8.60723%, progress (thread 0): 97.0253%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_901.66_901.87, WER: 0%, TER: 0%, total WER: 19.3367%, total TER: 8.60715%, progress (thread 0): 97.0332%]
|T|: y e a h
|P|: h u h
[sample: TS3003b_H01_MTD011UID_1547.15_1547.36, WER: 100%, TER: 75%, total WER: 19.3376%, total TER: 8.60777%, progress (thread 0): 97.0411%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1091.82_1092.03, WER: 0%, TER: 0%, total WER: 19.3374%, total TER: 8.60769%, progress (thread 0): 97.049%]
|T|: y e a h
|P|: h u h
[sample: TS3003c_H01_MTD011UID_1210.7_1210.91, WER: 100%, TER: 75%, total WER: 19.3383%, total TER: 8.60831%, progress (thread 0): 97.057%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_530.79_531, WER: 100%, TER: 75%, total WER: 19.3392%, total TER: 8.60893%, progress (thread 0): 97.0649%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1465.64_1465.85, WER: 0%, TER: 0%, total WER: 19.3389%, total TER: 8.60885%, progress (thread 0): 97.0728%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1695.15_1695.36, WER: 0%, TER: 0%, total WER: 19.3387%, total TER: 8.60877%, progress (thread 0): 97.0807%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1711.66_1711.87, WER: 0%, TER: 0%, total WER: 19.3385%, total TER: 8.60869%, progress (thread 0): 97.0886%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1789.64_1789.85, WER: 0%, TER: 0%, total WER: 19.3383%, total TER: 8.60861%, progress (thread 0): 97.0965%]
|T|: n o
|P|: n o
[sample: TS3003d_H03_MTD012ME_2014.88_2015.09, WER: 0%, TER: 0%, total WER: 19.3381%, total TER: 8.60857%, progress (thread 0): 97.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2064.37_2064.58, WER: 0%, TER: 0%, total WER: 19.3379%, total TER: 8.60849%, progress (thread 0): 97.1123%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2107.64_2107.85, WER: 0%, TER: 0%, total WER: 19.3376%, total TER: 8.60841%, progress (thread 0): 97.1203%]
|T|: y e a h
|P|: y m
[sample: TS3003d_H01_MTD011UID_2462.55_2462.76, WER: 100%, TER: 75%, total WER: 19.3385%, total TER: 8.60903%, progress (thread 0): 97.1282%]
|T|: s o r r y
|P|: s o r r y
[sample: EN2002a_H02_FEO072_377.93_378.14, WER: 0%, TER: 0%, total WER: 19.3383%, total TER: 8.60893%, progress (thread 0): 97.1361%]
|T|: o h
|P|: u h
[sample: EN2002a_H03_MEE071_483.42_483.63, WER: 100%, TER: 50%, total WER: 19.3392%, total TER: 8.60912%, progress (thread 0): 97.144%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_648.41_648.62, WER: 0%, TER: 0%, total WER: 19.339%, total TER: 8.60904%, progress (thread 0): 97.1519%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1319.66_1319.87, WER: 0%, TER: 0%, total WER: 19.3388%, total TER: 8.60896%, progress (thread 0): 97.1598%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1429.36_1429.57, WER: 0%, TER: 0%, total WER: 19.3386%, total TER: 8.60888%, progress (thread 0): 97.1677%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1896.08_1896.29, WER: 0%, TER: 0%, total WER: 19.3384%, total TER: 8.6088%, progress (thread 0): 97.1756%]
|T|: y e a h
|P|: h e h
[sample: EN2002a_H03_MEE071_2122.73_2122.94, WER: 100%, TER: 50%, total WER: 19.3393%, total TER: 8.60918%, progress (thread 0): 97.1835%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_266.76_266.97, WER: 0%, TER: 0%, total WER: 19.3391%, total TER: 8.6091%, progress (thread 0): 97.1915%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_291.79_292, WER: 0%, TER: 0%, total WER: 19.3388%, total TER: 8.60902%, progress (thread 0): 97.1994%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1335.23_1335.44, WER: 0%, TER: 0%, total WER: 19.3386%, total TER: 8.60894%, progress (thread 0): 97.2073%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_21.49_21.7, WER: 100%, TER: 25%, total WER: 19.3395%, total TER: 8.6091%, progress (thread 0): 97.2152%]
|T|: o k a y
|P|: o m a y
[sample: EN2002c_H03_MEE073_183.87_184.08, WER: 100%, TER: 25%, total WER: 19.3404%, total TER: 8.60925%, progress (thread 0): 97.2231%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1748.27_1748.48, WER: 0%, TER: 0%, total WER: 19.3402%, total TER: 8.60915%, progress (thread 0): 97.231%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1810.36_1810.57, WER: 100%, TER: 100%, total WER: 19.342%, total TER: 8.61064%, progress (thread 0): 97.2389%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1861.15_1861.36, WER: 0%, TER: 0%, total WER: 19.3418%, total TER: 8.61056%, progress (thread 0): 97.2468%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2180.5_2180.71, WER: 0%, TER: 0%, total WER: 19.3416%, total TER: 8.61048%, progress (thread 0): 97.2547%]
|T|: t r u e
|P|: t r u e
[sample: EN2002c_H03_MEE073_2453.86_2454.07, WER: 0%, TER: 0%, total WER: 19.3414%, total TER: 8.6104%, progress (thread 0): 97.2627%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2819.2_2819.41, WER: 0%, TER: 0%, total WER: 19.3411%, total TER: 8.61032%, progress (thread 0): 97.2706%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_164.51_164.72, WER: 100%, TER: 40%, total WER: 19.3421%, total TER: 8.61068%, progress (thread 0): 97.2785%]
|T|: r i g h t
|P|: y e a h
[sample: EN2002d_H03_MEE073_338.56_338.77, WER: 100%, TER: 80%, total WER: 19.343%, total TER: 8.61151%, progress (thread 0): 97.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_647.49_647.7, WER: 0%, TER: 0%, total WER: 19.3427%, total TER: 8.61143%, progress (thread 0): 97.2943%]
|T|: m m
|P|: y e a h
[sample: EN2002d_H03_MEE073_782.98_783.19, WER: 100%, TER: 200%, total WER: 19.3436%, total TER: 8.61232%, progress (thread 0): 97.3022%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_998.01_998.22, WER: 0%, TER: 0%, total WER: 19.3434%, total TER: 8.61222%, progress (thread 0): 97.3101%]
|T|: o h | y e a h
|P|: o
[sample: EN2002d_H00_FEO070_1377.89_1378.1, WER: 100%, TER: 85.7143%, total WER: 19.3452%, total TER: 8.61348%, progress (thread 0): 97.318%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1391.57_1391.78, WER: 0%, TER: 0%, total WER: 19.345%, total TER: 8.6134%, progress (thread 0): 97.326%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1912.21_1912.42, WER: 50%, TER: 42.8571%, total WER: 19.3457%, total TER: 8.61396%, progress (thread 0): 97.3339%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_2147.17_2147.38, WER: 100%, TER: 33.3333%, total WER: 19.3466%, total TER: 8.61413%, progress (thread 0): 97.3418%]
|T|: o k a y
|P|: h m m
[sample: ES2004b_H00_MEO015_338.55_338.75, WER: 100%, TER: 100%, total WER: 19.3475%, total TER: 8.61498%, progress (thread 0): 97.3497%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1514.44_1514.64, WER: 0%, TER: 0%, total WER: 19.3473%, total TER: 8.6149%, progress (thread 0): 97.3576%]
|T|: o o p s
|P|: o p
[sample: ES2004c_H00_MEO015_54.23_54.43, WER: 100%, TER: 50%, total WER: 19.3482%, total TER: 8.61529%, progress (thread 0): 97.3655%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1395.98_1396.18, WER: 100%, TER: 40%, total WER: 19.3491%, total TER: 8.61565%, progress (thread 0): 97.3734%]
|T|: ' k a y
|P|: t
[sample: ES2004d_H00_MEO015_562.82_563.02, WER: 100%, TER: 100%, total WER: 19.35%, total TER: 8.6165%, progress (thread 0): 97.3813%]
|T|: y e p
|P|: y e a h
[sample: ES2004d_H02_MEE014_922.35_922.55, WER: 100%, TER: 66.6667%, total WER: 19.3509%, total TER: 8.61691%, progress (thread 0): 97.3892%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1214.04_1214.24, WER: 0%, TER: 0%, total WER: 19.3507%, total TER: 8.61683%, progress (thread 0): 97.3972%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1952.42_1952.62, WER: 0%, TER: 0%, total WER: 19.3505%, total TER: 8.61675%, progress (thread 0): 97.4051%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_784.26_784.46, WER: 0%, TER: 0%, total WER: 19.3503%, total TER: 8.61667%, progress (thread 0): 97.413%]
|T|: ' k a y
|P|: k e a y
[sample: IS1009a_H03_FIO089_794.48_794.68, WER: 100%, TER: 50%, total WER: 19.3512%, total TER: 8.61705%, progress (thread 0): 97.4209%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H02_FIO084_179.12_179.32, WER: 100%, TER: 200%, total WER: 19.3521%, total TER: 8.61794%, progress (thread 0): 97.4288%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1111.83_1112.03, WER: 0%, TER: 0%, total WER: 19.3518%, total TER: 8.61786%, progress (thread 0): 97.4367%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_2011.99_2012.19, WER: 100%, TER: 60%, total WER: 19.3527%, total TER: 8.61846%, progress (thread 0): 97.4446%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_285.99_286.19, WER: 0%, TER: 0%, total WER: 19.3525%, total TER: 8.61838%, progress (thread 0): 97.4525%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1689.26_1689.46, WER: 0%, TER: 0%, total WER: 19.3523%, total TER: 8.6183%, progress (thread 0): 97.4604%]
|T|: m m
|P|: m m
[sample: IS1009d_H01_FIO087_964.59_964.79, WER: 0%, TER: 0%, total WER: 19.3521%, total TER: 8.61826%, progress (thread 0): 97.4684%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1286.22_1286.42, WER: 0%, TER: 0%, total WER: 19.3519%, total TER: 8.61818%, progress (thread 0): 97.4763%]
|T|: m m
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1431.86_1432.06, WER: 100%, TER: 200%, total WER: 19.3528%, total TER: 8.61907%, progress (thread 0): 97.4842%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_550.33_550.53, WER: 100%, TER: 25%, total WER: 19.3537%, total TER: 8.61922%, progress (thread 0): 97.4921%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_576.96_577.16, WER: 0%, TER: 0%, total WER: 19.3535%, total TER: 8.61918%, progress (thread 0): 97.5%]
|T|: y e a h
|P|: e h
[sample: TS3003b_H01_MTD011UID_1340.71_1340.91, WER: 100%, TER: 50%, total WER: 19.3544%, total TER: 8.61957%, progress (thread 0): 97.5079%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1635.98_1636.18, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61953%, progress (thread 0): 97.5158%]
|T|: m m
|P|: m m
[sample: TS3003c_H03_MTD012ME_1936.99_1937.19, WER: 0%, TER: 0%, total WER: 19.3539%, total TER: 8.61949%, progress (thread 0): 97.5237%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_214.36_214.56, WER: 0%, TER: 0%, total WER: 19.3537%, total TER: 8.61941%, progress (thread 0): 97.5316%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_418.75_418.95, WER: 100%, TER: 66.6667%, total WER: 19.3546%, total TER: 8.61981%, progress (thread 0): 97.5396%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1236.15_1236.35, WER: 0%, TER: 0%, total WER: 19.3544%, total TER: 8.61973%, progress (thread 0): 97.5475%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1334.1_1334.3, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61965%, progress (thread 0): 97.5554%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1736.65_1736.85, WER: 0%, TER: 0%, total WER: 19.354%, total TER: 8.61957%, progress (thread 0): 97.5633%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1862.16_1862.36, WER: 0%, TER: 0%, total WER: 19.3538%, total TER: 8.61949%, progress (thread 0): 97.5712%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2423.71_2423.91, WER: 100%, TER: 33.3333%, total WER: 19.3547%, total TER: 8.61967%, progress (thread 0): 97.5791%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_6.65_6.85, WER: 0%, TER: 0%, total WER: 19.3544%, total TER: 8.61959%, progress (thread 0): 97.587%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_207.6_207.8, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61951%, progress (thread 0): 97.5949%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H00_MEE073_325.97_326.17, WER: 0%, TER: 0%, total WER: 19.354%, total TER: 8.61945%, progress (thread 0): 97.6029%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_517.59_517.79, WER: 0%, TER: 0%, total WER: 19.3538%, total TER: 8.61937%, progress (thread 0): 97.6108%]
|T|: m m h m m
|P|: m m
[sample: EN2002a_H00_MEE073_625.1_625.3, WER: 100%, TER: 60%, total WER: 19.3547%, total TER: 8.61996%, progress (thread 0): 97.6187%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1082.64_1082.84, WER: 0%, TER: 0%, total WER: 19.3545%, total TER: 8.61988%, progress (thread 0): 97.6266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1296.98_1297.18, WER: 0%, TER: 0%, total WER: 19.3543%, total TER: 8.6198%, progress (thread 0): 97.6345%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1544.23_1544.43, WER: 100%, TER: 33.3333%, total WER: 19.3552%, total TER: 8.61998%, progress (thread 0): 97.6424%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_1988.65_1988.85, WER: 0%, TER: 0%, total WER: 19.3549%, total TER: 8.6199%, progress (thread 0): 97.6503%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_58.73_58.93, WER: 100%, TER: 66.6667%, total WER: 19.3558%, total TER: 8.6203%, progress (thread 0): 97.6582%]
|T|: d o | w e | d
|P|: d o m e
[sample: EN2002b_H01_MEE071_223.55_223.75, WER: 100%, TER: 57.1429%, total WER: 19.3586%, total TER: 8.62109%, progress (thread 0): 97.6661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_233.29_233.49, WER: 0%, TER: 0%, total WER: 19.3583%, total TER: 8.62101%, progress (thread 0): 97.674%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_259.41_259.61, WER: 100%, TER: 33.3333%, total WER: 19.3592%, total TER: 8.62118%, progress (thread 0): 97.682%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_498.07_498.27, WER: 0%, TER: 0%, total WER: 19.359%, total TER: 8.6211%, progress (thread 0): 97.6899%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1591.2_1591.4, WER: 0%, TER: 0%, total WER: 19.3588%, total TER: 8.621%, progress (thread 0): 97.6978%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H00_FEO070_1723.38_1723.58, WER: 0%, TER: 0%, total WER: 19.3586%, total TER: 8.62094%, progress (thread 0): 97.7057%]
|T|: o k a y
|P|: y e a y
[sample: EN2002c_H03_MEE073_241.42_241.62, WER: 100%, TER: 50%, total WER: 19.3595%, total TER: 8.62133%, progress (thread 0): 97.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_404.9_405.1, WER: 0%, TER: 0%, total WER: 19.3593%, total TER: 8.62123%, progress (thread 0): 97.7215%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_817.73_817.93, WER: 100%, TER: 33.3333%, total WER: 19.3602%, total TER: 8.6214%, progress (thread 0): 97.7294%]
|T|: y e a h
|P|: m e m
[sample: EN2002c_H02_MEE071_1319.11_1319.31, WER: 100%, TER: 75%, total WER: 19.3611%, total TER: 8.62202%, progress (thread 0): 97.7373%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1392.95_1393.15, WER: 0%, TER: 0%, total WER: 19.3609%, total TER: 8.62194%, progress (thread 0): 97.7453%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1395.54_1395.74, WER: 0%, TER: 0%, total WER: 19.3606%, total TER: 8.62184%, progress (thread 0): 97.7532%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1637.64_1637.84, WER: 0%, TER: 0%, total WER: 19.3604%, total TER: 8.62174%, progress (thread 0): 97.7611%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1911.7_1911.9, WER: 0%, TER: 0%, total WER: 19.3602%, total TER: 8.62164%, progress (thread 0): 97.769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1939.05_1939.25, WER: 0%, TER: 0%, total WER: 19.36%, total TER: 8.62156%, progress (thread 0): 97.7769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_327.41_327.61, WER: 0%, TER: 0%, total WER: 19.3598%, total TER: 8.62148%, progress (thread 0): 97.7848%]
|T|: y e a h
|P|: y e m m
[sample: EN2002d_H03_MEE073_967.12_967.32, WER: 100%, TER: 50%, total WER: 19.3607%, total TER: 8.62186%, progress (thread 0): 97.7927%]
|T|: y e p
|P|: y e a p
[sample: ES2004a_H03_FEE016_1048.29_1048.48, WER: 100%, TER: 33.3333%, total WER: 19.3616%, total TER: 8.62203%, progress (thread 0): 97.8006%]
|T|: o o p s
|P|: m
[sample: ES2004b_H00_MEO015_572.79_572.98, WER: 100%, TER: 100%, total WER: 19.3625%, total TER: 8.62288%, progress (thread 0): 97.8085%]
|T|: a l r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1308.81_1309, WER: 100%, TER: 28.5714%, total WER: 19.3634%, total TER: 8.62321%, progress (thread 0): 97.8165%]
|T|: h u h
|P|: m m
[sample: ES2004b_H02_MEE014_1454.52_1454.71, WER: 100%, TER: 100%, total WER: 19.3643%, total TER: 8.62385%, progress (thread 0): 97.8244%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H03_FEE016_1635.41_1635.6, WER: 100%, TER: 200%, total WER: 19.3652%, total TER: 8.62474%, progress (thread 0): 97.8323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1436.81_1437, WER: 0%, TER: 0%, total WER: 19.365%, total TER: 8.62466%, progress (thread 0): 97.8402%]
|T|: t h i n g
|P|: t h i n g
[sample: ES2004d_H02_MEE014_2209.98_2210.17, WER: 0%, TER: 0%, total WER: 19.3648%, total TER: 8.62456%, progress (thread 0): 97.8481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_805.49_805.68, WER: 0%, TER: 0%, total WER: 19.3645%, total TER: 8.62448%, progress (thread 0): 97.856%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_690.57_690.76, WER: 0%, TER: 0%, total WER: 19.3643%, total TER: 8.62438%, progress (thread 0): 97.8639%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H00_FIE088_808.35_808.54, WER: 100%, TER: 80%, total WER: 19.3652%, total TER: 8.62521%, progress (thread 0): 97.8718%]
|T|: b a t t e r y
|P|: t h a t
[sample: IS1009c_H01_FIO087_1631.84_1632.03, WER: 100%, TER: 85.7143%, total WER: 19.3661%, total TER: 8.62646%, progress (thread 0): 97.8798%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1314.07_1314.26, WER: 0%, TER: 0%, total WER: 19.3659%, total TER: 8.62638%, progress (thread 0): 97.8877%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_506.74_506.93, WER: 100%, TER: 25%, total WER: 19.3668%, total TER: 8.62653%, progress (thread 0): 97.8956%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1214.55_1214.74, WER: 0%, TER: 0%, total WER: 19.3666%, total TER: 8.62649%, progress (thread 0): 97.9035%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_1401.33_1401.52, WER: 100%, TER: 25%, total WER: 19.3675%, total TER: 8.62665%, progress (thread 0): 97.9114%]
|T|: y e a h
|P|: n o h
[sample: TS3003b_H01_MTD011UID_2147.67_2147.86, WER: 100%, TER: 75%, total WER: 19.3684%, total TER: 8.62726%, progress (thread 0): 97.9193%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1090.92_1091.11, WER: 0%, TER: 0%, total WER: 19.3682%, total TER: 8.62718%, progress (thread 0): 97.9272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1632.95_1633.14, WER: 0%, TER: 0%, total WER: 19.368%, total TER: 8.6271%, progress (thread 0): 97.9351%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_375.66_375.85, WER: 100%, TER: 75%, total WER: 19.3689%, total TER: 8.62772%, progress (thread 0): 97.943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_507.6_507.79, WER: 0%, TER: 0%, total WER: 19.3687%, total TER: 8.62764%, progress (thread 0): 97.951%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_816.71_816.9, WER: 0%, TER: 0%, total WER: 19.3684%, total TER: 8.62756%, progress (thread 0): 97.9589%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_966.74_966.93, WER: 0%, TER: 0%, total WER: 19.3682%, total TER: 8.62752%, progress (thread 0): 97.9668%]
|T|: m m
|P|: m m
[sample: TS3003d_H00_MTD009PM_1884.46_1884.65, WER: 0%, TER: 0%, total WER: 19.368%, total TER: 8.62748%, progress (thread 0): 97.9747%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1975.04_1975.23, WER: 100%, TER: 66.6667%, total WER: 19.3689%, total TER: 8.62789%, progress (thread 0): 97.9826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1998.6_1998.79, WER: 0%, TER: 0%, total WER: 19.3687%, total TER: 8.6278%, progress (thread 0): 97.9905%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H00_MEE073_399.03_399.22, WER: 100%, TER: 66.6667%, total WER: 19.3696%, total TER: 8.62821%, progress (thread 0): 97.9984%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H03_MEE071_741.39_741.58, WER: 100%, TER: 100%, total WER: 19.3705%, total TER: 8.62906%, progress (thread 0): 98.0063%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1037.67_1037.86, WER: 0%, TER: 0%, total WER: 19.3703%, total TER: 8.62898%, progress (thread 0): 98.0142%]
|T|: w h
|P|: m m
[sample: EN2002a_H03_MEE071_1305.67_1305.86, WER: 100%, TER: 100%, total WER: 19.3712%, total TER: 8.62941%, progress (thread 0): 98.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1781.11_1781.3, WER: 0%, TER: 0%, total WER: 19.371%, total TER: 8.62932%, progress (thread 0): 98.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1823.51_1823.7, WER: 0%, TER: 0%, total WER: 19.3707%, total TER: 8.62924%, progress (thread 0): 98.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1836.03_1836.22, WER: 0%, TER: 0%, total WER: 19.3705%, total TER: 8.62916%, progress (thread 0): 98.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1886.9_1887.09, WER: 0%, TER: 0%, total WER: 19.3703%, total TER: 8.62908%, progress (thread 0): 98.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1917.91_1918.1, WER: 0%, TER: 0%, total WER: 19.3701%, total TER: 8.629%, progress (thread 0): 98.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_142.44_142.63, WER: 0%, TER: 0%, total WER: 19.3699%, total TER: 8.62892%, progress (thread 0): 98.0696%]
|T|: u h h u h
|P|: m m
[sample: EN2002c_H03_MEE073_1527.18_1527.37, WER: 100%, TER: 100%, total WER: 19.3708%, total TER: 8.62999%, progress (thread 0): 98.0775%]
|T|: h m m
|P|: m e h
[sample: EN2002c_H03_MEE073_1630_1630.19, WER: 100%, TER: 100%, total WER: 19.3717%, total TER: 8.63062%, progress (thread 0): 98.0854%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_2672.39_2672.58, WER: 100%, TER: 33.3333%, total WER: 19.3726%, total TER: 8.6308%, progress (thread 0): 98.0934%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2679.33_2679.52, WER: 0%, TER: 0%, total WER: 19.3724%, total TER: 8.6307%, progress (thread 0): 98.1013%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_647.1_647.29, WER: 0%, TER: 0%, total WER: 19.3722%, total TER: 8.6306%, progress (thread 0): 98.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_649.08_649.27, WER: 0%, TER: 0%, total WER: 19.3719%, total TER: 8.63051%, progress (thread 0): 98.1171%]
|T|: o k
|P|: o k a y
[sample: EN2002d_H03_MEE073_909.71_909.9, WER: 100%, TER: 100%, total WER: 19.3728%, total TER: 8.63094%, progress (thread 0): 98.125%]
|T|: s u r e
|P|: t r u e
[sample: EN2002d_H03_MEE073_1568.39_1568.58, WER: 100%, TER: 75%, total WER: 19.3737%, total TER: 8.63156%, progress (thread 0): 98.1329%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_1708.21_1708.4, WER: 100%, TER: 66.6667%, total WER: 19.3746%, total TER: 8.63196%, progress (thread 0): 98.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2037.78_2037.97, WER: 0%, TER: 0%, total WER: 19.3744%, total TER: 8.63188%, progress (thread 0): 98.1487%]
|T|: s o
|P|: s o
[sample: ES2004a_H03_FEE016_605.84_606.02, WER: 0%, TER: 0%, total WER: 19.3742%, total TER: 8.63184%, progress (thread 0): 98.1566%]
|T|: ' k a y
|P|:
[sample: ES2004b_H00_MEO015_1027.78_1027.96, WER: 100%, TER: 100%, total WER: 19.3751%, total TER: 8.63269%, progress (thread 0): 98.1646%]
|T|: m m h m m
|P|: m m
[sample: ES2004c_H03_FEE016_1360.96_1361.14, WER: 100%, TER: 60%, total WER: 19.376%, total TER: 8.63329%, progress (thread 0): 98.1725%]
|T|: m m
|P|: h m m
[sample: ES2004d_H02_MEE014_2221.15_2221.33, WER: 100%, TER: 50%, total WER: 19.3769%, total TER: 8.63348%, progress (thread 0): 98.1804%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_361.5_361.68, WER: 100%, TER: 60%, total WER: 19.3778%, total TER: 8.63408%, progress (thread 0): 98.1883%]
|T|: u m
|P|: i m a n
[sample: IS1009c_H03_FIO089_1368.34_1368.52, WER: 100%, TER: 150%, total WER: 19.3787%, total TER: 8.63474%, progress (thread 0): 98.1962%]
|T|: a n d
|P|: a n d
[sample: IS1009c_H01_FIO087_1687.91_1688.09, WER: 0%, TER: 0%, total WER: 19.3785%, total TER: 8.63468%, progress (thread 0): 98.2041%]
|T|: m m
|P|: m m
[sample: IS1009d_H00_FIE088_1039.16_1039.34, WER: 0%, TER: 0%, total WER: 19.3783%, total TER: 8.63464%, progress (thread 0): 98.212%]
|T|: y e s
|P|: y e a
[sample: IS1009d_H01_FIO087_1532.02_1532.2, WER: 100%, TER: 33.3333%, total WER: 19.3792%, total TER: 8.63481%, progress (thread 0): 98.2199%]
|T|: n o
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1017.51_1017.69, WER: 100%, TER: 200%, total WER: 19.3801%, total TER: 8.6357%, progress (thread 0): 98.2278%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_583.19_583.37, WER: 0%, TER: 0%, total WER: 19.3799%, total TER: 8.63562%, progress (thread 0): 98.2358%]
|T|: m m
|P|: h m h
[sample: TS3003b_H01_MTD011UID_1188.77_1188.95, WER: 100%, TER: 100%, total WER: 19.3808%, total TER: 8.63604%, progress (thread 0): 98.2437%]
|T|: y e a h
|P|: y e m
[sample: TS3003b_H02_MTD0010ID_1801.24_1801.42, WER: 100%, TER: 50%, total WER: 19.3817%, total TER: 8.63643%, progress (thread 0): 98.2516%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1801.82_1802, WER: 0%, TER: 0%, total WER: 19.3815%, total TER: 8.63639%, progress (thread 0): 98.2595%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1131_1131.18, WER: 100%, TER: 33.3333%, total WER: 19.3824%, total TER: 8.63656%, progress (thread 0): 98.2674%]
|T|: a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_2394.1_2394.28, WER: 100%, TER: 50%, total WER: 19.3833%, total TER: 8.63675%, progress (thread 0): 98.2753%]
|T|: n o
|P|: m h
[sample: EN2002a_H00_MEE073_23.11_23.29, WER: 100%, TER: 100%, total WER: 19.3842%, total TER: 8.63718%, progress (thread 0): 98.2832%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_32.53_32.71, WER: 0%, TER: 0%, total WER: 19.3839%, total TER: 8.6371%, progress (thread 0): 98.2911%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_106.69_106.87, WER: 100%, TER: 33.3333%, total WER: 19.3848%, total TER: 8.63727%, progress (thread 0): 98.299%]
|T|: y e a h
|P|: m h
[sample: EN2002a_H00_MEE073_1441.15_1441.33, WER: 100%, TER: 75%, total WER: 19.3857%, total TER: 8.63789%, progress (thread 0): 98.307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1717.2_1717.38, WER: 0%, TER: 0%, total WER: 19.3855%, total TER: 8.63781%, progress (thread 0): 98.3149%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_530.52_530.7, WER: 0%, TER: 0%, total WER: 19.3853%, total TER: 8.63773%, progress (thread 0): 98.3228%]
|T|: i s | i t
|P|: i s | i t
[sample: EN2002b_H01_MEE071_1142.07_1142.25, WER: 0%, TER: 0%, total WER: 19.3849%, total TER: 8.63763%, progress (thread 0): 98.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1339.32_1339.5, WER: 0%, TER: 0%, total WER: 19.3847%, total TER: 8.63755%, progress (thread 0): 98.3386%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_186.7_186.88, WER: 0%, TER: 0%, total WER: 19.3844%, total TER: 8.63744%, progress (thread 0): 98.3465%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_536.86_537.04, WER: 100%, TER: 28.5714%, total WER: 19.3853%, total TER: 8.63777%, progress (thread 0): 98.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1667.09_1667.27, WER: 0%, TER: 0%, total WER: 19.3851%, total TER: 8.63769%, progress (thread 0): 98.3623%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_363.49_363.67, WER: 100%, TER: 33.3333%, total WER: 19.386%, total TER: 8.63786%, progress (thread 0): 98.3703%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_500.44_500.62, WER: 0%, TER: 0%, total WER: 19.3858%, total TER: 8.63776%, progress (thread 0): 98.3782%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_508.48_508.66, WER: 0%, TER: 0%, total WER: 19.3856%, total TER: 8.63768%, progress (thread 0): 98.3861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_512.59_512.77, WER: 0%, TER: 0%, total WER: 19.3854%, total TER: 8.6376%, progress (thread 0): 98.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_518.66_518.84, WER: 0%, TER: 0%, total WER: 19.3852%, total TER: 8.63752%, progress (thread 0): 98.4019%]
|T|: y e a h
|P|: h u h
[sample: EN2002d_H00_FEO070_882.18_882.36, WER: 100%, TER: 75%, total WER: 19.3861%, total TER: 8.63814%, progress (thread 0): 98.4098%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_1451.31_1451.49, WER: 0%, TER: 0%, total WER: 19.3859%, total TER: 8.6381%, progress (thread 0): 98.4177%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1581.55_1581.73, WER: 0%, TER: 0%, total WER: 19.3856%, total TER: 8.638%, progress (thread 0): 98.4256%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_2062.62_2062.8, WER: 0%, TER: 0%, total WER: 19.3854%, total TER: 8.63792%, progress (thread 0): 98.4335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2166.41_2166.59, WER: 0%, TER: 0%, total WER: 19.3852%, total TER: 8.63784%, progress (thread 0): 98.4415%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_2172.96_2173.14, WER: 0%, TER: 0%, total WER: 19.385%, total TER: 8.63774%, progress (thread 0): 98.4494%]
|T|: a h
|P|: e h
[sample: ES2004b_H01_FEE013_843.75_843.92, WER: 100%, TER: 50%, total WER: 19.3859%, total TER: 8.63793%, progress (thread 0): 98.4573%]
|T|: y e p
|P|: y e a h
[sample: ES2004c_H00_MEO015_2220.58_2220.75, WER: 100%, TER: 66.6667%, total WER: 19.3868%, total TER: 8.63833%, progress (thread 0): 98.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_381.27_381.44, WER: 0%, TER: 0%, total WER: 19.3866%, total TER: 8.63825%, progress (thread 0): 98.4731%]
|T|: h i
|P|: h i
[sample: IS1009c_H02_FIO084_43.25_43.42, WER: 0%, TER: 0%, total WER: 19.3864%, total TER: 8.63821%, progress (thread 0): 98.481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_347.27_347.44, WER: 0%, TER: 0%, total WER: 19.3861%, total TER: 8.63813%, progress (thread 0): 98.4889%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_784.48_784.65, WER: 0%, TER: 0%, total WER: 19.3859%, total TER: 8.63809%, progress (thread 0): 98.4968%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_182.69_182.86, WER: 100%, TER: 66.6667%, total WER: 19.3868%, total TER: 8.6385%, progress (thread 0): 98.5047%]
|T|: h m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_748.2_748.37, WER: 100%, TER: 33.3333%, total WER: 19.3877%, total TER: 8.63867%, progress (thread 0): 98.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1432.6_1432.77, WER: 0%, TER: 0%, total WER: 19.3875%, total TER: 8.63859%, progress (thread 0): 98.5206%]
|T|: n o
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1442.45_1442.62, WER: 100%, TER: 200%, total WER: 19.3884%, total TER: 8.63948%, progress (thread 0): 98.5285%]
|T|: y e a h
|P|: a h
[sample: TS3003b_H01_MTD011UID_1584.22_1584.39, WER: 100%, TER: 50%, total WER: 19.3893%, total TER: 8.63986%, progress (thread 0): 98.5364%]
|T|: n o
|P|: u h
[sample: TS3003b_H01_MTD011UID_1779.4_1779.57, WER: 100%, TER: 100%, total WER: 19.3902%, total TER: 8.64029%, progress (thread 0): 98.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2009.37_2009.54, WER: 0%, TER: 0%, total WER: 19.39%, total TER: 8.64021%, progress (thread 0): 98.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1183.13_1183.3, WER: 0%, TER: 0%, total WER: 19.3898%, total TER: 8.64013%, progress (thread 0): 98.5601%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2272.21_2272.38, WER: 0%, TER: 0%, total WER: 19.3896%, total TER: 8.64005%, progress (thread 0): 98.568%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_306.52_306.69, WER: 0%, TER: 0%, total WER: 19.3893%, total TER: 8.63997%, progress (thread 0): 98.576%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1283.14_1283.31, WER: 100%, TER: 75%, total WER: 19.3902%, total TER: 8.64058%, progress (thread 0): 98.5839%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_549.83_550, WER: 100%, TER: 33.3333%, total WER: 19.3911%, total TER: 8.64076%, progress (thread 0): 98.5918%]
|T|: h m m
|P|: m h
[sample: EN2002a_H00_MEE073_1153.54_1153.71, WER: 100%, TER: 66.6667%, total WER: 19.392%, total TER: 8.64116%, progress (thread 0): 98.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1180.31_1180.48, WER: 0%, TER: 0%, total WER: 19.3918%, total TER: 8.64108%, progress (thread 0): 98.6076%]
|T|: h m m
|P|: m m
[sample: EN2002a_H02_FEO072_2053.69_2053.86, WER: 100%, TER: 33.3333%, total WER: 19.3927%, total TER: 8.64125%, progress (thread 0): 98.6155%]
|T|: y e a h
|P|: y e a m
[sample: EN2002a_H01_FEO070_2086.54_2086.71, WER: 100%, TER: 25%, total WER: 19.3936%, total TER: 8.6414%, progress (thread 0): 98.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_423.51_423.68, WER: 0%, TER: 0%, total WER: 19.3934%, total TER: 8.64132%, progress (thread 0): 98.6313%]
|T|: y e a h
|P|: m h
[sample: EN2002b_H03_MEE073_1344.27_1344.44, WER: 100%, TER: 75%, total WER: 19.3943%, total TER: 8.64194%, progress (thread 0): 98.6392%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_1400.92_1401.09, WER: 100%, TER: 100%, total WER: 19.3952%, total TER: 8.64279%, progress (thread 0): 98.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1518.9_1519.07, WER: 0%, TER: 0%, total WER: 19.395%, total TER: 8.64271%, progress (thread 0): 98.6551%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_55.6_55.77, WER: 0%, TER: 0%, total WER: 19.3948%, total TER: 8.64263%, progress (thread 0): 98.663%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_711.52_711.69, WER: 0%, TER: 0%, total WER: 19.3946%, total TER: 8.64255%, progress (thread 0): 98.6709%]
|T|: b u t
|P|: m
[sample: EN2002d_H02_MEE071_431.16_431.33, WER: 100%, TER: 100%, total WER: 19.3955%, total TER: 8.64319%, progress (thread 0): 98.6788%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_649.62_649.79, WER: 0%, TER: 0%, total WER: 19.3952%, total TER: 8.64311%, progress (thread 0): 98.6867%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_831.51_831.68, WER: 0%, TER: 0%, total WER: 19.395%, total TER: 8.64303%, progress (thread 0): 98.6946%]
|T|: s o
|P|: m
[sample: EN2002d_H03_MEE073_1058.82_1058.99, WER: 100%, TER: 100%, total WER: 19.3959%, total TER: 8.64345%, progress (thread 0): 98.7025%]
|T|: o h
|P|: o h
[sample: EN2002d_H00_FEO070_1459.48_1459.65, WER: 0%, TER: 0%, total WER: 19.3957%, total TER: 8.64341%, progress (thread 0): 98.7104%]
|T|: r i g h t
|P|: r i g t
[sample: EN2002d_H03_MEE073_1858.1_1858.27, WER: 100%, TER: 20%, total WER: 19.3966%, total TER: 8.64354%, progress (thread 0): 98.7184%]
|T|: i | m e a n
|P|: a n y
[sample: ES2004c_H03_FEE016_691.32_691.48, WER: 100%, TER: 83.3333%, total WER: 19.3984%, total TER: 8.64458%, progress (thread 0): 98.7263%]
|T|: o k a y
|P|: m
[sample: ES2004c_H03_FEE016_1560.36_1560.52, WER: 100%, TER: 100%, total WER: 19.3993%, total TER: 8.64543%, progress (thread 0): 98.7342%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H00_FIE088_757.84_758, WER: 0%, TER: 0%, total WER: 19.3991%, total TER: 8.64535%, progress (thread 0): 98.7421%]
|T|: n o
|P|: n o
[sample: IS1009d_H01_FIO087_584.12_584.28, WER: 0%, TER: 0%, total WER: 19.3989%, total TER: 8.64531%, progress (thread 0): 98.75%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_740.48_740.64, WER: 0%, TER: 0%, total WER: 19.3987%, total TER: 8.64523%, progress (thread 0): 98.7579%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_826.94_827.1, WER: 0%, TER: 0%, total WER: 19.3984%, total TER: 8.64515%, progress (thread 0): 98.7658%]
|T|: n o
|P|: n o
[sample: IS1009d_H03_FIO089_862.59_862.75, WER: 0%, TER: 0%, total WER: 19.3982%, total TER: 8.64511%, progress (thread 0): 98.7737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1025.48_1025.64, WER: 0%, TER: 0%, total WER: 19.398%, total TER: 8.64503%, progress (thread 0): 98.7816%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_580.47_580.63, WER: 0%, TER: 0%, total WER: 19.3978%, total TER: 8.64495%, progress (thread 0): 98.7896%]
|T|: u h
|P|: i h
[sample: TS3003a_H00_MTD009PM_617.55_617.71, WER: 100%, TER: 50%, total WER: 19.3987%, total TER: 8.64514%, progress (thread 0): 98.7975%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1421.57_1421.73, WER: 100%, TER: 150%, total WER: 19.3996%, total TER: 8.6458%, progress (thread 0): 98.8054%]
|T|: h m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1789.09_1789.25, WER: 100%, TER: 33.3333%, total WER: 19.4005%, total TER: 8.64597%, progress (thread 0): 98.8133%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2009.54_2009.7, WER: 0%, TER: 0%, total WER: 19.4003%, total TER: 8.64589%, progress (thread 0): 98.8212%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1865.26_1865.42, WER: 100%, TER: 66.6667%, total WER: 19.4012%, total TER: 8.6463%, progress (thread 0): 98.8291%]
|T|: o h
|P|: t o
[sample: TS3003d_H00_MTD009PM_2587.23_2587.39, WER: 100%, TER: 100%, total WER: 19.4021%, total TER: 8.64672%, progress (thread 0): 98.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_376.27_376.43, WER: 0%, TER: 0%, total WER: 19.4019%, total TER: 8.64664%, progress (thread 0): 98.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_720.58_720.74, WER: 0%, TER: 0%, total WER: 19.4017%, total TER: 8.64656%, progress (thread 0): 98.8529%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H00_MEE073_737.85_738.01, WER: 100%, TER: 100%, total WER: 19.4026%, total TER: 8.64741%, progress (thread 0): 98.8608%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_844.91_845.07, WER: 0%, TER: 0%, total WER: 19.4023%, total TER: 8.64733%, progress (thread 0): 98.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1041.33_1041.49, WER: 0%, TER: 0%, total WER: 19.4021%, total TER: 8.64725%, progress (thread 0): 98.8766%]
|T|: y e a h
|P|: m e h
[sample: EN2002a_H00_MEE073_1050.03_1050.19, WER: 100%, TER: 50%, total WER: 19.403%, total TER: 8.64763%, progress (thread 0): 98.8845%]
|T|: w h y
|P|: m
[sample: EN2002a_H00_MEE073_1237.91_1238.07, WER: 100%, TER: 100%, total WER: 19.4039%, total TER: 8.64827%, progress (thread 0): 98.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1596.11_1596.27, WER: 0%, TER: 0%, total WER: 19.4037%, total TER: 8.64819%, progress (thread 0): 98.9003%]
|T|: s o r r y
|P|: s u r e
[sample: EN2002a_H00_MEE073_1813.61_1813.77, WER: 100%, TER: 60%, total WER: 19.4046%, total TER: 8.64879%, progress (thread 0): 98.9082%]
|T|: s u r e
|P|: s u e
[sample: EN2002a_H00_MEE073_1881.96_1882.12, WER: 100%, TER: 25%, total WER: 19.4055%, total TER: 8.64894%, progress (thread 0): 98.9161%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_39.76_39.92, WER: 0%, TER: 0%, total WER: 19.4053%, total TER: 8.64886%, progress (thread 0): 98.924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_317.94_318.1, WER: 0%, TER: 0%, total WER: 19.4051%, total TER: 8.64878%, progress (thread 0): 98.932%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1075.72_1075.88, WER: 0%, TER: 0%, total WER: 19.4049%, total TER: 8.64868%, progress (thread 0): 98.9399%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_1155.04_1155.2, WER: 100%, TER: 100%, total WER: 19.4058%, total TER: 8.64953%, progress (thread 0): 98.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_713.06_713.22, WER: 0%, TER: 0%, total WER: 19.4055%, total TER: 8.64945%, progress (thread 0): 98.9557%]
|T|: o h
|P|: m m
[sample: EN2002c_H03_MEE073_1210.59_1210.75, WER: 100%, TER: 100%, total WER: 19.4064%, total TER: 8.64987%, progress (thread 0): 98.9636%]
|T|: r i g h t
|P|: o r h
[sample: EN2002c_H03_MEE073_1484.69_1484.85, WER: 100%, TER: 80%, total WER: 19.4073%, total TER: 8.6507%, progress (thread 0): 98.9715%]
|T|: y e a h
|P|: m m
[sample: EN2002c_H03_MEE073_2554.98_2555.14, WER: 100%, TER: 100%, total WER: 19.4082%, total TER: 8.65155%, progress (thread 0): 98.9794%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_2776.86_2777.02, WER: 100%, TER: 33.3333%, total WER: 19.4091%, total TER: 8.65172%, progress (thread 0): 98.9873%]
|T|: m m
|P|: m m
[sample: EN2002d_H03_MEE073_645.29_645.45, WER: 0%, TER: 0%, total WER: 19.4089%, total TER: 8.65168%, progress (thread 0): 98.9952%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1397.11_1397.27, WER: 0%, TER: 0%, total WER: 19.4087%, total TER: 8.6516%, progress (thread 0): 99.0032%]
|T|: o o p s
|P|: m
[sample: ES2004a_H00_MEO015_188.28_188.43, WER: 100%, TER: 100%, total WER: 19.4096%, total TER: 8.65245%, progress (thread 0): 99.0111%]
|T|: o o h
|P|: m m
[sample: ES2004b_H02_MEE014_534.45_534.6, WER: 100%, TER: 100%, total WER: 19.4105%, total TER: 8.65309%, progress (thread 0): 99.019%]
|T|: ' k a y
|P|: y e a h
[sample: ES2004c_H01_FEE013_472.18_472.33, WER: 100%, TER: 75%, total WER: 19.4114%, total TER: 8.6537%, progress (thread 0): 99.0269%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_693.82_693.97, WER: 100%, TER: 66.6667%, total WER: 19.4123%, total TER: 8.65411%, progress (thread 0): 99.0348%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1647.59_1647.74, WER: 0%, TER: 0%, total WER: 19.4121%, total TER: 8.65403%, progress (thread 0): 99.0427%]
|T|: y e a h
|P|: s o m
[sample: IS1009d_H02_FIO084_1392_1392.15, WER: 100%, TER: 100%, total WER: 19.413%, total TER: 8.65488%, progress (thread 0): 99.0506%]
|T|: t w o
|P|: d o
[sample: IS1009d_H01_FIO087_1586.28_1586.43, WER: 100%, TER: 66.6667%, total WER: 19.4139%, total TER: 8.65528%, progress (thread 0): 99.0585%]
|T|: n o
|P|: y e h
[sample: IS1009d_H01_FIO087_1824.98_1825.13, WER: 100%, TER: 150%, total WER: 19.4148%, total TER: 8.65594%, progress (thread 0): 99.0665%]
|T|: y e a h
|P|: m e a h
[sample: IS1009d_H02_FIO084_1883.61_1883.76, WER: 100%, TER: 25%, total WER: 19.4157%, total TER: 8.65609%, progress (thread 0): 99.0744%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_567.51_567.66, WER: 0%, TER: 0%, total WER: 19.4155%, total TER: 8.65601%, progress (thread 0): 99.0823%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_2049.54_2049.69, WER: 0%, TER: 0%, total WER: 19.4153%, total TER: 8.65593%, progress (thread 0): 99.0902%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2041.85_2042, WER: 0%, TER: 0%, total WER: 19.415%, total TER: 8.65589%, progress (thread 0): 99.0981%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_639.89_640.04, WER: 100%, TER: 66.6667%, total WER: 19.4159%, total TER: 8.65629%, progress (thread 0): 99.106%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_967.58_967.73, WER: 0%, TER: 0%, total WER: 19.4157%, total TER: 8.65621%, progress (thread 0): 99.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1030.96_1031.11, WER: 0%, TER: 0%, total WER: 19.4155%, total TER: 8.65613%, progress (thread 0): 99.1218%]
|T|: y e a h
|P|: o h
[sample: EN2002a_H01_FEO070_1104.83_1104.98, WER: 100%, TER: 75%, total WER: 19.4164%, total TER: 8.65675%, progress (thread 0): 99.1297%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1527.69_1527.84, WER: 100%, TER: 33.3333%, total WER: 19.4173%, total TER: 8.65692%, progress (thread 0): 99.1377%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H00_MEE073_1539.77_1539.92, WER: 100%, TER: 100%, total WER: 19.4182%, total TER: 8.65777%, progress (thread 0): 99.1456%]
|T|: y e a h
|P|: m h
[sample: EN2002a_H00_MEE073_1744.01_1744.16, WER: 100%, TER: 75%, total WER: 19.4191%, total TER: 8.65839%, progress (thread 0): 99.1535%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1785.21_1785.36, WER: 0%, TER: 0%, total WER: 19.4189%, total TER: 8.65831%, progress (thread 0): 99.1614%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H03_MEE073_382.32_382.47, WER: 100%, TER: 66.6667%, total WER: 19.4198%, total TER: 8.65871%, progress (thread 0): 99.1693%]
|T|: u h
|P|: y e a h
[sample: EN2002b_H02_FEO072_424.35_424.5, WER: 100%, TER: 150%, total WER: 19.4207%, total TER: 8.65937%, progress (thread 0): 99.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_496.03_496.18, WER: 0%, TER: 0%, total WER: 19.4205%, total TER: 8.65929%, progress (thread 0): 99.1851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_897.7_897.85, WER: 0%, TER: 0%, total WER: 19.4203%, total TER: 8.65921%, progress (thread 0): 99.193%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2198.65_2198.8, WER: 0%, TER: 0%, total WER: 19.42%, total TER: 8.65913%, progress (thread 0): 99.201%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1290.13_1290.28, WER: 0%, TER: 0%, total WER: 19.4198%, total TER: 8.65904%, progress (thread 0): 99.2089%]
|T|: n o
|P|: o h
[sample: EN2002d_H03_MEE073_1424.03_1424.18, WER: 100%, TER: 100%, total WER: 19.4207%, total TER: 8.65947%, progress (thread 0): 99.2168%]
|T|: r i g h t
|P|: h
[sample: EN2002d_H03_MEE073_1909.61_1909.76, WER: 100%, TER: 80%, total WER: 19.4216%, total TER: 8.6603%, progress (thread 0): 99.2247%]
|T|: o k a y
|P|: m
[sample: EN2002d_H03_MEE073_1985.35_1985.5, WER: 100%, TER: 100%, total WER: 19.4225%, total TER: 8.66115%, progress (thread 0): 99.2326%]
|T|: i | t h
|P|: i
[sample: ES2004b_H01_FEE013_2305.5_2305.64, WER: 50%, TER: 75%, total WER: 19.4232%, total TER: 8.66176%, progress (thread 0): 99.2405%]
|T|: m m
|P|: m
[sample: ES2004c_H03_FEE016_777.13_777.27, WER: 100%, TER: 50%, total WER: 19.4241%, total TER: 8.66196%, progress (thread 0): 99.2484%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_56.9_57.04, WER: 0%, TER: 0%, total WER: 19.4239%, total TER: 8.66187%, progress (thread 0): 99.2563%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_645.27_645.41, WER: 100%, TER: 66.6667%, total WER: 19.4248%, total TER: 8.66228%, progress (thread 0): 99.2642%]
|T|: m m h m m
|P|: s o
[sample: IS1009d_H00_FIE088_770.97_771.11, WER: 100%, TER: 100%, total WER: 19.4257%, total TER: 8.66334%, progress (thread 0): 99.2721%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_189.69_189.83, WER: 100%, TER: 66.6667%, total WER: 19.4266%, total TER: 8.66374%, progress (thread 0): 99.2801%]
|T|: m m
|P|: m
[sample: TS3003a_H01_MTD011UID_903.08_903.22, WER: 100%, TER: 50%, total WER: 19.4275%, total TER: 8.66394%, progress (thread 0): 99.288%]
|T|: m m
|P|: m
[sample: TS3003b_H01_MTD011UID_2047.35_2047.49, WER: 100%, TER: 50%, total WER: 19.4284%, total TER: 8.66413%, progress (thread 0): 99.2959%]
|T|: ' k a y
|P|: y h
[sample: TS3003b_H03_MTD012ME_2140.48_2140.62, WER: 100%, TER: 100%, total WER: 19.4293%, total TER: 8.66498%, progress (thread 0): 99.3038%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1869.23_1869.37, WER: 0%, TER: 0%, total WER: 19.4291%, total TER: 8.6649%, progress (thread 0): 99.3117%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_1390.61_1390.75, WER: 100%, TER: 25%, total WER: 19.43%, total TER: 8.66505%, progress (thread 0): 99.3196%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_2021.71_2021.85, WER: 100%, TER: 25%, total WER: 19.4309%, total TER: 8.6652%, progress (thread 0): 99.3275%]
|T|: r i g h t
|P|: i h
[sample: EN2002a_H00_MEE073_1184.15_1184.29, WER: 100%, TER: 60%, total WER: 19.4318%, total TER: 8.6658%, progress (thread 0): 99.3354%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_1193.94_1194.08, WER: 100%, TER: 66.6667%, total WER: 19.4327%, total TER: 8.6662%, progress (thread 0): 99.3434%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H00_MEE073_2048.07_2048.21, WER: 100%, TER: 66.6667%, total WER: 19.4336%, total TER: 8.6666%, progress (thread 0): 99.3513%]
|T|: ' k a y
|P|:
[sample: EN2002c_H03_MEE073_103.84_103.98, WER: 100%, TER: 100%, total WER: 19.4345%, total TER: 8.66745%, progress (thread 0): 99.3592%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_531.32_531.46, WER: 0%, TER: 0%, total WER: 19.4342%, total TER: 8.66737%, progress (thread 0): 99.3671%]
|T|: y e a h
|P|: y e h
[sample: EN2002c_H03_MEE073_667.22_667.36, WER: 100%, TER: 25%, total WER: 19.4351%, total TER: 8.66752%, progress (thread 0): 99.375%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1476.08_1476.22, WER: 0%, TER: 0%, total WER: 19.4349%, total TER: 8.66744%, progress (thread 0): 99.3829%]
|T|: m m
|P|: m
[sample: EN2002c_H03_MEE073_2096.91_2097.05, WER: 100%, TER: 50%, total WER: 19.4358%, total TER: 8.66764%, progress (thread 0): 99.3908%]
|T|: o k a y
|P|: m e m
[sample: EN2002d_H00_FEO070_1251.9_1252.04, WER: 100%, TER: 100%, total WER: 19.4367%, total TER: 8.66848%, progress (thread 0): 99.3987%]
|T|: m m
|P|: m e a
[sample: ES2004c_H03_FEE016_98.28_98.41, WER: 100%, TER: 100%, total WER: 19.4376%, total TER: 8.66891%, progress (thread 0): 99.4066%]
|T|: s
|P|: m
[sample: ES2004d_H03_FEE016_1370.74_1370.87, WER: 100%, TER: 100%, total WER: 19.4385%, total TER: 8.66912%, progress (thread 0): 99.4146%]
|T|: o r | a | b
|P|: m a
[sample: IS1009a_H01_FIO087_573.12_573.25, WER: 100%, TER: 83.3333%, total WER: 19.4412%, total TER: 8.67016%, progress (thread 0): 99.4225%]
|T|: h m m
|P|: m m
[sample: IS1009c_H02_FIO084_144.08_144.21, WER: 100%, TER: 33.3333%, total WER: 19.4421%, total TER: 8.67033%, progress (thread 0): 99.4304%]
|T|: o k a y
|P|: m
[sample: IS1009d_H00_FIE088_604.16_604.29, WER: 100%, TER: 100%, total WER: 19.443%, total TER: 8.67118%, progress (thread 0): 99.4383%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_942.55_942.68, WER: 100%, TER: 66.6667%, total WER: 19.4439%, total TER: 8.67159%, progress (thread 0): 99.4462%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_392.63_392.76, WER: 100%, TER: 66.6667%, total WER: 19.4448%, total TER: 8.67199%, progress (thread 0): 99.4541%]
|T|: h u h
|P|: m
[sample: TS3003a_H01_MTD011UID_1266.08_1266.21, WER: 100%, TER: 100%, total WER: 19.4457%, total TER: 8.67263%, progress (thread 0): 99.462%]
|T|: o h
|P|: m h
[sample: TS3003a_H01_MTD011UID_1335.11_1335.24, WER: 100%, TER: 50%, total WER: 19.4466%, total TER: 8.67282%, progress (thread 0): 99.4699%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1475.42_1475.55, WER: 100%, TER: 66.6667%, total WER: 19.4475%, total TER: 8.67322%, progress (thread 0): 99.4778%]
|T|: y e a h
|P|: m m
[sample: TS3003b_H01_MTD011UID_1528.52_1528.65, WER: 100%, TER: 100%, total WER: 19.4484%, total TER: 8.67407%, progress (thread 0): 99.4858%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1173.23_1173.36, WER: 0%, TER: 0%, total WER: 19.4482%, total TER: 8.67403%, progress (thread 0): 99.4937%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_1693.45_1693.58, WER: 100%, TER: 25%, total WER: 19.4491%, total TER: 8.67418%, progress (thread 0): 99.5016%]
|T|: ' k a y
|P|: m e m
[sample: EN2002a_H00_MEE073_32.58_32.71, WER: 100%, TER: 100%, total WER: 19.45%, total TER: 8.67503%, progress (thread 0): 99.5095%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_984.87_985, WER: 100%, TER: 66.6667%, total WER: 19.4509%, total TER: 8.67544%, progress (thread 0): 99.5174%]
|T|: w e l l | f
|P|: h
[sample: EN2002a_H00_MEE073_1458.73_1458.86, WER: 100%, TER: 100%, total WER: 19.4527%, total TER: 8.67671%, progress (thread 0): 99.5253%]
|T|: y e p
|P|: y e a
[sample: EN2002b_H01_MEE071_493.19_493.32, WER: 100%, TER: 33.3333%, total WER: 19.4536%, total TER: 8.67688%, progress (thread 0): 99.5332%]
|T|: y e a h
|P|: y e a
[sample: EN2002c_H03_MEE073_665.01_665.14, WER: 100%, TER: 25%, total WER: 19.4545%, total TER: 8.67703%, progress (thread 0): 99.5411%]
|T|: o h
|P|: m
[sample: EN2002d_H01_FEO072_135.16_135.29, WER: 100%, TER: 100%, total WER: 19.4554%, total TER: 8.67746%, progress (thread 0): 99.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_726.53_726.66, WER: 0%, TER: 0%, total WER: 19.4552%, total TER: 8.67738%, progress (thread 0): 99.557%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_794.79_794.92, WER: 0%, TER: 0%, total WER: 19.455%, total TER: 8.67729%, progress (thread 0): 99.5649%]
|T|: y e a h
|P|: m e a
[sample: EN2002d_H00_FEO070_1595.77_1595.9, WER: 100%, TER: 50%, total WER: 19.4559%, total TER: 8.67768%, progress (thread 0): 99.5728%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_1641.64_1641.77, WER: 100%, TER: 100%, total WER: 19.4568%, total TER: 8.6781%, progress (thread 0): 99.5807%]
|T|: y e a h
|P|: m m
[sample: ES2004b_H00_MEO015_922.62_922.74, WER: 100%, TER: 100%, total WER: 19.4576%, total TER: 8.67895%, progress (thread 0): 99.5886%]
|T|: o k a y
|P|: m h
[sample: ES2004b_H00_MEO015_1977.87_1977.99, WER: 100%, TER: 100%, total WER: 19.4585%, total TER: 8.6798%, progress (thread 0): 99.5965%]
|T|: y e a h
|P|: m e m
[sample: IS1009b_H02_FIO084_173.86_173.98, WER: 100%, TER: 75%, total WER: 19.4594%, total TER: 8.68042%, progress (thread 0): 99.6044%]
|T|: h m m
|P|: m
[sample: IS1009c_H02_FIO084_1703.14_1703.26, WER: 100%, TER: 66.6667%, total WER: 19.4603%, total TER: 8.68082%, progress (thread 0): 99.6123%]
|T|: m m | m m
|P|:
[sample: IS1009d_H03_FIO089_870.76_870.88, WER: 100%, TER: 100%, total WER: 19.4621%, total TER: 8.68188%, progress (thread 0): 99.6203%]
|T|: y e p
|P|: y e a
[sample: TS3003a_H01_MTD011UID_257.2_257.32, WER: 100%, TER: 33.3333%, total WER: 19.463%, total TER: 8.68205%, progress (thread 0): 99.6282%]
|T|: y e a h
|P|: m
[sample: EN2002a_H03_MEE071_170.81_170.93, WER: 100%, TER: 100%, total WER: 19.4639%, total TER: 8.6829%, progress (thread 0): 99.6361%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_1468.26_1468.38, WER: 100%, TER: 66.6667%, total WER: 19.4648%, total TER: 8.6833%, progress (thread 0): 99.644%]
|T|: n o
|P|: m h
[sample: EN2002b_H03_MEE073_4.83_4.95, WER: 100%, TER: 100%, total WER: 19.4657%, total TER: 8.68373%, progress (thread 0): 99.6519%]
|T|: a l t e r
|P|: y e a
[sample: EN2002b_H03_MEE073_170.46_170.58, WER: 100%, TER: 80%, total WER: 19.4666%, total TER: 8.68456%, progress (thread 0): 99.6598%]
|T|: y e a h
|P|: m
[sample: EN2002b_H03_MEE073_1641.92_1642.04, WER: 100%, TER: 100%, total WER: 19.4675%, total TER: 8.6854%, progress (thread 0): 99.6677%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H03_MEE073_806.78_806.9, WER: 100%, TER: 25%, total WER: 19.4684%, total TER: 8.68556%, progress (thread 0): 99.6756%]
|T|: o k a y
|P|: y e a
[sample: ES2004a_H00_MEO015_71.95_72.06, WER: 100%, TER: 75%, total WER: 19.4693%, total TER: 8.68617%, progress (thread 0): 99.6835%]
|T|: o
|P|: y a
[sample: ES2004a_H03_FEE016_403.24_403.35, WER: 100%, TER: 200%, total WER: 19.4702%, total TER: 8.68662%, progress (thread 0): 99.6915%]
|T|: ' k a y
|P|:
[sample: ES2004a_H03_FEE016_1042.19_1042.3, WER: 100%, TER: 100%, total WER: 19.4711%, total TER: 8.68746%, progress (thread 0): 99.6994%]
|T|: m m
|P|: m m
[sample: IS1009c_H02_FIO084_620.84_620.95, WER: 0%, TER: 0%, total WER: 19.4709%, total TER: 8.68742%, progress (thread 0): 99.7073%]
|T|: y e a h
|P|: m
[sample: TS3003a_H01_MTD011UID_436.27_436.38, WER: 100%, TER: 100%, total WER: 19.4718%, total TER: 8.68827%, progress (thread 0): 99.7152%]
|T|: y e a h
|P|: m
[sample: EN2002a_H00_MEE073_669.47_669.58, WER: 100%, TER: 100%, total WER: 19.4727%, total TER: 8.68912%, progress (thread 0): 99.7231%]
|T|: y e a h
|P|: y a
[sample: EN2002a_H01_FEO070_1169.61_1169.72, WER: 100%, TER: 50%, total WER: 19.4736%, total TER: 8.6895%, progress (thread 0): 99.731%]
|T|: n o
|P|: y e
[sample: EN2002b_H02_FEO072_4.48_4.59, WER: 100%, TER: 100%, total WER: 19.4745%, total TER: 8.68993%, progress (thread 0): 99.7389%]
|T|: y e p
|P|: m
[sample: EN2002b_H03_MEE073_307.97_308.08, WER: 100%, TER: 100%, total WER: 19.4754%, total TER: 8.69056%, progress (thread 0): 99.7468%]
|T|: h m m
|P|: m
[sample: EN2002b_H03_MEE073_1578.82_1578.93, WER: 100%, TER: 66.6667%, total WER: 19.4763%, total TER: 8.69097%, progress (thread 0): 99.7547%]
|T|: h m m
|P|: m e
[sample: EN2002c_H03_MEE073_1446.36_1446.47, WER: 100%, TER: 66.6667%, total WER: 19.4772%, total TER: 8.69137%, progress (thread 0): 99.7627%]
|T|: o h
|P|: h
[sample: EN2002d_H01_FEO072_118.11_118.22, WER: 100%, TER: 50%, total WER: 19.4781%, total TER: 8.69156%, progress (thread 0): 99.7706%]
|T|: s o
|P|: m
[sample: EN2002d_H02_MEE071_2107.05_2107.16, WER: 100%, TER: 100%, total WER: 19.479%, total TER: 8.69199%, progress (thread 0): 99.7785%]
|T|: t
|P|: m
[sample: ES2004c_H02_MEE014_1184.81_1184.91, WER: 100%, TER: 100%, total WER: 19.4799%, total TER: 8.6922%, progress (thread 0): 99.7864%]
|T|: y e s
|P|: m a
[sample: IS1009d_H01_FIO087_1744.56_1744.66, WER: 100%, TER: 100%, total WER: 19.4808%, total TER: 8.69284%, progress (thread 0): 99.7943%]
|T|: o h
|P|: h
[sample: TS3003c_H02_MTD0010ID_1023.66_1023.76, WER: 100%, TER: 50%, total WER: 19.4817%, total TER: 8.69303%, progress (thread 0): 99.8022%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_2013_2013.1, WER: 100%, TER: 66.6667%, total WER: 19.4826%, total TER: 8.69343%, progress (thread 0): 99.8101%]
|T|: y e a h
|P|: m
[sample: EN2002b_H00_FEO070_43.63_43.73, WER: 100%, TER: 100%, total WER: 19.4835%, total TER: 8.69428%, progress (thread 0): 99.818%]
|T|: y e a h
|P|: y e a
[sample: EN2002b_H03_MEE073_293.52_293.62, WER: 100%, TER: 25%, total WER: 19.4844%, total TER: 8.69443%, progress (thread 0): 99.826%]
|T|: y e p
|P|: y e
[sample: EN2002c_H03_MEE073_763_763.1, WER: 100%, TER: 33.3333%, total WER: 19.4853%, total TER: 8.6946%, progress (thread 0): 99.8339%]
|T|: m m
|P|: m
[sample: EN2002d_H03_MEE073_2099.51_2099.61, WER: 100%, TER: 50%, total WER: 19.4862%, total TER: 8.69479%, progress (thread 0): 99.8418%]
|T|: ' k a y
|P|: m
[sample: ES2004a_H00_MEO015_168.79_168.88, WER: 100%, TER: 100%, total WER: 19.4871%, total TER: 8.69564%, progress (thread 0): 99.8497%]
|T|: y e p
|P|: y e
[sample: ES2004c_H00_MEO015_360.73_360.82, WER: 100%, TER: 33.3333%, total WER: 19.488%, total TER: 8.69581%, progress (thread 0): 99.8576%]
|T|: m m h m m
|P|: m
[sample: ES2004c_H03_FEE016_1132.2_1132.29, WER: 100%, TER: 80%, total WER: 19.4889%, total TER: 8.69664%, progress (thread 0): 99.8655%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1651.84_1651.93, WER: 100%, TER: 100%, total WER: 19.4898%, total TER: 8.69728%, progress (thread 0): 99.8734%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H02_FIO084_1753.49_1753.58, WER: 100%, TER: 80%, total WER: 19.4907%, total TER: 8.69811%, progress (thread 0): 99.8813%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_749.88_749.97, WER: 100%, TER: 100%, total WER: 19.4916%, total TER: 8.69874%, progress (thread 0): 99.8892%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1718.32_1718.41, WER: 100%, TER: 100%, total WER: 19.4925%, total TER: 8.6998%, progress (thread 0): 99.8972%]
|T|: w h a t
|P|: m
[sample: TS3003d_H00_MTD009PM_1597.33_1597.42, WER: 100%, TER: 100%, total WER: 19.4934%, total TER: 8.70065%, progress (thread 0): 99.9051%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_887.36_887.45, WER: 100%, TER: 66.6667%, total WER: 19.4943%, total TER: 8.70105%, progress (thread 0): 99.913%]
|T|: d a m n
|P|: m
[sample: EN2002a_H00_MEE073_1500.91_1501, WER: 100%, TER: 75%, total WER: 19.4952%, total TER: 8.70167%, progress (thread 0): 99.9209%]
|T|: h m m
|P|: m
[sample: EN2002c_H03_MEE073_429.63_429.72, WER: 100%, TER: 66.6667%, total WER: 19.4961%, total TER: 8.70207%, progress (thread 0): 99.9288%]
|T|: y e a h
|P|: y a
[sample: EN2002c_H02_MEE071_2578.94_2579.03, WER: 100%, TER: 50%, total WER: 19.497%, total TER: 8.70246%, progress (thread 0): 99.9367%]
|T|: d a m n
|P|: y e a
[sample: EN2002d_H03_MEE073_999.94_1000.03, WER: 100%, TER: 100%, total WER: 19.4979%, total TER: 8.7033%, progress (thread 0): 99.9446%]
|T|: m m
|P|:
[sample: ES2004a_H00_MEO015_252.48_252.56, WER: 100%, TER: 100%, total WER: 19.4988%, total TER: 8.70373%, progress (thread 0): 99.9525%]
|T|: m m
|P|:
[sample: ES2004c_H00_MEO015_1885.03_1885.11, WER: 100%, TER: 100%, total WER: 19.4997%, total TER: 8.70415%, progress (thread 0): 99.9604%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1313.86_1313.94, WER: 100%, TER: 100%, total WER: 19.5006%, total TER: 8.70458%, progress (thread 0): 99.9684%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_185.9_185.98, WER: 100%, TER: 100%, total WER: 19.5014%, total TER: 8.70521%, progress (thread 0): 99.9763%]
|T|: g
|P|: m
[sample: EN2002d_H03_MEE073_241.62_241.69, WER: 100%, TER: 100%, total WER: 19.5023%, total TER: 8.70542%, progress (thread 0): 99.9842%]
|T|: h m m
|P|:
[sample: IS1009a_H02_FIO084_490.4_490.46, WER: 100%, TER: 100%, total WER: 19.5032%, total TER: 8.70606%, progress (thread 0): 99.9921%]
|T|: ' k a y
|P|:
[sample: EN2002b_H03_MEE073_613.94_614, WER: 100%, TER: 100%, total WER: 19.5041%, total TER: 8.70691%, progress (thread 0): 100%]
I1224 07:09:02.842401 11830 Test.cpp:418] ------
I1224 07:09:02.842427 11830 Test.cpp:419] [Test ami_limited_supervision/test.lst (12640 samples) in 361.478s (actual decoding time 0.0286s/sample) -- WER: 19.5041%, TER: 8.70691%]
###Markdown
Viterbi WER improved from 26.6% to 19.5% after 1 epoch with finetuning... Beam Search decoding with a language model To do this, download the finetuned model and use the [Inference CTC tutorial](https://colab.research.google.com/github/flashlight/flashlight/blob/master/flashlight/app/asr/tutorial/notebooks/InferenceAndAlignmentCTC.ipynb) Step 5: Running with your own data To finetune on your own data, create `train`, `dev` and `test` list files and run the finetuning step. Each list file consists of multiple lines with each line describing one sample in the following format : ``` ```For example, let's take a look at the `dev.lst` file from AMI corpus.
###Code
! head ami_limited_supervision/dev.lst
###Output
ES2011a_H00_FEE041_34.27_37.14 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_34.27_37.14.flac 2870.0 here we go
ES2011a_H00_FEE041_37.14_39.15 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_37.14_39.15.flac 2010.0 welcome everybody
ES2011a_H00_FEE041_43.32_44.39 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_43.32_44.39.flac 1070.0 you can call me abbie
ES2011a_H00_FEE041_39.15_43.32 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_39.15_43.32.flac 4170.0 um i'm abigail claflin
ES2011a_H00_FEE041_46.43_47.63 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_46.43_47.63.flac 1200.0 's see
ES2011a_H00_FEE041_51.33_55.53 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_51.33_55.53.flac 4200.0 so this is our kick off meeting
ES2011a_H00_FEE041_55.53_56.85 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_55.53_56.85.flac 1320.0 um
ES2011a_H00_FEE041_47.63_50.2 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_47.63_50.2.flac 2570.0 powerpoint that's not it
ES2011a_H00_FEE041_50.2_51.33 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_50.2_51.33.flac 1130.0 there we go
ES2011a_H00_FEE041_62.17_64.28 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_62.17_64.28.flac 2110.0 let's shall we all introduce ourselves
###Markdown
Recording your own audioFor example, you can record your own audio and finetune the model...Installing a few packages first...
###Code
!apt-get install sox
!pip install ffmpeg-python sox
from flashlight.scripts.colab.record import record_audio
###Output
_____no_output_____
###Markdown
**Let's record now the following sentences:****1:** A flashlight or torch is a small, portable spotlight.
###Code
record_audio("recorded_audio_1")
###Output
_____no_output_____
###Markdown
**2:** Its function is a beam of light which helps to see and it usually requires batteries.
###Code
record_audio("recorded_audio_2")
###Output
_____no_output_____
###Markdown
**3:** In 1896, the first dry cell battery was invented.
###Code
record_audio("recorded_audio_3")
###Output
_____no_output_____
###Markdown
**4:** Unlike previous batteries, it used a paste electrolyte instead of a liquid.
###Code
record_audio("recorded_audio_4")
###Output
_____no_output_____
###Markdown
**5** This was the first battery suitable for portable electrical devices, as it did not spill or break easily and worked in any orientation.
###Code
record_audio("recorded_audio_5")
###Output
_____no_output_____
###Markdown
Create now new training/dev lists:(yes, you need to edit transcriptions below to your recordings)
###Code
import sox
transcriptions = [
"a flashlight or torch is a small portable spotlight",
"its function is a beam of light which helps to see and it usually requires batteries",
"in eighteen ninthy six the first dry cell battery was invented",
"unlike previous batteries it used a paste electrolyte instead of a liquid",
"this was the first battery suitable for portable electrical devices, as it did not spill or break easily and worked in any orientation"
]
with open("own_train.lst", "w") as f_train, open("own_dev.lst", "w") as f_dev:
for index, transcription in enumerate(transcriptions):
fname = "recorded_audio_" + str(index + 1) + ".wav"
duration_ms = sox.file_info.duration(fname) * 1000
if index % 2 == 0:
f_train.write("{}\t{}\t{}\t{}\n".format(
index + 1, fname, duration_ms, transcription))
else:
f_dev.write("{}\t{}\t{}\t{}\n".format(
index + 1, fname, duration_ms, transcription))
###Output
_____no_output_____
###Markdown
Check at first model quality on dev before finetuning
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am model.bin --datadir '' --emission_dir '' --uselexicon false \
--test own_dev.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
_____no_output_____
###Markdown
Finetune on recorded audio samplesPlay with parameters if needed.
###Code
! ./flashlight/build/bin/asr/fl_asr_tutorial_finetune_ctc model.bin \
--datadir= \
--train own_train.lst \
--valid dev:own_dev.lst \
--arch arch.txt \
--tokens tokens.txt \
--lexicon lexicon.txt \
--rundir own_checkpoint \
--lr 0.025 \
--netoptim sgd \
--momentum 0.8 \
--reportiters 1000 \
--lr_decay 100 \
--lr_decay_step 50 \
--iter 25000 \
--batchsize 4 \
--warmup 0
###Output
_____no_output_____
###Markdown
Test finetuned model(unlikely you get significant improvement with just five phrases, but let's check!)
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am own_checkpoint/001_model_dev.bin --datadir '' --emission_dir '' --uselexicon false \
--test own_dev.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
_____no_output_____
###Markdown
Tutorial on ASR Finetuning with CTC model Let's finetune a pretrained ASR model!Here we provide pre-trained speech recognition model with CTC loss that is trained on many open-sourced datasets. Details can be found in [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://arxiv.org/abs/2010.11745) Step 1: Install `Flashlight`First we install `Flashlight` and its dependencies. Flashlight is built from source with either CPU/CUDA backend and installation takes **~16 minutes**. For installation out of colab notebook please use [link](https://github.com/fairinternal/flashlightbuilding).
###Code
# First, choose backend to build with
backend = 'CUDA' #@param ["CPU", "CUDA"]
# Clone Flashlight
!git clone https://github.com/facebookresearch/flashlight.git
# install all dependencies for colab notebook
!source flashlight/scripts/colab/colab_install_deps.sh
###Output
_____no_output_____
###Markdown
Build CPU/CUDA Backend of `Flashlight`:- Build from current master. - Builds the ASR app. - Resulting binaries in `/content/flashlight/build/bin/asr`.If using a GPU Colab runtime, build the CUDA backend; else build the CPU backend.
###Code
# export necessary env variables
%env MKLROOT=/opt/intel/mkl
%env ArrayFire_DIR=/opt/arrayfire/share/ArrayFire/cmake
%env DNNL_DIR=/opt/dnnl/dnnl_lnx_2.0.0_cpu_iomp/lib/cmake/dnnl
if backend == "CUDA":
# Total time: ~13 minutes
!cd flashlight && mkdir -p build && cd build && \
cmake .. -DCMAKE_BUILD_TYPE=Release \
-DFL_BUILD_TESTS=OFF \
-DFL_BUILD_EXAMPLES=OFF \
-DFL_BUILD_APP_IMGCLASS=OFF \
-DFL_BUILD_APP_LM=OFF && \
make -j$(nproc)
elif backend == "CPU":
# Total time: ~14 minutes
!cd flashlight && mkdir -p build && cd build && \
cmake .. -DFL_BACKEND=CPU \
-DCMAKE_BUILD_TYPE=Release \
-DFL_BUILD_TESTS=OFF \
-DFL_BUILD_EXAMPLES=OFF \
-DFL_BUILD_APP_IMGCLASS=OFF \
-DFL_BUILD_APP_LM=OFF && \
make -j$(nproc)
else:
raise ValueError(f"Unknown backend {backend}")
###Output
_____no_output_____
###Markdown
Let's take a look around.
###Code
# Binaries are located in
!ls flashlight/build/bin/asr
###Output
fl_asr_align fl_asr_tutorial_finetune_ctc
fl_asr_decode fl_asr_tutorial_inference_ctc
fl_asr_test fl_asr_voice_activity_detection_ctc
fl_asr_train
###Markdown
Step 2: Setup Finetuning Downloading the model filesFirst, let's download the pretrained models for finetuning. For acoustic model, you can choose from >Architecture | Params | Criterion | Model Name | Arch Name >---|---|:---|:---:|:---:> Transformer|70Mil|CTC|am_transformer_ctc_stride3_letters_70Mparams.bin |am_transformer_ctc_stride3_letters_70Mparams.arch> Transformer|300Mil|CTC|am_transformer_ctc_stride3_letters_300Mparams.bin | am_transformer_ctc_stride3_letters_300Mparams.arch> Conformer|25Mil|CTC|am_conformer_ctc_stride3_letters_25Mparams.bin|am_conformer_ctc_stride3_letters_25Mparams.arch> Conformer|87Mil|CTC|am_conformer_ctc_stride3_letters_87Mparams.bin|am_conformer_ctc_stride3_letters_87Mparams.arch> Conformer|300Mil|CTC|am_conformer_ctc_stride3_letters_300Mparams.bin| am_conformer_ctc_stride3_letters_300Mparams.archFor demonstration, we will use the model in first row and download the model and its arch file.
###Code
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/am_transformer_ctc_stride3_letters_70Mparams.bin -O model.bin # acoustic model
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/am_transformer_ctc_stride3_letters_70Mparams.arch -O arch.txt # model architecture file
###Output
_____no_output_____
###Markdown
Along with the acoustic model, we will also download the tokens file, lexicon file
###Code
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/tokens.txt -O tokens.txt # tokens (defines predicted tokens)
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/lexicon.txt -O lexicon.txt # lexicon files (defines mapping between words)
###Output
_____no_output_____
###Markdown
Downloading the datasetFor finetuning the model, we provide a limited supervision dataset based on [AMI Corpus](http://groups.inf.ed.ac.uk/ami/corpus/). It consists of 10m, 1hr and 10hr subsets organized as follows. ```dev.lst development set test.lst test set train_10min_0.lst first 10 min foldtrain_10min_1.lsttrain_10min_2.lsttrain_10min_3.lsttrain_10min_4.lsttrain_10min_5.lsttrain_9hr.lst remaining data of the 10h split (10h=1h+9h)```The 10h split is created by combining the data from the 9h split and the 1h split. The 1h split is itself made of 6 folds of 10 min splits.The recipe used for preparing this corpus can be found [here](https://github.com/facebookresearch/wav2letter/tree/master/data/ami). **You can also use your own dataset to finetune the model instead of AMI Corpus.**
###Code
!rm ami_limited_supervision.tar.gz
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/ami_limited_supervision.tar.gz -O ami_limited_supervision.tar.gz
!tar -xf ami_limited_supervision.tar.gz
!ls ami_limited_supervision
###Output
rm: cannot remove 'ami_limited_supervision.tar.gz': No such file or directory
audio train_10min_0.lst train_10min_3.lst train_9hr.lst
dev.lst train_10min_1.lst train_10min_4.lst
test.lst train_10min_2.lst train_10min_5.lst
###Markdown
Get baseline WER before finetuningBefore proceeding to finetuning, let's test (viterbi) WER on AMI dataset to we have something to compare results after finetuning.
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am model.bin --datadir '' --emission_dir '' --uselexicon false \
--test ami_limited_supervision/test.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1046.72_1047.07, WER: 100%, TER: 50%, total WER: 25.9066%, total TER: 12.6062%, progress (thread 0): 86.8275%]
|T|: m m
|P|: m m
[sample: ES2004c_H01_FEE013_1294.34_1294.69, WER: 0%, TER: 0%, total WER: 25.9063%, total TER: 12.6061%, progress (thread 0): 86.8354%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1302.15_1302.5, WER: 100%, TER: 40%, total WER: 25.9071%, total TER: 12.6065%, progress (thread 0): 86.8434%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1515.72_1516.07, WER: 0%, TER: 0%, total WER: 25.9068%, total TER: 12.6063%, progress (thread 0): 86.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1690.13_1690.48, WER: 0%, TER: 0%, total WER: 25.9065%, total TER: 12.6062%, progress (thread 0): 86.8592%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_2078.48_2078.83, WER: 100%, TER: 40%, total WER: 25.9074%, total TER: 12.6065%, progress (thread 0): 86.8671%]
|T|: o k a y
|P|: i t
[sample: ES2004c_H02_MEE014_2291.54_2291.89, WER: 100%, TER: 100%, total WER: 25.9082%, total TER: 12.6074%, progress (thread 0): 86.875%]
|T|: o h
|P|:
[sample: ES2004d_H00_MEO015_127.17_127.52, WER: 100%, TER: 100%, total WER: 25.9091%, total TER: 12.6078%, progress (thread 0): 86.8829%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_561.86_562.21, WER: 0%, TER: 0%, total WER: 25.9088%, total TER: 12.6077%, progress (thread 0): 86.8908%]
|T|: ' k a y
|P|: k a y
[sample: ES2004d_H00_MEO015_640.16_640.51, WER: 100%, TER: 25%, total WER: 25.9096%, total TER: 12.6078%, progress (thread 0): 86.8987%]
|T|: r i g h t
|P|: l i k e
[sample: ES2004d_H00_MEO015_821.44_821.79, WER: 100%, TER: 80%, total WER: 25.9105%, total TER: 12.6086%, progress (thread 0): 86.9066%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H01_FEE013_822.51_822.86, WER: 100%, TER: 40%, total WER: 25.9113%, total TER: 12.6089%, progress (thread 0): 86.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1778.02_1778.37, WER: 0%, TER: 0%, total WER: 25.911%, total TER: 12.6088%, progress (thread 0): 86.9225%]
|T|: m m h m m
|P|: u | h u h
[sample: IS1009a_H00_FIE088_76.08_76.43, WER: 200%, TER: 80%, total WER: 25.913%, total TER: 12.6096%, progress (thread 0): 86.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_431.19_431.54, WER: 0%, TER: 0%, total WER: 25.9127%, total TER: 12.6094%, progress (thread 0): 86.9383%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_492.57_492.92, WER: 0%, TER: 0%, total WER: 25.9124%, total TER: 12.6094%, progress (thread 0): 86.9462%]
|T|: y e a h
|P|: h e | w a s
[sample: IS1009b_H02_FIO084_232.67_233.02, WER: 200%, TER: 100%, total WER: 25.9144%, total TER: 12.6102%, progress (thread 0): 86.9541%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_361.11_361.46, WER: 0%, TER: 0%, total WER: 25.9141%, total TER: 12.6101%, progress (thread 0): 86.962%]
|T|: y e p
|P|: y e a h
[sample: IS1009b_H00_FIE088_723.57_723.92, WER: 100%, TER: 66.6667%, total WER: 25.9149%, total TER: 12.6104%, progress (thread 0): 86.9699%]
|T|: m m h m m
|P|: e
[sample: IS1009b_H02_FIO084_993.71_994.06, WER: 100%, TER: 100%, total WER: 25.9158%, total TER: 12.6115%, progress (thread 0): 86.9778%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1007.39_1007.74, WER: 0%, TER: 0%, total WER: 25.9155%, total TER: 12.6114%, progress (thread 0): 86.9858%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1079.18_1079.53, WER: 100%, TER: 40%, total WER: 25.9163%, total TER: 12.6117%, progress (thread 0): 86.9937%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1082.71_1083.06, WER: 100%, TER: 40%, total WER: 25.9172%, total TER: 12.612%, progress (thread 0): 87.0016%]
|T|: t h i s | o n e
|P|: t h i s | o n e
[sample: IS1009b_H00_FIE088_1179.44_1179.79, WER: 0%, TER: 0%, total WER: 25.9166%, total TER: 12.6118%, progress (thread 0): 87.0095%]
|T|: y o u ' r e | t h r e e
|P|: i t h r i
[sample: IS1009b_H00_FIE088_1203.06_1203.41, WER: 100%, TER: 75%, total WER: 25.9183%, total TER: 12.6136%, progress (thread 0): 87.0174%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1699.07_1699.42, WER: 0%, TER: 0%, total WER: 25.918%, total TER: 12.6134%, progress (thread 0): 87.0253%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1821.76_1822.11, WER: 0%, TER: 0%, total WER: 25.9177%, total TER: 12.6133%, progress (thread 0): 87.0332%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_527.63_527.98, WER: 100%, TER: 40%, total WER: 25.9185%, total TER: 12.6136%, progress (thread 0): 87.0411%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_546.82_547.17, WER: 100%, TER: 40%, total WER: 25.9194%, total TER: 12.614%, progress (thread 0): 87.049%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_609.9_610.25, WER: 100%, TER: 40%, total WER: 25.9202%, total TER: 12.6143%, progress (thread 0): 87.057%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009c_H00_FIE088_688.49_688.84, WER: 200%, TER: 100%, total WER: 25.9222%, total TER: 12.6153%, progress (thread 0): 87.0649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1303.58_1303.93, WER: 0%, TER: 0%, total WER: 25.9219%, total TER: 12.6152%, progress (thread 0): 87.0728%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H01_FIO087_1514.93_1515.28, WER: 100%, TER: 40%, total WER: 25.9227%, total TER: 12.6155%, progress (thread 0): 87.0807%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1737.76_1738.11, WER: 100%, TER: 40%, total WER: 25.9236%, total TER: 12.6158%, progress (thread 0): 87.0886%]
|T|: a h
|P|: i
[sample: IS1009d_H02_FIO084_734.11_734.46, WER: 100%, TER: 100%, total WER: 25.9244%, total TER: 12.6163%, progress (thread 0): 87.0965%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_742.93_743.28, WER: 0%, TER: 0%, total WER: 25.9241%, total TER: 12.6162%, progress (thread 0): 87.1044%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_743.52_743.87, WER: 0%, TER: 0%, total WER: 25.9238%, total TER: 12.616%, progress (thread 0): 87.1123%]
|T|: y e a h
|P|: w h e r e
[sample: IS1009d_H02_FIO084_953.71_954.06, WER: 100%, TER: 100%, total WER: 25.9247%, total TER: 12.6169%, progress (thread 0): 87.1203%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1243.4_1243.75, WER: 0%, TER: 0%, total WER: 25.9244%, total TER: 12.6168%, progress (thread 0): 87.1282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1323.32_1323.67, WER: 0%, TER: 0%, total WER: 25.9241%, total TER: 12.6166%, progress (thread 0): 87.1361%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_1417.8_1418.15, WER: 100%, TER: 40%, total WER: 25.9249%, total TER: 12.617%, progress (thread 0): 87.144%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_1674.44_1674.79, WER: 100%, TER: 100%, total WER: 25.9258%, total TER: 12.618%, progress (thread 0): 87.1519%]
|T|: m m h m m
|P|: m h
[sample: IS1009d_H02_FIO084_1745.56_1745.91, WER: 100%, TER: 60%, total WER: 25.9266%, total TER: 12.6185%, progress (thread 0): 87.1598%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_506.21_506.56, WER: 0%, TER: 0%, total WER: 25.9263%, total TER: 12.6184%, progress (thread 0): 87.1677%]
|T|: t h e | p e n
|P|: b
[sample: TS3003a_H02_MTD0010ID_1074.9_1075.25, WER: 100%, TER: 100%, total WER: 25.928%, total TER: 12.6199%, progress (thread 0): 87.1756%]
|T|: r i g h t
|P|: r i g h t
[sample: TS3003a_H03_MTD012ME_1219.55_1219.9, WER: 0%, TER: 0%, total WER: 25.9277%, total TER: 12.6197%, progress (thread 0): 87.1835%]
|T|: m m h m m
|P|: m h m
[sample: TS3003a_H01_MTD011UID_1371.45_1371.8, WER: 100%, TER: 40%, total WER: 25.9285%, total TER: 12.62%, progress (thread 0): 87.1915%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_1453.73_1454.08, WER: 0%, TER: 0%, total WER: 25.9282%, total TER: 12.6199%, progress (thread 0): 87.1994%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1295.49_1295.84, WER: 0%, TER: 0%, total WER: 25.9279%, total TER: 12.6199%, progress (thread 0): 87.2073%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1351.56_1351.91, WER: 100%, TER: 50%, total WER: 25.9288%, total TER: 12.62%, progress (thread 0): 87.2152%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_180.89_181.24, WER: 0%, TER: 0%, total WER: 25.9285%, total TER: 12.6199%, progress (thread 0): 87.2231%]
|T|: y e a h
|P|: y e a p
[sample: TS3003c_H00_MTD009PM_1380.5_1380.85, WER: 100%, TER: 25%, total WER: 25.9293%, total TER: 12.62%, progress (thread 0): 87.231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1531.23_1531.58, WER: 0%, TER: 0%, total WER: 25.929%, total TER: 12.6199%, progress (thread 0): 87.2389%]
|T|: t h a t ' s | g o o d
|P|: t h a t ' s g o
[sample: TS3003c_H03_MTD012ME_1567.88_1568.23, WER: 100%, TER: 27.2727%, total WER: 25.9307%, total TER: 12.6203%, progress (thread 0): 87.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1713.34_1713.69, WER: 0%, TER: 0%, total WER: 25.9304%, total TER: 12.6202%, progress (thread 0): 87.2547%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_2280.01_2280.36, WER: 0%, TER: 0%, total WER: 25.9301%, total TER: 12.6201%, progress (thread 0): 87.2627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_306.6_306.95, WER: 0%, TER: 0%, total WER: 25.9298%, total TER: 12.6199%, progress (thread 0): 87.2706%]
|T|: n o
|P|: o
[sample: TS3003d_H00_MTD009PM_819.47_819.82, WER: 100%, TER: 50%, total WER: 25.9307%, total TER: 12.6201%, progress (thread 0): 87.2785%]
|T|: a l r i g h t
|P|:
[sample: TS3003d_H00_MTD009PM_965.64_965.99, WER: 100%, TER: 100%, total WER: 25.9315%, total TER: 12.6216%, progress (thread 0): 87.2864%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H00_MTD009PM_1373.9_1374.25, WER: 0%, TER: 0%, total WER: 25.9312%, total TER: 12.6215%, progress (thread 0): 87.2943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1847.37_1847.72, WER: 0%, TER: 0%, total WER: 25.9309%, total TER: 12.6213%, progress (thread 0): 87.3022%]
|T|: n i n e
|P|: n i n e t h
[sample: TS3003d_H03_MTD012ME_1899.7_1900.05, WER: 100%, TER: 50%, total WER: 25.9318%, total TER: 12.6217%, progress (thread 0): 87.3101%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H01_MTD011UID_2218.91_2219.26, WER: 0%, TER: 0%, total WER: 25.9315%, total TER: 12.6216%, progress (thread 0): 87.318%]
|T|: r i g h t
|P|: b u t
[sample: EN2002a_H03_MEE071_142.16_142.51, WER: 100%, TER: 80%, total WER: 25.9323%, total TER: 12.6224%, progress (thread 0): 87.326%]
|T|: h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_203.92_204.27, WER: 100%, TER: 66.6667%, total WER: 25.9332%, total TER: 12.6228%, progress (thread 0): 87.3339%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_220.21_220.56, WER: 0%, TER: 0%, total WER: 25.9329%, total TER: 12.6226%, progress (thread 0): 87.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1393.11_1393.46, WER: 0%, TER: 0%, total WER: 25.9326%, total TER: 12.6225%, progress (thread 0): 87.3497%]
|T|: n o
|P|: n o
[sample: EN2002a_H02_FEO072_1494.01_1494.36, WER: 0%, TER: 0%, total WER: 25.9323%, total TER: 12.6225%, progress (thread 0): 87.3576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1616.41_1616.76, WER: 0%, TER: 0%, total WER: 25.932%, total TER: 12.6224%, progress (thread 0): 87.3655%]
|T|: y e a h
|P|: y e h
[sample: EN2002a_H01_FEO070_2062.28_2062.63, WER: 100%, TER: 25%, total WER: 25.9328%, total TER: 12.6225%, progress (thread 0): 87.3734%]
|T|: t h e n | u m
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_306.19_306.54, WER: 100%, TER: 114.286%, total WER: 25.9345%, total TER: 12.6242%, progress (thread 0): 87.3813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_679.83_680.18, WER: 0%, TER: 0%, total WER: 25.9342%, total TER: 12.6241%, progress (thread 0): 87.3892%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1132.98_1133.33, WER: 0%, TER: 0%, total WER: 25.9339%, total TER: 12.624%, progress (thread 0): 87.3972%]
|T|: s h o u l d n ' t | n o
|P|: n i
[sample: EN2002b_H01_MEE071_1400.16_1400.51, WER: 100%, TER: 91.6667%, total WER: 25.9356%, total TER: 12.6262%, progress (thread 0): 87.4051%]
|T|: n o
|P|: k n o w
[sample: EN2002b_H02_FEO072_1650.79_1651.14, WER: 100%, TER: 100%, total WER: 25.9364%, total TER: 12.6266%, progress (thread 0): 87.413%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_550.49_550.84, WER: 0%, TER: 0%, total WER: 25.9362%, total TER: 12.6265%, progress (thread 0): 87.4209%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_586.84_587.19, WER: 0%, TER: 0%, total WER: 25.9359%, total TER: 12.6264%, progress (thread 0): 87.4288%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_725.66_726.01, WER: 0%, TER: 0%, total WER: 25.9356%, total TER: 12.6263%, progress (thread 0): 87.4367%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_743.92_744.27, WER: 0%, TER: 0%, total WER: 25.9353%, total TER: 12.6262%, progress (thread 0): 87.4446%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_975_975.35, WER: 0%, TER: 0%, total WER: 25.935%, total TER: 12.6261%, progress (thread 0): 87.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1520.32_1520.67, WER: 0%, TER: 0%, total WER: 25.9347%, total TER: 12.626%, progress (thread 0): 87.4604%]
|T|: s o
|P|:
[sample: EN2002c_H02_MEE071_1667.21_1667.56, WER: 100%, TER: 100%, total WER: 25.9355%, total TER: 12.6264%, progress (thread 0): 87.4684%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_2075.98_2076.33, WER: 0%, TER: 0%, total WER: 25.9352%, total TER: 12.6264%, progress (thread 0): 87.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2182.63_2182.98, WER: 0%, TER: 0%, total WER: 25.9349%, total TER: 12.6262%, progress (thread 0): 87.4842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_352.2_352.55, WER: 0%, TER: 0%, total WER: 25.9346%, total TER: 12.6261%, progress (thread 0): 87.4921%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002d_H03_MEE073_387.18_387.53, WER: 0%, TER: 0%, total WER: 25.934%, total TER: 12.6259%, progress (thread 0): 87.5%]
|T|: m m
|P|:
[sample: EN2002d_H01_FEO072_831.78_832.13, WER: 100%, TER: 100%, total WER: 25.9349%, total TER: 12.6263%, progress (thread 0): 87.5079%]
|T|: n o
|P|: n o
[sample: EN2002d_H03_MEE073_909.36_909.71, WER: 0%, TER: 0%, total WER: 25.9346%, total TER: 12.6263%, progress (thread 0): 87.5158%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002d_H03_MEE073_1113.08_1113.43, WER: 200%, TER: 175%, total WER: 25.9366%, total TER: 12.6278%, progress (thread 0): 87.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1233.84_1234.19, WER: 0%, TER: 0%, total WER: 25.9363%, total TER: 12.6277%, progress (thread 0): 87.5316%]
|T|: i | d o n ' t | k n o w
|P|:
[sample: EN2002d_H01_FEO072_1245.04_1245.39, WER: 100%, TER: 100%, total WER: 25.9388%, total TER: 12.6301%, progress (thread 0): 87.5396%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1798.12_1798.47, WER: 0%, TER: 0%, total WER: 25.9385%, total TER: 12.63%, progress (thread 0): 87.5475%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1926.51_1926.86, WER: 0%, TER: 0%, total WER: 25.9382%, total TER: 12.6299%, progress (thread 0): 87.5554%]
|T|: u h h u h
|P|: u h u h
[sample: EN2002d_H00_FEO070_2027.85_2028.2, WER: 100%, TER: 20%, total WER: 25.9391%, total TER: 12.63%, progress (thread 0): 87.5633%]
|T|: o h | s o r r y
|P|:
[sample: ES2004a_H00_MEO015_255.91_256.25, WER: 100%, TER: 100%, total WER: 25.9407%, total TER: 12.6316%, progress (thread 0): 87.5712%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H01_FEE013_545.3_545.64, WER: 100%, TER: 40%, total WER: 25.9416%, total TER: 12.632%, progress (thread 0): 87.5791%]
|T|: r e a l l y
|P|: r e a l l y
[sample: ES2004a_H01_FEE013_603.88_604.22, WER: 0%, TER: 0%, total WER: 25.9413%, total TER: 12.6318%, progress (thread 0): 87.587%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_635.54_635.88, WER: 100%, TER: 40%, total WER: 25.9421%, total TER: 12.6321%, progress (thread 0): 87.5949%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H03_FEE016_811.15_811.49, WER: 0%, TER: 0%, total WER: 25.9418%, total TER: 12.632%, progress (thread 0): 87.6028%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_954.93_955.27, WER: 100%, TER: 40%, total WER: 25.9427%, total TER: 12.6323%, progress (thread 0): 87.6108%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H03_FEE016_1445.71_1446.05, WER: 100%, TER: 60%, total WER: 25.9435%, total TER: 12.6329%, progress (thread 0): 87.6187%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1995.5_1995.84, WER: 100%, TER: 40%, total WER: 25.9444%, total TER: 12.6332%, progress (thread 0): 87.6266%]
|T|: ' k a y
|P|: t a n
[sample: ES2004c_H03_FEE016_22.85_23.19, WER: 100%, TER: 75%, total WER: 25.9452%, total TER: 12.6338%, progress (thread 0): 87.6345%]
|T|: t h a n k | y o u
|P|:
[sample: ES2004c_H03_FEE016_201.1_201.44, WER: 100%, TER: 100%, total WER: 25.9469%, total TER: 12.6356%, progress (thread 0): 87.6424%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_451.06_451.4, WER: 100%, TER: 25%, total WER: 25.9477%, total TER: 12.6357%, progress (thread 0): 87.6503%]
|T|: n o
|P|: n o t
[sample: ES2004c_H02_MEE014_535.19_535.53, WER: 100%, TER: 50%, total WER: 25.9486%, total TER: 12.6359%, progress (thread 0): 87.6582%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1178.32_1178.66, WER: 0%, TER: 0%, total WER: 25.9483%, total TER: 12.6358%, progress (thread 0): 87.6661%]
|T|: h m m
|P|: m m
[sample: ES2004c_H02_MEE014_1181.26_1181.6, WER: 100%, TER: 33.3333%, total WER: 25.9491%, total TER: 12.6359%, progress (thread 0): 87.674%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1478.47_1478.81, WER: 100%, TER: 50%, total WER: 25.9499%, total TER: 12.6361%, progress (thread 0): 87.682%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1640.61_1640.95, WER: 100%, TER: 40%, total WER: 25.9508%, total TER: 12.6364%, progress (thread 0): 87.6899%]
|T|: m m h m m
|P|: h e
[sample: ES2004c_H03_FEE016_2025.11_2025.45, WER: 100%, TER: 80%, total WER: 25.9516%, total TER: 12.6372%, progress (thread 0): 87.6978%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_2072.31_2072.65, WER: 100%, TER: 40%, total WER: 25.9525%, total TER: 12.6376%, progress (thread 0): 87.7057%]
|T|: y e a h
|P|: y e s
[sample: ES2004d_H00_MEO015_100.76_101.1, WER: 100%, TER: 50%, total WER: 25.9533%, total TER: 12.6379%, progress (thread 0): 87.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H01_FEE013_139.4_139.74, WER: 0%, TER: 0%, total WER: 25.953%, total TER: 12.6378%, progress (thread 0): 87.7215%]
|T|: y e p
|P|: y u p
[sample: ES2004d_H00_MEO015_155.57_155.91, WER: 100%, TER: 33.3333%, total WER: 25.9539%, total TER: 12.6379%, progress (thread 0): 87.7294%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_178.12_178.46, WER: 0%, TER: 0%, total WER: 25.9536%, total TER: 12.6378%, progress (thread 0): 87.7373%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_405.44_405.78, WER: 0%, TER: 0%, total WER: 25.9533%, total TER: 12.6377%, progress (thread 0): 87.7453%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_548.5_548.84, WER: 100%, TER: 40%, total WER: 25.9541%, total TER: 12.638%, progress (thread 0): 87.7532%]
|T|: t h r e e
|P|: t h r e e
[sample: ES2004d_H02_MEE014_646.42_646.76, WER: 0%, TER: 0%, total WER: 25.9538%, total TER: 12.6378%, progress (thread 0): 87.7611%]
|T|: f o u r
|P|: f o r
[sample: ES2004d_H01_FEE013_911.53_911.87, WER: 100%, TER: 25%, total WER: 25.9547%, total TER: 12.638%, progress (thread 0): 87.769%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1135.09_1135.43, WER: 0%, TER: 0%, total WER: 25.9544%, total TER: 12.6378%, progress (thread 0): 87.7769%]
|T|: m m
|P|: h m
[sample: ES2004d_H01_FEE013_1953.89_1954.23, WER: 100%, TER: 50%, total WER: 25.9552%, total TER: 12.638%, progress (thread 0): 87.7848%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_792.79_793.13, WER: 0%, TER: 0%, total WER: 25.9549%, total TER: 12.6379%, progress (thread 0): 87.7927%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_43.16_43.5, WER: 100%, TER: 40%, total WER: 25.9558%, total TER: 12.6383%, progress (thread 0): 87.8006%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_260.98_261.32, WER: 0%, TER: 0%, total WER: 25.9555%, total TER: 12.6381%, progress (thread 0): 87.8085%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_425.4_425.74, WER: 0%, TER: 0%, total WER: 25.9552%, total TER: 12.638%, progress (thread 0): 87.8165%]
|T|: y e p
|P|: o
[sample: IS1009b_H00_FIE088_598.28_598.62, WER: 100%, TER: 100%, total WER: 25.956%, total TER: 12.6386%, progress (thread 0): 87.8244%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_613.29_613.63, WER: 0%, TER: 0%, total WER: 25.9557%, total TER: 12.6385%, progress (thread 0): 87.8323%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1002.09_1002.43, WER: 0%, TER: 0%, total WER: 25.9554%, total TER: 12.6384%, progress (thread 0): 87.8402%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009b_H00_FIE088_1223.66_1224, WER: 200%, TER: 100%, total WER: 25.9574%, total TER: 12.6395%, progress (thread 0): 87.8481%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1225.44_1225.78, WER: 100%, TER: 40%, total WER: 25.9582%, total TER: 12.6398%, progress (thread 0): 87.856%]
|T|: m m
|P|: e a h
[sample: IS1009b_H03_FIO089_1734.63_1734.97, WER: 100%, TER: 150%, total WER: 25.9591%, total TER: 12.6404%, progress (thread 0): 87.8639%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1834.47_1834.81, WER: 0%, TER: 0%, total WER: 25.9588%, total TER: 12.6403%, progress (thread 0): 87.8718%]
|T|: o k a y
|P|: k
[sample: IS1009b_H01_FIO087_1882.63_1882.97, WER: 100%, TER: 75%, total WER: 25.9596%, total TER: 12.6409%, progress (thread 0): 87.8797%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H01_FIO087_1960.82_1961.16, WER: 100%, TER: 40%, total WER: 25.9605%, total TER: 12.6412%, progress (thread 0): 87.8877%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_535.66_536, WER: 100%, TER: 40%, total WER: 25.9613%, total TER: 12.6415%, progress (thread 0): 87.8956%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_575.77_576.11, WER: 100%, TER: 40%, total WER: 25.9621%, total TER: 12.6419%, progress (thread 0): 87.9035%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H03_FIO089_950.04_950.38, WER: 100%, TER: 80%, total WER: 25.963%, total TER: 12.6426%, progress (thread 0): 87.9114%]
|T|: i t | w o r k s
|P|: w u t
[sample: IS1009c_H01_FIO087_1083.82_1084.16, WER: 100%, TER: 87.5%, total WER: 25.9647%, total TER: 12.6441%, progress (thread 0): 87.9193%]
|T|: m m h m m
|P|: y e h
[sample: IS1009c_H03_FIO089_1109.75_1110.09, WER: 100%, TER: 80%, total WER: 25.9655%, total TER: 12.6449%, progress (thread 0): 87.9272%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1603.38_1603.72, WER: 100%, TER: 40%, total WER: 25.9663%, total TER: 12.6452%, progress (thread 0): 87.9351%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1613.06_1613.4, WER: 100%, TER: 40%, total WER: 25.9672%, total TER: 12.6455%, progress (thread 0): 87.943%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_1633.76_1634.1, WER: 0%, TER: 0%, total WER: 25.9669%, total TER: 12.6454%, progress (thread 0): 87.951%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_434.93_435.27, WER: 100%, TER: 40%, total WER: 25.9677%, total TER: 12.6457%, progress (thread 0): 87.9589%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_637.66_638, WER: 100%, TER: 60%, total WER: 25.9686%, total TER: 12.6463%, progress (thread 0): 87.9668%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_649.7_650.04, WER: 0%, TER: 0%, total WER: 25.9683%, total TER: 12.6461%, progress (thread 0): 87.9747%]
|T|: h m m
|P|: r i g h t
[sample: IS1009d_H02_FIO084_674.88_675.22, WER: 100%, TER: 166.667%, total WER: 25.9691%, total TER: 12.6472%, progress (thread 0): 87.9826%]
|T|: o o p s
|P|: u p
[sample: IS1009d_H00_FIE088_1068.99_1069.33, WER: 100%, TER: 75%, total WER: 25.97%, total TER: 12.6478%, progress (thread 0): 87.9905%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H01_FIO087_1509.75_1510.09, WER: 0%, TER: 0%, total WER: 25.9697%, total TER: 12.6477%, progress (thread 0): 87.9984%]
|T|: t h a n k | y o u
|P|: t h a n k | y o u
[sample: TS3003a_H03_MTD012ME_48.1_48.44, WER: 0%, TER: 0%, total WER: 25.9691%, total TER: 12.6475%, progress (thread 0): 88.0063%]
|T|: g u e s s
|P|: y e a f
[sample: TS3003a_H01_MTD011UID_983.96_984.3, WER: 100%, TER: 80%, total WER: 25.9699%, total TER: 12.6482%, progress (thread 0): 88.0142%]
|T|: t h e | p e n
|P|:
[sample: TS3003a_H02_MTD0010ID_1090.49_1090.83, WER: 100%, TER: 100%, total WER: 25.9716%, total TER: 12.6497%, progress (thread 0): 88.0222%]
|T|: t h i n g
|P|: t h i n g
[sample: TS3003a_H00_MTD009PM_1320.19_1320.53, WER: 0%, TER: 0%, total WER: 25.9713%, total TER: 12.6495%, progress (thread 0): 88.0301%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_132.84_133.18, WER: 0%, TER: 0%, total WER: 25.971%, total TER: 12.6495%, progress (thread 0): 88.038%]
|T|: r i g h t
|P|:
[sample: TS3003b_H01_MTD011UID_2086.12_2086.46, WER: 100%, TER: 100%, total WER: 25.9718%, total TER: 12.6505%, progress (thread 0): 88.0459%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2089.85_2090.19, WER: 0%, TER: 0%, total WER: 25.9715%, total TER: 12.6504%, progress (thread 0): 88.0538%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_982.32_982.66, WER: 0%, TER: 0%, total WER: 25.9713%, total TER: 12.6503%, progress (thread 0): 88.0617%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_1007.54_1007.88, WER: 0%, TER: 0%, total WER: 25.971%, total TER: 12.6502%, progress (thread 0): 88.0696%]
|T|: y e a h
|P|: y e h
[sample: TS3003c_H00_MTD009PM_1240.35_1240.69, WER: 100%, TER: 25%, total WER: 25.9718%, total TER: 12.6503%, progress (thread 0): 88.0775%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1413.87_1414.21, WER: 0%, TER: 0%, total WER: 25.9715%, total TER: 12.6501%, progress (thread 0): 88.0854%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1849.58_1849.92, WER: 0%, TER: 0%, total WER: 25.9712%, total TER: 12.65%, progress (thread 0): 88.0934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_595.75_596.09, WER: 0%, TER: 0%, total WER: 25.9709%, total TER: 12.6499%, progress (thread 0): 88.1013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_921.62_921.96, WER: 0%, TER: 0%, total WER: 25.9706%, total TER: 12.6498%, progress (thread 0): 88.1092%]
|T|: u h | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1308.11_1308.45, WER: 50%, TER: 42.8571%, total WER: 25.9712%, total TER: 12.6503%, progress (thread 0): 88.1171%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1388.52_1388.86, WER: 0%, TER: 0%, total WER: 25.9709%, total TER: 12.6502%, progress (thread 0): 88.125%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1802.71_1803.05, WER: 100%, TER: 75%, total WER: 25.9717%, total TER: 12.6508%, progress (thread 0): 88.1329%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1835.63_1835.97, WER: 100%, TER: 25%, total WER: 25.9726%, total TER: 12.6509%, progress (thread 0): 88.1408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2091.81_2092.15, WER: 0%, TER: 0%, total WER: 25.9723%, total TER: 12.6508%, progress (thread 0): 88.1487%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2217.62_2217.96, WER: 0%, TER: 0%, total WER: 25.972%, total TER: 12.6506%, progress (thread 0): 88.1566%]
|T|: m m | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2323.41_2323.75, WER: 50%, TER: 42.8571%, total WER: 25.9725%, total TER: 12.6511%, progress (thread 0): 88.1646%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2420.39_2420.73, WER: 0%, TER: 0%, total WER: 25.9722%, total TER: 12.651%, progress (thread 0): 88.1725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_406.15_406.49, WER: 0%, TER: 0%, total WER: 25.9719%, total TER: 12.6509%, progress (thread 0): 88.1804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_601.54_601.88, WER: 0%, TER: 0%, total WER: 25.9716%, total TER: 12.6508%, progress (thread 0): 88.1883%]
|T|: h m m
|P|: h m
[sample: EN2002a_H02_FEO072_713.14_713.48, WER: 100%, TER: 33.3333%, total WER: 25.9725%, total TER: 12.6509%, progress (thread 0): 88.1962%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_859.83_860.17, WER: 100%, TER: 66.6667%, total WER: 25.9733%, total TER: 12.6513%, progress (thread 0): 88.2041%]
|T|: a l r i g h t
|P|: i t
[sample: EN2002a_H01_FEO070_920.16_920.5, WER: 100%, TER: 71.4286%, total WER: 25.9741%, total TER: 12.6523%, progress (thread 0): 88.212%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1586.73_1587.07, WER: 0%, TER: 0%, total WER: 25.9738%, total TER: 12.6522%, progress (thread 0): 88.2199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1927.75_1928.09, WER: 0%, TER: 0%, total WER: 25.9736%, total TER: 12.652%, progress (thread 0): 88.2279%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1929.86_1930.2, WER: 0%, TER: 0%, total WER: 25.9733%, total TER: 12.6519%, progress (thread 0): 88.2358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2048.6_2048.94, WER: 0%, TER: 0%, total WER: 25.973%, total TER: 12.6518%, progress (thread 0): 88.2437%]
|T|: s o | i t ' s
|P|: t w a
[sample: EN2002a_H03_MEE071_2098.66_2099, WER: 100%, TER: 85.7143%, total WER: 25.9746%, total TER: 12.653%, progress (thread 0): 88.2516%]
|T|: o k a y
|P|: o k
[sample: EN2002a_H00_MEE073_2129.89_2130.23, WER: 100%, TER: 50%, total WER: 25.9755%, total TER: 12.6533%, progress (thread 0): 88.2595%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_144.51_144.85, WER: 0%, TER: 0%, total WER: 25.9752%, total TER: 12.6532%, progress (thread 0): 88.2674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_190.18_190.52, WER: 0%, TER: 0%, total WER: 25.9749%, total TER: 12.6531%, progress (thread 0): 88.2753%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_302.27_302.61, WER: 100%, TER: 66.6667%, total WER: 25.9757%, total TER: 12.6535%, progress (thread 0): 88.2832%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_309.52_309.86, WER: 0%, TER: 0%, total WER: 25.9754%, total TER: 12.6534%, progress (thread 0): 88.2911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_363.35_363.69, WER: 0%, TER: 0%, total WER: 25.9751%, total TER: 12.6533%, progress (thread 0): 88.299%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_409.14_409.48, WER: 0%, TER: 0%, total WER: 25.9749%, total TER: 12.6531%, progress (thread 0): 88.307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_736.4_736.74, WER: 0%, TER: 0%, total WER: 25.9746%, total TER: 12.653%, progress (thread 0): 88.3149%]
|T|: y e a h
|P|: y o
[sample: EN2002b_H03_MEE073_935.86_936.2, WER: 100%, TER: 75%, total WER: 25.9754%, total TER: 12.6536%, progress (thread 0): 88.3228%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002b_H03_MEE073_1252.52_1252.86, WER: 0%, TER: 0%, total WER: 25.9748%, total TER: 12.6534%, progress (thread 0): 88.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1563.14_1563.48, WER: 0%, TER: 0%, total WER: 25.9745%, total TER: 12.6533%, progress (thread 0): 88.3386%]
|T|: y e a h
|P|: y e h
[sample: EN2002b_H00_FEO070_1626.33_1626.67, WER: 100%, TER: 25%, total WER: 25.9754%, total TER: 12.6534%, progress (thread 0): 88.3465%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H01_FEO072_67.59_67.93, WER: 0%, TER: 0%, total WER: 25.9751%, total TER: 12.6533%, progress (thread 0): 88.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_273.14_273.48, WER: 0%, TER: 0%, total WER: 25.9748%, total TER: 12.6532%, progress (thread 0): 88.3623%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_444.82_445.16, WER: 100%, TER: 40%, total WER: 25.9756%, total TER: 12.6535%, progress (thread 0): 88.3703%]
|T|: o r
|P|: e
[sample: EN2002c_H01_FEO072_486.4_486.74, WER: 100%, TER: 100%, total WER: 25.9764%, total TER: 12.6539%, progress (thread 0): 88.3782%]
|T|: o h | w e l l
|P|: h o w
[sample: EN2002c_H03_MEE073_497.23_497.57, WER: 100%, TER: 71.4286%, total WER: 25.9781%, total TER: 12.6549%, progress (thread 0): 88.3861%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002c_H01_FEO072_721.91_722.25, WER: 200%, TER: 14.2857%, total WER: 25.9801%, total TER: 12.6549%, progress (thread 0): 88.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1305.34_1305.68, WER: 0%, TER: 0%, total WER: 25.9798%, total TER: 12.6548%, progress (thread 0): 88.4019%]
|T|: o h
|P|: o h
[sample: EN2002c_H01_FEO072_1925.9_1926.24, WER: 0%, TER: 0%, total WER: 25.9795%, total TER: 12.6547%, progress (thread 0): 88.4098%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2040.49_2040.83, WER: 0%, TER: 0%, total WER: 25.9792%, total TER: 12.6546%, progress (thread 0): 88.4177%]
|T|: m m h m m
|P|: m p
[sample: EN2002c_H03_MEE073_2245.81_2246.15, WER: 100%, TER: 80%, total WER: 25.9801%, total TER: 12.6554%, progress (thread 0): 88.4256%]
|T|: y e a h
|P|: r e a l l y
[sample: EN2002d_H03_MEE073_344.02_344.36, WER: 100%, TER: 100%, total WER: 25.9809%, total TER: 12.6562%, progress (thread 0): 88.4335%]
|T|: w h a t | w a s | i t
|P|: o h | w a s | i t
[sample: EN2002d_H03_MEE073_508.22_508.56, WER: 33.3333%, TER: 27.2727%, total WER: 25.9811%, total TER: 12.6566%, progress (thread 0): 88.4415%]
|T|: y e a h
|P|: g o o d
[sample: EN2002d_H02_MEE071_688.4_688.74, WER: 100%, TER: 100%, total WER: 25.982%, total TER: 12.6574%, progress (thread 0): 88.4494%]
|T|: y e a h
|P|: y e s
[sample: EN2002d_H02_MEE071_715.16_715.5, WER: 100%, TER: 50%, total WER: 25.9828%, total TER: 12.6577%, progress (thread 0): 88.4573%]
|T|: u h
|P|:
[sample: EN2002d_H03_MEE073_1125.77_1126.11, WER: 100%, TER: 100%, total WER: 25.9837%, total TER: 12.6582%, progress (thread 0): 88.4652%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1325.04_1325.38, WER: 100%, TER: 33.3333%, total WER: 25.9845%, total TER: 12.6583%, progress (thread 0): 88.4731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1423.14_1423.48, WER: 0%, TER: 0%, total WER: 25.9842%, total TER: 12.6582%, progress (thread 0): 88.481%]
|T|: n o | w e | p
|P|: s o | w e
[sample: EN2002d_H00_FEO070_1437.94_1438.28, WER: 66.6667%, TER: 42.8571%, total WER: 25.9856%, total TER: 12.6587%, progress (thread 0): 88.4889%]
|T|: w i t h
|P|: w i t h
[sample: EN2002d_H02_MEE071_2073.15_2073.49, WER: 0%, TER: 0%, total WER: 25.9853%, total TER: 12.6586%, progress (thread 0): 88.4968%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_258.31_258.64, WER: 100%, TER: 40%, total WER: 25.9861%, total TER: 12.6589%, progress (thread 0): 88.5048%]
|T|: m m | y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_1048.05_1048.38, WER: 50%, TER: 42.8571%, total WER: 25.9867%, total TER: 12.6594%, progress (thread 0): 88.5127%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_736.03_736.36, WER: 0%, TER: 0%, total WER: 25.9864%, total TER: 12.6593%, progress (thread 0): 88.5206%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1313.86_1314.19, WER: 0%, TER: 0%, total WER: 25.9861%, total TER: 12.6591%, progress (thread 0): 88.5285%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H01_FEE013_1711.46_1711.79, WER: 100%, TER: 200%, total WER: 25.9869%, total TER: 12.66%, progress (thread 0): 88.5364%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004b_H03_FEE016_2208.79_2209.12, WER: 200%, TER: 20%, total WER: 25.9889%, total TER: 12.6601%, progress (thread 0): 88.5443%]
|T|: o k a y
|P|: o k a y
[sample: ES2004b_H03_FEE016_2308.52_2308.85, WER: 0%, TER: 0%, total WER: 25.9886%, total TER: 12.66%, progress (thread 0): 88.5522%]
|T|: y e a h
|P|: y u p
[sample: ES2004c_H02_MEE014_172.32_172.65, WER: 100%, TER: 75%, total WER: 25.9895%, total TER: 12.6606%, progress (thread 0): 88.5601%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_560.34_560.67, WER: 100%, TER: 50%, total WER: 25.9903%, total TER: 12.6608%, progress (thread 0): 88.568%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H01_FEE013_873.37_873.7, WER: 100%, TER: 40%, total WER: 25.9911%, total TER: 12.6611%, progress (thread 0): 88.576%]
|T|: w e l l
|P|: w e l l
[sample: ES2004c_H02_MEE014_925.27_925.6, WER: 0%, TER: 0%, total WER: 25.9908%, total TER: 12.661%, progress (thread 0): 88.5839%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1268.11_1268.44, WER: 0%, TER: 0%, total WER: 25.9905%, total TER: 12.6608%, progress (thread 0): 88.5918%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1965.56_1965.89, WER: 100%, TER: 40%, total WER: 25.9914%, total TER: 12.6612%, progress (thread 0): 88.5997%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H01_FEE013_607.94_608.27, WER: 100%, TER: 40%, total WER: 25.9922%, total TER: 12.6615%, progress (thread 0): 88.6076%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_753.08_753.41, WER: 100%, TER: 33.3333%, total WER: 25.9931%, total TER: 12.6616%, progress (thread 0): 88.6155%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H03_FEE016_876.07_876.4, WER: 100%, TER: 40%, total WER: 25.9939%, total TER: 12.6619%, progress (thread 0): 88.6234%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H00_MEO015_1304.99_1305.32, WER: 0%, TER: 0%, total WER: 25.9936%, total TER: 12.6619%, progress (thread 0): 88.6313%]
|T|: s u r e
|P|: s u r e
[sample: ES2004d_H03_FEE016_1499.95_1500.28, WER: 0%, TER: 0%, total WER: 25.9933%, total TER: 12.6617%, progress (thread 0): 88.6392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1845.84_1846.17, WER: 0%, TER: 0%, total WER: 25.993%, total TER: 12.6616%, progress (thread 0): 88.6471%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1856.75_1857.08, WER: 0%, TER: 0%, total WER: 25.9927%, total TER: 12.6615%, progress (thread 0): 88.6551%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1984.68_1985.01, WER: 0%, TER: 0%, total WER: 25.9924%, total TER: 12.6614%, progress (thread 0): 88.663%]
|T|: m m
|P|: o h
[sample: ES2004d_H01_FEE013_2126.04_2126.37, WER: 100%, TER: 100%, total WER: 25.9933%, total TER: 12.6618%, progress (thread 0): 88.6709%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_55.7_56.03, WER: 0%, TER: 0%, total WER: 25.993%, total TER: 12.6617%, progress (thread 0): 88.6788%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_226.94_227.27, WER: 100%, TER: 40%, total WER: 25.9938%, total TER: 12.662%, progress (thread 0): 88.6867%]
|T|: o k a y
|P|: o k a t
[sample: IS1009a_H03_FIO089_318.12_318.45, WER: 100%, TER: 25%, total WER: 25.9946%, total TER: 12.6621%, progress (thread 0): 88.6946%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_652.91_653.24, WER: 100%, TER: 40%, total WER: 25.9955%, total TER: 12.6624%, progress (thread 0): 88.7025%]
|T|: t h e n
|P|: a n d | t h e n
[sample: IS1009a_H00_FIE088_714.21_714.54, WER: 100%, TER: 100%, total WER: 25.9963%, total TER: 12.6633%, progress (thread 0): 88.7104%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_741.37_741.7, WER: 100%, TER: 40%, total WER: 25.9972%, total TER: 12.6636%, progress (thread 0): 88.7184%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_790.95_791.28, WER: 100%, TER: 40%, total WER: 25.998%, total TER: 12.6639%, progress (thread 0): 88.7263%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_787.47_787.8, WER: 100%, TER: 40%, total WER: 25.9988%, total TER: 12.6642%, progress (thread 0): 88.7342%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1063.18_1063.51, WER: 100%, TER: 40%, total WER: 25.9997%, total TER: 12.6645%, progress (thread 0): 88.7421%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1809.47_1809.8, WER: 0%, TER: 0%, total WER: 25.9994%, total TER: 12.6644%, progress (thread 0): 88.75%]
|T|: m m h m m
|P|: u h
[sample: IS1009b_H03_FIO089_1882.22_1882.55, WER: 100%, TER: 80%, total WER: 26.0002%, total TER: 12.6652%, progress (thread 0): 88.7579%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_408.02_408.35, WER: 100%, TER: 40%, total WER: 26.0011%, total TER: 12.6655%, progress (thread 0): 88.7658%]
|T|: m m h m m
|P|: u h
[sample: IS1009c_H00_FIE088_430.74_431.07, WER: 100%, TER: 80%, total WER: 26.0019%, total TER: 12.6663%, progress (thread 0): 88.7737%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1552.21_1552.54, WER: 0%, TER: 0%, total WER: 26.0016%, total TER: 12.6662%, progress (thread 0): 88.7816%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_383.89_384.22, WER: 100%, TER: 40%, total WER: 26.0024%, total TER: 12.6665%, progress (thread 0): 88.7896%]
|T|: m m h m m
|P|: m
[sample: IS1009d_H03_FIO089_388.95_389.28, WER: 100%, TER: 80%, total WER: 26.0033%, total TER: 12.6673%, progress (thread 0): 88.7975%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_483.18_483.51, WER: 100%, TER: 40%, total WER: 26.0041%, total TER: 12.6676%, progress (thread 0): 88.8054%]
|T|: t h a t ' s | r i g h t
|P|:
[sample: IS1009d_H00_FIE088_757.72_758.05, WER: 100%, TER: 100%, total WER: 26.0058%, total TER: 12.6701%, progress (thread 0): 88.8133%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H01_FIO087_784.18_784.51, WER: 100%, TER: 40%, total WER: 26.0066%, total TER: 12.6704%, progress (thread 0): 88.8212%]
|T|: y e a h
|P|: i t
[sample: IS1009d_H02_FIO084_975.71_976.04, WER: 100%, TER: 100%, total WER: 26.0075%, total TER: 12.6713%, progress (thread 0): 88.8291%]
|T|: m m
|P|: h m
[sample: IS1009d_H02_FIO084_1183.1_1183.43, WER: 100%, TER: 50%, total WER: 26.0083%, total TER: 12.6714%, progress (thread 0): 88.837%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H02_FIO084_1185.85_1186.18, WER: 100%, TER: 40%, total WER: 26.0092%, total TER: 12.6717%, progress (thread 0): 88.8449%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1310.32_1310.65, WER: 100%, TER: 100%, total WER: 26.01%, total TER: 12.6728%, progress (thread 0): 88.8528%]
|T|: u h
|P|: u m
[sample: TS3003a_H01_MTD011UID_915.22_915.55, WER: 100%, TER: 50%, total WER: 26.0108%, total TER: 12.6729%, progress (thread 0): 88.8608%]
|T|: o r | w h o
|P|: o | w h o
[sample: TS3003a_H01_MTD011UID_975.03_975.36, WER: 50%, TER: 16.6667%, total WER: 26.0114%, total TER: 12.673%, progress (thread 0): 88.8687%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1261.99_1262.32, WER: 100%, TER: 50%, total WER: 26.0122%, total TER: 12.6732%, progress (thread 0): 88.8766%]
|T|: s o
|P|: s o
[sample: TS3003b_H03_MTD012ME_280.47_280.8, WER: 0%, TER: 0%, total WER: 26.0119%, total TER: 12.6731%, progress (thread 0): 88.8845%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H00_MTD009PM_842.62_842.95, WER: 100%, TER: 50%, total WER: 26.0128%, total TER: 12.6735%, progress (thread 0): 88.8924%]
|T|: o k a y
|P|: h
[sample: TS3003b_H00_MTD009PM_923.51_923.84, WER: 100%, TER: 100%, total WER: 26.0136%, total TER: 12.6743%, progress (thread 0): 88.9003%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1173.66_1173.99, WER: 0%, TER: 0%, total WER: 26.0133%, total TER: 12.6742%, progress (thread 0): 88.9082%]
|T|: n a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1009.07_1009.4, WER: 100%, TER: 66.6667%, total WER: 26.0141%, total TER: 12.6746%, progress (thread 0): 88.9161%]
|T|: ' k a y
|P|: s o
[sample: TS3003c_H03_MTD012ME_1321.92_1322.25, WER: 100%, TER: 100%, total WER: 26.015%, total TER: 12.6754%, progress (thread 0): 88.924%]
|T|: m m
|P|: a n
[sample: TS3003c_H01_MTD011UID_1395.11_1395.44, WER: 100%, TER: 100%, total WER: 26.0158%, total TER: 12.6758%, progress (thread 0): 88.932%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1507.79_1508.12, WER: 0%, TER: 0%, total WER: 26.0155%, total TER: 12.6757%, progress (thread 0): 88.9399%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1540.01_1540.34, WER: 100%, TER: 40%, total WER: 26.0164%, total TER: 12.676%, progress (thread 0): 88.9478%]
|T|: y e p
|P|: e h
[sample: TS3003c_H00_MTD009PM_1653.43_1653.76, WER: 100%, TER: 66.6667%, total WER: 26.0172%, total TER: 12.6764%, progress (thread 0): 88.9557%]
|T|: u h
|P|: m m
[sample: TS3003c_H01_MTD011UID_1692.63_1692.96, WER: 100%, TER: 100%, total WER: 26.018%, total TER: 12.6768%, progress (thread 0): 88.9636%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2074.03_2074.36, WER: 0%, TER: 0%, total WER: 26.0178%, total TER: 12.6767%, progress (thread 0): 88.9715%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2241.48_2241.81, WER: 0%, TER: 0%, total WER: 26.0175%, total TER: 12.6765%, progress (thread 0): 88.9794%]
|T|: y e a h
|P|: y e r e
[sample: TS3003d_H01_MTD011UID_693.96_694.29, WER: 100%, TER: 50%, total WER: 26.0183%, total TER: 12.6769%, progress (thread 0): 88.9873%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_872.34_872.67, WER: 0%, TER: 0%, total WER: 26.018%, total TER: 12.6768%, progress (thread 0): 88.9953%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_889.13_889.46, WER: 0%, TER: 0%, total WER: 26.0177%, total TER: 12.6767%, progress (thread 0): 89.0032%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_1661.82_1662.15, WER: 0%, TER: 0%, total WER: 26.0174%, total TER: 12.6765%, progress (thread 0): 89.0111%]
|T|: y e a h
|P|: h u h
[sample: TS3003d_H01_MTD011UID_2072.21_2072.54, WER: 100%, TER: 75%, total WER: 26.0183%, total TER: 12.6771%, progress (thread 0): 89.019%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_2085.82_2086.15, WER: 100%, TER: 40%, total WER: 26.0191%, total TER: 12.6774%, progress (thread 0): 89.0269%]
|T|: y e a h
|P|: y e s
[sample: TS3003d_H00_MTD009PM_2164.01_2164.34, WER: 100%, TER: 50%, total WER: 26.0199%, total TER: 12.6778%, progress (thread 0): 89.0348%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2179.8_2180.13, WER: 0%, TER: 0%, total WER: 26.0196%, total TER: 12.6777%, progress (thread 0): 89.0427%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_2461.76_2462.09, WER: 100%, TER: 40%, total WER: 26.0205%, total TER: 12.678%, progress (thread 0): 89.0506%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_299.48_299.81, WER: 0%, TER: 0%, total WER: 26.0202%, total TER: 12.6779%, progress (thread 0): 89.0585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_359.5_359.83, WER: 0%, TER: 0%, total WER: 26.0199%, total TER: 12.6778%, progress (thread 0): 89.0665%]
|T|: w a i t
|P|: w a i t
[sample: EN2002a_H02_FEO072_387.53_387.86, WER: 0%, TER: 0%, total WER: 26.0196%, total TER: 12.6776%, progress (thread 0): 89.0744%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_593.65_593.98, WER: 50%, TER: 55.5556%, total WER: 26.0201%, total TER: 12.6785%, progress (thread 0): 89.0823%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_594.32_594.65, WER: 0%, TER: 0%, total WER: 26.0198%, total TER: 12.6784%, progress (thread 0): 89.0902%]
|T|: y e a h
|P|: y n o
[sample: EN2002a_H01_FEO070_690.97_691.3, WER: 100%, TER: 75%, total WER: 26.0207%, total TER: 12.679%, progress (thread 0): 89.0981%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_1018.98_1019.31, WER: 0%, TER: 0%, total WER: 26.0204%, total TER: 12.6789%, progress (thread 0): 89.106%]
|T|: y e a h
|P|: y e s
[sample: EN2002a_H01_FEO070_1028.68_1029.01, WER: 100%, TER: 50%, total WER: 26.0212%, total TER: 12.6792%, progress (thread 0): 89.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1142.23_1142.56, WER: 0%, TER: 0%, total WER: 26.0209%, total TER: 12.6791%, progress (thread 0): 89.1218%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1361.22_1361.55, WER: 0%, TER: 0%, total WER: 26.0206%, total TER: 12.679%, progress (thread 0): 89.1297%]
|T|: r i g h t
|P|: e h
[sample: EN2002a_H03_MEE071_1383.46_1383.79, WER: 100%, TER: 80%, total WER: 26.0215%, total TER: 12.6798%, progress (thread 0): 89.1377%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1864.48_1864.81, WER: 0%, TER: 0%, total WER: 26.0212%, total TER: 12.6797%, progress (thread 0): 89.1456%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1964.31_1964.64, WER: 0%, TER: 0%, total WER: 26.0209%, total TER: 12.6796%, progress (thread 0): 89.1535%]
|T|: u m
|P|: u m
[sample: EN2002b_H03_MEE073_139.44_139.77, WER: 0%, TER: 0%, total WER: 26.0206%, total TER: 12.6795%, progress (thread 0): 89.1614%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_417.94_418.27, WER: 200%, TER: 175%, total WER: 26.0226%, total TER: 12.681%, progress (thread 0): 89.1693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_420.46_420.79, WER: 0%, TER: 0%, total WER: 26.0223%, total TER: 12.6809%, progress (thread 0): 89.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_705.52_705.85, WER: 0%, TER: 0%, total WER: 26.022%, total TER: 12.6808%, progress (thread 0): 89.1851%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_731.78_732.11, WER: 0%, TER: 0%, total WER: 26.0217%, total TER: 12.6807%, progress (thread 0): 89.193%]
|T|: y e a h | t
|P|: y e a h
[sample: EN2002b_H02_FEO072_1235.77_1236.1, WER: 50%, TER: 33.3333%, total WER: 26.0222%, total TER: 12.681%, progress (thread 0): 89.201%]
|T|: h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_1236.72_1237.05, WER: 100%, TER: 66.6667%, total WER: 26.0231%, total TER: 12.6813%, progress (thread 0): 89.2089%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_1256.5_1256.83, WER: 0%, TER: 0%, total WER: 26.0228%, total TER: 12.6812%, progress (thread 0): 89.2168%]
|T|: h m m
|P|: m
[sample: EN2002b_H02_FEO072_1475.51_1475.84, WER: 100%, TER: 66.6667%, total WER: 26.0236%, total TER: 12.6816%, progress (thread 0): 89.2247%]
|T|: o h | r i g h t
|P|: a l l | r i g h t
[sample: EN2002b_H02_FEO072_1611.8_1612.13, WER: 50%, TER: 37.5%, total WER: 26.0241%, total TER: 12.6821%, progress (thread 0): 89.2326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_93.86_94.19, WER: 0%, TER: 0%, total WER: 26.0238%, total TER: 12.6819%, progress (thread 0): 89.2405%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_245.2_245.53, WER: 0%, TER: 0%, total WER: 26.0235%, total TER: 12.6818%, progress (thread 0): 89.2484%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H01_FEO072_447.06_447.39, WER: 100%, TER: 40%, total WER: 26.0244%, total TER: 12.6822%, progress (thread 0): 89.2563%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002c_H03_MEE073_823.26_823.59, WER: 0%, TER: 0%, total WER: 26.0238%, total TER: 12.6819%, progress (thread 0): 89.2642%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_850.62_850.95, WER: 0%, TER: 0%, total WER: 26.0235%, total TER: 12.6818%, progress (thread 0): 89.2722%]
|T|: t h a t ' s | a
|P|: t h a t ' s | a
[sample: EN2002c_H03_MEE073_903.73_904.06, WER: 0%, TER: 0%, total WER: 26.0229%, total TER: 12.6816%, progress (thread 0): 89.2801%]
|T|: o h
|P|: n o w
[sample: EN2002c_H03_MEE073_962_962.33, WER: 100%, TER: 100%, total WER: 26.0237%, total TER: 12.682%, progress (thread 0): 89.288%]
|T|: y e a h | r i g h t
|P|: y a | r i h
[sample: EN2002c_H03_MEE073_1287.41_1287.74, WER: 100%, TER: 40%, total WER: 26.0254%, total TER: 12.6826%, progress (thread 0): 89.2959%]
|T|: t h a t ' s | t r u e
|P|: t h a t ' s | r u e
[sample: EN2002c_H03_MEE073_1299.73_1300.06, WER: 50%, TER: 9.09091%, total WER: 26.026%, total TER: 12.6825%, progress (thread 0): 89.3038%]
|T|: y o u | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2044.96_2045.29, WER: 0%, TER: 0%, total WER: 26.0254%, total TER: 12.6823%, progress (thread 0): 89.3117%]
|T|: m m
|P|: h m
[sample: EN2002c_H02_MEE071_2072_2072.33, WER: 100%, TER: 50%, total WER: 26.0262%, total TER: 12.6825%, progress (thread 0): 89.3196%]
|T|: o o h
|P|: o o
[sample: EN2002c_H01_FEO072_2817.88_2818.21, WER: 100%, TER: 33.3333%, total WER: 26.0271%, total TER: 12.6826%, progress (thread 0): 89.3275%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002d_H01_FEO072_419.86_420.19, WER: 200%, TER: 14.2857%, total WER: 26.029%, total TER: 12.6827%, progress (thread 0): 89.3354%]
|T|: m m
|P|: i
[sample: EN2002d_H03_MEE073_488.3_488.63, WER: 100%, TER: 100%, total WER: 26.0299%, total TER: 12.6831%, progress (thread 0): 89.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1089.65_1089.98, WER: 0%, TER: 0%, total WER: 26.0296%, total TER: 12.6829%, progress (thread 0): 89.3513%]
|T|: w e l l
|P|: n o
[sample: EN2002d_H03_MEE073_1132.11_1132.44, WER: 100%, TER: 100%, total WER: 26.0304%, total TER: 12.6838%, progress (thread 0): 89.3592%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1131.27_1131.6, WER: 100%, TER: 33.3333%, total WER: 26.0312%, total TER: 12.6839%, progress (thread 0): 89.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1257.89_1258.22, WER: 0%, TER: 0%, total WER: 26.031%, total TER: 12.6838%, progress (thread 0): 89.375%]
|T|: r e a l l y
|P|: r e a l l y
[sample: EN2002d_H01_FEO072_1345.97_1346.3, WER: 0%, TER: 0%, total WER: 26.0307%, total TER: 12.6836%, progress (thread 0): 89.3829%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1429.74_1430.07, WER: 100%, TER: 33.3333%, total WER: 26.0315%, total TER: 12.6838%, progress (thread 0): 89.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1957.8_1958.13, WER: 0%, TER: 0%, total WER: 26.0312%, total TER: 12.6836%, progress (thread 0): 89.3987%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_863.21_863.53, WER: 100%, TER: 40%, total WER: 26.032%, total TER: 12.684%, progress (thread 0): 89.4066%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_895.43_895.75, WER: 100%, TER: 40%, total WER: 26.0329%, total TER: 12.6843%, progress (thread 0): 89.4146%]
|T|: m m
|P|: h m
[sample: ES2004b_H01_FEE013_1140.58_1140.9, WER: 100%, TER: 50%, total WER: 26.0337%, total TER: 12.6845%, progress (thread 0): 89.4225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1182.75_1183.07, WER: 0%, TER: 0%, total WER: 26.0334%, total TER: 12.6843%, progress (thread 0): 89.4304%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1471.32_1471.64, WER: 100%, TER: 40%, total WER: 26.0343%, total TER: 12.6847%, progress (thread 0): 89.4383%]
|T|: s
|P|: s h
[sample: ES2004b_H01_FEE013_1922.51_1922.83, WER: 100%, TER: 100%, total WER: 26.0351%, total TER: 12.6849%, progress (thread 0): 89.4462%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H03_FEE016_1953.34_1953.66, WER: 100%, TER: 60%, total WER: 26.0359%, total TER: 12.6854%, progress (thread 0): 89.4541%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1966.24_1966.56, WER: 0%, TER: 0%, total WER: 26.0356%, total TER: 12.6853%, progress (thread 0): 89.462%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_806.95_807.27, WER: 0%, TER: 0%, total WER: 26.0353%, total TER: 12.6852%, progress (thread 0): 89.4699%]
|T|: o k a y
|P|: o k a
[sample: ES2004c_H03_FEE016_1066.11_1066.43, WER: 100%, TER: 25%, total WER: 26.0362%, total TER: 12.6853%, progress (thread 0): 89.4779%]
|T|: m m
|P|: y e m
[sample: ES2004c_H00_MEO015_1890.24_1890.56, WER: 100%, TER: 100%, total WER: 26.037%, total TER: 12.6857%, progress (thread 0): 89.4858%]
|T|: y e a h
|P|: y e p
[sample: ES2004d_H00_MEO015_341.64_341.96, WER: 100%, TER: 50%, total WER: 26.0379%, total TER: 12.6861%, progress (thread 0): 89.4937%]
|T|: n o
|P|: n o
[sample: ES2004d_H00_MEO015_400.96_401.28, WER: 0%, TER: 0%, total WER: 26.0376%, total TER: 12.686%, progress (thread 0): 89.5016%]
|T|: o o p s
|P|:
[sample: ES2004d_H03_FEE016_430.55_430.87, WER: 100%, TER: 100%, total WER: 26.0384%, total TER: 12.6868%, progress (thread 0): 89.5095%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_774.15_774.47, WER: 0%, TER: 0%, total WER: 26.0381%, total TER: 12.6867%, progress (thread 0): 89.5174%]
|T|: o n e
|P|: w e l l
[sample: ES2004d_H02_MEE014_785.53_785.85, WER: 100%, TER: 133.333%, total WER: 26.0389%, total TER: 12.6875%, progress (thread 0): 89.5253%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_840.47_840.79, WER: 0%, TER: 0%, total WER: 26.0386%, total TER: 12.6875%, progress (thread 0): 89.5332%]
|T|: m m h m m
|P|:
[sample: ES2004d_H03_FEE016_1930.95_1931.27, WER: 100%, TER: 100%, total WER: 26.0395%, total TER: 12.6885%, progress (thread 0): 89.5411%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2146.44_2146.76, WER: 0%, TER: 0%, total WER: 26.0392%, total TER: 12.6884%, progress (thread 0): 89.549%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_290.82_291.14, WER: 100%, TER: 40%, total WER: 26.04%, total TER: 12.6887%, progress (thread 0): 89.557%]
|T|: o o p s
|P|: s
[sample: IS1009a_H03_FIO089_420.14_420.46, WER: 100%, TER: 75%, total WER: 26.0409%, total TER: 12.6893%, progress (thread 0): 89.5649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H01_FIO087_494.72_495.04, WER: 0%, TER: 0%, total WER: 26.0406%, total TER: 12.6892%, progress (thread 0): 89.5728%]
|T|: y e a h
|P|:
[sample: IS1009a_H02_FIO084_577.05_577.37, WER: 100%, TER: 100%, total WER: 26.0414%, total TER: 12.69%, progress (thread 0): 89.5807%]
|T|: m m h m m
|P|: u h h u h
[sample: IS1009a_H02_FIO084_641.8_642.12, WER: 100%, TER: 80%, total WER: 26.0422%, total TER: 12.6908%, progress (thread 0): 89.5886%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_197.33_197.65, WER: 0%, TER: 0%, total WER: 26.0419%, total TER: 12.6906%, progress (thread 0): 89.5965%]
|T|: m m h m m
|P|: u
[sample: IS1009b_H00_FIE088_595.82_596.14, WER: 100%, TER: 100%, total WER: 26.0428%, total TER: 12.6917%, progress (thread 0): 89.6044%]
|T|: m m h m m
|P|: r i h
[sample: IS1009b_H03_FIO089_1073.83_1074.15, WER: 100%, TER: 80%, total WER: 26.0436%, total TER: 12.6925%, progress (thread 0): 89.6123%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1139.48_1139.8, WER: 100%, TER: 40%, total WER: 26.0445%, total TER: 12.6928%, progress (thread 0): 89.6202%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1268.25_1268.57, WER: 100%, TER: 40%, total WER: 26.0453%, total TER: 12.6931%, progress (thread 0): 89.6282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1877.89_1878.21, WER: 0%, TER: 0%, total WER: 26.045%, total TER: 12.693%, progress (thread 0): 89.6361%]
|T|: m m h m m
|P|: m e n
[sample: IS1009b_H03_FIO089_1894.73_1895.05, WER: 100%, TER: 80%, total WER: 26.0458%, total TER: 12.6938%, progress (thread 0): 89.644%]
|T|: h i
|P|: k a y
[sample: IS1009c_H01_FIO087_43.77_44.09, WER: 100%, TER: 150%, total WER: 26.0467%, total TER: 12.6944%, progress (thread 0): 89.6519%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009c_H00_FIE088_405.29_405.61, WER: 200%, TER: 100%, total WER: 26.0487%, total TER: 12.6954%, progress (thread 0): 89.6598%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_524.19_524.51, WER: 100%, TER: 40%, total WER: 26.0495%, total TER: 12.6958%, progress (thread 0): 89.6677%]
|T|: y e s
|P|:
[sample: IS1009c_H01_FIO087_1640_1640.32, WER: 100%, TER: 100%, total WER: 26.0503%, total TER: 12.6964%, progress (thread 0): 89.6756%]
|T|: m e n u
|P|: m e n u
[sample: IS1009d_H00_FIE088_523.67_523.99, WER: 0%, TER: 0%, total WER: 26.05%, total TER: 12.6963%, progress (thread 0): 89.6835%]
|T|: p a r d o n | m e
|P|: b u
[sample: IS1009d_H01_FIO087_699.16_699.48, WER: 100%, TER: 100%, total WER: 26.0517%, total TER: 12.6981%, progress (thread 0): 89.6915%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_799.6_799.92, WER: 100%, TER: 100%, total WER: 26.0525%, total TER: 12.6991%, progress (thread 0): 89.6994%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1056.69_1057.01, WER: 0%, TER: 0%, total WER: 26.0522%, total TER: 12.699%, progress (thread 0): 89.7073%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1722.84_1723.16, WER: 100%, TER: 40%, total WER: 26.0531%, total TER: 12.6993%, progress (thread 0): 89.7152%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1741.45_1741.77, WER: 100%, TER: 40%, total WER: 26.0539%, total TER: 12.6997%, progress (thread 0): 89.7231%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1791.05_1791.37, WER: 0%, TER: 0%, total WER: 26.0536%, total TER: 12.6995%, progress (thread 0): 89.731%]
|T|: y e a h
|P|: h m
[sample: TS3003b_H01_MTD011UID_1651.31_1651.63, WER: 100%, TER: 100%, total WER: 26.0545%, total TER: 12.7004%, progress (thread 0): 89.7389%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_1722.53_1722.85, WER: 100%, TER: 25%, total WER: 26.0553%, total TER: 12.7005%, progress (thread 0): 89.7468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2090.25_2090.57, WER: 0%, TER: 0%, total WER: 26.055%, total TER: 12.7004%, progress (thread 0): 89.7547%]
|T|: m m h m m
|P|: a h
[sample: TS3003c_H03_MTD012ME_2074.3_2074.62, WER: 100%, TER: 80%, total WER: 26.0558%, total TER: 12.7011%, progress (thread 0): 89.7627%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_2134.48_2134.8, WER: 100%, TER: 40%, total WER: 26.0567%, total TER: 12.7015%, progress (thread 0): 89.7706%]
|T|: n a y
|P|: n o
[sample: TS3003d_H01_MTD011UID_417.84_418.16, WER: 100%, TER: 66.6667%, total WER: 26.0575%, total TER: 12.7018%, progress (thread 0): 89.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_694.59_694.91, WER: 0%, TER: 0%, total WER: 26.0572%, total TER: 12.7017%, progress (thread 0): 89.7864%]
|T|: o r | b
|P|: r
[sample: TS3003d_H03_MTD012ME_896.59_896.91, WER: 100%, TER: 75%, total WER: 26.0589%, total TER: 12.7023%, progress (thread 0): 89.7943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1403.4_1403.72, WER: 0%, TER: 0%, total WER: 26.0586%, total TER: 12.7022%, progress (thread 0): 89.8022%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1492.24_1492.56, WER: 0%, TER: 0%, total WER: 26.0583%, total TER: 12.7021%, progress (thread 0): 89.8101%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_2024.39_2024.71, WER: 100%, TER: 100%, total WER: 26.0591%, total TER: 12.7029%, progress (thread 0): 89.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2381.84_2382.16, WER: 0%, TER: 0%, total WER: 26.0588%, total TER: 12.7028%, progress (thread 0): 89.826%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_68.23_68.55, WER: 0%, TER: 0%, total WER: 26.0586%, total TER: 12.7026%, progress (thread 0): 89.8339%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_359.54_359.86, WER: 50%, TER: 42.8571%, total WER: 26.0591%, total TER: 12.7031%, progress (thread 0): 89.8418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_496.88_497.2, WER: 0%, TER: 0%, total WER: 26.0588%, total TER: 12.703%, progress (thread 0): 89.8497%]
|T|: h m m
|P|: y n
[sample: EN2002a_H02_FEO072_504.18_504.5, WER: 100%, TER: 100%, total WER: 26.0596%, total TER: 12.7036%, progress (thread 0): 89.8576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_567.01_567.33, WER: 0%, TER: 0%, total WER: 26.0593%, total TER: 12.7036%, progress (thread 0): 89.8655%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_604.34_604.66, WER: 100%, TER: 40%, total WER: 26.0602%, total TER: 12.7039%, progress (thread 0): 89.8734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_729.25_729.57, WER: 0%, TER: 0%, total WER: 26.0599%, total TER: 12.7038%, progress (thread 0): 89.8813%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_800.36_800.68, WER: 0%, TER: 0%, total WER: 26.0596%, total TER: 12.7036%, progress (thread 0): 89.8892%]
|T|: o h
|P|: n o
[sample: EN2002a_H02_FEO072_856.5_856.82, WER: 100%, TER: 100%, total WER: 26.0604%, total TER: 12.704%, progress (thread 0): 89.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1050.6_1050.92, WER: 0%, TER: 0%, total WER: 26.0601%, total TER: 12.7039%, progress (thread 0): 89.9051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1101.18_1101.5, WER: 0%, TER: 0%, total WER: 26.0598%, total TER: 12.7038%, progress (thread 0): 89.913%]
|T|: o h | n o
|P|: y o u | k n o w
[sample: EN2002a_H01_FEO070_1448.67_1448.99, WER: 100%, TER: 80%, total WER: 26.0615%, total TER: 12.7046%, progress (thread 0): 89.9209%]
|T|: h m m
|P|: y e a h
[sample: EN2002a_H00_MEE073_1448.83_1449.15, WER: 100%, TER: 133.333%, total WER: 26.0623%, total TER: 12.7054%, progress (thread 0): 89.9288%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1537.39_1537.71, WER: 0%, TER: 0%, total WER: 26.0621%, total TER: 12.7053%, progress (thread 0): 89.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1862.6_1862.92, WER: 0%, TER: 0%, total WER: 26.0618%, total TER: 12.7052%, progress (thread 0): 89.9446%]
|T|: y e a h
|P|: i
[sample: EN2002b_H03_MEE073_283.05_283.37, WER: 100%, TER: 100%, total WER: 26.0626%, total TER: 12.706%, progress (thread 0): 89.9525%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_363.29_363.61, WER: 200%, TER: 175%, total WER: 26.0646%, total TER: 12.7075%, progress (thread 0): 89.9604%]
|T|: o h | y e a h
|P|:
[sample: EN2002b_H03_MEE073_679.41_679.73, WER: 100%, TER: 100%, total WER: 26.0662%, total TER: 12.709%, progress (thread 0): 89.9684%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_922.82_923.14, WER: 0%, TER: 0%, total WER: 26.0659%, total TER: 12.7088%, progress (thread 0): 89.9763%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H02_FEO072_1100.2_1100.52, WER: 100%, TER: 40%, total WER: 26.0668%, total TER: 12.7092%, progress (thread 0): 89.9842%]
|T|: g o o d | p o i n t
|P|: t h e | p o i n t
[sample: EN2002b_H03_MEE073_1217.83_1218.15, WER: 50%, TER: 40%, total WER: 26.0673%, total TER: 12.7098%, progress (thread 0): 89.9921%]
|T|: y e a h
|P|: o
[sample: EN2002b_H03_MEE073_1261.76_1262.08, WER: 100%, TER: 100%, total WER: 26.0682%, total TER: 12.7106%, progress (thread 0): 90%]
|T|: h m m
|P|: y n
[sample: EN2002b_H03_MEE073_1598.38_1598.7, WER: 100%, TER: 100%, total WER: 26.069%, total TER: 12.7112%, progress (thread 0): 90.0079%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1611.81_1612.13, WER: 50%, TER: 55.5556%, total WER: 26.0695%, total TER: 12.7121%, progress (thread 0): 90.0158%]
|T|: o k a y
|P|:
[sample: EN2002b_H03_MEE073_1705.25_1705.57, WER: 100%, TER: 100%, total WER: 26.0704%, total TER: 12.713%, progress (thread 0): 90.0237%]
|T|: g o o d
|P|: o k a y
[sample: EN2002c_H01_FEO072_489.73_490.05, WER: 100%, TER: 100%, total WER: 26.0712%, total TER: 12.7138%, progress (thread 0): 90.0316%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_653.98_654.3, WER: 0%, TER: 0%, total WER: 26.0709%, total TER: 12.7137%, progress (thread 0): 90.0396%]
|T|: y e a h
|P|: u h
[sample: EN2002c_H01_FEO072_1352.96_1353.28, WER: 100%, TER: 75%, total WER: 26.0718%, total TER: 12.7143%, progress (thread 0): 90.0475%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_2296.14_2296.46, WER: 100%, TER: 50%, total WER: 26.0726%, total TER: 12.7145%, progress (thread 0): 90.0554%]
|T|: i | t h i n k
|P|: i | t h i n k
[sample: EN2002c_H01_FEO072_2336.35_2336.67, WER: 0%, TER: 0%, total WER: 26.072%, total TER: 12.7143%, progress (thread 0): 90.0633%]
|T|: o h
|P|: i
[sample: EN2002c_H01_FEO072_2368.69_2369.01, WER: 100%, TER: 100%, total WER: 26.0728%, total TER: 12.7147%, progress (thread 0): 90.0712%]
|T|: w h a t
|P|: w e l l
[sample: EN2002c_H01_FEO072_2484.6_2484.92, WER: 100%, TER: 75%, total WER: 26.0737%, total TER: 12.7153%, progress (thread 0): 90.0791%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2800.26_2800.58, WER: 100%, TER: 33.3333%, total WER: 26.0745%, total TER: 12.7154%, progress (thread 0): 90.087%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_2829.54_2829.86, WER: 0%, TER: 0%, total WER: 26.0742%, total TER: 12.7153%, progress (thread 0): 90.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_347.79_348.11, WER: 0%, TER: 0%, total WER: 26.0739%, total TER: 12.7152%, progress (thread 0): 90.1028%]
|T|: i t ' s | g o o d
|P|:
[sample: EN2002d_H00_FEO070_420.4_420.72, WER: 100%, TER: 100%, total WER: 26.0756%, total TER: 12.717%, progress (thread 0): 90.1108%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_574.1_574.42, WER: 100%, TER: 40%, total WER: 26.0764%, total TER: 12.7173%, progress (thread 0): 90.1187%]
|T|: b u t
|P|: u h
[sample: EN2002d_H01_FEO072_590.21_590.53, WER: 100%, TER: 66.6667%, total WER: 26.0773%, total TER: 12.7177%, progress (thread 0): 90.1266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_769.35_769.67, WER: 0%, TER: 0%, total WER: 26.077%, total TER: 12.7176%, progress (thread 0): 90.1345%]
|T|: o h
|P|: n
[sample: EN2002d_H03_MEE073_939.78_940.1, WER: 100%, TER: 100%, total WER: 26.0778%, total TER: 12.718%, progress (thread 0): 90.1424%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_954.69_955.01, WER: 0%, TER: 0%, total WER: 26.0775%, total TER: 12.7179%, progress (thread 0): 90.1503%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1189.57_1189.89, WER: 0%, TER: 0%, total WER: 26.0772%, total TER: 12.7178%, progress (thread 0): 90.1582%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1203.2_1203.52, WER: 0%, TER: 0%, total WER: 26.0769%, total TER: 12.7176%, progress (thread 0): 90.1661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1255.64_1255.96, WER: 0%, TER: 0%, total WER: 26.0766%, total TER: 12.7175%, progress (thread 0): 90.174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1754.3_1754.62, WER: 0%, TER: 0%, total WER: 26.0763%, total TER: 12.7174%, progress (thread 0): 90.182%]
|T|: s o
|P|: s o
[sample: EN2002d_H01_FEO072_1974.1_1974.42, WER: 0%, TER: 0%, total WER: 26.076%, total TER: 12.7173%, progress (thread 0): 90.1899%]
|T|: m m
|P|: h m
[sample: EN2002d_H02_MEE071_2190.35_2190.67, WER: 100%, TER: 50%, total WER: 26.0769%, total TER: 12.7175%, progress (thread 0): 90.1978%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_582.6_582.91, WER: 0%, TER: 0%, total WER: 26.0766%, total TER: 12.7174%, progress (thread 0): 90.2057%]
|T|: m m h m m
|P|: u h | h u h
[sample: ES2004a_H00_MEO015_687.25_687.56, WER: 200%, TER: 100%, total WER: 26.0785%, total TER: 12.7184%, progress (thread 0): 90.2136%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_688.13_688.44, WER: 0%, TER: 0%, total WER: 26.0782%, total TER: 12.7183%, progress (thread 0): 90.2215%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_777.34_777.65, WER: 100%, TER: 40%, total WER: 26.0791%, total TER: 12.7186%, progress (thread 0): 90.2294%]
|T|: y
|P|: y
[sample: ES2004a_H02_MEE014_949.9_950.21, WER: 0%, TER: 0%, total WER: 26.0788%, total TER: 12.7186%, progress (thread 0): 90.2373%]
|T|: n o
|P|: t o
[sample: ES2004b_H03_FEE016_607.28_607.59, WER: 100%, TER: 50%, total WER: 26.0796%, total TER: 12.7188%, progress (thread 0): 90.2453%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H00_MEO015_1612.5_1612.81, WER: 100%, TER: 60%, total WER: 26.0805%, total TER: 12.7193%, progress (thread 0): 90.2532%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_2237.11_2237.42, WER: 0%, TER: 0%, total WER: 26.0802%, total TER: 12.7192%, progress (thread 0): 90.2611%]
|T|: b u t
|P|: b u t
[sample: ES2004c_H02_MEE014_571.73_572.04, WER: 0%, TER: 0%, total WER: 26.0799%, total TER: 12.7191%, progress (thread 0): 90.269%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1321.04_1321.35, WER: 0%, TER: 0%, total WER: 26.0796%, total TER: 12.719%, progress (thread 0): 90.2769%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_1582.98_1583.29, WER: 0%, TER: 0%, total WER: 26.0793%, total TER: 12.7189%, progress (thread 0): 90.2848%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2288.73_2289.04, WER: 0%, TER: 0%, total WER: 26.079%, total TER: 12.7188%, progress (thread 0): 90.2927%]
|T|: m m
|P|: h m
[sample: ES2004d_H00_MEO015_846.15_846.46, WER: 100%, TER: 50%, total WER: 26.0798%, total TER: 12.7189%, progress (thread 0): 90.3006%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1669.25_1669.56, WER: 0%, TER: 0%, total WER: 26.0795%, total TER: 12.7188%, progress (thread 0): 90.3085%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1669.66_1669.97, WER: 0%, TER: 0%, total WER: 26.0792%, total TER: 12.7187%, progress (thread 0): 90.3165%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1825.79_1826.1, WER: 0%, TER: 0%, total WER: 26.0789%, total TER: 12.7186%, progress (thread 0): 90.3244%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1901.99_1902.3, WER: 0%, TER: 0%, total WER: 26.0786%, total TER: 12.7185%, progress (thread 0): 90.3323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1967.13_1967.44, WER: 0%, TER: 0%, total WER: 26.0783%, total TER: 12.7183%, progress (thread 0): 90.3402%]
|T|: w e l l
|P|: y e h
[sample: ES2004d_H02_MEE014_2085.81_2086.12, WER: 100%, TER: 75%, total WER: 26.0792%, total TER: 12.7189%, progress (thread 0): 90.3481%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2220.03_2220.34, WER: 0%, TER: 0%, total WER: 26.0789%, total TER: 12.7188%, progress (thread 0): 90.356%]
|T|: o k a y
|P|: k
[sample: IS1009a_H00_FIE088_90.21_90.52, WER: 100%, TER: 75%, total WER: 26.0797%, total TER: 12.7194%, progress (thread 0): 90.3639%]
|T|: u h
|P|:
[sample: IS1009a_H02_FIO084_451.06_451.37, WER: 100%, TER: 100%, total WER: 26.0806%, total TER: 12.7198%, progress (thread 0): 90.3718%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_702.97_703.28, WER: 0%, TER: 0%, total WER: 26.0803%, total TER: 12.7197%, progress (thread 0): 90.3797%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_795.57_795.88, WER: 0%, TER: 0%, total WER: 26.08%, total TER: 12.7196%, progress (thread 0): 90.3877%]
|T|: m m
|P|: e
[sample: IS1009b_H02_FIO084_218.1_218.41, WER: 100%, TER: 100%, total WER: 26.0808%, total TER: 12.72%, progress (thread 0): 90.3956%]
|T|: m m h m m
|P|: u h m
[sample: IS1009b_H00_FIE088_616.32_616.63, WER: 100%, TER: 60%, total WER: 26.0816%, total TER: 12.7205%, progress (thread 0): 90.4035%]
|T|: y e a h
|P|: y e
[sample: IS1009b_H02_FIO084_1692.92_1693.23, WER: 100%, TER: 50%, total WER: 26.0825%, total TER: 12.7209%, progress (thread 0): 90.4114%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1832.99_1833.3, WER: 0%, TER: 0%, total WER: 26.0822%, total TER: 12.7208%, progress (thread 0): 90.4193%]
|T|: y e s
|P|: i s
[sample: IS1009b_H01_FIO087_1844.23_1844.54, WER: 100%, TER: 66.6667%, total WER: 26.083%, total TER: 12.7211%, progress (thread 0): 90.4272%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H03_FIO089_1904.39_1904.7, WER: 0%, TER: 0%, total WER: 26.0827%, total TER: 12.721%, progress (thread 0): 90.4351%]
|T|: m m h m m
|P|: t a m l e
[sample: IS1009c_H00_FIE088_1140.15_1140.46, WER: 100%, TER: 100%, total WER: 26.0836%, total TER: 12.722%, progress (thread 0): 90.443%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1163.03_1163.34, WER: 100%, TER: 40%, total WER: 26.0844%, total TER: 12.7223%, progress (thread 0): 90.451%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1237.53_1237.84, WER: 100%, TER: 40%, total WER: 26.0852%, total TER: 12.7227%, progress (thread 0): 90.4589%]
|T|: ' k a y
|P|: c a n
[sample: IS1009c_H01_FIO087_1429.75_1430.06, WER: 100%, TER: 75%, total WER: 26.0861%, total TER: 12.7232%, progress (thread 0): 90.4668%]
|T|: m m h m m
|P|: h
[sample: IS1009c_H02_FIO084_1554.5_1554.81, WER: 100%, TER: 80%, total WER: 26.0869%, total TER: 12.724%, progress (thread 0): 90.4747%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H01_FIO087_1580.06_1580.37, WER: 100%, TER: 40%, total WER: 26.0877%, total TER: 12.7243%, progress (thread 0): 90.4826%]
|T|: o h
|P|: u m
[sample: IS1009c_H00_FIE088_1694.98_1695.29, WER: 100%, TER: 100%, total WER: 26.0886%, total TER: 12.7248%, progress (thread 0): 90.4905%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_363.39_363.7, WER: 100%, TER: 40%, total WER: 26.0894%, total TER: 12.7251%, progress (thread 0): 90.4984%]
|T|: m m
|P|: h m
[sample: IS1009d_H02_FIO084_894.86_895.17, WER: 100%, TER: 50%, total WER: 26.0903%, total TER: 12.7253%, progress (thread 0): 90.5063%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_903.74_904.05, WER: 0%, TER: 0%, total WER: 26.09%, total TER: 12.7251%, progress (thread 0): 90.5142%]
|T|: n o
|P|: n o
[sample: IS1009d_H00_FIE088_1011.62_1011.93, WER: 0%, TER: 0%, total WER: 26.0897%, total TER: 12.7251%, progress (thread 0): 90.5222%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1012.22_1012.53, WER: 100%, TER: 40%, total WER: 26.0905%, total TER: 12.7254%, progress (thread 0): 90.5301%]
|T|: m m h m m
|P|: m | h m
[sample: IS1009d_H03_FIO089_1105.97_1106.28, WER: 200%, TER: 40%, total WER: 26.0925%, total TER: 12.7257%, progress (thread 0): 90.538%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1171.27_1171.58, WER: 0%, TER: 0%, total WER: 26.0922%, total TER: 12.7256%, progress (thread 0): 90.5459%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1243.74_1244.05, WER: 100%, TER: 66.6667%, total WER: 26.093%, total TER: 12.726%, progress (thread 0): 90.5538%]
|T|: w h y
|P|: w o w
[sample: IS1009d_H00_FIE088_1442.55_1442.86, WER: 100%, TER: 66.6667%, total WER: 26.0938%, total TER: 12.7264%, progress (thread 0): 90.5617%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1681.21_1681.52, WER: 0%, TER: 0%, total WER: 26.0935%, total TER: 12.7262%, progress (thread 0): 90.5696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_216.48_216.79, WER: 0%, TER: 0%, total WER: 26.0932%, total TER: 12.7261%, progress (thread 0): 90.5775%]
|T|: m e a t
|P|: m e a t
[sample: TS3003a_H03_MTD012ME_595.66_595.97, WER: 0%, TER: 0%, total WER: 26.093%, total TER: 12.726%, progress (thread 0): 90.5854%]
|T|: s o
|P|: s o
[sample: TS3003a_H03_MTD012ME_884.45_884.76, WER: 0%, TER: 0%, total WER: 26.0927%, total TER: 12.7259%, progress (thread 0): 90.5934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1418.64_1418.95, WER: 0%, TER: 0%, total WER: 26.0924%, total TER: 12.7258%, progress (thread 0): 90.6013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_71.8_72.11, WER: 0%, TER: 0%, total WER: 26.0921%, total TER: 12.7257%, progress (thread 0): 90.6092%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_538.03_538.34, WER: 0%, TER: 0%, total WER: 26.0918%, total TER: 12.7256%, progress (thread 0): 90.6171%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1469.03_1469.34, WER: 100%, TER: 100%, total WER: 26.0926%, total TER: 12.726%, progress (thread 0): 90.625%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1715.8_1716.11, WER: 0%, TER: 0%, total WER: 26.0923%, total TER: 12.7259%, progress (thread 0): 90.6329%]
|T|: y e a h
|P|: t h a t
[sample: TS3003b_H00_MTD009PM_1822.91_1823.22, WER: 100%, TER: 75%, total WER: 26.0932%, total TER: 12.7265%, progress (thread 0): 90.6408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_794.24_794.55, WER: 0%, TER: 0%, total WER: 26.0929%, total TER: 12.7264%, progress (thread 0): 90.6487%]
|T|: ' k a y
|P|: k a y
[sample: TS3003c_H00_MTD009PM_934.63_934.94, WER: 100%, TER: 25%, total WER: 26.0937%, total TER: 12.7265%, progress (thread 0): 90.6566%]
|T|: m m
|P|: h m
[sample: TS3003c_H01_MTD011UID_1136.65_1136.96, WER: 100%, TER: 50%, total WER: 26.0945%, total TER: 12.7267%, progress (thread 0): 90.6646%]
|T|: y e a h
|P|: a n c e
[sample: TS3003c_H01_MTD011UID_1444.43_1444.74, WER: 100%, TER: 100%, total WER: 26.0954%, total TER: 12.7275%, progress (thread 0): 90.6725%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1813.2_1813.51, WER: 0%, TER: 0%, total WER: 26.0951%, total TER: 12.7274%, progress (thread 0): 90.6804%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_255.36_255.67, WER: 0%, TER: 0%, total WER: 26.0948%, total TER: 12.7273%, progress (thread 0): 90.6883%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_453.54_453.85, WER: 100%, TER: 50%, total WER: 26.0956%, total TER: 12.7274%, progress (thread 0): 90.6962%]
|T|: y e p
|P|: n o
[sample: TS3003d_H02_MTD0010ID_562.3_562.61, WER: 100%, TER: 100%, total WER: 26.0964%, total TER: 12.728%, progress (thread 0): 90.7041%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H02_MTD0010ID_1317.08_1317.39, WER: 100%, TER: 100%, total WER: 26.0973%, total TER: 12.7289%, progress (thread 0): 90.712%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_1546.63_1546.94, WER: 100%, TER: 40%, total WER: 26.0981%, total TER: 12.7292%, progress (thread 0): 90.7199%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2324.36_2324.67, WER: 0%, TER: 0%, total WER: 26.0978%, total TER: 12.7291%, progress (thread 0): 90.7278%]
|T|: y e a h
|P|: e h
[sample: TS3003d_H01_MTD011UID_2373.13_2373.44, WER: 100%, TER: 50%, total WER: 26.0987%, total TER: 12.7294%, progress (thread 0): 90.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_595.97_596.28, WER: 0%, TER: 0%, total WER: 26.0984%, total TER: 12.7293%, progress (thread 0): 90.7437%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_694.14_694.45, WER: 0%, TER: 0%, total WER: 26.0981%, total TER: 12.7292%, progress (thread 0): 90.7516%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_704.35_704.66, WER: 0%, TER: 0%, total WER: 26.0978%, total TER: 12.7291%, progress (thread 0): 90.7595%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_910.51_910.82, WER: 0%, TER: 0%, total WER: 26.0975%, total TER: 12.7289%, progress (thread 0): 90.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1104.75_1105.06, WER: 0%, TER: 0%, total WER: 26.0972%, total TER: 12.7288%, progress (thread 0): 90.7753%]
|T|: h m m
|P|: h
[sample: EN2002a_H00_MEE073_1619.48_1619.79, WER: 100%, TER: 66.6667%, total WER: 26.098%, total TER: 12.7292%, progress (thread 0): 90.7832%]
|T|: o h
|P|:
[sample: EN2002a_H02_FEO072_1674.71_1675.02, WER: 100%, TER: 100%, total WER: 26.0989%, total TER: 12.7296%, progress (thread 0): 90.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1973.2_1973.51, WER: 0%, TER: 0%, total WER: 26.0986%, total TER: 12.7295%, progress (thread 0): 90.799%]
|T|: o h | r i g h t
|P|: a l | r i g h t
[sample: EN2002a_H03_MEE071_1971.01_1971.32, WER: 50%, TER: 25%, total WER: 26.0991%, total TER: 12.7297%, progress (thread 0): 90.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_346.95_347.26, WER: 0%, TER: 0%, total WER: 26.0988%, total TER: 12.7296%, progress (thread 0): 90.8149%]
|T|: y e a h
|P|: w e l l
[sample: EN2002b_H03_MEE073_372.58_372.89, WER: 100%, TER: 75%, total WER: 26.0996%, total TER: 12.7302%, progress (thread 0): 90.8228%]
|T|: m m
|P|:
[sample: EN2002b_H03_MEE073_565.42_565.73, WER: 100%, TER: 100%, total WER: 26.1005%, total TER: 12.7306%, progress (thread 0): 90.8307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_729.79_730.1, WER: 0%, TER: 0%, total WER: 26.1002%, total TER: 12.7305%, progress (thread 0): 90.8386%]
|T|: y e a h
|P|: r i g h
[sample: EN2002b_H03_MEE073_1214.71_1215.02, WER: 100%, TER: 75%, total WER: 26.101%, total TER: 12.7311%, progress (thread 0): 90.8465%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1323.81_1324.12, WER: 0%, TER: 0%, total WER: 26.1007%, total TER: 12.7309%, progress (thread 0): 90.8544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1461.61_1461.92, WER: 0%, TER: 0%, total WER: 26.1004%, total TER: 12.7308%, progress (thread 0): 90.8623%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1750.66_1750.97, WER: 0%, TER: 0%, total WER: 26.1001%, total TER: 12.7307%, progress (thread 0): 90.8703%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_441.29_441.6, WER: 0%, TER: 0%, total WER: 26.0998%, total TER: 12.7306%, progress (thread 0): 90.8782%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_543.24_543.55, WER: 0%, TER: 0%, total WER: 26.0995%, total TER: 12.7306%, progress (thread 0): 90.8861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_543.24_543.55, WER: 0%, TER: 0%, total WER: 26.0992%, total TER: 12.7305%, progress (thread 0): 90.894%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_982.53_982.84, WER: 100%, TER: 33.3333%, total WER: 26.1001%, total TER: 12.7306%, progress (thread 0): 90.9019%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1524.82_1525.13, WER: 0%, TER: 0%, total WER: 26.0998%, total TER: 12.7305%, progress (thread 0): 90.9098%]
|T|: u m
|P|: u m
[sample: EN2002c_H03_MEE073_1904.01_1904.32, WER: 0%, TER: 0%, total WER: 26.0995%, total TER: 12.7304%, progress (thread 0): 90.9177%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1958.34_1958.65, WER: 0%, TER: 0%, total WER: 26.0992%, total TER: 12.7303%, progress (thread 0): 90.9256%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_2240.67_2240.98, WER: 100%, TER: 40%, total WER: 26.1%, total TER: 12.7306%, progress (thread 0): 90.9335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2358.92_2359.23, WER: 0%, TER: 0%, total WER: 26.0997%, total TER: 12.7305%, progress (thread 0): 90.9415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2477.51_2477.82, WER: 0%, TER: 0%, total WER: 26.0994%, total TER: 12.7304%, progress (thread 0): 90.9494%]
|T|: n o
|P|: n i e
[sample: EN2002c_H02_MEE071_2608.15_2608.46, WER: 100%, TER: 100%, total WER: 26.1003%, total TER: 12.7308%, progress (thread 0): 90.9573%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2724.28_2724.59, WER: 0%, TER: 0%, total WER: 26.1%, total TER: 12.7307%, progress (thread 0): 90.9652%]
|T|: t i c k
|P|: t i k
[sample: EN2002c_H01_FEO072_2878.5_2878.81, WER: 100%, TER: 25%, total WER: 26.1008%, total TER: 12.7308%, progress (thread 0): 90.9731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_391.81_392.12, WER: 0%, TER: 0%, total WER: 26.1005%, total TER: 12.7307%, progress (thread 0): 90.981%]
|T|: y e a h | o k a y
|P|: o k a y
[sample: EN2002d_H00_FEO070_1379.88_1380.19, WER: 50%, TER: 55.5556%, total WER: 26.1011%, total TER: 12.7316%, progress (thread 0): 90.9889%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_1512.76_1513.07, WER: 100%, TER: 40%, total WER: 26.1019%, total TER: 12.7319%, progress (thread 0): 90.9968%]
|T|: s o
|P|: t h e
[sample: EN2002d_H00_FEO070_1542.52_1542.83, WER: 100%, TER: 150%, total WER: 26.1027%, total TER: 12.7325%, progress (thread 0): 91.0047%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1546.76_1547.07, WER: 0%, TER: 0%, total WER: 26.1024%, total TER: 12.7324%, progress (thread 0): 91.0127%]
|T|: s o
|P|: s o
[sample: EN2002d_H03_MEE073_1822.41_1822.72, WER: 0%, TER: 0%, total WER: 26.1021%, total TER: 12.7324%, progress (thread 0): 91.0206%]
|T|: m m
|P|: h m
[sample: EN2002d_H00_FEO070_1872.39_1872.7, WER: 100%, TER: 50%, total WER: 26.103%, total TER: 12.7325%, progress (thread 0): 91.0285%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2114.3_2114.61, WER: 0%, TER: 0%, total WER: 26.1027%, total TER: 12.7324%, progress (thread 0): 91.0364%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_819.96_820.26, WER: 0%, TER: 0%, total WER: 26.1024%, total TER: 12.7323%, progress (thread 0): 91.0443%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1232.15_1232.45, WER: 0%, TER: 0%, total WER: 26.1021%, total TER: 12.7322%, progress (thread 0): 91.0522%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1572.74_1573.04, WER: 100%, TER: 40%, total WER: 26.1029%, total TER: 12.7325%, progress (thread 0): 91.0601%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1784.08_1784.38, WER: 100%, TER: 40%, total WER: 26.1038%, total TER: 12.7328%, progress (thread 0): 91.068%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_2252.76_2253.06, WER: 0%, TER: 0%, total WER: 26.1035%, total TER: 12.7327%, progress (thread 0): 91.076%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_849.84_850.14, WER: 0%, TER: 0%, total WER: 26.1032%, total TER: 12.7326%, progress (thread 0): 91.0839%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1555.92_1556.22, WER: 0%, TER: 0%, total WER: 26.1029%, total TER: 12.7325%, progress (thread 0): 91.0918%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1640.82_1641.12, WER: 100%, TER: 40%, total WER: 26.1037%, total TER: 12.7328%, progress (thread 0): 91.0997%]
|T|: ' k a y
|P|: k e
[sample: ES2004d_H00_MEO015_271.77_272.07, WER: 100%, TER: 75%, total WER: 26.1045%, total TER: 12.7334%, progress (thread 0): 91.1076%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_639.49_639.79, WER: 0%, TER: 0%, total WER: 26.1042%, total TER: 12.7332%, progress (thread 0): 91.1155%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1390.02_1390.32, WER: 0%, TER: 0%, total WER: 26.104%, total TER: 12.7331%, progress (thread 0): 91.1234%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1711.3_1711.6, WER: 0%, TER: 0%, total WER: 26.1037%, total TER: 12.733%, progress (thread 0): 91.1313%]
|T|: m m
|P|: h m
[sample: ES2004d_H01_FEE013_2002_2002.3, WER: 100%, TER: 50%, total WER: 26.1045%, total TER: 12.7332%, progress (thread 0): 91.1392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2096.66_2096.96, WER: 0%, TER: 0%, total WER: 26.1042%, total TER: 12.7331%, progress (thread 0): 91.1472%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_617.96_618.26, WER: 100%, TER: 40%, total WER: 26.105%, total TER: 12.7334%, progress (thread 0): 91.1551%]
|T|: o k a y
|P|: c a n
[sample: IS1009a_H01_FIO087_782.9_783.2, WER: 100%, TER: 75%, total WER: 26.1059%, total TER: 12.734%, progress (thread 0): 91.163%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_182.06_182.36, WER: 0%, TER: 0%, total WER: 26.1056%, total TER: 12.7338%, progress (thread 0): 91.1709%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_574.49_574.79, WER: 100%, TER: 40%, total WER: 26.1064%, total TER: 12.7342%, progress (thread 0): 91.1788%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_707.08_707.38, WER: 100%, TER: 40%, total WER: 26.1072%, total TER: 12.7345%, progress (thread 0): 91.1867%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1025.61_1025.91, WER: 0%, TER: 0%, total WER: 26.1069%, total TER: 12.7344%, progress (thread 0): 91.1946%]
|T|: m m
|P|: m e a n
[sample: IS1009b_H02_FIO084_1366.95_1367.25, WER: 100%, TER: 150%, total WER: 26.1078%, total TER: 12.735%, progress (thread 0): 91.2025%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1371.17_1371.47, WER: 100%, TER: 40%, total WER: 26.1086%, total TER: 12.7353%, progress (thread 0): 91.2104%]
|T|: h m m
|P|: k a y
[sample: IS1009b_H02_FIO084_1604.76_1605.06, WER: 100%, TER: 100%, total WER: 26.1095%, total TER: 12.7359%, progress (thread 0): 91.2184%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1617.66_1617.96, WER: 0%, TER: 0%, total WER: 26.1092%, total TER: 12.7358%, progress (thread 0): 91.2263%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_335.7_336, WER: 0%, TER: 0%, total WER: 26.1089%, total TER: 12.7357%, progress (thread 0): 91.2342%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H03_FIO089_1106.15_1106.45, WER: 100%, TER: 40%, total WER: 26.1097%, total TER: 12.736%, progress (thread 0): 91.2421%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1237.83_1238.13, WER: 0%, TER: 0%, total WER: 26.1094%, total TER: 12.7359%, progress (thread 0): 91.25%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1259.97_1260.27, WER: 0%, TER: 0%, total WER: 26.1091%, total TER: 12.7358%, progress (thread 0): 91.2579%]
|T|: o k a y
|P|: k a m
[sample: IS1009c_H01_FIO087_1297.17_1297.47, WER: 100%, TER: 50%, total WER: 26.1099%, total TER: 12.7362%, progress (thread 0): 91.2658%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_607.11_607.41, WER: 100%, TER: 40%, total WER: 26.1108%, total TER: 12.7365%, progress (thread 0): 91.2737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_769.42_769.72, WER: 0%, TER: 0%, total WER: 26.1105%, total TER: 12.7364%, progress (thread 0): 91.2816%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1062.93_1063.23, WER: 0%, TER: 0%, total WER: 26.1102%, total TER: 12.7362%, progress (thread 0): 91.2896%]
|T|: o n e
|P|: b u t
[sample: IS1009d_H01_FIO087_1482_1482.3, WER: 100%, TER: 100%, total WER: 26.111%, total TER: 12.7369%, progress (thread 0): 91.2975%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H00_FIE088_1528.1_1528.4, WER: 0%, TER: 0%, total WER: 26.1107%, total TER: 12.7368%, progress (thread 0): 91.3054%]
|T|: y e a h
|P|: i
[sample: IS1009d_H02_FIO084_1824.41_1824.71, WER: 100%, TER: 100%, total WER: 26.1116%, total TER: 12.7376%, progress (thread 0): 91.3133%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1853.09_1853.39, WER: 0%, TER: 0%, total WER: 26.1113%, total TER: 12.7375%, progress (thread 0): 91.3212%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_238.31_238.61, WER: 0%, TER: 0%, total WER: 26.111%, total TER: 12.7373%, progress (thread 0): 91.3291%]
|T|: s o
|P|: s o
[sample: TS3003a_H02_MTD0010ID_1080.77_1081.07, WER: 0%, TER: 0%, total WER: 26.1107%, total TER: 12.7373%, progress (thread 0): 91.337%]
|T|: h m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1308.88_1309.18, WER: 100%, TER: 33.3333%, total WER: 26.1115%, total TER: 12.7374%, progress (thread 0): 91.3449%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_1423.96_1424.26, WER: 0%, TER: 0%, total WER: 26.1112%, total TER: 12.7373%, progress (thread 0): 91.3529%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H00_MTD009PM_1463.25_1463.55, WER: 100%, TER: 50%, total WER: 26.1121%, total TER: 12.7377%, progress (thread 0): 91.3608%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_1535.95_1536.25, WER: 0%, TER: 0%, total WER: 26.1118%, total TER: 12.7376%, progress (thread 0): 91.3687%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1635.81_1636.11, WER: 100%, TER: 40%, total WER: 26.1126%, total TER: 12.7379%, progress (thread 0): 91.3766%]
|T|: h m
|P|: i s
[sample: TS3003b_H01_MTD011UID_1834.41_1834.71, WER: 100%, TER: 100%, total WER: 26.1134%, total TER: 12.7383%, progress (thread 0): 91.3845%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1871.5_1871.8, WER: 100%, TER: 100%, total WER: 26.1143%, total TER: 12.7387%, progress (thread 0): 91.3924%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2279.98_2280.28, WER: 0%, TER: 0%, total WER: 26.114%, total TER: 12.7386%, progress (thread 0): 91.4003%]
|T|: s
|P|:
[sample: TS3003d_H01_MTD011UID_856.51_856.81, WER: 100%, TER: 100%, total WER: 26.1148%, total TER: 12.7388%, progress (thread 0): 91.4082%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1057.72_1058.02, WER: 0%, TER: 0%, total WER: 26.1145%, total TER: 12.7387%, progress (thread 0): 91.4161%]
|T|: y e a h
|P|: y e a n
[sample: TS3003d_H02_MTD0010ID_1709.82_1710.12, WER: 100%, TER: 25%, total WER: 26.1153%, total TER: 12.7388%, progress (thread 0): 91.424%]
|T|: y e s
|P|: y e s
[sample: TS3003d_H02_MTD0010ID_1732.89_1733.19, WER: 0%, TER: 0%, total WER: 26.115%, total TER: 12.7387%, progress (thread 0): 91.432%]
|T|: y e a h
|P|: y e h
[sample: TS3003d_H00_MTD009PM_1821.73_1822.03, WER: 100%, TER: 25%, total WER: 26.1159%, total TER: 12.7388%, progress (thread 0): 91.4399%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2250.87_2251.17, WER: 0%, TER: 0%, total WER: 26.1156%, total TER: 12.7387%, progress (thread 0): 91.4478%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2482.46_2482.76, WER: 0%, TER: 0%, total WER: 26.1153%, total TER: 12.7386%, progress (thread 0): 91.4557%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_11.83_12.13, WER: 0%, TER: 0%, total WER: 26.115%, total TER: 12.7385%, progress (thread 0): 91.4636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_48.72_49.02, WER: 0%, TER: 0%, total WER: 26.1147%, total TER: 12.7383%, progress (thread 0): 91.4715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_203.25_203.55, WER: 0%, TER: 0%, total WER: 26.1144%, total TER: 12.7382%, progress (thread 0): 91.4794%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_261.14_261.44, WER: 100%, TER: 33.3333%, total WER: 26.1152%, total TER: 12.7384%, progress (thread 0): 91.4873%]
|T|: w h y | n o t
|P|: w h y | d o n ' t
[sample: EN2002a_H03_MEE071_375.98_376.28, WER: 50%, TER: 42.8571%, total WER: 26.1158%, total TER: 12.7389%, progress (thread 0): 91.4953%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_469.41_469.71, WER: 0%, TER: 0%, total WER: 26.1155%, total TER: 12.7387%, progress (thread 0): 91.5032%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_486.08_486.38, WER: 0%, TER: 0%, total WER: 26.1152%, total TER: 12.7386%, progress (thread 0): 91.5111%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_508.71_509.01, WER: 0%, TER: 0%, total WER: 26.1149%, total TER: 12.7385%, progress (thread 0): 91.519%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_521.78_522.08, WER: 0%, TER: 0%, total WER: 26.1146%, total TER: 12.7384%, progress (thread 0): 91.5269%]
|T|: s o
|P|:
[sample: EN2002a_H03_MEE071_738.15_738.45, WER: 100%, TER: 100%, total WER: 26.1154%, total TER: 12.7388%, progress (thread 0): 91.5348%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_747.85_748.15, WER: 100%, TER: 40%, total WER: 26.1163%, total TER: 12.7391%, progress (thread 0): 91.5427%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_795.73_796.03, WER: 100%, TER: 33.3333%, total WER: 26.1171%, total TER: 12.7393%, progress (thread 0): 91.5506%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_877.67_877.97, WER: 100%, TER: 40%, total WER: 26.1179%, total TER: 12.7396%, progress (thread 0): 91.5585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_918.33_918.63, WER: 0%, TER: 0%, total WER: 26.1176%, total TER: 12.7395%, progress (thread 0): 91.5665%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1889.11_1889.41, WER: 0%, TER: 0%, total WER: 26.1173%, total TER: 12.7393%, progress (thread 0): 91.5744%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H02_FEO072_2116.83_2117.13, WER: 0%, TER: 0%, total WER: 26.1171%, total TER: 12.7392%, progress (thread 0): 91.5823%]
|T|: o h | y e a h
|P|: h e l
[sample: EN2002b_H03_MEE073_211.33_211.63, WER: 100%, TER: 71.4286%, total WER: 26.1187%, total TER: 12.7402%, progress (thread 0): 91.5902%]
|T|: w e ' l l | s e e
|P|: w e l l | s | e
[sample: EN2002b_H00_FEO070_255.08_255.38, WER: 150%, TER: 22.2222%, total WER: 26.1215%, total TER: 12.7404%, progress (thread 0): 91.5981%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_368.42_368.72, WER: 0%, TER: 0%, total WER: 26.1212%, total TER: 12.7402%, progress (thread 0): 91.606%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_391.92_392.22, WER: 100%, TER: 40%, total WER: 26.1221%, total TER: 12.7406%, progress (thread 0): 91.6139%]
|T|: m m
|P|: m
[sample: EN2002b_H03_MEE073_526.11_526.41, WER: 100%, TER: 50%, total WER: 26.1229%, total TER: 12.7407%, progress (thread 0): 91.6218%]
|T|: a l r i g h t
|P|: a l l r i g h t
[sample: EN2002b_H02_FEO072_627.98_628.28, WER: 100%, TER: 14.2857%, total WER: 26.1237%, total TER: 12.7408%, progress (thread 0): 91.6298%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1551.52_1551.82, WER: 0%, TER: 0%, total WER: 26.1234%, total TER: 12.7406%, progress (thread 0): 91.6377%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1611.31_1611.61, WER: 0%, TER: 0%, total WER: 26.1231%, total TER: 12.7405%, progress (thread 0): 91.6456%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1729.42_1729.72, WER: 0%, TER: 0%, total WER: 26.1228%, total TER: 12.7404%, progress (thread 0): 91.6535%]
|T|: i | t h i n k
|P|: i
[sample: EN2002c_H01_FEO072_232.37_232.67, WER: 50%, TER: 85.7143%, total WER: 26.1234%, total TER: 12.7416%, progress (thread 0): 91.6614%]
|T|: y e a h
|P|: y e s
[sample: EN2002c_H02_MEE071_355.18_355.48, WER: 100%, TER: 50%, total WER: 26.1242%, total TER: 12.742%, progress (thread 0): 91.6693%]
|T|: n o | n o
|P|:
[sample: EN2002c_H03_MEE073_543.55_543.85, WER: 100%, TER: 100%, total WER: 26.1259%, total TER: 12.743%, progress (thread 0): 91.6772%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_700.09_700.39, WER: 100%, TER: 40%, total WER: 26.1267%, total TER: 12.7433%, progress (thread 0): 91.6851%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H02_MEE071_1131.17_1131.47, WER: 100%, TER: 66.6667%, total WER: 26.1276%, total TER: 12.7437%, progress (thread 0): 91.693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1143.34_1143.64, WER: 0%, TER: 0%, total WER: 26.1273%, total TER: 12.7436%, progress (thread 0): 91.701%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_1321.8_1322.1, WER: 0%, TER: 0%, total WER: 26.127%, total TER: 12.7435%, progress (thread 0): 91.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1600.62_1600.92, WER: 0%, TER: 0%, total WER: 26.1267%, total TER: 12.7434%, progress (thread 0): 91.7168%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2301.16_2301.46, WER: 0%, TER: 0%, total WER: 26.1264%, total TER: 12.7433%, progress (thread 0): 91.7247%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2443.4_2443.7, WER: 0%, TER: 0%, total WER: 26.1261%, total TER: 12.7432%, progress (thread 0): 91.7326%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H02_MEE071_2847.83_2848.13, WER: 100%, TER: 40%, total WER: 26.1269%, total TER: 12.7435%, progress (thread 0): 91.7405%]
|T|: y e a h
|P|: y e
[sample: EN2002d_H02_MEE071_673.96_674.26, WER: 100%, TER: 50%, total WER: 26.1277%, total TER: 12.7438%, progress (thread 0): 91.7484%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_834.35_834.65, WER: 0%, TER: 0%, total WER: 26.1274%, total TER: 12.7437%, progress (thread 0): 91.7563%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_925.24_925.54, WER: 0%, TER: 0%, total WER: 26.1272%, total TER: 12.7436%, progress (thread 0): 91.7642%]
|T|: u m
|P|: u m
[sample: EN2002d_H01_FEO072_1191.17_1191.47, WER: 0%, TER: 0%, total WER: 26.1269%, total TER: 12.7435%, progress (thread 0): 91.7721%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1205.4_1205.7, WER: 0%, TER: 0%, total WER: 26.1266%, total TER: 12.7434%, progress (thread 0): 91.7801%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H03_MEE073_1221.17_1221.47, WER: 100%, TER: 40%, total WER: 26.1274%, total TER: 12.7437%, progress (thread 0): 91.788%]
|T|: y e a h
|P|: h m
[sample: EN2002d_H00_FEO070_1249.7_1250, WER: 100%, TER: 100%, total WER: 26.1282%, total TER: 12.7445%, progress (thread 0): 91.7959%]
|T|: s u r e
|P|: s u r e
[sample: EN2002d_H03_MEE073_1672.53_1672.83, WER: 0%, TER: 0%, total WER: 26.1279%, total TER: 12.7444%, progress (thread 0): 91.8038%]
|T|: m m
|P|: h m
[sample: EN2002d_H02_MEE071_2192.66_2192.96, WER: 100%, TER: 50%, total WER: 26.1288%, total TER: 12.7446%, progress (thread 0): 91.8117%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_747.66_747.95, WER: 0%, TER: 0%, total WER: 26.1285%, total TER: 12.7444%, progress (thread 0): 91.8196%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_784.99_785.28, WER: 100%, TER: 40%, total WER: 26.1293%, total TER: 12.7448%, progress (thread 0): 91.8275%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_826.06_826.35, WER: 0%, TER: 0%, total WER: 26.129%, total TER: 12.7446%, progress (thread 0): 91.8354%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2019.84_2020.13, WER: 0%, TER: 0%, total WER: 26.1287%, total TER: 12.7445%, progress (thread 0): 91.8434%]
|T|: o k a y
|P|: a
[sample: ES2004b_H02_MEE014_2295.26_2295.55, WER: 100%, TER: 75%, total WER: 26.1296%, total TER: 12.7451%, progress (thread 0): 91.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_316.02_316.31, WER: 0%, TER: 0%, total WER: 26.1293%, total TER: 12.745%, progress (thread 0): 91.8592%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004c_H00_MEO015_1261.19_1261.48, WER: 200%, TER: 20%, total WER: 26.1312%, total TER: 12.745%, progress (thread 0): 91.8671%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H01_FEE013_1987.8_1988.09, WER: 100%, TER: 40%, total WER: 26.1321%, total TER: 12.7454%, progress (thread 0): 91.875%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_2244.26_2244.55, WER: 0%, TER: 0%, total WER: 26.1318%, total TER: 12.7452%, progress (thread 0): 91.8829%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_211.03_211.32, WER: 0%, TER: 0%, total WER: 26.1315%, total TER: 12.7451%, progress (thread 0): 91.8908%]
|T|: r i g h t
|P|: i
[sample: ES2004d_H03_FEE016_810.67_810.96, WER: 100%, TER: 80%, total WER: 26.1323%, total TER: 12.7459%, progress (thread 0): 91.8987%]
|T|: m m
|P|: m h m
[sample: ES2004d_H03_FEE016_1204.42_1204.71, WER: 100%, TER: 50%, total WER: 26.1331%, total TER: 12.7461%, progress (thread 0): 91.9066%]
|T|: u m
|P|:
[sample: ES2004d_H01_FEE013_1227.88_1228.17, WER: 100%, TER: 100%, total WER: 26.134%, total TER: 12.7465%, progress (thread 0): 91.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1634.52_1634.81, WER: 0%, TER: 0%, total WER: 26.1337%, total TER: 12.7463%, progress (thread 0): 91.9225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1787.34_1787.63, WER: 0%, TER: 0%, total WER: 26.1334%, total TER: 12.7462%, progress (thread 0): 91.9304%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1964.63_1964.92, WER: 0%, TER: 0%, total WER: 26.1331%, total TER: 12.7461%, progress (thread 0): 91.9383%]
|T|: o k a y
|P|: o k y
[sample: IS1009a_H03_FIO089_118.76_119.05, WER: 100%, TER: 25%, total WER: 26.1339%, total TER: 12.7462%, progress (thread 0): 91.9462%]
|T|: h m m
|P|: c a s e
[sample: IS1009a_H02_FIO084_803.39_803.68, WER: 100%, TER: 133.333%, total WER: 26.1347%, total TER: 12.7471%, progress (thread 0): 91.9541%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_708.64_708.93, WER: 100%, TER: 40%, total WER: 26.1356%, total TER: 12.7474%, progress (thread 0): 91.962%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_998.44_998.73, WER: 0%, TER: 0%, total WER: 26.1353%, total TER: 12.7473%, progress (thread 0): 91.9699%]
|T|: m m h m m
|P|: h
[sample: IS1009b_H03_FIO089_1311.53_1311.82, WER: 100%, TER: 80%, total WER: 26.1361%, total TER: 12.7481%, progress (thread 0): 91.9778%]
|T|: m m h m m
|P|: a m
[sample: IS1009b_H00_FIE088_1356.24_1356.53, WER: 100%, TER: 80%, total WER: 26.137%, total TER: 12.7488%, progress (thread 0): 91.9858%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_1658.74_1659.03, WER: 100%, TER: 60%, total WER: 26.1378%, total TER: 12.7494%, progress (thread 0): 91.9937%]
|T|: h m m
|P|: m h m
[sample: IS1009b_H02_FIO084_1899.66_1899.95, WER: 100%, TER: 66.6667%, total WER: 26.1386%, total TER: 12.7498%, progress (thread 0): 92.0016%]
|T|: m m h m m
|P|: w h m
[sample: IS1009b_H03_FIO089_1999.16_1999.45, WER: 100%, TER: 60%, total WER: 26.1395%, total TER: 12.7503%, progress (thread 0): 92.0095%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_284.27_284.56, WER: 0%, TER: 0%, total WER: 26.1392%, total TER: 12.7502%, progress (thread 0): 92.0174%]
|T|: m m h m m
|P|: u m | h u m
[sample: IS1009c_H00_FIE088_381_381.29, WER: 200%, TER: 60%, total WER: 26.1411%, total TER: 12.7508%, progress (thread 0): 92.0253%]
|T|: w e | c a n
|P|:
[sample: IS1009c_H01_FIO087_755.64_755.93, WER: 100%, TER: 100%, total WER: 26.1428%, total TER: 12.752%, progress (thread 0): 92.0332%]
|T|: y e a h | b u t | w
|P|:
[sample: IS1009c_H02_FIO084_1559.51_1559.8, WER: 100%, TER: 100%, total WER: 26.1453%, total TER: 12.7541%, progress (thread 0): 92.0411%]
|T|: m m h m m
|P|: y
[sample: IS1009c_H02_FIO084_1608.9_1609.19, WER: 100%, TER: 100%, total WER: 26.1461%, total TER: 12.7551%, progress (thread 0): 92.049%]
|T|: h e l l o
|P|: y o u | k n o w
[sample: IS1009d_H01_FIO087_41.27_41.56, WER: 200%, TER: 140%, total WER: 26.1481%, total TER: 12.7566%, progress (thread 0): 92.057%]
|T|: n o
|P|: y o u | k n o w
[sample: IS1009d_H02_FIO084_952.51_952.8, WER: 200%, TER: 300%, total WER: 26.15%, total TER: 12.7579%, progress (thread 0): 92.0649%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1056.71_1057, WER: 100%, TER: 100%, total WER: 26.1509%, total TER: 12.7589%, progress (thread 0): 92.0728%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H03_FIO089_1344.32_1344.61, WER: 100%, TER: 80%, total WER: 26.1517%, total TER: 12.7597%, progress (thread 0): 92.0807%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_1389.87_1390.16, WER: 0%, TER: 0%, total WER: 26.1514%, total TER: 12.7596%, progress (thread 0): 92.0886%]
|T|: m m h m m
|P|: y e a
[sample: IS1009d_H02_FIO084_1790.06_1790.35, WER: 100%, TER: 100%, total WER: 26.1522%, total TER: 12.7606%, progress (thread 0): 92.0965%]
|T|: o k a y
|P|: i
[sample: TS3003a_H01_MTD011UID_704.99_705.28, WER: 100%, TER: 100%, total WER: 26.1531%, total TER: 12.7615%, progress (thread 0): 92.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_977.31_977.6, WER: 0%, TER: 0%, total WER: 26.1528%, total TER: 12.7613%, progress (thread 0): 92.1123%]
|T|: y o u
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1267.17_1267.46, WER: 100%, TER: 100%, total WER: 26.1536%, total TER: 12.7619%, progress (thread 0): 92.1203%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_178.78_179.07, WER: 0%, TER: 0%, total WER: 26.1533%, total TER: 12.7619%, progress (thread 0): 92.1282%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1577.63_1577.92, WER: 0%, TER: 0%, total WER: 26.153%, total TER: 12.7617%, progress (thread 0): 92.1361%]
|T|: w e l l
|P|: w e l l
[sample: TS3003b_H03_MTD012ME_1752.79_1753.08, WER: 0%, TER: 0%, total WER: 26.1527%, total TER: 12.7616%, progress (thread 0): 92.144%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_2033.23_2033.52, WER: 0%, TER: 0%, total WER: 26.1524%, total TER: 12.7615%, progress (thread 0): 92.1519%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2083.17_2083.46, WER: 0%, TER: 0%, total WER: 26.1521%, total TER: 12.7614%, progress (thread 0): 92.1598%]
|T|: y e a h
|P|: n o
[sample: TS3003b_H02_MTD0010ID_2083.37_2083.66, WER: 100%, TER: 100%, total WER: 26.153%, total TER: 12.7622%, progress (thread 0): 92.1677%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H01_MTD011UID_1289.74_1290.03, WER: 100%, TER: 40%, total WER: 26.1538%, total TER: 12.7625%, progress (thread 0): 92.1756%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1452.67_1452.96, WER: 0%, TER: 0%, total WER: 26.1535%, total TER: 12.7624%, progress (thread 0): 92.1835%]
|T|: m m h m m
|P|: h
[sample: TS3003d_H03_MTD012ME_695.88_696.17, WER: 100%, TER: 80%, total WER: 26.1543%, total TER: 12.7632%, progress (thread 0): 92.1915%]
|T|: n a y
|P|: h m
[sample: TS3003d_H01_MTD011UID_835.49_835.78, WER: 100%, TER: 100%, total WER: 26.1552%, total TER: 12.7638%, progress (thread 0): 92.1994%]
|T|: h m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_1010.19_1010.48, WER: 100%, TER: 33.3333%, total WER: 26.156%, total TER: 12.7639%, progress (thread 0): 92.2073%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H02_MTD0010ID_1075.11_1075.4, WER: 100%, TER: 100%, total WER: 26.1568%, total TER: 12.7648%, progress (thread 0): 92.2152%]
|T|: t r u e
|P|: t r u e
[sample: TS3003d_H03_MTD012ME_1129.3_1129.59, WER: 0%, TER: 0%, total WER: 26.1566%, total TER: 12.7646%, progress (thread 0): 92.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1862.62_1862.91, WER: 0%, TER: 0%, total WER: 26.1563%, total TER: 12.7645%, progress (thread 0): 92.231%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_2370.37_2370.66, WER: 100%, TER: 50%, total WER: 26.1571%, total TER: 12.7647%, progress (thread 0): 92.2389%]
|T|: h m m
|P|: h m
[sample: TS3003d_H00_MTD009PM_2517.23_2517.52, WER: 100%, TER: 33.3333%, total WER: 26.1579%, total TER: 12.7648%, progress (thread 0): 92.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2571.69_2571.98, WER: 0%, TER: 0%, total WER: 26.1576%, total TER: 12.7647%, progress (thread 0): 92.2547%]
|T|: h m m
|P|: y e h
[sample: EN2002a_H00_MEE073_218.41_218.7, WER: 100%, TER: 100%, total WER: 26.1585%, total TER: 12.7653%, progress (thread 0): 92.2627%]
|T|: i | d o n ' t | k n o w
|P|: i | d n '
[sample: EN2002a_H00_MEE073_234.63_234.92, WER: 66.6667%, TER: 58.3333%, total WER: 26.1598%, total TER: 12.7666%, progress (thread 0): 92.2706%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_526.91_527.2, WER: 100%, TER: 66.6667%, total WER: 26.1607%, total TER: 12.767%, progress (thread 0): 92.2785%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_646.99_647.28, WER: 0%, TER: 0%, total WER: 26.1604%, total TER: 12.7669%, progress (thread 0): 92.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_723.91_724.2, WER: 0%, TER: 0%, total WER: 26.1601%, total TER: 12.7667%, progress (thread 0): 92.2943%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_749.38_749.67, WER: 0%, TER: 0%, total WER: 26.1598%, total TER: 12.7666%, progress (thread 0): 92.3022%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_856.03_856.32, WER: 0%, TER: 0%, total WER: 26.1595%, total TER: 12.7665%, progress (thread 0): 92.3101%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_872.78_873.07, WER: 0%, TER: 0%, total WER: 26.1592%, total TER: 12.7664%, progress (thread 0): 92.318%]
|T|: h m m
|P|: y e h
[sample: EN2002a_H00_MEE073_1076.05_1076.34, WER: 100%, TER: 100%, total WER: 26.16%, total TER: 12.767%, progress (thread 0): 92.326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1255.44_1255.73, WER: 0%, TER: 0%, total WER: 26.1597%, total TER: 12.7669%, progress (thread 0): 92.3339%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1364.93_1365.22, WER: 0%, TER: 0%, total WER: 26.1594%, total TER: 12.7668%, progress (thread 0): 92.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1593.85_1594.14, WER: 0%, TER: 0%, total WER: 26.1591%, total TER: 12.7666%, progress (thread 0): 92.3497%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1595.68_1595.97, WER: 0%, TER: 0%, total WER: 26.1588%, total TER: 12.7665%, progress (thread 0): 92.3576%]
|T|: m m h m m
|P|: w e
[sample: EN2002a_H02_FEO072_1620.66_1620.95, WER: 100%, TER: 100%, total WER: 26.1597%, total TER: 12.7675%, progress (thread 0): 92.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1960.6_1960.89, WER: 0%, TER: 0%, total WER: 26.1594%, total TER: 12.7674%, progress (thread 0): 92.3734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2127.38_2127.67, WER: 0%, TER: 0%, total WER: 26.1591%, total TER: 12.7673%, progress (thread 0): 92.3813%]
|T|: v e r y | g o o d
|P|: w h e n
[sample: EN2002b_H02_FEO072_142.65_142.94, WER: 100%, TER: 100%, total WER: 26.1608%, total TER: 12.7691%, progress (thread 0): 92.3892%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_382.41_382.7, WER: 0%, TER: 0%, total WER: 26.1605%, total TER: 12.769%, progress (thread 0): 92.3972%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_419.68_419.97, WER: 0%, TER: 0%, total WER: 26.1602%, total TER: 12.7689%, progress (thread 0): 92.4051%]
|T|: o k a y
|P|: o k a
[sample: EN2002b_H00_FEO070_734.04_734.33, WER: 100%, TER: 25%, total WER: 26.161%, total TER: 12.769%, progress (thread 0): 92.413%]
|T|: h m m
|P|: n o
[sample: EN2002b_H03_MEE073_1150.51_1150.8, WER: 100%, TER: 100%, total WER: 26.1618%, total TER: 12.7696%, progress (thread 0): 92.4209%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_1286.29_1286.58, WER: 100%, TER: 40%, total WER: 26.1627%, total TER: 12.77%, progress (thread 0): 92.4288%]
|T|: s o
|P|: s o
[sample: EN2002b_H03_MEE073_1517.43_1517.72, WER: 0%, TER: 0%, total WER: 26.1624%, total TER: 12.7699%, progress (thread 0): 92.4367%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_61.76_62.05, WER: 0%, TER: 0%, total WER: 26.1621%, total TER: 12.7698%, progress (thread 0): 92.4446%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_697.18_697.47, WER: 100%, TER: 50%, total WER: 26.1629%, total TER: 12.7699%, progress (thread 0): 92.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1026.29_1026.58, WER: 0%, TER: 0%, total WER: 26.1626%, total TER: 12.7698%, progress (thread 0): 92.4604%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_1088_1088.29, WER: 100%, TER: 40%, total WER: 26.1634%, total TER: 12.7701%, progress (thread 0): 92.4684%]
|T|: t h a t
|P|: o
[sample: EN2002c_H03_MEE073_1302.84_1303.13, WER: 100%, TER: 100%, total WER: 26.1643%, total TER: 12.771%, progress (thread 0): 92.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1313.99_1314.28, WER: 0%, TER: 0%, total WER: 26.164%, total TER: 12.7708%, progress (thread 0): 92.4842%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_1543.66_1543.95, WER: 100%, TER: 50%, total WER: 26.1648%, total TER: 12.771%, progress (thread 0): 92.4921%]
|T|: ' c a u s e
|P|: t a u s
[sample: EN2002c_H03_MEE073_2609.79_2610.08, WER: 100%, TER: 50%, total WER: 26.1656%, total TER: 12.7715%, progress (thread 0): 92.5%]
|T|: a c t u a l l y
|P|: a c t u a l l y
[sample: EN2002d_H03_MEE073_783.19_783.48, WER: 0%, TER: 0%, total WER: 26.1653%, total TER: 12.7713%, progress (thread 0): 92.5079%]
|T|: r i g h t
|P|: u
[sample: EN2002d_H02_MEE071_833.43_833.72, WER: 100%, TER: 100%, total WER: 26.1662%, total TER: 12.7723%, progress (thread 0): 92.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1031.38_1031.67, WER: 0%, TER: 0%, total WER: 26.1659%, total TER: 12.7722%, progress (thread 0): 92.5237%]
|T|: y e a h
|P|: n o
[sample: EN2002d_H03_MEE073_1490.73_1491.02, WER: 100%, TER: 100%, total WER: 26.1667%, total TER: 12.773%, progress (thread 0): 92.5316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2069.6_2069.89, WER: 0%, TER: 0%, total WER: 26.1664%, total TER: 12.7729%, progress (thread 0): 92.5396%]
|T|: n o
|P|: n
[sample: ES2004b_H03_FEE016_602.63_602.91, WER: 100%, TER: 50%, total WER: 26.1673%, total TER: 12.7731%, progress (thread 0): 92.5475%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1146.25_1146.53, WER: 0%, TER: 0%, total WER: 26.167%, total TER: 12.773%, progress (thread 0): 92.5554%]
|T|: m m
|P|: h m
[sample: ES2004b_H03_FEE016_1316.47_1316.75, WER: 100%, TER: 50%, total WER: 26.1678%, total TER: 12.7731%, progress (thread 0): 92.5633%]
|T|: r i g h t
|P|: r i h t
[sample: ES2004b_H00_MEO015_1939.86_1940.14, WER: 100%, TER: 20%, total WER: 26.1686%, total TER: 12.7732%, progress (thread 0): 92.5712%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2216.73_2217.01, WER: 0%, TER: 0%, total WER: 26.1683%, total TER: 12.7731%, progress (thread 0): 92.5791%]
|T|: t h a n k s
|P|:
[sample: ES2004c_H01_FEE013_54.47_54.75, WER: 100%, TER: 100%, total WER: 26.1692%, total TER: 12.7743%, progress (thread 0): 92.587%]
|T|: u h
|P|: u h
[sample: ES2004c_H02_MEE014_1126.8_1127.08, WER: 0%, TER: 0%, total WER: 26.1689%, total TER: 12.7743%, progress (thread 0): 92.5949%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1180.98_1181.26, WER: 100%, TER: 50%, total WER: 26.1697%, total TER: 12.7744%, progress (thread 0): 92.6029%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1398.69_1398.97, WER: 0%, TER: 0%, total WER: 26.1694%, total TER: 12.7743%, progress (thread 0): 92.6108%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2123.64_2123.92, WER: 0%, TER: 0%, total WER: 26.1691%, total TER: 12.7742%, progress (thread 0): 92.6187%]
|T|: n o
|P|: n o
[sample: ES2004d_H02_MEE014_1050.44_1050.72, WER: 0%, TER: 0%, total WER: 26.1688%, total TER: 12.7741%, progress (thread 0): 92.6266%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_1500.31_1500.59, WER: 100%, TER: 40%, total WER: 26.1696%, total TER: 12.7745%, progress (thread 0): 92.6345%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1843.24_1843.52, WER: 0%, TER: 0%, total WER: 26.1694%, total TER: 12.7743%, progress (thread 0): 92.6424%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_2015.69_2015.97, WER: 0%, TER: 0%, total WER: 26.1691%, total TER: 12.7742%, progress (thread 0): 92.6503%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H01_FEE013_2120.6_2120.88, WER: 0%, TER: 0%, total WER: 26.1688%, total TER: 12.7741%, progress (thread 0): 92.6582%]
|T|: o k a y
|P|: o
[sample: IS1009a_H03_FIO089_188.06_188.34, WER: 100%, TER: 75%, total WER: 26.1696%, total TER: 12.7746%, progress (thread 0): 92.6661%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_793.23_793.51, WER: 100%, TER: 40%, total WER: 26.1704%, total TER: 12.775%, progress (thread 0): 92.674%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_804.79_805.07, WER: 0%, TER: 0%, total WER: 26.1701%, total TER: 12.7748%, progress (thread 0): 92.682%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_894.05_894.33, WER: 0%, TER: 0%, total WER: 26.1698%, total TER: 12.7747%, progress (thread 0): 92.6899%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1764.17_1764.45, WER: 0%, TER: 0%, total WER: 26.1695%, total TER: 12.7746%, progress (thread 0): 92.6978%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009c_H02_FIO084_724.39_724.67, WER: 100%, TER: 100%, total WER: 26.1704%, total TER: 12.7756%, progress (thread 0): 92.7057%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1140.8_1141.08, WER: 0%, TER: 0%, total WER: 26.1701%, total TER: 12.7755%, progress (thread 0): 92.7136%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1619.41_1619.69, WER: 0%, TER: 0%, total WER: 26.1698%, total TER: 12.7754%, progress (thread 0): 92.7215%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_617.29_617.57, WER: 100%, TER: 40%, total WER: 26.1706%, total TER: 12.7757%, progress (thread 0): 92.7294%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_773.28_773.56, WER: 0%, TER: 0%, total WER: 26.1703%, total TER: 12.7756%, progress (thread 0): 92.7373%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_980.1_980.38, WER: 0%, TER: 0%, total WER: 26.17%, total TER: 12.7755%, progress (thread 0): 92.7452%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1792.74_1793.02, WER: 100%, TER: 100%, total WER: 26.1709%, total TER: 12.7765%, progress (thread 0): 92.7532%]
|T|: h m m
|P|: h m
[sample: IS1009d_H02_FIO084_1830.44_1830.72, WER: 100%, TER: 33.3333%, total WER: 26.1717%, total TER: 12.7766%, progress (thread 0): 92.7611%]
|T|: m m h m m
|P|: u h | h u
[sample: IS1009d_H03_FIO089_1876.62_1876.9, WER: 200%, TER: 100%, total WER: 26.1736%, total TER: 12.7777%, progress (thread 0): 92.769%]
|T|: m o r n i n g
|P|: o n t
[sample: TS3003a_H02_MTD0010ID_16.42_16.7, WER: 100%, TER: 71.4286%, total WER: 26.1745%, total TER: 12.7786%, progress (thread 0): 92.7769%]
|T|: s u r e
|P|: s u r e
[sample: TS3003a_H03_MTD012ME_163.72_164, WER: 0%, TER: 0%, total WER: 26.1742%, total TER: 12.7785%, progress (thread 0): 92.7848%]
|T|: h m m
|P|: h m
[sample: TS3003a_H03_MTD012ME_661.22_661.5, WER: 100%, TER: 33.3333%, total WER: 26.175%, total TER: 12.7786%, progress (thread 0): 92.7927%]
|T|: h m m
|P|: h m
[sample: TS3003a_H00_MTD009PM_1235.68_1235.96, WER: 100%, TER: 33.3333%, total WER: 26.1759%, total TER: 12.7788%, progress (thread 0): 92.8006%]
|T|: y e a h
|P|: t h a
[sample: TS3003b_H00_MTD009PM_823.58_823.86, WER: 100%, TER: 75%, total WER: 26.1767%, total TER: 12.7794%, progress (thread 0): 92.8085%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1892.2_1892.48, WER: 0%, TER: 0%, total WER: 26.1764%, total TER: 12.7792%, progress (thread 0): 92.8165%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1860.06_1860.34, WER: 0%, TER: 0%, total WER: 26.1761%, total TER: 12.7791%, progress (thread 0): 92.8244%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1891.82_1892.1, WER: 100%, TER: 40%, total WER: 26.1769%, total TER: 12.7794%, progress (thread 0): 92.8323%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_267.26_267.54, WER: 0%, TER: 0%, total WER: 26.1766%, total TER: 12.7793%, progress (thread 0): 92.8402%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_365.09_365.37, WER: 0%, TER: 0%, total WER: 26.1763%, total TER: 12.7792%, progress (thread 0): 92.8481%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H00_MTD009PM_816.6_816.88, WER: 0%, TER: 0%, total WER: 26.176%, total TER: 12.7791%, progress (thread 0): 92.856%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_860.45_860.73, WER: 0%, TER: 0%, total WER: 26.1757%, total TER: 12.779%, progress (thread 0): 92.8639%]
|T|: a n d | i t
|P|: a n d | i t
[sample: TS3003d_H02_MTD0010ID_1310.28_1310.56, WER: 0%, TER: 0%, total WER: 26.1752%, total TER: 12.7788%, progress (thread 0): 92.8718%]
|T|: s h
|P|: s
[sample: TS3003d_H02_MTD0010ID_1311.62_1311.9, WER: 100%, TER: 50%, total WER: 26.176%, total TER: 12.779%, progress (thread 0): 92.8797%]
|T|: y e p
|P|: y u p
[sample: TS3003d_H02_MTD0010ID_1403.4_1403.68, WER: 100%, TER: 33.3333%, total WER: 26.1768%, total TER: 12.7791%, progress (thread 0): 92.8877%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1945.66_1945.94, WER: 0%, TER: 0%, total WER: 26.1765%, total TER: 12.779%, progress (thread 0): 92.8956%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2142.04_2142.32, WER: 0%, TER: 0%, total WER: 26.1762%, total TER: 12.7789%, progress (thread 0): 92.9035%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_49.78_50.06, WER: 100%, TER: 50%, total WER: 26.1771%, total TER: 12.7792%, progress (thread 0): 92.9114%]
|T|: y e p
|P|: y e p
[sample: EN2002a_H01_FEO070_478.66_478.94, WER: 0%, TER: 0%, total WER: 26.1768%, total TER: 12.7792%, progress (thread 0): 92.9193%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_758.4_758.68, WER: 0%, TER: 0%, total WER: 26.1765%, total TER: 12.779%, progress (thread 0): 92.9272%]
|T|: y e a h
|P|: h m
[sample: EN2002a_H01_FEO070_762.35_762.63, WER: 100%, TER: 100%, total WER: 26.1773%, total TER: 12.7799%, progress (thread 0): 92.9351%]
|T|: b u t
|P|: b u t
[sample: EN2002a_H01_FEO070_1024.58_1024.86, WER: 0%, TER: 0%, total WER: 26.177%, total TER: 12.7798%, progress (thread 0): 92.943%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1306.05_1306.33, WER: 0%, TER: 0%, total WER: 26.1767%, total TER: 12.7796%, progress (thread 0): 92.951%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1392.09_1392.37, WER: 0%, TER: 0%, total WER: 26.1764%, total TER: 12.7795%, progress (thread 0): 92.9589%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002a_H00_MEE073_1638.28_1638.56, WER: 200%, TER: 175%, total WER: 26.1784%, total TER: 12.781%, progress (thread 0): 92.9668%]
|T|: s e e
|P|: s e e
[sample: EN2002a_H01_FEO070_1702.66_1702.94, WER: 0%, TER: 0%, total WER: 26.1781%, total TER: 12.781%, progress (thread 0): 92.9747%]
|T|: y e a h
|P|: n o
[sample: EN2002a_H01_FEO070_1796.46_1796.74, WER: 100%, TER: 100%, total WER: 26.1789%, total TER: 12.7818%, progress (thread 0): 92.9826%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1831.52_1831.8, WER: 0%, TER: 0%, total WER: 26.1786%, total TER: 12.7816%, progress (thread 0): 92.9905%]
|T|: o h
|P|: u h
[sample: EN2002a_H02_FEO072_1981.37_1981.65, WER: 100%, TER: 50%, total WER: 26.1794%, total TER: 12.7818%, progress (thread 0): 92.9984%]
|T|: i | t h
|P|: i
[sample: EN2002a_H00_MEE073_2026.22_2026.5, WER: 50%, TER: 75%, total WER: 26.18%, total TER: 12.7824%, progress (thread 0): 93.0063%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H01_MEE071_16.64_16.92, WER: 0%, TER: 0%, total WER: 26.1797%, total TER: 12.7823%, progress (thread 0): 93.0142%]
|T|: m m h m m
|P|: h m
[sample: EN2002b_H03_MEE073_335.63_335.91, WER: 100%, TER: 60%, total WER: 26.1805%, total TER: 12.7828%, progress (thread 0): 93.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_356.11_356.39, WER: 0%, TER: 0%, total WER: 26.1802%, total TER: 12.7827%, progress (thread 0): 93.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_987.48_987.76, WER: 0%, TER: 0%, total WER: 26.1799%, total TER: 12.7826%, progress (thread 0): 93.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1285.46_1285.74, WER: 0%, TER: 0%, total WER: 26.1796%, total TER: 12.7825%, progress (thread 0): 93.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1450.71_1450.99, WER: 0%, TER: 0%, total WER: 26.1793%, total TER: 12.7824%, progress (thread 0): 93.0538%]
|T|: y e a h
|P|: y e a k n
[sample: EN2002b_H03_MEE073_1588.08_1588.36, WER: 100%, TER: 50%, total WER: 26.1802%, total TER: 12.7827%, progress (thread 0): 93.0617%]
|T|: y e a h
|P|: y o a h
[sample: EN2002c_H03_MEE073_264.61_264.89, WER: 100%, TER: 25%, total WER: 26.181%, total TER: 12.7828%, progress (thread 0): 93.0696%]
|T|: m m h m m
|P|: m
[sample: EN2002c_H03_MEE073_638.33_638.61, WER: 100%, TER: 80%, total WER: 26.1818%, total TER: 12.7836%, progress (thread 0): 93.0775%]
|T|: a h
|P|: u h
[sample: EN2002c_H01_FEO072_1466.83_1467.11, WER: 100%, TER: 50%, total WER: 26.1827%, total TER: 12.7838%, progress (thread 0): 93.0854%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1508.55_1508.83, WER: 100%, TER: 28.5714%, total WER: 26.1835%, total TER: 12.784%, progress (thread 0): 93.0934%]
|T|: i | k n o w
|P|: k i n d | o f
[sample: EN2002c_H03_MEE073_1627.18_1627.46, WER: 100%, TER: 83.3333%, total WER: 26.1852%, total TER: 12.785%, progress (thread 0): 93.1013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2613.98_2614.26, WER: 0%, TER: 0%, total WER: 26.1849%, total TER: 12.7849%, progress (thread 0): 93.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2738.83_2739.11, WER: 0%, TER: 0%, total WER: 26.1846%, total TER: 12.7848%, progress (thread 0): 93.1171%]
|T|: w e l l
|P|: o
[sample: EN2002c_H03_MEE073_2837.32_2837.6, WER: 100%, TER: 100%, total WER: 26.1854%, total TER: 12.7856%, progress (thread 0): 93.125%]
|T|: i | t h
|P|: i
[sample: EN2002d_H03_MEE073_209.34_209.62, WER: 50%, TER: 75%, total WER: 26.1859%, total TER: 12.7862%, progress (thread 0): 93.1329%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H03_MEE073_379.93_380.21, WER: 100%, TER: 40%, total WER: 26.1868%, total TER: 12.7865%, progress (thread 0): 93.1408%]
|T|: y e a h
|P|:
[sample: EN2002d_H03_MEE073_446.25_446.53, WER: 100%, TER: 100%, total WER: 26.1876%, total TER: 12.7873%, progress (thread 0): 93.1487%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_622.33_622.61, WER: 0%, TER: 0%, total WER: 26.1873%, total TER: 12.7872%, progress (thread 0): 93.1566%]
|T|: y e a h
|P|: h m
[sample: EN2002d_H03_MEE073_989.71_989.99, WER: 100%, TER: 100%, total WER: 26.1881%, total TER: 12.788%, progress (thread 0): 93.1646%]
|T|: h m m
|P|: h m
[sample: EN2002d_H02_MEE071_1294.26_1294.54, WER: 100%, TER: 33.3333%, total WER: 26.189%, total TER: 12.7882%, progress (thread 0): 93.1725%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1744.58_1744.86, WER: 0%, TER: 0%, total WER: 26.1887%, total TER: 12.788%, progress (thread 0): 93.1804%]
|T|: t h e
|P|: e
[sample: EN2002d_H03_MEE073_1888.86_1889.14, WER: 100%, TER: 66.6667%, total WER: 26.1895%, total TER: 12.7884%, progress (thread 0): 93.1883%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2067.26_2067.54, WER: 0%, TER: 0%, total WER: 26.1892%, total TER: 12.7883%, progress (thread 0): 93.1962%]
|T|: s u r e
|P|: t r u e
[sample: ES2004b_H00_MEO015_1306.29_1306.56, WER: 100%, TER: 75%, total WER: 26.19%, total TER: 12.7889%, progress (thread 0): 93.2041%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1834.89_1835.16, WER: 0%, TER: 0%, total WER: 26.1898%, total TER: 12.7888%, progress (thread 0): 93.212%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1954.73_1955, WER: 100%, TER: 40%, total WER: 26.1906%, total TER: 12.7891%, progress (thread 0): 93.2199%]
|T|: m m h m m
|P|: h m
[sample: ES2004c_H03_FEE016_652.96_653.23, WER: 100%, TER: 60%, total WER: 26.1914%, total TER: 12.7896%, progress (thread 0): 93.2278%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1141.18_1141.45, WER: 100%, TER: 40%, total WER: 26.1922%, total TER: 12.79%, progress (thread 0): 93.2358%]
|T|: m m
|P|: j u
[sample: ES2004c_H03_FEE016_1281.44_1281.71, WER: 100%, TER: 100%, total WER: 26.1931%, total TER: 12.7904%, progress (thread 0): 93.2437%]
|T|: o k a y
|P|: k a y
[sample: ES2004c_H00_MEO015_1403.89_1404.16, WER: 100%, TER: 25%, total WER: 26.1939%, total TER: 12.7905%, progress (thread 0): 93.2516%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1730.76_1731.03, WER: 0%, TER: 0%, total WER: 26.1936%, total TER: 12.7904%, progress (thread 0): 93.2595%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1835.22_1835.49, WER: 100%, TER: 40%, total WER: 26.1944%, total TER: 12.7907%, progress (thread 0): 93.2674%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1921.81_1922.08, WER: 0%, TER: 0%, total WER: 26.1942%, total TER: 12.7906%, progress (thread 0): 93.2753%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_2149.62_2149.89, WER: 0%, TER: 0%, total WER: 26.1939%, total TER: 12.7904%, progress (thread 0): 93.2832%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_654.95_655.22, WER: 100%, TER: 40%, total WER: 26.1947%, total TER: 12.7908%, progress (thread 0): 93.2911%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_738.4_738.67, WER: 100%, TER: 33.3333%, total WER: 26.1955%, total TER: 12.7909%, progress (thread 0): 93.299%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_793.57_793.84, WER: 0%, TER: 0%, total WER: 26.1952%, total TER: 12.7908%, progress (thread 0): 93.307%]
|T|: n o
|P|: n o
[sample: ES2004d_H03_FEE016_1418.58_1418.85, WER: 0%, TER: 0%, total WER: 26.1949%, total TER: 12.7907%, progress (thread 0): 93.3149%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1497.73_1498, WER: 0%, TER: 0%, total WER: 26.1946%, total TER: 12.7906%, progress (thread 0): 93.3228%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1559.56_1559.83, WER: 0%, TER: 0%, total WER: 26.1943%, total TER: 12.7905%, progress (thread 0): 93.3307%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2099.96_2100.23, WER: 0%, TER: 0%, total WER: 26.194%, total TER: 12.7904%, progress (thread 0): 93.3386%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_159.51_159.78, WER: 100%, TER: 40%, total WER: 26.1949%, total TER: 12.7907%, progress (thread 0): 93.3465%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_495.24_495.51, WER: 0%, TER: 0%, total WER: 26.1946%, total TER: 12.7906%, progress (thread 0): 93.3544%]
|T|: m m h m m
|P|: m h | h m
[sample: IS1009b_H02_FIO084_130.02_130.29, WER: 200%, TER: 60%, total WER: 26.1965%, total TER: 12.7911%, progress (thread 0): 93.3623%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_195.35_195.62, WER: 100%, TER: 60%, total WER: 26.1974%, total TER: 12.7917%, progress (thread 0): 93.3703%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1093_1093.27, WER: 100%, TER: 40%, total WER: 26.1982%, total TER: 12.792%, progress (thread 0): 93.3782%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H01_FIO087_323.83_324.1, WER: 0%, TER: 0%, total WER: 26.1979%, total TER: 12.7918%, progress (thread 0): 93.3861%]
|T|: m m h m m
|P|:
[sample: IS1009c_H00_FIE088_770.63_770.9, WER: 100%, TER: 100%, total WER: 26.1987%, total TER: 12.7929%, progress (thread 0): 93.394%]
|T|: m m h m m
|P|: u h | h
[sample: IS1009c_H03_FIO089_922.16_922.43, WER: 200%, TER: 80%, total WER: 26.2007%, total TER: 12.7936%, progress (thread 0): 93.4019%]
|T|: m m h m m
|P|: u h | h m
[sample: IS1009c_H03_FIO089_1095.56_1095.83, WER: 200%, TER: 80%, total WER: 26.2026%, total TER: 12.7944%, progress (thread 0): 93.4098%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1374.42_1374.69, WER: 0%, TER: 0%, total WER: 26.2024%, total TER: 12.7943%, progress (thread 0): 93.4177%]
|T|: o k a y
|P|: l i c
[sample: IS1009d_H03_FIO089_215.46_215.73, WER: 100%, TER: 100%, total WER: 26.2032%, total TER: 12.7951%, progress (thread 0): 93.4256%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_377.8_378.07, WER: 100%, TER: 100%, total WER: 26.204%, total TER: 12.7961%, progress (thread 0): 93.4335%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_381.16_381.43, WER: 100%, TER: 40%, total WER: 26.2048%, total TER: 12.7965%, progress (thread 0): 93.4415%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_938.34_938.61, WER: 100%, TER: 60%, total WER: 26.2057%, total TER: 12.797%, progress (thread 0): 93.4494%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H03_FIO089_1114.59_1114.86, WER: 100%, TER: 80%, total WER: 26.2065%, total TER: 12.7978%, progress (thread 0): 93.4573%]
|T|: o h
|P|: o h
[sample: IS1009d_H00_FIE088_1436.69_1436.96, WER: 0%, TER: 0%, total WER: 26.2062%, total TER: 12.7977%, progress (thread 0): 93.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1775.25_1775.52, WER: 0%, TER: 0%, total WER: 26.2059%, total TER: 12.7976%, progress (thread 0): 93.4731%]
|T|: o k a y
|P|: o h
[sample: IS1009d_H01_FIO087_1908.84_1909.11, WER: 100%, TER: 75%, total WER: 26.2067%, total TER: 12.7982%, progress (thread 0): 93.481%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_684.57_684.84, WER: 100%, TER: 150%, total WER: 26.2076%, total TER: 12.7988%, progress (thread 0): 93.4889%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_790.17_790.44, WER: 100%, TER: 25%, total WER: 26.2084%, total TER: 12.799%, progress (thread 0): 93.4968%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_1326.95_1327.22, WER: 0%, TER: 0%, total WER: 26.2081%, total TER: 12.7989%, progress (thread 0): 93.5047%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1909.01_1909.28, WER: 0%, TER: 0%, total WER: 26.2078%, total TER: 12.7988%, progress (thread 0): 93.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1979.53_1979.8, WER: 0%, TER: 0%, total WER: 26.2075%, total TER: 12.7987%, progress (thread 0): 93.5206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2145.84_2146.11, WER: 0%, TER: 0%, total WER: 26.2072%, total TER: 12.7985%, progress (thread 0): 93.5285%]
|T|: o h
|P|: h m
[sample: TS3003d_H01_MTD011UID_364.71_364.98, WER: 100%, TER: 100%, total WER: 26.2081%, total TER: 12.7989%, progress (thread 0): 93.5364%]
|T|: t e n
|P|: t e n
[sample: TS3003d_H02_MTD0010ID_864.45_864.72, WER: 0%, TER: 0%, total WER: 26.2078%, total TER: 12.7989%, progress (thread 0): 93.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_958.91_959.18, WER: 0%, TER: 0%, total WER: 26.2075%, total TER: 12.7987%, progress (thread 0): 93.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1378.52_1378.79, WER: 0%, TER: 0%, total WER: 26.2072%, total TER: 12.7986%, progress (thread 0): 93.5601%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_2176.17_2176.44, WER: 0%, TER: 0%, total WER: 26.2069%, total TER: 12.7986%, progress (thread 0): 93.568%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_2345.04_2345.31, WER: 100%, TER: 50%, total WER: 26.2077%, total TER: 12.7987%, progress (thread 0): 93.576%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2364.31_2364.58, WER: 0%, TER: 0%, total WER: 26.2074%, total TER: 12.7986%, progress (thread 0): 93.5839%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_2435.3_2435.57, WER: 100%, TER: 25%, total WER: 26.2082%, total TER: 12.7987%, progress (thread 0): 93.5918%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_501.72_501.99, WER: 100%, TER: 33.3333%, total WER: 26.2091%, total TER: 12.7989%, progress (thread 0): 93.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_727.19_727.46, WER: 0%, TER: 0%, total WER: 26.2088%, total TER: 12.7987%, progress (thread 0): 93.6076%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_806.36_806.63, WER: 0%, TER: 0%, total WER: 26.2085%, total TER: 12.7986%, progress (thread 0): 93.6155%]
|T|: o k a y
|P|:
[sample: EN2002a_H00_MEE073_927.86_928.13, WER: 100%, TER: 100%, total WER: 26.2093%, total TER: 12.7994%, progress (thread 0): 93.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1223.91_1224.18, WER: 0%, TER: 0%, total WER: 26.209%, total TER: 12.7993%, progress (thread 0): 93.6313%]
|T|: y e a h
|P|: y o u n w
[sample: EN2002a_H00_MEE073_1319.85_1320.12, WER: 100%, TER: 100%, total WER: 26.2099%, total TER: 12.8001%, progress (thread 0): 93.6392%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_1452_1452.27, WER: 100%, TER: 50%, total WER: 26.2107%, total TER: 12.8005%, progress (thread 0): 93.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1516.15_1516.42, WER: 0%, TER: 0%, total WER: 26.2104%, total TER: 12.8004%, progress (thread 0): 93.6551%]
|T|: y e a h
|P|: y e s
[sample: EN2002a_H03_MEE071_1533.19_1533.46, WER: 100%, TER: 50%, total WER: 26.2112%, total TER: 12.8007%, progress (thread 0): 93.663%]
|T|: y e a h
|P|: y o n
[sample: EN2002a_H00_MEE073_1556.33_1556.6, WER: 100%, TER: 75%, total WER: 26.2121%, total TER: 12.8013%, progress (thread 0): 93.6709%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1945.11_1945.38, WER: 0%, TER: 0%, total WER: 26.2118%, total TER: 12.8012%, progress (thread 0): 93.6788%]
|T|: o h
|P|: o h
[sample: EN2002a_H00_MEE073_1989_1989.27, WER: 0%, TER: 0%, total WER: 26.2115%, total TER: 12.8011%, progress (thread 0): 93.6867%]
|T|: o h
|P|: u m
[sample: EN2002a_H00_MEE073_2003.83_2004.1, WER: 100%, TER: 100%, total WER: 26.2123%, total TER: 12.8015%, progress (thread 0): 93.6946%]
|T|: n o
|P|: y o u | k n o w
[sample: EN2002a_H01_FEO070_2105.6_2105.87, WER: 200%, TER: 300%, total WER: 26.2142%, total TER: 12.8029%, progress (thread 0): 93.7025%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_355.59_355.86, WER: 0%, TER: 0%, total WER: 26.214%, total TER: 12.8028%, progress (thread 0): 93.7104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_485.61_485.88, WER: 0%, TER: 0%, total WER: 26.2137%, total TER: 12.8027%, progress (thread 0): 93.7184%]
|T|: r i g h t
|P|: g r i a t
[sample: EN2002b_H03_MEE073_1027.72_1027.99, WER: 100%, TER: 60%, total WER: 26.2145%, total TER: 12.8032%, progress (thread 0): 93.7263%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_1296.06_1296.33, WER: 0%, TER: 0%, total WER: 26.2142%, total TER: 12.8032%, progress (thread 0): 93.7342%]
|T|: s o
|P|: s
[sample: EN2002b_H01_MEE071_1333.58_1333.85, WER: 100%, TER: 50%, total WER: 26.215%, total TER: 12.8034%, progress (thread 0): 93.7421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1339.47_1339.74, WER: 0%, TER: 0%, total WER: 26.2147%, total TER: 12.8032%, progress (thread 0): 93.75%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1448.9_1449.17, WER: 0%, TER: 0%, total WER: 26.2144%, total TER: 12.8031%, progress (thread 0): 93.7579%]
|T|: a l r i g h t
|P|: a l r i g h t
[sample: EN2002c_H03_MEE073_558.45_558.72, WER: 0%, TER: 0%, total WER: 26.2141%, total TER: 12.8029%, progress (thread 0): 93.7658%]
|T|: h m m
|P|: h m
[sample: EN2002c_H01_FEO072_655.42_655.69, WER: 100%, TER: 33.3333%, total WER: 26.215%, total TER: 12.803%, progress (thread 0): 93.7737%]
|T|: c o o l
|P|: c o o l
[sample: EN2002c_H01_FEO072_778.13_778.4, WER: 0%, TER: 0%, total WER: 26.2147%, total TER: 12.8029%, progress (thread 0): 93.7816%]
|T|: o r
|P|: o l
[sample: EN2002c_H01_FEO072_798.93_799.2, WER: 100%, TER: 50%, total WER: 26.2155%, total TER: 12.8031%, progress (thread 0): 93.7896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_938.98_939.25, WER: 0%, TER: 0%, total WER: 26.2152%, total TER: 12.803%, progress (thread 0): 93.7975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1122.75_1123.02, WER: 0%, TER: 0%, total WER: 26.2149%, total TER: 12.8029%, progress (thread 0): 93.8054%]
|T|: o k a y
|P|:
[sample: EN2002c_H02_MEE071_1353.05_1353.32, WER: 100%, TER: 100%, total WER: 26.2157%, total TER: 12.8037%, progress (thread 0): 93.8133%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1574.37_1574.64, WER: 0%, TER: 0%, total WER: 26.2154%, total TER: 12.8035%, progress (thread 0): 93.8212%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_1672.35_1672.62, WER: 100%, TER: 25%, total WER: 26.2163%, total TER: 12.8036%, progress (thread 0): 93.8291%]
|T|: y e a h
|P|: n o
[sample: EN2002c_H03_MEE073_1740.26_1740.53, WER: 100%, TER: 100%, total WER: 26.2171%, total TER: 12.8045%, progress (thread 0): 93.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1980.58_1980.85, WER: 0%, TER: 0%, total WER: 26.2168%, total TER: 12.8043%, progress (thread 0): 93.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2155.14_2155.41, WER: 0%, TER: 0%, total WER: 26.2165%, total TER: 12.8042%, progress (thread 0): 93.8528%]
|T|: a l r i g h t
|P|: a h
[sample: EN2002c_H03_MEE073_2202.51_2202.78, WER: 100%, TER: 71.4286%, total WER: 26.2174%, total TER: 12.8052%, progress (thread 0): 93.8608%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2272.51_2272.78, WER: 200%, TER: 175%, total WER: 26.2193%, total TER: 12.8067%, progress (thread 0): 93.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2294.4_2294.67, WER: 0%, TER: 0%, total WER: 26.219%, total TER: 12.8066%, progress (thread 0): 93.8766%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2396.65_2396.92, WER: 0%, TER: 0%, total WER: 26.2187%, total TER: 12.8065%, progress (thread 0): 93.8845%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2568.46_2568.73, WER: 0%, TER: 0%, total WER: 26.2184%, total TER: 12.8063%, progress (thread 0): 93.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2705.6_2705.87, WER: 0%, TER: 0%, total WER: 26.2181%, total TER: 12.8062%, progress (thread 0): 93.9003%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_2790.29_2790.56, WER: 100%, TER: 50%, total WER: 26.219%, total TER: 12.8064%, progress (thread 0): 93.9082%]
|T|: i | k n o w
|P|: n
[sample: EN2002d_H03_MEE073_903.75_904.02, WER: 100%, TER: 83.3333%, total WER: 26.2206%, total TER: 12.8074%, progress (thread 0): 93.9161%]
|T|: b u t
|P|: k h a t
[sample: EN2002d_H01_FEO072_946.69_946.96, WER: 100%, TER: 100%, total WER: 26.2214%, total TER: 12.808%, progress (thread 0): 93.924%]
|T|: s o r r y
|P|: s o
[sample: EN2002d_H00_FEO070_966.35_966.62, WER: 100%, TER: 60%, total WER: 26.2223%, total TER: 12.8085%, progress (thread 0): 93.932%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1127.55_1127.82, WER: 0%, TER: 0%, total WER: 26.222%, total TER: 12.8084%, progress (thread 0): 93.9399%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1281.17_1281.44, WER: 0%, TER: 0%, total WER: 26.2217%, total TER: 12.8083%, progress (thread 0): 93.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1305.04_1305.31, WER: 0%, TER: 0%, total WER: 26.2214%, total TER: 12.8082%, progress (thread 0): 93.9557%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002d_H00_FEO070_1507.8_1508.07, WER: 100%, TER: 100%, total WER: 26.2231%, total TER: 12.8096%, progress (thread 0): 93.9636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_1832.82_1833.09, WER: 0%, TER: 0%, total WER: 26.2228%, total TER: 12.8095%, progress (thread 0): 93.9715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_2000.66_2000.93, WER: 0%, TER: 0%, total WER: 26.2225%, total TER: 12.8094%, progress (thread 0): 93.9794%]
|T|: w h a t
|P|: w e l l
[sample: EN2002d_H02_MEE071_2072.88_2073.15, WER: 100%, TER: 75%, total WER: 26.2233%, total TER: 12.8099%, progress (thread 0): 93.9873%]
|T|: y e p
|P|: y e p
[sample: EN2002d_H03_MEE073_2169.42_2169.69, WER: 0%, TER: 0%, total WER: 26.223%, total TER: 12.8099%, progress (thread 0): 93.9953%]
|T|: y e a h
|P|: y o a | h
[sample: ES2004a_H00_MEO015_17.88_18.14, WER: 200%, TER: 50%, total WER: 26.225%, total TER: 12.8102%, progress (thread 0): 94.0032%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_966.22_966.48, WER: 0%, TER: 0%, total WER: 26.2247%, total TER: 12.8101%, progress (thread 0): 94.0111%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_89.12_89.38, WER: 100%, TER: 40%, total WER: 26.2255%, total TER: 12.8104%, progress (thread 0): 94.019%]
|T|: t h e r e | w e | g o
|P|:
[sample: ES2004b_H02_MEE014_830.72_830.98, WER: 100%, TER: 100%, total WER: 26.228%, total TER: 12.8126%, progress (thread 0): 94.0269%]
|T|: h u h
|P|: a
[sample: ES2004b_H02_MEE014_1039.01_1039.27, WER: 100%, TER: 100%, total WER: 26.2288%, total TER: 12.8132%, progress (thread 0): 94.0348%]
|T|: p e n s
|P|: b e e n
[sample: ES2004b_H00_MEO015_1162.44_1162.7, WER: 100%, TER: 75%, total WER: 26.2296%, total TER: 12.8138%, progress (thread 0): 94.0427%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1848.94_1849.2, WER: 0%, TER: 0%, total WER: 26.2293%, total TER: 12.8137%, progress (thread 0): 94.0506%]
|T|: ' k a y
|P|: t o
[sample: ES2004c_H00_MEO015_188.1_188.36, WER: 100%, TER: 100%, total WER: 26.2302%, total TER: 12.8145%, progress (thread 0): 94.0585%]
|T|: y e p
|P|: y u p
[sample: ES2004c_H00_MEO015_540.57_540.83, WER: 100%, TER: 33.3333%, total WER: 26.231%, total TER: 12.8146%, progress (thread 0): 94.0665%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1192.94_1193.2, WER: 0%, TER: 0%, total WER: 26.2307%, total TER: 12.8145%, progress (thread 0): 94.0744%]
|T|: m m h m m
|P|: u h | h u m
[sample: ES2004d_H00_MEO015_39.85_40.11, WER: 200%, TER: 80%, total WER: 26.2327%, total TER: 12.8153%, progress (thread 0): 94.0823%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_129.84_130.1, WER: 0%, TER: 0%, total WER: 26.2324%, total TER: 12.8152%, progress (thread 0): 94.0902%]
|T|: o h
|P|: o m
[sample: ES2004d_H03_FEE016_1400.02_1400.28, WER: 100%, TER: 50%, total WER: 26.2332%, total TER: 12.8154%, progress (thread 0): 94.0981%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1553.35_1553.61, WER: 0%, TER: 0%, total WER: 26.2329%, total TER: 12.8152%, progress (thread 0): 94.106%]
|T|: n o
|P|: n o
[sample: ES2004d_H01_FEE013_1608.41_1608.67, WER: 0%, TER: 0%, total WER: 26.2326%, total TER: 12.8152%, progress (thread 0): 94.1139%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_1680.41_1680.67, WER: 100%, TER: 40%, total WER: 26.2334%, total TER: 12.8155%, progress (thread 0): 94.1218%]
|T|: m m h m m
|P|: h
[sample: IS1009a_H03_FIO089_195.56_195.82, WER: 100%, TER: 80%, total WER: 26.2343%, total TER: 12.8163%, progress (thread 0): 94.1297%]
|T|: o k a y
|P|: a k
[sample: IS1009b_H01_FIO087_250.63_250.89, WER: 100%, TER: 75%, total WER: 26.2351%, total TER: 12.8169%, progress (thread 0): 94.1377%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_289.01_289.27, WER: 0%, TER: 0%, total WER: 26.2348%, total TER: 12.8168%, progress (thread 0): 94.1456%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_892.83_893.09, WER: 100%, TER: 100%, total WER: 26.2356%, total TER: 12.8178%, progress (thread 0): 94.1535%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_2020.18_2020.44, WER: 0%, TER: 0%, total WER: 26.2353%, total TER: 12.8177%, progress (thread 0): 94.1614%]
|T|: m m h m m
|P|: y e | h
[sample: IS1009c_H00_FIE088_626.71_626.97, WER: 200%, TER: 100%, total WER: 26.2373%, total TER: 12.8187%, progress (thread 0): 94.1693%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_766.43_766.69, WER: 0%, TER: 0%, total WER: 26.237%, total TER: 12.8186%, progress (thread 0): 94.1772%]
|T|: m m
|P|:
[sample: IS1009d_H01_FIO087_656.85_657.11, WER: 100%, TER: 100%, total WER: 26.2378%, total TER: 12.819%, progress (thread 0): 94.1851%]
|T|: ' c a u s e
|P|:
[sample: TS3003a_H03_MTD012ME_1376.63_1376.89, WER: 100%, TER: 100%, total WER: 26.2387%, total TER: 12.8202%, progress (thread 0): 94.193%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H02_MTD0010ID_1474.04_1474.3, WER: 0%, TER: 0%, total WER: 26.2384%, total TER: 12.8201%, progress (thread 0): 94.201%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_206.59_206.85, WER: 100%, TER: 25%, total WER: 26.2392%, total TER: 12.8202%, progress (thread 0): 94.2089%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H01_MTD011UID_581.65_581.91, WER: 0%, TER: 0%, total WER: 26.2389%, total TER: 12.8201%, progress (thread 0): 94.2168%]
|T|: h m m
|P|: h m
[sample: TS3003b_H03_MTD012ME_883.3_883.56, WER: 100%, TER: 33.3333%, total WER: 26.2397%, total TER: 12.8202%, progress (thread 0): 94.2247%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1533.07_1533.33, WER: 100%, TER: 40%, total WER: 26.2406%, total TER: 12.8205%, progress (thread 0): 94.2326%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_2048.3_2048.56, WER: 0%, TER: 0%, total WER: 26.2403%, total TER: 12.8205%, progress (thread 0): 94.2405%]
|T|: m m
|P|: t e
[sample: TS3003c_H01_MTD011UID_1933.15_1933.41, WER: 100%, TER: 100%, total WER: 26.2411%, total TER: 12.8209%, progress (thread 0): 94.2484%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1980.3_1980.56, WER: 0%, TER: 0%, total WER: 26.2408%, total TER: 12.8207%, progress (thread 0): 94.2563%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_694.34_694.6, WER: 0%, TER: 0%, total WER: 26.2405%, total TER: 12.8206%, progress (thread 0): 94.2642%]
|T|: t w o
|P|: t o
[sample: TS3003d_H03_MTD012ME_1897.81_1898.07, WER: 100%, TER: 33.3333%, total WER: 26.2413%, total TER: 12.8208%, progress (thread 0): 94.2722%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2141.52_2141.78, WER: 0%, TER: 0%, total WER: 26.241%, total TER: 12.8206%, progress (thread 0): 94.2801%]
|T|: m m h m m
|P|: m h | h
[sample: EN2002a_H02_FEO072_36.34_36.6, WER: 200%, TER: 60%, total WER: 26.243%, total TER: 12.8212%, progress (thread 0): 94.288%]
|T|: y e a h
|P|: y h
[sample: EN2002a_H00_MEE073_319.23_319.49, WER: 100%, TER: 50%, total WER: 26.2438%, total TER: 12.8215%, progress (thread 0): 94.2959%]
|T|: y e a h
|P|: e h
[sample: EN2002a_H03_MEE071_671.05_671.31, WER: 100%, TER: 50%, total WER: 26.2446%, total TER: 12.8219%, progress (thread 0): 94.3038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_721.89_722.15, WER: 0%, TER: 0%, total WER: 26.2443%, total TER: 12.8218%, progress (thread 0): 94.3117%]
|T|: t r u e
|P|: t r u e
[sample: EN2002a_H00_MEE073_751.8_752.06, WER: 0%, TER: 0%, total WER: 26.2441%, total TER: 12.8217%, progress (thread 0): 94.3196%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1052.1_1052.36, WER: 0%, TER: 0%, total WER: 26.2438%, total TER: 12.8215%, progress (thread 0): 94.3275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1446.67_1446.93, WER: 0%, TER: 0%, total WER: 26.2435%, total TER: 12.8214%, progress (thread 0): 94.3354%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1777.39_1777.65, WER: 0%, TER: 0%, total WER: 26.2432%, total TER: 12.8213%, progress (thread 0): 94.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1868.15_1868.41, WER: 0%, TER: 0%, total WER: 26.2429%, total TER: 12.8212%, progress (thread 0): 94.3513%]
|T|: o k a y
|P|: g
[sample: EN2002b_H02_FEO072_93.22_93.48, WER: 100%, TER: 100%, total WER: 26.2437%, total TER: 12.822%, progress (thread 0): 94.3592%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_252.07_252.33, WER: 200%, TER: 175%, total WER: 26.2457%, total TER: 12.8235%, progress (thread 0): 94.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_460.58_460.84, WER: 0%, TER: 0%, total WER: 26.2454%, total TER: 12.8234%, progress (thread 0): 94.375%]
|T|: o h
|P|: o h
[sample: EN2002b_H03_MEE073_674.06_674.32, WER: 0%, TER: 0%, total WER: 26.2451%, total TER: 12.8233%, progress (thread 0): 94.3829%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_1026.26_1026.52, WER: 200%, TER: 175%, total WER: 26.247%, total TER: 12.8248%, progress (thread 0): 94.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1104.98_1105.24, WER: 0%, TER: 0%, total WER: 26.2467%, total TER: 12.8247%, progress (thread 0): 94.3987%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_1549.41_1549.67, WER: 100%, TER: 33.3333%, total WER: 26.2476%, total TER: 12.8249%, progress (thread 0): 94.4066%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1643.55_1643.81, WER: 0%, TER: 0%, total WER: 26.2473%, total TER: 12.8247%, progress (thread 0): 94.4146%]
|T|: r i g h t
|P|: r i
[sample: EN2002c_H02_MEE071_46.45_46.71, WER: 100%, TER: 60%, total WER: 26.2481%, total TER: 12.8253%, progress (thread 0): 94.4225%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_577.1_577.36, WER: 0%, TER: 0%, total WER: 26.2478%, total TER: 12.8252%, progress (thread 0): 94.4304%]
|T|: y e p
|P|: n o
[sample: EN2002c_H01_FEO072_600.57_600.83, WER: 100%, TER: 100%, total WER: 26.2486%, total TER: 12.8258%, progress (thread 0): 94.4383%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_775.24_775.5, WER: 0%, TER: 0%, total WER: 26.2483%, total TER: 12.8257%, progress (thread 0): 94.4462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1079.16_1079.42, WER: 0%, TER: 0%, total WER: 26.248%, total TER: 12.8256%, progress (thread 0): 94.4541%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_1105.57_1105.83, WER: 100%, TER: 40%, total WER: 26.2489%, total TER: 12.8259%, progress (thread 0): 94.462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1119.48_1119.74, WER: 0%, TER: 0%, total WER: 26.2486%, total TER: 12.8258%, progress (thread 0): 94.4699%]
|T|: m m h m m
|P|: s o
[sample: EN2002c_H03_MEE073_1983.15_1983.41, WER: 100%, TER: 100%, total WER: 26.2494%, total TER: 12.8268%, progress (thread 0): 94.4779%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1999.1_1999.36, WER: 0%, TER: 0%, total WER: 26.2491%, total TER: 12.8266%, progress (thread 0): 94.4858%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2082.11_2082.37, WER: 0%, TER: 0%, total WER: 26.2488%, total TER: 12.8265%, progress (thread 0): 94.4937%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2586.11_2586.37, WER: 0%, TER: 0%, total WER: 26.2485%, total TER: 12.8264%, progress (thread 0): 94.5016%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_347.04_347.3, WER: 0%, TER: 0%, total WER: 26.2482%, total TER: 12.8263%, progress (thread 0): 94.5095%]
|T|: w h o
|P|: h m
[sample: EN2002d_H00_FEO070_501.31_501.57, WER: 100%, TER: 66.6667%, total WER: 26.249%, total TER: 12.8267%, progress (thread 0): 94.5174%]
|T|: y e a h
|P|: y e p
[sample: EN2002d_H02_MEE071_728.46_728.72, WER: 100%, TER: 50%, total WER: 26.2499%, total TER: 12.827%, progress (thread 0): 94.5253%]
|T|: w e l l | i t ' s
|P|: j u s t
[sample: EN2002d_H03_MEE073_882.54_882.8, WER: 100%, TER: 88.8889%, total WER: 26.2515%, total TER: 12.8286%, progress (thread 0): 94.5332%]
|T|: s u p e r
|P|: s u p
[sample: EN2002d_H03_MEE073_953.8_954.06, WER: 100%, TER: 40%, total WER: 26.2524%, total TER: 12.8289%, progress (thread 0): 94.5411%]
|T|: o h | y e a h
|P|:
[sample: EN2002d_H00_FEO070_1517.63_1517.89, WER: 100%, TER: 100%, total WER: 26.254%, total TER: 12.8303%, progress (thread 0): 94.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1704.28_1704.54, WER: 0%, TER: 0%, total WER: 26.2537%, total TER: 12.8302%, progress (thread 0): 94.557%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_2135.36_2135.62, WER: 100%, TER: 33.3333%, total WER: 26.2546%, total TER: 12.8304%, progress (thread 0): 94.5649%]
|T|: m m
|P|: h m
[sample: ES2004a_H03_FEE016_666.69_666.94, WER: 100%, TER: 50%, total WER: 26.2554%, total TER: 12.8305%, progress (thread 0): 94.5728%]
|T|: y e p
|P|: y e p
[sample: ES2004b_H00_MEO015_817.97_818.22, WER: 0%, TER: 0%, total WER: 26.2551%, total TER: 12.8305%, progress (thread 0): 94.5807%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_2175.08_2175.33, WER: 100%, TER: 40%, total WER: 26.2559%, total TER: 12.8308%, progress (thread 0): 94.5886%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_620.36_620.61, WER: 100%, TER: 40%, total WER: 26.2567%, total TER: 12.8311%, progress (thread 0): 94.5965%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1866.87_1867.12, WER: 100%, TER: 100%, total WER: 26.2576%, total TER: 12.8317%, progress (thread 0): 94.6044%]
|T|: s o r r y
|P|: s o r r y
[sample: ES2004d_H01_FEE013_455.38_455.63, WER: 0%, TER: 0%, total WER: 26.2573%, total TER: 12.8315%, progress (thread 0): 94.6123%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_607.67_607.92, WER: 0%, TER: 0%, total WER: 26.257%, total TER: 12.8314%, progress (thread 0): 94.6203%]
|T|: y e s
|P|: y e a h
[sample: ES2004d_H03_FEE016_1135.45_1135.7, WER: 100%, TER: 66.6667%, total WER: 26.2578%, total TER: 12.8318%, progress (thread 0): 94.6282%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1553.38_1553.63, WER: 0%, TER: 0%, total WER: 26.2575%, total TER: 12.8317%, progress (thread 0): 94.6361%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1086.32_1086.57, WER: 100%, TER: 40%, total WER: 26.2583%, total TER: 12.832%, progress (thread 0): 94.644%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1900.42_1900.67, WER: 0%, TER: 0%, total WER: 26.2581%, total TER: 12.8319%, progress (thread 0): 94.6519%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1909.04_1909.29, WER: 0%, TER: 0%, total WER: 26.2578%, total TER: 12.8318%, progress (thread 0): 94.6598%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H00_FIE088_324.58_324.83, WER: 0%, TER: 0%, total WER: 26.2575%, total TER: 12.8316%, progress (thread 0): 94.6677%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1287.33_1287.58, WER: 0%, TER: 0%, total WER: 26.2572%, total TER: 12.8315%, progress (thread 0): 94.6756%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1781.33_1781.58, WER: 0%, TER: 0%, total WER: 26.2569%, total TER: 12.8314%, progress (thread 0): 94.6835%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_351.32_351.57, WER: 0%, TER: 0%, total WER: 26.2566%, total TER: 12.8313%, progress (thread 0): 94.6915%]
|T|: m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_427.43_427.68, WER: 100%, TER: 200%, total WER: 26.2574%, total TER: 12.8322%, progress (thread 0): 94.6994%]
|T|: m m
|P|: a
[sample: IS1009d_H02_FIO084_826.51_826.76, WER: 100%, TER: 100%, total WER: 26.2582%, total TER: 12.8326%, progress (thread 0): 94.7073%]
|T|: n o
|P|: n o
[sample: TS3003a_H03_MTD012ME_1222.74_1222.99, WER: 0%, TER: 0%, total WER: 26.2579%, total TER: 12.8325%, progress (thread 0): 94.7152%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_281.83_282.08, WER: 100%, TER: 50%, total WER: 26.2588%, total TER: 12.8327%, progress (thread 0): 94.7231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1559.52_1559.77, WER: 0%, TER: 0%, total WER: 26.2585%, total TER: 12.8326%, progress (thread 0): 94.731%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_1839.11_1839.36, WER: 0%, TER: 0%, total WER: 26.2582%, total TER: 12.8324%, progress (thread 0): 94.7389%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H02_MTD0010ID_2094.57_2094.82, WER: 100%, TER: 50%, total WER: 26.259%, total TER: 12.8328%, progress (thread 0): 94.7468%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1972.52_1972.77, WER: 100%, TER: 40%, total WER: 26.2598%, total TER: 12.8331%, progress (thread 0): 94.7548%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_303.27_303.52, WER: 100%, TER: 75%, total WER: 26.2607%, total TER: 12.8337%, progress (thread 0): 94.7627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_583.29_583.54, WER: 0%, TER: 0%, total WER: 26.2604%, total TER: 12.8336%, progress (thread 0): 94.7706%]
|T|: y e a h
|P|: y e h
[sample: TS3003d_H02_MTD0010ID_847.16_847.41, WER: 100%, TER: 25%, total WER: 26.2612%, total TER: 12.8337%, progress (thread 0): 94.7785%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1370.99_1371.24, WER: 100%, TER: 75%, total WER: 26.262%, total TER: 12.8343%, progress (thread 0): 94.7864%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1371.48_1371.73, WER: 0%, TER: 0%, total WER: 26.2617%, total TER: 12.8341%, progress (thread 0): 94.7943%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1404.32_1404.57, WER: 100%, TER: 75%, total WER: 26.2626%, total TER: 12.8347%, progress (thread 0): 94.8022%]
|T|: g o o d
|P|: c a u s e
[sample: TS3003d_H03_MTD012ME_1696.35_1696.6, WER: 100%, TER: 125%, total WER: 26.2634%, total TER: 12.8358%, progress (thread 0): 94.8101%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2064.36_2064.61, WER: 0%, TER: 0%, total WER: 26.2631%, total TER: 12.8356%, progress (thread 0): 94.818%]
|T|: y e a h
|P|:
[sample: TS3003d_H02_MTD0010ID_2111_2111.25, WER: 100%, TER: 100%, total WER: 26.2639%, total TER: 12.8365%, progress (thread 0): 94.826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2138.4_2138.65, WER: 0%, TER: 0%, total WER: 26.2636%, total TER: 12.8363%, progress (thread 0): 94.8339%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2216.27_2216.52, WER: 0%, TER: 0%, total WER: 26.2633%, total TER: 12.8362%, progress (thread 0): 94.8418%]
|T|: a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2381.92_2382.17, WER: 100%, TER: 100%, total WER: 26.2642%, total TER: 12.8366%, progress (thread 0): 94.8497%]
|T|: s o
|P|: s o
[sample: EN2002a_H00_MEE073_202.42_202.67, WER: 0%, TER: 0%, total WER: 26.2639%, total TER: 12.8366%, progress (thread 0): 94.8576%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002a_H00_MEE073_749.45_749.7, WER: 200%, TER: 175%, total WER: 26.2658%, total TER: 12.8381%, progress (thread 0): 94.8655%]
|T|: y e a h
|P|: t o
[sample: EN2002a_H00_MEE073_1642_1642.25, WER: 100%, TER: 100%, total WER: 26.2666%, total TER: 12.8389%, progress (thread 0): 94.8734%]
|T|: s o
|P|: s o
[sample: EN2002a_H02_FEO072_1664.11_1664.36, WER: 0%, TER: 0%, total WER: 26.2663%, total TER: 12.8388%, progress (thread 0): 94.8813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1964.06_1964.31, WER: 0%, TER: 0%, total WER: 26.2661%, total TER: 12.8387%, progress (thread 0): 94.8892%]
|T|: y o u ' d | b e | s
|P|: i n p
[sample: EN2002a_H00_MEE073_2096.92_2097.17, WER: 100%, TER: 100%, total WER: 26.2685%, total TER: 12.8408%, progress (thread 0): 94.8971%]
|T|: y e a h
|P|: y e p
[sample: EN2002b_H01_MEE071_93.68_93.93, WER: 100%, TER: 50%, total WER: 26.2694%, total TER: 12.8411%, progress (thread 0): 94.9051%]
|T|: r i g h t
|P|: i
[sample: EN2002b_H03_MEE073_311.61_311.86, WER: 100%, TER: 80%, total WER: 26.2702%, total TER: 12.8419%, progress (thread 0): 94.913%]
|T|: a l r i g h t
|P|: r g h t
[sample: EN2002b_H03_MEE073_1129.86_1130.11, WER: 100%, TER: 42.8571%, total WER: 26.271%, total TER: 12.8424%, progress (thread 0): 94.9209%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1222.15_1222.4, WER: 0%, TER: 0%, total WER: 26.2707%, total TER: 12.8423%, progress (thread 0): 94.9288%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1237.83_1238.08, WER: 0%, TER: 0%, total WER: 26.2704%, total TER: 12.8422%, progress (thread 0): 94.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1245.09_1245.34, WER: 0%, TER: 0%, total WER: 26.2701%, total TER: 12.842%, progress (thread 0): 94.9446%]
|T|: a n d
|P|: t h e n
[sample: EN2002b_H01_MEE071_1309.75_1310, WER: 100%, TER: 133.333%, total WER: 26.271%, total TER: 12.8429%, progress (thread 0): 94.9525%]
|T|: y e p
|P|: n o
[sample: EN2002b_H02_FEO072_1594.84_1595.09, WER: 100%, TER: 100%, total WER: 26.2718%, total TER: 12.8435%, progress (thread 0): 94.9604%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_1643.59_1643.84, WER: 200%, TER: 175%, total WER: 26.2737%, total TER: 12.845%, progress (thread 0): 94.9684%]
|T|: s u r e
|P|: s u e
[sample: EN2002c_H02_MEE071_67.06_67.31, WER: 100%, TER: 25%, total WER: 26.2746%, total TER: 12.8451%, progress (thread 0): 94.9763%]
|T|: o k a y
|P|: n
[sample: EN2002c_H03_MEE073_76.13_76.38, WER: 100%, TER: 100%, total WER: 26.2754%, total TER: 12.8459%, progress (thread 0): 94.9842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_253.63_253.88, WER: 0%, TER: 0%, total WER: 26.2751%, total TER: 12.8458%, progress (thread 0): 94.9921%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_470.85_471.1, WER: 100%, TER: 40%, total WER: 26.2759%, total TER: 12.8461%, progress (thread 0): 95%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_759.06_759.31, WER: 0%, TER: 0%, total WER: 26.2756%, total TER: 12.846%, progress (thread 0): 95.0079%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1000.82_1001.07, WER: 0%, TER: 0%, total WER: 26.2753%, total TER: 12.8459%, progress (thread 0): 95.0158%]
|T|: y e a h
|P|: y e a | h | w
[sample: EN2002c_H03_MEE073_1023.06_1023.31, WER: 300%, TER: 75%, total WER: 26.2784%, total TER: 12.8464%, progress (thread 0): 95.0237%]
|T|: m m h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1102.48_1102.73, WER: 100%, TER: 60%, total WER: 26.2793%, total TER: 12.847%, progress (thread 0): 95.0316%]
|T|: y e a h
|P|: y e s
[sample: EN2002c_H02_MEE071_1122.76_1123.01, WER: 100%, TER: 50%, total WER: 26.2801%, total TER: 12.8473%, progress (thread 0): 95.0396%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_1359.23_1359.48, WER: 100%, TER: 66.6667%, total WER: 26.2809%, total TER: 12.8477%, progress (thread 0): 95.0475%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_1508.23_1508.48, WER: 0%, TER: 0%, total WER: 26.2806%, total TER: 12.8476%, progress (thread 0): 95.0554%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2100.94_2101.19, WER: 0%, TER: 0%, total WER: 26.2803%, total TER: 12.8475%, progress (thread 0): 95.0633%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2354.17_2354.42, WER: 0%, TER: 0%, total WER: 26.28%, total TER: 12.8474%, progress (thread 0): 95.0712%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_146.37_146.62, WER: 100%, TER: 28.5714%, total WER: 26.2808%, total TER: 12.8476%, progress (thread 0): 95.0791%]
|T|: o h
|P|: o
[sample: EN2002d_H00_FEO070_286.77_287.02, WER: 100%, TER: 50%, total WER: 26.2817%, total TER: 12.8478%, progress (thread 0): 95.087%]
|T|: y e a h
|P|: y h m
[sample: EN2002d_H03_MEE073_589.91_590.16, WER: 100%, TER: 75%, total WER: 26.2825%, total TER: 12.8484%, progress (thread 0): 95.0949%]
|T|: y e a h
|P|: c a n c e
[sample: EN2002d_H03_MEE073_850.49_850.74, WER: 100%, TER: 125%, total WER: 26.2833%, total TER: 12.8494%, progress (thread 0): 95.1028%]
|T|: s o
|P|: s o
[sample: EN2002d_H02_MEE071_1251.8_1252.05, WER: 0%, TER: 0%, total WER: 26.283%, total TER: 12.8494%, progress (thread 0): 95.1108%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_1830.85_1831.1, WER: 100%, TER: 33.3333%, total WER: 26.2839%, total TER: 12.8495%, progress (thread 0): 95.1187%]
|T|: o k a y
|P|: c o n
[sample: EN2002d_H03_MEE073_1854.42_1854.67, WER: 100%, TER: 100%, total WER: 26.2847%, total TER: 12.8503%, progress (thread 0): 95.1266%]
|T|: m m
|P|: o
[sample: EN2002d_H01_FEO072_2083.17_2083.42, WER: 100%, TER: 100%, total WER: 26.2855%, total TER: 12.8508%, progress (thread 0): 95.1345%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2192.17_2192.42, WER: 0%, TER: 0%, total WER: 26.2852%, total TER: 12.8506%, progress (thread 0): 95.1424%]
|T|: s
|P|: s
[sample: ES2004a_H02_MEE014_484.48_484.72, WER: 0%, TER: 0%, total WER: 26.2849%, total TER: 12.8506%, progress (thread 0): 95.1503%]
|T|: m m
|P|:
[sample: ES2004a_H03_FEE016_775.6_775.84, WER: 100%, TER: 100%, total WER: 26.2858%, total TER: 12.851%, progress (thread 0): 95.1582%]
|T|: s o r r y
|P|: t
[sample: ES2004b_H00_MEO015_100.7_100.94, WER: 100%, TER: 100%, total WER: 26.2866%, total TER: 12.852%, progress (thread 0): 95.1661%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1311.92_1312.16, WER: 0%, TER: 0%, total WER: 26.2863%, total TER: 12.8519%, progress (thread 0): 95.174%]
|T|: m m h m m
|P|: m h u m
[sample: ES2004b_H00_MEO015_1636.65_1636.89, WER: 100%, TER: 40%, total WER: 26.2871%, total TER: 12.8522%, progress (thread 0): 95.182%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1305.92_1306.16, WER: 100%, TER: 25%, total WER: 26.2879%, total TER: 12.8523%, progress (thread 0): 95.1899%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1345.16_1345.4, WER: 100%, TER: 50%, total WER: 26.2888%, total TER: 12.8525%, progress (thread 0): 95.1978%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_1364.93_1365.17, WER: 100%, TER: 100%, total WER: 26.2896%, total TER: 12.8529%, progress (thread 0): 95.2057%]
|T|: f i n e
|P|: p o i n t
[sample: ES2004c_H00_MEO015_2156.57_2156.81, WER: 100%, TER: 75%, total WER: 26.2904%, total TER: 12.8535%, progress (thread 0): 95.2136%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_973.73_973.97, WER: 0%, TER: 0%, total WER: 26.2901%, total TER: 12.8534%, progress (thread 0): 95.2215%]
|T|: m m
|P|: h
[sample: ES2004d_H03_FEE016_1286.7_1286.94, WER: 100%, TER: 100%, total WER: 26.291%, total TER: 12.8538%, progress (thread 0): 95.2294%]
|T|: o h
|P|: o h
[sample: ES2004d_H01_FEE013_2176.67_2176.91, WER: 0%, TER: 0%, total WER: 26.2907%, total TER: 12.8537%, progress (thread 0): 95.2373%]
|T|: n o
|P|: h
[sample: IS1009a_H03_FIO089_145.55_145.79, WER: 100%, TER: 100%, total WER: 26.2915%, total TER: 12.8541%, progress (thread 0): 95.2453%]
|T|: m m h m m
|P|:
[sample: IS1009a_H00_FIE088_466.21_466.45, WER: 100%, TER: 100%, total WER: 26.2923%, total TER: 12.8551%, progress (thread 0): 95.2532%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_556.58_556.82, WER: 0%, TER: 0%, total WER: 26.292%, total TER: 12.855%, progress (thread 0): 95.2611%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1033.16_1033.4, WER: 0%, TER: 0%, total WER: 26.2917%, total TER: 12.8549%, progress (thread 0): 95.269%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_1099.19_1099.43, WER: 0%, TER: 0%, total WER: 26.2914%, total TER: 12.8548%, progress (thread 0): 95.2769%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1247.84_1248.08, WER: 100%, TER: 60%, total WER: 26.2923%, total TER: 12.8553%, progress (thread 0): 95.2848%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H03_FIO089_752.07_752.31, WER: 100%, TER: 40%, total WER: 26.2931%, total TER: 12.8557%, progress (thread 0): 95.2927%]
|T|: m m | r i g h t
|P|: r i g t
[sample: IS1009c_H00_FIE088_764.18_764.42, WER: 100%, TER: 50%, total WER: 26.2947%, total TER: 12.8563%, progress (thread 0): 95.3006%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1110.14_1110.38, WER: 100%, TER: 40%, total WER: 26.2956%, total TER: 12.8567%, progress (thread 0): 95.3085%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1123.59_1123.83, WER: 100%, TER: 40%, total WER: 26.2964%, total TER: 12.857%, progress (thread 0): 95.3165%]
|T|: h e l l o
|P|: n o
[sample: IS1009d_H02_FIO084_41.31_41.55, WER: 100%, TER: 80%, total WER: 26.2972%, total TER: 12.8578%, progress (thread 0): 95.3244%]
|T|: m m
|P|: m m
[sample: IS1009d_H03_FIO089_516.52_516.76, WER: 0%, TER: 0%, total WER: 26.2969%, total TER: 12.8577%, progress (thread 0): 95.3323%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_656.9_657.14, WER: 100%, TER: 60%, total WER: 26.2978%, total TER: 12.8583%, progress (thread 0): 95.3402%]
|T|: y e a h
|P|: y o n o
[sample: IS1009d_H02_FIO084_1011.72_1011.96, WER: 100%, TER: 75%, total WER: 26.2986%, total TER: 12.8588%, progress (thread 0): 95.3481%]
|T|: o k a y
|P|: o k a
[sample: IS1009d_H03_FIO089_1883.01_1883.25, WER: 100%, TER: 25%, total WER: 26.2994%, total TER: 12.8589%, progress (thread 0): 95.356%]
|T|: w e
|P|: w
[sample: TS3003a_H00_MTD009PM_1109.9_1110.14, WER: 100%, TER: 50%, total WER: 26.3002%, total TER: 12.8591%, progress (thread 0): 95.3639%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1420.32_1420.56, WER: 100%, TER: 50%, total WER: 26.3011%, total TER: 12.8593%, progress (thread 0): 95.3718%]
|T|: n o
|P|: n o
[sample: TS3003b_H02_MTD0010ID_1474.4_1474.64, WER: 0%, TER: 0%, total WER: 26.3008%, total TER: 12.8592%, progress (thread 0): 95.3797%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_1677.79_1678.03, WER: 0%, TER: 0%, total WER: 26.3005%, total TER: 12.8591%, progress (thread 0): 95.3877%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1975.67_1975.91, WER: 100%, TER: 100%, total WER: 26.3013%, total TER: 12.8595%, progress (thread 0): 95.3956%]
|T|: y e a h
|P|: h u m
[sample: TS3003b_H01_MTD011UID_2083.9_2084.14, WER: 100%, TER: 100%, total WER: 26.3021%, total TER: 12.8603%, progress (thread 0): 95.4035%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_1240.02_1240.26, WER: 100%, TER: 75%, total WER: 26.303%, total TER: 12.8609%, progress (thread 0): 95.4114%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1254.68_1254.92, WER: 0%, TER: 0%, total WER: 26.3027%, total TER: 12.8608%, progress (thread 0): 95.4193%]
|T|: s o
|P|: s o
[sample: TS3003c_H01_MTD011UID_1417.83_1418.07, WER: 0%, TER: 0%, total WER: 26.3024%, total TER: 12.8607%, progress (thread 0): 95.4272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1477.96_1478.2, WER: 0%, TER: 0%, total WER: 26.3021%, total TER: 12.8606%, progress (thread 0): 95.4351%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1566.64_1566.88, WER: 100%, TER: 25%, total WER: 26.3029%, total TER: 12.8607%, progress (thread 0): 95.443%]
|T|: y e a h
|P|: a h
[sample: TS3003c_H01_MTD011UID_1716.46_1716.7, WER: 100%, TER: 50%, total WER: 26.3037%, total TER: 12.8611%, progress (thread 0): 95.451%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2217.59_2217.83, WER: 0%, TER: 0%, total WER: 26.3034%, total TER: 12.861%, progress (thread 0): 95.4589%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2243.54_2243.78, WER: 0%, TER: 0%, total WER: 26.3031%, total TER: 12.8608%, progress (thread 0): 95.4668%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1049.33_1049.57, WER: 0%, TER: 0%, total WER: 26.3028%, total TER: 12.8607%, progress (thread 0): 95.4747%]
|T|: y e a h
|P|: o h
[sample: TS3003d_H01_MTD011UID_1496.68_1496.92, WER: 100%, TER: 75%, total WER: 26.3037%, total TER: 12.8613%, progress (thread 0): 95.4826%]
|T|: d | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_106.38_106.62, WER: 50%, TER: 33.3333%, total WER: 26.3042%, total TER: 12.8616%, progress (thread 0): 95.4905%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_140.88_141.12, WER: 0%, TER: 0%, total WER: 26.3039%, total TER: 12.8615%, progress (thread 0): 95.4984%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_333.3_333.54, WER: 0%, TER: 0%, total WER: 26.3036%, total TER: 12.8613%, progress (thread 0): 95.5063%]
|T|: y e a h
|P|: h m
[sample: EN2002a_H01_FEO070_398.18_398.42, WER: 100%, TER: 100%, total WER: 26.3044%, total TER: 12.8621%, progress (thread 0): 95.5142%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_481.62_481.86, WER: 100%, TER: 50%, total WER: 26.3053%, total TER: 12.8625%, progress (thread 0): 95.5222%]
|T|: y e a h
|P|: e m
[sample: EN2002a_H00_MEE073_682.97_683.21, WER: 100%, TER: 75%, total WER: 26.3061%, total TER: 12.863%, progress (thread 0): 95.5301%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H02_FEO072_1009.94_1010.18, WER: 100%, TER: 40%, total WER: 26.3069%, total TER: 12.8634%, progress (thread 0): 95.538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1041.03_1041.27, WER: 0%, TER: 0%, total WER: 26.3066%, total TER: 12.8632%, progress (thread 0): 95.5459%]
|T|: i | s e e
|P|: a s
[sample: EN2002a_H00_MEE073_1171.72_1171.96, WER: 100%, TER: 80%, total WER: 26.3083%, total TER: 12.864%, progress (thread 0): 95.5538%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002a_H00_MEE073_1209.32_1209.56, WER: 100%, TER: 85.7143%, total WER: 26.3099%, total TER: 12.8652%, progress (thread 0): 95.5617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1310.69_1310.93, WER: 0%, TER: 0%, total WER: 26.3096%, total TER: 12.8651%, progress (thread 0): 95.5696%]
|T|: o p e n
|P|:
[sample: EN2002a_H02_FEO072_1350.04_1350.28, WER: 100%, TER: 100%, total WER: 26.3105%, total TER: 12.8659%, progress (thread 0): 95.5775%]
|T|: m m
|P|: h m
[sample: EN2002a_H03_MEE071_1405.73_1405.97, WER: 100%, TER: 50%, total WER: 26.3113%, total TER: 12.8661%, progress (thread 0): 95.5854%]
|T|: r i g h t
|P|: r i g t
[sample: EN2002a_H00_MEE073_1791.17_1791.41, WER: 100%, TER: 20%, total WER: 26.3121%, total TER: 12.8662%, progress (thread 0): 95.5934%]
|T|: y e a h
|P|: t e h
[sample: EN2002a_H00_MEE073_1834.42_1834.66, WER: 100%, TER: 50%, total WER: 26.313%, total TER: 12.8665%, progress (thread 0): 95.6013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2028.12_2028.36, WER: 0%, TER: 0%, total WER: 26.3127%, total TER: 12.8664%, progress (thread 0): 95.6092%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_2040.58_2040.82, WER: 0%, TER: 0%, total WER: 26.3124%, total TER: 12.8663%, progress (thread 0): 95.6171%]
|T|: o h
|P|: o h
[sample: EN2002a_H01_FEO070_2098.91_2099.15, WER: 0%, TER: 0%, total WER: 26.3121%, total TER: 12.8662%, progress (thread 0): 95.625%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_522.48_522.72, WER: 0%, TER: 0%, total WER: 26.3118%, total TER: 12.8661%, progress (thread 0): 95.6329%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_612.23_612.47, WER: 0%, TER: 0%, total WER: 26.3115%, total TER: 12.866%, progress (thread 0): 95.6408%]
|T|: m m
|P|:
[sample: EN2002b_H02_FEO072_1475.84_1476.08, WER: 100%, TER: 100%, total WER: 26.3123%, total TER: 12.8664%, progress (thread 0): 95.6487%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_235.84_236.08, WER: 100%, TER: 33.3333%, total WER: 26.3131%, total TER: 12.8665%, progress (thread 0): 95.6566%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_571.47_571.71, WER: 0%, TER: 0%, total WER: 26.3128%, total TER: 12.8664%, progress (thread 0): 95.6646%]
|T|: h m m
|P|: y o u h
[sample: EN2002c_H03_MEE073_574.34_574.58, WER: 100%, TER: 133.333%, total WER: 26.3137%, total TER: 12.8672%, progress (thread 0): 95.6725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_598.8_599.04, WER: 0%, TER: 0%, total WER: 26.3134%, total TER: 12.8671%, progress (thread 0): 95.6804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1009.87_1010.11, WER: 0%, TER: 0%, total WER: 26.3131%, total TER: 12.867%, progress (thread 0): 95.6883%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1018.66_1018.9, WER: 0%, TER: 0%, total WER: 26.3128%, total TER: 12.8669%, progress (thread 0): 95.6962%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1281.36_1281.6, WER: 100%, TER: 33.3333%, total WER: 26.3136%, total TER: 12.867%, progress (thread 0): 95.7041%]
|T|: y e a h
|P|: y e a | k n | w
[sample: EN2002c_H03_MEE073_1396.34_1396.58, WER: 300%, TER: 125%, total WER: 26.3167%, total TER: 12.868%, progress (thread 0): 95.712%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1906.84_1907.08, WER: 0%, TER: 0%, total WER: 26.3164%, total TER: 12.8679%, progress (thread 0): 95.7199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1913.84_1914.08, WER: 0%, TER: 0%, total WER: 26.3161%, total TER: 12.8678%, progress (thread 0): 95.7279%]
|T|: ' c a u s e
|P|: a s
[sample: EN2002c_H03_MEE073_2264.19_2264.43, WER: 100%, TER: 66.6667%, total WER: 26.3169%, total TER: 12.8686%, progress (thread 0): 95.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2309.3_2309.54, WER: 0%, TER: 0%, total WER: 26.3166%, total TER: 12.8684%, progress (thread 0): 95.7437%]
|T|: r i g h t
|P|: i
[sample: EN2002c_H03_MEE073_2341.41_2341.65, WER: 100%, TER: 80%, total WER: 26.3174%, total TER: 12.8692%, progress (thread 0): 95.7516%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2370.76_2371, WER: 0%, TER: 0%, total WER: 26.3171%, total TER: 12.8691%, progress (thread 0): 95.7595%]
|T|: g o o d
|P|: g o o d
[sample: EN2002d_H01_FEO072_337.97_338.21, WER: 0%, TER: 0%, total WER: 26.3169%, total TER: 12.869%, progress (thread 0): 95.7674%]
|T|: y e a h
|P|: t o
[sample: EN2002d_H03_MEE073_390.81_391.05, WER: 100%, TER: 100%, total WER: 26.3177%, total TER: 12.8698%, progress (thread 0): 95.7753%]
|T|: r i g h t
|P|: r i g h
[sample: EN2002d_H01_FEO072_496.78_497.02, WER: 100%, TER: 20%, total WER: 26.3185%, total TER: 12.8698%, progress (thread 0): 95.7832%]
|T|: o k a y
|P|: u h
[sample: EN2002d_H00_FEO070_513.66_513.9, WER: 100%, TER: 100%, total WER: 26.3193%, total TER: 12.8707%, progress (thread 0): 95.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1190.34_1190.58, WER: 0%, TER: 0%, total WER: 26.319%, total TER: 12.8705%, progress (thread 0): 95.799%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1371.83_1372.07, WER: 0%, TER: 0%, total WER: 26.3187%, total TER: 12.8704%, progress (thread 0): 95.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1477.13_1477.37, WER: 0%, TER: 0%, total WER: 26.3184%, total TER: 12.8703%, progress (thread 0): 95.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1706.39_1706.63, WER: 0%, TER: 0%, total WER: 26.3182%, total TER: 12.8702%, progress (thread 0): 95.8228%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H03_FEE016_1717.78_1718.01, WER: 100%, TER: 200%, total WER: 26.319%, total TER: 12.8711%, progress (thread 0): 95.8307%]
|T|: m m h m m
|P|: h
[sample: ES2004b_H00_MEO015_1860.91_1861.14, WER: 100%, TER: 80%, total WER: 26.3198%, total TER: 12.8718%, progress (thread 0): 95.8386%]
|T|: t r u e
|P|: j u
[sample: ES2004b_H00_MEO015_2294.79_2295.02, WER: 100%, TER: 75%, total WER: 26.3206%, total TER: 12.8724%, progress (thread 0): 95.8465%]
|T|: y e a h
|P|: a n d
[sample: ES2004c_H00_MEO015_85.13_85.36, WER: 100%, TER: 100%, total WER: 26.3215%, total TER: 12.8732%, progress (thread 0): 95.8544%]
|T|: ' k a y
|P|: k a
[sample: ES2004c_H00_MEO015_356.94_357.17, WER: 100%, TER: 50%, total WER: 26.3223%, total TER: 12.8736%, progress (thread 0): 95.8623%]
|T|: s o
|P|: a
[sample: ES2004c_H02_MEE014_748.27_748.5, WER: 100%, TER: 100%, total WER: 26.3231%, total TER: 12.874%, progress (thread 0): 95.8702%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_1121.33_1121.56, WER: 100%, TER: 100%, total WER: 26.3239%, total TER: 12.8744%, progress (thread 0): 95.8782%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1169.98_1170.21, WER: 0%, TER: 0%, total WER: 26.3236%, total TER: 12.8743%, progress (thread 0): 95.8861%]
|T|: o k a y
|P|: o
[sample: ES2004d_H03_FEE016_711.82_712.05, WER: 100%, TER: 75%, total WER: 26.3245%, total TER: 12.8748%, progress (thread 0): 95.894%]
|T|: t w o
|P|: d o
[sample: ES2004d_H00_MEO015_732.46_732.69, WER: 100%, TER: 66.6667%, total WER: 26.3253%, total TER: 12.8752%, progress (thread 0): 95.9019%]
|T|: m m h m m
|P|: h
[sample: ES2004d_H00_MEO015_822.98_823.21, WER: 100%, TER: 80%, total WER: 26.3261%, total TER: 12.876%, progress (thread 0): 95.9098%]
|T|: ' k a y
|P|: s c h o o
[sample: ES2004d_H00_MEO015_1813.16_1813.39, WER: 100%, TER: 125%, total WER: 26.3269%, total TER: 12.877%, progress (thread 0): 95.9177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_195.04_195.27, WER: 0%, TER: 0%, total WER: 26.3267%, total TER: 12.8769%, progress (thread 0): 95.9256%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_532.46_532.69, WER: 0%, TER: 0%, total WER: 26.3264%, total TER: 12.8768%, progress (thread 0): 95.9335%]
|T|: m m
|P|: h m
[sample: IS1009b_H02_FIO084_55.21_55.44, WER: 100%, TER: 50%, total WER: 26.3272%, total TER: 12.877%, progress (thread 0): 95.9415%]
|T|: y e a h
|P|: y e
[sample: IS1009b_H02_FIO084_1919.19_1919.42, WER: 100%, TER: 50%, total WER: 26.328%, total TER: 12.8774%, progress (thread 0): 95.9494%]
|T|: m m h m m
|P|: h m
[sample: IS1009c_H00_FIE088_554.54_554.77, WER: 100%, TER: 60%, total WER: 26.3288%, total TER: 12.8779%, progress (thread 0): 95.9573%]
|T|: m m | y e s
|P|: y e a h
[sample: IS1009c_H01_FIO087_728.73_728.96, WER: 100%, TER: 83.3333%, total WER: 26.3305%, total TER: 12.8789%, progress (thread 0): 95.9652%]
|T|: y e p
|P|: y e a h
[sample: IS1009c_H03_FIO089_1639.78_1640.01, WER: 100%, TER: 66.6667%, total WER: 26.3313%, total TER: 12.8793%, progress (thread 0): 95.9731%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_747.97_748.2, WER: 100%, TER: 66.6667%, total WER: 26.3321%, total TER: 12.8796%, progress (thread 0): 95.981%]
|T|: h m m
|P|:
[sample: IS1009d_H02_FIO084_748.42_748.65, WER: 100%, TER: 100%, total WER: 26.333%, total TER: 12.8803%, progress (thread 0): 95.9889%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_758.1_758.33, WER: 100%, TER: 100%, total WER: 26.3338%, total TER: 12.8813%, progress (thread 0): 95.9968%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_1695.75_1695.98, WER: 0%, TER: 0%, total WER: 26.3335%, total TER: 12.8812%, progress (thread 0): 96.0047%]
|T|: o k a y
|P|: y e
[sample: IS1009d_H03_FIO089_1889.3_1889.53, WER: 100%, TER: 100%, total WER: 26.3343%, total TER: 12.882%, progress (thread 0): 96.0127%]
|T|: ' k a y
|P|: o k a
[sample: TS3003a_H03_MTD012ME_843.57_843.8, WER: 100%, TER: 50%, total WER: 26.3352%, total TER: 12.8823%, progress (thread 0): 96.0206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1334.2_1334.43, WER: 0%, TER: 0%, total WER: 26.3349%, total TER: 12.8822%, progress (thread 0): 96.0285%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1346.08_1346.31, WER: 0%, TER: 0%, total WER: 26.3346%, total TER: 12.8821%, progress (thread 0): 96.0364%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1078.3_1078.53, WER: 100%, TER: 50%, total WER: 26.3354%, total TER: 12.8823%, progress (thread 0): 96.0443%]
|T|: n o
|P|: n o
[sample: TS3003b_H01_MTD011UID_1716.14_1716.37, WER: 0%, TER: 0%, total WER: 26.3351%, total TER: 12.8822%, progress (thread 0): 96.0522%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_1250.59_1250.82, WER: 0%, TER: 0%, total WER: 26.3348%, total TER: 12.8822%, progress (thread 0): 96.0601%]
|T|: y e s
|P|: j u s t
[sample: TS3003c_H02_MTD0010ID_1518.39_1518.62, WER: 100%, TER: 100%, total WER: 26.3356%, total TER: 12.8828%, progress (thread 0): 96.068%]
|T|: m m
|P|: a m
[sample: TS3003c_H01_MTD011UID_1654.26_1654.49, WER: 100%, TER: 50%, total WER: 26.3365%, total TER: 12.8829%, progress (thread 0): 96.076%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_2049.46_2049.69, WER: 100%, TER: 75%, total WER: 26.3373%, total TER: 12.8835%, progress (thread 0): 96.0839%]
|T|: s o
|P|: s o
[sample: TS3003d_H01_MTD011UID_261.45_261.68, WER: 0%, TER: 0%, total WER: 26.337%, total TER: 12.8835%, progress (thread 0): 96.0918%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H02_MTD0010ID_1021.27_1021.5, WER: 0%, TER: 0%, total WER: 26.3367%, total TER: 12.8834%, progress (thread 0): 96.0997%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_1945.96_1946.19, WER: 100%, TER: 100%, total WER: 26.3375%, total TER: 12.8842%, progress (thread 0): 96.1076%]
|T|: y e a h
|P|: e a h
[sample: TS3003d_H01_MTD011UID_2064.77_2065, WER: 100%, TER: 25%, total WER: 26.3383%, total TER: 12.8843%, progress (thread 0): 96.1155%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_424.58_424.81, WER: 0%, TER: 0%, total WER: 26.338%, total TER: 12.8841%, progress (thread 0): 96.1234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_781.91_782.14, WER: 0%, TER: 0%, total WER: 26.3377%, total TER: 12.884%, progress (thread 0): 96.1313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_855.85_856.08, WER: 0%, TER: 0%, total WER: 26.3375%, total TER: 12.8839%, progress (thread 0): 96.1392%]
|T|: h m m
|P|:
[sample: EN2002a_H02_FEO072_877.24_877.47, WER: 100%, TER: 100%, total WER: 26.3383%, total TER: 12.8845%, progress (thread 0): 96.1471%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1019.4_1019.63, WER: 100%, TER: 33.3333%, total WER: 26.3391%, total TER: 12.8847%, progress (thread 0): 96.1551%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_1188.61_1188.84, WER: 100%, TER: 66.6667%, total WER: 26.3399%, total TER: 12.885%, progress (thread 0): 96.163%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1295.18_1295.41, WER: 0%, TER: 0%, total WER: 26.3396%, total TER: 12.8849%, progress (thread 0): 96.1709%]
|T|: y e a h
|P|: t o
[sample: EN2002a_H01_FEO070_1408.29_1408.52, WER: 100%, TER: 100%, total WER: 26.3405%, total TER: 12.8857%, progress (thread 0): 96.1788%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1571.85_1572.08, WER: 0%, TER: 0%, total WER: 26.3402%, total TER: 12.8857%, progress (thread 0): 96.1867%]
|T|: o h
|P|: o
[sample: EN2002b_H03_MEE073_29.06_29.29, WER: 100%, TER: 50%, total WER: 26.341%, total TER: 12.8858%, progress (thread 0): 96.1946%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_194.27_194.5, WER: 0%, TER: 0%, total WER: 26.3407%, total TER: 12.8857%, progress (thread 0): 96.2025%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_211.63_211.86, WER: 0%, TER: 0%, total WER: 26.3404%, total TER: 12.8856%, progress (thread 0): 96.2104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_493.43_493.66, WER: 0%, TER: 0%, total WER: 26.3401%, total TER: 12.8855%, progress (thread 0): 96.2184%]
|T|: h m m
|P|: h m
[sample: EN2002b_H00_FEO070_534.29_534.52, WER: 100%, TER: 33.3333%, total WER: 26.3409%, total TER: 12.8856%, progress (thread 0): 96.2263%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_672.54_672.77, WER: 100%, TER: 33.3333%, total WER: 26.3418%, total TER: 12.8858%, progress (thread 0): 96.2342%]
|T|: m m h m m
|P|: y e
[sample: EN2002b_H03_MEE073_1361.79_1362.02, WER: 100%, TER: 100%, total WER: 26.3426%, total TER: 12.8868%, progress (thread 0): 96.2421%]
|T|: y e a h
|P|: e a h
[sample: EN2002c_H03_MEE073_555.02_555.25, WER: 100%, TER: 25%, total WER: 26.3434%, total TER: 12.8869%, progress (thread 0): 96.25%]
|T|: h m m
|P|: b u h
[sample: EN2002c_H01_FEO072_699.88_700.11, WER: 100%, TER: 100%, total WER: 26.3442%, total TER: 12.8875%, progress (thread 0): 96.2579%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1900.45_1900.68, WER: 0%, TER: 0%, total WER: 26.3439%, total TER: 12.8874%, progress (thread 0): 96.2658%]
|T|: d o h
|P|: d o
[sample: EN2002c_H02_MEE071_2395.6_2395.83, WER: 100%, TER: 33.3333%, total WER: 26.3448%, total TER: 12.8875%, progress (thread 0): 96.2737%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2817.5_2817.73, WER: 0%, TER: 0%, total WER: 26.3445%, total TER: 12.8874%, progress (thread 0): 96.2816%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2846.79_2847.02, WER: 0%, TER: 0%, total WER: 26.3442%, total TER: 12.8873%, progress (thread 0): 96.2896%]
|T|: o k a y
|P|: k a
[sample: EN2002d_H03_MEE073_107.69_107.92, WER: 100%, TER: 50%, total WER: 26.345%, total TER: 12.8876%, progress (thread 0): 96.2975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_503.45_503.68, WER: 0%, TER: 0%, total WER: 26.3447%, total TER: 12.8875%, progress (thread 0): 96.3054%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_548.74_548.97, WER: 100%, TER: 40%, total WER: 26.3455%, total TER: 12.8878%, progress (thread 0): 96.3133%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_767.8_768.03, WER: 0%, TER: 0%, total WER: 26.3452%, total TER: 12.8877%, progress (thread 0): 96.3212%]
|T|: n o
|P|: n o
[sample: EN2002d_H00_FEO070_1427.88_1428.11, WER: 0%, TER: 0%, total WER: 26.3449%, total TER: 12.8876%, progress (thread 0): 96.3291%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1760.63_1760.86, WER: 100%, TER: 33.3333%, total WER: 26.3458%, total TER: 12.8878%, progress (thread 0): 96.337%]
|T|: o k a y
|P|:
[sample: EN2002d_H00_FEO070_2207.62_2207.85, WER: 100%, TER: 100%, total WER: 26.3466%, total TER: 12.8886%, progress (thread 0): 96.3449%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_1022.74_1022.96, WER: 0%, TER: 0%, total WER: 26.3463%, total TER: 12.8885%, progress (thread 0): 96.3528%]
|T|: m m
|P|: h m
[sample: ES2004b_H02_MEE014_1537.12_1537.34, WER: 100%, TER: 50%, total WER: 26.3471%, total TER: 12.8886%, progress (thread 0): 96.3608%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004c_H00_MEO015_575.7_575.92, WER: 0%, TER: 0%, total WER: 26.3468%, total TER: 12.8885%, progress (thread 0): 96.3687%]
|T|: m m h m m
|P|: y m | h
[sample: ES2004c_H03_FEE016_1311.86_1312.08, WER: 200%, TER: 80%, total WER: 26.3488%, total TER: 12.8893%, progress (thread 0): 96.3766%]
|T|: r i g h t
|P|: i
[sample: ES2004c_H00_MEO015_2116.51_2116.73, WER: 100%, TER: 80%, total WER: 26.3496%, total TER: 12.8901%, progress (thread 0): 96.3845%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_2159.26_2159.48, WER: 0%, TER: 0%, total WER: 26.3493%, total TER: 12.8899%, progress (thread 0): 96.3924%]
|T|: y e a h
|P|: e h
[sample: ES2004d_H02_MEE014_968.07_968.29, WER: 100%, TER: 50%, total WER: 26.3501%, total TER: 12.8903%, progress (thread 0): 96.4003%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1220.58_1220.8, WER: 0%, TER: 0%, total WER: 26.3498%, total TER: 12.8902%, progress (thread 0): 96.4082%]
|T|: o k a y
|P|:
[sample: ES2004d_H03_FEE016_1281.25_1281.47, WER: 100%, TER: 100%, total WER: 26.3507%, total TER: 12.891%, progress (thread 0): 96.4161%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1684.26_1684.48, WER: 0%, TER: 0%, total WER: 26.3504%, total TER: 12.8909%, progress (thread 0): 96.424%]
|T|: o k a y
|P|:
[sample: IS1009b_H03_FIO089_45.13_45.35, WER: 100%, TER: 100%, total WER: 26.3512%, total TER: 12.8917%, progress (thread 0): 96.432%]
|T|: ' k a y
|P|: c o
[sample: IS1009b_H02_FIO084_155.16_155.38, WER: 100%, TER: 100%, total WER: 26.352%, total TER: 12.8925%, progress (thread 0): 96.4399%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_404.12_404.34, WER: 100%, TER: 60%, total WER: 26.3528%, total TER: 12.893%, progress (thread 0): 96.4478%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_961.79_962.01, WER: 0%, TER: 0%, total WER: 26.3525%, total TER: 12.8929%, progress (thread 0): 96.4557%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1007.66_1007.88, WER: 0%, TER: 0%, total WER: 26.3523%, total TER: 12.8928%, progress (thread 0): 96.4636%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H00_FIE088_337.53_337.75, WER: 0%, TER: 0%, total WER: 26.352%, total TER: 12.8927%, progress (thread 0): 96.4715%]
|T|: o k a y
|P|: o
[sample: IS1009c_H01_FIO087_1263.61_1263.83, WER: 100%, TER: 75%, total WER: 26.3528%, total TER: 12.8933%, progress (thread 0): 96.4794%]
|T|: m m
|P|: e
[sample: IS1009d_H03_FIO089_590.61_590.83, WER: 100%, TER: 100%, total WER: 26.3536%, total TER: 12.8937%, progress (thread 0): 96.4873%]
|T|: m m
|P|:
[sample: IS1009d_H02_FIO084_801.03_801.25, WER: 100%, TER: 100%, total WER: 26.3544%, total TER: 12.8941%, progress (thread 0): 96.4953%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H02_FIO084_1687.52_1687.74, WER: 100%, TER: 80%, total WER: 26.3553%, total TER: 12.8948%, progress (thread 0): 96.5032%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1775.76_1775.98, WER: 0%, TER: 0%, total WER: 26.355%, total TER: 12.8947%, progress (thread 0): 96.5111%]
|T|: o k a y
|P|:
[sample: IS1009d_H03_FIO089_1846.51_1846.73, WER: 100%, TER: 100%, total WER: 26.3558%, total TER: 12.8955%, progress (thread 0): 96.519%]
|T|: m o r n i n g
|P|: m o n e y
[sample: TS3003a_H01_MTD011UID_15.34_15.56, WER: 100%, TER: 57.1429%, total WER: 26.3566%, total TER: 12.8963%, progress (thread 0): 96.5269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1316.76_1316.98, WER: 0%, TER: 0%, total WER: 26.3563%, total TER: 12.8961%, progress (thread 0): 96.5348%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1357.36_1357.58, WER: 100%, TER: 50%, total WER: 26.3571%, total TER: 12.8963%, progress (thread 0): 96.5427%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_888.56_888.78, WER: 0%, TER: 0%, total WER: 26.3568%, total TER: 12.8962%, progress (thread 0): 96.5506%]
|T|: h m m
|P|: h m
[sample: TS3003b_H03_MTD012ME_1371.93_1372.15, WER: 100%, TER: 33.3333%, total WER: 26.3577%, total TER: 12.8963%, progress (thread 0): 96.5585%]
|T|: y e a h
|P|: a n d
[sample: TS3003b_H01_MTD011UID_1600.07_1600.29, WER: 100%, TER: 100%, total WER: 26.3585%, total TER: 12.8971%, progress (thread 0): 96.5665%]
|T|: n o
|P|: n
[sample: TS3003c_H01_MTD011UID_109.75_109.97, WER: 100%, TER: 50%, total WER: 26.3593%, total TER: 12.8973%, progress (thread 0): 96.5744%]
|T|: m a y b
|P|: i
[sample: TS3003c_H00_MTD009PM_437.36_437.58, WER: 100%, TER: 100%, total WER: 26.3602%, total TER: 12.8981%, progress (thread 0): 96.5823%]
|T|: m m h m m
|P|:
[sample: TS3003c_H01_MTD011UID_1100.48_1100.7, WER: 100%, TER: 100%, total WER: 26.361%, total TER: 12.8991%, progress (thread 0): 96.5902%]
|T|: y e a h
|P|: h m
[sample: TS3003c_H01_MTD011UID_1304.78_1305, WER: 100%, TER: 100%, total WER: 26.3618%, total TER: 12.9%, progress (thread 0): 96.5981%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_2263.6_2263.82, WER: 0%, TER: 0%, total WER: 26.3615%, total TER: 12.8998%, progress (thread 0): 96.606%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_563.58_563.8, WER: 100%, TER: 75%, total WER: 26.3623%, total TER: 12.9004%, progress (thread 0): 96.6139%]
|T|: y e p
|P|: y u p
[sample: TS3003d_H02_MTD0010ID_577.11_577.33, WER: 100%, TER: 33.3333%, total WER: 26.3632%, total TER: 12.9006%, progress (thread 0): 96.6218%]
|T|: n o
|P|: u h
[sample: TS3003d_H01_MTD011UID_1458.81_1459.03, WER: 100%, TER: 100%, total WER: 26.364%, total TER: 12.901%, progress (thread 0): 96.6297%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_1674.77_1674.99, WER: 100%, TER: 50%, total WER: 26.3648%, total TER: 12.9011%, progress (thread 0): 96.6377%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1736.91_1737.13, WER: 0%, TER: 0%, total WER: 26.3645%, total TER: 12.901%, progress (thread 0): 96.6456%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_1751.36_1751.58, WER: 0%, TER: 0%, total WER: 26.3642%, total TER: 12.901%, progress (thread 0): 96.6535%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1991.39_1991.61, WER: 0%, TER: 0%, total WER: 26.3639%, total TER: 12.9008%, progress (thread 0): 96.6614%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H03_MTD012ME_1998.36_1998.58, WER: 100%, TER: 25%, total WER: 26.3647%, total TER: 12.9009%, progress (thread 0): 96.6693%]
|T|: y e a h
|P|: y o u | k n o
[sample: EN2002a_H00_MEE073_482.53_482.75, WER: 200%, TER: 150%, total WER: 26.3667%, total TER: 12.9022%, progress (thread 0): 96.6772%]
|T|: h m m
|P|: y m
[sample: EN2002a_H01_FEO070_706.29_706.51, WER: 100%, TER: 66.6667%, total WER: 26.3675%, total TER: 12.9026%, progress (thread 0): 96.6851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_805.91_806.13, WER: 0%, TER: 0%, total WER: 26.3672%, total TER: 12.9025%, progress (thread 0): 96.693%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1103.91_1104.13, WER: 0%, TER: 0%, total WER: 26.3669%, total TER: 12.9023%, progress (thread 0): 96.701%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_1216.04_1216.26, WER: 100%, TER: 66.6667%, total WER: 26.3678%, total TER: 12.9027%, progress (thread 0): 96.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1537.4_1537.62, WER: 0%, TER: 0%, total WER: 26.3675%, total TER: 12.9026%, progress (thread 0): 96.7168%]
|T|: j u s t
|P|: j u s t
[sample: EN2002a_H01_FEO070_1871.92_1872.14, WER: 0%, TER: 0%, total WER: 26.3672%, total TER: 12.9025%, progress (thread 0): 96.7247%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1963.02_1963.24, WER: 0%, TER: 0%, total WER: 26.3669%, total TER: 12.9023%, progress (thread 0): 96.7326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_639.41_639.63, WER: 0%, TER: 0%, total WER: 26.3666%, total TER: 12.9022%, progress (thread 0): 96.7405%]
|T|: r i g h t
|P|: w u t
[sample: EN2002b_H03_MEE073_1397.63_1397.85, WER: 100%, TER: 80%, total WER: 26.3674%, total TER: 12.903%, progress (thread 0): 96.7484%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_1547.56_1547.78, WER: 100%, TER: 33.3333%, total WER: 26.3682%, total TER: 12.9032%, progress (thread 0): 96.7563%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_575.92_576.14, WER: 100%, TER: 28.5714%, total WER: 26.369%, total TER: 12.9034%, progress (thread 0): 96.7642%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H02_MEE071_589.02_589.24, WER: 100%, TER: 80%, total WER: 26.3699%, total TER: 12.9042%, progress (thread 0): 96.7722%]
|T|: ' k a y
|P|: y e h
[sample: EN2002c_H03_MEE073_684.52_684.74, WER: 100%, TER: 100%, total WER: 26.3707%, total TER: 12.905%, progress (thread 0): 96.7801%]
|T|: o h | y e a h
|P|: e
[sample: EN2002c_H03_MEE073_1506.68_1506.9, WER: 100%, TER: 85.7143%, total WER: 26.3723%, total TER: 12.9062%, progress (thread 0): 96.788%]
|T|: y e a h
|P|: t o
[sample: EN2002c_H03_MEE073_1602.82_1603.04, WER: 100%, TER: 100%, total WER: 26.3732%, total TER: 12.907%, progress (thread 0): 96.7959%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1707.51_1707.73, WER: 100%, TER: 33.3333%, total WER: 26.374%, total TER: 12.9071%, progress (thread 0): 96.8038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2260.94_2261.16, WER: 0%, TER: 0%, total WER: 26.3737%, total TER: 12.907%, progress (thread 0): 96.8117%]
|T|: y e a h
|P|: e m
[sample: EN2002c_H03_MEE073_2697.66_2697.88, WER: 100%, TER: 75%, total WER: 26.3745%, total TER: 12.9076%, progress (thread 0): 96.8196%]
|T|: o k a y
|P|:
[sample: EN2002d_H03_MEE073_402.37_402.59, WER: 100%, TER: 100%, total WER: 26.3754%, total TER: 12.9084%, progress (thread 0): 96.8275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_736.63_736.85, WER: 0%, TER: 0%, total WER: 26.3751%, total TER: 12.9083%, progress (thread 0): 96.8354%]
|T|: o h
|P|: h o m
[sample: EN2002d_H00_FEO070_909.94_910.16, WER: 100%, TER: 100%, total WER: 26.3759%, total TER: 12.9087%, progress (thread 0): 96.8434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1061.24_1061.46, WER: 0%, TER: 0%, total WER: 26.3756%, total TER: 12.9086%, progress (thread 0): 96.8513%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1400.88_1401.1, WER: 0%, TER: 0%, total WER: 26.3753%, total TER: 12.9085%, progress (thread 0): 96.8592%]
|T|: b u t
|P|: b u t
[sample: EN2002d_H00_FEO070_1658_1658.22, WER: 0%, TER: 0%, total WER: 26.375%, total TER: 12.9084%, progress (thread 0): 96.8671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1732.66_1732.88, WER: 0%, TER: 0%, total WER: 26.3747%, total TER: 12.9082%, progress (thread 0): 96.875%]
|T|: m m
|P|: h m
[sample: ES2004a_H03_FEE016_622.02_622.23, WER: 100%, TER: 50%, total WER: 26.3755%, total TER: 12.9084%, progress (thread 0): 96.8829%]
|T|: y e a h
|P|: y u p
[sample: ES2004b_H00_MEO015_1323.05_1323.26, WER: 100%, TER: 75%, total WER: 26.3763%, total TER: 12.909%, progress (thread 0): 96.8908%]
|T|: m m h m m
|P|: u | h m
[sample: ES2004b_H00_MEO015_1464.74_1464.95, WER: 200%, TER: 60%, total WER: 26.3783%, total TER: 12.9095%, progress (thread 0): 96.8987%]
|T|: m m h m m
|P|: u h u
[sample: ES2004c_H00_MEO015_96.42_96.63, WER: 100%, TER: 80%, total WER: 26.3791%, total TER: 12.9103%, progress (thread 0): 96.9066%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1385.07_1385.28, WER: 0%, TER: 0%, total WER: 26.3788%, total TER: 12.9102%, progress (thread 0): 96.9146%]
|T|: h m m
|P|: h m
[sample: ES2004d_H00_MEO015_26.63_26.84, WER: 100%, TER: 33.3333%, total WER: 26.3796%, total TER: 12.9103%, progress (thread 0): 96.9225%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_627.79_628, WER: 100%, TER: 66.6667%, total WER: 26.3805%, total TER: 12.9107%, progress (thread 0): 96.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_39.06_39.27, WER: 0%, TER: 0%, total WER: 26.3802%, total TER: 12.9106%, progress (thread 0): 96.9383%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_169.39_169.6, WER: 100%, TER: 100%, total WER: 26.381%, total TER: 12.9116%, progress (thread 0): 96.9462%]
|T|: m m h m m
|P|:
[sample: IS1009b_H00_FIE088_1286.43_1286.64, WER: 100%, TER: 100%, total WER: 26.3818%, total TER: 12.9126%, progress (thread 0): 96.9541%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H00_FIE088_1289.44_1289.65, WER: 0%, TER: 0%, total WER: 26.3815%, total TER: 12.9125%, progress (thread 0): 96.962%]
|T|: m m
|P|: a n
[sample: IS1009b_H02_FIO084_1632.09_1632.3, WER: 100%, TER: 100%, total WER: 26.3824%, total TER: 12.9129%, progress (thread 0): 96.9699%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_284.08_284.29, WER: 0%, TER: 0%, total WER: 26.3821%, total TER: 12.9128%, progress (thread 0): 96.9778%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1563.53_1563.74, WER: 0%, TER: 0%, total WER: 26.3818%, total TER: 12.9126%, progress (thread 0): 96.9858%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1707.23_1707.44, WER: 0%, TER: 0%, total WER: 26.3815%, total TER: 12.9125%, progress (thread 0): 96.9937%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_741.38_741.59, WER: 0%, TER: 0%, total WER: 26.3812%, total TER: 12.9124%, progress (thread 0): 97.0016%]
|T|: y e a h
|P|:
[sample: IS1009d_H02_FIO084_1371.67_1371.88, WER: 100%, TER: 100%, total WER: 26.382%, total TER: 12.9132%, progress (thread 0): 97.0095%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1880.74_1880.95, WER: 100%, TER: 100%, total WER: 26.3828%, total TER: 12.9142%, progress (thread 0): 97.0174%]
|T|: y e a h
|P|: y e s
[sample: TS3003a_H01_MTD011UID_1295.8_1296.01, WER: 100%, TER: 50%, total WER: 26.3836%, total TER: 12.9146%, progress (thread 0): 97.0253%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_901.66_901.87, WER: 0%, TER: 0%, total WER: 26.3834%, total TER: 12.9145%, progress (thread 0): 97.0332%]
|T|: y e a h
|P|: h u h
[sample: TS3003b_H01_MTD011UID_1547.15_1547.36, WER: 100%, TER: 75%, total WER: 26.3842%, total TER: 12.915%, progress (thread 0): 97.0411%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1091.82_1092.03, WER: 0%, TER: 0%, total WER: 26.3839%, total TER: 12.9149%, progress (thread 0): 97.049%]
|T|: y e a h
|P|: h u h
[sample: TS3003c_H01_MTD011UID_1210.7_1210.91, WER: 100%, TER: 75%, total WER: 26.3847%, total TER: 12.9155%, progress (thread 0): 97.057%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_530.79_531, WER: 100%, TER: 75%, total WER: 26.3855%, total TER: 12.9161%, progress (thread 0): 97.0649%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1465.64_1465.85, WER: 0%, TER: 0%, total WER: 26.3852%, total TER: 12.916%, progress (thread 0): 97.0728%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1695.15_1695.36, WER: 0%, TER: 0%, total WER: 26.3849%, total TER: 12.9158%, progress (thread 0): 97.0807%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1711.66_1711.87, WER: 0%, TER: 0%, total WER: 26.3846%, total TER: 12.9157%, progress (thread 0): 97.0886%]
|T|: y e a h
|P|: y e p
[sample: TS3003d_H00_MTD009PM_1789.64_1789.85, WER: 100%, TER: 50%, total WER: 26.3855%, total TER: 12.9161%, progress (thread 0): 97.0965%]
|T|: n o
|P|: n o
[sample: TS3003d_H03_MTD012ME_2014.88_2015.09, WER: 0%, TER: 0%, total WER: 26.3852%, total TER: 12.916%, progress (thread 0): 97.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2064.37_2064.58, WER: 0%, TER: 0%, total WER: 26.3849%, total TER: 12.9159%, progress (thread 0): 97.1123%]
|T|: y e a h
|P|: e h
[sample: TS3003d_H01_MTD011UID_2107.64_2107.85, WER: 100%, TER: 50%, total WER: 26.3857%, total TER: 12.9162%, progress (thread 0): 97.1203%]
|T|: y e a h
|P|: h m
[sample: TS3003d_H01_MTD011UID_2462.55_2462.76, WER: 100%, TER: 100%, total WER: 26.3865%, total TER: 12.917%, progress (thread 0): 97.1282%]
|T|: s o r r y
|P|: s o r r
[sample: EN2002a_H02_FEO072_377.93_378.14, WER: 100%, TER: 20%, total WER: 26.3873%, total TER: 12.9171%, progress (thread 0): 97.1361%]
|T|: o h
|P|:
[sample: EN2002a_H03_MEE071_483.42_483.63, WER: 100%, TER: 100%, total WER: 26.3882%, total TER: 12.9175%, progress (thread 0): 97.144%]
|T|: y e a h
|P|: y a h
[sample: EN2002a_H03_MEE071_648.41_648.62, WER: 100%, TER: 25%, total WER: 26.389%, total TER: 12.9176%, progress (thread 0): 97.1519%]
|T|: y e a h
|P|: c o m
[sample: EN2002a_H01_FEO070_1319.66_1319.87, WER: 100%, TER: 100%, total WER: 26.3898%, total TER: 12.9184%, progress (thread 0): 97.1598%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1429.36_1429.57, WER: 0%, TER: 0%, total WER: 26.3895%, total TER: 12.9183%, progress (thread 0): 97.1677%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1896.08_1896.29, WER: 0%, TER: 0%, total WER: 26.3892%, total TER: 12.9182%, progress (thread 0): 97.1756%]
|T|: y e a h
|P|: y e
[sample: EN2002a_H03_MEE071_2122.73_2122.94, WER: 100%, TER: 50%, total WER: 26.3901%, total TER: 12.9185%, progress (thread 0): 97.1835%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_266.76_266.97, WER: 0%, TER: 0%, total WER: 26.3898%, total TER: 12.9184%, progress (thread 0): 97.1915%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_291.79_292, WER: 0%, TER: 0%, total WER: 26.3895%, total TER: 12.9183%, progress (thread 0): 97.1994%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1335.23_1335.44, WER: 0%, TER: 0%, total WER: 26.3892%, total TER: 12.9182%, progress (thread 0): 97.2073%]
|T|: ' k a y
|P|: k u
[sample: EN2002c_H03_MEE073_21.49_21.7, WER: 100%, TER: 75%, total WER: 26.39%, total TER: 12.9188%, progress (thread 0): 97.2152%]
|T|: o k a y
|P|:
[sample: EN2002c_H03_MEE073_183.87_184.08, WER: 100%, TER: 100%, total WER: 26.3908%, total TER: 12.9196%, progress (thread 0): 97.2231%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1748.27_1748.48, WER: 0%, TER: 0%, total WER: 26.3905%, total TER: 12.9194%, progress (thread 0): 97.231%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002c_H03_MEE073_1810.36_1810.57, WER: 100%, TER: 85.7143%, total WER: 26.3922%, total TER: 12.9206%, progress (thread 0): 97.2389%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1861.15_1861.36, WER: 0%, TER: 0%, total WER: 26.3919%, total TER: 12.9205%, progress (thread 0): 97.2468%]
|T|: y e a h
|P|: y o u | k n
[sample: EN2002c_H03_MEE073_2180.5_2180.71, WER: 200%, TER: 125%, total WER: 26.3938%, total TER: 12.9215%, progress (thread 0): 97.2547%]
|T|: t r u e
|P|: t r u e
[sample: EN2002c_H03_MEE073_2453.86_2454.07, WER: 0%, TER: 0%, total WER: 26.3935%, total TER: 12.9214%, progress (thread 0): 97.2627%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2819.2_2819.41, WER: 0%, TER: 0%, total WER: 26.3932%, total TER: 12.9213%, progress (thread 0): 97.2706%]
|T|: m m h m m
|P|: u h m
[sample: EN2002d_H01_FEO072_164.51_164.72, WER: 100%, TER: 60%, total WER: 26.3941%, total TER: 12.9218%, progress (thread 0): 97.2785%]
|T|: r i g h t
|P|: t o
[sample: EN2002d_H03_MEE073_338.56_338.77, WER: 100%, TER: 100%, total WER: 26.3949%, total TER: 12.9229%, progress (thread 0): 97.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_647.49_647.7, WER: 0%, TER: 0%, total WER: 26.3946%, total TER: 12.9227%, progress (thread 0): 97.2943%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_782.98_783.19, WER: 100%, TER: 100%, total WER: 26.3954%, total TER: 12.9231%, progress (thread 0): 97.3022%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_998.01_998.22, WER: 0%, TER: 0%, total WER: 26.3951%, total TER: 12.923%, progress (thread 0): 97.3101%]
|T|: o h | y e a h
|P|: c
[sample: EN2002d_H00_FEO070_1377.89_1378.1, WER: 100%, TER: 100%, total WER: 26.3968%, total TER: 12.9244%, progress (thread 0): 97.318%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1391.57_1391.78, WER: 0%, TER: 0%, total WER: 26.3965%, total TER: 12.9243%, progress (thread 0): 97.326%]
|T|: o h | y e a h
|P|: y e
[sample: EN2002d_H03_MEE073_1912.21_1912.42, WER: 100%, TER: 71.4286%, total WER: 26.3981%, total TER: 12.9252%, progress (thread 0): 97.3339%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_2147.17_2147.38, WER: 100%, TER: 33.3333%, total WER: 26.3989%, total TER: 12.9254%, progress (thread 0): 97.3418%]
|T|: o k a y
|P|: j
[sample: ES2004b_H00_MEO015_338.55_338.75, WER: 100%, TER: 100%, total WER: 26.3998%, total TER: 12.9262%, progress (thread 0): 97.3497%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1514.44_1514.64, WER: 0%, TER: 0%, total WER: 26.3995%, total TER: 12.9261%, progress (thread 0): 97.3576%]
|T|: o o p s
|P|:
[sample: ES2004c_H00_MEO015_54.23_54.43, WER: 100%, TER: 100%, total WER: 26.4003%, total TER: 12.9269%, progress (thread 0): 97.3655%]
|T|: m m h m m
|P|: h
[sample: ES2004c_H00_MEO015_1395.98_1396.18, WER: 100%, TER: 80%, total WER: 26.4011%, total TER: 12.9277%, progress (thread 0): 97.3734%]
|T|: ' k a y
|P|: t
[sample: ES2004d_H00_MEO015_562.82_563.02, WER: 100%, TER: 100%, total WER: 26.4019%, total TER: 12.9285%, progress (thread 0): 97.3813%]
|T|: y e p
|P|: y e a h
[sample: ES2004d_H02_MEE014_922.35_922.55, WER: 100%, TER: 66.6667%, total WER: 26.4028%, total TER: 12.9289%, progress (thread 0): 97.3892%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1214.04_1214.24, WER: 0%, TER: 0%, total WER: 26.4025%, total TER: 12.9287%, progress (thread 0): 97.3972%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1952.42_1952.62, WER: 0%, TER: 0%, total WER: 26.4022%, total TER: 12.9286%, progress (thread 0): 97.4051%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_784.26_784.46, WER: 0%, TER: 0%, total WER: 26.4019%, total TER: 12.9285%, progress (thread 0): 97.413%]
|T|: ' k a y
|P|: t o
[sample: IS1009a_H03_FIO089_794.48_794.68, WER: 100%, TER: 100%, total WER: 26.4027%, total TER: 12.9293%, progress (thread 0): 97.4209%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H02_FIO084_179.12_179.32, WER: 100%, TER: 200%, total WER: 26.4035%, total TER: 12.9302%, progress (thread 0): 97.4288%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1111.83_1112.03, WER: 0%, TER: 0%, total WER: 26.4032%, total TER: 12.9301%, progress (thread 0): 97.4367%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_2011.99_2012.19, WER: 100%, TER: 100%, total WER: 26.404%, total TER: 12.9311%, progress (thread 0): 97.4446%]
|T|: y e a h
|P|: y e a
[sample: IS1009c_H03_FIO089_285.99_286.19, WER: 100%, TER: 25%, total WER: 26.4049%, total TER: 12.9312%, progress (thread 0): 97.4525%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1689.26_1689.46, WER: 0%, TER: 0%, total WER: 26.4046%, total TER: 12.9311%, progress (thread 0): 97.4604%]
|T|: m m
|P|:
[sample: IS1009d_H01_FIO087_964.59_964.79, WER: 100%, TER: 100%, total WER: 26.4054%, total TER: 12.9315%, progress (thread 0): 97.4684%]
|T|: y e a h
|P|: a n d
[sample: TS3003a_H01_MTD011UID_1286.22_1286.42, WER: 100%, TER: 100%, total WER: 26.4062%, total TER: 12.9323%, progress (thread 0): 97.4763%]
|T|: m m
|P|: y
[sample: TS3003a_H01_MTD011UID_1431.86_1432.06, WER: 100%, TER: 100%, total WER: 26.407%, total TER: 12.9327%, progress (thread 0): 97.4842%]
|T|: ' k a y
|P|: i n g
[sample: TS3003b_H03_MTD012ME_550.33_550.53, WER: 100%, TER: 100%, total WER: 26.4079%, total TER: 12.9335%, progress (thread 0): 97.4921%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_576.96_577.16, WER: 100%, TER: 50%, total WER: 26.4087%, total TER: 12.9337%, progress (thread 0): 97.5%]
|T|: y e a h
|P|: u h
[sample: TS3003b_H01_MTD011UID_1340.71_1340.91, WER: 100%, TER: 75%, total WER: 26.4095%, total TER: 12.9342%, progress (thread 0): 97.5079%]
|T|: m m
|P|: y e
[sample: TS3003b_H01_MTD011UID_1635.98_1636.18, WER: 100%, TER: 100%, total WER: 26.4103%, total TER: 12.9346%, progress (thread 0): 97.5158%]
|T|: m m
|P|: h m
[sample: TS3003c_H03_MTD012ME_1936.99_1937.19, WER: 100%, TER: 50%, total WER: 26.4112%, total TER: 12.9348%, progress (thread 0): 97.5237%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_214.36_214.56, WER: 0%, TER: 0%, total WER: 26.4109%, total TER: 12.9347%, progress (thread 0): 97.5316%]
|T|: y e p
|P|: y e
[sample: TS3003d_H00_MTD009PM_418.75_418.95, WER: 100%, TER: 33.3333%, total WER: 26.4117%, total TER: 12.9348%, progress (thread 0): 97.5396%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1236.15_1236.35, WER: 0%, TER: 0%, total WER: 26.4114%, total TER: 12.9347%, progress (thread 0): 97.5475%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1334.1_1334.3, WER: 0%, TER: 0%, total WER: 26.4111%, total TER: 12.9346%, progress (thread 0): 97.5554%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1736.65_1736.85, WER: 0%, TER: 0%, total WER: 26.4108%, total TER: 12.9345%, progress (thread 0): 97.5633%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1862.16_1862.36, WER: 0%, TER: 0%, total WER: 26.4105%, total TER: 12.9344%, progress (thread 0): 97.5712%]
|T|: h m m
|P|: w h e n
[sample: TS3003d_H01_MTD011UID_2423.71_2423.91, WER: 100%, TER: 100%, total WER: 26.4113%, total TER: 12.935%, progress (thread 0): 97.5791%]
|T|: y e a h
|P|: h
[sample: EN2002a_H00_MEE073_6.65_6.85, WER: 100%, TER: 75%, total WER: 26.4122%, total TER: 12.9355%, progress (thread 0): 97.587%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_207.6_207.8, WER: 0%, TER: 0%, total WER: 26.4119%, total TER: 12.9354%, progress (thread 0): 97.5949%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_325.97_326.17, WER: 100%, TER: 33.3333%, total WER: 26.4127%, total TER: 12.9356%, progress (thread 0): 97.6029%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_517.59_517.79, WER: 0%, TER: 0%, total WER: 26.4124%, total TER: 12.9354%, progress (thread 0): 97.6108%]
|T|: m m h m m
|P|: e h
[sample: EN2002a_H00_MEE073_625.1_625.3, WER: 100%, TER: 80%, total WER: 26.4132%, total TER: 12.9362%, progress (thread 0): 97.6187%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1082.64_1082.84, WER: 0%, TER: 0%, total WER: 26.4129%, total TER: 12.9361%, progress (thread 0): 97.6266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1296.98_1297.18, WER: 0%, TER: 0%, total WER: 26.4126%, total TER: 12.936%, progress (thread 0): 97.6345%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1544.23_1544.43, WER: 100%, TER: 33.3333%, total WER: 26.4134%, total TER: 12.9361%, progress (thread 0): 97.6424%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_1988.65_1988.85, WER: 0%, TER: 0%, total WER: 26.4132%, total TER: 12.936%, progress (thread 0): 97.6503%]
|T|: y e p
|P|: y e p
[sample: EN2002b_H00_FEO070_58.73_58.93, WER: 0%, TER: 0%, total WER: 26.4129%, total TER: 12.9359%, progress (thread 0): 97.6582%]
|T|: d o | w e | d
|P|: d w e
[sample: EN2002b_H01_MEE071_223.55_223.75, WER: 100%, TER: 57.1429%, total WER: 26.4153%, total TER: 12.9366%, progress (thread 0): 97.6661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_233.29_233.49, WER: 0%, TER: 0%, total WER: 26.415%, total TER: 12.9365%, progress (thread 0): 97.674%]
|T|: h m m
|P|: y e h
[sample: EN2002b_H03_MEE073_259.41_259.61, WER: 100%, TER: 100%, total WER: 26.4159%, total TER: 12.9371%, progress (thread 0): 97.682%]
|T|: y e a h
|P|: t o
[sample: EN2002b_H03_MEE073_498.07_498.27, WER: 100%, TER: 100%, total WER: 26.4167%, total TER: 12.9379%, progress (thread 0): 97.6899%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1591.2_1591.4, WER: 0%, TER: 0%, total WER: 26.4164%, total TER: 12.9378%, progress (thread 0): 97.6978%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H00_FEO070_1723.38_1723.58, WER: 0%, TER: 0%, total WER: 26.4161%, total TER: 12.9377%, progress (thread 0): 97.7057%]
|T|: o k a y
|P|: g o
[sample: EN2002c_H03_MEE073_241.42_241.62, WER: 100%, TER: 100%, total WER: 26.4169%, total TER: 12.9385%, progress (thread 0): 97.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_404.9_405.1, WER: 0%, TER: 0%, total WER: 26.4166%, total TER: 12.9384%, progress (thread 0): 97.7215%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_817.73_817.93, WER: 100%, TER: 33.3333%, total WER: 26.4174%, total TER: 12.9385%, progress (thread 0): 97.7294%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1319.11_1319.31, WER: 0%, TER: 0%, total WER: 26.4171%, total TER: 12.9384%, progress (thread 0): 97.7373%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1392.95_1393.15, WER: 0%, TER: 0%, total WER: 26.4168%, total TER: 12.9383%, progress (thread 0): 97.7453%]
|T|: r i g h t
|P|: f r i r t
[sample: EN2002c_H03_MEE073_1395.54_1395.74, WER: 100%, TER: 60%, total WER: 26.4177%, total TER: 12.9388%, progress (thread 0): 97.7532%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1637.64_1637.84, WER: 0%, TER: 0%, total WER: 26.4174%, total TER: 12.9387%, progress (thread 0): 97.7611%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1911.7_1911.9, WER: 0%, TER: 0%, total WER: 26.4171%, total TER: 12.9385%, progress (thread 0): 97.769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1939.05_1939.25, WER: 0%, TER: 0%, total WER: 26.4168%, total TER: 12.9384%, progress (thread 0): 97.7769%]
|T|: y e a h
|P|: t o
[sample: EN2002d_H03_MEE073_327.41_327.61, WER: 100%, TER: 100%, total WER: 26.4176%, total TER: 12.9392%, progress (thread 0): 97.7848%]
|T|: y e a h
|P|: s h n g
[sample: EN2002d_H03_MEE073_967.12_967.32, WER: 100%, TER: 100%, total WER: 26.4184%, total TER: 12.94%, progress (thread 0): 97.7927%]
|T|: y e p
|P|: y u p
[sample: ES2004a_H03_FEE016_1048.29_1048.48, WER: 100%, TER: 33.3333%, total WER: 26.4193%, total TER: 12.9401%, progress (thread 0): 97.8006%]
|T|: o o p s
|P|:
[sample: ES2004b_H00_MEO015_572.79_572.98, WER: 100%, TER: 100%, total WER: 26.4201%, total TER: 12.941%, progress (thread 0): 97.8085%]
|T|: a l r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1308.81_1309, WER: 100%, TER: 28.5714%, total WER: 26.4209%, total TER: 12.9412%, progress (thread 0): 97.8165%]
|T|: h u h
|P|: h
[sample: ES2004b_H02_MEE014_1454.52_1454.71, WER: 100%, TER: 66.6667%, total WER: 26.4217%, total TER: 12.9416%, progress (thread 0): 97.8244%]
|T|: m m
|P|: y o u | k
[sample: ES2004b_H03_FEE016_1635.41_1635.6, WER: 200%, TER: 250%, total WER: 26.4237%, total TER: 12.9427%, progress (thread 0): 97.8323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1436.81_1437, WER: 0%, TER: 0%, total WER: 26.4234%, total TER: 12.9426%, progress (thread 0): 97.8402%]
|T|: t h i n g
|P|: t h i n g
[sample: ES2004d_H02_MEE014_2209.98_2210.17, WER: 0%, TER: 0%, total WER: 26.4231%, total TER: 12.9424%, progress (thread 0): 97.8481%]
|T|: y e a h
|P|: t h e r
[sample: IS1009a_H02_FIO084_805.49_805.68, WER: 100%, TER: 100%, total WER: 26.4239%, total TER: 12.9432%, progress (thread 0): 97.856%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_690.57_690.76, WER: 0%, TER: 0%, total WER: 26.4236%, total TER: 12.9431%, progress (thread 0): 97.8639%]
|T|: m m h m m
|P|:
[sample: IS1009c_H00_FIE088_808.35_808.54, WER: 100%, TER: 100%, total WER: 26.4244%, total TER: 12.9441%, progress (thread 0): 97.8718%]
|T|: b a t t e r y
|P|: t h a t
[sample: IS1009c_H01_FIO087_1631.84_1632.03, WER: 100%, TER: 85.7143%, total WER: 26.4252%, total TER: 12.9453%, progress (thread 0): 97.8798%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1314.07_1314.26, WER: 0%, TER: 0%, total WER: 26.425%, total TER: 12.9452%, progress (thread 0): 97.8877%]
|T|: ' k a y
|P|: k a n
[sample: TS3003a_H03_MTD012ME_506.74_506.93, WER: 100%, TER: 50%, total WER: 26.4258%, total TER: 12.9455%, progress (thread 0): 97.8956%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1214.55_1214.74, WER: 100%, TER: 50%, total WER: 26.4266%, total TER: 12.9457%, progress (thread 0): 97.9035%]
|T|: ' k a y
|P|: g a y
[sample: TS3003a_H01_MTD011UID_1401.33_1401.52, WER: 100%, TER: 50%, total WER: 26.4274%, total TER: 12.946%, progress (thread 0): 97.9114%]
|T|: y e a h
|P|: n o
[sample: TS3003b_H01_MTD011UID_2147.67_2147.86, WER: 100%, TER: 100%, total WER: 26.4282%, total TER: 12.9468%, progress (thread 0): 97.9193%]
|T|: y e a h
|P|: i
[sample: TS3003c_H01_MTD011UID_1090.92_1091.11, WER: 100%, TER: 100%, total WER: 26.4291%, total TER: 12.9476%, progress (thread 0): 97.9272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1632.95_1633.14, WER: 0%, TER: 0%, total WER: 26.4288%, total TER: 12.9475%, progress (thread 0): 97.9351%]
|T|: y e a h
|P|: a n
[sample: TS3003d_H01_MTD011UID_375.66_375.85, WER: 100%, TER: 75%, total WER: 26.4296%, total TER: 12.9481%, progress (thread 0): 97.943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_507.6_507.79, WER: 0%, TER: 0%, total WER: 26.4293%, total TER: 12.948%, progress (thread 0): 97.951%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_816.71_816.9, WER: 0%, TER: 0%, total WER: 26.429%, total TER: 12.9479%, progress (thread 0): 97.9589%]
|T|: m m
|P|:
[sample: TS3003d_H01_MTD011UID_966.74_966.93, WER: 100%, TER: 100%, total WER: 26.4298%, total TER: 12.9483%, progress (thread 0): 97.9668%]
|T|: m m
|P|: h m
[sample: TS3003d_H00_MTD009PM_1884.46_1884.65, WER: 100%, TER: 50%, total WER: 26.4306%, total TER: 12.9484%, progress (thread 0): 97.9747%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1975.04_1975.23, WER: 100%, TER: 66.6667%, total WER: 26.4315%, total TER: 12.9488%, progress (thread 0): 97.9826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1998.6_1998.79, WER: 0%, TER: 0%, total WER: 26.4312%, total TER: 12.9487%, progress (thread 0): 97.9905%]
|T|: y e p
|P|: y u p
[sample: EN2002a_H00_MEE073_399.03_399.22, WER: 100%, TER: 33.3333%, total WER: 26.432%, total TER: 12.9488%, progress (thread 0): 97.9984%]
|T|: y e a h
|P|: r i g h t
[sample: EN2002a_H03_MEE071_741.39_741.58, WER: 100%, TER: 100%, total WER: 26.4328%, total TER: 12.9496%, progress (thread 0): 98.0063%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1037.67_1037.86, WER: 0%, TER: 0%, total WER: 26.4325%, total TER: 12.9495%, progress (thread 0): 98.0142%]
|T|: w h
|P|:
[sample: EN2002a_H03_MEE071_1305.67_1305.86, WER: 100%, TER: 100%, total WER: 26.4333%, total TER: 12.9499%, progress (thread 0): 98.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1781.11_1781.3, WER: 0%, TER: 0%, total WER: 26.4331%, total TER: 12.9498%, progress (thread 0): 98.0301%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H02_FEO072_1823.51_1823.7, WER: 100%, TER: 50%, total WER: 26.4339%, total TER: 12.9501%, progress (thread 0): 98.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1836.03_1836.22, WER: 0%, TER: 0%, total WER: 26.4336%, total TER: 12.95%, progress (thread 0): 98.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1886.9_1887.09, WER: 0%, TER: 0%, total WER: 26.4333%, total TER: 12.9499%, progress (thread 0): 98.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1917.91_1918.1, WER: 0%, TER: 0%, total WER: 26.433%, total TER: 12.9498%, progress (thread 0): 98.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_142.44_142.63, WER: 0%, TER: 0%, total WER: 26.4327%, total TER: 12.9497%, progress (thread 0): 98.0696%]
|T|: u h h u h
|P|: w
[sample: EN2002c_H03_MEE073_1527.18_1527.37, WER: 100%, TER: 100%, total WER: 26.4335%, total TER: 12.9507%, progress (thread 0): 98.0775%]
|T|: h m m
|P|: y e a
[sample: EN2002c_H03_MEE073_1630_1630.19, WER: 100%, TER: 100%, total WER: 26.4343%, total TER: 12.9513%, progress (thread 0): 98.0854%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2672.39_2672.58, WER: 100%, TER: 33.3333%, total WER: 26.4352%, total TER: 12.9514%, progress (thread 0): 98.0934%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H03_MEE073_2679.33_2679.52, WER: 100%, TER: 80%, total WER: 26.436%, total TER: 12.9522%, progress (thread 0): 98.1013%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_647.1_647.29, WER: 0%, TER: 0%, total WER: 26.4357%, total TER: 12.9521%, progress (thread 0): 98.1092%]
|T|: y e a h
|P|: t h
[sample: EN2002d_H03_MEE073_649.08_649.27, WER: 100%, TER: 75%, total WER: 26.4365%, total TER: 12.9526%, progress (thread 0): 98.1171%]
|T|: o k
|P|: i ' m
[sample: EN2002d_H03_MEE073_909.71_909.9, WER: 100%, TER: 150%, total WER: 26.4373%, total TER: 12.9533%, progress (thread 0): 98.125%]
|T|: s u r e
|P|: t r e
[sample: EN2002d_H03_MEE073_1568.39_1568.58, WER: 100%, TER: 50%, total WER: 26.4382%, total TER: 12.9536%, progress (thread 0): 98.1329%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_1708.21_1708.4, WER: 100%, TER: 66.6667%, total WER: 26.439%, total TER: 12.954%, progress (thread 0): 98.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2037.78_2037.97, WER: 0%, TER: 0%, total WER: 26.4387%, total TER: 12.9539%, progress (thread 0): 98.1487%]
|T|: s o
|P|: s h
[sample: ES2004a_H03_FEE016_605.84_606.02, WER: 100%, TER: 50%, total WER: 26.4395%, total TER: 12.954%, progress (thread 0): 98.1566%]
|T|: ' k a y
|P|:
[sample: ES2004b_H00_MEO015_1027.78_1027.96, WER: 100%, TER: 100%, total WER: 26.4403%, total TER: 12.9549%, progress (thread 0): 98.1646%]
|T|: m m h m m
|P|:
[sample: ES2004c_H03_FEE016_1360.96_1361.14, WER: 100%, TER: 100%, total WER: 26.4412%, total TER: 12.9559%, progress (thread 0): 98.1725%]
|T|: m m
|P|: h m
[sample: ES2004d_H02_MEE014_2221.15_2221.33, WER: 100%, TER: 50%, total WER: 26.442%, total TER: 12.956%, progress (thread 0): 98.1804%]
|T|: m m h m m
|P|: a
[sample: IS1009b_H02_FIO084_361.5_361.68, WER: 100%, TER: 100%, total WER: 26.4428%, total TER: 12.957%, progress (thread 0): 98.1883%]
|T|: u m
|P|: i | m a
[sample: IS1009c_H03_FIO089_1368.34_1368.52, WER: 200%, TER: 150%, total WER: 26.4447%, total TER: 12.9577%, progress (thread 0): 98.1962%]
|T|: a n d
|P|: a n d
[sample: IS1009c_H01_FIO087_1687.91_1688.09, WER: 0%, TER: 0%, total WER: 26.4444%, total TER: 12.9576%, progress (thread 0): 98.2041%]
|T|: m m
|P|: h m
[sample: IS1009d_H00_FIE088_1039.16_1039.34, WER: 100%, TER: 50%, total WER: 26.4453%, total TER: 12.9578%, progress (thread 0): 98.212%]
|T|: y e s
|P|: i t ' s
[sample: IS1009d_H01_FIO087_1532.02_1532.2, WER: 100%, TER: 100%, total WER: 26.4461%, total TER: 12.9584%, progress (thread 0): 98.2199%]
|T|: n o
|P|: y o u | k
[sample: TS3003a_H00_MTD009PM_1017.51_1017.69, WER: 200%, TER: 200%, total WER: 26.448%, total TER: 12.9592%, progress (thread 0): 98.2278%]
|T|: y e a h
|P|: y e a
[sample: TS3003b_H00_MTD009PM_583.19_583.37, WER: 100%, TER: 25%, total WER: 26.4488%, total TER: 12.9594%, progress (thread 0): 98.2358%]
|T|: m m
|P|: h h
[sample: TS3003b_H01_MTD011UID_1188.77_1188.95, WER: 100%, TER: 100%, total WER: 26.4497%, total TER: 12.9598%, progress (thread 0): 98.2437%]
|T|: y e a h
|P|: y o
[sample: TS3003b_H02_MTD0010ID_1801.24_1801.42, WER: 100%, TER: 75%, total WER: 26.4505%, total TER: 12.9603%, progress (thread 0): 98.2516%]
|T|: m m
|P|:
[sample: TS3003b_H01_MTD011UID_1801.82_1802, WER: 100%, TER: 100%, total WER: 26.4513%, total TER: 12.9607%, progress (thread 0): 98.2595%]
|T|: h m m
|P|: r i g h t
[sample: TS3003d_H01_MTD011UID_1131_1131.18, WER: 100%, TER: 166.667%, total WER: 26.4521%, total TER: 12.9618%, progress (thread 0): 98.2674%]
|T|: a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_2394.1_2394.28, WER: 100%, TER: 50%, total WER: 26.453%, total TER: 12.962%, progress (thread 0): 98.2753%]
|T|: n o
|P|: h m
[sample: EN2002a_H00_MEE073_23.11_23.29, WER: 100%, TER: 100%, total WER: 26.4538%, total TER: 12.9624%, progress (thread 0): 98.2832%]
|T|: y e a h
|P|: y o
[sample: EN2002a_H01_FEO070_32.53_32.71, WER: 100%, TER: 75%, total WER: 26.4546%, total TER: 12.963%, progress (thread 0): 98.2911%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_106.69_106.87, WER: 100%, TER: 33.3333%, total WER: 26.4554%, total TER: 12.9631%, progress (thread 0): 98.299%]
|T|: y e a h
|P|: a n
[sample: EN2002a_H00_MEE073_1441.15_1441.33, WER: 100%, TER: 75%, total WER: 26.4563%, total TER: 12.9637%, progress (thread 0): 98.307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1717.2_1717.38, WER: 0%, TER: 0%, total WER: 26.456%, total TER: 12.9636%, progress (thread 0): 98.3149%]
|T|: o k a y
|P|:
[sample: EN2002b_H03_MEE073_530.52_530.7, WER: 100%, TER: 100%, total WER: 26.4568%, total TER: 12.9644%, progress (thread 0): 98.3228%]
|T|: i s | i t
|P|: i s
[sample: EN2002b_H01_MEE071_1142.07_1142.25, WER: 50%, TER: 60%, total WER: 26.4573%, total TER: 12.9649%, progress (thread 0): 98.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1339.32_1339.5, WER: 0%, TER: 0%, total WER: 26.457%, total TER: 12.9648%, progress (thread 0): 98.3386%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_186.7_186.88, WER: 0%, TER: 0%, total WER: 26.4567%, total TER: 12.9647%, progress (thread 0): 98.3465%]
|T|: a l r i g h t
|P|: r
[sample: EN2002c_H03_MEE073_536.86_537.04, WER: 100%, TER: 85.7143%, total WER: 26.4575%, total TER: 12.9658%, progress (thread 0): 98.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1667.09_1667.27, WER: 0%, TER: 0%, total WER: 26.4572%, total TER: 12.9657%, progress (thread 0): 98.3623%]
|T|: h m m
|P|:
[sample: EN2002d_H01_FEO072_363.49_363.67, WER: 100%, TER: 100%, total WER: 26.4581%, total TER: 12.9663%, progress (thread 0): 98.3703%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_500.44_500.62, WER: 0%, TER: 0%, total WER: 26.4578%, total TER: 12.9662%, progress (thread 0): 98.3782%]
|T|: w h a t
|P|: w h i t
[sample: EN2002d_H00_FEO070_508.48_508.66, WER: 100%, TER: 25%, total WER: 26.4586%, total TER: 12.9663%, progress (thread 0): 98.3861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_512.59_512.77, WER: 0%, TER: 0%, total WER: 26.4583%, total TER: 12.9662%, progress (thread 0): 98.394%]
|T|: y e a h
|P|: i t s
[sample: EN2002d_H02_MEE071_518.66_518.84, WER: 100%, TER: 100%, total WER: 26.4591%, total TER: 12.967%, progress (thread 0): 98.4019%]
|T|: y e a h
|P|: h u h
[sample: EN2002d_H00_FEO070_882.18_882.36, WER: 100%, TER: 75%, total WER: 26.4599%, total TER: 12.9676%, progress (thread 0): 98.4098%]
|T|: m m
|P|:
[sample: EN2002d_H02_MEE071_1451.31_1451.49, WER: 100%, TER: 100%, total WER: 26.4608%, total TER: 12.968%, progress (thread 0): 98.4177%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1581.55_1581.73, WER: 0%, TER: 0%, total WER: 26.4605%, total TER: 12.9678%, progress (thread 0): 98.4256%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_2062.62_2062.8, WER: 0%, TER: 0%, total WER: 26.4602%, total TER: 12.9677%, progress (thread 0): 98.4335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2166.41_2166.59, WER: 0%, TER: 0%, total WER: 26.4599%, total TER: 12.9676%, progress (thread 0): 98.4415%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_2172.96_2173.14, WER: 0%, TER: 0%, total WER: 26.4596%, total TER: 12.9674%, progress (thread 0): 98.4494%]
|T|: a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_843.75_843.92, WER: 100%, TER: 100%, total WER: 26.4604%, total TER: 12.9678%, progress (thread 0): 98.4573%]
|T|: y e p
|P|: y e a h
[sample: ES2004c_H00_MEO015_2220.58_2220.75, WER: 100%, TER: 66.6667%, total WER: 26.4612%, total TER: 12.9682%, progress (thread 0): 98.4652%]
|T|: y e a h
|P|:
[sample: IS1009b_H02_FIO084_381.27_381.44, WER: 100%, TER: 100%, total WER: 26.462%, total TER: 12.969%, progress (thread 0): 98.4731%]
|T|: h i
|P|: i n
[sample: IS1009c_H02_FIO084_43.25_43.42, WER: 100%, TER: 100%, total WER: 26.4629%, total TER: 12.9694%, progress (thread 0): 98.481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_347.27_347.44, WER: 0%, TER: 0%, total WER: 26.4626%, total TER: 12.9693%, progress (thread 0): 98.4889%]
|T|: m m
|P|: h
[sample: IS1009d_H02_FIO084_784.48_784.65, WER: 100%, TER: 100%, total WER: 26.4634%, total TER: 12.9697%, progress (thread 0): 98.4968%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_182.69_182.86, WER: 100%, TER: 66.6667%, total WER: 26.4642%, total TER: 12.9701%, progress (thread 0): 98.5047%]
|T|: h m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_748.2_748.37, WER: 100%, TER: 33.3333%, total WER: 26.465%, total TER: 12.9702%, progress (thread 0): 98.5127%]
|T|: y e a h
|P|: a n d
[sample: TS3003a_H01_MTD011UID_1432.6_1432.77, WER: 100%, TER: 100%, total WER: 26.4659%, total TER: 12.971%, progress (thread 0): 98.5206%]
|T|: n o
|P|: t h e t
[sample: TS3003a_H00_MTD009PM_1442.45_1442.62, WER: 100%, TER: 200%, total WER: 26.4667%, total TER: 12.9719%, progress (thread 0): 98.5285%]
|T|: y e a h
|P|:
[sample: TS3003b_H01_MTD011UID_1584.22_1584.39, WER: 100%, TER: 100%, total WER: 26.4675%, total TER: 12.9727%, progress (thread 0): 98.5364%]
|T|: n o
|P|: u h
[sample: TS3003b_H01_MTD011UID_1779.4_1779.57, WER: 100%, TER: 100%, total WER: 26.4683%, total TER: 12.9731%, progress (thread 0): 98.5443%]
|T|: y e a h
|P|: o p
[sample: TS3003b_H03_MTD012ME_2009.37_2009.54, WER: 100%, TER: 100%, total WER: 26.4691%, total TER: 12.9739%, progress (thread 0): 98.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1183.13_1183.3, WER: 0%, TER: 0%, total WER: 26.4688%, total TER: 12.9738%, progress (thread 0): 98.5601%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2272.21_2272.38, WER: 0%, TER: 0%, total WER: 26.4686%, total TER: 12.9737%, progress (thread 0): 98.568%]
|T|: y e a h
|P|: e a h
[sample: TS3003d_H01_MTD011UID_306.52_306.69, WER: 100%, TER: 25%, total WER: 26.4694%, total TER: 12.9738%, progress (thread 0): 98.576%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_1283.14_1283.31, WER: 100%, TER: 100%, total WER: 26.4702%, total TER: 12.9746%, progress (thread 0): 98.5839%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_549.83_550, WER: 100%, TER: 100%, total WER: 26.471%, total TER: 12.9752%, progress (thread 0): 98.5918%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_1153.54_1153.71, WER: 100%, TER: 100%, total WER: 26.4718%, total TER: 12.9758%, progress (thread 0): 98.5997%]
|T|: y e a h
|P|:
[sample: EN2002a_H00_MEE073_1180.31_1180.48, WER: 100%, TER: 100%, total WER: 26.4727%, total TER: 12.9766%, progress (thread 0): 98.6076%]
|T|: h m m
|P|: h m
[sample: EN2002a_H02_FEO072_2053.69_2053.86, WER: 100%, TER: 33.3333%, total WER: 26.4735%, total TER: 12.9768%, progress (thread 0): 98.6155%]
|T|: y e a h
|P|: j u s
[sample: EN2002a_H01_FEO070_2086.54_2086.71, WER: 100%, TER: 100%, total WER: 26.4743%, total TER: 12.9776%, progress (thread 0): 98.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_423.51_423.68, WER: 0%, TER: 0%, total WER: 26.474%, total TER: 12.9774%, progress (thread 0): 98.6313%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1344.27_1344.44, WER: 100%, TER: 100%, total WER: 26.4748%, total TER: 12.9783%, progress (thread 0): 98.6392%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1400.92_1401.09, WER: 0%, TER: 0%, total WER: 26.4745%, total TER: 12.9781%, progress (thread 0): 98.6472%]
|T|: y e a h
|P|: e s t
[sample: EN2002b_H01_MEE071_1518.9_1519.07, WER: 100%, TER: 75%, total WER: 26.4754%, total TER: 12.9787%, progress (thread 0): 98.6551%]
|T|: y e a h
|P|: i n
[sample: EN2002c_H03_MEE073_55.6_55.77, WER: 100%, TER: 100%, total WER: 26.4762%, total TER: 12.9795%, progress (thread 0): 98.663%]
|T|: y e a h
|P|:
[sample: EN2002c_H03_MEE073_711.52_711.69, WER: 100%, TER: 100%, total WER: 26.477%, total TER: 12.9803%, progress (thread 0): 98.6709%]
|T|: b u t
|P|: t a e
[sample: EN2002d_H02_MEE071_431.16_431.33, WER: 100%, TER: 100%, total WER: 26.4778%, total TER: 12.9809%, progress (thread 0): 98.6788%]
|T|: y e a h
|P|: i s
[sample: EN2002d_H02_MEE071_649.62_649.79, WER: 100%, TER: 100%, total WER: 26.4786%, total TER: 12.9817%, progress (thread 0): 98.6867%]
|T|: y e a h
|P|: h
[sample: EN2002d_H03_MEE073_831.51_831.68, WER: 100%, TER: 75%, total WER: 26.4795%, total TER: 12.9823%, progress (thread 0): 98.6946%]
|T|: s o
|P|:
[sample: EN2002d_H03_MEE073_1058.82_1058.99, WER: 100%, TER: 100%, total WER: 26.4803%, total TER: 12.9827%, progress (thread 0): 98.7025%]
|T|: o h
|P|: i
[sample: EN2002d_H00_FEO070_1459.48_1459.65, WER: 100%, TER: 100%, total WER: 26.4811%, total TER: 12.9831%, progress (thread 0): 98.7104%]
|T|: r i g h t
|P|: r i h t
[sample: EN2002d_H03_MEE073_1858.1_1858.27, WER: 100%, TER: 20%, total WER: 26.4819%, total TER: 12.9832%, progress (thread 0): 98.7184%]
|T|: i | m e a n
|P|: i | m n
[sample: ES2004c_H03_FEE016_691.32_691.48, WER: 50%, TER: 33.3333%, total WER: 26.4825%, total TER: 12.9835%, progress (thread 0): 98.7263%]
|T|: o k a y
|P|:
[sample: ES2004c_H03_FEE016_1560.36_1560.52, WER: 100%, TER: 100%, total WER: 26.4833%, total TER: 12.9843%, progress (thread 0): 98.7342%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H00_FIE088_757.84_758, WER: 0%, TER: 0%, total WER: 26.483%, total TER: 12.9842%, progress (thread 0): 98.7421%]
|T|: n o
|P|: n o
[sample: IS1009d_H01_FIO087_584.12_584.28, WER: 0%, TER: 0%, total WER: 26.4827%, total TER: 12.9841%, progress (thread 0): 98.75%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_740.48_740.64, WER: 0%, TER: 0%, total WER: 26.4824%, total TER: 12.984%, progress (thread 0): 98.7579%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_826.94_827.1, WER: 0%, TER: 0%, total WER: 26.4821%, total TER: 12.9839%, progress (thread 0): 98.7658%]
|T|: n o
|P|: n o
[sample: IS1009d_H03_FIO089_862.59_862.75, WER: 0%, TER: 0%, total WER: 26.4818%, total TER: 12.9838%, progress (thread 0): 98.7737%]
|T|: y e a h
|P|: y e a
[sample: IS1009d_H02_FIO084_1025.48_1025.64, WER: 100%, TER: 25%, total WER: 26.4826%, total TER: 12.9839%, progress (thread 0): 98.7816%]
|T|: y e a h
|P|: a n
[sample: TS3003a_H01_MTD011UID_580.47_580.63, WER: 100%, TER: 75%, total WER: 26.4834%, total TER: 12.9845%, progress (thread 0): 98.7896%]
|T|: u h
|P|: r i h
[sample: TS3003a_H00_MTD009PM_617.55_617.71, WER: 100%, TER: 100%, total WER: 26.4843%, total TER: 12.9849%, progress (thread 0): 98.7975%]
|T|: u h
|P|: e a h
[sample: TS3003a_H00_MTD009PM_1421.57_1421.73, WER: 100%, TER: 100%, total WER: 26.4851%, total TER: 12.9853%, progress (thread 0): 98.8054%]
|T|: h m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1789.09_1789.25, WER: 100%, TER: 33.3333%, total WER: 26.4859%, total TER: 12.9855%, progress (thread 0): 98.8133%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2009.54_2009.7, WER: 0%, TER: 0%, total WER: 26.4856%, total TER: 12.9853%, progress (thread 0): 98.8212%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1865.26_1865.42, WER: 100%, TER: 66.6667%, total WER: 26.4864%, total TER: 12.9857%, progress (thread 0): 98.8291%]
|T|: o h
|P|: t o
[sample: TS3003d_H00_MTD009PM_2587.23_2587.39, WER: 100%, TER: 100%, total WER: 26.4872%, total TER: 12.9861%, progress (thread 0): 98.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_376.27_376.43, WER: 0%, TER: 0%, total WER: 26.487%, total TER: 12.986%, progress (thread 0): 98.8449%]
|T|: y e a h
|P|: s i n e
[sample: EN2002a_H00_MEE073_720.58_720.74, WER: 100%, TER: 100%, total WER: 26.4878%, total TER: 12.9868%, progress (thread 0): 98.8529%]
|T|: y e a h
|P|: b u t
[sample: EN2002a_H00_MEE073_737.85_738.01, WER: 100%, TER: 100%, total WER: 26.4886%, total TER: 12.9876%, progress (thread 0): 98.8608%]
|T|: y e a h
|P|: y o
[sample: EN2002a_H00_MEE073_844.91_845.07, WER: 100%, TER: 75%, total WER: 26.4894%, total TER: 12.9882%, progress (thread 0): 98.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1041.33_1041.49, WER: 0%, TER: 0%, total WER: 26.4891%, total TER: 12.9881%, progress (thread 0): 98.8766%]
|T|: y e a h
|P|: b u t
[sample: EN2002a_H00_MEE073_1050.03_1050.19, WER: 100%, TER: 100%, total WER: 26.4899%, total TER: 12.9889%, progress (thread 0): 98.8845%]
|T|: w h y
|P|: w
[sample: EN2002a_H00_MEE073_1237.91_1238.07, WER: 100%, TER: 66.6667%, total WER: 26.4908%, total TER: 12.9893%, progress (thread 0): 98.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1596.11_1596.27, WER: 0%, TER: 0%, total WER: 26.4905%, total TER: 12.9891%, progress (thread 0): 98.9003%]
|T|: s o r r y
|P|: t u e
[sample: EN2002a_H00_MEE073_1813.61_1813.77, WER: 100%, TER: 100%, total WER: 26.4913%, total TER: 12.9901%, progress (thread 0): 98.9082%]
|T|: s u r e
|P|:
[sample: EN2002a_H00_MEE073_1881.96_1882.12, WER: 100%, TER: 100%, total WER: 26.4921%, total TER: 12.991%, progress (thread 0): 98.9161%]
|T|: y e a h
|P|: y o u
[sample: EN2002b_H02_FEO072_39.76_39.92, WER: 100%, TER: 75%, total WER: 26.4929%, total TER: 12.9915%, progress (thread 0): 98.924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_317.94_318.1, WER: 0%, TER: 0%, total WER: 26.4926%, total TER: 12.9914%, progress (thread 0): 98.932%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1075.72_1075.88, WER: 0%, TER: 0%, total WER: 26.4923%, total TER: 12.9913%, progress (thread 0): 98.9399%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1155.04_1155.2, WER: 100%, TER: 100%, total WER: 26.4932%, total TER: 12.9921%, progress (thread 0): 98.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_713.06_713.22, WER: 0%, TER: 0%, total WER: 26.4929%, total TER: 12.9919%, progress (thread 0): 98.9557%]
|T|: o h
|P|:
[sample: EN2002c_H03_MEE073_1210.59_1210.75, WER: 100%, TER: 100%, total WER: 26.4937%, total TER: 12.9924%, progress (thread 0): 98.9636%]
|T|: r i g h t
|P|: o r
[sample: EN2002c_H03_MEE073_1484.69_1484.85, WER: 100%, TER: 100%, total WER: 26.4945%, total TER: 12.9934%, progress (thread 0): 98.9715%]
|T|: y e a h
|P|: e
[sample: EN2002c_H03_MEE073_2554.98_2555.14, WER: 100%, TER: 75%, total WER: 26.4953%, total TER: 12.9939%, progress (thread 0): 98.9794%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2776.86_2777.02, WER: 100%, TER: 33.3333%, total WER: 26.4961%, total TER: 12.9941%, progress (thread 0): 98.9873%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_645.29_645.45, WER: 100%, TER: 100%, total WER: 26.497%, total TER: 12.9945%, progress (thread 0): 98.9952%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1397.11_1397.27, WER: 0%, TER: 0%, total WER: 26.4967%, total TER: 12.9944%, progress (thread 0): 99.0032%]
|T|: o o p s
|P|:
[sample: ES2004a_H00_MEO015_188.28_188.43, WER: 100%, TER: 100%, total WER: 26.4975%, total TER: 12.9952%, progress (thread 0): 99.0111%]
|T|: o o h
|P|: i t
[sample: ES2004b_H02_MEE014_534.45_534.6, WER: 100%, TER: 100%, total WER: 26.4983%, total TER: 12.9958%, progress (thread 0): 99.019%]
|T|: ' k a y
|P|:
[sample: ES2004c_H01_FEE013_472.18_472.33, WER: 100%, TER: 100%, total WER: 26.4991%, total TER: 12.9966%, progress (thread 0): 99.0269%]
|T|: y e s
|P|:
[sample: IS1009a_H01_FIO087_693.82_693.97, WER: 100%, TER: 100%, total WER: 26.5%, total TER: 12.9972%, progress (thread 0): 99.0348%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1647.59_1647.74, WER: 0%, TER: 0%, total WER: 26.4997%, total TER: 12.9971%, progress (thread 0): 99.0427%]
|T|: y e a h
|P|: s a m e
[sample: IS1009d_H02_FIO084_1392_1392.15, WER: 100%, TER: 100%, total WER: 26.5005%, total TER: 12.9979%, progress (thread 0): 99.0506%]
|T|: t w o
|P|: d o
[sample: IS1009d_H01_FIO087_1586.28_1586.43, WER: 100%, TER: 66.6667%, total WER: 26.5013%, total TER: 12.9983%, progress (thread 0): 99.0585%]
|T|: n o
|P|:
[sample: IS1009d_H01_FIO087_1824.98_1825.13, WER: 100%, TER: 100%, total WER: 26.5021%, total TER: 12.9987%, progress (thread 0): 99.0665%]
|T|: y e a h
|P|: n
[sample: IS1009d_H02_FIO084_1883.61_1883.76, WER: 100%, TER: 100%, total WER: 26.5029%, total TER: 12.9995%, progress (thread 0): 99.0744%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_567.51_567.66, WER: 0%, TER: 0%, total WER: 26.5026%, total TER: 12.9993%, progress (thread 0): 99.0823%]
|T|: y e a h
|P|:
[sample: TS3003c_H02_MTD0010ID_2049.54_2049.69, WER: 100%, TER: 100%, total WER: 26.5035%, total TER: 13.0002%, progress (thread 0): 99.0902%]
|T|: m m
|P|: m
[sample: TS3003d_H01_MTD011UID_2041.85_2042, WER: 100%, TER: 50%, total WER: 26.5043%, total TER: 13.0003%, progress (thread 0): 99.0981%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_639.89_640.04, WER: 100%, TER: 66.6667%, total WER: 26.5051%, total TER: 13.0007%, progress (thread 0): 99.106%]
|T|: y e a h
|P|: a n
[sample: EN2002a_H00_MEE073_967.58_967.73, WER: 100%, TER: 75%, total WER: 26.5059%, total TER: 13.0013%, progress (thread 0): 99.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1030.96_1031.11, WER: 0%, TER: 0%, total WER: 26.5056%, total TER: 13.0012%, progress (thread 0): 99.1218%]
|T|: y e a h
|P|: h o
[sample: EN2002a_H01_FEO070_1104.83_1104.98, WER: 100%, TER: 100%, total WER: 26.5065%, total TER: 13.002%, progress (thread 0): 99.1297%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1527.69_1527.84, WER: 100%, TER: 33.3333%, total WER: 26.5073%, total TER: 13.0021%, progress (thread 0): 99.1377%]
|T|: y e a h
|P|:
[sample: EN2002a_H00_MEE073_1539.77_1539.92, WER: 100%, TER: 100%, total WER: 26.5081%, total TER: 13.0029%, progress (thread 0): 99.1456%]
|T|: y e a h
|P|: r e
[sample: EN2002a_H00_MEE073_1744.01_1744.16, WER: 100%, TER: 75%, total WER: 26.5089%, total TER: 13.0035%, progress (thread 0): 99.1535%]
|T|: y e a h
|P|: y o a
[sample: EN2002a_H01_FEO070_1785.21_1785.36, WER: 100%, TER: 50%, total WER: 26.5097%, total TER: 13.0038%, progress (thread 0): 99.1614%]
|T|: y e p
|P|: y e p
[sample: EN2002b_H03_MEE073_382.32_382.47, WER: 0%, TER: 0%, total WER: 26.5094%, total TER: 13.0037%, progress (thread 0): 99.1693%]
|T|: u h
|P|: y e a h
[sample: EN2002b_H02_FEO072_424.35_424.5, WER: 100%, TER: 150%, total WER: 26.5103%, total TER: 13.0044%, progress (thread 0): 99.1772%]
|T|: y e a h
|P|:
[sample: EN2002b_H00_FEO070_496.03_496.18, WER: 100%, TER: 100%, total WER: 26.5111%, total TER: 13.0052%, progress (thread 0): 99.1851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_897.7_897.85, WER: 0%, TER: 0%, total WER: 26.5108%, total TER: 13.0051%, progress (thread 0): 99.193%]
|T|: y e a h
|P|: t h
[sample: EN2002c_H03_MEE073_2198.65_2198.8, WER: 100%, TER: 75%, total WER: 26.5116%, total TER: 13.0056%, progress (thread 0): 99.201%]
|T|: y e a h
|P|: i t
[sample: EN2002d_H00_FEO070_1290.13_1290.28, WER: 100%, TER: 100%, total WER: 26.5124%, total TER: 13.0065%, progress (thread 0): 99.2089%]
|T|: n o
|P|:
[sample: EN2002d_H03_MEE073_1424.03_1424.18, WER: 100%, TER: 100%, total WER: 26.5132%, total TER: 13.0069%, progress (thread 0): 99.2168%]
|T|: r i g h t
|P|:
[sample: EN2002d_H03_MEE073_1909.61_1909.76, WER: 100%, TER: 100%, total WER: 26.5141%, total TER: 13.0079%, progress (thread 0): 99.2247%]
|T|: o k a y
|P|:
[sample: EN2002d_H03_MEE073_1985.35_1985.5, WER: 100%, TER: 100%, total WER: 26.5149%, total TER: 13.0087%, progress (thread 0): 99.2326%]
|T|: i | t h
|P|: i
[sample: ES2004b_H01_FEE013_2305.5_2305.64, WER: 50%, TER: 75%, total WER: 26.5154%, total TER: 13.0093%, progress (thread 0): 99.2405%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_777.13_777.27, WER: 100%, TER: 100%, total WER: 26.5162%, total TER: 13.0097%, progress (thread 0): 99.2484%]
|T|: y e a h
|P|:
[sample: IS1009a_H02_FIO084_56.9_57.04, WER: 100%, TER: 100%, total WER: 26.5171%, total TER: 13.0105%, progress (thread 0): 99.2563%]
|T|: y e s
|P|: y e a
[sample: IS1009d_H01_FIO087_645.27_645.41, WER: 100%, TER: 33.3333%, total WER: 26.5179%, total TER: 13.0106%, progress (thread 0): 99.2642%]
|T|: m m h m m
|P|:
[sample: IS1009d_H00_FIE088_770.97_771.11, WER: 100%, TER: 100%, total WER: 26.5187%, total TER: 13.0116%, progress (thread 0): 99.2721%]
|T|: h m m
|P|:
[sample: TS3003a_H01_MTD011UID_189.69_189.83, WER: 100%, TER: 100%, total WER: 26.5195%, total TER: 13.0122%, progress (thread 0): 99.2801%]
|T|: m m
|P|:
[sample: TS3003a_H01_MTD011UID_903.08_903.22, WER: 100%, TER: 100%, total WER: 26.5203%, total TER: 13.0126%, progress (thread 0): 99.288%]
|T|: m m
|P|:
[sample: TS3003b_H01_MTD011UID_2047.35_2047.49, WER: 100%, TER: 100%, total WER: 26.5212%, total TER: 13.013%, progress (thread 0): 99.2959%]
|T|: ' k a y
|P|:
[sample: TS3003b_H03_MTD012ME_2140.48_2140.62, WER: 100%, TER: 100%, total WER: 26.522%, total TER: 13.0138%, progress (thread 0): 99.3038%]
|T|: y e a h
|P|:
[sample: TS3003c_H03_MTD012ME_1869.23_1869.37, WER: 100%, TER: 100%, total WER: 26.5228%, total TER: 13.0147%, progress (thread 0): 99.3117%]
|T|: y e a h
|P|:
[sample: TS3003d_H00_MTD009PM_1390.61_1390.75, WER: 100%, TER: 100%, total WER: 26.5236%, total TER: 13.0155%, progress (thread 0): 99.3196%]
|T|: y e a h
|P|:
[sample: TS3003d_H00_MTD009PM_2021.71_2021.85, WER: 100%, TER: 100%, total WER: 26.5244%, total TER: 13.0163%, progress (thread 0): 99.3275%]
|T|: r i g h t
|P|: i k
[sample: EN2002a_H00_MEE073_1184.15_1184.29, WER: 100%, TER: 80%, total WER: 26.5253%, total TER: 13.017%, progress (thread 0): 99.3354%]
|T|: h m m
|P|: i h m
[sample: EN2002a_H00_MEE073_1193.94_1194.08, WER: 100%, TER: 66.6667%, total WER: 26.5261%, total TER: 13.0174%, progress (thread 0): 99.3434%]
|T|: y e p
|P|: y e h
[sample: EN2002a_H00_MEE073_2048.07_2048.21, WER: 100%, TER: 33.3333%, total WER: 26.5269%, total TER: 13.0176%, progress (thread 0): 99.3513%]
|T|: ' k a y
|P|:
[sample: EN2002c_H03_MEE073_103.84_103.98, WER: 100%, TER: 100%, total WER: 26.5277%, total TER: 13.0184%, progress (thread 0): 99.3592%]
|T|: y e a h
|P|: g e
[sample: EN2002c_H02_MEE071_531.32_531.46, WER: 100%, TER: 75%, total WER: 26.5285%, total TER: 13.0189%, progress (thread 0): 99.3671%]
|T|: y e a h
|P|: s o
[sample: EN2002c_H03_MEE073_667.22_667.36, WER: 100%, TER: 100%, total WER: 26.5294%, total TER: 13.0198%, progress (thread 0): 99.375%]
|T|: y e a h
|P|: y e
[sample: EN2002c_H02_MEE071_1476.08_1476.22, WER: 100%, TER: 50%, total WER: 26.5302%, total TER: 13.0201%, progress (thread 0): 99.3829%]
|T|: m m
|P|:
[sample: EN2002c_H03_MEE073_2096.91_2097.05, WER: 100%, TER: 100%, total WER: 26.531%, total TER: 13.0205%, progress (thread 0): 99.3908%]
|T|: o k a y
|P|: e
[sample: EN2002d_H00_FEO070_1251.9_1252.04, WER: 100%, TER: 100%, total WER: 26.5318%, total TER: 13.0213%, progress (thread 0): 99.3987%]
|T|: m m
|P|: y e a
[sample: ES2004c_H03_FEE016_98.28_98.41, WER: 100%, TER: 150%, total WER: 26.5326%, total TER: 13.0219%, progress (thread 0): 99.4066%]
|T|: s
|P|:
[sample: ES2004d_H03_FEE016_1370.74_1370.87, WER: 100%, TER: 100%, total WER: 26.5335%, total TER: 13.0221%, progress (thread 0): 99.4146%]
|T|: o r | a | b
|P|:
[sample: IS1009a_H01_FIO087_573.12_573.25, WER: 100%, TER: 100%, total WER: 26.5359%, total TER: 13.0234%, progress (thread 0): 99.4225%]
|T|: h m m
|P|: y e
[sample: IS1009c_H02_FIO084_144.08_144.21, WER: 100%, TER: 100%, total WER: 26.5367%, total TER: 13.024%, progress (thread 0): 99.4304%]
|T|: o k a y
|P|: j u s
[sample: IS1009d_H00_FIE088_604.16_604.29, WER: 100%, TER: 100%, total WER: 26.5376%, total TER: 13.0248%, progress (thread 0): 99.4383%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_942.55_942.68, WER: 100%, TER: 100%, total WER: 26.5384%, total TER: 13.0254%, progress (thread 0): 99.4462%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_392.63_392.76, WER: 100%, TER: 66.6667%, total WER: 26.5392%, total TER: 13.0258%, progress (thread 0): 99.4541%]
|T|: h u h
|P|:
[sample: TS3003a_H01_MTD011UID_1266.08_1266.21, WER: 100%, TER: 100%, total WER: 26.54%, total TER: 13.0264%, progress (thread 0): 99.462%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1335.11_1335.24, WER: 100%, TER: 100%, total WER: 26.5408%, total TER: 13.0268%, progress (thread 0): 99.4699%]
|T|: y e p
|P|: y h a
[sample: TS3003a_H01_MTD011UID_1475.42_1475.55, WER: 100%, TER: 66.6667%, total WER: 26.5417%, total TER: 13.0271%, progress (thread 0): 99.4778%]
|T|: y e a h
|P|:
[sample: TS3003b_H01_MTD011UID_1528.52_1528.65, WER: 100%, TER: 100%, total WER: 26.5425%, total TER: 13.0279%, progress (thread 0): 99.4858%]
|T|: m m
|P|:
[sample: TS3003c_H01_MTD011UID_1173.23_1173.36, WER: 100%, TER: 100%, total WER: 26.5433%, total TER: 13.0284%, progress (thread 0): 99.4937%]
|T|: y e a h
|P|: t h
[sample: TS3003d_H00_MTD009PM_1693.45_1693.58, WER: 100%, TER: 75%, total WER: 26.5441%, total TER: 13.0289%, progress (thread 0): 99.5016%]
|T|: ' k a y
|P|:
[sample: EN2002a_H00_MEE073_32.58_32.71, WER: 100%, TER: 100%, total WER: 26.5449%, total TER: 13.0297%, progress (thread 0): 99.5095%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_984.87_985, WER: 100%, TER: 100%, total WER: 26.5458%, total TER: 13.0303%, progress (thread 0): 99.5174%]
|T|: w e l l | f
|P|: b u h
[sample: EN2002a_H00_MEE073_1458.73_1458.86, WER: 100%, TER: 100%, total WER: 26.5474%, total TER: 13.0316%, progress (thread 0): 99.5253%]
|T|: y e p
|P|: y e a
[sample: EN2002b_H01_MEE071_493.19_493.32, WER: 100%, TER: 33.3333%, total WER: 26.5482%, total TER: 13.0317%, progress (thread 0): 99.5332%]
|T|: y e a h
|P|:
[sample: EN2002c_H03_MEE073_665.01_665.14, WER: 100%, TER: 100%, total WER: 26.549%, total TER: 13.0325%, progress (thread 0): 99.5411%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_135.16_135.29, WER: 100%, TER: 100%, total WER: 26.5499%, total TER: 13.0329%, progress (thread 0): 99.549%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H01_FEO072_726.53_726.66, WER: 100%, TER: 25%, total WER: 26.5507%, total TER: 13.033%, progress (thread 0): 99.557%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H01_FEO072_794.79_794.92, WER: 100%, TER: 25%, total WER: 26.5515%, total TER: 13.0331%, progress (thread 0): 99.5649%]
|T|: y e a h
|P|: g o
[sample: EN2002d_H00_FEO070_1595.77_1595.9, WER: 100%, TER: 100%, total WER: 26.5523%, total TER: 13.0339%, progress (thread 0): 99.5728%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_1641.64_1641.77, WER: 100%, TER: 100%, total WER: 26.5531%, total TER: 13.0343%, progress (thread 0): 99.5807%]
|T|: y e a h
|P|:
[sample: ES2004b_H00_MEO015_922.62_922.74, WER: 100%, TER: 100%, total WER: 26.554%, total TER: 13.0351%, progress (thread 0): 99.5886%]
|T|: o k a y
|P|: j u
[sample: ES2004b_H00_MEO015_1977.87_1977.99, WER: 100%, TER: 100%, total WER: 26.5548%, total TER: 13.036%, progress (thread 0): 99.5965%]
|T|: y e a h
|P|: i
[sample: IS1009b_H02_FIO084_173.86_173.98, WER: 100%, TER: 100%, total WER: 26.5556%, total TER: 13.0368%, progress (thread 0): 99.6044%]
|T|: h m m
|P|:
[sample: IS1009c_H02_FIO084_1703.14_1703.26, WER: 100%, TER: 100%, total WER: 26.5564%, total TER: 13.0374%, progress (thread 0): 99.6123%]
|T|: m m | m m
|P|:
[sample: IS1009d_H03_FIO089_870.76_870.88, WER: 100%, TER: 100%, total WER: 26.5581%, total TER: 13.0384%, progress (thread 0): 99.6203%]
|T|: y e p
|P|: i f
[sample: TS3003a_H01_MTD011UID_257.2_257.32, WER: 100%, TER: 100%, total WER: 26.5589%, total TER: 13.039%, progress (thread 0): 99.6282%]
|T|: y e a h
|P|: j u s
[sample: EN2002a_H03_MEE071_170.81_170.93, WER: 100%, TER: 100%, total WER: 26.5597%, total TER: 13.0398%, progress (thread 0): 99.6361%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1468.26_1468.38, WER: 100%, TER: 33.3333%, total WER: 26.5605%, total TER: 13.0399%, progress (thread 0): 99.644%]
|T|: n o
|P|: n h
[sample: EN2002b_H03_MEE073_4.83_4.95, WER: 100%, TER: 50%, total WER: 26.5613%, total TER: 13.0401%, progress (thread 0): 99.6519%]
|T|: a l t e r
|P|: u
[sample: EN2002b_H03_MEE073_170.46_170.58, WER: 100%, TER: 100%, total WER: 26.5622%, total TER: 13.0411%, progress (thread 0): 99.6598%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1641.92_1642.04, WER: 100%, TER: 100%, total WER: 26.563%, total TER: 13.0419%, progress (thread 0): 99.6677%]
|T|: y e a h
|P|: y e
[sample: EN2002d_H03_MEE073_806.78_806.9, WER: 100%, TER: 50%, total WER: 26.5638%, total TER: 13.0423%, progress (thread 0): 99.6756%]
|T|: o k a y
|P|:
[sample: ES2004a_H00_MEO015_71.95_72.06, WER: 100%, TER: 100%, total WER: 26.5646%, total TER: 13.0431%, progress (thread 0): 99.6835%]
|T|: o
|P|:
[sample: ES2004a_H03_FEE016_403.24_403.35, WER: 100%, TER: 100%, total WER: 26.5654%, total TER: 13.0433%, progress (thread 0): 99.6915%]
|T|: ' k a y
|P|:
[sample: ES2004a_H03_FEE016_1042.19_1042.3, WER: 100%, TER: 100%, total WER: 26.5662%, total TER: 13.0441%, progress (thread 0): 99.6994%]
|T|: m m
|P|:
[sample: IS1009c_H02_FIO084_620.84_620.95, WER: 100%, TER: 100%, total WER: 26.5671%, total TER: 13.0445%, progress (thread 0): 99.7073%]
|T|: y e a h
|P|:
[sample: TS3003a_H01_MTD011UID_436.27_436.38, WER: 100%, TER: 100%, total WER: 26.5679%, total TER: 13.0453%, progress (thread 0): 99.7152%]
|T|: y e a h
|P|: o
[sample: EN2002a_H00_MEE073_669.47_669.58, WER: 100%, TER: 100%, total WER: 26.5687%, total TER: 13.0461%, progress (thread 0): 99.7231%]
|T|: y e a h
|P|:
[sample: EN2002a_H01_FEO070_1169.61_1169.72, WER: 100%, TER: 100%, total WER: 26.5695%, total TER: 13.0469%, progress (thread 0): 99.731%]
|T|: n o
|P|:
[sample: EN2002b_H02_FEO072_4.48_4.59, WER: 100%, TER: 100%, total WER: 26.5703%, total TER: 13.0473%, progress (thread 0): 99.7389%]
|T|: y e p
|P|:
[sample: EN2002b_H03_MEE073_307.97_308.08, WER: 100%, TER: 100%, total WER: 26.5712%, total TER: 13.0479%, progress (thread 0): 99.7468%]
|T|: h m m
|P|:
[sample: EN2002b_H03_MEE073_1578.82_1578.93, WER: 100%, TER: 100%, total WER: 26.572%, total TER: 13.0485%, progress (thread 0): 99.7547%]
|T|: h m m
|P|:
[sample: EN2002c_H03_MEE073_1446.36_1446.47, WER: 100%, TER: 100%, total WER: 26.5728%, total TER: 13.0491%, progress (thread 0): 99.7627%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_118.11_118.22, WER: 100%, TER: 100%, total WER: 26.5736%, total TER: 13.0495%, progress (thread 0): 99.7706%]
|T|: s o
|P|:
[sample: EN2002d_H02_MEE071_2107.05_2107.16, WER: 100%, TER: 100%, total WER: 26.5744%, total TER: 13.0499%, progress (thread 0): 99.7785%]
|T|: t
|P|:
[sample: ES2004c_H02_MEE014_1184.81_1184.91, WER: 100%, TER: 100%, total WER: 26.5753%, total TER: 13.0501%, progress (thread 0): 99.7864%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_1744.56_1744.66, WER: 100%, TER: 100%, total WER: 26.5761%, total TER: 13.0507%, progress (thread 0): 99.7943%]
|T|: o h
|P|:
[sample: TS3003c_H02_MTD0010ID_1023.66_1023.76, WER: 100%, TER: 100%, total WER: 26.5769%, total TER: 13.0512%, progress (thread 0): 99.8022%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_2013_2013.1, WER: 100%, TER: 100%, total WER: 26.5777%, total TER: 13.0518%, progress (thread 0): 99.8101%]
|T|: y e a h
|P|:
[sample: EN2002b_H00_FEO070_43.63_43.73, WER: 100%, TER: 100%, total WER: 26.5785%, total TER: 13.0526%, progress (thread 0): 99.818%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_293.52_293.62, WER: 100%, TER: 100%, total WER: 26.5794%, total TER: 13.0534%, progress (thread 0): 99.826%]
|T|: y e p
|P|: e
[sample: EN2002c_H03_MEE073_763_763.1, WER: 100%, TER: 66.6667%, total WER: 26.5802%, total TER: 13.0537%, progress (thread 0): 99.8339%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_2099.51_2099.61, WER: 100%, TER: 100%, total WER: 26.581%, total TER: 13.0541%, progress (thread 0): 99.8418%]
|T|: ' k a y
|P|:
[sample: ES2004a_H00_MEO015_168.79_168.88, WER: 100%, TER: 100%, total WER: 26.5818%, total TER: 13.055%, progress (thread 0): 99.8497%]
|T|: y e p
|P|:
[sample: ES2004c_H00_MEO015_360.73_360.82, WER: 100%, TER: 100%, total WER: 26.5826%, total TER: 13.0556%, progress (thread 0): 99.8576%]
|T|: m m h m m
|P|:
[sample: ES2004c_H03_FEE016_1132.2_1132.29, WER: 100%, TER: 100%, total WER: 26.5835%, total TER: 13.0566%, progress (thread 0): 99.8655%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1651.84_1651.93, WER: 100%, TER: 100%, total WER: 26.5843%, total TER: 13.0572%, progress (thread 0): 99.8734%]
|T|: m m h m m
|P|:
[sample: IS1009c_H02_FIO084_1753.49_1753.58, WER: 100%, TER: 100%, total WER: 26.5851%, total TER: 13.0582%, progress (thread 0): 99.8813%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_749.88_749.97, WER: 100%, TER: 100%, total WER: 26.5859%, total TER: 13.0588%, progress (thread 0): 99.8892%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1718.32_1718.41, WER: 100%, TER: 100%, total WER: 26.5867%, total TER: 13.0598%, progress (thread 0): 99.8972%]
|T|: w h a t
|P|:
[sample: TS3003d_H00_MTD009PM_1597.33_1597.42, WER: 100%, TER: 100%, total WER: 26.5875%, total TER: 13.0606%, progress (thread 0): 99.9051%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_887.36_887.45, WER: 100%, TER: 100%, total WER: 26.5884%, total TER: 13.0612%, progress (thread 0): 99.913%]
|T|: d a m n
|P|:
[sample: EN2002a_H00_MEE073_1500.91_1501, WER: 100%, TER: 100%, total WER: 26.5892%, total TER: 13.062%, progress (thread 0): 99.9209%]
|T|: h m m
|P|:
[sample: EN2002c_H03_MEE073_429.63_429.72, WER: 100%, TER: 100%, total WER: 26.59%, total TER: 13.0626%, progress (thread 0): 99.9288%]
|T|: y e a h
|P|: a
[sample: EN2002c_H02_MEE071_2578.94_2579.03, WER: 100%, TER: 75%, total WER: 26.5908%, total TER: 13.0632%, progress (thread 0): 99.9367%]
|T|: d a m n
|P|:
[sample: EN2002d_H03_MEE073_999.94_1000.03, WER: 100%, TER: 100%, total WER: 26.5916%, total TER: 13.064%, progress (thread 0): 99.9446%]
|T|: m m
|P|:
[sample: ES2004a_H00_MEO015_252.48_252.56, WER: 100%, TER: 100%, total WER: 26.5925%, total TER: 13.0644%, progress (thread 0): 99.9525%]
|T|: m m
|P|:
[sample: ES2004c_H00_MEO015_1885.03_1885.11, WER: 100%, TER: 100%, total WER: 26.5933%, total TER: 13.0648%, progress (thread 0): 99.9604%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1313.86_1313.94, WER: 100%, TER: 100%, total WER: 26.5941%, total TER: 13.0652%, progress (thread 0): 99.9684%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_185.9_185.98, WER: 100%, TER: 100%, total WER: 26.5949%, total TER: 13.0658%, progress (thread 0): 99.9763%]
|T|: g
|P|:
[sample: EN2002d_H03_MEE073_241.62_241.69, WER: 100%, TER: 100%, total WER: 26.5957%, total TER: 13.066%, progress (thread 0): 99.9842%]
|T|: h m m
|P|:
[sample: IS1009a_H02_FIO084_490.4_490.46, WER: 100%, TER: 100%, total WER: 26.5966%, total TER: 13.0666%, progress (thread 0): 99.9921%]
|T|: ' k a y
|P|:
[sample: EN2002b_H03_MEE073_613.94_614, WER: 100%, TER: 100%, total WER: 26.5974%, total TER: 13.0674%, progress (thread 0): 100%]
I1224 05:05:23.029392 9187 Test.cpp:418] ------
I1224 05:05:23.029417 9187 Test.cpp:419] [Test ami_limited_supervision/test.lst (12640 samples) in 363.179s (actual decoding time 0.0287s/sample) -- WER: 26.5974%, TER: 13.0674%]
###Markdown
We can see that the viterbi WER is 26.6% before finetuning. Step 3: Run FinetuningNow, let's run finetuning with the AMI Corpus to see if we can improve the WER. Important parameters for `fl_asr_finetune_ctc`:`--train`, `--valid` - list files for training and validation sets respectively. Use comma to separate multiple files`--datadir` - [optional] base path to be used for `--train`, `--valid` flags`--lr` - learning rate for SGD`--momentum` - SGD momentum `--lr_decay` - epoch at which learning decay starts `--lr_decay_step` - learning rate halves after this epoch interval starting from epoch given by `lr_decay` `--arch` - architecture file. Tune droupout if necessary. `--tokens` - tokens file `--batchsize` - batchsize per process`--lexicon` - lexicon file `--rundir` - path to store checkpoint logs`--reportiters` - Number of updates after which we will run evaluation on validation data and save model, if 0 we only do this at end of each epoch>Amount of train data | Config to use >---|---|> 10 min| --train train_10min_0.lst> 1 hr| --train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst> 10 hr| --train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lstLet's run finetuning with 10hr AMI data (**~7min** for 1000 updates with evaluation on dev set)
###Code
! ./flashlight/build/bin/asr/fl_asr_tutorial_finetune_ctc model.bin \
--datadir ami_limited_supervision \
--train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lst \
--valid dev:dev.lst \
--arch arch.txt \
--tokens tokens.txt \
--lexicon lexicon.txt \
--rundir checkpoint \
--lr 0.025 \
--netoptim sgd \
--momentum 0.8 \
--reportiters 1000 \
--lr_decay 100 \
--lr_decay_step 50 \
--iter 25000 \
--batchsize 4 \
--warmup 0
###Output
I1224 06:39:48.599629 11517 FinetuneCTC.cpp:76] Parsing command line flags
Initialized NCCL 2.7.8 successfully!
I1224 06:39:49.002488 11517 FinetuneCTC.cpp:106] Gflags after parsing
--flagfile=; --fromenv=; --tryfromenv=; --undefok=; --tab_completion_columns=80; --tab_completion_word=; --help=false; --helpfull=false; --helpmatch=; --helpon=; --helppackage=false; --helpshort=false; --helpxml=false; --version=false; --adambeta1=0.94999999999999996; --adambeta2=0.98999999999999999; --am=; --am_decoder_tr_dropout=0.20000000000000001; --am_decoder_tr_layerdrop=0.20000000000000001; --am_decoder_tr_layers=6; --arch=arch.txt; --attention=keyvalue; --attentionthreshold=2147483647; --attnWindow=softPretrain; --attnconvchannel=0; --attnconvkernel=0; --attndim=0; --batching_max_duration=0; --batching_strategy=none; --batchsize=4; --beamsize=2500; --beamsizetoken=250000; --beamthreshold=25; --channels=1; --criterion=ctc; --critoptim=adagrad; --datadir=ami_limited_supervision; --decoderattnround=1; --decoderdropout=0; --decoderrnnlayer=1; --decodertype=wrd; --devwin=0; --emission_dir=; --emission_queue_size=3000; --enable_distributed=true; --encoderdim=256; --eosscore=0; --eostoken=false; --everstoredb=false; --fftcachesize=1; --filterbanks=80; --fl_amp_max_scale_factor=32000; --fl_amp_scale_factor=4096; --fl_amp_scale_factor_update_interval=2000; --fl_amp_use_mixed_precision=false; --fl_benchmark_mode=true; --fl_log_level=; --fl_optim_mode=; --fl_vlog_level=0; --flagsfile=; --framesizems=25; --framestridems=10; --gamma=1; --gumbeltemperature=1; --highfreqfilterbank=-1; --inputfeeding=false; --isbeamdump=false; --iter=25000; --itersave=false; --labelsmooth=0.050000000000000003; --leftWindowSize=50; --lexicon=lexicon.txt; --linlr=-1; --linlrcrit=-1; --linseg=0; --lm=; --lm_memory=5000; --lm_vocab=; --lmtype=kenlm; --lmweight=0; --lmweight_high=4; --lmweight_low=0; --lmweight_step=0.20000000000000001; --localnrmlleftctx=0; --localnrmlrightctx=0; --logadd=false; --lowfreqfilterbank=0; --lr=0.025000000000000001; --lr_decay=100; --lr_decay_step=50; --lrcosine=false; --lrcrit=0.02; --max_devices_per_node=8; --maxdecoderoutputlen=400; --maxgradnorm=0.10000000000000001; --maxload=-1; --maxrate=10; --maxsil=50; --maxword=-1; --melfloor=1; --mfcc=false; --mfcccoeffs=13; --mfsc=true; --minrate=3; --minsil=0; --momentum=0.80000000000000004; --netoptim=sgd; --nthread=6; --nthread_decoder=1; --nthread_decoder_am_forward=1; --numattnhead=8; --onorm=target; --optimepsilon=1e-08; --optimrho=0.90000000000000002; --pctteacherforcing=99; --pcttraineval=1; --pow=false; --pretrainWindow=0; --replabel=0; --reportiters=1000; --rightWindowSize=50; --rndv_filepath=; --rundir=checkpoint; --samplerate=16000; --sampletarget=0.01; --samplingstrategy=rand; --saug_fmaskf=30; --saug_fmaskn=2; --saug_start_update=24000; --saug_tmaskn=10; --saug_tmaskp=0.050000000000000003; --saug_tmaskt=30; --sclite=; --seed=0; --sfx_config=; --show=false; --showletters=false; --silscore=0; --smearing=none; --smoothingtemperature=1; --softwoffset=10; --softwrate=5; --softwstd=4; --sqnorm=true; --stepsize=9223372036854775807; --surround=; --test=; --tokens=tokens.txt; --train=train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lst; --trainWithWindow=true; --transdiag=0; --unkscore=-inf; --use_memcache=false; --uselexicon=true; --usewordpiece=false; --valid=dev:dev.lst; --validbatchsize=-1; --warmup=0; --weightdecay=0; --wordscore=0; --wordseparator=|; --world_rank=0; --world_size=1; --alsologtoemail=; --alsologtostderr=false; --colorlogtostderr=false; --drop_log_memory=true; --log_backtrace_at=; --log_dir=; --log_link=; --log_prefix=true; --logbuflevel=0; --logbufsecs=30; --logemaillevel=999; --logfile_mode=436; --logmailer=/bin/mail; --logtostderr=true; --max_log_size=1800; --minloglevel=0; --stderrthreshold=2; --stop_logging_if_full_disk=false; --symbolize_stacktrace=true; --v=0; --vmodule=;
I1224 06:39:49.002910 11517 FinetuneCTC.cpp:107] Experiment path: checkpoint
I1224 06:39:49.002919 11517 FinetuneCTC.cpp:108] Experiment runidx: 1
I1224 06:39:49.003252 11517 FinetuneCTC.cpp:153] Number of classes (network): 29
I1224 06:39:49.248888 11517 FinetuneCTC.cpp:160] Number of words: 200001
I1224 06:39:50.344347 11517 FinetuneCTC.cpp:248] Loading architecture file from arch.txt
I1224 06:39:50.868463 11517 FinetuneCTC.cpp:277] [Network] Sequential [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> (19) -> (20) -> (21) -> (22) -> (23) -> (24) -> (25) -> (26) -> (27) -> (28) -> (29) -> (30) -> (31) -> (32) -> (33) -> (34) -> (35) -> (36) -> (37) -> (38) -> (39) -> (40) -> (41) -> (42) -> output]
(0): View (-1 1 80 0)
(1): LayerNorm ( axis : { 0 1 2 } , size : -1)
(2): Conv2D (80->768, 7x1, 3,1, SAME,0, 1, 1) (with bias)
(3): GatedLinearUnit (2)
(4): Dropout (0.050000)
(5): Reorder (2,0,3,1)
(6): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(7): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(8): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(9): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(10): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(11): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(12): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(13): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(14): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(15): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(16): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(17): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(18): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(19): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(20): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(21): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(22): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(23): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(24): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(25): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(26): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(27): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(28): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(29): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(30): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(31): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(32): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(33): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(34): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(35): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(36): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(37): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(38): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(39): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(40): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(41): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(42): Linear (384->29) (with bias)
I1224 06:39:50.868662 11517 FinetuneCTC.cpp:278] [Network Params: 70498735]
I1224 06:39:50.868705 11517 FinetuneCTC.cpp:283] [Criterion] ConnectionistTemporalClassificationCriterion
I1224 06:39:51.004284 11517 FinetuneCTC.cpp:287] [Network Optimizer] SGD (momentum=0.8)
I1224 06:39:51.005266 11517 FinetuneCTC.cpp:547] Shuffling trainset
I1224 06:39:51.005805 11517 FinetuneCTC.cpp:554] Epoch 1 started!
I1224 06:46:52.988443 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 1000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:32 | bch(ms): 212.42 | smp(ms): 3.21 | fwd(ms): 82.29 | crit-fwd(ms): 2.90 | bwd(ms): 94.35 | optim(ms): 31.26 | loss: 2.78453 | train-TER: 9.68 | train-WER: 21.00 | dev-loss: 2.59365 | dev-TER: 10.09 | dev-WER: 19.50 | avg-isz: 287 | avg-tsz: 040 | max-tsz: 339 | avr-batchsz: 4.00 | hrs: 3.20 | thrpt(sec/sec): 54.18 | timestamp: 2020-12-24 06:46:52
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 06:53:31.970283 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 2000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:06 | bch(ms): 186.76 | smp(ms): 0.04 | fwd(ms): 69.67 | crit-fwd(ms): 2.02 | bwd(ms): 86.59 | optim(ms): 30.22 | loss: 2.63802 | train-TER: 9.86 | train-WER: 22.43 | dev-loss: 2.54714 | dev-TER: 9.84 | dev-WER: 18.86 | avg-isz: 259 | avg-tsz: 036 | max-tsz: 345 | avr-batchsz: 4.00 | hrs: 2.88 | thrpt(sec/sec): 55.57 | timestamp: 2020-12-24 06:53:31
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 07:00:07.246326 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 3000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:02 | bch(ms): 182.40 | smp(ms): 0.03 | fwd(ms): 67.86 | crit-fwd(ms): 1.92 | bwd(ms): 84.27 | optim(ms): 30.00 | loss: 2.57714 | train-TER: 12.22 | train-WER: 24.38 | dev-loss: 2.45296 | dev-TER: 9.55 | dev-WER: 18.37 | avg-isz: 248 | avg-tsz: 035 | max-tsz: 257 | avr-batchsz: 4.00 | hrs: 2.76 | thrpt(sec/sec): 54.53 | timestamp: 2020-12-24 07:00:07
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 07:01:30.886020 11517 FinetuneCTC.cpp:547] Shuffling trainset
I1224 07:01:30.886448 11517 FinetuneCTC.cpp:554] Epoch 2 started!
[5e8e495af856:11519] *** Process received signal ***
[5e8e495af856:11519] Signal: Segmentation fault (11)
[5e8e495af856:11519] Signal code: Address not mapped (1)
[5e8e495af856:11519] Failing at address: 0x7f848b62120d
[5e8e495af856:11519] [ 0] /lib/x86_64-linux-gnu/libpthread.so.0(+0x12980)[0x7f848e2cd980]
[5e8e495af856:11519] [ 1] /lib/x86_64-linux-gnu/libc.so.6(getenv+0xa5)[0x7f848df0c8a5]
[5e8e495af856:11519] [ 2] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(_ZN13TCMallocGuardD1Ev+0x34)[0x7f848e777e44]
[5e8e495af856:11519] [ 3] /lib/x86_64-linux-gnu/libc.so.6(__cxa_finalize+0xf5)[0x7f848df0d735]
[5e8e495af856:11519] [ 4] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(+0x13cb3)[0x7f848e775cb3]
[5e8e495af856:11519] *** End of error message ***
^C
###Markdown
Step 4: Run Decoding Viterbi decoding
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am checkpoint/001_model_dev.bin --datadir '' --emission_dir '' --uselexicon false \
--test ami_limited_supervision/test.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1046.72_1047.07, WER: 0%, TER: 0%, total WER: 18.9985%, total TER: 8.47729%, progress (thread 0): 86.8275%]
|T|: m m
|P|: m m
[sample: ES2004c_H01_FEE013_1294.34_1294.69, WER: 0%, TER: 0%, total WER: 18.9983%, total TER: 8.47725%, progress (thread 0): 86.8354%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1302.15_1302.5, WER: 100%, TER: 0%, total WER: 18.9992%, total TER: 8.47715%, progress (thread 0): 86.8434%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1515.72_1516.07, WER: 0%, TER: 0%, total WER: 18.999%, total TER: 8.47707%, progress (thread 0): 86.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1690.13_1690.48, WER: 0%, TER: 0%, total WER: 18.9988%, total TER: 8.47699%, progress (thread 0): 86.8592%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2078.48_2078.83, WER: 100%, TER: 0%, total WER: 18.9997%, total TER: 8.47689%, progress (thread 0): 86.8671%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H02_MEE014_2291.54_2291.89, WER: 0%, TER: 0%, total WER: 18.9995%, total TER: 8.47681%, progress (thread 0): 86.875%]
|T|: o h
|P|: u m
[sample: ES2004d_H00_MEO015_127.17_127.52, WER: 100%, TER: 100%, total WER: 19.0004%, total TER: 8.47725%, progress (thread 0): 86.8829%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_561.86_562.21, WER: 0%, TER: 0%, total WER: 19.0002%, total TER: 8.47717%, progress (thread 0): 86.8908%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004d_H00_MEO015_640.16_640.51, WER: 100%, TER: 25%, total WER: 19.0011%, total TER: 8.47732%, progress (thread 0): 86.8987%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_821.44_821.79, WER: 0%, TER: 0%, total WER: 19.0009%, total TER: 8.47722%, progress (thread 0): 86.9066%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H01_FEE013_822.51_822.86, WER: 100%, TER: 0%, total WER: 19.0019%, total TER: 8.47712%, progress (thread 0): 86.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1778.02_1778.37, WER: 0%, TER: 0%, total WER: 19.0016%, total TER: 8.47704%, progress (thread 0): 86.9225%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_76.08_76.43, WER: 100%, TER: 0%, total WER: 19.0026%, total TER: 8.47694%, progress (thread 0): 86.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_431.19_431.54, WER: 0%, TER: 0%, total WER: 19.0023%, total TER: 8.47686%, progress (thread 0): 86.9383%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_492.57_492.92, WER: 100%, TER: 66.6667%, total WER: 19.0033%, total TER: 8.47727%, progress (thread 0): 86.9462%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_232.67_233.02, WER: 0%, TER: 0%, total WER: 19.003%, total TER: 8.47719%, progress (thread 0): 86.9541%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_361.11_361.46, WER: 0%, TER: 0%, total WER: 19.0028%, total TER: 8.47711%, progress (thread 0): 86.962%]
|T|: y e p
|P|: y e a h
[sample: IS1009b_H00_FIE088_723.57_723.92, WER: 100%, TER: 66.6667%, total WER: 19.0038%, total TER: 8.47752%, progress (thread 0): 86.9699%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_993.71_994.06, WER: 100%, TER: 60%, total WER: 19.0047%, total TER: 8.47813%, progress (thread 0): 86.9778%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1007.39_1007.74, WER: 0%, TER: 0%, total WER: 19.0045%, total TER: 8.47807%, progress (thread 0): 86.9858%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1079.18_1079.53, WER: 100%, TER: 0%, total WER: 19.0054%, total TER: 8.47797%, progress (thread 0): 86.9937%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1082.71_1083.06, WER: 100%, TER: 0%, total WER: 19.0063%, total TER: 8.47787%, progress (thread 0): 87.0016%]
|T|: t h i s | o n e
|P|: t h i s | o n e
[sample: IS1009b_H00_FIE088_1179.44_1179.79, WER: 0%, TER: 0%, total WER: 19.0059%, total TER: 8.47771%, progress (thread 0): 87.0095%]
|T|: y o u ' r e | t h r e e
|P|: y o u | t h r e e
[sample: IS1009b_H00_FIE088_1203.06_1203.41, WER: 50%, TER: 25%, total WER: 19.0066%, total TER: 8.47818%, progress (thread 0): 87.0174%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1699.07_1699.42, WER: 0%, TER: 0%, total WER: 19.0064%, total TER: 8.4781%, progress (thread 0): 87.0253%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1821.76_1822.11, WER: 0%, TER: 0%, total WER: 19.0061%, total TER: 8.47802%, progress (thread 0): 87.0332%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_527.63_527.98, WER: 100%, TER: 0%, total WER: 19.0071%, total TER: 8.47792%, progress (thread 0): 87.0411%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_546.82_547.17, WER: 100%, TER: 0%, total WER: 19.008%, total TER: 8.47782%, progress (thread 0): 87.049%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_609.9_610.25, WER: 100%, TER: 0%, total WER: 19.0089%, total TER: 8.47772%, progress (thread 0): 87.057%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_688.49_688.84, WER: 100%, TER: 20%, total WER: 19.0098%, total TER: 8.47786%, progress (thread 0): 87.0649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1303.58_1303.93, WER: 0%, TER: 0%, total WER: 19.0096%, total TER: 8.47778%, progress (thread 0): 87.0728%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H01_FIO087_1514.93_1515.28, WER: 100%, TER: 0%, total WER: 19.0105%, total TER: 8.47768%, progress (thread 0): 87.0807%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1737.76_1738.11, WER: 100%, TER: 0%, total WER: 19.0115%, total TER: 8.47758%, progress (thread 0): 87.0886%]
|T|: a h
|P|: m m
[sample: IS1009d_H02_FIO084_734.11_734.46, WER: 100%, TER: 100%, total WER: 19.0124%, total TER: 8.47801%, progress (thread 0): 87.0965%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_742.93_743.28, WER: 0%, TER: 0%, total WER: 19.0122%, total TER: 8.47795%, progress (thread 0): 87.1044%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_743.52_743.87, WER: 0%, TER: 0%, total WER: 19.0119%, total TER: 8.47787%, progress (thread 0): 87.1123%]
|T|: y e a h
|P|: w h a t
[sample: IS1009d_H02_FIO084_953.71_954.06, WER: 100%, TER: 75%, total WER: 19.0129%, total TER: 8.4785%, progress (thread 0): 87.1203%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1243.4_1243.75, WER: 0%, TER: 0%, total WER: 19.0126%, total TER: 8.47842%, progress (thread 0): 87.1282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1323.32_1323.67, WER: 0%, TER: 0%, total WER: 19.0124%, total TER: 8.47834%, progress (thread 0): 87.1361%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_1417.8_1418.15, WER: 100%, TER: 0%, total WER: 19.0133%, total TER: 8.47824%, progress (thread 0): 87.144%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1674.44_1674.79, WER: 100%, TER: 0%, total WER: 19.0143%, total TER: 8.47814%, progress (thread 0): 87.1519%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_1745.56_1745.91, WER: 100%, TER: 40%, total WER: 19.0152%, total TER: 8.47851%, progress (thread 0): 87.1598%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_506.21_506.56, WER: 0%, TER: 0%, total WER: 19.015%, total TER: 8.47843%, progress (thread 0): 87.1677%]
|T|: t h e | p e n
|P|: t e
[sample: TS3003a_H02_MTD0010ID_1074.9_1075.25, WER: 100%, TER: 71.4286%, total WER: 19.0168%, total TER: 8.47947%, progress (thread 0): 87.1756%]
|T|: r i g h t
|P|: r i g h t
[sample: TS3003a_H03_MTD012ME_1219.55_1219.9, WER: 0%, TER: 0%, total WER: 19.0166%, total TER: 8.47937%, progress (thread 0): 87.1835%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003a_H01_MTD011UID_1371.45_1371.8, WER: 100%, TER: 20%, total WER: 19.0175%, total TER: 8.4795%, progress (thread 0): 87.1915%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_1453.73_1454.08, WER: 0%, TER: 0%, total WER: 19.0173%, total TER: 8.47942%, progress (thread 0): 87.1994%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1295.49_1295.84, WER: 0%, TER: 0%, total WER: 19.0171%, total TER: 8.47938%, progress (thread 0): 87.2073%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1351.56_1351.91, WER: 0%, TER: 0%, total WER: 19.0169%, total TER: 8.47934%, progress (thread 0): 87.2152%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_180.89_181.24, WER: 0%, TER: 0%, total WER: 19.0167%, total TER: 8.47926%, progress (thread 0): 87.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1380.5_1380.85, WER: 0%, TER: 0%, total WER: 19.0164%, total TER: 8.47918%, progress (thread 0): 87.231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1531.23_1531.58, WER: 0%, TER: 0%, total WER: 19.0162%, total TER: 8.4791%, progress (thread 0): 87.2389%]
|T|: t h a t ' s | g o o d
|P|: t h a t ' s g o
[sample: TS3003c_H03_MTD012ME_1567.88_1568.23, WER: 100%, TER: 27.2727%, total WER: 19.0181%, total TER: 8.47959%, progress (thread 0): 87.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1713.34_1713.69, WER: 0%, TER: 0%, total WER: 19.0178%, total TER: 8.47951%, progress (thread 0): 87.2547%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_2280.01_2280.36, WER: 0%, TER: 0%, total WER: 19.0176%, total TER: 8.47943%, progress (thread 0): 87.2627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_306.6_306.95, WER: 0%, TER: 0%, total WER: 19.0174%, total TER: 8.47935%, progress (thread 0): 87.2706%]
|T|: n o
|P|: n o
[sample: TS3003d_H00_MTD009PM_819.47_819.82, WER: 0%, TER: 0%, total WER: 19.0172%, total TER: 8.47931%, progress (thread 0): 87.2785%]
|T|: a l r i g h t
|P|: i
[sample: TS3003d_H00_MTD009PM_965.64_965.99, WER: 100%, TER: 85.7143%, total WER: 19.0181%, total TER: 8.48058%, progress (thread 0): 87.2864%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1373.9_1374.25, WER: 100%, TER: 66.6667%, total WER: 19.019%, total TER: 8.48099%, progress (thread 0): 87.2943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1847.37_1847.72, WER: 0%, TER: 0%, total WER: 19.0188%, total TER: 8.48091%, progress (thread 0): 87.3022%]
|T|: n i n e
|P|: n i n e
[sample: TS3003d_H03_MTD012ME_1899.7_1900.05, WER: 0%, TER: 0%, total WER: 19.0186%, total TER: 8.48083%, progress (thread 0): 87.3101%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H01_MTD011UID_2218.91_2219.26, WER: 0%, TER: 0%, total WER: 19.0184%, total TER: 8.48075%, progress (thread 0): 87.318%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_142.16_142.51, WER: 0%, TER: 0%, total WER: 19.0182%, total TER: 8.48065%, progress (thread 0): 87.326%]
|T|: h m m
|P|: m m m m
[sample: EN2002a_H00_MEE073_203.92_204.27, WER: 100%, TER: 66.6667%, total WER: 19.0191%, total TER: 8.48107%, progress (thread 0): 87.3339%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_220.21_220.56, WER: 0%, TER: 0%, total WER: 19.0189%, total TER: 8.48099%, progress (thread 0): 87.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1393.11_1393.46, WER: 0%, TER: 0%, total WER: 19.0187%, total TER: 8.48091%, progress (thread 0): 87.3497%]
|T|: n o
|P|: n o
[sample: EN2002a_H02_FEO072_1494.01_1494.36, WER: 0%, TER: 0%, total WER: 19.0184%, total TER: 8.48087%, progress (thread 0): 87.3576%]
|T|: n o
|P|: y e a h
[sample: EN2002a_H01_FEO070_1616.41_1616.76, WER: 100%, TER: 200%, total WER: 19.0194%, total TER: 8.48177%, progress (thread 0): 87.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2062.28_2062.63, WER: 0%, TER: 0%, total WER: 19.0192%, total TER: 8.48169%, progress (thread 0): 87.3734%]
|T|: t h e n | u m
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_306.19_306.54, WER: 100%, TER: 114.286%, total WER: 19.021%, total TER: 8.48343%, progress (thread 0): 87.3813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_679.83_680.18, WER: 0%, TER: 0%, total WER: 19.0208%, total TER: 8.48335%, progress (thread 0): 87.3892%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1132.98_1133.33, WER: 0%, TER: 0%, total WER: 19.0206%, total TER: 8.48327%, progress (thread 0): 87.3972%]
|T|: s h o u l d n ' t | n o
|P|: s h o u d n ' n
[sample: EN2002b_H01_MEE071_1400.16_1400.51, WER: 100%, TER: 33.3333%, total WER: 19.0224%, total TER: 8.48398%, progress (thread 0): 87.4051%]
|T|: n o
|P|: y e a h
[sample: EN2002b_H02_FEO072_1650.79_1651.14, WER: 100%, TER: 200%, total WER: 19.0233%, total TER: 8.48488%, progress (thread 0): 87.413%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_550.49_550.84, WER: 0%, TER: 0%, total WER: 19.0231%, total TER: 8.4848%, progress (thread 0): 87.4209%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_586.84_587.19, WER: 0%, TER: 0%, total WER: 19.0229%, total TER: 8.48472%, progress (thread 0): 87.4288%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_725.66_726.01, WER: 0%, TER: 0%, total WER: 19.0227%, total TER: 8.48468%, progress (thread 0): 87.4367%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_743.92_744.27, WER: 0%, TER: 0%, total WER: 19.0225%, total TER: 8.48464%, progress (thread 0): 87.4446%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_975_975.35, WER: 0%, TER: 0%, total WER: 19.0222%, total TER: 8.48456%, progress (thread 0): 87.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1520.32_1520.67, WER: 0%, TER: 0%, total WER: 19.022%, total TER: 8.48448%, progress (thread 0): 87.4604%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_1667.21_1667.56, WER: 0%, TER: 0%, total WER: 19.0218%, total TER: 8.48444%, progress (thread 0): 87.4684%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_2075.98_2076.33, WER: 0%, TER: 0%, total WER: 19.0216%, total TER: 8.4844%, progress (thread 0): 87.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2182.63_2182.98, WER: 0%, TER: 0%, total WER: 19.0214%, total TER: 8.48432%, progress (thread 0): 87.4842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_352.2_352.55, WER: 0%, TER: 0%, total WER: 19.0212%, total TER: 8.48424%, progress (thread 0): 87.4921%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002d_H03_MEE073_387.18_387.53, WER: 0%, TER: 0%, total WER: 19.0207%, total TER: 8.4841%, progress (thread 0): 87.5%]
|T|: m m
|P|: m m
[sample: EN2002d_H01_FEO072_831.78_832.13, WER: 0%, TER: 0%, total WER: 19.0205%, total TER: 8.48406%, progress (thread 0): 87.5079%]
|T|: n o
|P|: n o
[sample: EN2002d_H03_MEE073_909.36_909.71, WER: 0%, TER: 0%, total WER: 19.0203%, total TER: 8.48402%, progress (thread 0): 87.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1113.08_1113.43, WER: 0%, TER: 0%, total WER: 19.0201%, total TER: 8.48394%, progress (thread 0): 87.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1233.84_1234.19, WER: 0%, TER: 0%, total WER: 19.0199%, total TER: 8.48386%, progress (thread 0): 87.5316%]
|T|: i | d o n ' t | k n o w
|P|: i d
[sample: EN2002d_H01_FEO072_1245.04_1245.39, WER: 100%, TER: 83.3333%, total WER: 19.0226%, total TER: 8.48597%, progress (thread 0): 87.5396%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1798.12_1798.47, WER: 0%, TER: 0%, total WER: 19.0224%, total TER: 8.48589%, progress (thread 0): 87.5475%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1926.51_1926.86, WER: 0%, TER: 0%, total WER: 19.0222%, total TER: 8.48581%, progress (thread 0): 87.5554%]
|T|: u h h u h
|P|: u m
[sample: EN2002d_H00_FEO070_2027.85_2028.2, WER: 100%, TER: 80%, total WER: 19.0231%, total TER: 8.48666%, progress (thread 0): 87.5633%]
|T|: o h | s o r r y
|P|: o k a y
[sample: ES2004a_H00_MEO015_255.91_256.25, WER: 100%, TER: 75%, total WER: 19.025%, total TER: 8.48791%, progress (thread 0): 87.5712%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H01_FEE013_545.3_545.64, WER: 100%, TER: 0%, total WER: 19.0259%, total TER: 8.48781%, progress (thread 0): 87.5791%]
|T|: r e a l l y
|P|: r e a l l y
[sample: ES2004a_H01_FEE013_603.88_604.22, WER: 0%, TER: 0%, total WER: 19.0257%, total TER: 8.48769%, progress (thread 0): 87.587%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_635.54_635.88, WER: 100%, TER: 0%, total WER: 19.0266%, total TER: 8.48759%, progress (thread 0): 87.5949%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H03_FEE016_811.15_811.49, WER: 0%, TER: 0%, total WER: 19.0264%, total TER: 8.48751%, progress (thread 0): 87.6028%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_954.93_955.27, WER: 100%, TER: 0%, total WER: 19.0273%, total TER: 8.48741%, progress (thread 0): 87.6108%]
|T|: m m h m m
|P|: m m
[sample: ES2004b_H03_FEE016_1445.71_1446.05, WER: 100%, TER: 60%, total WER: 19.0282%, total TER: 8.48802%, progress (thread 0): 87.6187%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1995.5_1995.84, WER: 100%, TER: 0%, total WER: 19.0291%, total TER: 8.48792%, progress (thread 0): 87.6266%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H03_FEE016_22.85_23.19, WER: 100%, TER: 25%, total WER: 19.03%, total TER: 8.48807%, progress (thread 0): 87.6345%]
|T|: t h a n k | y o u
|P|: o k a y
[sample: ES2004c_H03_FEE016_201.1_201.44, WER: 100%, TER: 77.7778%, total WER: 19.0319%, total TER: 8.48954%, progress (thread 0): 87.6424%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_451.06_451.4, WER: 100%, TER: 25%, total WER: 19.0328%, total TER: 8.4897%, progress (thread 0): 87.6503%]
|T|: n o
|P|: n o t
[sample: ES2004c_H02_MEE014_535.19_535.53, WER: 100%, TER: 50%, total WER: 19.0337%, total TER: 8.48989%, progress (thread 0): 87.6582%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1178.32_1178.66, WER: 0%, TER: 0%, total WER: 19.0335%, total TER: 8.48981%, progress (thread 0): 87.6661%]
|T|: h m m
|P|: m m
[sample: ES2004c_H02_MEE014_1181.26_1181.6, WER: 100%, TER: 33.3333%, total WER: 19.0344%, total TER: 8.48999%, progress (thread 0): 87.674%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1478.47_1478.81, WER: 0%, TER: 0%, total WER: 19.0342%, total TER: 8.48995%, progress (thread 0): 87.682%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1640.61_1640.95, WER: 100%, TER: 0%, total WER: 19.0351%, total TER: 8.48985%, progress (thread 0): 87.6899%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2025.11_2025.45, WER: 100%, TER: 0%, total WER: 19.036%, total TER: 8.48975%, progress (thread 0): 87.6978%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2072.31_2072.65, WER: 100%, TER: 0%, total WER: 19.037%, total TER: 8.48965%, progress (thread 0): 87.7057%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_100.76_101.1, WER: 0%, TER: 0%, total WER: 19.0367%, total TER: 8.48957%, progress (thread 0): 87.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H01_FEE013_139.4_139.74, WER: 0%, TER: 0%, total WER: 19.0365%, total TER: 8.48947%, progress (thread 0): 87.7215%]
|T|: y e p
|P|: y e p
[sample: ES2004d_H00_MEO015_155.57_155.91, WER: 0%, TER: 0%, total WER: 19.0363%, total TER: 8.48941%, progress (thread 0): 87.7294%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_178.12_178.46, WER: 0%, TER: 0%, total WER: 19.0361%, total TER: 8.48933%, progress (thread 0): 87.7373%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_405.44_405.78, WER: 0%, TER: 0%, total WER: 19.0359%, total TER: 8.48925%, progress (thread 0): 87.7453%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H00_MEO015_548.5_548.84, WER: 100%, TER: 0%, total WER: 19.0368%, total TER: 8.48915%, progress (thread 0): 87.7532%]
|T|: t h r e e
|P|: t h r e e
[sample: ES2004d_H02_MEE014_646.42_646.76, WER: 0%, TER: 0%, total WER: 19.0366%, total TER: 8.48905%, progress (thread 0): 87.7611%]
|T|: f o u r
|P|: f o u r
[sample: ES2004d_H01_FEE013_911.53_911.87, WER: 0%, TER: 0%, total WER: 19.0364%, total TER: 8.48897%, progress (thread 0): 87.769%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1135.09_1135.43, WER: 0%, TER: 0%, total WER: 19.0362%, total TER: 8.48889%, progress (thread 0): 87.7769%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_1953.89_1954.23, WER: 0%, TER: 0%, total WER: 19.0359%, total TER: 8.48885%, progress (thread 0): 87.7848%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_792.79_793.13, WER: 0%, TER: 0%, total WER: 19.0357%, total TER: 8.48879%, progress (thread 0): 87.7927%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_43.16_43.5, WER: 100%, TER: 0%, total WER: 19.0366%, total TER: 8.48869%, progress (thread 0): 87.8006%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_260.98_261.32, WER: 0%, TER: 0%, total WER: 19.0364%, total TER: 8.48861%, progress (thread 0): 87.8085%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_425.4_425.74, WER: 0%, TER: 0%, total WER: 19.0362%, total TER: 8.48853%, progress (thread 0): 87.8165%]
|T|: y e p
|P|: y o h
[sample: IS1009b_H00_FIE088_598.28_598.62, WER: 100%, TER: 66.6667%, total WER: 19.0371%, total TER: 8.48894%, progress (thread 0): 87.8244%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_613.29_613.63, WER: 0%, TER: 0%, total WER: 19.0369%, total TER: 8.48886%, progress (thread 0): 87.8323%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1002.09_1002.43, WER: 0%, TER: 0%, total WER: 19.0367%, total TER: 8.4888%, progress (thread 0): 87.8402%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H00_FIE088_1223.66_1224, WER: 100%, TER: 20%, total WER: 19.0376%, total TER: 8.48894%, progress (thread 0): 87.8481%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1225.44_1225.78, WER: 100%, TER: 0%, total WER: 19.0385%, total TER: 8.48884%, progress (thread 0): 87.856%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H03_FIO089_1734.63_1734.97, WER: 100%, TER: 200%, total WER: 19.0395%, total TER: 8.48974%, progress (thread 0): 87.8639%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1834.47_1834.81, WER: 0%, TER: 0%, total WER: 19.0392%, total TER: 8.48966%, progress (thread 0): 87.8718%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_1882.63_1882.97, WER: 0%, TER: 0%, total WER: 19.039%, total TER: 8.48958%, progress (thread 0): 87.8797%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H01_FIO087_1960.82_1961.16, WER: 100%, TER: 0%, total WER: 19.0399%, total TER: 8.48948%, progress (thread 0): 87.8877%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_535.66_536, WER: 100%, TER: 0%, total WER: 19.0409%, total TER: 8.48938%, progress (thread 0): 87.8956%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_575.77_576.11, WER: 100%, TER: 0%, total WER: 19.0418%, total TER: 8.48928%, progress (thread 0): 87.9035%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_950.04_950.38, WER: 100%, TER: 0%, total WER: 19.0427%, total TER: 8.48918%, progress (thread 0): 87.9114%]
|T|: i t | w o r k s
|P|: b u t
[sample: IS1009c_H01_FIO087_1083.82_1084.16, WER: 100%, TER: 100%, total WER: 19.0445%, total TER: 8.4909%, progress (thread 0): 87.9193%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_1109.75_1110.09, WER: 100%, TER: 0%, total WER: 19.0455%, total TER: 8.4908%, progress (thread 0): 87.9272%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1603.38_1603.72, WER: 100%, TER: 0%, total WER: 19.0464%, total TER: 8.4907%, progress (thread 0): 87.9351%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1613.06_1613.4, WER: 100%, TER: 0%, total WER: 19.0473%, total TER: 8.4906%, progress (thread 0): 87.943%]
|T|: y e s
|P|: y e a s
[sample: IS1009c_H01_FIO087_1633.76_1634.1, WER: 100%, TER: 33.3333%, total WER: 19.0482%, total TER: 8.49078%, progress (thread 0): 87.951%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_434.93_435.27, WER: 100%, TER: 0%, total WER: 19.0491%, total TER: 8.49068%, progress (thread 0): 87.9589%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_637.66_638, WER: 100%, TER: 40%, total WER: 19.0501%, total TER: 8.49105%, progress (thread 0): 87.9668%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_649.7_650.04, WER: 0%, TER: 0%, total WER: 19.0498%, total TER: 8.49095%, progress (thread 0): 87.9747%]
|T|: h m m
|P|: m h t
[sample: IS1009d_H02_FIO084_674.88_675.22, WER: 100%, TER: 100%, total WER: 19.0508%, total TER: 8.49159%, progress (thread 0): 87.9826%]
|T|: o o p s
|P|: u p s
[sample: IS1009d_H00_FIE088_1068.99_1069.33, WER: 100%, TER: 50%, total WER: 19.0517%, total TER: 8.49199%, progress (thread 0): 87.9905%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H01_FIO087_1509.75_1510.09, WER: 0%, TER: 0%, total WER: 19.0515%, total TER: 8.49193%, progress (thread 0): 87.9984%]
|T|: t h a n k | y o u
|P|: t h a n k | y o u
[sample: TS3003a_H03_MTD012ME_48.1_48.44, WER: 0%, TER: 0%, total WER: 19.051%, total TER: 8.49175%, progress (thread 0): 88.0063%]
|T|: g u e s s
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_983.96_984.3, WER: 100%, TER: 80%, total WER: 19.0519%, total TER: 8.49259%, progress (thread 0): 88.0142%]
|T|: t h e | p e n
|P|: m m h
[sample: TS3003a_H02_MTD0010ID_1090.49_1090.83, WER: 100%, TER: 100%, total WER: 19.0538%, total TER: 8.49409%, progress (thread 0): 88.0222%]
|T|: t h i n g
|P|: t h i n g
[sample: TS3003a_H00_MTD009PM_1320.19_1320.53, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49399%, progress (thread 0): 88.0301%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_132.84_133.18, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49395%, progress (thread 0): 88.038%]
|T|: r i g h t
|P|: b u h
[sample: TS3003b_H01_MTD011UID_2086.12_2086.46, WER: 100%, TER: 80%, total WER: 19.0543%, total TER: 8.4948%, progress (thread 0): 88.0459%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2089.85_2090.19, WER: 0%, TER: 0%, total WER: 19.0541%, total TER: 8.49472%, progress (thread 0): 88.0538%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_982.32_982.66, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49464%, progress (thread 0): 88.0617%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_1007.54_1007.88, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49456%, progress (thread 0): 88.0696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1240.35_1240.69, WER: 0%, TER: 0%, total WER: 19.0534%, total TER: 8.49448%, progress (thread 0): 88.0775%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1413.87_1414.21, WER: 0%, TER: 0%, total WER: 19.0532%, total TER: 8.4944%, progress (thread 0): 88.0854%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1849.58_1849.92, WER: 0%, TER: 0%, total WER: 19.053%, total TER: 8.49432%, progress (thread 0): 88.0934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_595.75_596.09, WER: 0%, TER: 0%, total WER: 19.0528%, total TER: 8.49424%, progress (thread 0): 88.1013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_921.62_921.96, WER: 0%, TER: 0%, total WER: 19.0525%, total TER: 8.49416%, progress (thread 0): 88.1092%]
|T|: u h | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1308.11_1308.45, WER: 50%, TER: 42.8571%, total WER: 19.0532%, total TER: 8.49472%, progress (thread 0): 88.1171%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1388.52_1388.86, WER: 0%, TER: 0%, total WER: 19.053%, total TER: 8.49464%, progress (thread 0): 88.125%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1802.71_1803.05, WER: 0%, TER: 0%, total WER: 19.0528%, total TER: 8.49456%, progress (thread 0): 88.1329%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1835.63_1835.97, WER: 100%, TER: 25%, total WER: 19.0537%, total TER: 8.49472%, progress (thread 0): 88.1408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2091.81_2092.15, WER: 0%, TER: 0%, total WER: 19.0535%, total TER: 8.49464%, progress (thread 0): 88.1487%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2217.62_2217.96, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49456%, progress (thread 0): 88.1566%]
|T|: m m | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2323.41_2323.75, WER: 50%, TER: 42.8571%, total WER: 19.054%, total TER: 8.49512%, progress (thread 0): 88.1646%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2420.39_2420.73, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49504%, progress (thread 0): 88.1725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_406.15_406.49, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49496%, progress (thread 0): 88.1804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_601.54_601.88, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49488%, progress (thread 0): 88.1883%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H02_FEO072_713.14_713.48, WER: 0%, TER: 0%, total WER: 19.0531%, total TER: 8.49482%, progress (thread 0): 88.1962%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_859.83_860.17, WER: 100%, TER: 66.6667%, total WER: 19.0541%, total TER: 8.49524%, progress (thread 0): 88.2041%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002a_H01_FEO070_920.16_920.5, WER: 100%, TER: 28.5714%, total WER: 19.055%, total TER: 8.49557%, progress (thread 0): 88.212%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1586.73_1587.07, WER: 0%, TER: 0%, total WER: 19.0548%, total TER: 8.49549%, progress (thread 0): 88.2199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1927.75_1928.09, WER: 0%, TER: 0%, total WER: 19.0545%, total TER: 8.49541%, progress (thread 0): 88.2279%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1929.86_1930.2, WER: 0%, TER: 0%, total WER: 19.0543%, total TER: 8.49533%, progress (thread 0): 88.2358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2048.6_2048.94, WER: 0%, TER: 0%, total WER: 19.0541%, total TER: 8.49525%, progress (thread 0): 88.2437%]
|T|: s o | i t ' s
|P|: t m
[sample: EN2002a_H03_MEE071_2098.66_2099, WER: 100%, TER: 85.7143%, total WER: 19.0559%, total TER: 8.49652%, progress (thread 0): 88.2516%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_2129.89_2130.23, WER: 0%, TER: 0%, total WER: 19.0557%, total TER: 8.49644%, progress (thread 0): 88.2595%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_144.51_144.85, WER: 0%, TER: 0%, total WER: 19.0555%, total TER: 8.49636%, progress (thread 0): 88.2674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_190.18_190.52, WER: 0%, TER: 0%, total WER: 19.0553%, total TER: 8.49628%, progress (thread 0): 88.2753%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_302.27_302.61, WER: 100%, TER: 66.6667%, total WER: 19.0562%, total TER: 8.49669%, progress (thread 0): 88.2832%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_309.52_309.86, WER: 0%, TER: 0%, total WER: 19.056%, total TER: 8.49661%, progress (thread 0): 88.2911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_363.35_363.69, WER: 0%, TER: 0%, total WER: 19.0558%, total TER: 8.49653%, progress (thread 0): 88.299%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_409.14_409.48, WER: 0%, TER: 0%, total WER: 19.0556%, total TER: 8.49645%, progress (thread 0): 88.307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_736.4_736.74, WER: 0%, TER: 0%, total WER: 19.0553%, total TER: 8.49637%, progress (thread 0): 88.3149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_935.86_936.2, WER: 0%, TER: 0%, total WER: 19.0551%, total TER: 8.49629%, progress (thread 0): 88.3228%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002b_H03_MEE073_1252.52_1252.86, WER: 0%, TER: 0%, total WER: 19.0547%, total TER: 8.49615%, progress (thread 0): 88.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1563.14_1563.48, WER: 0%, TER: 0%, total WER: 19.0545%, total TER: 8.49607%, progress (thread 0): 88.3386%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1626.33_1626.67, WER: 0%, TER: 0%, total WER: 19.0543%, total TER: 8.49599%, progress (thread 0): 88.3465%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H01_FEO072_67.59_67.93, WER: 0%, TER: 0%, total WER: 19.054%, total TER: 8.49591%, progress (thread 0): 88.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_273.14_273.48, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49583%, progress (thread 0): 88.3623%]
|T|: m m h m m
|P|: m m m m
[sample: EN2002c_H03_MEE073_444.82_445.16, WER: 100%, TER: 20%, total WER: 19.0548%, total TER: 8.49596%, progress (thread 0): 88.3703%]
|T|: o r
|P|: m h
[sample: EN2002c_H01_FEO072_486.4_486.74, WER: 100%, TER: 100%, total WER: 19.0557%, total TER: 8.4964%, progress (thread 0): 88.3782%]
|T|: o h | w e l l
|P|: o h
[sample: EN2002c_H03_MEE073_497.23_497.57, WER: 50%, TER: 71.4286%, total WER: 19.0564%, total TER: 8.49743%, progress (thread 0): 88.3861%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002c_H01_FEO072_721.91_722.25, WER: 200%, TER: 14.2857%, total WER: 19.0584%, total TER: 8.49753%, progress (thread 0): 88.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1305.34_1305.68, WER: 0%, TER: 0%, total WER: 19.0582%, total TER: 8.49745%, progress (thread 0): 88.4019%]
|T|: o h
|P|: o h
[sample: EN2002c_H01_FEO072_1925.9_1926.24, WER: 0%, TER: 0%, total WER: 19.058%, total TER: 8.49741%, progress (thread 0): 88.4098%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2040.49_2040.83, WER: 0%, TER: 0%, total WER: 19.0578%, total TER: 8.49733%, progress (thread 0): 88.4177%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002c_H03_MEE073_2245.81_2246.15, WER: 100%, TER: 20%, total WER: 19.0587%, total TER: 8.49746%, progress (thread 0): 88.4256%]
|T|: y e a h
|P|: r e a h
[sample: EN2002d_H03_MEE073_344.02_344.36, WER: 100%, TER: 25%, total WER: 19.0596%, total TER: 8.49762%, progress (thread 0): 88.4335%]
|T|: w h a t | w a s | i t
|P|: w h a | w a s | i t
[sample: EN2002d_H03_MEE073_508.22_508.56, WER: 33.3333%, TER: 9.09091%, total WER: 19.0601%, total TER: 8.49763%, progress (thread 0): 88.4415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_688.4_688.74, WER: 0%, TER: 0%, total WER: 19.0599%, total TER: 8.49755%, progress (thread 0): 88.4494%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_715.16_715.5, WER: 0%, TER: 0%, total WER: 19.0597%, total TER: 8.49747%, progress (thread 0): 88.4573%]
|T|: u h
|P|: m m
[sample: EN2002d_H03_MEE073_1125.77_1126.11, WER: 100%, TER: 100%, total WER: 19.0606%, total TER: 8.4979%, progress (thread 0): 88.4652%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1325.04_1325.38, WER: 100%, TER: 33.3333%, total WER: 19.0615%, total TER: 8.49808%, progress (thread 0): 88.4731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1423.14_1423.48, WER: 0%, TER: 0%, total WER: 19.0613%, total TER: 8.498%, progress (thread 0): 88.481%]
|T|: n o | w e | p
|P|: n o | w e
[sample: EN2002d_H00_FEO070_1437.94_1438.28, WER: 33.3333%, TER: 28.5714%, total WER: 19.0618%, total TER: 8.49833%, progress (thread 0): 88.4889%]
|T|: w i t h
|P|: w i t h
[sample: EN2002d_H02_MEE071_2073.15_2073.49, WER: 0%, TER: 0%, total WER: 19.0616%, total TER: 8.49825%, progress (thread 0): 88.4968%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_258.31_258.64, WER: 100%, TER: 0%, total WER: 19.0625%, total TER: 8.49815%, progress (thread 0): 88.5048%]
|T|: m m | y e a h
|P|: m y e a h
[sample: ES2004a_H00_MEO015_1048.05_1048.38, WER: 100%, TER: 28.5714%, total WER: 19.0643%, total TER: 8.49848%, progress (thread 0): 88.5127%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_736.03_736.36, WER: 0%, TER: 0%, total WER: 19.0641%, total TER: 8.4984%, progress (thread 0): 88.5206%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1313.86_1314.19, WER: 0%, TER: 0%, total WER: 19.0639%, total TER: 8.49832%, progress (thread 0): 88.5285%]
|T|: m m
|P|: m m
[sample: ES2004b_H01_FEE013_1711.46_1711.79, WER: 0%, TER: 0%, total WER: 19.0637%, total TER: 8.49828%, progress (thread 0): 88.5364%]
|T|: u h h u h
|P|: m h | h u h
[sample: ES2004b_H03_FEE016_2208.79_2209.12, WER: 200%, TER: 40%, total WER: 19.0657%, total TER: 8.49865%, progress (thread 0): 88.5443%]
|T|: o k a y
|P|: o k a y
[sample: ES2004b_H03_FEE016_2308.52_2308.85, WER: 0%, TER: 0%, total WER: 19.0655%, total TER: 8.49857%, progress (thread 0): 88.5522%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_172.32_172.65, WER: 0%, TER: 0%, total WER: 19.0653%, total TER: 8.49849%, progress (thread 0): 88.5601%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_560.34_560.67, WER: 0%, TER: 0%, total WER: 19.0651%, total TER: 8.49845%, progress (thread 0): 88.568%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H01_FEE013_873.37_873.7, WER: 100%, TER: 0%, total WER: 19.066%, total TER: 8.49835%, progress (thread 0): 88.576%]
|T|: w e l l
|P|: w e l l
[sample: ES2004c_H02_MEE014_925.27_925.6, WER: 0%, TER: 0%, total WER: 19.0658%, total TER: 8.49827%, progress (thread 0): 88.5839%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1268.11_1268.44, WER: 0%, TER: 0%, total WER: 19.0656%, total TER: 8.49819%, progress (thread 0): 88.5918%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1965.56_1965.89, WER: 100%, TER: 0%, total WER: 19.0665%, total TER: 8.49809%, progress (thread 0): 88.5997%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H01_FEE013_607.94_608.27, WER: 100%, TER: 0%, total WER: 19.0674%, total TER: 8.49799%, progress (thread 0): 88.6076%]
|T|: t w o
|P|: t w o
[sample: ES2004d_H02_MEE014_753.08_753.41, WER: 0%, TER: 0%, total WER: 19.0672%, total TER: 8.49793%, progress (thread 0): 88.6155%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H03_FEE016_876.07_876.4, WER: 100%, TER: 0%, total WER: 19.0681%, total TER: 8.49783%, progress (thread 0): 88.6234%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H00_MEO015_1304.99_1305.32, WER: 0%, TER: 0%, total WER: 19.0679%, total TER: 8.49777%, progress (thread 0): 88.6313%]
|T|: s u r e
|P|: s u r e
[sample: ES2004d_H03_FEE016_1499.95_1500.28, WER: 0%, TER: 0%, total WER: 19.0677%, total TER: 8.49769%, progress (thread 0): 88.6392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1845.84_1846.17, WER: 0%, TER: 0%, total WER: 19.0674%, total TER: 8.49761%, progress (thread 0): 88.6471%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1856.75_1857.08, WER: 0%, TER: 0%, total WER: 19.0672%, total TER: 8.49753%, progress (thread 0): 88.6551%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1984.68_1985.01, WER: 0%, TER: 0%, total WER: 19.067%, total TER: 8.49745%, progress (thread 0): 88.663%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_2126.04_2126.37, WER: 0%, TER: 0%, total WER: 19.0668%, total TER: 8.49741%, progress (thread 0): 88.6709%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_55.7_56.03, WER: 0%, TER: 0%, total WER: 19.0666%, total TER: 8.49733%, progress (thread 0): 88.6788%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_226.94_227.27, WER: 100%, TER: 0%, total WER: 19.0675%, total TER: 8.49723%, progress (thread 0): 88.6867%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_318.12_318.45, WER: 0%, TER: 0%, total WER: 19.0673%, total TER: 8.49715%, progress (thread 0): 88.6946%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_652.91_653.24, WER: 100%, TER: 0%, total WER: 19.0682%, total TER: 8.49705%, progress (thread 0): 88.7025%]
|T|: t h e n
|P|: a n d | t h e n
[sample: IS1009a_H00_FIE088_714.21_714.54, WER: 100%, TER: 100%, total WER: 19.0691%, total TER: 8.49791%, progress (thread 0): 88.7104%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_741.37_741.7, WER: 100%, TER: 0%, total WER: 19.07%, total TER: 8.49781%, progress (thread 0): 88.7184%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_790.95_791.28, WER: 100%, TER: 0%, total WER: 19.0709%, total TER: 8.49771%, progress (thread 0): 88.7263%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_787.47_787.8, WER: 100%, TER: 0%, total WER: 19.0719%, total TER: 8.49761%, progress (thread 0): 88.7342%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1063.18_1063.51, WER: 100%, TER: 0%, total WER: 19.0728%, total TER: 8.49751%, progress (thread 0): 88.7421%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1809.47_1809.8, WER: 0%, TER: 0%, total WER: 19.0726%, total TER: 8.49743%, progress (thread 0): 88.75%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1882.22_1882.55, WER: 100%, TER: 0%, total WER: 19.0735%, total TER: 8.49733%, progress (thread 0): 88.7579%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_408.02_408.35, WER: 100%, TER: 0%, total WER: 19.0744%, total TER: 8.49723%, progress (thread 0): 88.7658%]
|T|: m m h m m
|P|: m m
[sample: IS1009c_H00_FIE088_430.74_431.07, WER: 100%, TER: 60%, total WER: 19.0753%, total TER: 8.49784%, progress (thread 0): 88.7737%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1552.21_1552.54, WER: 0%, TER: 0%, total WER: 19.0751%, total TER: 8.49776%, progress (thread 0): 88.7816%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_383.89_384.22, WER: 100%, TER: 0%, total WER: 19.076%, total TER: 8.49766%, progress (thread 0): 88.7896%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_388.95_389.28, WER: 100%, TER: 0%, total WER: 19.0769%, total TER: 8.49756%, progress (thread 0): 88.7975%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_483.18_483.51, WER: 100%, TER: 0%, total WER: 19.0779%, total TER: 8.49746%, progress (thread 0): 88.8054%]
|T|: t h a t ' s | r i g h t
|P|: t h a t ' s | r g h t
[sample: IS1009d_H00_FIE088_757.72_758.05, WER: 50%, TER: 8.33333%, total WER: 19.0786%, total TER: 8.49745%, progress (thread 0): 88.8133%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H01_FIO087_784.18_784.51, WER: 100%, TER: 0%, total WER: 19.0795%, total TER: 8.49735%, progress (thread 0): 88.8212%]
|T|: y e a h
|P|: m m
[sample: IS1009d_H02_FIO084_975.71_976.04, WER: 100%, TER: 100%, total WER: 19.0804%, total TER: 8.49822%, progress (thread 0): 88.8291%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_1183.1_1183.43, WER: 0%, TER: 0%, total WER: 19.0802%, total TER: 8.49818%, progress (thread 0): 88.837%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H02_FIO084_1185.85_1186.18, WER: 100%, TER: 0%, total WER: 19.0811%, total TER: 8.49808%, progress (thread 0): 88.8449%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_1310.32_1310.65, WER: 100%, TER: 40%, total WER: 19.082%, total TER: 8.49845%, progress (thread 0): 88.8528%]
|T|: u h
|P|: u m
[sample: TS3003a_H01_MTD011UID_915.22_915.55, WER: 100%, TER: 50%, total WER: 19.0829%, total TER: 8.49864%, progress (thread 0): 88.8608%]
|T|: o r | w h o
|P|: o h | w h o
[sample: TS3003a_H01_MTD011UID_975.03_975.36, WER: 50%, TER: 16.6667%, total WER: 19.0836%, total TER: 8.49876%, progress (thread 0): 88.8687%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1261.99_1262.32, WER: 0%, TER: 0%, total WER: 19.0834%, total TER: 8.49872%, progress (thread 0): 88.8766%]
|T|: s o
|P|: s o
[sample: TS3003b_H03_MTD012ME_280.47_280.8, WER: 0%, TER: 0%, total WER: 19.0832%, total TER: 8.49868%, progress (thread 0): 88.8845%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_842.62_842.95, WER: 0%, TER: 0%, total WER: 19.083%, total TER: 8.4986%, progress (thread 0): 88.8924%]
|T|: o k a y
|P|: h m h
[sample: TS3003b_H00_MTD009PM_923.51_923.84, WER: 100%, TER: 100%, total WER: 19.0839%, total TER: 8.49946%, progress (thread 0): 88.9003%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1173.66_1173.99, WER: 0%, TER: 0%, total WER: 19.0837%, total TER: 8.49938%, progress (thread 0): 88.9082%]
|T|: n a h
|P|: n e a h
[sample: TS3003c_H01_MTD011UID_1009.07_1009.4, WER: 100%, TER: 33.3333%, total WER: 19.0846%, total TER: 8.49955%, progress (thread 0): 88.9161%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1321.92_1322.25, WER: 100%, TER: 25%, total WER: 19.0855%, total TER: 8.49971%, progress (thread 0): 88.924%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1395.11_1395.44, WER: 0%, TER: 0%, total WER: 19.0853%, total TER: 8.49967%, progress (thread 0): 88.932%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1507.79_1508.12, WER: 0%, TER: 0%, total WER: 19.0851%, total TER: 8.49959%, progress (thread 0): 88.9399%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_1540.01_1540.34, WER: 100%, TER: 0%, total WER: 19.086%, total TER: 8.49949%, progress (thread 0): 88.9478%]
|T|: y e p
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1653.43_1653.76, WER: 100%, TER: 66.6667%, total WER: 19.0869%, total TER: 8.4999%, progress (thread 0): 88.9557%]
|T|: u h
|P|: m h m m
[sample: TS3003c_H01_MTD011UID_1692.63_1692.96, WER: 100%, TER: 150%, total WER: 19.0878%, total TER: 8.50056%, progress (thread 0): 88.9636%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2074.03_2074.36, WER: 0%, TER: 0%, total WER: 19.0876%, total TER: 8.50048%, progress (thread 0): 88.9715%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2241.48_2241.81, WER: 0%, TER: 0%, total WER: 19.0874%, total TER: 8.5004%, progress (thread 0): 88.9794%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_693.96_694.29, WER: 0%, TER: 0%, total WER: 19.0872%, total TER: 8.50032%, progress (thread 0): 88.9873%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_872.34_872.67, WER: 0%, TER: 0%, total WER: 19.087%, total TER: 8.50024%, progress (thread 0): 88.9953%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_889.13_889.46, WER: 0%, TER: 0%, total WER: 19.0868%, total TER: 8.50016%, progress (thread 0): 89.0032%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_1661.82_1662.15, WER: 0%, TER: 0%, total WER: 19.0865%, total TER: 8.50008%, progress (thread 0): 89.0111%]
|T|: y e a h
|P|: n a h
[sample: TS3003d_H01_MTD011UID_2072.21_2072.54, WER: 100%, TER: 50%, total WER: 19.0875%, total TER: 8.50047%, progress (thread 0): 89.019%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_2085.82_2086.15, WER: 100%, TER: 0%, total WER: 19.0884%, total TER: 8.50037%, progress (thread 0): 89.0269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2164.01_2164.34, WER: 0%, TER: 0%, total WER: 19.0882%, total TER: 8.50029%, progress (thread 0): 89.0348%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2179.8_2180.13, WER: 0%, TER: 0%, total WER: 19.0879%, total TER: 8.50021%, progress (thread 0): 89.0427%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_2461.76_2462.09, WER: 100%, TER: 0%, total WER: 19.0889%, total TER: 8.50011%, progress (thread 0): 89.0506%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_299.48_299.81, WER: 0%, TER: 0%, total WER: 19.0886%, total TER: 8.50003%, progress (thread 0): 89.0585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_359.5_359.83, WER: 0%, TER: 0%, total WER: 19.0884%, total TER: 8.49995%, progress (thread 0): 89.0665%]
|T|: w a i t
|P|: w a i t
[sample: EN2002a_H02_FEO072_387.53_387.86, WER: 0%, TER: 0%, total WER: 19.0882%, total TER: 8.49987%, progress (thread 0): 89.0744%]
|T|: y e a h | y e a h
|P|: e h | y e a h
[sample: EN2002a_H03_MEE071_593.65_593.98, WER: 50%, TER: 22.2222%, total WER: 19.0889%, total TER: 8.50016%, progress (thread 0): 89.0823%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_594.32_594.65, WER: 0%, TER: 0%, total WER: 19.0887%, total TER: 8.50008%, progress (thread 0): 89.0902%]
|T|: y e a h
|P|: n e a h
[sample: EN2002a_H01_FEO070_690.97_691.3, WER: 100%, TER: 25%, total WER: 19.0896%, total TER: 8.50024%, progress (thread 0): 89.0981%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_1018.98_1019.31, WER: 0%, TER: 0%, total WER: 19.0894%, total TER: 8.50016%, progress (thread 0): 89.106%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1028.68_1029.01, WER: 0%, TER: 0%, total WER: 19.0892%, total TER: 8.50008%, progress (thread 0): 89.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1142.23_1142.56, WER: 0%, TER: 0%, total WER: 19.089%, total TER: 8.5%, progress (thread 0): 89.1218%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1361.22_1361.55, WER: 0%, TER: 0%, total WER: 19.0887%, total TER: 8.49992%, progress (thread 0): 89.1297%]
|T|: r i g h t
|P|: i h
[sample: EN2002a_H03_MEE071_1383.46_1383.79, WER: 100%, TER: 60%, total WER: 19.0897%, total TER: 8.50053%, progress (thread 0): 89.1377%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1864.48_1864.81, WER: 0%, TER: 0%, total WER: 19.0894%, total TER: 8.50045%, progress (thread 0): 89.1456%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1964.31_1964.64, WER: 0%, TER: 0%, total WER: 19.0892%, total TER: 8.50037%, progress (thread 0): 89.1535%]
|T|: u m
|P|: m m
[sample: EN2002b_H03_MEE073_139.44_139.77, WER: 100%, TER: 50%, total WER: 19.0901%, total TER: 8.50056%, progress (thread 0): 89.1614%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_417.94_418.27, WER: 0%, TER: 0%, total WER: 19.0899%, total TER: 8.50048%, progress (thread 0): 89.1693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_420.46_420.79, WER: 0%, TER: 0%, total WER: 19.0897%, total TER: 8.5004%, progress (thread 0): 89.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_705.52_705.85, WER: 0%, TER: 0%, total WER: 19.0895%, total TER: 8.50032%, progress (thread 0): 89.1851%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_731.78_732.11, WER: 0%, TER: 0%, total WER: 19.0893%, total TER: 8.50024%, progress (thread 0): 89.193%]
|T|: y e a h | t
|P|: y e a h
[sample: EN2002b_H02_FEO072_1235.77_1236.1, WER: 50%, TER: 33.3333%, total WER: 19.09%, total TER: 8.50059%, progress (thread 0): 89.201%]
|T|: h m m
|P|: m m m
[sample: EN2002b_H03_MEE073_1236.72_1237.05, WER: 100%, TER: 33.3333%, total WER: 19.0909%, total TER: 8.50077%, progress (thread 0): 89.2089%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_1256.5_1256.83, WER: 0%, TER: 0%, total WER: 19.0907%, total TER: 8.50069%, progress (thread 0): 89.2168%]
|T|: h m m
|P|: m m m
[sample: EN2002b_H02_FEO072_1475.51_1475.84, WER: 100%, TER: 33.3333%, total WER: 19.0916%, total TER: 8.50086%, progress (thread 0): 89.2247%]
|T|: o h | r i g h t
|P|: a l | r i g h t
[sample: EN2002b_H02_FEO072_1611.8_1612.13, WER: 50%, TER: 25%, total WER: 19.0923%, total TER: 8.50117%, progress (thread 0): 89.2326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_93.86_94.19, WER: 0%, TER: 0%, total WER: 19.0921%, total TER: 8.50109%, progress (thread 0): 89.2405%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_245.2_245.53, WER: 0%, TER: 0%, total WER: 19.0919%, total TER: 8.50101%, progress (thread 0): 89.2484%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H01_FEO072_447.06_447.39, WER: 100%, TER: 0%, total WER: 19.0928%, total TER: 8.50091%, progress (thread 0): 89.2563%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002c_H03_MEE073_823.26_823.59, WER: 0%, TER: 0%, total WER: 19.0924%, total TER: 8.50077%, progress (thread 0): 89.2642%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_850.62_850.95, WER: 0%, TER: 0%, total WER: 19.0921%, total TER: 8.50069%, progress (thread 0): 89.2722%]
|T|: t h a t ' s | a
|P|: o s a y
[sample: EN2002c_H03_MEE073_903.73_904.06, WER: 100%, TER: 87.5%, total WER: 19.094%, total TER: 8.50218%, progress (thread 0): 89.2801%]
|T|: o h
|P|: o h
[sample: EN2002c_H03_MEE073_962_962.33, WER: 0%, TER: 0%, total WER: 19.0938%, total TER: 8.50214%, progress (thread 0): 89.288%]
|T|: y e a h | r i g h t
|P|: y e a h | r g h t
[sample: EN2002c_H03_MEE073_1287.41_1287.74, WER: 50%, TER: 10%, total WER: 19.0945%, total TER: 8.50217%, progress (thread 0): 89.2959%]
|T|: t h a t ' s | t r u e
|P|: t h a t ' s | t r e
[sample: EN2002c_H03_MEE073_1299.73_1300.06, WER: 50%, TER: 9.09091%, total WER: 19.0952%, total TER: 8.50219%, progress (thread 0): 89.3038%]
|T|: y o u | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2044.96_2045.29, WER: 0%, TER: 0%, total WER: 19.0947%, total TER: 8.50203%, progress (thread 0): 89.3117%]
|T|: m m
|P|: m m
[sample: EN2002c_H02_MEE071_2072_2072.33, WER: 0%, TER: 0%, total WER: 19.0945%, total TER: 8.50199%, progress (thread 0): 89.3196%]
|T|: o o h
|P|: o o h
[sample: EN2002c_H01_FEO072_2817.88_2818.21, WER: 0%, TER: 0%, total WER: 19.0943%, total TER: 8.50193%, progress (thread 0): 89.3275%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002d_H01_FEO072_419.86_420.19, WER: 200%, TER: 14.2857%, total WER: 19.0963%, total TER: 8.50202%, progress (thread 0): 89.3354%]
|T|: m m
|P|: u m
[sample: EN2002d_H03_MEE073_488.3_488.63, WER: 100%, TER: 50%, total WER: 19.0973%, total TER: 8.50222%, progress (thread 0): 89.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1089.65_1089.98, WER: 0%, TER: 0%, total WER: 19.097%, total TER: 8.50214%, progress (thread 0): 89.3513%]
|T|: w e l l
|P|: n o
[sample: EN2002d_H03_MEE073_1132.11_1132.44, WER: 100%, TER: 100%, total WER: 19.098%, total TER: 8.503%, progress (thread 0): 89.3592%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1131.27_1131.6, WER: 100%, TER: 33.3333%, total WER: 19.0989%, total TER: 8.50317%, progress (thread 0): 89.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1257.89_1258.22, WER: 0%, TER: 0%, total WER: 19.0987%, total TER: 8.50309%, progress (thread 0): 89.375%]
|T|: r e a l l y
|P|: r e a l l y
[sample: EN2002d_H01_FEO072_1345.97_1346.3, WER: 0%, TER: 0%, total WER: 19.0984%, total TER: 8.50297%, progress (thread 0): 89.3829%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1429.74_1430.07, WER: 100%, TER: 33.3333%, total WER: 19.0994%, total TER: 8.50315%, progress (thread 0): 89.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1957.8_1958.13, WER: 0%, TER: 0%, total WER: 19.0991%, total TER: 8.50307%, progress (thread 0): 89.3987%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_863.21_863.53, WER: 100%, TER: 0%, total WER: 19.1001%, total TER: 8.50297%, progress (thread 0): 89.4066%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_895.43_895.75, WER: 100%, TER: 0%, total WER: 19.101%, total TER: 8.50287%, progress (thread 0): 89.4146%]
|T|: m m
|P|: m m
[sample: ES2004b_H01_FEE013_1140.58_1140.9, WER: 0%, TER: 0%, total WER: 19.1008%, total TER: 8.50283%, progress (thread 0): 89.4225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1182.75_1183.07, WER: 0%, TER: 0%, total WER: 19.1005%, total TER: 8.50275%, progress (thread 0): 89.4304%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1471.32_1471.64, WER: 100%, TER: 0%, total WER: 19.1015%, total TER: 8.50265%, progress (thread 0): 89.4383%]
|T|: s
|P|: m m
[sample: ES2004b_H01_FEE013_1922.51_1922.83, WER: 100%, TER: 200%, total WER: 19.1024%, total TER: 8.5031%, progress (thread 0): 89.4462%]
|T|: m m h m m
|P|: m m
[sample: ES2004b_H03_FEE016_1953.34_1953.66, WER: 100%, TER: 60%, total WER: 19.1033%, total TER: 8.5037%, progress (thread 0): 89.4541%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1966.24_1966.56, WER: 0%, TER: 0%, total WER: 19.1031%, total TER: 8.50362%, progress (thread 0): 89.462%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_806.95_807.27, WER: 0%, TER: 0%, total WER: 19.1029%, total TER: 8.50354%, progress (thread 0): 89.4699%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H03_FEE016_1066.11_1066.43, WER: 0%, TER: 0%, total WER: 19.1026%, total TER: 8.50346%, progress (thread 0): 89.4779%]
|T|: m m
|P|: m m
[sample: ES2004c_H00_MEO015_1890.24_1890.56, WER: 0%, TER: 0%, total WER: 19.1024%, total TER: 8.50342%, progress (thread 0): 89.4858%]
|T|: y e a h
|P|: y e a p
[sample: ES2004d_H00_MEO015_341.64_341.96, WER: 100%, TER: 25%, total WER: 19.1033%, total TER: 8.50358%, progress (thread 0): 89.4937%]
|T|: n o
|P|: n o
[sample: ES2004d_H00_MEO015_400.96_401.28, WER: 0%, TER: 0%, total WER: 19.1031%, total TER: 8.50354%, progress (thread 0): 89.5016%]
|T|: o o p s
|P|: i p s
[sample: ES2004d_H03_FEE016_430.55_430.87, WER: 100%, TER: 50%, total WER: 19.104%, total TER: 8.50393%, progress (thread 0): 89.5095%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_774.15_774.47, WER: 0%, TER: 0%, total WER: 19.1038%, total TER: 8.50385%, progress (thread 0): 89.5174%]
|T|: o n e
|P|: w e l l
[sample: ES2004d_H02_MEE014_785.53_785.85, WER: 100%, TER: 133.333%, total WER: 19.1047%, total TER: 8.50473%, progress (thread 0): 89.5253%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_840.47_840.79, WER: 0%, TER: 0%, total WER: 19.1045%, total TER: 8.50467%, progress (thread 0): 89.5332%]
|T|: m m h m m
|P|: m m m m
[sample: ES2004d_H03_FEE016_1930.95_1931.27, WER: 100%, TER: 20%, total WER: 19.1054%, total TER: 8.5048%, progress (thread 0): 89.5411%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2146.44_2146.76, WER: 0%, TER: 0%, total WER: 19.1052%, total TER: 8.50472%, progress (thread 0): 89.549%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_290.82_291.14, WER: 100%, TER: 0%, total WER: 19.1061%, total TER: 8.50462%, progress (thread 0): 89.557%]
|T|: o o p s
|P|: s o
[sample: IS1009a_H03_FIO089_420.14_420.46, WER: 100%, TER: 75%, total WER: 19.1071%, total TER: 8.50525%, progress (thread 0): 89.5649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H01_FIO087_494.72_495.04, WER: 0%, TER: 0%, total WER: 19.1068%, total TER: 8.50517%, progress (thread 0): 89.5728%]
|T|: y e a h
|P|: h
[sample: IS1009a_H02_FIO084_577.05_577.37, WER: 100%, TER: 75%, total WER: 19.1078%, total TER: 8.50579%, progress (thread 0): 89.5807%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H02_FIO084_641.8_642.12, WER: 100%, TER: 20%, total WER: 19.1087%, total TER: 8.50593%, progress (thread 0): 89.5886%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_197.33_197.65, WER: 0%, TER: 0%, total WER: 19.1085%, total TER: 8.50585%, progress (thread 0): 89.5965%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_595.82_596.14, WER: 100%, TER: 60%, total WER: 19.1094%, total TER: 8.50645%, progress (thread 0): 89.6044%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1073.83_1074.15, WER: 100%, TER: 0%, total WER: 19.1103%, total TER: 8.50635%, progress (thread 0): 89.6123%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1139.48_1139.8, WER: 100%, TER: 0%, total WER: 19.1112%, total TER: 8.50625%, progress (thread 0): 89.6202%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1268.25_1268.57, WER: 100%, TER: 0%, total WER: 19.1121%, total TER: 8.50615%, progress (thread 0): 89.6282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1877.89_1878.21, WER: 0%, TER: 0%, total WER: 19.1119%, total TER: 8.50607%, progress (thread 0): 89.6361%]
|T|: m m h m m
|P|: m e n
[sample: IS1009b_H03_FIO089_1894.73_1895.05, WER: 100%, TER: 80%, total WER: 19.1128%, total TER: 8.50691%, progress (thread 0): 89.644%]
|T|: h i
|P|: k a y
[sample: IS1009c_H01_FIO087_43.77_44.09, WER: 100%, TER: 150%, total WER: 19.1137%, total TER: 8.50758%, progress (thread 0): 89.6519%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_405.29_405.61, WER: 100%, TER: 0%, total WER: 19.1146%, total TER: 8.50748%, progress (thread 0): 89.6598%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_524.19_524.51, WER: 100%, TER: 0%, total WER: 19.1156%, total TER: 8.50738%, progress (thread 0): 89.6677%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_1640_1640.32, WER: 0%, TER: 0%, total WER: 19.1153%, total TER: 8.50732%, progress (thread 0): 89.6756%]
|T|: m e n u
|P|: m e n u
[sample: IS1009d_H00_FIE088_523.67_523.99, WER: 0%, TER: 0%, total WER: 19.1151%, total TER: 8.50724%, progress (thread 0): 89.6835%]
|T|: p a r d o n | m e
|P|: b u d d o n
[sample: IS1009d_H01_FIO087_699.16_699.48, WER: 100%, TER: 66.6667%, total WER: 19.117%, total TER: 8.50847%, progress (thread 0): 89.6915%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_799.6_799.92, WER: 100%, TER: 60%, total WER: 19.1179%, total TER: 8.50907%, progress (thread 0): 89.6994%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1056.69_1057.01, WER: 0%, TER: 0%, total WER: 19.1177%, total TER: 8.50899%, progress (thread 0): 89.7073%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1722.84_1723.16, WER: 100%, TER: 0%, total WER: 19.1186%, total TER: 8.50889%, progress (thread 0): 89.7152%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1741.45_1741.77, WER: 100%, TER: 0%, total WER: 19.1195%, total TER: 8.50879%, progress (thread 0): 89.7231%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1791.05_1791.37, WER: 0%, TER: 0%, total WER: 19.1193%, total TER: 8.50871%, progress (thread 0): 89.731%]
|T|: y e a h
|P|: m m
[sample: TS3003b_H01_MTD011UID_1651.31_1651.63, WER: 100%, TER: 100%, total WER: 19.1202%, total TER: 8.50957%, progress (thread 0): 89.7389%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_1722.53_1722.85, WER: 100%, TER: 25%, total WER: 19.1211%, total TER: 8.50973%, progress (thread 0): 89.7468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2090.25_2090.57, WER: 0%, TER: 0%, total WER: 19.1209%, total TER: 8.50965%, progress (thread 0): 89.7547%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_2074.3_2074.62, WER: 100%, TER: 0%, total WER: 19.1218%, total TER: 8.50955%, progress (thread 0): 89.7627%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_2134.48_2134.8, WER: 100%, TER: 0%, total WER: 19.1227%, total TER: 8.50945%, progress (thread 0): 89.7706%]
|T|: n a y
|P|: n o
[sample: TS3003d_H01_MTD011UID_417.84_418.16, WER: 100%, TER: 66.6667%, total WER: 19.1236%, total TER: 8.50986%, progress (thread 0): 89.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_694.59_694.91, WER: 0%, TER: 0%, total WER: 19.1234%, total TER: 8.50978%, progress (thread 0): 89.7864%]
|T|: o r | b
|P|: o r
[sample: TS3003d_H03_MTD012ME_896.59_896.91, WER: 50%, TER: 50%, total WER: 19.1241%, total TER: 8.51017%, progress (thread 0): 89.7943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1403.4_1403.72, WER: 0%, TER: 0%, total WER: 19.1239%, total TER: 8.51009%, progress (thread 0): 89.8022%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1492.24_1492.56, WER: 0%, TER: 0%, total WER: 19.1237%, total TER: 8.51001%, progress (thread 0): 89.8101%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_2024.39_2024.71, WER: 100%, TER: 100%, total WER: 19.1246%, total TER: 8.51087%, progress (thread 0): 89.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2381.84_2382.16, WER: 0%, TER: 0%, total WER: 19.1244%, total TER: 8.51079%, progress (thread 0): 89.826%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_68.23_68.55, WER: 0%, TER: 0%, total WER: 19.1242%, total TER: 8.51069%, progress (thread 0): 89.8339%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_359.54_359.86, WER: 50%, TER: 42.8571%, total WER: 19.1249%, total TER: 8.51125%, progress (thread 0): 89.8418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_496.88_497.2, WER: 0%, TER: 0%, total WER: 19.1247%, total TER: 8.51117%, progress (thread 0): 89.8497%]
|T|: h m m
|P|: n m m
[sample: EN2002a_H02_FEO072_504.18_504.5, WER: 100%, TER: 33.3333%, total WER: 19.1256%, total TER: 8.51135%, progress (thread 0): 89.8576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_567.01_567.33, WER: 0%, TER: 0%, total WER: 19.1254%, total TER: 8.51131%, progress (thread 0): 89.8655%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_604.34_604.66, WER: 100%, TER: 0%, total WER: 19.1263%, total TER: 8.51121%, progress (thread 0): 89.8734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_729.25_729.57, WER: 0%, TER: 0%, total WER: 19.1261%, total TER: 8.51113%, progress (thread 0): 89.8813%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_800.36_800.68, WER: 0%, TER: 0%, total WER: 19.1258%, total TER: 8.51103%, progress (thread 0): 89.8892%]
|T|: o h
|P|: n o
[sample: EN2002a_H02_FEO072_856.5_856.82, WER: 100%, TER: 100%, total WER: 19.1268%, total TER: 8.51146%, progress (thread 0): 89.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1050.6_1050.92, WER: 0%, TER: 0%, total WER: 19.1265%, total TER: 8.51138%, progress (thread 0): 89.9051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1101.18_1101.5, WER: 0%, TER: 0%, total WER: 19.1263%, total TER: 8.5113%, progress (thread 0): 89.913%]
|T|: o h | n o
|P|: o o
[sample: EN2002a_H01_FEO070_1448.67_1448.99, WER: 100%, TER: 60%, total WER: 19.1282%, total TER: 8.5119%, progress (thread 0): 89.9209%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1448.83_1449.15, WER: 100%, TER: 33.3333%, total WER: 19.1291%, total TER: 8.51208%, progress (thread 0): 89.9288%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1537.39_1537.71, WER: 0%, TER: 0%, total WER: 19.1289%, total TER: 8.512%, progress (thread 0): 89.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1862.6_1862.92, WER: 0%, TER: 0%, total WER: 19.1286%, total TER: 8.51192%, progress (thread 0): 89.9446%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_283.05_283.37, WER: 100%, TER: 100%, total WER: 19.1295%, total TER: 8.51278%, progress (thread 0): 89.9525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_363.29_363.61, WER: 0%, TER: 0%, total WER: 19.1293%, total TER: 8.5127%, progress (thread 0): 89.9604%]
|T|: o h | y e a h
|P|: m e a h
[sample: EN2002b_H03_MEE073_679.41_679.73, WER: 100%, TER: 57.1429%, total WER: 19.1312%, total TER: 8.5135%, progress (thread 0): 89.9684%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_922.82_923.14, WER: 0%, TER: 0%, total WER: 19.1309%, total TER: 8.51342%, progress (thread 0): 89.9763%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H02_FEO072_1100.2_1100.52, WER: 100%, TER: 0%, total WER: 19.1319%, total TER: 8.51332%, progress (thread 0): 89.9842%]
|T|: g o o d | p o i n t
|P|: t o e | p o i n t
[sample: EN2002b_H03_MEE073_1217.83_1218.15, WER: 50%, TER: 30%, total WER: 19.1326%, total TER: 8.51382%, progress (thread 0): 89.9921%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1261.76_1262.08, WER: 0%, TER: 0%, total WER: 19.1323%, total TER: 8.51374%, progress (thread 0): 90%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1598.38_1598.7, WER: 100%, TER: 33.3333%, total WER: 19.1333%, total TER: 8.51392%, progress (thread 0): 90.0079%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1611.81_1612.13, WER: 50%, TER: 55.5556%, total WER: 19.134%, total TER: 8.51491%, progress (thread 0): 90.0158%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1705.25_1705.57, WER: 0%, TER: 0%, total WER: 19.1337%, total TER: 8.51483%, progress (thread 0): 90.0237%]
|T|: g o o d
|P|: o k a y
[sample: EN2002c_H01_FEO072_489.73_490.05, WER: 100%, TER: 100%, total WER: 19.1347%, total TER: 8.51569%, progress (thread 0): 90.0316%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_653.98_654.3, WER: 0%, TER: 0%, total WER: 19.1344%, total TER: 8.51565%, progress (thread 0): 90.0396%]
|T|: y e a h
|P|: e h
[sample: EN2002c_H01_FEO072_1352.96_1353.28, WER: 100%, TER: 50%, total WER: 19.1354%, total TER: 8.51604%, progress (thread 0): 90.0475%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_2296.14_2296.46, WER: 0%, TER: 0%, total WER: 19.1351%, total TER: 8.516%, progress (thread 0): 90.0554%]
|T|: i | t h i n k
|P|: i | t h i n k
[sample: EN2002c_H01_FEO072_2336.35_2336.67, WER: 0%, TER: 0%, total WER: 19.1347%, total TER: 8.51586%, progress (thread 0): 90.0633%]
|T|: o h
|P|: e h
[sample: EN2002c_H01_FEO072_2368.69_2369.01, WER: 100%, TER: 50%, total WER: 19.1356%, total TER: 8.51605%, progress (thread 0): 90.0712%]
|T|: w h a t
|P|: w e l l
[sample: EN2002c_H01_FEO072_2484.6_2484.92, WER: 100%, TER: 75%, total WER: 19.1365%, total TER: 8.51668%, progress (thread 0): 90.0791%]
|T|: h m m
|P|: m m m
[sample: EN2002c_H03_MEE073_2800.26_2800.58, WER: 100%, TER: 33.3333%, total WER: 19.1375%, total TER: 8.51685%, progress (thread 0): 90.087%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_2829.54_2829.86, WER: 0%, TER: 0%, total WER: 19.1372%, total TER: 8.51677%, progress (thread 0): 90.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_347.79_348.11, WER: 0%, TER: 0%, total WER: 19.137%, total TER: 8.51669%, progress (thread 0): 90.1028%]
|T|: i t ' s | g o o d
|P|: t h a ' s | g o
[sample: EN2002d_H00_FEO070_420.4_420.72, WER: 100%, TER: 55.5556%, total WER: 19.1388%, total TER: 8.51769%, progress (thread 0): 90.1108%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002d_H01_FEO072_574.1_574.42, WER: 100%, TER: 20%, total WER: 19.1398%, total TER: 8.51782%, progress (thread 0): 90.1187%]
|T|: b u t
|P|: b u t
[sample: EN2002d_H01_FEO072_590.21_590.53, WER: 0%, TER: 0%, total WER: 19.1395%, total TER: 8.51776%, progress (thread 0): 90.1266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_769.35_769.67, WER: 0%, TER: 0%, total WER: 19.1393%, total TER: 8.51768%, progress (thread 0): 90.1345%]
|T|: o h
|P|: o h
[sample: EN2002d_H03_MEE073_939.78_940.1, WER: 0%, TER: 0%, total WER: 19.1391%, total TER: 8.51764%, progress (thread 0): 90.1424%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_954.69_955.01, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.51756%, progress (thread 0): 90.1503%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1189.57_1189.89, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51748%, progress (thread 0): 90.1582%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1203.2_1203.52, WER: 0%, TER: 0%, total WER: 19.1385%, total TER: 8.5174%, progress (thread 0): 90.1661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1255.64_1255.96, WER: 0%, TER: 0%, total WER: 19.1382%, total TER: 8.51732%, progress (thread 0): 90.174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1754.3_1754.62, WER: 0%, TER: 0%, total WER: 19.138%, total TER: 8.51724%, progress (thread 0): 90.182%]
|T|: s o
|P|: s o
[sample: EN2002d_H01_FEO072_1974.1_1974.42, WER: 0%, TER: 0%, total WER: 19.1378%, total TER: 8.5172%, progress (thread 0): 90.1899%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_2190.35_2190.67, WER: 0%, TER: 0%, total WER: 19.1376%, total TER: 8.51716%, progress (thread 0): 90.1978%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_582.6_582.91, WER: 0%, TER: 0%, total WER: 19.1374%, total TER: 8.51708%, progress (thread 0): 90.2057%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_687.25_687.56, WER: 100%, TER: 0%, total WER: 19.1383%, total TER: 8.51698%, progress (thread 0): 90.2136%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_688.13_688.44, WER: 0%, TER: 0%, total WER: 19.1381%, total TER: 8.5169%, progress (thread 0): 90.2215%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_777.34_777.65, WER: 100%, TER: 0%, total WER: 19.139%, total TER: 8.5168%, progress (thread 0): 90.2294%]
|T|: y
|P|: y
[sample: ES2004a_H02_MEE014_949.9_950.21, WER: 0%, TER: 0%, total WER: 19.1388%, total TER: 8.51678%, progress (thread 0): 90.2373%]
|T|: n o
|P|: n o
[sample: ES2004b_H03_FEE016_607.28_607.59, WER: 0%, TER: 0%, total WER: 19.1386%, total TER: 8.51674%, progress (thread 0): 90.2453%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1612.5_1612.81, WER: 100%, TER: 0%, total WER: 19.1395%, total TER: 8.51664%, progress (thread 0): 90.2532%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_2237.11_2237.42, WER: 0%, TER: 0%, total WER: 19.1393%, total TER: 8.51656%, progress (thread 0): 90.2611%]
|T|: b u t
|P|: b u t
[sample: ES2004c_H02_MEE014_571.73_572.04, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.5165%, progress (thread 0): 90.269%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1321.04_1321.35, WER: 0%, TER: 0%, total WER: 19.1388%, total TER: 8.51642%, progress (thread 0): 90.2769%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_1582.98_1583.29, WER: 0%, TER: 0%, total WER: 19.1386%, total TER: 8.51634%, progress (thread 0): 90.2848%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2288.73_2289.04, WER: 0%, TER: 0%, total WER: 19.1384%, total TER: 8.51626%, progress (thread 0): 90.2927%]
|T|: m m
|P|: h m m
[sample: ES2004d_H00_MEO015_846.15_846.46, WER: 100%, TER: 50%, total WER: 19.1393%, total TER: 8.51646%, progress (thread 0): 90.3006%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1669.25_1669.56, WER: 0%, TER: 0%, total WER: 19.1391%, total TER: 8.51638%, progress (thread 0): 90.3085%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1669.66_1669.97, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.5163%, progress (thread 0): 90.3165%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1825.79_1826.1, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51622%, progress (thread 0): 90.3244%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1901.99_1902.3, WER: 0%, TER: 0%, total WER: 19.1384%, total TER: 8.51614%, progress (thread 0): 90.3323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1967.13_1967.44, WER: 0%, TER: 0%, total WER: 19.1382%, total TER: 8.51606%, progress (thread 0): 90.3402%]
|T|: w e l l
|P|: o h
[sample: ES2004d_H02_MEE014_2085.81_2086.12, WER: 100%, TER: 100%, total WER: 19.1391%, total TER: 8.51692%, progress (thread 0): 90.3481%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2220.03_2220.34, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.51684%, progress (thread 0): 90.356%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_90.21_90.52, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51676%, progress (thread 0): 90.3639%]
|T|: u h
|P|: m m
[sample: IS1009a_H02_FIO084_451.06_451.37, WER: 100%, TER: 100%, total WER: 19.1396%, total TER: 8.51719%, progress (thread 0): 90.3718%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_702.97_703.28, WER: 0%, TER: 0%, total WER: 19.1394%, total TER: 8.51711%, progress (thread 0): 90.3797%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_795.57_795.88, WER: 0%, TER: 0%, total WER: 19.1392%, total TER: 8.51703%, progress (thread 0): 90.3877%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_218.1_218.41, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.51699%, progress (thread 0): 90.3956%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_616.32_616.63, WER: 100%, TER: 0%, total WER: 19.1399%, total TER: 8.51689%, progress (thread 0): 90.4035%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1692.92_1693.23, WER: 0%, TER: 0%, total WER: 19.1397%, total TER: 8.51681%, progress (thread 0): 90.4114%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1832.99_1833.3, WER: 0%, TER: 0%, total WER: 19.1395%, total TER: 8.51673%, progress (thread 0): 90.4193%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1844.23_1844.54, WER: 0%, TER: 0%, total WER: 19.1392%, total TER: 8.51667%, progress (thread 0): 90.4272%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H03_FIO089_1904.39_1904.7, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.51657%, progress (thread 0): 90.4351%]
|T|: m m h m m
|P|: m e m
[sample: IS1009c_H00_FIE088_1140.15_1140.46, WER: 100%, TER: 60%, total WER: 19.1399%, total TER: 8.51717%, progress (thread 0): 90.443%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_1163.03_1163.34, WER: 100%, TER: 0%, total WER: 19.1409%, total TER: 8.51707%, progress (thread 0): 90.451%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_1237.53_1237.84, WER: 100%, TER: 0%, total WER: 19.1418%, total TER: 8.51697%, progress (thread 0): 90.4589%]
|T|: ' k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1429.75_1430.06, WER: 100%, TER: 25%, total WER: 19.1427%, total TER: 8.51712%, progress (thread 0): 90.4668%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1554.5_1554.81, WER: 100%, TER: 0%, total WER: 19.1436%, total TER: 8.51702%, progress (thread 0): 90.4747%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H01_FIO087_1580.06_1580.37, WER: 100%, TER: 0%, total WER: 19.1445%, total TER: 8.51692%, progress (thread 0): 90.4826%]
|T|: o h
|P|: o h
[sample: IS1009c_H00_FIE088_1694.98_1695.29, WER: 0%, TER: 0%, total WER: 19.1443%, total TER: 8.51688%, progress (thread 0): 90.4905%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_363.39_363.7, WER: 100%, TER: 0%, total WER: 19.1452%, total TER: 8.51678%, progress (thread 0): 90.4984%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_894.86_895.17, WER: 0%, TER: 0%, total WER: 19.145%, total TER: 8.51674%, progress (thread 0): 90.5063%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_903.74_904.05, WER: 0%, TER: 0%, total WER: 19.1448%, total TER: 8.51666%, progress (thread 0): 90.5142%]
|T|: n o
|P|: n o
[sample: IS1009d_H00_FIE088_1011.62_1011.93, WER: 0%, TER: 0%, total WER: 19.1446%, total TER: 8.51662%, progress (thread 0): 90.5222%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1012.22_1012.53, WER: 100%, TER: 0%, total WER: 19.1455%, total TER: 8.51652%, progress (thread 0): 90.5301%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1105.97_1106.28, WER: 100%, TER: 0%, total WER: 19.1464%, total TER: 8.51642%, progress (thread 0): 90.538%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1171.27_1171.58, WER: 0%, TER: 0%, total WER: 19.1462%, total TER: 8.51634%, progress (thread 0): 90.5459%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1243.74_1244.05, WER: 100%, TER: 66.6667%, total WER: 19.1471%, total TER: 8.51675%, progress (thread 0): 90.5538%]
|T|: w h y
|P|: w o w
[sample: IS1009d_H00_FIE088_1442.55_1442.86, WER: 100%, TER: 66.6667%, total WER: 19.148%, total TER: 8.51716%, progress (thread 0): 90.5617%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1681.21_1681.52, WER: 0%, TER: 0%, total WER: 19.1478%, total TER: 8.51708%, progress (thread 0): 90.5696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_216.48_216.79, WER: 0%, TER: 0%, total WER: 19.1476%, total TER: 8.517%, progress (thread 0): 90.5775%]
|T|: m e a t
|P|: n e a t
[sample: TS3003a_H03_MTD012ME_595.66_595.97, WER: 100%, TER: 25%, total WER: 19.1485%, total TER: 8.51716%, progress (thread 0): 90.5854%]
|T|: s o
|P|: s o
[sample: TS3003a_H03_MTD012ME_884.45_884.76, WER: 0%, TER: 0%, total WER: 19.1483%, total TER: 8.51712%, progress (thread 0): 90.5934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1418.64_1418.95, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51704%, progress (thread 0): 90.6013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_71.8_72.11, WER: 0%, TER: 0%, total WER: 19.1478%, total TER: 8.51696%, progress (thread 0): 90.6092%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_538.03_538.34, WER: 0%, TER: 0%, total WER: 19.1476%, total TER: 8.51688%, progress (thread 0): 90.6171%]
|T|: m m
|P|: m h
[sample: TS3003b_H01_MTD011UID_1469.03_1469.34, WER: 100%, TER: 50%, total WER: 19.1485%, total TER: 8.51707%, progress (thread 0): 90.625%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1715.8_1716.11, WER: 0%, TER: 0%, total WER: 19.1483%, total TER: 8.51703%, progress (thread 0): 90.6329%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1822.91_1823.22, WER: 0%, TER: 0%, total WER: 19.1481%, total TER: 8.51695%, progress (thread 0): 90.6408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_794.24_794.55, WER: 0%, TER: 0%, total WER: 19.1479%, total TER: 8.51687%, progress (thread 0): 90.6487%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_934.63_934.94, WER: 100%, TER: 25%, total WER: 19.1488%, total TER: 8.51703%, progress (thread 0): 90.6566%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1136.65_1136.96, WER: 0%, TER: 0%, total WER: 19.1486%, total TER: 8.51699%, progress (thread 0): 90.6646%]
|T|: y e a h
|P|: y e m
[sample: TS3003c_H01_MTD011UID_1444.43_1444.74, WER: 100%, TER: 50%, total WER: 19.1495%, total TER: 8.51738%, progress (thread 0): 90.6725%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1813.2_1813.51, WER: 0%, TER: 0%, total WER: 19.1493%, total TER: 8.5173%, progress (thread 0): 90.6804%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_255.36_255.67, WER: 0%, TER: 0%, total WER: 19.1491%, total TER: 8.51722%, progress (thread 0): 90.6883%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_453.54_453.85, WER: 0%, TER: 0%, total WER: 19.1488%, total TER: 8.51718%, progress (thread 0): 90.6962%]
|T|: y e p
|P|: o h
[sample: TS3003d_H02_MTD0010ID_562.3_562.61, WER: 100%, TER: 100%, total WER: 19.1498%, total TER: 8.51782%, progress (thread 0): 90.7041%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1317.08_1317.39, WER: 0%, TER: 0%, total WER: 19.1495%, total TER: 8.51774%, progress (thread 0): 90.712%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_1546.63_1546.94, WER: 100%, TER: 0%, total WER: 19.1505%, total TER: 8.51764%, progress (thread 0): 90.7199%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2324.36_2324.67, WER: 0%, TER: 0%, total WER: 19.1502%, total TER: 8.51756%, progress (thread 0): 90.7278%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2373.13_2373.44, WER: 0%, TER: 0%, total WER: 19.15%, total TER: 8.51748%, progress (thread 0): 90.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_595.97_596.28, WER: 0%, TER: 0%, total WER: 19.1498%, total TER: 8.5174%, progress (thread 0): 90.7437%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_694.14_694.45, WER: 0%, TER: 0%, total WER: 19.1496%, total TER: 8.51732%, progress (thread 0): 90.7516%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_704.35_704.66, WER: 0%, TER: 0%, total WER: 19.1494%, total TER: 8.51724%, progress (thread 0): 90.7595%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_910.51_910.82, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51716%, progress (thread 0): 90.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1104.75_1105.06, WER: 0%, TER: 0%, total WER: 19.1489%, total TER: 8.51708%, progress (thread 0): 90.7753%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1619.48_1619.79, WER: 100%, TER: 33.3333%, total WER: 19.1499%, total TER: 8.51726%, progress (thread 0): 90.7832%]
|T|: o h
|P|: m m
[sample: EN2002a_H02_FEO072_1674.71_1675.02, WER: 100%, TER: 100%, total WER: 19.1508%, total TER: 8.51769%, progress (thread 0): 90.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1973.2_1973.51, WER: 0%, TER: 0%, total WER: 19.1505%, total TER: 8.51761%, progress (thread 0): 90.799%]
|T|: o h | r i g h t
|P|: o h | r i g h t
[sample: EN2002a_H03_MEE071_1971.01_1971.32, WER: 0%, TER: 0%, total WER: 19.1501%, total TER: 8.51745%, progress (thread 0): 90.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_346.95_347.26, WER: 0%, TER: 0%, total WER: 19.1499%, total TER: 8.51737%, progress (thread 0): 90.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_372.58_372.89, WER: 0%, TER: 0%, total WER: 19.1497%, total TER: 8.51729%, progress (thread 0): 90.8228%]
|T|: m m
|P|: m m
[sample: EN2002b_H03_MEE073_565.42_565.73, WER: 0%, TER: 0%, total WER: 19.1495%, total TER: 8.51725%, progress (thread 0): 90.8307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_729.79_730.1, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51717%, progress (thread 0): 90.8386%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1214.71_1215.02, WER: 0%, TER: 0%, total WER: 19.149%, total TER: 8.51709%, progress (thread 0): 90.8465%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1323.81_1324.12, WER: 0%, TER: 0%, total WER: 19.1488%, total TER: 8.51701%, progress (thread 0): 90.8544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1461.61_1461.92, WER: 0%, TER: 0%, total WER: 19.1486%, total TER: 8.51693%, progress (thread 0): 90.8623%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1750.66_1750.97, WER: 0%, TER: 0%, total WER: 19.1484%, total TER: 8.51685%, progress (thread 0): 90.8703%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_441.29_441.6, WER: 0%, TER: 0%, total WER: 19.1482%, total TER: 8.51681%, progress (thread 0): 90.8782%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_543.24_543.55, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51677%, progress (thread 0): 90.8861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_543.24_543.55, WER: 0%, TER: 0%, total WER: 19.1477%, total TER: 8.51669%, progress (thread 0): 90.894%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_982.53_982.84, WER: 100%, TER: 33.3333%, total WER: 19.1486%, total TER: 8.51686%, progress (thread 0): 90.9019%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1524.82_1525.13, WER: 0%, TER: 0%, total WER: 19.1484%, total TER: 8.51678%, progress (thread 0): 90.9098%]
|T|: u m
|P|: u m
[sample: EN2002c_H03_MEE073_1904.01_1904.32, WER: 0%, TER: 0%, total WER: 19.1482%, total TER: 8.51674%, progress (thread 0): 90.9177%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1958.34_1958.65, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51666%, progress (thread 0): 90.9256%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_2240.67_2240.98, WER: 100%, TER: 0%, total WER: 19.1489%, total TER: 8.51656%, progress (thread 0): 90.9335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2358.92_2359.23, WER: 0%, TER: 0%, total WER: 19.1487%, total TER: 8.51648%, progress (thread 0): 90.9415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2477.51_2477.82, WER: 0%, TER: 0%, total WER: 19.1485%, total TER: 8.5164%, progress (thread 0): 90.9494%]
|T|: n o
|P|: n i c
[sample: EN2002c_H02_MEE071_2608.15_2608.46, WER: 100%, TER: 100%, total WER: 19.1494%, total TER: 8.51683%, progress (thread 0): 90.9573%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2724.28_2724.59, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51675%, progress (thread 0): 90.9652%]
|T|: t i c k
|P|: t i c k
[sample: EN2002c_H01_FEO072_2878.5_2878.81, WER: 0%, TER: 0%, total WER: 19.149%, total TER: 8.51667%, progress (thread 0): 90.9731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_391.81_392.12, WER: 0%, TER: 0%, total WER: 19.1487%, total TER: 8.51659%, progress (thread 0): 90.981%]
|T|: y e a h | o k a y
|P|: o k a y
[sample: EN2002d_H00_FEO070_1379.88_1380.19, WER: 50%, TER: 55.5556%, total WER: 19.1494%, total TER: 8.51758%, progress (thread 0): 90.9889%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H01_FEO072_1512.76_1513.07, WER: 100%, TER: 0%, total WER: 19.1504%, total TER: 8.51748%, progress (thread 0): 90.9968%]
|T|: s o
|P|: y e a h
[sample: EN2002d_H00_FEO070_1542.52_1542.83, WER: 100%, TER: 200%, total WER: 19.1513%, total TER: 8.51838%, progress (thread 0): 91.0047%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1546.76_1547.07, WER: 0%, TER: 0%, total WER: 19.1511%, total TER: 8.5183%, progress (thread 0): 91.0127%]
|T|: s o
|P|: s o
[sample: EN2002d_H03_MEE073_1822.41_1822.72, WER: 0%, TER: 0%, total WER: 19.1508%, total TER: 8.51826%, progress (thread 0): 91.0206%]
|T|: m m
|P|: m m
[sample: EN2002d_H00_FEO070_1872.39_1872.7, WER: 0%, TER: 0%, total WER: 19.1506%, total TER: 8.51822%, progress (thread 0): 91.0285%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2114.3_2114.61, WER: 0%, TER: 0%, total WER: 19.1504%, total TER: 8.51814%, progress (thread 0): 91.0364%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_819.96_820.26, WER: 0%, TER: 0%, total WER: 19.1502%, total TER: 8.51806%, progress (thread 0): 91.0443%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1232.15_1232.45, WER: 0%, TER: 0%, total WER: 19.15%, total TER: 8.51798%, progress (thread 0): 91.0522%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1572.74_1573.04, WER: 100%, TER: 0%, total WER: 19.1509%, total TER: 8.51788%, progress (thread 0): 91.0601%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1784.08_1784.38, WER: 100%, TER: 0%, total WER: 19.1518%, total TER: 8.51778%, progress (thread 0): 91.068%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_2252.76_2253.06, WER: 0%, TER: 0%, total WER: 19.1516%, total TER: 8.5177%, progress (thread 0): 91.076%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_849.84_850.14, WER: 0%, TER: 0%, total WER: 19.1514%, total TER: 8.51762%, progress (thread 0): 91.0839%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1555.92_1556.22, WER: 0%, TER: 0%, total WER: 19.1511%, total TER: 8.51754%, progress (thread 0): 91.0918%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H00_MEO015_1640.82_1641.12, WER: 100%, TER: 0%, total WER: 19.1521%, total TER: 8.51744%, progress (thread 0): 91.0997%]
|T|: ' k a y
|P|: k e y
[sample: ES2004d_H00_MEO015_271.77_272.07, WER: 100%, TER: 50%, total WER: 19.153%, total TER: 8.51783%, progress (thread 0): 91.1076%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_639.49_639.79, WER: 0%, TER: 0%, total WER: 19.1528%, total TER: 8.51775%, progress (thread 0): 91.1155%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1390.02_1390.32, WER: 0%, TER: 0%, total WER: 19.1525%, total TER: 8.51767%, progress (thread 0): 91.1234%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1711.3_1711.6, WER: 0%, TER: 0%, total WER: 19.1523%, total TER: 8.51759%, progress (thread 0): 91.1313%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_2002_2002.3, WER: 0%, TER: 0%, total WER: 19.1521%, total TER: 8.51755%, progress (thread 0): 91.1392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2096.66_2096.96, WER: 0%, TER: 0%, total WER: 19.1519%, total TER: 8.51747%, progress (thread 0): 91.1472%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_617.96_618.26, WER: 100%, TER: 0%, total WER: 19.1528%, total TER: 8.51737%, progress (thread 0): 91.1551%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H01_FIO087_782.9_783.2, WER: 0%, TER: 0%, total WER: 19.1526%, total TER: 8.51729%, progress (thread 0): 91.163%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_182.06_182.36, WER: 0%, TER: 0%, total WER: 19.1524%, total TER: 8.51721%, progress (thread 0): 91.1709%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_574.49_574.79, WER: 100%, TER: 0%, total WER: 19.1533%, total TER: 8.51711%, progress (thread 0): 91.1788%]
|T|: m m h m m
|P|: m m m
[sample: IS1009b_H03_FIO089_707.08_707.38, WER: 100%, TER: 40%, total WER: 19.1542%, total TER: 8.51748%, progress (thread 0): 91.1867%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1025.61_1025.91, WER: 0%, TER: 0%, total WER: 19.154%, total TER: 8.5174%, progress (thread 0): 91.1946%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_1366.95_1367.25, WER: 0%, TER: 0%, total WER: 19.1538%, total TER: 8.51736%, progress (thread 0): 91.2025%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1371.17_1371.47, WER: 100%, TER: 0%, total WER: 19.1547%, total TER: 8.51726%, progress (thread 0): 91.2104%]
|T|: h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1604.76_1605.06, WER: 100%, TER: 33.3333%, total WER: 19.1556%, total TER: 8.51744%, progress (thread 0): 91.2184%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1617.66_1617.96, WER: 0%, TER: 0%, total WER: 19.1554%, total TER: 8.51736%, progress (thread 0): 91.2263%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_335.7_336, WER: 0%, TER: 0%, total WER: 19.1552%, total TER: 8.51728%, progress (thread 0): 91.2342%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_1106.15_1106.45, WER: 100%, TER: 0%, total WER: 19.1561%, total TER: 8.51718%, progress (thread 0): 91.2421%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1237.83_1238.13, WER: 0%, TER: 0%, total WER: 19.1559%, total TER: 8.51712%, progress (thread 0): 91.25%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1259.97_1260.27, WER: 0%, TER: 0%, total WER: 19.1556%, total TER: 8.51704%, progress (thread 0): 91.2579%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1297.17_1297.47, WER: 0%, TER: 0%, total WER: 19.1554%, total TER: 8.51696%, progress (thread 0): 91.2658%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_607.11_607.41, WER: 100%, TER: 0%, total WER: 19.1563%, total TER: 8.51686%, progress (thread 0): 91.2737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_769.42_769.72, WER: 0%, TER: 0%, total WER: 19.1561%, total TER: 8.51678%, progress (thread 0): 91.2816%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1062.93_1063.23, WER: 0%, TER: 0%, total WER: 19.1559%, total TER: 8.5167%, progress (thread 0): 91.2896%]
|T|: o n e
|P|: b u t
[sample: IS1009d_H01_FIO087_1482_1482.3, WER: 100%, TER: 100%, total WER: 19.1568%, total TER: 8.51734%, progress (thread 0): 91.2975%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H00_FIE088_1528.1_1528.4, WER: 0%, TER: 0%, total WER: 19.1566%, total TER: 8.51728%, progress (thread 0): 91.3054%]
|T|: y e a h
|P|: i
[sample: IS1009d_H02_FIO084_1824.41_1824.71, WER: 100%, TER: 100%, total WER: 19.1575%, total TER: 8.51814%, progress (thread 0): 91.3133%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1853.09_1853.39, WER: 0%, TER: 0%, total WER: 19.1573%, total TER: 8.51806%, progress (thread 0): 91.3212%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_238.31_238.61, WER: 0%, TER: 0%, total WER: 19.1571%, total TER: 8.51798%, progress (thread 0): 91.3291%]
|T|: s o
|P|: s o
[sample: TS3003a_H02_MTD0010ID_1080.77_1081.07, WER: 0%, TER: 0%, total WER: 19.1569%, total TER: 8.51794%, progress (thread 0): 91.337%]
|T|: h m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1308.88_1309.18, WER: 100%, TER: 33.3333%, total WER: 19.1578%, total TER: 8.51812%, progress (thread 0): 91.3449%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_1423.96_1424.26, WER: 0%, TER: 0%, total WER: 19.1576%, total TER: 8.51806%, progress (thread 0): 91.3529%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1463.25_1463.55, WER: 0%, TER: 0%, total WER: 19.1573%, total TER: 8.51798%, progress (thread 0): 91.3608%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_1535.95_1536.25, WER: 0%, TER: 0%, total WER: 19.1571%, total TER: 8.51792%, progress (thread 0): 91.3687%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003b_H03_MTD012ME_1635.81_1636.11, WER: 100%, TER: 0%, total WER: 19.158%, total TER: 8.51782%, progress (thread 0): 91.3766%]
|T|: h m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1834.41_1834.71, WER: 100%, TER: 50%, total WER: 19.159%, total TER: 8.51801%, progress (thread 0): 91.3845%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1871.5_1871.8, WER: 0%, TER: 0%, total WER: 19.1587%, total TER: 8.51797%, progress (thread 0): 91.3924%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2279.98_2280.28, WER: 0%, TER: 0%, total WER: 19.1585%, total TER: 8.51789%, progress (thread 0): 91.4003%]
|T|: s
|P|: y m
[sample: TS3003d_H01_MTD011UID_856.51_856.81, WER: 100%, TER: 200%, total WER: 19.1594%, total TER: 8.51834%, progress (thread 0): 91.4082%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1057.72_1058.02, WER: 0%, TER: 0%, total WER: 19.1592%, total TER: 8.51826%, progress (thread 0): 91.4161%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1709.82_1710.12, WER: 0%, TER: 0%, total WER: 19.159%, total TER: 8.51818%, progress (thread 0): 91.424%]
|T|: y e s
|P|: y e s
[sample: TS3003d_H02_MTD0010ID_1732.89_1733.19, WER: 0%, TER: 0%, total WER: 19.1588%, total TER: 8.51812%, progress (thread 0): 91.432%]
|T|: y e a h
|P|: y m a m
[sample: TS3003d_H00_MTD009PM_1821.73_1822.03, WER: 100%, TER: 50%, total WER: 19.1597%, total TER: 8.51851%, progress (thread 0): 91.4399%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2250.87_2251.17, WER: 0%, TER: 0%, total WER: 19.1595%, total TER: 8.51843%, progress (thread 0): 91.4478%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2482.46_2482.76, WER: 0%, TER: 0%, total WER: 19.1593%, total TER: 8.51835%, progress (thread 0): 91.4557%]
|T|: r i g h t
|P|: y o u r e r i g h t
[sample: EN2002a_H03_MEE071_11.83_12.13, WER: 100%, TER: 100%, total WER: 19.1602%, total TER: 8.51942%, progress (thread 0): 91.4636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_48.72_49.02, WER: 0%, TER: 0%, total WER: 19.16%, total TER: 8.51934%, progress (thread 0): 91.4715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_203.25_203.55, WER: 0%, TER: 0%, total WER: 19.1597%, total TER: 8.51926%, progress (thread 0): 91.4794%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_261.14_261.44, WER: 100%, TER: 33.3333%, total WER: 19.1607%, total TER: 8.51944%, progress (thread 0): 91.4873%]
|T|: w h y | n o t
|P|: w h y | d o n ' t
[sample: EN2002a_H03_MEE071_375.98_376.28, WER: 50%, TER: 42.8571%, total WER: 19.1614%, total TER: 8.52%, progress (thread 0): 91.4953%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_469.41_469.71, WER: 0%, TER: 0%, total WER: 19.1611%, total TER: 8.51992%, progress (thread 0): 91.5032%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_486.08_486.38, WER: 0%, TER: 0%, total WER: 19.1609%, total TER: 8.51984%, progress (thread 0): 91.5111%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_508.71_509.01, WER: 0%, TER: 0%, total WER: 19.1607%, total TER: 8.51976%, progress (thread 0): 91.519%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_521.78_522.08, WER: 0%, TER: 0%, total WER: 19.1605%, total TER: 8.51968%, progress (thread 0): 91.5269%]
|T|: s o
|P|: s o
[sample: EN2002a_H03_MEE071_738.15_738.45, WER: 0%, TER: 0%, total WER: 19.1603%, total TER: 8.51964%, progress (thread 0): 91.5348%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_747.85_748.15, WER: 100%, TER: 0%, total WER: 19.1612%, total TER: 8.51954%, progress (thread 0): 91.5427%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_795.73_796.03, WER: 100%, TER: 33.3333%, total WER: 19.1621%, total TER: 8.51971%, progress (thread 0): 91.5506%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_877.67_877.97, WER: 100%, TER: 0%, total WER: 19.163%, total TER: 8.51961%, progress (thread 0): 91.5585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_918.33_918.63, WER: 0%, TER: 0%, total WER: 19.1628%, total TER: 8.51954%, progress (thread 0): 91.5665%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1889.11_1889.41, WER: 0%, TER: 0%, total WER: 19.1626%, total TER: 8.51946%, progress (thread 0): 91.5744%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H02_FEO072_2116.83_2117.13, WER: 0%, TER: 0%, total WER: 19.1624%, total TER: 8.51936%, progress (thread 0): 91.5823%]
|T|: o h | y e a h
|P|: h m h
[sample: EN2002b_H03_MEE073_211.33_211.63, WER: 100%, TER: 71.4286%, total WER: 19.1642%, total TER: 8.52039%, progress (thread 0): 91.5902%]
|T|: w e ' l l | s e e
|P|: w e l l | s e
[sample: EN2002b_H00_FEO070_255.08_255.38, WER: 100%, TER: 22.2222%, total WER: 19.166%, total TER: 8.52068%, progress (thread 0): 91.5981%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_368.42_368.72, WER: 0%, TER: 0%, total WER: 19.1658%, total TER: 8.5206%, progress (thread 0): 91.606%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H03_MEE073_391.92_392.22, WER: 100%, TER: 0%, total WER: 19.1667%, total TER: 8.5205%, progress (thread 0): 91.6139%]
|T|: m m
|P|: m m
[sample: EN2002b_H03_MEE073_526.11_526.41, WER: 0%, TER: 0%, total WER: 19.1665%, total TER: 8.52046%, progress (thread 0): 91.6218%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002b_H02_FEO072_627.98_628.28, WER: 200%, TER: 14.2857%, total WER: 19.1685%, total TER: 8.52055%, progress (thread 0): 91.6298%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1551.52_1551.82, WER: 0%, TER: 0%, total WER: 19.1683%, total TER: 8.52047%, progress (thread 0): 91.6377%]
|T|: b u t
|P|: b h u t
[sample: EN2002b_H01_MEE071_1611.31_1611.61, WER: 100%, TER: 33.3333%, total WER: 19.1692%, total TER: 8.52065%, progress (thread 0): 91.6456%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1729.42_1729.72, WER: 0%, TER: 0%, total WER: 19.169%, total TER: 8.52057%, progress (thread 0): 91.6535%]
|T|: i | t h i n k
|P|: i
[sample: EN2002c_H01_FEO072_232.37_232.67, WER: 50%, TER: 85.7143%, total WER: 19.1697%, total TER: 8.52183%, progress (thread 0): 91.6614%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_355.18_355.48, WER: 0%, TER: 0%, total WER: 19.1695%, total TER: 8.52175%, progress (thread 0): 91.6693%]
|T|: n o | n o
|P|: m m
[sample: EN2002c_H03_MEE073_543.55_543.85, WER: 100%, TER: 100%, total WER: 19.1713%, total TER: 8.52282%, progress (thread 0): 91.6772%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_700.09_700.39, WER: 100%, TER: 0%, total WER: 19.1722%, total TER: 8.52272%, progress (thread 0): 91.6851%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H02_MEE071_1131.17_1131.47, WER: 100%, TER: 66.6667%, total WER: 19.1731%, total TER: 8.52313%, progress (thread 0): 91.693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1143.34_1143.64, WER: 0%, TER: 0%, total WER: 19.1729%, total TER: 8.52305%, progress (thread 0): 91.701%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_1321.8_1322.1, WER: 0%, TER: 0%, total WER: 19.1727%, total TER: 8.52301%, progress (thread 0): 91.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1600.62_1600.92, WER: 0%, TER: 0%, total WER: 19.1725%, total TER: 8.52293%, progress (thread 0): 91.7168%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2301.16_2301.46, WER: 0%, TER: 0%, total WER: 19.1723%, total TER: 8.52285%, progress (thread 0): 91.7247%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2443.4_2443.7, WER: 0%, TER: 0%, total WER: 19.1721%, total TER: 8.52277%, progress (thread 0): 91.7326%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H02_MEE071_2847.83_2848.13, WER: 100%, TER: 0%, total WER: 19.173%, total TER: 8.52267%, progress (thread 0): 91.7405%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_673.96_674.26, WER: 0%, TER: 0%, total WER: 19.1728%, total TER: 8.52259%, progress (thread 0): 91.7484%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_834.35_834.65, WER: 0%, TER: 0%, total WER: 19.1725%, total TER: 8.52251%, progress (thread 0): 91.7563%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_925.24_925.54, WER: 0%, TER: 0%, total WER: 19.1723%, total TER: 8.52243%, progress (thread 0): 91.7642%]
|T|: u m
|P|: u m
[sample: EN2002d_H01_FEO072_1191.17_1191.47, WER: 0%, TER: 0%, total WER: 19.1721%, total TER: 8.52239%, progress (thread 0): 91.7721%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1205.4_1205.7, WER: 0%, TER: 0%, total WER: 19.1719%, total TER: 8.52229%, progress (thread 0): 91.7801%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H03_MEE073_1221.17_1221.47, WER: 100%, TER: 0%, total WER: 19.1728%, total TER: 8.52219%, progress (thread 0): 91.788%]
|T|: y e a h
|P|: m m
[sample: EN2002d_H00_FEO070_1249.7_1250, WER: 100%, TER: 100%, total WER: 19.1737%, total TER: 8.52305%, progress (thread 0): 91.7959%]
|T|: s u r e
|P|: s u r e
[sample: EN2002d_H03_MEE073_1672.53_1672.83, WER: 0%, TER: 0%, total WER: 19.1735%, total TER: 8.52297%, progress (thread 0): 91.8038%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_2192.66_2192.96, WER: 0%, TER: 0%, total WER: 19.1733%, total TER: 8.52293%, progress (thread 0): 91.8117%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_747.66_747.95, WER: 0%, TER: 0%, total WER: 19.1731%, total TER: 8.52285%, progress (thread 0): 91.8196%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004a_H00_MEO015_784.99_785.28, WER: 100%, TER: 20%, total WER: 19.174%, total TER: 8.52299%, progress (thread 0): 91.8275%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_826.06_826.35, WER: 0%, TER: 0%, total WER: 19.1738%, total TER: 8.52289%, progress (thread 0): 91.8354%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2019.84_2020.13, WER: 0%, TER: 0%, total WER: 19.1736%, total TER: 8.52281%, progress (thread 0): 91.8434%]
|T|: o k a y
|P|: m m
[sample: ES2004b_H02_MEE014_2295.26_2295.55, WER: 100%, TER: 100%, total WER: 19.1745%, total TER: 8.52366%, progress (thread 0): 91.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_316.02_316.31, WER: 0%, TER: 0%, total WER: 19.1742%, total TER: 8.52358%, progress (thread 0): 91.8592%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004c_H00_MEO015_1261.19_1261.48, WER: 200%, TER: 20%, total WER: 19.1763%, total TER: 8.52372%, progress (thread 0): 91.8671%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H01_FEE013_1987.8_1988.09, WER: 100%, TER: 0%, total WER: 19.1772%, total TER: 8.52362%, progress (thread 0): 91.875%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_2244.26_2244.55, WER: 0%, TER: 0%, total WER: 19.177%, total TER: 8.52354%, progress (thread 0): 91.8829%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_211.03_211.32, WER: 0%, TER: 0%, total WER: 19.1768%, total TER: 8.52344%, progress (thread 0): 91.8908%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H03_FEE016_810.67_810.96, WER: 0%, TER: 0%, total WER: 19.1766%, total TER: 8.52334%, progress (thread 0): 91.8987%]
|T|: m m
|P|: m m
[sample: ES2004d_H03_FEE016_1204.42_1204.71, WER: 0%, TER: 0%, total WER: 19.1763%, total TER: 8.5233%, progress (thread 0): 91.9066%]
|T|: u m
|P|: u m
[sample: ES2004d_H01_FEE013_1227.88_1228.17, WER: 0%, TER: 0%, total WER: 19.1761%, total TER: 8.52326%, progress (thread 0): 91.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1634.52_1634.81, WER: 0%, TER: 0%, total WER: 19.1759%, total TER: 8.52318%, progress (thread 0): 91.9225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1787.34_1787.63, WER: 0%, TER: 0%, total WER: 19.1757%, total TER: 8.5231%, progress (thread 0): 91.9304%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1964.63_1964.92, WER: 0%, TER: 0%, total WER: 19.1755%, total TER: 8.52302%, progress (thread 0): 91.9383%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_118.76_119.05, WER: 0%, TER: 0%, total WER: 19.1753%, total TER: 8.52294%, progress (thread 0): 91.9462%]
|T|: h m m
|P|: m m
[sample: IS1009a_H02_FIO084_803.39_803.68, WER: 100%, TER: 33.3333%, total WER: 19.1762%, total TER: 8.52311%, progress (thread 0): 91.9541%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_708.64_708.93, WER: 100%, TER: 0%, total WER: 19.1771%, total TER: 8.52301%, progress (thread 0): 91.962%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_998.44_998.73, WER: 0%, TER: 0%, total WER: 19.1769%, total TER: 8.52293%, progress (thread 0): 91.9699%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1311.53_1311.82, WER: 100%, TER: 0%, total WER: 19.1778%, total TER: 8.52284%, progress (thread 0): 91.9778%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1356.24_1356.53, WER: 100%, TER: 60%, total WER: 19.1787%, total TER: 8.52344%, progress (thread 0): 91.9858%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1658.74_1659.03, WER: 100%, TER: 60%, total WER: 19.1796%, total TER: 8.52404%, progress (thread 0): 91.9937%]
|T|: h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1899.66_1899.95, WER: 100%, TER: 33.3333%, total WER: 19.1805%, total TER: 8.52422%, progress (thread 0): 92.0016%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H03_FIO089_1999.16_1999.45, WER: 100%, TER: 20%, total WER: 19.1814%, total TER: 8.52435%, progress (thread 0): 92.0095%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_284.27_284.56, WER: 0%, TER: 0%, total WER: 19.1812%, total TER: 8.52429%, progress (thread 0): 92.0174%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_381_381.29, WER: 100%, TER: 20%, total WER: 19.1821%, total TER: 8.52442%, progress (thread 0): 92.0253%]
|T|: w e | c a n
|P|: w e g e t
[sample: IS1009c_H01_FIO087_755.64_755.93, WER: 100%, TER: 66.6667%, total WER: 19.1839%, total TER: 8.52524%, progress (thread 0): 92.0332%]
|T|: y e a h | b u t | w
|P|: y e a h
[sample: IS1009c_H02_FIO084_1559.51_1559.8, WER: 66.6667%, TER: 60%, total WER: 19.1855%, total TER: 8.52645%, progress (thread 0): 92.0411%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H02_FIO084_1608.9_1609.19, WER: 100%, TER: 20%, total WER: 19.1865%, total TER: 8.52658%, progress (thread 0): 92.049%]
|T|: h e l l o
|P|: o
[sample: IS1009d_H01_FIO087_41.27_41.56, WER: 100%, TER: 80%, total WER: 19.1874%, total TER: 8.52742%, progress (thread 0): 92.057%]
|T|: n o
|P|: n
[sample: IS1009d_H02_FIO084_952.51_952.8, WER: 100%, TER: 50%, total WER: 19.1883%, total TER: 8.52761%, progress (thread 0): 92.0649%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1056.71_1057, WER: 100%, TER: 0%, total WER: 19.1892%, total TER: 8.52751%, progress (thread 0): 92.0728%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1344.32_1344.61, WER: 100%, TER: 0%, total WER: 19.1901%, total TER: 8.52741%, progress (thread 0): 92.0807%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_1389.87_1390.16, WER: 0%, TER: 0%, total WER: 19.1899%, total TER: 8.52733%, progress (thread 0): 92.0886%]
|T|: m m h m m
|P|: y e a m
[sample: IS1009d_H02_FIO084_1790.06_1790.35, WER: 100%, TER: 80%, total WER: 19.1908%, total TER: 8.52817%, progress (thread 0): 92.0965%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_704.99_705.28, WER: 0%, TER: 0%, total WER: 19.1906%, total TER: 8.52809%, progress (thread 0): 92.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_977.31_977.6, WER: 0%, TER: 0%, total WER: 19.1904%, total TER: 8.52801%, progress (thread 0): 92.1123%]
|T|: y o u
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1267.17_1267.46, WER: 100%, TER: 100%, total WER: 19.1913%, total TER: 8.52865%, progress (thread 0): 92.1203%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_178.78_179.07, WER: 0%, TER: 0%, total WER: 19.1911%, total TER: 8.52859%, progress (thread 0): 92.1282%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1577.63_1577.92, WER: 0%, TER: 0%, total WER: 19.1908%, total TER: 8.52851%, progress (thread 0): 92.1361%]
|T|: w e l l
|P|: w e l l
[sample: TS3003b_H03_MTD012ME_1752.79_1753.08, WER: 0%, TER: 0%, total WER: 19.1906%, total TER: 8.52843%, progress (thread 0): 92.144%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_2033.23_2033.52, WER: 0%, TER: 0%, total WER: 19.1904%, total TER: 8.52835%, progress (thread 0): 92.1519%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2083.17_2083.46, WER: 0%, TER: 0%, total WER: 19.1902%, total TER: 8.52827%, progress (thread 0): 92.1598%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_2083.37_2083.66, WER: 0%, TER: 0%, total WER: 19.19%, total TER: 8.52819%, progress (thread 0): 92.1677%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H01_MTD011UID_1289.74_1290.03, WER: 100%, TER: 0%, total WER: 19.1909%, total TER: 8.52809%, progress (thread 0): 92.1756%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1452.67_1452.96, WER: 0%, TER: 0%, total WER: 19.1907%, total TER: 8.52801%, progress (thread 0): 92.1835%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003d_H03_MTD012ME_695.88_696.17, WER: 100%, TER: 20%, total WER: 19.1916%, total TER: 8.52815%, progress (thread 0): 92.1915%]
|T|: n a y
|P|: n m h
[sample: TS3003d_H01_MTD011UID_835.49_835.78, WER: 100%, TER: 66.6667%, total WER: 19.1925%, total TER: 8.52856%, progress (thread 0): 92.1994%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1010.19_1010.48, WER: 100%, TER: 33.3333%, total WER: 19.1934%, total TER: 8.52873%, progress (thread 0): 92.2073%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H02_MTD0010ID_1075.11_1075.4, WER: 100%, TER: 75%, total WER: 19.1943%, total TER: 8.52935%, progress (thread 0): 92.2152%]
|T|: t r u e
|P|: t r u e
[sample: TS3003d_H03_MTD012ME_1129.3_1129.59, WER: 0%, TER: 0%, total WER: 19.1941%, total TER: 8.52927%, progress (thread 0): 92.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1862.62_1862.91, WER: 0%, TER: 0%, total WER: 19.1939%, total TER: 8.52919%, progress (thread 0): 92.231%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2370.37_2370.66, WER: 0%, TER: 0%, total WER: 19.1937%, total TER: 8.52915%, progress (thread 0): 92.2389%]
|T|: h m m
|P|: m m
[sample: TS3003d_H00_MTD009PM_2517.23_2517.52, WER: 100%, TER: 33.3333%, total WER: 19.1946%, total TER: 8.52933%, progress (thread 0): 92.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2571.69_2571.98, WER: 0%, TER: 0%, total WER: 19.1944%, total TER: 8.52925%, progress (thread 0): 92.2547%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_218.41_218.7, WER: 100%, TER: 33.3333%, total WER: 19.1953%, total TER: 8.52942%, progress (thread 0): 92.2627%]
|T|: i | d o n ' t | k n o w
|P|: i | d o n t
[sample: EN2002a_H00_MEE073_234.63_234.92, WER: 66.6667%, TER: 50%, total WER: 19.1969%, total TER: 8.53059%, progress (thread 0): 92.2706%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_526.91_527.2, WER: 100%, TER: 66.6667%, total WER: 19.1978%, total TER: 8.531%, progress (thread 0): 92.2785%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_646.99_647.28, WER: 0%, TER: 0%, total WER: 19.1976%, total TER: 8.53092%, progress (thread 0): 92.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_723.91_724.2, WER: 0%, TER: 0%, total WER: 19.1974%, total TER: 8.53084%, progress (thread 0): 92.2943%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_749.38_749.67, WER: 0%, TER: 0%, total WER: 19.1971%, total TER: 8.53076%, progress (thread 0): 92.3022%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_856.03_856.32, WER: 0%, TER: 0%, total WER: 19.1969%, total TER: 8.53068%, progress (thread 0): 92.3101%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_872.78_873.07, WER: 0%, TER: 0%, total WER: 19.1967%, total TER: 8.5306%, progress (thread 0): 92.318%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1076.05_1076.34, WER: 100%, TER: 33.3333%, total WER: 19.1976%, total TER: 8.53077%, progress (thread 0): 92.326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1255.44_1255.73, WER: 0%, TER: 0%, total WER: 19.1974%, total TER: 8.53069%, progress (thread 0): 92.3339%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1364.93_1365.22, WER: 0%, TER: 0%, total WER: 19.1972%, total TER: 8.53061%, progress (thread 0): 92.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1593.85_1594.14, WER: 0%, TER: 0%, total WER: 19.197%, total TER: 8.53053%, progress (thread 0): 92.3497%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1595.68_1595.97, WER: 0%, TER: 0%, total WER: 19.1968%, total TER: 8.53045%, progress (thread 0): 92.3576%]
|T|: m m h m m
|P|: m m
[sample: EN2002a_H02_FEO072_1620.66_1620.95, WER: 100%, TER: 60%, total WER: 19.1977%, total TER: 8.53105%, progress (thread 0): 92.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1960.6_1960.89, WER: 0%, TER: 0%, total WER: 19.1975%, total TER: 8.53097%, progress (thread 0): 92.3734%]
|T|: y e a h
|P|: o m
[sample: EN2002a_H00_MEE073_2127.38_2127.67, WER: 100%, TER: 100%, total WER: 19.1984%, total TER: 8.53183%, progress (thread 0): 92.3813%]
|T|: v e r y | g o o d
|P|: m o n
[sample: EN2002b_H02_FEO072_142.65_142.94, WER: 100%, TER: 88.8889%, total WER: 19.2002%, total TER: 8.53352%, progress (thread 0): 92.3892%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_382.41_382.7, WER: 0%, TER: 0%, total WER: 19.2%, total TER: 8.53344%, progress (thread 0): 92.3972%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_419.68_419.97, WER: 0%, TER: 0%, total WER: 19.1998%, total TER: 8.53336%, progress (thread 0): 92.4051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_734.04_734.33, WER: 0%, TER: 0%, total WER: 19.1995%, total TER: 8.53328%, progress (thread 0): 92.413%]
|T|: h m m
|P|: n o
[sample: EN2002b_H03_MEE073_1150.51_1150.8, WER: 100%, TER: 100%, total WER: 19.2005%, total TER: 8.53393%, progress (thread 0): 92.4209%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H03_MEE073_1286.29_1286.58, WER: 100%, TER: 0%, total WER: 19.2014%, total TER: 8.53383%, progress (thread 0): 92.4288%]
|T|: s o
|P|: s o
[sample: EN2002b_H03_MEE073_1517.43_1517.72, WER: 0%, TER: 0%, total WER: 19.2011%, total TER: 8.53379%, progress (thread 0): 92.4367%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_61.76_62.05, WER: 0%, TER: 0%, total WER: 19.2009%, total TER: 8.53371%, progress (thread 0): 92.4446%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_697.18_697.47, WER: 0%, TER: 0%, total WER: 19.2007%, total TER: 8.53367%, progress (thread 0): 92.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1026.29_1026.58, WER: 0%, TER: 0%, total WER: 19.2005%, total TER: 8.53359%, progress (thread 0): 92.4604%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_1088_1088.29, WER: 100%, TER: 0%, total WER: 19.2014%, total TER: 8.53349%, progress (thread 0): 92.4684%]
|T|: t h a t
|P|: o k y
[sample: EN2002c_H03_MEE073_1302.84_1303.13, WER: 100%, TER: 100%, total WER: 19.2023%, total TER: 8.53434%, progress (thread 0): 92.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1313.99_1314.28, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53426%, progress (thread 0): 92.4842%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_1543.66_1543.95, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53422%, progress (thread 0): 92.4921%]
|T|: ' c a u s e
|P|: t h e s
[sample: EN2002c_H03_MEE073_2609.79_2610.08, WER: 100%, TER: 83.3333%, total WER: 19.2028%, total TER: 8.53527%, progress (thread 0): 92.5%]
|T|: a c t u a l l y
|P|: a c t u a l l y
[sample: EN2002d_H03_MEE073_783.19_783.48, WER: 0%, TER: 0%, total WER: 19.2026%, total TER: 8.53512%, progress (thread 0): 92.5079%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H02_MEE071_833.43_833.72, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53502%, progress (thread 0): 92.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1031.38_1031.67, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53494%, progress (thread 0): 92.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1490.73_1491.02, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53486%, progress (thread 0): 92.5316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2069.6_2069.89, WER: 0%, TER: 0%, total WER: 19.2017%, total TER: 8.53478%, progress (thread 0): 92.5396%]
|T|: n o
|P|: n m m
[sample: ES2004b_H03_FEE016_602.63_602.91, WER: 100%, TER: 100%, total WER: 19.2026%, total TER: 8.5352%, progress (thread 0): 92.5475%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1146.25_1146.53, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53512%, progress (thread 0): 92.5554%]
|T|: m m
|P|: m m
[sample: ES2004b_H03_FEE016_1316.47_1316.75, WER: 0%, TER: 0%, total WER: 19.2022%, total TER: 8.53508%, progress (thread 0): 92.5633%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1939.86_1940.14, WER: 0%, TER: 0%, total WER: 19.202%, total TER: 8.53498%, progress (thread 0): 92.5712%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2216.73_2217.01, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.5349%, progress (thread 0): 92.5791%]
|T|: t h a n k s
|P|: m
[sample: ES2004c_H01_FEE013_54.47_54.75, WER: 100%, TER: 100%, total WER: 19.2027%, total TER: 8.53619%, progress (thread 0): 92.587%]
|T|: u h
|P|: u h
[sample: ES2004c_H02_MEE014_1126.8_1127.08, WER: 0%, TER: 0%, total WER: 19.2025%, total TER: 8.53615%, progress (thread 0): 92.5949%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1180.98_1181.26, WER: 0%, TER: 0%, total WER: 19.2022%, total TER: 8.53611%, progress (thread 0): 92.6029%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1398.69_1398.97, WER: 0%, TER: 0%, total WER: 19.202%, total TER: 8.53603%, progress (thread 0): 92.6108%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2123.64_2123.92, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.53595%, progress (thread 0): 92.6187%]
|T|: n o
|P|: n o
[sample: ES2004d_H02_MEE014_1050.44_1050.72, WER: 0%, TER: 0%, total WER: 19.2016%, total TER: 8.53591%, progress (thread 0): 92.6266%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H00_MEO015_1500.31_1500.59, WER: 100%, TER: 0%, total WER: 19.2025%, total TER: 8.53581%, progress (thread 0): 92.6345%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1843.24_1843.52, WER: 0%, TER: 0%, total WER: 19.2023%, total TER: 8.53573%, progress (thread 0): 92.6424%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_2015.69_2015.97, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53563%, progress (thread 0): 92.6503%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H01_FEE013_2120.6_2120.88, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.53555%, progress (thread 0): 92.6582%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_188.06_188.34, WER: 0%, TER: 0%, total WER: 19.2016%, total TER: 8.53547%, progress (thread 0): 92.6661%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_793.23_793.51, WER: 100%, TER: 0%, total WER: 19.2025%, total TER: 8.53537%, progress (thread 0): 92.674%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_804.79_805.07, WER: 0%, TER: 0%, total WER: 19.2023%, total TER: 8.53529%, progress (thread 0): 92.682%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_894.05_894.33, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53521%, progress (thread 0): 92.6899%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1764.17_1764.45, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53513%, progress (thread 0): 92.6978%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009c_H02_FIO084_724.39_724.67, WER: 100%, TER: 100%, total WER: 19.2028%, total TER: 8.5362%, progress (thread 0): 92.7057%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1140.8_1141.08, WER: 0%, TER: 0%, total WER: 19.2026%, total TER: 8.53614%, progress (thread 0): 92.7136%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1619.41_1619.69, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53606%, progress (thread 0): 92.7215%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_617.29_617.57, WER: 100%, TER: 0%, total WER: 19.2033%, total TER: 8.53596%, progress (thread 0): 92.7294%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_773.28_773.56, WER: 0%, TER: 0%, total WER: 19.2031%, total TER: 8.53586%, progress (thread 0): 92.7373%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_980.1_980.38, WER: 0%, TER: 0%, total WER: 19.2028%, total TER: 8.53578%, progress (thread 0): 92.7452%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1792.74_1793.02, WER: 100%, TER: 20%, total WER: 19.2038%, total TER: 8.53591%, progress (thread 0): 92.7532%]
|T|: h m m
|P|: m m
[sample: IS1009d_H02_FIO084_1830.44_1830.72, WER: 100%, TER: 33.3333%, total WER: 19.2047%, total TER: 8.53609%, progress (thread 0): 92.7611%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1876.62_1876.9, WER: 100%, TER: 20%, total WER: 19.2056%, total TER: 8.53622%, progress (thread 0): 92.769%]
|T|: m o r n i n g
|P|: o
[sample: TS3003a_H02_MTD0010ID_16.42_16.7, WER: 100%, TER: 85.7143%, total WER: 19.2065%, total TER: 8.53749%, progress (thread 0): 92.7769%]
|T|: s u r e
|P|: s u r e
[sample: TS3003a_H03_MTD012ME_163.72_164, WER: 0%, TER: 0%, total WER: 19.2063%, total TER: 8.53741%, progress (thread 0): 92.7848%]
|T|: h m m
|P|: h m m
[sample: TS3003a_H03_MTD012ME_661.22_661.5, WER: 0%, TER: 0%, total WER: 19.2061%, total TER: 8.53735%, progress (thread 0): 92.7927%]
|T|: h m m
|P|: m m
[sample: TS3003a_H00_MTD009PM_1235.68_1235.96, WER: 100%, TER: 33.3333%, total WER: 19.207%, total TER: 8.53752%, progress (thread 0): 92.8006%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_823.58_823.86, WER: 0%, TER: 0%, total WER: 19.2068%, total TER: 8.53744%, progress (thread 0): 92.8085%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1892.2_1892.48, WER: 0%, TER: 0%, total WER: 19.2065%, total TER: 8.53736%, progress (thread 0): 92.8165%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1860.06_1860.34, WER: 0%, TER: 0%, total WER: 19.2063%, total TER: 8.53728%, progress (thread 0): 92.8244%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_1891.82_1892.1, WER: 100%, TER: 0%, total WER: 19.2072%, total TER: 8.53718%, progress (thread 0): 92.8323%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_267.26_267.54, WER: 0%, TER: 0%, total WER: 19.207%, total TER: 8.5371%, progress (thread 0): 92.8402%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_365.09_365.37, WER: 0%, TER: 0%, total WER: 19.2068%, total TER: 8.53702%, progress (thread 0): 92.8481%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_816.6_816.88, WER: 100%, TER: 66.6667%, total WER: 19.2077%, total TER: 8.53743%, progress (thread 0): 92.856%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_860.45_860.73, WER: 0%, TER: 0%, total WER: 19.2075%, total TER: 8.53735%, progress (thread 0): 92.8639%]
|T|: a n d | i t
|P|: a n d | i t
[sample: TS3003d_H02_MTD0010ID_1310.28_1310.56, WER: 0%, TER: 0%, total WER: 19.2071%, total TER: 8.53723%, progress (thread 0): 92.8718%]
|T|: s h
|P|: s
[sample: TS3003d_H02_MTD0010ID_1311.62_1311.9, WER: 100%, TER: 50%, total WER: 19.208%, total TER: 8.53742%, progress (thread 0): 92.8797%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1403.4_1403.68, WER: 100%, TER: 66.6667%, total WER: 19.2089%, total TER: 8.53783%, progress (thread 0): 92.8877%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1945.66_1945.94, WER: 0%, TER: 0%, total WER: 19.2087%, total TER: 8.53775%, progress (thread 0): 92.8956%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2142.04_2142.32, WER: 0%, TER: 0%, total WER: 19.2084%, total TER: 8.53767%, progress (thread 0): 92.9035%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_49.78_50.06, WER: 0%, TER: 0%, total WER: 19.2082%, total TER: 8.53759%, progress (thread 0): 92.9114%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_478.66_478.94, WER: 100%, TER: 66.6667%, total WER: 19.2091%, total TER: 8.538%, progress (thread 0): 92.9193%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_758.4_758.68, WER: 0%, TER: 0%, total WER: 19.2089%, total TER: 8.53792%, progress (thread 0): 92.9272%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H01_FEO070_762.35_762.63, WER: 100%, TER: 100%, total WER: 19.2098%, total TER: 8.53878%, progress (thread 0): 92.9351%]
|T|: b u t
|P|: b u t
[sample: EN2002a_H01_FEO070_1024.58_1024.86, WER: 0%, TER: 0%, total WER: 19.2096%, total TER: 8.53872%, progress (thread 0): 92.943%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1306.05_1306.33, WER: 0%, TER: 0%, total WER: 19.2094%, total TER: 8.53864%, progress (thread 0): 92.951%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1392.09_1392.37, WER: 0%, TER: 0%, total WER: 19.2092%, total TER: 8.53856%, progress (thread 0): 92.9589%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1638.28_1638.56, WER: 0%, TER: 0%, total WER: 19.209%, total TER: 8.53848%, progress (thread 0): 92.9668%]
|T|: s e e
|P|: s e e
[sample: EN2002a_H01_FEO070_1702.66_1702.94, WER: 0%, TER: 0%, total WER: 19.2088%, total TER: 8.53842%, progress (thread 0): 92.9747%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1796.46_1796.74, WER: 0%, TER: 0%, total WER: 19.2085%, total TER: 8.53834%, progress (thread 0): 92.9826%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1831.52_1831.8, WER: 0%, TER: 0%, total WER: 19.2083%, total TER: 8.53826%, progress (thread 0): 92.9905%]
|T|: o h
|P|: a h
[sample: EN2002a_H02_FEO072_1981.37_1981.65, WER: 100%, TER: 50%, total WER: 19.2092%, total TER: 8.53845%, progress (thread 0): 92.9984%]
|T|: i | t h
|P|: i
[sample: EN2002a_H00_MEE073_2026.22_2026.5, WER: 50%, TER: 75%, total WER: 19.2099%, total TER: 8.53907%, progress (thread 0): 93.0063%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H01_MEE071_16.64_16.92, WER: 0%, TER: 0%, total WER: 19.2097%, total TER: 8.53899%, progress (thread 0): 93.0142%]
|T|: m m h m m
|P|: m m
[sample: EN2002b_H03_MEE073_335.63_335.91, WER: 100%, TER: 60%, total WER: 19.2106%, total TER: 8.5396%, progress (thread 0): 93.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_356.11_356.39, WER: 0%, TER: 0%, total WER: 19.2104%, total TER: 8.53952%, progress (thread 0): 93.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_987.48_987.76, WER: 0%, TER: 0%, total WER: 19.2102%, total TER: 8.53944%, progress (thread 0): 93.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1285.46_1285.74, WER: 0%, TER: 0%, total WER: 19.21%, total TER: 8.53936%, progress (thread 0): 93.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1450.71_1450.99, WER: 0%, TER: 0%, total WER: 19.2098%, total TER: 8.53928%, progress (thread 0): 93.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1588.08_1588.36, WER: 0%, TER: 0%, total WER: 19.2095%, total TER: 8.5392%, progress (thread 0): 93.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_264.61_264.89, WER: 0%, TER: 0%, total WER: 19.2093%, total TER: 8.53912%, progress (thread 0): 93.0696%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_638.33_638.61, WER: 100%, TER: 0%, total WER: 19.2102%, total TER: 8.53902%, progress (thread 0): 93.0775%]
|T|: a h
|P|: o h
[sample: EN2002c_H01_FEO072_1466.83_1467.11, WER: 100%, TER: 50%, total WER: 19.2111%, total TER: 8.53921%, progress (thread 0): 93.0854%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1508.55_1508.83, WER: 100%, TER: 28.5714%, total WER: 19.2121%, total TER: 8.53954%, progress (thread 0): 93.0934%]
|T|: i | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_1627.18_1627.46, WER: 50%, TER: 50%, total WER: 19.2127%, total TER: 8.54012%, progress (thread 0): 93.1013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2613.98_2614.26, WER: 0%, TER: 0%, total WER: 19.2125%, total TER: 8.54004%, progress (thread 0): 93.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2738.83_2739.11, WER: 0%, TER: 0%, total WER: 19.2123%, total TER: 8.53996%, progress (thread 0): 93.1171%]
|T|: w e l l
|P|: m m
[sample: EN2002c_H03_MEE073_2837.32_2837.6, WER: 100%, TER: 100%, total WER: 19.2132%, total TER: 8.54082%, progress (thread 0): 93.125%]
|T|: i | t h
|P|: i
[sample: EN2002d_H03_MEE073_209.34_209.62, WER: 50%, TER: 75%, total WER: 19.2139%, total TER: 8.54144%, progress (thread 0): 93.1329%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H03_MEE073_379.93_380.21, WER: 100%, TER: 0%, total WER: 19.2148%, total TER: 8.54134%, progress (thread 0): 93.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_446.25_446.53, WER: 0%, TER: 0%, total WER: 19.2146%, total TER: 8.54126%, progress (thread 0): 93.1487%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_622.33_622.61, WER: 0%, TER: 0%, total WER: 19.2144%, total TER: 8.54118%, progress (thread 0): 93.1566%]
|T|: y e a h
|P|: y m a m
[sample: EN2002d_H03_MEE073_989.71_989.99, WER: 100%, TER: 50%, total WER: 19.2153%, total TER: 8.54157%, progress (thread 0): 93.1646%]
|T|: h m m
|P|: m m
[sample: EN2002d_H02_MEE071_1294.26_1294.54, WER: 100%, TER: 33.3333%, total WER: 19.2162%, total TER: 8.54174%, progress (thread 0): 93.1725%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1744.58_1744.86, WER: 0%, TER: 0%, total WER: 19.216%, total TER: 8.54166%, progress (thread 0): 93.1804%]
|T|: t h e
|P|: y m a h
[sample: EN2002d_H03_MEE073_1888.86_1889.14, WER: 100%, TER: 133.333%, total WER: 19.2169%, total TER: 8.54254%, progress (thread 0): 93.1883%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2067.26_2067.54, WER: 0%, TER: 0%, total WER: 19.2167%, total TER: 8.54246%, progress (thread 0): 93.1962%]
|T|: s u r e
|P|: s r u e
[sample: ES2004b_H00_MEO015_1306.29_1306.56, WER: 100%, TER: 50%, total WER: 19.2176%, total TER: 8.54284%, progress (thread 0): 93.2041%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1834.89_1835.16, WER: 0%, TER: 0%, total WER: 19.2174%, total TER: 8.54276%, progress (thread 0): 93.212%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1954.73_1955, WER: 100%, TER: 0%, total WER: 19.2183%, total TER: 8.54266%, progress (thread 0): 93.2199%]
|T|: m m h m m
|P|: m m m
[sample: ES2004c_H03_FEE016_652.96_653.23, WER: 100%, TER: 40%, total WER: 19.2192%, total TER: 8.54303%, progress (thread 0): 93.2278%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004c_H00_MEO015_1141.18_1141.45, WER: 100%, TER: 20%, total WER: 19.2201%, total TER: 8.54317%, progress (thread 0): 93.2358%]
|T|: m m
|P|: s o
[sample: ES2004c_H03_FEE016_1281.44_1281.71, WER: 100%, TER: 100%, total WER: 19.221%, total TER: 8.54359%, progress (thread 0): 93.2437%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1403.89_1404.16, WER: 0%, TER: 0%, total WER: 19.2208%, total TER: 8.54351%, progress (thread 0): 93.2516%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1730.76_1731.03, WER: 0%, TER: 0%, total WER: 19.2206%, total TER: 8.54343%, progress (thread 0): 93.2595%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H00_MEO015_1835.22_1835.49, WER: 100%, TER: 0%, total WER: 19.2215%, total TER: 8.54333%, progress (thread 0): 93.2674%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1921.81_1922.08, WER: 0%, TER: 0%, total WER: 19.2213%, total TER: 8.54325%, progress (thread 0): 93.2753%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_2149.62_2149.89, WER: 0%, TER: 0%, total WER: 19.2211%, total TER: 8.54317%, progress (thread 0): 93.2832%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_654.95_655.22, WER: 100%, TER: 20%, total WER: 19.222%, total TER: 8.54331%, progress (thread 0): 93.2911%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_738.4_738.67, WER: 100%, TER: 33.3333%, total WER: 19.2229%, total TER: 8.54348%, progress (thread 0): 93.299%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_793.57_793.84, WER: 0%, TER: 0%, total WER: 19.2227%, total TER: 8.5434%, progress (thread 0): 93.307%]
|T|: n o
|P|: n o
[sample: ES2004d_H03_FEE016_1418.58_1418.85, WER: 0%, TER: 0%, total WER: 19.2225%, total TER: 8.54336%, progress (thread 0): 93.3149%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1497.73_1498, WER: 0%, TER: 0%, total WER: 19.2222%, total TER: 8.54328%, progress (thread 0): 93.3228%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1559.56_1559.83, WER: 0%, TER: 0%, total WER: 19.222%, total TER: 8.5432%, progress (thread 0): 93.3307%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2099.96_2100.23, WER: 0%, TER: 0%, total WER: 19.2218%, total TER: 8.54312%, progress (thread 0): 93.3386%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_159.51_159.78, WER: 100%, TER: 0%, total WER: 19.2227%, total TER: 8.54302%, progress (thread 0): 93.3465%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_495.24_495.51, WER: 0%, TER: 0%, total WER: 19.2225%, total TER: 8.54294%, progress (thread 0): 93.3544%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H02_FIO084_130.02_130.29, WER: 100%, TER: 20%, total WER: 19.2234%, total TER: 8.54308%, progress (thread 0): 93.3623%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_195.35_195.62, WER: 100%, TER: 60%, total WER: 19.2243%, total TER: 8.54368%, progress (thread 0): 93.3703%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H00_FIE088_1093_1093.27, WER: 100%, TER: 20%, total WER: 19.2252%, total TER: 8.54381%, progress (thread 0): 93.3782%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H01_FIO087_323.83_324.1, WER: 0%, TER: 0%, total WER: 19.225%, total TER: 8.54371%, progress (thread 0): 93.3861%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_770.63_770.9, WER: 100%, TER: 0%, total WER: 19.2259%, total TER: 8.54361%, progress (thread 0): 93.394%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_922.16_922.43, WER: 100%, TER: 20%, total WER: 19.2268%, total TER: 8.54375%, progress (thread 0): 93.4019%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_1095.56_1095.83, WER: 100%, TER: 20%, total WER: 19.2277%, total TER: 8.54388%, progress (thread 0): 93.4098%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1374.42_1374.69, WER: 0%, TER: 0%, total WER: 19.2275%, total TER: 8.5438%, progress (thread 0): 93.4177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_215.46_215.73, WER: 0%, TER: 0%, total WER: 19.2273%, total TER: 8.54372%, progress (thread 0): 93.4256%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_377.8_378.07, WER: 100%, TER: 0%, total WER: 19.2282%, total TER: 8.54362%, progress (thread 0): 93.4335%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_381.16_381.43, WER: 100%, TER: 0%, total WER: 19.2291%, total TER: 8.54352%, progress (thread 0): 93.4415%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H02_FIO084_938.34_938.61, WER: 100%, TER: 20%, total WER: 19.23%, total TER: 8.54365%, progress (thread 0): 93.4494%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1114.59_1114.86, WER: 100%, TER: 20%, total WER: 19.2309%, total TER: 8.54379%, progress (thread 0): 93.4573%]
|T|: o h
|P|: o h
[sample: IS1009d_H00_FIE088_1436.69_1436.96, WER: 0%, TER: 0%, total WER: 19.2307%, total TER: 8.54375%, progress (thread 0): 93.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1775.25_1775.52, WER: 0%, TER: 0%, total WER: 19.2305%, total TER: 8.54367%, progress (thread 0): 93.4731%]
|T|: o k a y
|P|: o h
[sample: IS1009d_H01_FIO087_1908.84_1909.11, WER: 100%, TER: 75%, total WER: 19.2314%, total TER: 8.54429%, progress (thread 0): 93.481%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_684.57_684.84, WER: 100%, TER: 150%, total WER: 19.2323%, total TER: 8.54495%, progress (thread 0): 93.4889%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_790.17_790.44, WER: 100%, TER: 25%, total WER: 19.2332%, total TER: 8.54511%, progress (thread 0): 93.4968%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_1326.95_1327.22, WER: 0%, TER: 0%, total WER: 19.233%, total TER: 8.54507%, progress (thread 0): 93.5047%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1909.01_1909.28, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54499%, progress (thread 0): 93.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1979.53_1979.8, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54491%, progress (thread 0): 93.5206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2145.84_2146.11, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54483%, progress (thread 0): 93.5285%]
|T|: o h
|P|: m m
[sample: TS3003d_H01_MTD011UID_364.71_364.98, WER: 100%, TER: 100%, total WER: 19.2333%, total TER: 8.54525%, progress (thread 0): 93.5364%]
|T|: t e n
|P|: t e n
[sample: TS3003d_H02_MTD0010ID_864.45_864.72, WER: 0%, TER: 0%, total WER: 19.2331%, total TER: 8.54519%, progress (thread 0): 93.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_958.91_959.18, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54511%, progress (thread 0): 93.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1378.52_1378.79, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54503%, progress (thread 0): 93.5601%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_2176.17_2176.44, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54499%, progress (thread 0): 93.568%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2345.04_2345.31, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54495%, progress (thread 0): 93.576%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2364.31_2364.58, WER: 0%, TER: 0%, total WER: 19.232%, total TER: 8.54487%, progress (thread 0): 93.5839%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_2435.3_2435.57, WER: 100%, TER: 25%, total WER: 19.2329%, total TER: 8.54503%, progress (thread 0): 93.5918%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_501.72_501.99, WER: 100%, TER: 33.3333%, total WER: 19.2338%, total TER: 8.5452%, progress (thread 0): 93.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_727.19_727.46, WER: 0%, TER: 0%, total WER: 19.2336%, total TER: 8.54512%, progress (thread 0): 93.6076%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_806.36_806.63, WER: 0%, TER: 0%, total WER: 19.2334%, total TER: 8.54504%, progress (thread 0): 93.6155%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_927.86_928.13, WER: 0%, TER: 0%, total WER: 19.2332%, total TER: 8.54496%, progress (thread 0): 93.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1223.91_1224.18, WER: 0%, TER: 0%, total WER: 19.2329%, total TER: 8.54488%, progress (thread 0): 93.6313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1319.85_1320.12, WER: 0%, TER: 0%, total WER: 19.2327%, total TER: 8.5448%, progress (thread 0): 93.6392%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1452_1452.27, WER: 0%, TER: 0%, total WER: 19.2325%, total TER: 8.54472%, progress (thread 0): 93.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1516.15_1516.42, WER: 0%, TER: 0%, total WER: 19.2323%, total TER: 8.54464%, progress (thread 0): 93.6551%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1533.19_1533.46, WER: 0%, TER: 0%, total WER: 19.2321%, total TER: 8.54456%, progress (thread 0): 93.663%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1556.33_1556.6, WER: 0%, TER: 0%, total WER: 19.2319%, total TER: 8.54448%, progress (thread 0): 93.6709%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1945.11_1945.38, WER: 0%, TER: 0%, total WER: 19.2316%, total TER: 8.5444%, progress (thread 0): 93.6788%]
|T|: o h
|P|: o h
[sample: EN2002a_H00_MEE073_1989_1989.27, WER: 0%, TER: 0%, total WER: 19.2314%, total TER: 8.54436%, progress (thread 0): 93.6867%]
|T|: o h
|P|: u m
[sample: EN2002a_H00_MEE073_2003.83_2004.1, WER: 100%, TER: 100%, total WER: 19.2323%, total TER: 8.54479%, progress (thread 0): 93.6946%]
|T|: n o
|P|: n
[sample: EN2002a_H01_FEO070_2105.6_2105.87, WER: 100%, TER: 50%, total WER: 19.2332%, total TER: 8.54498%, progress (thread 0): 93.7025%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_355.59_355.86, WER: 0%, TER: 0%, total WER: 19.233%, total TER: 8.54494%, progress (thread 0): 93.7104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_485.61_485.88, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54486%, progress (thread 0): 93.7184%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1027.72_1027.99, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54476%, progress (thread 0): 93.7263%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_1296.06_1296.33, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54472%, progress (thread 0): 93.7342%]
|T|: s o
|P|: s o
[sample: EN2002b_H01_MEE071_1333.58_1333.85, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54468%, progress (thread 0): 93.7421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1339.47_1339.74, WER: 0%, TER: 0%, total WER: 19.2319%, total TER: 8.5446%, progress (thread 0): 93.75%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1448.9_1449.17, WER: 0%, TER: 0%, total WER: 19.2317%, total TER: 8.54452%, progress (thread 0): 93.7579%]
|T|: a l r i g h t
|P|: a l r i g h t
[sample: EN2002c_H03_MEE073_558.45_558.72, WER: 0%, TER: 0%, total WER: 19.2315%, total TER: 8.54439%, progress (thread 0): 93.7658%]
|T|: h m m
|P|: m m
[sample: EN2002c_H01_FEO072_655.42_655.69, WER: 100%, TER: 33.3333%, total WER: 19.2324%, total TER: 8.54456%, progress (thread 0): 93.7737%]
|T|: c o o l
|P|: c o o l
[sample: EN2002c_H01_FEO072_778.13_778.4, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54448%, progress (thread 0): 93.7816%]
|T|: o r
|P|: o h
[sample: EN2002c_H01_FEO072_798.93_799.2, WER: 100%, TER: 50%, total WER: 19.2331%, total TER: 8.54467%, progress (thread 0): 93.7896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_938.98_939.25, WER: 0%, TER: 0%, total WER: 19.2329%, total TER: 8.54459%, progress (thread 0): 93.7975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1122.75_1123.02, WER: 0%, TER: 0%, total WER: 19.2327%, total TER: 8.54451%, progress (thread 0): 93.8054%]
|T|: o k a y
|P|: m h m m
[sample: EN2002c_H02_MEE071_1353.05_1353.32, WER: 100%, TER: 100%, total WER: 19.2336%, total TER: 8.54537%, progress (thread 0): 93.8133%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1574.37_1574.64, WER: 0%, TER: 0%, total WER: 19.2334%, total TER: 8.54527%, progress (thread 0): 93.8212%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_1672.35_1672.62, WER: 100%, TER: 25%, total WER: 19.2343%, total TER: 8.54542%, progress (thread 0): 93.8291%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1740.26_1740.53, WER: 0%, TER: 0%, total WER: 19.2341%, total TER: 8.54534%, progress (thread 0): 93.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1980.58_1980.85, WER: 0%, TER: 0%, total WER: 19.2338%, total TER: 8.54526%, progress (thread 0): 93.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2155.14_2155.41, WER: 0%, TER: 0%, total WER: 19.2336%, total TER: 8.54518%, progress (thread 0): 93.8528%]
|T|: a l r i g h t
|P|: o h r i
[sample: EN2002c_H03_MEE073_2202.51_2202.78, WER: 100%, TER: 71.4286%, total WER: 19.2345%, total TER: 8.54621%, progress (thread 0): 93.8608%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2272.51_2272.78, WER: 0%, TER: 0%, total WER: 19.2343%, total TER: 8.54613%, progress (thread 0): 93.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2294.4_2294.67, WER: 0%, TER: 0%, total WER: 19.2341%, total TER: 8.54605%, progress (thread 0): 93.8766%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2396.65_2396.92, WER: 0%, TER: 0%, total WER: 19.2339%, total TER: 8.54597%, progress (thread 0): 93.8845%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2568.46_2568.73, WER: 0%, TER: 0%, total WER: 19.2337%, total TER: 8.54589%, progress (thread 0): 93.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2705.6_2705.87, WER: 0%, TER: 0%, total WER: 19.2335%, total TER: 8.54581%, progress (thread 0): 93.9003%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_2790.29_2790.56, WER: 0%, TER: 0%, total WER: 19.2332%, total TER: 8.54577%, progress (thread 0): 93.9082%]
|T|: i | k n o w
|P|: y o u | k n o w
[sample: EN2002d_H03_MEE073_903.75_904.02, WER: 50%, TER: 50%, total WER: 19.2339%, total TER: 8.54635%, progress (thread 0): 93.9161%]
|T|: b u t
|P|: w h l
[sample: EN2002d_H01_FEO072_946.69_946.96, WER: 100%, TER: 100%, total WER: 19.2348%, total TER: 8.54699%, progress (thread 0): 93.924%]
|T|: s o r r y
|P|: s o r r y
[sample: EN2002d_H00_FEO070_966.35_966.62, WER: 0%, TER: 0%, total WER: 19.2346%, total TER: 8.54689%, progress (thread 0): 93.932%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1127.55_1127.82, WER: 0%, TER: 0%, total WER: 19.2344%, total TER: 8.54681%, progress (thread 0): 93.9399%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1281.17_1281.44, WER: 0%, TER: 0%, total WER: 19.2342%, total TER: 8.54673%, progress (thread 0): 93.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1305.04_1305.31, WER: 0%, TER: 0%, total WER: 19.234%, total TER: 8.54665%, progress (thread 0): 93.9557%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002d_H00_FEO070_1507.8_1508.07, WER: 100%, TER: 100%, total WER: 19.2358%, total TER: 8.54815%, progress (thread 0): 93.9636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_1832.82_1833.09, WER: 0%, TER: 0%, total WER: 19.2356%, total TER: 8.54807%, progress (thread 0): 93.9715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_2000.66_2000.93, WER: 0%, TER: 0%, total WER: 19.2354%, total TER: 8.54799%, progress (thread 0): 93.9794%]
|T|: w h a t
|P|: w e a l
[sample: EN2002d_H02_MEE071_2072.88_2073.15, WER: 100%, TER: 50%, total WER: 19.2363%, total TER: 8.54838%, progress (thread 0): 93.9873%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_2169.42_2169.69, WER: 100%, TER: 66.6667%, total WER: 19.2372%, total TER: 8.54879%, progress (thread 0): 93.9953%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_17.88_18.14, WER: 0%, TER: 0%, total WER: 19.237%, total TER: 8.54871%, progress (thread 0): 94.0032%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_966.22_966.48, WER: 0%, TER: 0%, total WER: 19.2367%, total TER: 8.54861%, progress (thread 0): 94.0111%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_89.12_89.38, WER: 100%, TER: 20%, total WER: 19.2376%, total TER: 8.54874%, progress (thread 0): 94.019%]
|T|: t h e r e | w e | g o
|P|: o k a y
[sample: ES2004b_H02_MEE014_830.72_830.98, WER: 100%, TER: 100%, total WER: 19.2404%, total TER: 8.55109%, progress (thread 0): 94.0269%]
|T|: h u h
|P|: m m
[sample: ES2004b_H02_MEE014_1039.01_1039.27, WER: 100%, TER: 100%, total WER: 19.2413%, total TER: 8.55173%, progress (thread 0): 94.0348%]
|T|: p e n s
|P|: i n
[sample: ES2004b_H00_MEO015_1162.44_1162.7, WER: 100%, TER: 75%, total WER: 19.2422%, total TER: 8.55235%, progress (thread 0): 94.0427%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1848.94_1849.2, WER: 0%, TER: 0%, total WER: 19.242%, total TER: 8.55227%, progress (thread 0): 94.0506%]
|T|: ' k a y
|P|: k a y
[sample: ES2004c_H00_MEO015_188.1_188.36, WER: 100%, TER: 25%, total WER: 19.2429%, total TER: 8.55243%, progress (thread 0): 94.0585%]
|T|: y e p
|P|: y e p
[sample: ES2004c_H00_MEO015_540.57_540.83, WER: 0%, TER: 0%, total WER: 19.2427%, total TER: 8.55237%, progress (thread 0): 94.0665%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1192.94_1193.2, WER: 0%, TER: 0%, total WER: 19.2425%, total TER: 8.55229%, progress (thread 0): 94.0744%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_39.85_40.11, WER: 100%, TER: 20%, total WER: 19.2434%, total TER: 8.55242%, progress (thread 0): 94.0823%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_129.84_130.1, WER: 0%, TER: 0%, total WER: 19.2431%, total TER: 8.55234%, progress (thread 0): 94.0902%]
|T|: o h
|P|: m h
[sample: ES2004d_H03_FEE016_1400.02_1400.28, WER: 100%, TER: 50%, total WER: 19.2441%, total TER: 8.55253%, progress (thread 0): 94.0981%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1553.35_1553.61, WER: 0%, TER: 0%, total WER: 19.2438%, total TER: 8.55245%, progress (thread 0): 94.106%]
|T|: n o
|P|: y e a h
[sample: ES2004d_H01_FEE013_1608.41_1608.67, WER: 100%, TER: 200%, total WER: 19.2447%, total TER: 8.55335%, progress (thread 0): 94.1139%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_1680.41_1680.67, WER: 100%, TER: 20%, total WER: 19.2457%, total TER: 8.55348%, progress (thread 0): 94.1218%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H03_FIO089_195.56_195.82, WER: 100%, TER: 20%, total WER: 19.2466%, total TER: 8.55362%, progress (thread 0): 94.1297%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_250.63_250.89, WER: 0%, TER: 0%, total WER: 19.2463%, total TER: 8.55354%, progress (thread 0): 94.1377%]
|T|: y e s
|P|: y e a h
[sample: IS1009b_H01_FIO087_289.01_289.27, WER: 100%, TER: 66.6667%, total WER: 19.2473%, total TER: 8.55394%, progress (thread 0): 94.1456%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_892.83_893.09, WER: 100%, TER: 60%, total WER: 19.2482%, total TER: 8.55455%, progress (thread 0): 94.1535%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_2020.18_2020.44, WER: 0%, TER: 0%, total WER: 19.2479%, total TER: 8.55447%, progress (thread 0): 94.1614%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_626.71_626.97, WER: 100%, TER: 40%, total WER: 19.2489%, total TER: 8.55483%, progress (thread 0): 94.1693%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_766.43_766.69, WER: 0%, TER: 0%, total WER: 19.2486%, total TER: 8.55475%, progress (thread 0): 94.1772%]
|T|: m m
|P|: y m m
[sample: IS1009d_H01_FIO087_656.85_657.11, WER: 100%, TER: 50%, total WER: 19.2495%, total TER: 8.55495%, progress (thread 0): 94.1851%]
|T|: ' c a u s e
|P|: s
[sample: TS3003a_H03_MTD012ME_1376.63_1376.89, WER: 100%, TER: 83.3333%, total WER: 19.2505%, total TER: 8.55599%, progress (thread 0): 94.193%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H02_MTD0010ID_1474.04_1474.3, WER: 0%, TER: 0%, total WER: 19.2502%, total TER: 8.55591%, progress (thread 0): 94.201%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_206.59_206.85, WER: 100%, TER: 25%, total WER: 19.2511%, total TER: 8.55607%, progress (thread 0): 94.2089%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H01_MTD011UID_581.65_581.91, WER: 0%, TER: 0%, total WER: 19.2509%, total TER: 8.55599%, progress (thread 0): 94.2168%]
|T|: h m m
|P|: m m
[sample: TS3003b_H03_MTD012ME_883.3_883.56, WER: 100%, TER: 33.3333%, total WER: 19.2518%, total TER: 8.55616%, progress (thread 0): 94.2247%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1533.07_1533.33, WER: 100%, TER: 40%, total WER: 19.2527%, total TER: 8.55653%, progress (thread 0): 94.2326%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_2048.3_2048.56, WER: 0%, TER: 0%, total WER: 19.2525%, total TER: 8.55647%, progress (thread 0): 94.2405%]
|T|: m m
|P|: h m m
[sample: TS3003c_H01_MTD011UID_1933.15_1933.41, WER: 100%, TER: 50%, total WER: 19.2534%, total TER: 8.55666%, progress (thread 0): 94.2484%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1980.3_1980.56, WER: 0%, TER: 0%, total WER: 19.2532%, total TER: 8.55658%, progress (thread 0): 94.2563%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_694.34_694.6, WER: 0%, TER: 0%, total WER: 19.253%, total TER: 8.5565%, progress (thread 0): 94.2642%]
|T|: t w o
|P|: t w o
[sample: TS3003d_H03_MTD012ME_1897.81_1898.07, WER: 0%, TER: 0%, total WER: 19.2528%, total TER: 8.55644%, progress (thread 0): 94.2722%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2141.52_2141.78, WER: 0%, TER: 0%, total WER: 19.2526%, total TER: 8.55636%, progress (thread 0): 94.2801%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002a_H02_FEO072_36.34_36.6, WER: 100%, TER: 20%, total WER: 19.2535%, total TER: 8.5565%, progress (thread 0): 94.288%]
|T|: y e a h
|P|: y m a m
[sample: EN2002a_H00_MEE073_319.23_319.49, WER: 100%, TER: 50%, total WER: 19.2544%, total TER: 8.55688%, progress (thread 0): 94.2959%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_671.05_671.31, WER: 0%, TER: 0%, total WER: 19.2542%, total TER: 8.5568%, progress (thread 0): 94.3038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_721.89_722.15, WER: 0%, TER: 0%, total WER: 19.254%, total TER: 8.55672%, progress (thread 0): 94.3117%]
|T|: t r u e
|P|: t r u e
[sample: EN2002a_H00_MEE073_751.8_752.06, WER: 0%, TER: 0%, total WER: 19.2537%, total TER: 8.55664%, progress (thread 0): 94.3196%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1052.1_1052.36, WER: 0%, TER: 0%, total WER: 19.2535%, total TER: 8.55656%, progress (thread 0): 94.3275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1446.67_1446.93, WER: 0%, TER: 0%, total WER: 19.2533%, total TER: 8.55648%, progress (thread 0): 94.3354%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1777.39_1777.65, WER: 0%, TER: 0%, total WER: 19.2531%, total TER: 8.5564%, progress (thread 0): 94.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1868.15_1868.41, WER: 0%, TER: 0%, total WER: 19.2529%, total TER: 8.55632%, progress (thread 0): 94.3513%]
|T|: o k a y
|P|: o g a y
[sample: EN2002b_H02_FEO072_93.22_93.48, WER: 100%, TER: 25%, total WER: 19.2538%, total TER: 8.55648%, progress (thread 0): 94.3592%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_252.07_252.33, WER: 0%, TER: 0%, total WER: 19.2536%, total TER: 8.5564%, progress (thread 0): 94.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_460.58_460.84, WER: 0%, TER: 0%, total WER: 19.2533%, total TER: 8.55632%, progress (thread 0): 94.375%]
|T|: o h
|P|: o h
[sample: EN2002b_H03_MEE073_674.06_674.32, WER: 0%, TER: 0%, total WER: 19.2531%, total TER: 8.55628%, progress (thread 0): 94.3829%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1026.26_1026.52, WER: 0%, TER: 0%, total WER: 19.2529%, total TER: 8.5562%, progress (thread 0): 94.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1104.98_1105.24, WER: 0%, TER: 0%, total WER: 19.2527%, total TER: 8.55612%, progress (thread 0): 94.3987%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1549.41_1549.67, WER: 100%, TER: 33.3333%, total WER: 19.2536%, total TER: 8.55629%, progress (thread 0): 94.4066%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1643.55_1643.81, WER: 0%, TER: 0%, total WER: 19.2534%, total TER: 8.55621%, progress (thread 0): 94.4146%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H02_MEE071_46.45_46.71, WER: 0%, TER: 0%, total WER: 19.2532%, total TER: 8.55611%, progress (thread 0): 94.4225%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_577.1_577.36, WER: 0%, TER: 0%, total WER: 19.253%, total TER: 8.55605%, progress (thread 0): 94.4304%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_600.57_600.83, WER: 100%, TER: 66.6667%, total WER: 19.2539%, total TER: 8.55646%, progress (thread 0): 94.4383%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_775.24_775.5, WER: 0%, TER: 0%, total WER: 19.2536%, total TER: 8.55638%, progress (thread 0): 94.4462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1079.16_1079.42, WER: 0%, TER: 0%, total WER: 19.2534%, total TER: 8.5563%, progress (thread 0): 94.4541%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002c_H03_MEE073_1105.57_1105.83, WER: 100%, TER: 20%, total WER: 19.2543%, total TER: 8.55643%, progress (thread 0): 94.462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1119.48_1119.74, WER: 0%, TER: 0%, total WER: 19.2541%, total TER: 8.55635%, progress (thread 0): 94.4699%]
|T|: m m h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1983.15_1983.41, WER: 100%, TER: 60%, total WER: 19.255%, total TER: 8.55695%, progress (thread 0): 94.4779%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1999.1_1999.36, WER: 0%, TER: 0%, total WER: 19.2548%, total TER: 8.55685%, progress (thread 0): 94.4858%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2082.11_2082.37, WER: 0%, TER: 0%, total WER: 19.2546%, total TER: 8.55677%, progress (thread 0): 94.4937%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2586.11_2586.37, WER: 0%, TER: 0%, total WER: 19.2544%, total TER: 8.55669%, progress (thread 0): 94.5016%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_347.04_347.3, WER: 0%, TER: 0%, total WER: 19.2542%, total TER: 8.55661%, progress (thread 0): 94.5095%]
|T|: w h o
|P|: h m m
[sample: EN2002d_H00_FEO070_501.31_501.57, WER: 100%, TER: 100%, total WER: 19.2551%, total TER: 8.55726%, progress (thread 0): 94.5174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_728.46_728.72, WER: 0%, TER: 0%, total WER: 19.2549%, total TER: 8.55718%, progress (thread 0): 94.5253%]
|T|: w e l l | i t ' s
|P|: j u s t
[sample: EN2002d_H03_MEE073_882.54_882.8, WER: 100%, TER: 88.8889%, total WER: 19.2567%, total TER: 8.55886%, progress (thread 0): 94.5332%]
|T|: s u p e r
|P|: s u p e
[sample: EN2002d_H03_MEE073_953.8_954.06, WER: 100%, TER: 20%, total WER: 19.2576%, total TER: 8.559%, progress (thread 0): 94.5411%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1517.63_1517.89, WER: 50%, TER: 42.8571%, total WER: 19.2583%, total TER: 8.55956%, progress (thread 0): 94.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1704.28_1704.54, WER: 0%, TER: 0%, total WER: 19.2581%, total TER: 8.55948%, progress (thread 0): 94.557%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_2135.36_2135.62, WER: 100%, TER: 33.3333%, total WER: 19.259%, total TER: 8.55965%, progress (thread 0): 94.5649%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_666.69_666.94, WER: 0%, TER: 0%, total WER: 19.2587%, total TER: 8.55961%, progress (thread 0): 94.5728%]
|T|: y e p
|P|: y e a h
[sample: ES2004b_H00_MEO015_817.97_818.22, WER: 100%, TER: 66.6667%, total WER: 19.2597%, total TER: 8.56002%, progress (thread 0): 94.5807%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_2175.08_2175.33, WER: 100%, TER: 20%, total WER: 19.2606%, total TER: 8.56015%, progress (thread 0): 94.5886%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004c_H00_MEO015_620.36_620.61, WER: 100%, TER: 20%, total WER: 19.2615%, total TER: 8.56029%, progress (thread 0): 94.5965%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1866.87_1867.12, WER: 100%, TER: 100%, total WER: 19.2624%, total TER: 8.56093%, progress (thread 0): 94.6044%]
|T|: s o r r y
|P|: s o r r y
[sample: ES2004d_H01_FEE013_455.38_455.63, WER: 0%, TER: 0%, total WER: 19.2622%, total TER: 8.56083%, progress (thread 0): 94.6123%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_607.67_607.92, WER: 0%, TER: 0%, total WER: 19.2619%, total TER: 8.56075%, progress (thread 0): 94.6203%]
|T|: y e s
|P|: y e a h
[sample: ES2004d_H03_FEE016_1135.45_1135.7, WER: 100%, TER: 66.6667%, total WER: 19.2628%, total TER: 8.56115%, progress (thread 0): 94.6282%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1553.38_1553.63, WER: 0%, TER: 0%, total WER: 19.2626%, total TER: 8.56107%, progress (thread 0): 94.6361%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H03_FIO089_1086.32_1086.57, WER: 100%, TER: 20%, total WER: 19.2635%, total TER: 8.56121%, progress (thread 0): 94.644%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1900.42_1900.67, WER: 0%, TER: 0%, total WER: 19.2633%, total TER: 8.56113%, progress (thread 0): 94.6519%]
|T|: y e s
|P|: y e a h
[sample: IS1009b_H01_FIO087_1909.04_1909.29, WER: 100%, TER: 66.6667%, total WER: 19.2642%, total TER: 8.56153%, progress (thread 0): 94.6598%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H00_FIE088_324.58_324.83, WER: 0%, TER: 0%, total WER: 19.264%, total TER: 8.56143%, progress (thread 0): 94.6677%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1287.33_1287.58, WER: 0%, TER: 0%, total WER: 19.2638%, total TER: 8.56135%, progress (thread 0): 94.6756%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1781.33_1781.58, WER: 0%, TER: 0%, total WER: 19.2636%, total TER: 8.56127%, progress (thread 0): 94.6835%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_351.32_351.57, WER: 0%, TER: 0%, total WER: 19.2634%, total TER: 8.56119%, progress (thread 0): 94.6915%]
|T|: m m
|P|: m m m
[sample: IS1009d_H03_FIO089_427.43_427.68, WER: 100%, TER: 50%, total WER: 19.2643%, total TER: 8.56139%, progress (thread 0): 94.6994%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_826.51_826.76, WER: 0%, TER: 0%, total WER: 19.2641%, total TER: 8.56135%, progress (thread 0): 94.7073%]
|T|: n o
|P|: n o
[sample: TS3003a_H03_MTD012ME_1222.74_1222.99, WER: 0%, TER: 0%, total WER: 19.2638%, total TER: 8.56131%, progress (thread 0): 94.7152%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_281.83_282.08, WER: 0%, TER: 0%, total WER: 19.2636%, total TER: 8.56127%, progress (thread 0): 94.7231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1559.52_1559.77, WER: 0%, TER: 0%, total WER: 19.2634%, total TER: 8.56119%, progress (thread 0): 94.731%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_1839.11_1839.36, WER: 0%, TER: 0%, total WER: 19.2632%, total TER: 8.56111%, progress (thread 0): 94.7389%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H02_MTD0010ID_2094.57_2094.82, WER: 100%, TER: 50%, total WER: 19.2641%, total TER: 8.5615%, progress (thread 0): 94.7468%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003c_H03_MTD012ME_1972.52_1972.77, WER: 100%, TER: 20%, total WER: 19.265%, total TER: 8.56163%, progress (thread 0): 94.7548%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_303.27_303.52, WER: 100%, TER: 75%, total WER: 19.2659%, total TER: 8.56225%, progress (thread 0): 94.7627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_583.29_583.54, WER: 0%, TER: 0%, total WER: 19.2657%, total TER: 8.56217%, progress (thread 0): 94.7706%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_847.16_847.41, WER: 0%, TER: 0%, total WER: 19.2655%, total TER: 8.56209%, progress (thread 0): 94.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1370.99_1371.24, WER: 0%, TER: 0%, total WER: 19.2653%, total TER: 8.56201%, progress (thread 0): 94.7864%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1371.48_1371.73, WER: 0%, TER: 0%, total WER: 19.265%, total TER: 8.56193%, progress (thread 0): 94.7943%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_1404.32_1404.57, WER: 100%, TER: 75%, total WER: 19.266%, total TER: 8.56255%, progress (thread 0): 94.8022%]
|T|: g o o d
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1696.35_1696.6, WER: 100%, TER: 100%, total WER: 19.2669%, total TER: 8.5634%, progress (thread 0): 94.8101%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2064.36_2064.61, WER: 0%, TER: 0%, total WER: 19.2666%, total TER: 8.56332%, progress (thread 0): 94.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_2111_2111.25, WER: 0%, TER: 0%, total WER: 19.2664%, total TER: 8.56324%, progress (thread 0): 94.826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2138.4_2138.65, WER: 0%, TER: 0%, total WER: 19.2662%, total TER: 8.56316%, progress (thread 0): 94.8339%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2216.27_2216.52, WER: 0%, TER: 0%, total WER: 19.266%, total TER: 8.56308%, progress (thread 0): 94.8418%]
|T|: a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2381.92_2382.17, WER: 100%, TER: 100%, total WER: 19.2669%, total TER: 8.56351%, progress (thread 0): 94.8497%]
|T|: s o
|P|: s o
[sample: EN2002a_H00_MEE073_202.42_202.67, WER: 0%, TER: 0%, total WER: 19.2667%, total TER: 8.56347%, progress (thread 0): 94.8576%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_749.45_749.7, WER: 0%, TER: 0%, total WER: 19.2665%, total TER: 8.56339%, progress (thread 0): 94.8655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1642_1642.25, WER: 0%, TER: 0%, total WER: 19.2663%, total TER: 8.56331%, progress (thread 0): 94.8734%]
|T|: s o
|P|: s o
[sample: EN2002a_H02_FEO072_1664.11_1664.36, WER: 0%, TER: 0%, total WER: 19.266%, total TER: 8.56327%, progress (thread 0): 94.8813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1964.06_1964.31, WER: 0%, TER: 0%, total WER: 19.2658%, total TER: 8.56319%, progress (thread 0): 94.8892%]
|T|: y o u ' d | b e | s
|P|: c i m p e
[sample: EN2002a_H00_MEE073_2096.92_2097.17, WER: 100%, TER: 90%, total WER: 19.2685%, total TER: 8.56509%, progress (thread 0): 94.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_93.68_93.93, WER: 0%, TER: 0%, total WER: 19.2683%, total TER: 8.56501%, progress (thread 0): 94.9051%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_311.61_311.86, WER: 0%, TER: 0%, total WER: 19.2681%, total TER: 8.56491%, progress (thread 0): 94.913%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1129.86_1130.11, WER: 100%, TER: 28.5714%, total WER: 19.269%, total TER: 8.56524%, progress (thread 0): 94.9209%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1222.15_1222.4, WER: 0%, TER: 0%, total WER: 19.2688%, total TER: 8.56518%, progress (thread 0): 94.9288%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1237.83_1238.08, WER: 0%, TER: 0%, total WER: 19.2686%, total TER: 8.5651%, progress (thread 0): 94.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1245.09_1245.34, WER: 0%, TER: 0%, total WER: 19.2684%, total TER: 8.56502%, progress (thread 0): 94.9446%]
|T|: a n d
|P|: t h e n
[sample: EN2002b_H01_MEE071_1309.75_1310, WER: 100%, TER: 133.333%, total WER: 19.2693%, total TER: 8.56589%, progress (thread 0): 94.9525%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H02_FEO072_1594.84_1595.09, WER: 100%, TER: 66.6667%, total WER: 19.2702%, total TER: 8.5663%, progress (thread 0): 94.9604%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1643.59_1643.84, WER: 0%, TER: 0%, total WER: 19.27%, total TER: 8.56622%, progress (thread 0): 94.9684%]
|T|: s u r e
|P|: s u r e
[sample: EN2002c_H02_MEE071_67.06_67.31, WER: 0%, TER: 0%, total WER: 19.2697%, total TER: 8.56614%, progress (thread 0): 94.9763%]
|T|: o k a y
|P|: m m
[sample: EN2002c_H03_MEE073_76.13_76.38, WER: 100%, TER: 100%, total WER: 19.2707%, total TER: 8.56699%, progress (thread 0): 94.9842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_253.63_253.88, WER: 0%, TER: 0%, total WER: 19.2704%, total TER: 8.56691%, progress (thread 0): 94.9921%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_470.85_471.1, WER: 100%, TER: 0%, total WER: 19.2713%, total TER: 8.56681%, progress (thread 0): 95%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_759.06_759.31, WER: 0%, TER: 0%, total WER: 19.2711%, total TER: 8.56671%, progress (thread 0): 95.0079%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1000.82_1001.07, WER: 0%, TER: 0%, total WER: 19.2709%, total TER: 8.56663%, progress (thread 0): 95.0158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1023.06_1023.31, WER: 0%, TER: 0%, total WER: 19.2707%, total TER: 8.56655%, progress (thread 0): 95.0237%]
|T|: m m h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1102.48_1102.73, WER: 100%, TER: 60%, total WER: 19.2716%, total TER: 8.56715%, progress (thread 0): 95.0316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1122.76_1123.01, WER: 0%, TER: 0%, total WER: 19.2714%, total TER: 8.56707%, progress (thread 0): 95.0396%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_1359.23_1359.48, WER: 100%, TER: 66.6667%, total WER: 19.2723%, total TER: 8.56748%, progress (thread 0): 95.0475%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_1508.23_1508.48, WER: 0%, TER: 0%, total WER: 19.2721%, total TER: 8.56742%, progress (thread 0): 95.0554%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2100.94_2101.19, WER: 0%, TER: 0%, total WER: 19.2719%, total TER: 8.56734%, progress (thread 0): 95.0633%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2354.17_2354.42, WER: 0%, TER: 0%, total WER: 19.2716%, total TER: 8.56726%, progress (thread 0): 95.0712%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_146.37_146.62, WER: 100%, TER: 28.5714%, total WER: 19.2725%, total TER: 8.56759%, progress (thread 0): 95.0791%]
|T|: o h
|P|: m m
[sample: EN2002d_H00_FEO070_286.77_287.02, WER: 100%, TER: 100%, total WER: 19.2735%, total TER: 8.56801%, progress (thread 0): 95.087%]
|T|: y e a h
|P|: m m
[sample: EN2002d_H03_MEE073_589.91_590.16, WER: 100%, TER: 100%, total WER: 19.2744%, total TER: 8.56887%, progress (thread 0): 95.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_850.49_850.74, WER: 0%, TER: 0%, total WER: 19.2741%, total TER: 8.56879%, progress (thread 0): 95.1028%]
|T|: s o
|P|: h m h
[sample: EN2002d_H02_MEE071_1251.8_1252.05, WER: 100%, TER: 150%, total WER: 19.2751%, total TER: 8.56945%, progress (thread 0): 95.1108%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_1830.85_1831.1, WER: 100%, TER: 33.3333%, total WER: 19.276%, total TER: 8.56962%, progress (thread 0): 95.1187%]
|T|: o k a y
|P|: c o y
[sample: EN2002d_H03_MEE073_1854.42_1854.67, WER: 100%, TER: 75%, total WER: 19.2769%, total TER: 8.57024%, progress (thread 0): 95.1266%]
|T|: m m
|P|: h m m
[sample: EN2002d_H01_FEO072_2083.17_2083.42, WER: 100%, TER: 50%, total WER: 19.2778%, total TER: 8.57044%, progress (thread 0): 95.1345%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2192.17_2192.42, WER: 0%, TER: 0%, total WER: 19.2776%, total TER: 8.57036%, progress (thread 0): 95.1424%]
|T|: s
|P|: s
[sample: ES2004a_H02_MEE014_484.48_484.72, WER: 0%, TER: 0%, total WER: 19.2773%, total TER: 8.57033%, progress (thread 0): 95.1503%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_775.6_775.84, WER: 0%, TER: 0%, total WER: 19.2771%, total TER: 8.57029%, progress (thread 0): 95.1582%]
|T|: s o r r y
|P|: m m
[sample: ES2004b_H00_MEO015_100.7_100.94, WER: 100%, TER: 100%, total WER: 19.278%, total TER: 8.57136%, progress (thread 0): 95.1661%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1311.92_1312.16, WER: 0%, TER: 0%, total WER: 19.2778%, total TER: 8.57126%, progress (thread 0): 95.174%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_1636.65_1636.89, WER: 100%, TER: 20%, total WER: 19.2787%, total TER: 8.5714%, progress (thread 0): 95.182%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1305.92_1306.16, WER: 100%, TER: 25%, total WER: 19.2796%, total TER: 8.57155%, progress (thread 0): 95.1899%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1345.16_1345.4, WER: 0%, TER: 0%, total WER: 19.2794%, total TER: 8.57151%, progress (thread 0): 95.1978%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1364.93_1365.17, WER: 0%, TER: 0%, total WER: 19.2792%, total TER: 8.57147%, progress (thread 0): 95.2057%]
|T|: f i n e
|P|: i n y
[sample: ES2004c_H00_MEO015_2156.57_2156.81, WER: 100%, TER: 50%, total WER: 19.2801%, total TER: 8.57186%, progress (thread 0): 95.2136%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_973.73_973.97, WER: 0%, TER: 0%, total WER: 19.2799%, total TER: 8.5718%, progress (thread 0): 95.2215%]
|T|: m m
|P|: m m
[sample: ES2004d_H03_FEE016_1286.7_1286.94, WER: 0%, TER: 0%, total WER: 19.2797%, total TER: 8.57176%, progress (thread 0): 95.2294%]
|T|: o h
|P|: o h
[sample: ES2004d_H01_FEE013_2176.67_2176.91, WER: 0%, TER: 0%, total WER: 19.2794%, total TER: 8.57172%, progress (thread 0): 95.2373%]
|T|: n o
|P|: m m
[sample: IS1009a_H03_FIO089_145.55_145.79, WER: 100%, TER: 100%, total WER: 19.2804%, total TER: 8.57214%, progress (thread 0): 95.2453%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H00_FIE088_466.21_466.45, WER: 100%, TER: 20%, total WER: 19.2813%, total TER: 8.57228%, progress (thread 0): 95.2532%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_556.58_556.82, WER: 0%, TER: 0%, total WER: 19.281%, total TER: 8.5722%, progress (thread 0): 95.2611%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1033.16_1033.4, WER: 0%, TER: 0%, total WER: 19.2808%, total TER: 8.57212%, progress (thread 0): 95.269%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_1099.19_1099.43, WER: 0%, TER: 0%, total WER: 19.2806%, total TER: 8.57204%, progress (thread 0): 95.2769%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1247.84_1248.08, WER: 100%, TER: 0%, total WER: 19.2815%, total TER: 8.57194%, progress (thread 0): 95.2848%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_752.07_752.31, WER: 100%, TER: 20%, total WER: 19.2824%, total TER: 8.57207%, progress (thread 0): 95.2927%]
|T|: m m | r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_764.18_764.42, WER: 50%, TER: 37.5%, total WER: 19.2831%, total TER: 8.57261%, progress (thread 0): 95.3006%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1110.14_1110.38, WER: 100%, TER: 40%, total WER: 19.284%, total TER: 8.57298%, progress (thread 0): 95.3085%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_1123.59_1123.83, WER: 100%, TER: 20%, total WER: 19.2849%, total TER: 8.57311%, progress (thread 0): 95.3165%]
|T|: h e l l o
|P|: n o
[sample: IS1009d_H02_FIO084_41.31_41.55, WER: 100%, TER: 80%, total WER: 19.2858%, total TER: 8.57394%, progress (thread 0): 95.3244%]
|T|: m m
|P|: m m
[sample: IS1009d_H03_FIO089_516.52_516.76, WER: 0%, TER: 0%, total WER: 19.2856%, total TER: 8.5739%, progress (thread 0): 95.3323%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_656.9_657.14, WER: 100%, TER: 60%, total WER: 19.2865%, total TER: 8.5745%, progress (thread 0): 95.3402%]
|T|: y e a h
|P|: n o
[sample: IS1009d_H02_FIO084_1011.72_1011.96, WER: 100%, TER: 100%, total WER: 19.2874%, total TER: 8.57536%, progress (thread 0): 95.3481%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_1883.01_1883.25, WER: 0%, TER: 0%, total WER: 19.2872%, total TER: 8.57528%, progress (thread 0): 95.356%]
|T|: w e
|P|: w e
[sample: TS3003a_H00_MTD009PM_1109.9_1110.14, WER: 0%, TER: 0%, total WER: 19.287%, total TER: 8.57524%, progress (thread 0): 95.3639%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1420.32_1420.56, WER: 0%, TER: 0%, total WER: 19.2868%, total TER: 8.5752%, progress (thread 0): 95.3718%]
|T|: n o
|P|: n o
[sample: TS3003b_H02_MTD0010ID_1474.4_1474.64, WER: 0%, TER: 0%, total WER: 19.2866%, total TER: 8.57516%, progress (thread 0): 95.3797%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_1677.79_1678.03, WER: 0%, TER: 0%, total WER: 19.2863%, total TER: 8.57508%, progress (thread 0): 95.3877%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1975.67_1975.91, WER: 100%, TER: 100%, total WER: 19.2873%, total TER: 8.5755%, progress (thread 0): 95.3956%]
|T|: y e a h
|P|: h m m
[sample: TS3003b_H01_MTD011UID_2083.9_2084.14, WER: 100%, TER: 100%, total WER: 19.2882%, total TER: 8.57635%, progress (thread 0): 95.4035%]
|T|: y e a h
|P|: a h
[sample: TS3003c_H01_MTD011UID_1240.02_1240.26, WER: 100%, TER: 50%, total WER: 19.2891%, total TER: 8.57674%, progress (thread 0): 95.4114%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1254.68_1254.92, WER: 0%, TER: 0%, total WER: 19.2888%, total TER: 8.57666%, progress (thread 0): 95.4193%]
|T|: s o
|P|: s o
[sample: TS3003c_H01_MTD011UID_1417.83_1418.07, WER: 0%, TER: 0%, total WER: 19.2886%, total TER: 8.57662%, progress (thread 0): 95.4272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1477.96_1478.2, WER: 0%, TER: 0%, total WER: 19.2884%, total TER: 8.57654%, progress (thread 0): 95.4351%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1566.64_1566.88, WER: 100%, TER: 25%, total WER: 19.2893%, total TER: 8.57669%, progress (thread 0): 95.443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1716.46_1716.7, WER: 0%, TER: 0%, total WER: 19.2891%, total TER: 8.57661%, progress (thread 0): 95.451%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2217.59_2217.83, WER: 0%, TER: 0%, total WER: 19.2889%, total TER: 8.57653%, progress (thread 0): 95.4589%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2243.54_2243.78, WER: 0%, TER: 0%, total WER: 19.2887%, total TER: 8.57645%, progress (thread 0): 95.4668%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1049.33_1049.57, WER: 0%, TER: 0%, total WER: 19.2885%, total TER: 8.57637%, progress (thread 0): 95.4747%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1496.68_1496.92, WER: 100%, TER: 75%, total WER: 19.2894%, total TER: 8.57699%, progress (thread 0): 95.4826%]
|T|: d | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_106.38_106.62, WER: 50%, TER: 33.3333%, total WER: 19.2901%, total TER: 8.57734%, progress (thread 0): 95.4905%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_140.88_141.12, WER: 0%, TER: 0%, total WER: 19.2898%, total TER: 8.57726%, progress (thread 0): 95.4984%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_333.3_333.54, WER: 0%, TER: 0%, total WER: 19.2896%, total TER: 8.57716%, progress (thread 0): 95.5063%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H01_FEO070_398.18_398.42, WER: 100%, TER: 100%, total WER: 19.2905%, total TER: 8.57801%, progress (thread 0): 95.5142%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_481.62_481.86, WER: 0%, TER: 0%, total WER: 19.2903%, total TER: 8.57793%, progress (thread 0): 95.5222%]
|T|: y e a h
|P|: y e a m
[sample: EN2002a_H00_MEE073_682.97_683.21, WER: 100%, TER: 25%, total WER: 19.2912%, total TER: 8.57809%, progress (thread 0): 95.5301%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002a_H02_FEO072_1009.94_1010.18, WER: 100%, TER: 20%, total WER: 19.2921%, total TER: 8.57822%, progress (thread 0): 95.538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1041.03_1041.27, WER: 0%, TER: 0%, total WER: 19.2919%, total TER: 8.57814%, progress (thread 0): 95.5459%]
|T|: i | s e e
|P|: a t ' s
[sample: EN2002a_H00_MEE073_1171.72_1171.96, WER: 100%, TER: 100%, total WER: 19.2937%, total TER: 8.57921%, progress (thread 0): 95.5538%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002a_H00_MEE073_1209.32_1209.56, WER: 100%, TER: 85.7143%, total WER: 19.2955%, total TER: 8.58047%, progress (thread 0): 95.5617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1310.69_1310.93, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.58039%, progress (thread 0): 95.5696%]
|T|: o p e n
|P|: o p a y
[sample: EN2002a_H02_FEO072_1350.04_1350.28, WER: 100%, TER: 50%, total WER: 19.2962%, total TER: 8.58077%, progress (thread 0): 95.5775%]
|T|: m m
|P|: m m
[sample: EN2002a_H03_MEE071_1405.73_1405.97, WER: 0%, TER: 0%, total WER: 19.296%, total TER: 8.58073%, progress (thread 0): 95.5854%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1791.17_1791.41, WER: 0%, TER: 0%, total WER: 19.2958%, total TER: 8.58063%, progress (thread 0): 95.5934%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1834.42_1834.66, WER: 0%, TER: 0%, total WER: 19.2956%, total TER: 8.58055%, progress (thread 0): 95.6013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2028.12_2028.36, WER: 0%, TER: 0%, total WER: 19.2954%, total TER: 8.58047%, progress (thread 0): 95.6092%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_2040.58_2040.82, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.58039%, progress (thread 0): 95.6171%]
|T|: o h
|P|: o h
[sample: EN2002a_H01_FEO070_2098.91_2099.15, WER: 0%, TER: 0%, total WER: 19.2949%, total TER: 8.58035%, progress (thread 0): 95.625%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_522.48_522.72, WER: 0%, TER: 0%, total WER: 19.2947%, total TER: 8.58027%, progress (thread 0): 95.6329%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_612.23_612.47, WER: 0%, TER: 0%, total WER: 19.2945%, total TER: 8.58019%, progress (thread 0): 95.6408%]
|T|: m m
|P|: m m
[sample: EN2002b_H02_FEO072_1475.84_1476.08, WER: 0%, TER: 0%, total WER: 19.2943%, total TER: 8.58015%, progress (thread 0): 95.6487%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_235.84_236.08, WER: 100%, TER: 33.3333%, total WER: 19.2952%, total TER: 8.58033%, progress (thread 0): 95.6566%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_571.47_571.71, WER: 0%, TER: 0%, total WER: 19.295%, total TER: 8.58025%, progress (thread 0): 95.6646%]
|T|: h m m
|P|: y e a h
[sample: EN2002c_H03_MEE073_574.34_574.58, WER: 100%, TER: 133.333%, total WER: 19.2959%, total TER: 8.58112%, progress (thread 0): 95.6725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_598.8_599.04, WER: 0%, TER: 0%, total WER: 19.2956%, total TER: 8.58104%, progress (thread 0): 95.6804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1009.87_1010.11, WER: 0%, TER: 0%, total WER: 19.2954%, total TER: 8.58096%, progress (thread 0): 95.6883%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1018.66_1018.9, WER: 0%, TER: 0%, total WER: 19.2952%, total TER: 8.58086%, progress (thread 0): 95.6962%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1281.36_1281.6, WER: 100%, TER: 33.3333%, total WER: 19.2961%, total TER: 8.58103%, progress (thread 0): 95.7041%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1396.34_1396.58, WER: 0%, TER: 0%, total WER: 19.2959%, total TER: 8.58095%, progress (thread 0): 95.712%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1906.84_1907.08, WER: 0%, TER: 0%, total WER: 19.2957%, total TER: 8.58087%, progress (thread 0): 95.7199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1913.84_1914.08, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58079%, progress (thread 0): 95.7279%]
|T|: ' c a u s e
|P|: t h e ' s
[sample: EN2002c_H03_MEE073_2264.19_2264.43, WER: 100%, TER: 83.3333%, total WER: 19.2964%, total TER: 8.58184%, progress (thread 0): 95.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2309.3_2309.54, WER: 0%, TER: 0%, total WER: 19.2962%, total TER: 8.58176%, progress (thread 0): 95.7437%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2341.41_2341.65, WER: 0%, TER: 0%, total WER: 19.2959%, total TER: 8.58166%, progress (thread 0): 95.7516%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2370.76_2371, WER: 0%, TER: 0%, total WER: 19.2957%, total TER: 8.58156%, progress (thread 0): 95.7595%]
|T|: g o o d
|P|: g o o d
[sample: EN2002d_H01_FEO072_337.97_338.21, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58148%, progress (thread 0): 95.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_390.81_391.05, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.5814%, progress (thread 0): 95.7753%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_496.78_497.02, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.5813%, progress (thread 0): 95.7832%]
|T|: o k a y
|P|: h u h
[sample: EN2002d_H00_FEO070_513.66_513.9, WER: 100%, TER: 100%, total WER: 19.296%, total TER: 8.58215%, progress (thread 0): 95.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1190.34_1190.58, WER: 0%, TER: 0%, total WER: 19.2958%, total TER: 8.58207%, progress (thread 0): 95.799%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1371.83_1372.07, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58199%, progress (thread 0): 95.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1477.13_1477.37, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.58191%, progress (thread 0): 95.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1706.39_1706.63, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.58183%, progress (thread 0): 95.8228%]
|T|: m m
|P|: m m
[sample: ES2004b_H03_FEE016_1717.78_1718.01, WER: 0%, TER: 0%, total WER: 19.2949%, total TER: 8.58179%, progress (thread 0): 95.8307%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1860.91_1861.14, WER: 100%, TER: 40%, total WER: 19.2958%, total TER: 8.58216%, progress (thread 0): 95.8386%]
|T|: t r u e
|P|: s u e
[sample: ES2004b_H00_MEO015_2294.79_2295.02, WER: 100%, TER: 50%, total WER: 19.2967%, total TER: 8.58254%, progress (thread 0): 95.8465%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_85.13_85.36, WER: 0%, TER: 0%, total WER: 19.2965%, total TER: 8.58246%, progress (thread 0): 95.8544%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_356.94_357.17, WER: 100%, TER: 25%, total WER: 19.2974%, total TER: 8.58262%, progress (thread 0): 95.8623%]
|T|: s o
|P|: s a e
[sample: ES2004c_H02_MEE014_748.27_748.5, WER: 100%, TER: 100%, total WER: 19.2983%, total TER: 8.58304%, progress (thread 0): 95.8702%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1121.33_1121.56, WER: 0%, TER: 0%, total WER: 19.2981%, total TER: 8.583%, progress (thread 0): 95.8782%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1169.98_1170.21, WER: 0%, TER: 0%, total WER: 19.2979%, total TER: 8.58292%, progress (thread 0): 95.8861%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H03_FEE016_711.82_712.05, WER: 0%, TER: 0%, total WER: 19.2977%, total TER: 8.58284%, progress (thread 0): 95.894%]
|T|: t w o
|P|: t o
[sample: ES2004d_H00_MEO015_732.46_732.69, WER: 100%, TER: 33.3333%, total WER: 19.2986%, total TER: 8.58301%, progress (thread 0): 95.9019%]
|T|: m m h m m
|P|: m e h m
[sample: ES2004d_H00_MEO015_822.98_823.21, WER: 100%, TER: 40%, total WER: 19.2995%, total TER: 8.58338%, progress (thread 0): 95.9098%]
|T|: ' k a y
|P|: s o
[sample: ES2004d_H00_MEO015_1813.16_1813.39, WER: 100%, TER: 100%, total WER: 19.3004%, total TER: 8.58423%, progress (thread 0): 95.9177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_195.04_195.27, WER: 0%, TER: 0%, total WER: 19.3002%, total TER: 8.58415%, progress (thread 0): 95.9256%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_532.46_532.69, WER: 100%, TER: 66.6667%, total WER: 19.3011%, total TER: 8.58456%, progress (thread 0): 95.9335%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_55.21_55.44, WER: 0%, TER: 0%, total WER: 19.3008%, total TER: 8.58452%, progress (thread 0): 95.9415%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1919.19_1919.42, WER: 0%, TER: 0%, total WER: 19.3006%, total TER: 8.58444%, progress (thread 0): 95.9494%]
|T|: m m h m m
|P|: m m
[sample: IS1009c_H00_FIE088_554.54_554.77, WER: 100%, TER: 60%, total WER: 19.3015%, total TER: 8.58504%, progress (thread 0): 95.9573%]
|T|: m m | y e s
|P|: y e a h
[sample: IS1009c_H01_FIO087_728.73_728.96, WER: 100%, TER: 83.3333%, total WER: 19.3033%, total TER: 8.58608%, progress (thread 0): 95.9652%]
|T|: y e p
|P|: y e a h
[sample: IS1009c_H03_FIO089_1639.78_1640.01, WER: 100%, TER: 66.6667%, total WER: 19.3042%, total TER: 8.58649%, progress (thread 0): 95.9731%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_747.97_748.2, WER: 100%, TER: 66.6667%, total WER: 19.3052%, total TER: 8.5869%, progress (thread 0): 95.981%]
|T|: h m m
|P|: m m
[sample: IS1009d_H02_FIO084_748.42_748.65, WER: 100%, TER: 33.3333%, total WER: 19.3061%, total TER: 8.58707%, progress (thread 0): 95.9889%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_758.1_758.33, WER: 100%, TER: 100%, total WER: 19.307%, total TER: 8.58814%, progress (thread 0): 95.9968%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1695.75_1695.98, WER: 100%, TER: 66.6667%, total WER: 19.3079%, total TER: 8.58854%, progress (thread 0): 96.0047%]
|T|: o k a y
|P|: y e a h
[sample: IS1009d_H03_FIO089_1889.3_1889.53, WER: 100%, TER: 75%, total WER: 19.3088%, total TER: 8.58916%, progress (thread 0): 96.0127%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_843.57_843.8, WER: 100%, TER: 25%, total WER: 19.3097%, total TER: 8.58931%, progress (thread 0): 96.0206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1334.2_1334.43, WER: 0%, TER: 0%, total WER: 19.3095%, total TER: 8.58923%, progress (thread 0): 96.0285%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1346.08_1346.31, WER: 0%, TER: 0%, total WER: 19.3092%, total TER: 8.58915%, progress (thread 0): 96.0364%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1078.3_1078.53, WER: 0%, TER: 0%, total WER: 19.309%, total TER: 8.58911%, progress (thread 0): 96.0443%]
|T|: n o
|P|: n o
[sample: TS3003b_H01_MTD011UID_1716.14_1716.37, WER: 0%, TER: 0%, total WER: 19.3088%, total TER: 8.58907%, progress (thread 0): 96.0522%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_1250.59_1250.82, WER: 0%, TER: 0%, total WER: 19.3086%, total TER: 8.58903%, progress (thread 0): 96.0601%]
|T|: y e s
|P|: j u s t
[sample: TS3003c_H02_MTD0010ID_1518.39_1518.62, WER: 100%, TER: 100%, total WER: 19.3095%, total TER: 8.58967%, progress (thread 0): 96.068%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1654.26_1654.49, WER: 0%, TER: 0%, total WER: 19.3093%, total TER: 8.58963%, progress (thread 0): 96.076%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_2049.46_2049.69, WER: 100%, TER: 75%, total WER: 19.3102%, total TER: 8.59025%, progress (thread 0): 96.0839%]
|T|: s o
|P|: s o
[sample: TS3003d_H01_MTD011UID_261.45_261.68, WER: 0%, TER: 0%, total WER: 19.31%, total TER: 8.59021%, progress (thread 0): 96.0918%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1021.27_1021.5, WER: 100%, TER: 66.6667%, total WER: 19.3109%, total TER: 8.59062%, progress (thread 0): 96.0997%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1945.96_1946.19, WER: 100%, TER: 75%, total WER: 19.3118%, total TER: 8.59124%, progress (thread 0): 96.1076%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2064.77_2065, WER: 0%, TER: 0%, total WER: 19.3116%, total TER: 8.59116%, progress (thread 0): 96.1155%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_424.58_424.81, WER: 0%, TER: 0%, total WER: 19.3114%, total TER: 8.59106%, progress (thread 0): 96.1234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_781.91_782.14, WER: 0%, TER: 0%, total WER: 19.3111%, total TER: 8.59098%, progress (thread 0): 96.1313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_855.85_856.08, WER: 0%, TER: 0%, total WER: 19.3109%, total TER: 8.5909%, progress (thread 0): 96.1392%]
|T|: h m m
|P|: m m
[sample: EN2002a_H02_FEO072_877.24_877.47, WER: 100%, TER: 33.3333%, total WER: 19.3118%, total TER: 8.59107%, progress (thread 0): 96.1471%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H00_MEE073_1019.4_1019.63, WER: 0%, TER: 0%, total WER: 19.3116%, total TER: 8.59101%, progress (thread 0): 96.1551%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_1188.61_1188.84, WER: 100%, TER: 66.6667%, total WER: 19.3125%, total TER: 8.59142%, progress (thread 0): 96.163%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1295.18_1295.41, WER: 0%, TER: 0%, total WER: 19.3123%, total TER: 8.59134%, progress (thread 0): 96.1709%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1408.29_1408.52, WER: 0%, TER: 0%, total WER: 19.3121%, total TER: 8.59126%, progress (thread 0): 96.1788%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1571.85_1572.08, WER: 0%, TER: 0%, total WER: 19.3119%, total TER: 8.59122%, progress (thread 0): 96.1867%]
|T|: o h
|P|: o
[sample: EN2002b_H03_MEE073_29.06_29.29, WER: 100%, TER: 50%, total WER: 19.3128%, total TER: 8.59141%, progress (thread 0): 96.1946%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_194.27_194.5, WER: 0%, TER: 0%, total WER: 19.3125%, total TER: 8.59133%, progress (thread 0): 96.2025%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_211.63_211.86, WER: 0%, TER: 0%, total WER: 19.3123%, total TER: 8.59125%, progress (thread 0): 96.2104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_493.43_493.66, WER: 0%, TER: 0%, total WER: 19.3121%, total TER: 8.59117%, progress (thread 0): 96.2184%]
|T|: h m m
|P|: m m
[sample: EN2002b_H00_FEO070_534.29_534.52, WER: 100%, TER: 33.3333%, total WER: 19.313%, total TER: 8.59134%, progress (thread 0): 96.2263%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_672.54_672.77, WER: 100%, TER: 33.3333%, total WER: 19.3139%, total TER: 8.59152%, progress (thread 0): 96.2342%]
|T|: m m h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1361.79_1362.02, WER: 100%, TER: 60%, total WER: 19.3148%, total TER: 8.59211%, progress (thread 0): 96.2421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_555.02_555.25, WER: 0%, TER: 0%, total WER: 19.3146%, total TER: 8.59203%, progress (thread 0): 96.25%]
|T|: h m m
|P|: h m m
[sample: EN2002c_H01_FEO072_699.88_700.11, WER: 0%, TER: 0%, total WER: 19.3144%, total TER: 8.59197%, progress (thread 0): 96.2579%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1900.45_1900.68, WER: 0%, TER: 0%, total WER: 19.3142%, total TER: 8.59189%, progress (thread 0): 96.2658%]
|T|: d o h
|P|: s o
[sample: EN2002c_H02_MEE071_2395.6_2395.83, WER: 100%, TER: 66.6667%, total WER: 19.3151%, total TER: 8.5923%, progress (thread 0): 96.2737%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2817.5_2817.73, WER: 0%, TER: 0%, total WER: 19.3149%, total TER: 8.59222%, progress (thread 0): 96.2816%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2846.79_2847.02, WER: 0%, TER: 0%, total WER: 19.3147%, total TER: 8.59214%, progress (thread 0): 96.2896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_107.69_107.92, WER: 0%, TER: 0%, total WER: 19.3144%, total TER: 8.59206%, progress (thread 0): 96.2975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_503.45_503.68, WER: 0%, TER: 0%, total WER: 19.3142%, total TER: 8.59198%, progress (thread 0): 96.3054%]
|T|: m m h m m
|P|: m m
[sample: EN2002d_H01_FEO072_548.74_548.97, WER: 100%, TER: 60%, total WER: 19.3151%, total TER: 8.59258%, progress (thread 0): 96.3133%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_767.8_768.03, WER: 0%, TER: 0%, total WER: 19.3149%, total TER: 8.5925%, progress (thread 0): 96.3212%]
|T|: n o
|P|: n o
[sample: EN2002d_H00_FEO070_1427.88_1428.11, WER: 0%, TER: 0%, total WER: 19.3147%, total TER: 8.59246%, progress (thread 0): 96.3291%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1760.63_1760.86, WER: 100%, TER: 33.3333%, total WER: 19.3156%, total TER: 8.59263%, progress (thread 0): 96.337%]
|T|: o k a y
|P|: y e h
[sample: EN2002d_H00_FEO070_2207.62_2207.85, WER: 100%, TER: 100%, total WER: 19.3165%, total TER: 8.59348%, progress (thread 0): 96.3449%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_1022.74_1022.96, WER: 0%, TER: 0%, total WER: 19.3163%, total TER: 8.5934%, progress (thread 0): 96.3528%]
|T|: m m
|P|: m m
[sample: ES2004b_H02_MEE014_1537.12_1537.34, WER: 0%, TER: 0%, total WER: 19.3161%, total TER: 8.59336%, progress (thread 0): 96.3608%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004c_H00_MEO015_575.7_575.92, WER: 0%, TER: 0%, total WER: 19.3159%, total TER: 8.59326%, progress (thread 0): 96.3687%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1311.86_1312.08, WER: 100%, TER: 40%, total WER: 19.3168%, total TER: 8.59363%, progress (thread 0): 96.3766%]
|T|: r i g h t
|P|: i k
[sample: ES2004c_H00_MEO015_2116.51_2116.73, WER: 100%, TER: 80%, total WER: 19.3177%, total TER: 8.59446%, progress (thread 0): 96.3845%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_2159.26_2159.48, WER: 0%, TER: 0%, total WER: 19.3174%, total TER: 8.59438%, progress (thread 0): 96.3924%]
|T|: y e a h
|P|: y e m
[sample: ES2004d_H02_MEE014_968.07_968.29, WER: 100%, TER: 50%, total WER: 19.3183%, total TER: 8.59477%, progress (thread 0): 96.4003%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1220.58_1220.8, WER: 0%, TER: 0%, total WER: 19.3181%, total TER: 8.59469%, progress (thread 0): 96.4082%]
|T|: o k a y
|P|: y o u t
[sample: ES2004d_H03_FEE016_1281.25_1281.47, WER: 100%, TER: 100%, total WER: 19.319%, total TER: 8.59554%, progress (thread 0): 96.4161%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1684.26_1684.48, WER: 0%, TER: 0%, total WER: 19.3188%, total TER: 8.59546%, progress (thread 0): 96.424%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_45.13_45.35, WER: 0%, TER: 0%, total WER: 19.3186%, total TER: 8.59538%, progress (thread 0): 96.432%]
|T|: ' k a y
|P|: m m
[sample: IS1009b_H02_FIO084_155.16_155.38, WER: 100%, TER: 100%, total WER: 19.3195%, total TER: 8.59623%, progress (thread 0): 96.4399%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_404.12_404.34, WER: 100%, TER: 60%, total WER: 19.3204%, total TER: 8.59683%, progress (thread 0): 96.4478%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_961.79_962.01, WER: 0%, TER: 0%, total WER: 19.3202%, total TER: 8.59675%, progress (thread 0): 96.4557%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1007.66_1007.88, WER: 0%, TER: 0%, total WER: 19.32%, total TER: 8.59667%, progress (thread 0): 96.4636%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H00_FIE088_337.53_337.75, WER: 0%, TER: 0%, total WER: 19.3198%, total TER: 8.59659%, progress (thread 0): 96.4715%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1263.61_1263.83, WER: 0%, TER: 0%, total WER: 19.3195%, total TER: 8.59651%, progress (thread 0): 96.4794%]
|T|: m m
|P|: m m m
[sample: IS1009d_H03_FIO089_590.61_590.83, WER: 100%, TER: 50%, total WER: 19.3204%, total TER: 8.5967%, progress (thread 0): 96.4873%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_801.03_801.25, WER: 0%, TER: 0%, total WER: 19.3202%, total TER: 8.59666%, progress (thread 0): 96.4953%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_1687.52_1687.74, WER: 100%, TER: 60%, total WER: 19.3211%, total TER: 8.59726%, progress (thread 0): 96.5032%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1775.76_1775.98, WER: 0%, TER: 0%, total WER: 19.3209%, total TER: 8.59718%, progress (thread 0): 96.5111%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_1846.51_1846.73, WER: 0%, TER: 0%, total WER: 19.3207%, total TER: 8.5971%, progress (thread 0): 96.519%]
|T|: m o r n i n g
|P|: m o n e y
[sample: TS3003a_H01_MTD011UID_15.34_15.56, WER: 100%, TER: 57.1429%, total WER: 19.3216%, total TER: 8.59789%, progress (thread 0): 96.5269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1316.76_1316.98, WER: 0%, TER: 0%, total WER: 19.3214%, total TER: 8.59781%, progress (thread 0): 96.5348%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1357.36_1357.58, WER: 0%, TER: 0%, total WER: 19.3212%, total TER: 8.59777%, progress (thread 0): 96.5427%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_888.56_888.78, WER: 0%, TER: 0%, total WER: 19.321%, total TER: 8.59769%, progress (thread 0): 96.5506%]
|T|: h m m
|P|: m m
[sample: TS3003b_H03_MTD012ME_1371.93_1372.15, WER: 100%, TER: 33.3333%, total WER: 19.3219%, total TER: 8.59787%, progress (thread 0): 96.5585%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1600.07_1600.29, WER: 0%, TER: 0%, total WER: 19.3216%, total TER: 8.59779%, progress (thread 0): 96.5665%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_109.75_109.97, WER: 0%, TER: 0%, total WER: 19.3214%, total TER: 8.59775%, progress (thread 0): 96.5744%]
|T|: m a y b
|P|: i
[sample: TS3003c_H00_MTD009PM_437.36_437.58, WER: 100%, TER: 100%, total WER: 19.3223%, total TER: 8.5986%, progress (thread 0): 96.5823%]
|T|: m m h m m
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_1100.48_1100.7, WER: 100%, TER: 100%, total WER: 19.3232%, total TER: 8.59966%, progress (thread 0): 96.5902%]
|T|: y e a h
|P|: m m
[sample: TS3003c_H01_MTD011UID_1304.78_1305, WER: 100%, TER: 100%, total WER: 19.3241%, total TER: 8.60051%, progress (thread 0): 96.5981%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_2263.6_2263.82, WER: 0%, TER: 0%, total WER: 19.3239%, total TER: 8.60043%, progress (thread 0): 96.606%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_563.58_563.8, WER: 0%, TER: 0%, total WER: 19.3237%, total TER: 8.60035%, progress (thread 0): 96.6139%]
|T|: y e p
|P|: y e a p
[sample: TS3003d_H02_MTD0010ID_577.11_577.33, WER: 100%, TER: 33.3333%, total WER: 19.3246%, total TER: 8.60053%, progress (thread 0): 96.6218%]
|T|: n o
|P|: o h
[sample: TS3003d_H01_MTD011UID_1458.81_1459.03, WER: 100%, TER: 100%, total WER: 19.3255%, total TER: 8.60095%, progress (thread 0): 96.6297%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1674.77_1674.99, WER: 0%, TER: 0%, total WER: 19.3253%, total TER: 8.60091%, progress (thread 0): 96.6377%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1736.91_1737.13, WER: 0%, TER: 0%, total WER: 19.3251%, total TER: 8.60083%, progress (thread 0): 96.6456%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_1751.36_1751.58, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60079%, progress (thread 0): 96.6535%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1991.39_1991.61, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60071%, progress (thread 0): 96.6614%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1998.36_1998.58, WER: 0%, TER: 0%, total WER: 19.3244%, total TER: 8.60063%, progress (thread 0): 96.6693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_482.53_482.75, WER: 0%, TER: 0%, total WER: 19.3242%, total TER: 8.60055%, progress (thread 0): 96.6772%]
|T|: h m m
|P|: m m
[sample: EN2002a_H01_FEO070_706.29_706.51, WER: 100%, TER: 33.3333%, total WER: 19.3251%, total TER: 8.60073%, progress (thread 0): 96.6851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_805.91_806.13, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60064%, progress (thread 0): 96.693%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1103.91_1104.13, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60054%, progress (thread 0): 96.701%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_1216.04_1216.26, WER: 100%, TER: 66.6667%, total WER: 19.3256%, total TER: 8.60095%, progress (thread 0): 96.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1537.4_1537.62, WER: 0%, TER: 0%, total WER: 19.3254%, total TER: 8.60087%, progress (thread 0): 96.7168%]
|T|: j u s t
|P|: j u s t
[sample: EN2002a_H01_FEO070_1871.92_1872.14, WER: 0%, TER: 0%, total WER: 19.3252%, total TER: 8.60079%, progress (thread 0): 96.7247%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1963.02_1963.24, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60071%, progress (thread 0): 96.7326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_639.41_639.63, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60063%, progress (thread 0): 96.7405%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1397.63_1397.85, WER: 0%, TER: 0%, total WER: 19.3245%, total TER: 8.60053%, progress (thread 0): 96.7484%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1547.56_1547.78, WER: 100%, TER: 33.3333%, total WER: 19.3254%, total TER: 8.6007%, progress (thread 0): 96.7563%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_575.92_576.14, WER: 100%, TER: 28.5714%, total WER: 19.3263%, total TER: 8.60103%, progress (thread 0): 96.7642%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H02_MEE071_589.02_589.24, WER: 100%, TER: 80%, total WER: 19.3272%, total TER: 8.60186%, progress (thread 0): 96.7722%]
|T|: ' k a y
|P|: y e a h
[sample: EN2002c_H03_MEE073_684.52_684.74, WER: 100%, TER: 75%, total WER: 19.3281%, total TER: 8.60248%, progress (thread 0): 96.7801%]
|T|: o h | y e a h
|P|: m h
[sample: EN2002c_H03_MEE073_1506.68_1506.9, WER: 100%, TER: 85.7143%, total WER: 19.3299%, total TER: 8.60374%, progress (thread 0): 96.788%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1602.82_1603.04, WER: 0%, TER: 0%, total WER: 19.3297%, total TER: 8.60366%, progress (thread 0): 96.7959%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1707.51_1707.73, WER: 100%, TER: 33.3333%, total WER: 19.3306%, total TER: 8.60383%, progress (thread 0): 96.8038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2260.94_2261.16, WER: 0%, TER: 0%, total WER: 19.3304%, total TER: 8.60375%, progress (thread 0): 96.8117%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2697.66_2697.88, WER: 0%, TER: 0%, total WER: 19.3302%, total TER: 8.60367%, progress (thread 0): 96.8196%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_402.37_402.59, WER: 0%, TER: 0%, total WER: 19.33%, total TER: 8.60359%, progress (thread 0): 96.8275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_736.63_736.85, WER: 0%, TER: 0%, total WER: 19.3298%, total TER: 8.60351%, progress (thread 0): 96.8354%]
|T|: o h
|P|: h u h
[sample: EN2002d_H00_FEO070_909.94_910.16, WER: 100%, TER: 100%, total WER: 19.3307%, total TER: 8.60393%, progress (thread 0): 96.8434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1061.24_1061.46, WER: 0%, TER: 0%, total WER: 19.3304%, total TER: 8.60385%, progress (thread 0): 96.8513%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1400.88_1401.1, WER: 0%, TER: 0%, total WER: 19.3302%, total TER: 8.60377%, progress (thread 0): 96.8592%]
|T|: b u t
|P|: b u h
[sample: EN2002d_H00_FEO070_1658_1658.22, WER: 100%, TER: 33.3333%, total WER: 19.3311%, total TER: 8.60395%, progress (thread 0): 96.8671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1732.66_1732.88, WER: 0%, TER: 0%, total WER: 19.3309%, total TER: 8.60387%, progress (thread 0): 96.875%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_622.02_622.23, WER: 0%, TER: 0%, total WER: 19.3307%, total TER: 8.60383%, progress (thread 0): 96.8829%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_1323.05_1323.26, WER: 0%, TER: 0%, total WER: 19.3305%, total TER: 8.60375%, progress (thread 0): 96.8908%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1464.74_1464.95, WER: 100%, TER: 40%, total WER: 19.3314%, total TER: 8.60411%, progress (thread 0): 96.8987%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_96.42_96.63, WER: 100%, TER: 40%, total WER: 19.3323%, total TER: 8.60448%, progress (thread 0): 96.9066%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1385.07_1385.28, WER: 0%, TER: 0%, total WER: 19.3321%, total TER: 8.6044%, progress (thread 0): 96.9146%]
|T|: h m m
|P|: m m
[sample: ES2004d_H00_MEO015_26.63_26.84, WER: 100%, TER: 33.3333%, total WER: 19.333%, total TER: 8.60457%, progress (thread 0): 96.9225%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_627.79_628, WER: 100%, TER: 66.6667%, total WER: 19.3339%, total TER: 8.60498%, progress (thread 0): 96.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_39.06_39.27, WER: 0%, TER: 0%, total WER: 19.3337%, total TER: 8.6049%, progress (thread 0): 96.9383%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_169.39_169.6, WER: 100%, TER: 60%, total WER: 19.3346%, total TER: 8.60549%, progress (thread 0): 96.9462%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1286.43_1286.64, WER: 100%, TER: 60%, total WER: 19.3355%, total TER: 8.60609%, progress (thread 0): 96.9541%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H00_FIE088_1289.44_1289.65, WER: 0%, TER: 0%, total WER: 19.3353%, total TER: 8.60599%, progress (thread 0): 96.962%]
|T|: m m
|P|: o n
[sample: IS1009b_H02_FIO084_1632.09_1632.3, WER: 100%, TER: 100%, total WER: 19.3362%, total TER: 8.60642%, progress (thread 0): 96.9699%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_284.08_284.29, WER: 0%, TER: 0%, total WER: 19.3359%, total TER: 8.60634%, progress (thread 0): 96.9778%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1563.53_1563.74, WER: 0%, TER: 0%, total WER: 19.3357%, total TER: 8.60626%, progress (thread 0): 96.9858%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1707.23_1707.44, WER: 0%, TER: 0%, total WER: 19.3355%, total TER: 8.60618%, progress (thread 0): 96.9937%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_741.38_741.59, WER: 0%, TER: 0%, total WER: 19.3353%, total TER: 8.6061%, progress (thread 0): 97.0016%]
|T|: y e a h
|P|:
[sample: IS1009d_H02_FIO084_1371.67_1371.88, WER: 100%, TER: 100%, total WER: 19.3362%, total TER: 8.60695%, progress (thread 0): 97.0095%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1880.74_1880.95, WER: 100%, TER: 40%, total WER: 19.3371%, total TER: 8.60731%, progress (thread 0): 97.0174%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1295.8_1296.01, WER: 0%, TER: 0%, total WER: 19.3369%, total TER: 8.60723%, progress (thread 0): 97.0253%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_901.66_901.87, WER: 0%, TER: 0%, total WER: 19.3367%, total TER: 8.60715%, progress (thread 0): 97.0332%]
|T|: y e a h
|P|: h u h
[sample: TS3003b_H01_MTD011UID_1547.15_1547.36, WER: 100%, TER: 75%, total WER: 19.3376%, total TER: 8.60777%, progress (thread 0): 97.0411%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1091.82_1092.03, WER: 0%, TER: 0%, total WER: 19.3374%, total TER: 8.60769%, progress (thread 0): 97.049%]
|T|: y e a h
|P|: h u h
[sample: TS3003c_H01_MTD011UID_1210.7_1210.91, WER: 100%, TER: 75%, total WER: 19.3383%, total TER: 8.60831%, progress (thread 0): 97.057%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_530.79_531, WER: 100%, TER: 75%, total WER: 19.3392%, total TER: 8.60893%, progress (thread 0): 97.0649%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1465.64_1465.85, WER: 0%, TER: 0%, total WER: 19.3389%, total TER: 8.60885%, progress (thread 0): 97.0728%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1695.15_1695.36, WER: 0%, TER: 0%, total WER: 19.3387%, total TER: 8.60877%, progress (thread 0): 97.0807%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1711.66_1711.87, WER: 0%, TER: 0%, total WER: 19.3385%, total TER: 8.60869%, progress (thread 0): 97.0886%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1789.64_1789.85, WER: 0%, TER: 0%, total WER: 19.3383%, total TER: 8.60861%, progress (thread 0): 97.0965%]
|T|: n o
|P|: n o
[sample: TS3003d_H03_MTD012ME_2014.88_2015.09, WER: 0%, TER: 0%, total WER: 19.3381%, total TER: 8.60857%, progress (thread 0): 97.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2064.37_2064.58, WER: 0%, TER: 0%, total WER: 19.3379%, total TER: 8.60849%, progress (thread 0): 97.1123%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2107.64_2107.85, WER: 0%, TER: 0%, total WER: 19.3376%, total TER: 8.60841%, progress (thread 0): 97.1203%]
|T|: y e a h
|P|: y m
[sample: TS3003d_H01_MTD011UID_2462.55_2462.76, WER: 100%, TER: 75%, total WER: 19.3385%, total TER: 8.60903%, progress (thread 0): 97.1282%]
|T|: s o r r y
|P|: s o r r y
[sample: EN2002a_H02_FEO072_377.93_378.14, WER: 0%, TER: 0%, total WER: 19.3383%, total TER: 8.60893%, progress (thread 0): 97.1361%]
|T|: o h
|P|: u h
[sample: EN2002a_H03_MEE071_483.42_483.63, WER: 100%, TER: 50%, total WER: 19.3392%, total TER: 8.60912%, progress (thread 0): 97.144%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_648.41_648.62, WER: 0%, TER: 0%, total WER: 19.339%, total TER: 8.60904%, progress (thread 0): 97.1519%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1319.66_1319.87, WER: 0%, TER: 0%, total WER: 19.3388%, total TER: 8.60896%, progress (thread 0): 97.1598%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1429.36_1429.57, WER: 0%, TER: 0%, total WER: 19.3386%, total TER: 8.60888%, progress (thread 0): 97.1677%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1896.08_1896.29, WER: 0%, TER: 0%, total WER: 19.3384%, total TER: 8.6088%, progress (thread 0): 97.1756%]
|T|: y e a h
|P|: h e h
[sample: EN2002a_H03_MEE071_2122.73_2122.94, WER: 100%, TER: 50%, total WER: 19.3393%, total TER: 8.60918%, progress (thread 0): 97.1835%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_266.76_266.97, WER: 0%, TER: 0%, total WER: 19.3391%, total TER: 8.6091%, progress (thread 0): 97.1915%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_291.79_292, WER: 0%, TER: 0%, total WER: 19.3388%, total TER: 8.60902%, progress (thread 0): 97.1994%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1335.23_1335.44, WER: 0%, TER: 0%, total WER: 19.3386%, total TER: 8.60894%, progress (thread 0): 97.2073%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_21.49_21.7, WER: 100%, TER: 25%, total WER: 19.3395%, total TER: 8.6091%, progress (thread 0): 97.2152%]
|T|: o k a y
|P|: o m a y
[sample: EN2002c_H03_MEE073_183.87_184.08, WER: 100%, TER: 25%, total WER: 19.3404%, total TER: 8.60925%, progress (thread 0): 97.2231%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1748.27_1748.48, WER: 0%, TER: 0%, total WER: 19.3402%, total TER: 8.60915%, progress (thread 0): 97.231%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1810.36_1810.57, WER: 100%, TER: 100%, total WER: 19.342%, total TER: 8.61064%, progress (thread 0): 97.2389%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1861.15_1861.36, WER: 0%, TER: 0%, total WER: 19.3418%, total TER: 8.61056%, progress (thread 0): 97.2468%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2180.5_2180.71, WER: 0%, TER: 0%, total WER: 19.3416%, total TER: 8.61048%, progress (thread 0): 97.2547%]
|T|: t r u e
|P|: t r u e
[sample: EN2002c_H03_MEE073_2453.86_2454.07, WER: 0%, TER: 0%, total WER: 19.3414%, total TER: 8.6104%, progress (thread 0): 97.2627%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2819.2_2819.41, WER: 0%, TER: 0%, total WER: 19.3411%, total TER: 8.61032%, progress (thread 0): 97.2706%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_164.51_164.72, WER: 100%, TER: 40%, total WER: 19.3421%, total TER: 8.61068%, progress (thread 0): 97.2785%]
|T|: r i g h t
|P|: y e a h
[sample: EN2002d_H03_MEE073_338.56_338.77, WER: 100%, TER: 80%, total WER: 19.343%, total TER: 8.61151%, progress (thread 0): 97.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_647.49_647.7, WER: 0%, TER: 0%, total WER: 19.3427%, total TER: 8.61143%, progress (thread 0): 97.2943%]
|T|: m m
|P|: y e a h
[sample: EN2002d_H03_MEE073_782.98_783.19, WER: 100%, TER: 200%, total WER: 19.3436%, total TER: 8.61232%, progress (thread 0): 97.3022%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_998.01_998.22, WER: 0%, TER: 0%, total WER: 19.3434%, total TER: 8.61222%, progress (thread 0): 97.3101%]
|T|: o h | y e a h
|P|: o
[sample: EN2002d_H00_FEO070_1377.89_1378.1, WER: 100%, TER: 85.7143%, total WER: 19.3452%, total TER: 8.61348%, progress (thread 0): 97.318%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1391.57_1391.78, WER: 0%, TER: 0%, total WER: 19.345%, total TER: 8.6134%, progress (thread 0): 97.326%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1912.21_1912.42, WER: 50%, TER: 42.8571%, total WER: 19.3457%, total TER: 8.61396%, progress (thread 0): 97.3339%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_2147.17_2147.38, WER: 100%, TER: 33.3333%, total WER: 19.3466%, total TER: 8.61413%, progress (thread 0): 97.3418%]
|T|: o k a y
|P|: h m m
[sample: ES2004b_H00_MEO015_338.55_338.75, WER: 100%, TER: 100%, total WER: 19.3475%, total TER: 8.61498%, progress (thread 0): 97.3497%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1514.44_1514.64, WER: 0%, TER: 0%, total WER: 19.3473%, total TER: 8.6149%, progress (thread 0): 97.3576%]
|T|: o o p s
|P|: o p
[sample: ES2004c_H00_MEO015_54.23_54.43, WER: 100%, TER: 50%, total WER: 19.3482%, total TER: 8.61529%, progress (thread 0): 97.3655%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1395.98_1396.18, WER: 100%, TER: 40%, total WER: 19.3491%, total TER: 8.61565%, progress (thread 0): 97.3734%]
|T|: ' k a y
|P|: t
[sample: ES2004d_H00_MEO015_562.82_563.02, WER: 100%, TER: 100%, total WER: 19.35%, total TER: 8.6165%, progress (thread 0): 97.3813%]
|T|: y e p
|P|: y e a h
[sample: ES2004d_H02_MEE014_922.35_922.55, WER: 100%, TER: 66.6667%, total WER: 19.3509%, total TER: 8.61691%, progress (thread 0): 97.3892%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1214.04_1214.24, WER: 0%, TER: 0%, total WER: 19.3507%, total TER: 8.61683%, progress (thread 0): 97.3972%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1952.42_1952.62, WER: 0%, TER: 0%, total WER: 19.3505%, total TER: 8.61675%, progress (thread 0): 97.4051%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_784.26_784.46, WER: 0%, TER: 0%, total WER: 19.3503%, total TER: 8.61667%, progress (thread 0): 97.413%]
|T|: ' k a y
|P|: k e a y
[sample: IS1009a_H03_FIO089_794.48_794.68, WER: 100%, TER: 50%, total WER: 19.3512%, total TER: 8.61705%, progress (thread 0): 97.4209%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H02_FIO084_179.12_179.32, WER: 100%, TER: 200%, total WER: 19.3521%, total TER: 8.61794%, progress (thread 0): 97.4288%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1111.83_1112.03, WER: 0%, TER: 0%, total WER: 19.3518%, total TER: 8.61786%, progress (thread 0): 97.4367%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_2011.99_2012.19, WER: 100%, TER: 60%, total WER: 19.3527%, total TER: 8.61846%, progress (thread 0): 97.4446%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_285.99_286.19, WER: 0%, TER: 0%, total WER: 19.3525%, total TER: 8.61838%, progress (thread 0): 97.4525%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1689.26_1689.46, WER: 0%, TER: 0%, total WER: 19.3523%, total TER: 8.6183%, progress (thread 0): 97.4604%]
|T|: m m
|P|: m m
[sample: IS1009d_H01_FIO087_964.59_964.79, WER: 0%, TER: 0%, total WER: 19.3521%, total TER: 8.61826%, progress (thread 0): 97.4684%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1286.22_1286.42, WER: 0%, TER: 0%, total WER: 19.3519%, total TER: 8.61818%, progress (thread 0): 97.4763%]
|T|: m m
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1431.86_1432.06, WER: 100%, TER: 200%, total WER: 19.3528%, total TER: 8.61907%, progress (thread 0): 97.4842%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_550.33_550.53, WER: 100%, TER: 25%, total WER: 19.3537%, total TER: 8.61922%, progress (thread 0): 97.4921%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_576.96_577.16, WER: 0%, TER: 0%, total WER: 19.3535%, total TER: 8.61918%, progress (thread 0): 97.5%]
|T|: y e a h
|P|: e h
[sample: TS3003b_H01_MTD011UID_1340.71_1340.91, WER: 100%, TER: 50%, total WER: 19.3544%, total TER: 8.61957%, progress (thread 0): 97.5079%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1635.98_1636.18, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61953%, progress (thread 0): 97.5158%]
|T|: m m
|P|: m m
[sample: TS3003c_H03_MTD012ME_1936.99_1937.19, WER: 0%, TER: 0%, total WER: 19.3539%, total TER: 8.61949%, progress (thread 0): 97.5237%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_214.36_214.56, WER: 0%, TER: 0%, total WER: 19.3537%, total TER: 8.61941%, progress (thread 0): 97.5316%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_418.75_418.95, WER: 100%, TER: 66.6667%, total WER: 19.3546%, total TER: 8.61981%, progress (thread 0): 97.5396%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1236.15_1236.35, WER: 0%, TER: 0%, total WER: 19.3544%, total TER: 8.61973%, progress (thread 0): 97.5475%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1334.1_1334.3, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61965%, progress (thread 0): 97.5554%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1736.65_1736.85, WER: 0%, TER: 0%, total WER: 19.354%, total TER: 8.61957%, progress (thread 0): 97.5633%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1862.16_1862.36, WER: 0%, TER: 0%, total WER: 19.3538%, total TER: 8.61949%, progress (thread 0): 97.5712%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2423.71_2423.91, WER: 100%, TER: 33.3333%, total WER: 19.3547%, total TER: 8.61967%, progress (thread 0): 97.5791%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_6.65_6.85, WER: 0%, TER: 0%, total WER: 19.3544%, total TER: 8.61959%, progress (thread 0): 97.587%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_207.6_207.8, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61951%, progress (thread 0): 97.5949%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H00_MEE073_325.97_326.17, WER: 0%, TER: 0%, total WER: 19.354%, total TER: 8.61945%, progress (thread 0): 97.6029%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_517.59_517.79, WER: 0%, TER: 0%, total WER: 19.3538%, total TER: 8.61937%, progress (thread 0): 97.6108%]
|T|: m m h m m
|P|: m m
[sample: EN2002a_H00_MEE073_625.1_625.3, WER: 100%, TER: 60%, total WER: 19.3547%, total TER: 8.61996%, progress (thread 0): 97.6187%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1082.64_1082.84, WER: 0%, TER: 0%, total WER: 19.3545%, total TER: 8.61988%, progress (thread 0): 97.6266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1296.98_1297.18, WER: 0%, TER: 0%, total WER: 19.3543%, total TER: 8.6198%, progress (thread 0): 97.6345%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1544.23_1544.43, WER: 100%, TER: 33.3333%, total WER: 19.3552%, total TER: 8.61998%, progress (thread 0): 97.6424%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_1988.65_1988.85, WER: 0%, TER: 0%, total WER: 19.3549%, total TER: 8.6199%, progress (thread 0): 97.6503%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_58.73_58.93, WER: 100%, TER: 66.6667%, total WER: 19.3558%, total TER: 8.6203%, progress (thread 0): 97.6582%]
|T|: d o | w e | d
|P|: d o m e
[sample: EN2002b_H01_MEE071_223.55_223.75, WER: 100%, TER: 57.1429%, total WER: 19.3586%, total TER: 8.62109%, progress (thread 0): 97.6661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_233.29_233.49, WER: 0%, TER: 0%, total WER: 19.3583%, total TER: 8.62101%, progress (thread 0): 97.674%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_259.41_259.61, WER: 100%, TER: 33.3333%, total WER: 19.3592%, total TER: 8.62118%, progress (thread 0): 97.682%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_498.07_498.27, WER: 0%, TER: 0%, total WER: 19.359%, total TER: 8.6211%, progress (thread 0): 97.6899%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1591.2_1591.4, WER: 0%, TER: 0%, total WER: 19.3588%, total TER: 8.621%, progress (thread 0): 97.6978%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H00_FEO070_1723.38_1723.58, WER: 0%, TER: 0%, total WER: 19.3586%, total TER: 8.62094%, progress (thread 0): 97.7057%]
|T|: o k a y
|P|: y e a y
[sample: EN2002c_H03_MEE073_241.42_241.62, WER: 100%, TER: 50%, total WER: 19.3595%, total TER: 8.62133%, progress (thread 0): 97.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_404.9_405.1, WER: 0%, TER: 0%, total WER: 19.3593%, total TER: 8.62123%, progress (thread 0): 97.7215%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_817.73_817.93, WER: 100%, TER: 33.3333%, total WER: 19.3602%, total TER: 8.6214%, progress (thread 0): 97.7294%]
|T|: y e a h
|P|: m e m
[sample: EN2002c_H02_MEE071_1319.11_1319.31, WER: 100%, TER: 75%, total WER: 19.3611%, total TER: 8.62202%, progress (thread 0): 97.7373%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1392.95_1393.15, WER: 0%, TER: 0%, total WER: 19.3609%, total TER: 8.62194%, progress (thread 0): 97.7453%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1395.54_1395.74, WER: 0%, TER: 0%, total WER: 19.3606%, total TER: 8.62184%, progress (thread 0): 97.7532%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1637.64_1637.84, WER: 0%, TER: 0%, total WER: 19.3604%, total TER: 8.62174%, progress (thread 0): 97.7611%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1911.7_1911.9, WER: 0%, TER: 0%, total WER: 19.3602%, total TER: 8.62164%, progress (thread 0): 97.769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1939.05_1939.25, WER: 0%, TER: 0%, total WER: 19.36%, total TER: 8.62156%, progress (thread 0): 97.7769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_327.41_327.61, WER: 0%, TER: 0%, total WER: 19.3598%, total TER: 8.62148%, progress (thread 0): 97.7848%]
|T|: y e a h
|P|: y e m m
[sample: EN2002d_H03_MEE073_967.12_967.32, WER: 100%, TER: 50%, total WER: 19.3607%, total TER: 8.62186%, progress (thread 0): 97.7927%]
|T|: y e p
|P|: y e a p
[sample: ES2004a_H03_FEE016_1048.29_1048.48, WER: 100%, TER: 33.3333%, total WER: 19.3616%, total TER: 8.62203%, progress (thread 0): 97.8006%]
|T|: o o p s
|P|: m
[sample: ES2004b_H00_MEO015_572.79_572.98, WER: 100%, TER: 100%, total WER: 19.3625%, total TER: 8.62288%, progress (thread 0): 97.8085%]
|T|: a l r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1308.81_1309, WER: 100%, TER: 28.5714%, total WER: 19.3634%, total TER: 8.62321%, progress (thread 0): 97.8165%]
|T|: h u h
|P|: m m
[sample: ES2004b_H02_MEE014_1454.52_1454.71, WER: 100%, TER: 100%, total WER: 19.3643%, total TER: 8.62385%, progress (thread 0): 97.8244%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H03_FEE016_1635.41_1635.6, WER: 100%, TER: 200%, total WER: 19.3652%, total TER: 8.62474%, progress (thread 0): 97.8323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1436.81_1437, WER: 0%, TER: 0%, total WER: 19.365%, total TER: 8.62466%, progress (thread 0): 97.8402%]
|T|: t h i n g
|P|: t h i n g
[sample: ES2004d_H02_MEE014_2209.98_2210.17, WER: 0%, TER: 0%, total WER: 19.3648%, total TER: 8.62456%, progress (thread 0): 97.8481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_805.49_805.68, WER: 0%, TER: 0%, total WER: 19.3645%, total TER: 8.62448%, progress (thread 0): 97.856%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_690.57_690.76, WER: 0%, TER: 0%, total WER: 19.3643%, total TER: 8.62438%, progress (thread 0): 97.8639%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H00_FIE088_808.35_808.54, WER: 100%, TER: 80%, total WER: 19.3652%, total TER: 8.62521%, progress (thread 0): 97.8718%]
|T|: b a t t e r y
|P|: t h a t
[sample: IS1009c_H01_FIO087_1631.84_1632.03, WER: 100%, TER: 85.7143%, total WER: 19.3661%, total TER: 8.62646%, progress (thread 0): 97.8798%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1314.07_1314.26, WER: 0%, TER: 0%, total WER: 19.3659%, total TER: 8.62638%, progress (thread 0): 97.8877%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_506.74_506.93, WER: 100%, TER: 25%, total WER: 19.3668%, total TER: 8.62653%, progress (thread 0): 97.8956%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1214.55_1214.74, WER: 0%, TER: 0%, total WER: 19.3666%, total TER: 8.62649%, progress (thread 0): 97.9035%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_1401.33_1401.52, WER: 100%, TER: 25%, total WER: 19.3675%, total TER: 8.62665%, progress (thread 0): 97.9114%]
|T|: y e a h
|P|: n o h
[sample: TS3003b_H01_MTD011UID_2147.67_2147.86, WER: 100%, TER: 75%, total WER: 19.3684%, total TER: 8.62726%, progress (thread 0): 97.9193%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1090.92_1091.11, WER: 0%, TER: 0%, total WER: 19.3682%, total TER: 8.62718%, progress (thread 0): 97.9272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1632.95_1633.14, WER: 0%, TER: 0%, total WER: 19.368%, total TER: 8.6271%, progress (thread 0): 97.9351%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_375.66_375.85, WER: 100%, TER: 75%, total WER: 19.3689%, total TER: 8.62772%, progress (thread 0): 97.943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_507.6_507.79, WER: 0%, TER: 0%, total WER: 19.3687%, total TER: 8.62764%, progress (thread 0): 97.951%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_816.71_816.9, WER: 0%, TER: 0%, total WER: 19.3684%, total TER: 8.62756%, progress (thread 0): 97.9589%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_966.74_966.93, WER: 0%, TER: 0%, total WER: 19.3682%, total TER: 8.62752%, progress (thread 0): 97.9668%]
|T|: m m
|P|: m m
[sample: TS3003d_H00_MTD009PM_1884.46_1884.65, WER: 0%, TER: 0%, total WER: 19.368%, total TER: 8.62748%, progress (thread 0): 97.9747%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1975.04_1975.23, WER: 100%, TER: 66.6667%, total WER: 19.3689%, total TER: 8.62789%, progress (thread 0): 97.9826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1998.6_1998.79, WER: 0%, TER: 0%, total WER: 19.3687%, total TER: 8.6278%, progress (thread 0): 97.9905%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H00_MEE073_399.03_399.22, WER: 100%, TER: 66.6667%, total WER: 19.3696%, total TER: 8.62821%, progress (thread 0): 97.9984%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H03_MEE071_741.39_741.58, WER: 100%, TER: 100%, total WER: 19.3705%, total TER: 8.62906%, progress (thread 0): 98.0063%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1037.67_1037.86, WER: 0%, TER: 0%, total WER: 19.3703%, total TER: 8.62898%, progress (thread 0): 98.0142%]
|T|: w h
|P|: m m
[sample: EN2002a_H03_MEE071_1305.67_1305.86, WER: 100%, TER: 100%, total WER: 19.3712%, total TER: 8.62941%, progress (thread 0): 98.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1781.11_1781.3, WER: 0%, TER: 0%, total WER: 19.371%, total TER: 8.62932%, progress (thread 0): 98.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1823.51_1823.7, WER: 0%, TER: 0%, total WER: 19.3707%, total TER: 8.62924%, progress (thread 0): 98.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1836.03_1836.22, WER: 0%, TER: 0%, total WER: 19.3705%, total TER: 8.62916%, progress (thread 0): 98.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1886.9_1887.09, WER: 0%, TER: 0%, total WER: 19.3703%, total TER: 8.62908%, progress (thread 0): 98.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1917.91_1918.1, WER: 0%, TER: 0%, total WER: 19.3701%, total TER: 8.629%, progress (thread 0): 98.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_142.44_142.63, WER: 0%, TER: 0%, total WER: 19.3699%, total TER: 8.62892%, progress (thread 0): 98.0696%]
|T|: u h h u h
|P|: m m
[sample: EN2002c_H03_MEE073_1527.18_1527.37, WER: 100%, TER: 100%, total WER: 19.3708%, total TER: 8.62999%, progress (thread 0): 98.0775%]
|T|: h m m
|P|: m e h
[sample: EN2002c_H03_MEE073_1630_1630.19, WER: 100%, TER: 100%, total WER: 19.3717%, total TER: 8.63062%, progress (thread 0): 98.0854%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_2672.39_2672.58, WER: 100%, TER: 33.3333%, total WER: 19.3726%, total TER: 8.6308%, progress (thread 0): 98.0934%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2679.33_2679.52, WER: 0%, TER: 0%, total WER: 19.3724%, total TER: 8.6307%, progress (thread 0): 98.1013%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_647.1_647.29, WER: 0%, TER: 0%, total WER: 19.3722%, total TER: 8.6306%, progress (thread 0): 98.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_649.08_649.27, WER: 0%, TER: 0%, total WER: 19.3719%, total TER: 8.63051%, progress (thread 0): 98.1171%]
|T|: o k
|P|: o k a y
[sample: EN2002d_H03_MEE073_909.71_909.9, WER: 100%, TER: 100%, total WER: 19.3728%, total TER: 8.63094%, progress (thread 0): 98.125%]
|T|: s u r e
|P|: t r u e
[sample: EN2002d_H03_MEE073_1568.39_1568.58, WER: 100%, TER: 75%, total WER: 19.3737%, total TER: 8.63156%, progress (thread 0): 98.1329%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_1708.21_1708.4, WER: 100%, TER: 66.6667%, total WER: 19.3746%, total TER: 8.63196%, progress (thread 0): 98.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2037.78_2037.97, WER: 0%, TER: 0%, total WER: 19.3744%, total TER: 8.63188%, progress (thread 0): 98.1487%]
|T|: s o
|P|: s o
[sample: ES2004a_H03_FEE016_605.84_606.02, WER: 0%, TER: 0%, total WER: 19.3742%, total TER: 8.63184%, progress (thread 0): 98.1566%]
|T|: ' k a y
|P|:
[sample: ES2004b_H00_MEO015_1027.78_1027.96, WER: 100%, TER: 100%, total WER: 19.3751%, total TER: 8.63269%, progress (thread 0): 98.1646%]
|T|: m m h m m
|P|: m m
[sample: ES2004c_H03_FEE016_1360.96_1361.14, WER: 100%, TER: 60%, total WER: 19.376%, total TER: 8.63329%, progress (thread 0): 98.1725%]
|T|: m m
|P|: h m m
[sample: ES2004d_H02_MEE014_2221.15_2221.33, WER: 100%, TER: 50%, total WER: 19.3769%, total TER: 8.63348%, progress (thread 0): 98.1804%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_361.5_361.68, WER: 100%, TER: 60%, total WER: 19.3778%, total TER: 8.63408%, progress (thread 0): 98.1883%]
|T|: u m
|P|: i m a n
[sample: IS1009c_H03_FIO089_1368.34_1368.52, WER: 100%, TER: 150%, total WER: 19.3787%, total TER: 8.63474%, progress (thread 0): 98.1962%]
|T|: a n d
|P|: a n d
[sample: IS1009c_H01_FIO087_1687.91_1688.09, WER: 0%, TER: 0%, total WER: 19.3785%, total TER: 8.63468%, progress (thread 0): 98.2041%]
|T|: m m
|P|: m m
[sample: IS1009d_H00_FIE088_1039.16_1039.34, WER: 0%, TER: 0%, total WER: 19.3783%, total TER: 8.63464%, progress (thread 0): 98.212%]
|T|: y e s
|P|: y e a
[sample: IS1009d_H01_FIO087_1532.02_1532.2, WER: 100%, TER: 33.3333%, total WER: 19.3792%, total TER: 8.63481%, progress (thread 0): 98.2199%]
|T|: n o
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1017.51_1017.69, WER: 100%, TER: 200%, total WER: 19.3801%, total TER: 8.6357%, progress (thread 0): 98.2278%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_583.19_583.37, WER: 0%, TER: 0%, total WER: 19.3799%, total TER: 8.63562%, progress (thread 0): 98.2358%]
|T|: m m
|P|: h m h
[sample: TS3003b_H01_MTD011UID_1188.77_1188.95, WER: 100%, TER: 100%, total WER: 19.3808%, total TER: 8.63604%, progress (thread 0): 98.2437%]
|T|: y e a h
|P|: y e m
[sample: TS3003b_H02_MTD0010ID_1801.24_1801.42, WER: 100%, TER: 50%, total WER: 19.3817%, total TER: 8.63643%, progress (thread 0): 98.2516%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1801.82_1802, WER: 0%, TER: 0%, total WER: 19.3815%, total TER: 8.63639%, progress (thread 0): 98.2595%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1131_1131.18, WER: 100%, TER: 33.3333%, total WER: 19.3824%, total TER: 8.63656%, progress (thread 0): 98.2674%]
|T|: a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_2394.1_2394.28, WER: 100%, TER: 50%, total WER: 19.3833%, total TER: 8.63675%, progress (thread 0): 98.2753%]
|T|: n o
|P|: m h
[sample: EN2002a_H00_MEE073_23.11_23.29, WER: 100%, TER: 100%, total WER: 19.3842%, total TER: 8.63718%, progress (thread 0): 98.2832%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_32.53_32.71, WER: 0%, TER: 0%, total WER: 19.3839%, total TER: 8.6371%, progress (thread 0): 98.2911%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_106.69_106.87, WER: 100%, TER: 33.3333%, total WER: 19.3848%, total TER: 8.63727%, progress (thread 0): 98.299%]
|T|: y e a h
|P|: m h
[sample: EN2002a_H00_MEE073_1441.15_1441.33, WER: 100%, TER: 75%, total WER: 19.3857%, total TER: 8.63789%, progress (thread 0): 98.307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1717.2_1717.38, WER: 0%, TER: 0%, total WER: 19.3855%, total TER: 8.63781%, progress (thread 0): 98.3149%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_530.52_530.7, WER: 0%, TER: 0%, total WER: 19.3853%, total TER: 8.63773%, progress (thread 0): 98.3228%]
|T|: i s | i t
|P|: i s | i t
[sample: EN2002b_H01_MEE071_1142.07_1142.25, WER: 0%, TER: 0%, total WER: 19.3849%, total TER: 8.63763%, progress (thread 0): 98.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1339.32_1339.5, WER: 0%, TER: 0%, total WER: 19.3847%, total TER: 8.63755%, progress (thread 0): 98.3386%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_186.7_186.88, WER: 0%, TER: 0%, total WER: 19.3844%, total TER: 8.63744%, progress (thread 0): 98.3465%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_536.86_537.04, WER: 100%, TER: 28.5714%, total WER: 19.3853%, total TER: 8.63777%, progress (thread 0): 98.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1667.09_1667.27, WER: 0%, TER: 0%, total WER: 19.3851%, total TER: 8.63769%, progress (thread 0): 98.3623%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_363.49_363.67, WER: 100%, TER: 33.3333%, total WER: 19.386%, total TER: 8.63786%, progress (thread 0): 98.3703%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_500.44_500.62, WER: 0%, TER: 0%, total WER: 19.3858%, total TER: 8.63776%, progress (thread 0): 98.3782%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_508.48_508.66, WER: 0%, TER: 0%, total WER: 19.3856%, total TER: 8.63768%, progress (thread 0): 98.3861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_512.59_512.77, WER: 0%, TER: 0%, total WER: 19.3854%, total TER: 8.6376%, progress (thread 0): 98.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_518.66_518.84, WER: 0%, TER: 0%, total WER: 19.3852%, total TER: 8.63752%, progress (thread 0): 98.4019%]
|T|: y e a h
|P|: h u h
[sample: EN2002d_H00_FEO070_882.18_882.36, WER: 100%, TER: 75%, total WER: 19.3861%, total TER: 8.63814%, progress (thread 0): 98.4098%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_1451.31_1451.49, WER: 0%, TER: 0%, total WER: 19.3859%, total TER: 8.6381%, progress (thread 0): 98.4177%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1581.55_1581.73, WER: 0%, TER: 0%, total WER: 19.3856%, total TER: 8.638%, progress (thread 0): 98.4256%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_2062.62_2062.8, WER: 0%, TER: 0%, total WER: 19.3854%, total TER: 8.63792%, progress (thread 0): 98.4335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2166.41_2166.59, WER: 0%, TER: 0%, total WER: 19.3852%, total TER: 8.63784%, progress (thread 0): 98.4415%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_2172.96_2173.14, WER: 0%, TER: 0%, total WER: 19.385%, total TER: 8.63774%, progress (thread 0): 98.4494%]
|T|: a h
|P|: e h
[sample: ES2004b_H01_FEE013_843.75_843.92, WER: 100%, TER: 50%, total WER: 19.3859%, total TER: 8.63793%, progress (thread 0): 98.4573%]
|T|: y e p
|P|: y e a h
[sample: ES2004c_H00_MEO015_2220.58_2220.75, WER: 100%, TER: 66.6667%, total WER: 19.3868%, total TER: 8.63833%, progress (thread 0): 98.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_381.27_381.44, WER: 0%, TER: 0%, total WER: 19.3866%, total TER: 8.63825%, progress (thread 0): 98.4731%]
|T|: h i
|P|: h i
[sample: IS1009c_H02_FIO084_43.25_43.42, WER: 0%, TER: 0%, total WER: 19.3864%, total TER: 8.63821%, progress (thread 0): 98.481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_347.27_347.44, WER: 0%, TER: 0%, total WER: 19.3861%, total TER: 8.63813%, progress (thread 0): 98.4889%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_784.48_784.65, WER: 0%, TER: 0%, total WER: 19.3859%, total TER: 8.63809%, progress (thread 0): 98.4968%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_182.69_182.86, WER: 100%, TER: 66.6667%, total WER: 19.3868%, total TER: 8.6385%, progress (thread 0): 98.5047%]
|T|: h m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_748.2_748.37, WER: 100%, TER: 33.3333%, total WER: 19.3877%, total TER: 8.63867%, progress (thread 0): 98.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1432.6_1432.77, WER: 0%, TER: 0%, total WER: 19.3875%, total TER: 8.63859%, progress (thread 0): 98.5206%]
|T|: n o
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1442.45_1442.62, WER: 100%, TER: 200%, total WER: 19.3884%, total TER: 8.63948%, progress (thread 0): 98.5285%]
|T|: y e a h
|P|: a h
[sample: TS3003b_H01_MTD011UID_1584.22_1584.39, WER: 100%, TER: 50%, total WER: 19.3893%, total TER: 8.63986%, progress (thread 0): 98.5364%]
|T|: n o
|P|: u h
[sample: TS3003b_H01_MTD011UID_1779.4_1779.57, WER: 100%, TER: 100%, total WER: 19.3902%, total TER: 8.64029%, progress (thread 0): 98.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2009.37_2009.54, WER: 0%, TER: 0%, total WER: 19.39%, total TER: 8.64021%, progress (thread 0): 98.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1183.13_1183.3, WER: 0%, TER: 0%, total WER: 19.3898%, total TER: 8.64013%, progress (thread 0): 98.5601%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2272.21_2272.38, WER: 0%, TER: 0%, total WER: 19.3896%, total TER: 8.64005%, progress (thread 0): 98.568%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_306.52_306.69, WER: 0%, TER: 0%, total WER: 19.3893%, total TER: 8.63997%, progress (thread 0): 98.576%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1283.14_1283.31, WER: 100%, TER: 75%, total WER: 19.3902%, total TER: 8.64058%, progress (thread 0): 98.5839%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_549.83_550, WER: 100%, TER: 33.3333%, total WER: 19.3911%, total TER: 8.64076%, progress (thread 0): 98.5918%]
|T|: h m m
|P|: m h
[sample: EN2002a_H00_MEE073_1153.54_1153.71, WER: 100%, TER: 66.6667%, total WER: 19.392%, total TER: 8.64116%, progress (thread 0): 98.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1180.31_1180.48, WER: 0%, TER: 0%, total WER: 19.3918%, total TER: 8.64108%, progress (thread 0): 98.6076%]
|T|: h m m
|P|: m m
[sample: EN2002a_H02_FEO072_2053.69_2053.86, WER: 100%, TER: 33.3333%, total WER: 19.3927%, total TER: 8.64125%, progress (thread 0): 98.6155%]
|T|: y e a h
|P|: y e a m
[sample: EN2002a_H01_FEO070_2086.54_2086.71, WER: 100%, TER: 25%, total WER: 19.3936%, total TER: 8.6414%, progress (thread 0): 98.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_423.51_423.68, WER: 0%, TER: 0%, total WER: 19.3934%, total TER: 8.64132%, progress (thread 0): 98.6313%]
|T|: y e a h
|P|: m h
[sample: EN2002b_H03_MEE073_1344.27_1344.44, WER: 100%, TER: 75%, total WER: 19.3943%, total TER: 8.64194%, progress (thread 0): 98.6392%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_1400.92_1401.09, WER: 100%, TER: 100%, total WER: 19.3952%, total TER: 8.64279%, progress (thread 0): 98.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1518.9_1519.07, WER: 0%, TER: 0%, total WER: 19.395%, total TER: 8.64271%, progress (thread 0): 98.6551%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_55.6_55.77, WER: 0%, TER: 0%, total WER: 19.3948%, total TER: 8.64263%, progress (thread 0): 98.663%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_711.52_711.69, WER: 0%, TER: 0%, total WER: 19.3946%, total TER: 8.64255%, progress (thread 0): 98.6709%]
|T|: b u t
|P|: m
[sample: EN2002d_H02_MEE071_431.16_431.33, WER: 100%, TER: 100%, total WER: 19.3955%, total TER: 8.64319%, progress (thread 0): 98.6788%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_649.62_649.79, WER: 0%, TER: 0%, total WER: 19.3952%, total TER: 8.64311%, progress (thread 0): 98.6867%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_831.51_831.68, WER: 0%, TER: 0%, total WER: 19.395%, total TER: 8.64303%, progress (thread 0): 98.6946%]
|T|: s o
|P|: m
[sample: EN2002d_H03_MEE073_1058.82_1058.99, WER: 100%, TER: 100%, total WER: 19.3959%, total TER: 8.64345%, progress (thread 0): 98.7025%]
|T|: o h
|P|: o h
[sample: EN2002d_H00_FEO070_1459.48_1459.65, WER: 0%, TER: 0%, total WER: 19.3957%, total TER: 8.64341%, progress (thread 0): 98.7104%]
|T|: r i g h t
|P|: r i g t
[sample: EN2002d_H03_MEE073_1858.1_1858.27, WER: 100%, TER: 20%, total WER: 19.3966%, total TER: 8.64354%, progress (thread 0): 98.7184%]
|T|: i | m e a n
|P|: a n y
[sample: ES2004c_H03_FEE016_691.32_691.48, WER: 100%, TER: 83.3333%, total WER: 19.3984%, total TER: 8.64458%, progress (thread 0): 98.7263%]
|T|: o k a y
|P|: m
[sample: ES2004c_H03_FEE016_1560.36_1560.52, WER: 100%, TER: 100%, total WER: 19.3993%, total TER: 8.64543%, progress (thread 0): 98.7342%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H00_FIE088_757.84_758, WER: 0%, TER: 0%, total WER: 19.3991%, total TER: 8.64535%, progress (thread 0): 98.7421%]
|T|: n o
|P|: n o
[sample: IS1009d_H01_FIO087_584.12_584.28, WER: 0%, TER: 0%, total WER: 19.3989%, total TER: 8.64531%, progress (thread 0): 98.75%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_740.48_740.64, WER: 0%, TER: 0%, total WER: 19.3987%, total TER: 8.64523%, progress (thread 0): 98.7579%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_826.94_827.1, WER: 0%, TER: 0%, total WER: 19.3984%, total TER: 8.64515%, progress (thread 0): 98.7658%]
|T|: n o
|P|: n o
[sample: IS1009d_H03_FIO089_862.59_862.75, WER: 0%, TER: 0%, total WER: 19.3982%, total TER: 8.64511%, progress (thread 0): 98.7737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1025.48_1025.64, WER: 0%, TER: 0%, total WER: 19.398%, total TER: 8.64503%, progress (thread 0): 98.7816%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_580.47_580.63, WER: 0%, TER: 0%, total WER: 19.3978%, total TER: 8.64495%, progress (thread 0): 98.7896%]
|T|: u h
|P|: i h
[sample: TS3003a_H00_MTD009PM_617.55_617.71, WER: 100%, TER: 50%, total WER: 19.3987%, total TER: 8.64514%, progress (thread 0): 98.7975%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1421.57_1421.73, WER: 100%, TER: 150%, total WER: 19.3996%, total TER: 8.6458%, progress (thread 0): 98.8054%]
|T|: h m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1789.09_1789.25, WER: 100%, TER: 33.3333%, total WER: 19.4005%, total TER: 8.64597%, progress (thread 0): 98.8133%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2009.54_2009.7, WER: 0%, TER: 0%, total WER: 19.4003%, total TER: 8.64589%, progress (thread 0): 98.8212%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1865.26_1865.42, WER: 100%, TER: 66.6667%, total WER: 19.4012%, total TER: 8.6463%, progress (thread 0): 98.8291%]
|T|: o h
|P|: t o
[sample: TS3003d_H00_MTD009PM_2587.23_2587.39, WER: 100%, TER: 100%, total WER: 19.4021%, total TER: 8.64672%, progress (thread 0): 98.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_376.27_376.43, WER: 0%, TER: 0%, total WER: 19.4019%, total TER: 8.64664%, progress (thread 0): 98.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_720.58_720.74, WER: 0%, TER: 0%, total WER: 19.4017%, total TER: 8.64656%, progress (thread 0): 98.8529%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H00_MEE073_737.85_738.01, WER: 100%, TER: 100%, total WER: 19.4026%, total TER: 8.64741%, progress (thread 0): 98.8608%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_844.91_845.07, WER: 0%, TER: 0%, total WER: 19.4023%, total TER: 8.64733%, progress (thread 0): 98.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1041.33_1041.49, WER: 0%, TER: 0%, total WER: 19.4021%, total TER: 8.64725%, progress (thread 0): 98.8766%]
|T|: y e a h
|P|: m e h
[sample: EN2002a_H00_MEE073_1050.03_1050.19, WER: 100%, TER: 50%, total WER: 19.403%, total TER: 8.64763%, progress (thread 0): 98.8845%]
|T|: w h y
|P|: m
[sample: EN2002a_H00_MEE073_1237.91_1238.07, WER: 100%, TER: 100%, total WER: 19.4039%, total TER: 8.64827%, progress (thread 0): 98.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1596.11_1596.27, WER: 0%, TER: 0%, total WER: 19.4037%, total TER: 8.64819%, progress (thread 0): 98.9003%]
|T|: s o r r y
|P|: s u r e
[sample: EN2002a_H00_MEE073_1813.61_1813.77, WER: 100%, TER: 60%, total WER: 19.4046%, total TER: 8.64879%, progress (thread 0): 98.9082%]
|T|: s u r e
|P|: s u e
[sample: EN2002a_H00_MEE073_1881.96_1882.12, WER: 100%, TER: 25%, total WER: 19.4055%, total TER: 8.64894%, progress (thread 0): 98.9161%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_39.76_39.92, WER: 0%, TER: 0%, total WER: 19.4053%, total TER: 8.64886%, progress (thread 0): 98.924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_317.94_318.1, WER: 0%, TER: 0%, total WER: 19.4051%, total TER: 8.64878%, progress (thread 0): 98.932%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1075.72_1075.88, WER: 0%, TER: 0%, total WER: 19.4049%, total TER: 8.64868%, progress (thread 0): 98.9399%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_1155.04_1155.2, WER: 100%, TER: 100%, total WER: 19.4058%, total TER: 8.64953%, progress (thread 0): 98.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_713.06_713.22, WER: 0%, TER: 0%, total WER: 19.4055%, total TER: 8.64945%, progress (thread 0): 98.9557%]
|T|: o h
|P|: m m
[sample: EN2002c_H03_MEE073_1210.59_1210.75, WER: 100%, TER: 100%, total WER: 19.4064%, total TER: 8.64987%, progress (thread 0): 98.9636%]
|T|: r i g h t
|P|: o r h
[sample: EN2002c_H03_MEE073_1484.69_1484.85, WER: 100%, TER: 80%, total WER: 19.4073%, total TER: 8.6507%, progress (thread 0): 98.9715%]
|T|: y e a h
|P|: m m
[sample: EN2002c_H03_MEE073_2554.98_2555.14, WER: 100%, TER: 100%, total WER: 19.4082%, total TER: 8.65155%, progress (thread 0): 98.9794%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_2776.86_2777.02, WER: 100%, TER: 33.3333%, total WER: 19.4091%, total TER: 8.65172%, progress (thread 0): 98.9873%]
|T|: m m
|P|: m m
[sample: EN2002d_H03_MEE073_645.29_645.45, WER: 0%, TER: 0%, total WER: 19.4089%, total TER: 8.65168%, progress (thread 0): 98.9952%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1397.11_1397.27, WER: 0%, TER: 0%, total WER: 19.4087%, total TER: 8.6516%, progress (thread 0): 99.0032%]
|T|: o o p s
|P|: m
[sample: ES2004a_H00_MEO015_188.28_188.43, WER: 100%, TER: 100%, total WER: 19.4096%, total TER: 8.65245%, progress (thread 0): 99.0111%]
|T|: o o h
|P|: m m
[sample: ES2004b_H02_MEE014_534.45_534.6, WER: 100%, TER: 100%, total WER: 19.4105%, total TER: 8.65309%, progress (thread 0): 99.019%]
|T|: ' k a y
|P|: y e a h
[sample: ES2004c_H01_FEE013_472.18_472.33, WER: 100%, TER: 75%, total WER: 19.4114%, total TER: 8.6537%, progress (thread 0): 99.0269%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_693.82_693.97, WER: 100%, TER: 66.6667%, total WER: 19.4123%, total TER: 8.65411%, progress (thread 0): 99.0348%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1647.59_1647.74, WER: 0%, TER: 0%, total WER: 19.4121%, total TER: 8.65403%, progress (thread 0): 99.0427%]
|T|: y e a h
|P|: s o m
[sample: IS1009d_H02_FIO084_1392_1392.15, WER: 100%, TER: 100%, total WER: 19.413%, total TER: 8.65488%, progress (thread 0): 99.0506%]
|T|: t w o
|P|: d o
[sample: IS1009d_H01_FIO087_1586.28_1586.43, WER: 100%, TER: 66.6667%, total WER: 19.4139%, total TER: 8.65528%, progress (thread 0): 99.0585%]
|T|: n o
|P|: y e h
[sample: IS1009d_H01_FIO087_1824.98_1825.13, WER: 100%, TER: 150%, total WER: 19.4148%, total TER: 8.65594%, progress (thread 0): 99.0665%]
|T|: y e a h
|P|: m e a h
[sample: IS1009d_H02_FIO084_1883.61_1883.76, WER: 100%, TER: 25%, total WER: 19.4157%, total TER: 8.65609%, progress (thread 0): 99.0744%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_567.51_567.66, WER: 0%, TER: 0%, total WER: 19.4155%, total TER: 8.65601%, progress (thread 0): 99.0823%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_2049.54_2049.69, WER: 0%, TER: 0%, total WER: 19.4153%, total TER: 8.65593%, progress (thread 0): 99.0902%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2041.85_2042, WER: 0%, TER: 0%, total WER: 19.415%, total TER: 8.65589%, progress (thread 0): 99.0981%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_639.89_640.04, WER: 100%, TER: 66.6667%, total WER: 19.4159%, total TER: 8.65629%, progress (thread 0): 99.106%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_967.58_967.73, WER: 0%, TER: 0%, total WER: 19.4157%, total TER: 8.65621%, progress (thread 0): 99.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1030.96_1031.11, WER: 0%, TER: 0%, total WER: 19.4155%, total TER: 8.65613%, progress (thread 0): 99.1218%]
|T|: y e a h
|P|: o h
[sample: EN2002a_H01_FEO070_1104.83_1104.98, WER: 100%, TER: 75%, total WER: 19.4164%, total TER: 8.65675%, progress (thread 0): 99.1297%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1527.69_1527.84, WER: 100%, TER: 33.3333%, total WER: 19.4173%, total TER: 8.65692%, progress (thread 0): 99.1377%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H00_MEE073_1539.77_1539.92, WER: 100%, TER: 100%, total WER: 19.4182%, total TER: 8.65777%, progress (thread 0): 99.1456%]
|T|: y e a h
|P|: m h
[sample: EN2002a_H00_MEE073_1744.01_1744.16, WER: 100%, TER: 75%, total WER: 19.4191%, total TER: 8.65839%, progress (thread 0): 99.1535%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1785.21_1785.36, WER: 0%, TER: 0%, total WER: 19.4189%, total TER: 8.65831%, progress (thread 0): 99.1614%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H03_MEE073_382.32_382.47, WER: 100%, TER: 66.6667%, total WER: 19.4198%, total TER: 8.65871%, progress (thread 0): 99.1693%]
|T|: u h
|P|: y e a h
[sample: EN2002b_H02_FEO072_424.35_424.5, WER: 100%, TER: 150%, total WER: 19.4207%, total TER: 8.65937%, progress (thread 0): 99.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_496.03_496.18, WER: 0%, TER: 0%, total WER: 19.4205%, total TER: 8.65929%, progress (thread 0): 99.1851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_897.7_897.85, WER: 0%, TER: 0%, total WER: 19.4203%, total TER: 8.65921%, progress (thread 0): 99.193%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2198.65_2198.8, WER: 0%, TER: 0%, total WER: 19.42%, total TER: 8.65913%, progress (thread 0): 99.201%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1290.13_1290.28, WER: 0%, TER: 0%, total WER: 19.4198%, total TER: 8.65904%, progress (thread 0): 99.2089%]
|T|: n o
|P|: o h
[sample: EN2002d_H03_MEE073_1424.03_1424.18, WER: 100%, TER: 100%, total WER: 19.4207%, total TER: 8.65947%, progress (thread 0): 99.2168%]
|T|: r i g h t
|P|: h
[sample: EN2002d_H03_MEE073_1909.61_1909.76, WER: 100%, TER: 80%, total WER: 19.4216%, total TER: 8.6603%, progress (thread 0): 99.2247%]
|T|: o k a y
|P|: m
[sample: EN2002d_H03_MEE073_1985.35_1985.5, WER: 100%, TER: 100%, total WER: 19.4225%, total TER: 8.66115%, progress (thread 0): 99.2326%]
|T|: i | t h
|P|: i
[sample: ES2004b_H01_FEE013_2305.5_2305.64, WER: 50%, TER: 75%, total WER: 19.4232%, total TER: 8.66176%, progress (thread 0): 99.2405%]
|T|: m m
|P|: m
[sample: ES2004c_H03_FEE016_777.13_777.27, WER: 100%, TER: 50%, total WER: 19.4241%, total TER: 8.66196%, progress (thread 0): 99.2484%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_56.9_57.04, WER: 0%, TER: 0%, total WER: 19.4239%, total TER: 8.66187%, progress (thread 0): 99.2563%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_645.27_645.41, WER: 100%, TER: 66.6667%, total WER: 19.4248%, total TER: 8.66228%, progress (thread 0): 99.2642%]
|T|: m m h m m
|P|: s o
[sample: IS1009d_H00_FIE088_770.97_771.11, WER: 100%, TER: 100%, total WER: 19.4257%, total TER: 8.66334%, progress (thread 0): 99.2721%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_189.69_189.83, WER: 100%, TER: 66.6667%, total WER: 19.4266%, total TER: 8.66374%, progress (thread 0): 99.2801%]
|T|: m m
|P|: m
[sample: TS3003a_H01_MTD011UID_903.08_903.22, WER: 100%, TER: 50%, total WER: 19.4275%, total TER: 8.66394%, progress (thread 0): 99.288%]
|T|: m m
|P|: m
[sample: TS3003b_H01_MTD011UID_2047.35_2047.49, WER: 100%, TER: 50%, total WER: 19.4284%, total TER: 8.66413%, progress (thread 0): 99.2959%]
|T|: ' k a y
|P|: y h
[sample: TS3003b_H03_MTD012ME_2140.48_2140.62, WER: 100%, TER: 100%, total WER: 19.4293%, total TER: 8.66498%, progress (thread 0): 99.3038%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1869.23_1869.37, WER: 0%, TER: 0%, total WER: 19.4291%, total TER: 8.6649%, progress (thread 0): 99.3117%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_1390.61_1390.75, WER: 100%, TER: 25%, total WER: 19.43%, total TER: 8.66505%, progress (thread 0): 99.3196%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_2021.71_2021.85, WER: 100%, TER: 25%, total WER: 19.4309%, total TER: 8.6652%, progress (thread 0): 99.3275%]
|T|: r i g h t
|P|: i h
[sample: EN2002a_H00_MEE073_1184.15_1184.29, WER: 100%, TER: 60%, total WER: 19.4318%, total TER: 8.6658%, progress (thread 0): 99.3354%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_1193.94_1194.08, WER: 100%, TER: 66.6667%, total WER: 19.4327%, total TER: 8.6662%, progress (thread 0): 99.3434%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H00_MEE073_2048.07_2048.21, WER: 100%, TER: 66.6667%, total WER: 19.4336%, total TER: 8.6666%, progress (thread 0): 99.3513%]
|T|: ' k a y
|P|:
[sample: EN2002c_H03_MEE073_103.84_103.98, WER: 100%, TER: 100%, total WER: 19.4345%, total TER: 8.66745%, progress (thread 0): 99.3592%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_531.32_531.46, WER: 0%, TER: 0%, total WER: 19.4342%, total TER: 8.66737%, progress (thread 0): 99.3671%]
|T|: y e a h
|P|: y e h
[sample: EN2002c_H03_MEE073_667.22_667.36, WER: 100%, TER: 25%, total WER: 19.4351%, total TER: 8.66752%, progress (thread 0): 99.375%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1476.08_1476.22, WER: 0%, TER: 0%, total WER: 19.4349%, total TER: 8.66744%, progress (thread 0): 99.3829%]
|T|: m m
|P|: m
[sample: EN2002c_H03_MEE073_2096.91_2097.05, WER: 100%, TER: 50%, total WER: 19.4358%, total TER: 8.66764%, progress (thread 0): 99.3908%]
|T|: o k a y
|P|: m e m
[sample: EN2002d_H00_FEO070_1251.9_1252.04, WER: 100%, TER: 100%, total WER: 19.4367%, total TER: 8.66848%, progress (thread 0): 99.3987%]
|T|: m m
|P|: m e a
[sample: ES2004c_H03_FEE016_98.28_98.41, WER: 100%, TER: 100%, total WER: 19.4376%, total TER: 8.66891%, progress (thread 0): 99.4066%]
|T|: s
|P|: m
[sample: ES2004d_H03_FEE016_1370.74_1370.87, WER: 100%, TER: 100%, total WER: 19.4385%, total TER: 8.66912%, progress (thread 0): 99.4146%]
|T|: o r | a | b
|P|: m a
[sample: IS1009a_H01_FIO087_573.12_573.25, WER: 100%, TER: 83.3333%, total WER: 19.4412%, total TER: 8.67016%, progress (thread 0): 99.4225%]
|T|: h m m
|P|: m m
[sample: IS1009c_H02_FIO084_144.08_144.21, WER: 100%, TER: 33.3333%, total WER: 19.4421%, total TER: 8.67033%, progress (thread 0): 99.4304%]
|T|: o k a y
|P|: m
[sample: IS1009d_H00_FIE088_604.16_604.29, WER: 100%, TER: 100%, total WER: 19.443%, total TER: 8.67118%, progress (thread 0): 99.4383%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_942.55_942.68, WER: 100%, TER: 66.6667%, total WER: 19.4439%, total TER: 8.67159%, progress (thread 0): 99.4462%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_392.63_392.76, WER: 100%, TER: 66.6667%, total WER: 19.4448%, total TER: 8.67199%, progress (thread 0): 99.4541%]
|T|: h u h
|P|: m
[sample: TS3003a_H01_MTD011UID_1266.08_1266.21, WER: 100%, TER: 100%, total WER: 19.4457%, total TER: 8.67263%, progress (thread 0): 99.462%]
|T|: o h
|P|: m h
[sample: TS3003a_H01_MTD011UID_1335.11_1335.24, WER: 100%, TER: 50%, total WER: 19.4466%, total TER: 8.67282%, progress (thread 0): 99.4699%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1475.42_1475.55, WER: 100%, TER: 66.6667%, total WER: 19.4475%, total TER: 8.67322%, progress (thread 0): 99.4778%]
|T|: y e a h
|P|: m m
[sample: TS3003b_H01_MTD011UID_1528.52_1528.65, WER: 100%, TER: 100%, total WER: 19.4484%, total TER: 8.67407%, progress (thread 0): 99.4858%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1173.23_1173.36, WER: 0%, TER: 0%, total WER: 19.4482%, total TER: 8.67403%, progress (thread 0): 99.4937%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_1693.45_1693.58, WER: 100%, TER: 25%, total WER: 19.4491%, total TER: 8.67418%, progress (thread 0): 99.5016%]
|T|: ' k a y
|P|: m e m
[sample: EN2002a_H00_MEE073_32.58_32.71, WER: 100%, TER: 100%, total WER: 19.45%, total TER: 8.67503%, progress (thread 0): 99.5095%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_984.87_985, WER: 100%, TER: 66.6667%, total WER: 19.4509%, total TER: 8.67544%, progress (thread 0): 99.5174%]
|T|: w e l l | f
|P|: h
[sample: EN2002a_H00_MEE073_1458.73_1458.86, WER: 100%, TER: 100%, total WER: 19.4527%, total TER: 8.67671%, progress (thread 0): 99.5253%]
|T|: y e p
|P|: y e a
[sample: EN2002b_H01_MEE071_493.19_493.32, WER: 100%, TER: 33.3333%, total WER: 19.4536%, total TER: 8.67688%, progress (thread 0): 99.5332%]
|T|: y e a h
|P|: y e a
[sample: EN2002c_H03_MEE073_665.01_665.14, WER: 100%, TER: 25%, total WER: 19.4545%, total TER: 8.67703%, progress (thread 0): 99.5411%]
|T|: o h
|P|: m
[sample: EN2002d_H01_FEO072_135.16_135.29, WER: 100%, TER: 100%, total WER: 19.4554%, total TER: 8.67746%, progress (thread 0): 99.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_726.53_726.66, WER: 0%, TER: 0%, total WER: 19.4552%, total TER: 8.67738%, progress (thread 0): 99.557%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_794.79_794.92, WER: 0%, TER: 0%, total WER: 19.455%, total TER: 8.67729%, progress (thread 0): 99.5649%]
|T|: y e a h
|P|: m e a
[sample: EN2002d_H00_FEO070_1595.77_1595.9, WER: 100%, TER: 50%, total WER: 19.4559%, total TER: 8.67768%, progress (thread 0): 99.5728%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_1641.64_1641.77, WER: 100%, TER: 100%, total WER: 19.4568%, total TER: 8.6781%, progress (thread 0): 99.5807%]
|T|: y e a h
|P|: m m
[sample: ES2004b_H00_MEO015_922.62_922.74, WER: 100%, TER: 100%, total WER: 19.4576%, total TER: 8.67895%, progress (thread 0): 99.5886%]
|T|: o k a y
|P|: m h
[sample: ES2004b_H00_MEO015_1977.87_1977.99, WER: 100%, TER: 100%, total WER: 19.4585%, total TER: 8.6798%, progress (thread 0): 99.5965%]
|T|: y e a h
|P|: m e m
[sample: IS1009b_H02_FIO084_173.86_173.98, WER: 100%, TER: 75%, total WER: 19.4594%, total TER: 8.68042%, progress (thread 0): 99.6044%]
|T|: h m m
|P|: m
[sample: IS1009c_H02_FIO084_1703.14_1703.26, WER: 100%, TER: 66.6667%, total WER: 19.4603%, total TER: 8.68082%, progress (thread 0): 99.6123%]
|T|: m m | m m
|P|:
[sample: IS1009d_H03_FIO089_870.76_870.88, WER: 100%, TER: 100%, total WER: 19.4621%, total TER: 8.68188%, progress (thread 0): 99.6203%]
|T|: y e p
|P|: y e a
[sample: TS3003a_H01_MTD011UID_257.2_257.32, WER: 100%, TER: 33.3333%, total WER: 19.463%, total TER: 8.68205%, progress (thread 0): 99.6282%]
|T|: y e a h
|P|: m
[sample: EN2002a_H03_MEE071_170.81_170.93, WER: 100%, TER: 100%, total WER: 19.4639%, total TER: 8.6829%, progress (thread 0): 99.6361%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_1468.26_1468.38, WER: 100%, TER: 66.6667%, total WER: 19.4648%, total TER: 8.6833%, progress (thread 0): 99.644%]
|T|: n o
|P|: m h
[sample: EN2002b_H03_MEE073_4.83_4.95, WER: 100%, TER: 100%, total WER: 19.4657%, total TER: 8.68373%, progress (thread 0): 99.6519%]
|T|: a l t e r
|P|: y e a
[sample: EN2002b_H03_MEE073_170.46_170.58, WER: 100%, TER: 80%, total WER: 19.4666%, total TER: 8.68456%, progress (thread 0): 99.6598%]
|T|: y e a h
|P|: m
[sample: EN2002b_H03_MEE073_1641.92_1642.04, WER: 100%, TER: 100%, total WER: 19.4675%, total TER: 8.6854%, progress (thread 0): 99.6677%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H03_MEE073_806.78_806.9, WER: 100%, TER: 25%, total WER: 19.4684%, total TER: 8.68556%, progress (thread 0): 99.6756%]
|T|: o k a y
|P|: y e a
[sample: ES2004a_H00_MEO015_71.95_72.06, WER: 100%, TER: 75%, total WER: 19.4693%, total TER: 8.68617%, progress (thread 0): 99.6835%]
|T|: o
|P|: y a
[sample: ES2004a_H03_FEE016_403.24_403.35, WER: 100%, TER: 200%, total WER: 19.4702%, total TER: 8.68662%, progress (thread 0): 99.6915%]
|T|: ' k a y
|P|:
[sample: ES2004a_H03_FEE016_1042.19_1042.3, WER: 100%, TER: 100%, total WER: 19.4711%, total TER: 8.68746%, progress (thread 0): 99.6994%]
|T|: m m
|P|: m m
[sample: IS1009c_H02_FIO084_620.84_620.95, WER: 0%, TER: 0%, total WER: 19.4709%, total TER: 8.68742%, progress (thread 0): 99.7073%]
|T|: y e a h
|P|: m
[sample: TS3003a_H01_MTD011UID_436.27_436.38, WER: 100%, TER: 100%, total WER: 19.4718%, total TER: 8.68827%, progress (thread 0): 99.7152%]
|T|: y e a h
|P|: m
[sample: EN2002a_H00_MEE073_669.47_669.58, WER: 100%, TER: 100%, total WER: 19.4727%, total TER: 8.68912%, progress (thread 0): 99.7231%]
|T|: y e a h
|P|: y a
[sample: EN2002a_H01_FEO070_1169.61_1169.72, WER: 100%, TER: 50%, total WER: 19.4736%, total TER: 8.6895%, progress (thread 0): 99.731%]
|T|: n o
|P|: y e
[sample: EN2002b_H02_FEO072_4.48_4.59, WER: 100%, TER: 100%, total WER: 19.4745%, total TER: 8.68993%, progress (thread 0): 99.7389%]
|T|: y e p
|P|: m
[sample: EN2002b_H03_MEE073_307.97_308.08, WER: 100%, TER: 100%, total WER: 19.4754%, total TER: 8.69056%, progress (thread 0): 99.7468%]
|T|: h m m
|P|: m
[sample: EN2002b_H03_MEE073_1578.82_1578.93, WER: 100%, TER: 66.6667%, total WER: 19.4763%, total TER: 8.69097%, progress (thread 0): 99.7547%]
|T|: h m m
|P|: m e
[sample: EN2002c_H03_MEE073_1446.36_1446.47, WER: 100%, TER: 66.6667%, total WER: 19.4772%, total TER: 8.69137%, progress (thread 0): 99.7627%]
|T|: o h
|P|: h
[sample: EN2002d_H01_FEO072_118.11_118.22, WER: 100%, TER: 50%, total WER: 19.4781%, total TER: 8.69156%, progress (thread 0): 99.7706%]
|T|: s o
|P|: m
[sample: EN2002d_H02_MEE071_2107.05_2107.16, WER: 100%, TER: 100%, total WER: 19.479%, total TER: 8.69199%, progress (thread 0): 99.7785%]
|T|: t
|P|: m
[sample: ES2004c_H02_MEE014_1184.81_1184.91, WER: 100%, TER: 100%, total WER: 19.4799%, total TER: 8.6922%, progress (thread 0): 99.7864%]
|T|: y e s
|P|: m a
[sample: IS1009d_H01_FIO087_1744.56_1744.66, WER: 100%, TER: 100%, total WER: 19.4808%, total TER: 8.69284%, progress (thread 0): 99.7943%]
|T|: o h
|P|: h
[sample: TS3003c_H02_MTD0010ID_1023.66_1023.76, WER: 100%, TER: 50%, total WER: 19.4817%, total TER: 8.69303%, progress (thread 0): 99.8022%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_2013_2013.1, WER: 100%, TER: 66.6667%, total WER: 19.4826%, total TER: 8.69343%, progress (thread 0): 99.8101%]
|T|: y e a h
|P|: m
[sample: EN2002b_H00_FEO070_43.63_43.73, WER: 100%, TER: 100%, total WER: 19.4835%, total TER: 8.69428%, progress (thread 0): 99.818%]
|T|: y e a h
|P|: y e a
[sample: EN2002b_H03_MEE073_293.52_293.62, WER: 100%, TER: 25%, total WER: 19.4844%, total TER: 8.69443%, progress (thread 0): 99.826%]
|T|: y e p
|P|: y e
[sample: EN2002c_H03_MEE073_763_763.1, WER: 100%, TER: 33.3333%, total WER: 19.4853%, total TER: 8.6946%, progress (thread 0): 99.8339%]
|T|: m m
|P|: m
[sample: EN2002d_H03_MEE073_2099.51_2099.61, WER: 100%, TER: 50%, total WER: 19.4862%, total TER: 8.69479%, progress (thread 0): 99.8418%]
|T|: ' k a y
|P|: m
[sample: ES2004a_H00_MEO015_168.79_168.88, WER: 100%, TER: 100%, total WER: 19.4871%, total TER: 8.69564%, progress (thread 0): 99.8497%]
|T|: y e p
|P|: y e
[sample: ES2004c_H00_MEO015_360.73_360.82, WER: 100%, TER: 33.3333%, total WER: 19.488%, total TER: 8.69581%, progress (thread 0): 99.8576%]
|T|: m m h m m
|P|: m
[sample: ES2004c_H03_FEE016_1132.2_1132.29, WER: 100%, TER: 80%, total WER: 19.4889%, total TER: 8.69664%, progress (thread 0): 99.8655%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1651.84_1651.93, WER: 100%, TER: 100%, total WER: 19.4898%, total TER: 8.69728%, progress (thread 0): 99.8734%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H02_FIO084_1753.49_1753.58, WER: 100%, TER: 80%, total WER: 19.4907%, total TER: 8.69811%, progress (thread 0): 99.8813%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_749.88_749.97, WER: 100%, TER: 100%, total WER: 19.4916%, total TER: 8.69874%, progress (thread 0): 99.8892%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1718.32_1718.41, WER: 100%, TER: 100%, total WER: 19.4925%, total TER: 8.6998%, progress (thread 0): 99.8972%]
|T|: w h a t
|P|: m
[sample: TS3003d_H00_MTD009PM_1597.33_1597.42, WER: 100%, TER: 100%, total WER: 19.4934%, total TER: 8.70065%, progress (thread 0): 99.9051%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_887.36_887.45, WER: 100%, TER: 66.6667%, total WER: 19.4943%, total TER: 8.70105%, progress (thread 0): 99.913%]
|T|: d a m n
|P|: m
[sample: EN2002a_H00_MEE073_1500.91_1501, WER: 100%, TER: 75%, total WER: 19.4952%, total TER: 8.70167%, progress (thread 0): 99.9209%]
|T|: h m m
|P|: m
[sample: EN2002c_H03_MEE073_429.63_429.72, WER: 100%, TER: 66.6667%, total WER: 19.4961%, total TER: 8.70207%, progress (thread 0): 99.9288%]
|T|: y e a h
|P|: y a
[sample: EN2002c_H02_MEE071_2578.94_2579.03, WER: 100%, TER: 50%, total WER: 19.497%, total TER: 8.70246%, progress (thread 0): 99.9367%]
|T|: d a m n
|P|: y e a
[sample: EN2002d_H03_MEE073_999.94_1000.03, WER: 100%, TER: 100%, total WER: 19.4979%, total TER: 8.7033%, progress (thread 0): 99.9446%]
|T|: m m
|P|:
[sample: ES2004a_H00_MEO015_252.48_252.56, WER: 100%, TER: 100%, total WER: 19.4988%, total TER: 8.70373%, progress (thread 0): 99.9525%]
|T|: m m
|P|:
[sample: ES2004c_H00_MEO015_1885.03_1885.11, WER: 100%, TER: 100%, total WER: 19.4997%, total TER: 8.70415%, progress (thread 0): 99.9604%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1313.86_1313.94, WER: 100%, TER: 100%, total WER: 19.5006%, total TER: 8.70458%, progress (thread 0): 99.9684%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_185.9_185.98, WER: 100%, TER: 100%, total WER: 19.5014%, total TER: 8.70521%, progress (thread 0): 99.9763%]
|T|: g
|P|: m
[sample: EN2002d_H03_MEE073_241.62_241.69, WER: 100%, TER: 100%, total WER: 19.5023%, total TER: 8.70542%, progress (thread 0): 99.9842%]
|T|: h m m
|P|:
[sample: IS1009a_H02_FIO084_490.4_490.46, WER: 100%, TER: 100%, total WER: 19.5032%, total TER: 8.70606%, progress (thread 0): 99.9921%]
|T|: ' k a y
|P|:
[sample: EN2002b_H03_MEE073_613.94_614, WER: 100%, TER: 100%, total WER: 19.5041%, total TER: 8.70691%, progress (thread 0): 100%]
I1224 07:09:02.842401 11830 Test.cpp:418] ------
I1224 07:09:02.842427 11830 Test.cpp:419] [Test ami_limited_supervision/test.lst (12640 samples) in 361.478s (actual decoding time 0.0286s/sample) -- WER: 19.5041%, TER: 8.70691%]
###Markdown
Viterbi WER improved from 26.6% to 19.5% after 1 epoch with finetuning... Beam Search decoding with a language model To do this, download the finetuned model and use the [Inference CTC tutorial](https://colab.research.google.com/github/facebookresearch/flashlight/blob/master/flashlight/app/asr/tutorial/notebooks/InferenceAndAlignmentCTC.ipynb) Step 5: Running with your own data To finetune on your own data, create `train`, `dev` and `test` list files and run the finetuning step. Each list file consists of multiple lines with each line describing one sample in the following format : ``` ```For example, let's take a look at the `dev.lst` file from AMI corpus.
###Code
! head ami_limited_supervision/dev.lst
###Output
ES2011a_H00_FEE041_34.27_37.14 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_34.27_37.14.flac 2870.0 here we go
ES2011a_H00_FEE041_37.14_39.15 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_37.14_39.15.flac 2010.0 welcome everybody
ES2011a_H00_FEE041_43.32_44.39 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_43.32_44.39.flac 1070.0 you can call me abbie
ES2011a_H00_FEE041_39.15_43.32 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_39.15_43.32.flac 4170.0 um i'm abigail claflin
ES2011a_H00_FEE041_46.43_47.63 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_46.43_47.63.flac 1200.0 's see
ES2011a_H00_FEE041_51.33_55.53 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_51.33_55.53.flac 4200.0 so this is our kick off meeting
ES2011a_H00_FEE041_55.53_56.85 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_55.53_56.85.flac 1320.0 um
ES2011a_H00_FEE041_47.63_50.2 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_47.63_50.2.flac 2570.0 powerpoint that's not it
ES2011a_H00_FEE041_50.2_51.33 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_50.2_51.33.flac 1130.0 there we go
ES2011a_H00_FEE041_62.17_64.28 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_62.17_64.28.flac 2110.0 let's shall we all introduce ourselves
###Markdown
Recording your own audioFor example, you can record your own audio and finetune the model...Installing a few packages first...
###Code
!apt-get install sox
!pip install ffmpeg-python sox
from flashlight.scripts.colab.record import record_audio
###Output
_____no_output_____
###Markdown
**Let's record now the following sentences:****1:** A flashlight or torch is a small, portable spotlight.
###Code
record_audio("recorded_audio_1")
###Output
_____no_output_____
###Markdown
**2:** Its function is a beam of light which helps to see and it usually requires batteries.
###Code
record_audio("recorded_audio_2")
###Output
_____no_output_____
###Markdown
**3:** In 1896, the first dry cell battery was invented.
###Code
record_audio("recorded_audio_3")
###Output
_____no_output_____
###Markdown
**4:** Unlike previous batteries, it used a paste electrolyte instead of a liquid.
###Code
record_audio("recorded_audio_4")
###Output
_____no_output_____
###Markdown
**5** This was the first battery suitable for portable electrical devices, as it did not spill or break easily and worked in any orientation.
###Code
record_audio("recorded_audio_5")
###Output
_____no_output_____
###Markdown
Create now new training/dev lists:(yes, you need to edit transcriptions below to your recordings)
###Code
import sox
transcriptions = [
"a flashlight or torch is a small portable spotlight",
"its function is a beam of light which helps to see and it usually requires batteries",
"in eighteen ninthy six the first dry cell battery was invented",
"unlike previous batteries it used a paste electrolyte instead of a liquid",
"this was the first battery suitable for portable electrical devices, as it did not spill or break easily and worked in any orientation"
]
with open("own_train.lst", "w") as f_train, open("own_dev.lst", "w") as f_dev:
for index, transcription in enumerate(transcriptions):
fname = "recorded_audio_" + str(index + 1) + ".wav"
duration_ms = sox.file_info.duration(fname) * 1000
if index % 2 == 0:
f_train.write("{}\t{}\t{}\t{}\n".format(
index + 1, fname, duration_ms, transcription))
else:
f_dev.write("{}\t{}\t{}\t{}\n".format(
index + 1, fname, duration_ms, transcription))
###Output
_____no_output_____
###Markdown
Check at first model quality on dev before finetuning
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am model.bin --datadir '' --emission_dir '' --uselexicon false \
--test own_dev.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
_____no_output_____
###Markdown
Finetune on recorded audio samplesPlay with parameters if needed.
###Code
! ./flashlight/build/bin/asr/fl_asr_tutorial_finetune_ctc model.bin \
--datadir= \
--train own_train.lst \
--valid dev:own_dev.lst \
--arch arch.txt \
--tokens tokens.txt \
--lexicon lexicon.txt \
--rundir own_checkpoint \
--lr 0.025 \
--netoptim sgd \
--momentum 0.8 \
--reportiters 1000 \
--lr_decay 100 \
--lr_decay_step 50 \
--iter 25000 \
--batchsize 4 \
--warmup 0
###Output
_____no_output_____
###Markdown
Test finetuned model(unlikely you get significant improvement with just five phrases, but let's check!)
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am own_checkpoint/001_model_dev.bin --datadir '' --emission_dir '' --uselexicon false \
--test own_dev.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
_____no_output_____
###Markdown
Tutorial on ASR Finetuning with CTC model Let's finetune a pretrained ASR model!Here we provide pre-trained speech recognition model with CTC loss that is trained on many open-sourced datasets. Details can be found in [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://arxiv.org/abs/2010.11745) Step 1: Install `Flashlight`First we install `Flashlight` and its dependencies. Flashlight is built from source with either CPU/CUDA backend and installation takes **~16 minutes**. For installation out of colab notebook please use [link](https://github.com/fairinternal/flashlightbuilding).
###Code
# First, choose backend to build with
backend = 'CUDA' #@param ["CPU", "CUDA"]
# Clone Flashlight
!git clone https://github.com/flashlight/flashlight.git
# install all dependencies for colab notebook
!source flashlight/scripts/colab/colab_install_deps.sh
###Output
_____no_output_____
###Markdown
Build CPU/CUDA Backend of `Flashlight`:- Build from current master. - Builds the ASR app. - Resulting binaries in `/content/flashlight/build/bin/asr`.If using a GPU Colab runtime, build the CUDA backend; else build the CPU backend.
###Code
# export necessary env variables
%env MKLROOT=/opt/intel/mkl
%env ArrayFire_DIR=/opt/arrayfire/share/ArrayFire/cmake
%env DNNL_DIR=/opt/dnnl/dnnl_lnx_2.0.0_cpu_iomp/lib/cmake/dnnl
if backend == "CUDA":
# Total time: ~13 minutes
!cd flashlight && git checkout d2e1924cb2a2b32b48cc326bb7e332ca3ea54f67 && mkdir -p build && cd build && \
cmake .. -DCMAKE_BUILD_TYPE=Release \
-DFL_BUILD_TESTS=OFF \
-DFL_BUILD_EXAMPLES=OFF \
-DFL_BUILD_APP_ASR=ON && \
make -j$(nproc)
elif backend == "CPU":
# Total time: ~14 minutes
!cd flashlight && git checkout d2e1924cb2a2b32b48cc326bb7e332ca3ea54f67 && mkdir -p build && cd build && \
cmake .. -DFL_BACKEND=CPU \
-DCMAKE_BUILD_TYPE=Release \
-DFL_BUILD_TESTS=OFF \
-DFL_BUILD_EXAMPLES=OFF \
-DFL_BUILD_APP_ASR=ON && \
make -j$(nproc)
else:
raise ValueError(f"Unknown backend {backend}")
###Output
_____no_output_____
###Markdown
Let's take a look around.
###Code
# Binaries are located in
!ls flashlight/build/bin/asr
###Output
fl_asr_align fl_asr_tutorial_finetune_ctc
fl_asr_decode fl_asr_tutorial_inference_ctc
fl_asr_test fl_asr_voice_activity_detection_ctc
fl_asr_train
###Markdown
Step 2: Setup Finetuning Downloading the model filesFirst, let's download the pretrained models for finetuning. For acoustic model, you can choose from >Architecture | Params | Criterion | Model Name | Arch Name >---|---|:---|:---:|:---:> Transformer|70Mil|CTC|am_transformer_ctc_stride3_letters_70Mparams.bin |am_transformer_ctc_stride3_letters_70Mparams.arch> Transformer|300Mil|CTC|am_transformer_ctc_stride3_letters_300Mparams.bin | am_transformer_ctc_stride3_letters_300Mparams.arch> Conformer|25Mil|CTC|am_conformer_ctc_stride3_letters_25Mparams.bin|am_conformer_ctc_stride3_letters_25Mparams.arch> Conformer|87Mil|CTC|am_conformer_ctc_stride3_letters_87Mparams.bin|am_conformer_ctc_stride3_letters_87Mparams.arch> Conformer|300Mil|CTC|am_conformer_ctc_stride3_letters_300Mparams.bin| am_conformer_ctc_stride3_letters_300Mparams.archFor demonstration, we will use the model in first row and download the model and its arch file.
###Code
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/am_transformer_ctc_stride3_letters_70Mparams.bin -O model.bin # acoustic model
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/am_transformer_ctc_stride3_letters_70Mparams.arch -O arch.txt # model architecture file
###Output
_____no_output_____
###Markdown
Along with the acoustic model, we will also download the tokens file, lexicon file
###Code
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/tokens.txt -O tokens.txt # tokens (defines predicted tokens)
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/lexicon.txt -O lexicon.txt # lexicon files (defines mapping between words)
###Output
_____no_output_____
###Markdown
Downloading the datasetFor finetuning the model, we provide a limited supervision dataset based on [AMI Corpus](http://groups.inf.ed.ac.uk/ami/corpus/). It consists of 10m, 1hr and 10hr subsets organized as follows. ```dev.lst development set test.lst test set train_10min_0.lst first 10 min foldtrain_10min_1.lsttrain_10min_2.lsttrain_10min_3.lsttrain_10min_4.lsttrain_10min_5.lsttrain_9hr.lst remaining data of the 10h split (10h=1h+9h)```The 10h split is created by combining the data from the 9h split and the 1h split. The 1h split is itself made of 6 folds of 10 min splits.The recipe used for preparing this corpus can be found [here](https://github.com/flashlight/wav2letter/tree/master/data/ami). **You can also use your own dataset to finetune the model instead of AMI Corpus.**
###Code
!rm ami_limited_supervision.tar.gz
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/ami_limited_supervision.tar.gz -O ami_limited_supervision.tar.gz
!tar -xf ami_limited_supervision.tar.gz
!ls ami_limited_supervision
###Output
rm: cannot remove 'ami_limited_supervision.tar.gz': No such file or directory
audio train_10min_0.lst train_10min_3.lst train_9hr.lst
dev.lst train_10min_1.lst train_10min_4.lst
test.lst train_10min_2.lst train_10min_5.lst
###Markdown
Get baseline WER before finetuningBefore proceeding to finetuning, let's test (viterbi) WER on AMI dataset to we have something to compare results after finetuning.
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am model.bin --datadir '' --emission_dir '' --uselexicon false \
--test ami_limited_supervision/test.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1046.72_1047.07, WER: 100%, TER: 50%, total WER: 25.9066%, total TER: 12.6062%, progress (thread 0): 86.8275%]
|T|: m m
|P|: m m
[sample: ES2004c_H01_FEE013_1294.34_1294.69, WER: 0%, TER: 0%, total WER: 25.9063%, total TER: 12.6061%, progress (thread 0): 86.8354%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1302.15_1302.5, WER: 100%, TER: 40%, total WER: 25.9071%, total TER: 12.6065%, progress (thread 0): 86.8434%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1515.72_1516.07, WER: 0%, TER: 0%, total WER: 25.9068%, total TER: 12.6063%, progress (thread 0): 86.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1690.13_1690.48, WER: 0%, TER: 0%, total WER: 25.9065%, total TER: 12.6062%, progress (thread 0): 86.8592%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_2078.48_2078.83, WER: 100%, TER: 40%, total WER: 25.9074%, total TER: 12.6065%, progress (thread 0): 86.8671%]
|T|: o k a y
|P|: i t
[sample: ES2004c_H02_MEE014_2291.54_2291.89, WER: 100%, TER: 100%, total WER: 25.9082%, total TER: 12.6074%, progress (thread 0): 86.875%]
|T|: o h
|P|:
[sample: ES2004d_H00_MEO015_127.17_127.52, WER: 100%, TER: 100%, total WER: 25.9091%, total TER: 12.6078%, progress (thread 0): 86.8829%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_561.86_562.21, WER: 0%, TER: 0%, total WER: 25.9088%, total TER: 12.6077%, progress (thread 0): 86.8908%]
|T|: ' k a y
|P|: k a y
[sample: ES2004d_H00_MEO015_640.16_640.51, WER: 100%, TER: 25%, total WER: 25.9096%, total TER: 12.6078%, progress (thread 0): 86.8987%]
|T|: r i g h t
|P|: l i k e
[sample: ES2004d_H00_MEO015_821.44_821.79, WER: 100%, TER: 80%, total WER: 25.9105%, total TER: 12.6086%, progress (thread 0): 86.9066%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H01_FEE013_822.51_822.86, WER: 100%, TER: 40%, total WER: 25.9113%, total TER: 12.6089%, progress (thread 0): 86.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1778.02_1778.37, WER: 0%, TER: 0%, total WER: 25.911%, total TER: 12.6088%, progress (thread 0): 86.9225%]
|T|: m m h m m
|P|: u | h u h
[sample: IS1009a_H00_FIE088_76.08_76.43, WER: 200%, TER: 80%, total WER: 25.913%, total TER: 12.6096%, progress (thread 0): 86.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_431.19_431.54, WER: 0%, TER: 0%, total WER: 25.9127%, total TER: 12.6094%, progress (thread 0): 86.9383%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_492.57_492.92, WER: 0%, TER: 0%, total WER: 25.9124%, total TER: 12.6094%, progress (thread 0): 86.9462%]
|T|: y e a h
|P|: h e | w a s
[sample: IS1009b_H02_FIO084_232.67_233.02, WER: 200%, TER: 100%, total WER: 25.9144%, total TER: 12.6102%, progress (thread 0): 86.9541%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_361.11_361.46, WER: 0%, TER: 0%, total WER: 25.9141%, total TER: 12.6101%, progress (thread 0): 86.962%]
|T|: y e p
|P|: y e a h
[sample: IS1009b_H00_FIE088_723.57_723.92, WER: 100%, TER: 66.6667%, total WER: 25.9149%, total TER: 12.6104%, progress (thread 0): 86.9699%]
|T|: m m h m m
|P|: e
[sample: IS1009b_H02_FIO084_993.71_994.06, WER: 100%, TER: 100%, total WER: 25.9158%, total TER: 12.6115%, progress (thread 0): 86.9778%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1007.39_1007.74, WER: 0%, TER: 0%, total WER: 25.9155%, total TER: 12.6114%, progress (thread 0): 86.9858%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1079.18_1079.53, WER: 100%, TER: 40%, total WER: 25.9163%, total TER: 12.6117%, progress (thread 0): 86.9937%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1082.71_1083.06, WER: 100%, TER: 40%, total WER: 25.9172%, total TER: 12.612%, progress (thread 0): 87.0016%]
|T|: t h i s | o n e
|P|: t h i s | o n e
[sample: IS1009b_H00_FIE088_1179.44_1179.79, WER: 0%, TER: 0%, total WER: 25.9166%, total TER: 12.6118%, progress (thread 0): 87.0095%]
|T|: y o u ' r e | t h r e e
|P|: i t h r i
[sample: IS1009b_H00_FIE088_1203.06_1203.41, WER: 100%, TER: 75%, total WER: 25.9183%, total TER: 12.6136%, progress (thread 0): 87.0174%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1699.07_1699.42, WER: 0%, TER: 0%, total WER: 25.918%, total TER: 12.6134%, progress (thread 0): 87.0253%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1821.76_1822.11, WER: 0%, TER: 0%, total WER: 25.9177%, total TER: 12.6133%, progress (thread 0): 87.0332%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_527.63_527.98, WER: 100%, TER: 40%, total WER: 25.9185%, total TER: 12.6136%, progress (thread 0): 87.0411%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_546.82_547.17, WER: 100%, TER: 40%, total WER: 25.9194%, total TER: 12.614%, progress (thread 0): 87.049%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_609.9_610.25, WER: 100%, TER: 40%, total WER: 25.9202%, total TER: 12.6143%, progress (thread 0): 87.057%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009c_H00_FIE088_688.49_688.84, WER: 200%, TER: 100%, total WER: 25.9222%, total TER: 12.6153%, progress (thread 0): 87.0649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1303.58_1303.93, WER: 0%, TER: 0%, total WER: 25.9219%, total TER: 12.6152%, progress (thread 0): 87.0728%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H01_FIO087_1514.93_1515.28, WER: 100%, TER: 40%, total WER: 25.9227%, total TER: 12.6155%, progress (thread 0): 87.0807%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1737.76_1738.11, WER: 100%, TER: 40%, total WER: 25.9236%, total TER: 12.6158%, progress (thread 0): 87.0886%]
|T|: a h
|P|: i
[sample: IS1009d_H02_FIO084_734.11_734.46, WER: 100%, TER: 100%, total WER: 25.9244%, total TER: 12.6163%, progress (thread 0): 87.0965%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_742.93_743.28, WER: 0%, TER: 0%, total WER: 25.9241%, total TER: 12.6162%, progress (thread 0): 87.1044%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_743.52_743.87, WER: 0%, TER: 0%, total WER: 25.9238%, total TER: 12.616%, progress (thread 0): 87.1123%]
|T|: y e a h
|P|: w h e r e
[sample: IS1009d_H02_FIO084_953.71_954.06, WER: 100%, TER: 100%, total WER: 25.9247%, total TER: 12.6169%, progress (thread 0): 87.1203%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1243.4_1243.75, WER: 0%, TER: 0%, total WER: 25.9244%, total TER: 12.6168%, progress (thread 0): 87.1282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1323.32_1323.67, WER: 0%, TER: 0%, total WER: 25.9241%, total TER: 12.6166%, progress (thread 0): 87.1361%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_1417.8_1418.15, WER: 100%, TER: 40%, total WER: 25.9249%, total TER: 12.617%, progress (thread 0): 87.144%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_1674.44_1674.79, WER: 100%, TER: 100%, total WER: 25.9258%, total TER: 12.618%, progress (thread 0): 87.1519%]
|T|: m m h m m
|P|: m h
[sample: IS1009d_H02_FIO084_1745.56_1745.91, WER: 100%, TER: 60%, total WER: 25.9266%, total TER: 12.6185%, progress (thread 0): 87.1598%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_506.21_506.56, WER: 0%, TER: 0%, total WER: 25.9263%, total TER: 12.6184%, progress (thread 0): 87.1677%]
|T|: t h e | p e n
|P|: b
[sample: TS3003a_H02_MTD0010ID_1074.9_1075.25, WER: 100%, TER: 100%, total WER: 25.928%, total TER: 12.6199%, progress (thread 0): 87.1756%]
|T|: r i g h t
|P|: r i g h t
[sample: TS3003a_H03_MTD012ME_1219.55_1219.9, WER: 0%, TER: 0%, total WER: 25.9277%, total TER: 12.6197%, progress (thread 0): 87.1835%]
|T|: m m h m m
|P|: m h m
[sample: TS3003a_H01_MTD011UID_1371.45_1371.8, WER: 100%, TER: 40%, total WER: 25.9285%, total TER: 12.62%, progress (thread 0): 87.1915%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_1453.73_1454.08, WER: 0%, TER: 0%, total WER: 25.9282%, total TER: 12.6199%, progress (thread 0): 87.1994%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1295.49_1295.84, WER: 0%, TER: 0%, total WER: 25.9279%, total TER: 12.6199%, progress (thread 0): 87.2073%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1351.56_1351.91, WER: 100%, TER: 50%, total WER: 25.9288%, total TER: 12.62%, progress (thread 0): 87.2152%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_180.89_181.24, WER: 0%, TER: 0%, total WER: 25.9285%, total TER: 12.6199%, progress (thread 0): 87.2231%]
|T|: y e a h
|P|: y e a p
[sample: TS3003c_H00_MTD009PM_1380.5_1380.85, WER: 100%, TER: 25%, total WER: 25.9293%, total TER: 12.62%, progress (thread 0): 87.231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1531.23_1531.58, WER: 0%, TER: 0%, total WER: 25.929%, total TER: 12.6199%, progress (thread 0): 87.2389%]
|T|: t h a t ' s | g o o d
|P|: t h a t ' s g o
[sample: TS3003c_H03_MTD012ME_1567.88_1568.23, WER: 100%, TER: 27.2727%, total WER: 25.9307%, total TER: 12.6203%, progress (thread 0): 87.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1713.34_1713.69, WER: 0%, TER: 0%, total WER: 25.9304%, total TER: 12.6202%, progress (thread 0): 87.2547%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_2280.01_2280.36, WER: 0%, TER: 0%, total WER: 25.9301%, total TER: 12.6201%, progress (thread 0): 87.2627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_306.6_306.95, WER: 0%, TER: 0%, total WER: 25.9298%, total TER: 12.6199%, progress (thread 0): 87.2706%]
|T|: n o
|P|: o
[sample: TS3003d_H00_MTD009PM_819.47_819.82, WER: 100%, TER: 50%, total WER: 25.9307%, total TER: 12.6201%, progress (thread 0): 87.2785%]
|T|: a l r i g h t
|P|:
[sample: TS3003d_H00_MTD009PM_965.64_965.99, WER: 100%, TER: 100%, total WER: 25.9315%, total TER: 12.6216%, progress (thread 0): 87.2864%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H00_MTD009PM_1373.9_1374.25, WER: 0%, TER: 0%, total WER: 25.9312%, total TER: 12.6215%, progress (thread 0): 87.2943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1847.37_1847.72, WER: 0%, TER: 0%, total WER: 25.9309%, total TER: 12.6213%, progress (thread 0): 87.3022%]
|T|: n i n e
|P|: n i n e t h
[sample: TS3003d_H03_MTD012ME_1899.7_1900.05, WER: 100%, TER: 50%, total WER: 25.9318%, total TER: 12.6217%, progress (thread 0): 87.3101%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H01_MTD011UID_2218.91_2219.26, WER: 0%, TER: 0%, total WER: 25.9315%, total TER: 12.6216%, progress (thread 0): 87.318%]
|T|: r i g h t
|P|: b u t
[sample: EN2002a_H03_MEE071_142.16_142.51, WER: 100%, TER: 80%, total WER: 25.9323%, total TER: 12.6224%, progress (thread 0): 87.326%]
|T|: h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_203.92_204.27, WER: 100%, TER: 66.6667%, total WER: 25.9332%, total TER: 12.6228%, progress (thread 0): 87.3339%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_220.21_220.56, WER: 0%, TER: 0%, total WER: 25.9329%, total TER: 12.6226%, progress (thread 0): 87.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1393.11_1393.46, WER: 0%, TER: 0%, total WER: 25.9326%, total TER: 12.6225%, progress (thread 0): 87.3497%]
|T|: n o
|P|: n o
[sample: EN2002a_H02_FEO072_1494.01_1494.36, WER: 0%, TER: 0%, total WER: 25.9323%, total TER: 12.6225%, progress (thread 0): 87.3576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1616.41_1616.76, WER: 0%, TER: 0%, total WER: 25.932%, total TER: 12.6224%, progress (thread 0): 87.3655%]
|T|: y e a h
|P|: y e h
[sample: EN2002a_H01_FEO070_2062.28_2062.63, WER: 100%, TER: 25%, total WER: 25.9328%, total TER: 12.6225%, progress (thread 0): 87.3734%]
|T|: t h e n | u m
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_306.19_306.54, WER: 100%, TER: 114.286%, total WER: 25.9345%, total TER: 12.6242%, progress (thread 0): 87.3813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_679.83_680.18, WER: 0%, TER: 0%, total WER: 25.9342%, total TER: 12.6241%, progress (thread 0): 87.3892%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1132.98_1133.33, WER: 0%, TER: 0%, total WER: 25.9339%, total TER: 12.624%, progress (thread 0): 87.3972%]
|T|: s h o u l d n ' t | n o
|P|: n i
[sample: EN2002b_H01_MEE071_1400.16_1400.51, WER: 100%, TER: 91.6667%, total WER: 25.9356%, total TER: 12.6262%, progress (thread 0): 87.4051%]
|T|: n o
|P|: k n o w
[sample: EN2002b_H02_FEO072_1650.79_1651.14, WER: 100%, TER: 100%, total WER: 25.9364%, total TER: 12.6266%, progress (thread 0): 87.413%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_550.49_550.84, WER: 0%, TER: 0%, total WER: 25.9362%, total TER: 12.6265%, progress (thread 0): 87.4209%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_586.84_587.19, WER: 0%, TER: 0%, total WER: 25.9359%, total TER: 12.6264%, progress (thread 0): 87.4288%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_725.66_726.01, WER: 0%, TER: 0%, total WER: 25.9356%, total TER: 12.6263%, progress (thread 0): 87.4367%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_743.92_744.27, WER: 0%, TER: 0%, total WER: 25.9353%, total TER: 12.6262%, progress (thread 0): 87.4446%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_975_975.35, WER: 0%, TER: 0%, total WER: 25.935%, total TER: 12.6261%, progress (thread 0): 87.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1520.32_1520.67, WER: 0%, TER: 0%, total WER: 25.9347%, total TER: 12.626%, progress (thread 0): 87.4604%]
|T|: s o
|P|:
[sample: EN2002c_H02_MEE071_1667.21_1667.56, WER: 100%, TER: 100%, total WER: 25.9355%, total TER: 12.6264%, progress (thread 0): 87.4684%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_2075.98_2076.33, WER: 0%, TER: 0%, total WER: 25.9352%, total TER: 12.6264%, progress (thread 0): 87.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2182.63_2182.98, WER: 0%, TER: 0%, total WER: 25.9349%, total TER: 12.6262%, progress (thread 0): 87.4842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_352.2_352.55, WER: 0%, TER: 0%, total WER: 25.9346%, total TER: 12.6261%, progress (thread 0): 87.4921%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002d_H03_MEE073_387.18_387.53, WER: 0%, TER: 0%, total WER: 25.934%, total TER: 12.6259%, progress (thread 0): 87.5%]
|T|: m m
|P|:
[sample: EN2002d_H01_FEO072_831.78_832.13, WER: 100%, TER: 100%, total WER: 25.9349%, total TER: 12.6263%, progress (thread 0): 87.5079%]
|T|: n o
|P|: n o
[sample: EN2002d_H03_MEE073_909.36_909.71, WER: 0%, TER: 0%, total WER: 25.9346%, total TER: 12.6263%, progress (thread 0): 87.5158%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002d_H03_MEE073_1113.08_1113.43, WER: 200%, TER: 175%, total WER: 25.9366%, total TER: 12.6278%, progress (thread 0): 87.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1233.84_1234.19, WER: 0%, TER: 0%, total WER: 25.9363%, total TER: 12.6277%, progress (thread 0): 87.5316%]
|T|: i | d o n ' t | k n o w
|P|:
[sample: EN2002d_H01_FEO072_1245.04_1245.39, WER: 100%, TER: 100%, total WER: 25.9388%, total TER: 12.6301%, progress (thread 0): 87.5396%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1798.12_1798.47, WER: 0%, TER: 0%, total WER: 25.9385%, total TER: 12.63%, progress (thread 0): 87.5475%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1926.51_1926.86, WER: 0%, TER: 0%, total WER: 25.9382%, total TER: 12.6299%, progress (thread 0): 87.5554%]
|T|: u h h u h
|P|: u h u h
[sample: EN2002d_H00_FEO070_2027.85_2028.2, WER: 100%, TER: 20%, total WER: 25.9391%, total TER: 12.63%, progress (thread 0): 87.5633%]
|T|: o h | s o r r y
|P|:
[sample: ES2004a_H00_MEO015_255.91_256.25, WER: 100%, TER: 100%, total WER: 25.9407%, total TER: 12.6316%, progress (thread 0): 87.5712%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H01_FEE013_545.3_545.64, WER: 100%, TER: 40%, total WER: 25.9416%, total TER: 12.632%, progress (thread 0): 87.5791%]
|T|: r e a l l y
|P|: r e a l l y
[sample: ES2004a_H01_FEE013_603.88_604.22, WER: 0%, TER: 0%, total WER: 25.9413%, total TER: 12.6318%, progress (thread 0): 87.587%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_635.54_635.88, WER: 100%, TER: 40%, total WER: 25.9421%, total TER: 12.6321%, progress (thread 0): 87.5949%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H03_FEE016_811.15_811.49, WER: 0%, TER: 0%, total WER: 25.9418%, total TER: 12.632%, progress (thread 0): 87.6028%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_954.93_955.27, WER: 100%, TER: 40%, total WER: 25.9427%, total TER: 12.6323%, progress (thread 0): 87.6108%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H03_FEE016_1445.71_1446.05, WER: 100%, TER: 60%, total WER: 25.9435%, total TER: 12.6329%, progress (thread 0): 87.6187%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1995.5_1995.84, WER: 100%, TER: 40%, total WER: 25.9444%, total TER: 12.6332%, progress (thread 0): 87.6266%]
|T|: ' k a y
|P|: t a n
[sample: ES2004c_H03_FEE016_22.85_23.19, WER: 100%, TER: 75%, total WER: 25.9452%, total TER: 12.6338%, progress (thread 0): 87.6345%]
|T|: t h a n k | y o u
|P|:
[sample: ES2004c_H03_FEE016_201.1_201.44, WER: 100%, TER: 100%, total WER: 25.9469%, total TER: 12.6356%, progress (thread 0): 87.6424%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_451.06_451.4, WER: 100%, TER: 25%, total WER: 25.9477%, total TER: 12.6357%, progress (thread 0): 87.6503%]
|T|: n o
|P|: n o t
[sample: ES2004c_H02_MEE014_535.19_535.53, WER: 100%, TER: 50%, total WER: 25.9486%, total TER: 12.6359%, progress (thread 0): 87.6582%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1178.32_1178.66, WER: 0%, TER: 0%, total WER: 25.9483%, total TER: 12.6358%, progress (thread 0): 87.6661%]
|T|: h m m
|P|: m m
[sample: ES2004c_H02_MEE014_1181.26_1181.6, WER: 100%, TER: 33.3333%, total WER: 25.9491%, total TER: 12.6359%, progress (thread 0): 87.674%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1478.47_1478.81, WER: 100%, TER: 50%, total WER: 25.9499%, total TER: 12.6361%, progress (thread 0): 87.682%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1640.61_1640.95, WER: 100%, TER: 40%, total WER: 25.9508%, total TER: 12.6364%, progress (thread 0): 87.6899%]
|T|: m m h m m
|P|: h e
[sample: ES2004c_H03_FEE016_2025.11_2025.45, WER: 100%, TER: 80%, total WER: 25.9516%, total TER: 12.6372%, progress (thread 0): 87.6978%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_2072.31_2072.65, WER: 100%, TER: 40%, total WER: 25.9525%, total TER: 12.6376%, progress (thread 0): 87.7057%]
|T|: y e a h
|P|: y e s
[sample: ES2004d_H00_MEO015_100.76_101.1, WER: 100%, TER: 50%, total WER: 25.9533%, total TER: 12.6379%, progress (thread 0): 87.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H01_FEE013_139.4_139.74, WER: 0%, TER: 0%, total WER: 25.953%, total TER: 12.6378%, progress (thread 0): 87.7215%]
|T|: y e p
|P|: y u p
[sample: ES2004d_H00_MEO015_155.57_155.91, WER: 100%, TER: 33.3333%, total WER: 25.9539%, total TER: 12.6379%, progress (thread 0): 87.7294%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_178.12_178.46, WER: 0%, TER: 0%, total WER: 25.9536%, total TER: 12.6378%, progress (thread 0): 87.7373%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_405.44_405.78, WER: 0%, TER: 0%, total WER: 25.9533%, total TER: 12.6377%, progress (thread 0): 87.7453%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_548.5_548.84, WER: 100%, TER: 40%, total WER: 25.9541%, total TER: 12.638%, progress (thread 0): 87.7532%]
|T|: t h r e e
|P|: t h r e e
[sample: ES2004d_H02_MEE014_646.42_646.76, WER: 0%, TER: 0%, total WER: 25.9538%, total TER: 12.6378%, progress (thread 0): 87.7611%]
|T|: f o u r
|P|: f o r
[sample: ES2004d_H01_FEE013_911.53_911.87, WER: 100%, TER: 25%, total WER: 25.9547%, total TER: 12.638%, progress (thread 0): 87.769%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1135.09_1135.43, WER: 0%, TER: 0%, total WER: 25.9544%, total TER: 12.6378%, progress (thread 0): 87.7769%]
|T|: m m
|P|: h m
[sample: ES2004d_H01_FEE013_1953.89_1954.23, WER: 100%, TER: 50%, total WER: 25.9552%, total TER: 12.638%, progress (thread 0): 87.7848%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_792.79_793.13, WER: 0%, TER: 0%, total WER: 25.9549%, total TER: 12.6379%, progress (thread 0): 87.7927%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_43.16_43.5, WER: 100%, TER: 40%, total WER: 25.9558%, total TER: 12.6383%, progress (thread 0): 87.8006%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_260.98_261.32, WER: 0%, TER: 0%, total WER: 25.9555%, total TER: 12.6381%, progress (thread 0): 87.8085%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_425.4_425.74, WER: 0%, TER: 0%, total WER: 25.9552%, total TER: 12.638%, progress (thread 0): 87.8165%]
|T|: y e p
|P|: o
[sample: IS1009b_H00_FIE088_598.28_598.62, WER: 100%, TER: 100%, total WER: 25.956%, total TER: 12.6386%, progress (thread 0): 87.8244%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_613.29_613.63, WER: 0%, TER: 0%, total WER: 25.9557%, total TER: 12.6385%, progress (thread 0): 87.8323%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1002.09_1002.43, WER: 0%, TER: 0%, total WER: 25.9554%, total TER: 12.6384%, progress (thread 0): 87.8402%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009b_H00_FIE088_1223.66_1224, WER: 200%, TER: 100%, total WER: 25.9574%, total TER: 12.6395%, progress (thread 0): 87.8481%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1225.44_1225.78, WER: 100%, TER: 40%, total WER: 25.9582%, total TER: 12.6398%, progress (thread 0): 87.856%]
|T|: m m
|P|: e a h
[sample: IS1009b_H03_FIO089_1734.63_1734.97, WER: 100%, TER: 150%, total WER: 25.9591%, total TER: 12.6404%, progress (thread 0): 87.8639%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1834.47_1834.81, WER: 0%, TER: 0%, total WER: 25.9588%, total TER: 12.6403%, progress (thread 0): 87.8718%]
|T|: o k a y
|P|: k
[sample: IS1009b_H01_FIO087_1882.63_1882.97, WER: 100%, TER: 75%, total WER: 25.9596%, total TER: 12.6409%, progress (thread 0): 87.8797%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H01_FIO087_1960.82_1961.16, WER: 100%, TER: 40%, total WER: 25.9605%, total TER: 12.6412%, progress (thread 0): 87.8877%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_535.66_536, WER: 100%, TER: 40%, total WER: 25.9613%, total TER: 12.6415%, progress (thread 0): 87.8956%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_575.77_576.11, WER: 100%, TER: 40%, total WER: 25.9621%, total TER: 12.6419%, progress (thread 0): 87.9035%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H03_FIO089_950.04_950.38, WER: 100%, TER: 80%, total WER: 25.963%, total TER: 12.6426%, progress (thread 0): 87.9114%]
|T|: i t | w o r k s
|P|: w u t
[sample: IS1009c_H01_FIO087_1083.82_1084.16, WER: 100%, TER: 87.5%, total WER: 25.9647%, total TER: 12.6441%, progress (thread 0): 87.9193%]
|T|: m m h m m
|P|: y e h
[sample: IS1009c_H03_FIO089_1109.75_1110.09, WER: 100%, TER: 80%, total WER: 25.9655%, total TER: 12.6449%, progress (thread 0): 87.9272%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1603.38_1603.72, WER: 100%, TER: 40%, total WER: 25.9663%, total TER: 12.6452%, progress (thread 0): 87.9351%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1613.06_1613.4, WER: 100%, TER: 40%, total WER: 25.9672%, total TER: 12.6455%, progress (thread 0): 87.943%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_1633.76_1634.1, WER: 0%, TER: 0%, total WER: 25.9669%, total TER: 12.6454%, progress (thread 0): 87.951%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_434.93_435.27, WER: 100%, TER: 40%, total WER: 25.9677%, total TER: 12.6457%, progress (thread 0): 87.9589%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_637.66_638, WER: 100%, TER: 60%, total WER: 25.9686%, total TER: 12.6463%, progress (thread 0): 87.9668%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_649.7_650.04, WER: 0%, TER: 0%, total WER: 25.9683%, total TER: 12.6461%, progress (thread 0): 87.9747%]
|T|: h m m
|P|: r i g h t
[sample: IS1009d_H02_FIO084_674.88_675.22, WER: 100%, TER: 166.667%, total WER: 25.9691%, total TER: 12.6472%, progress (thread 0): 87.9826%]
|T|: o o p s
|P|: u p
[sample: IS1009d_H00_FIE088_1068.99_1069.33, WER: 100%, TER: 75%, total WER: 25.97%, total TER: 12.6478%, progress (thread 0): 87.9905%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H01_FIO087_1509.75_1510.09, WER: 0%, TER: 0%, total WER: 25.9697%, total TER: 12.6477%, progress (thread 0): 87.9984%]
|T|: t h a n k | y o u
|P|: t h a n k | y o u
[sample: TS3003a_H03_MTD012ME_48.1_48.44, WER: 0%, TER: 0%, total WER: 25.9691%, total TER: 12.6475%, progress (thread 0): 88.0063%]
|T|: g u e s s
|P|: y e a f
[sample: TS3003a_H01_MTD011UID_983.96_984.3, WER: 100%, TER: 80%, total WER: 25.9699%, total TER: 12.6482%, progress (thread 0): 88.0142%]
|T|: t h e | p e n
|P|:
[sample: TS3003a_H02_MTD0010ID_1090.49_1090.83, WER: 100%, TER: 100%, total WER: 25.9716%, total TER: 12.6497%, progress (thread 0): 88.0222%]
|T|: t h i n g
|P|: t h i n g
[sample: TS3003a_H00_MTD009PM_1320.19_1320.53, WER: 0%, TER: 0%, total WER: 25.9713%, total TER: 12.6495%, progress (thread 0): 88.0301%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_132.84_133.18, WER: 0%, TER: 0%, total WER: 25.971%, total TER: 12.6495%, progress (thread 0): 88.038%]
|T|: r i g h t
|P|:
[sample: TS3003b_H01_MTD011UID_2086.12_2086.46, WER: 100%, TER: 100%, total WER: 25.9718%, total TER: 12.6505%, progress (thread 0): 88.0459%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2089.85_2090.19, WER: 0%, TER: 0%, total WER: 25.9715%, total TER: 12.6504%, progress (thread 0): 88.0538%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_982.32_982.66, WER: 0%, TER: 0%, total WER: 25.9713%, total TER: 12.6503%, progress (thread 0): 88.0617%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_1007.54_1007.88, WER: 0%, TER: 0%, total WER: 25.971%, total TER: 12.6502%, progress (thread 0): 88.0696%]
|T|: y e a h
|P|: y e h
[sample: TS3003c_H00_MTD009PM_1240.35_1240.69, WER: 100%, TER: 25%, total WER: 25.9718%, total TER: 12.6503%, progress (thread 0): 88.0775%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1413.87_1414.21, WER: 0%, TER: 0%, total WER: 25.9715%, total TER: 12.6501%, progress (thread 0): 88.0854%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1849.58_1849.92, WER: 0%, TER: 0%, total WER: 25.9712%, total TER: 12.65%, progress (thread 0): 88.0934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_595.75_596.09, WER: 0%, TER: 0%, total WER: 25.9709%, total TER: 12.6499%, progress (thread 0): 88.1013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_921.62_921.96, WER: 0%, TER: 0%, total WER: 25.9706%, total TER: 12.6498%, progress (thread 0): 88.1092%]
|T|: u h | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1308.11_1308.45, WER: 50%, TER: 42.8571%, total WER: 25.9712%, total TER: 12.6503%, progress (thread 0): 88.1171%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1388.52_1388.86, WER: 0%, TER: 0%, total WER: 25.9709%, total TER: 12.6502%, progress (thread 0): 88.125%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1802.71_1803.05, WER: 100%, TER: 75%, total WER: 25.9717%, total TER: 12.6508%, progress (thread 0): 88.1329%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1835.63_1835.97, WER: 100%, TER: 25%, total WER: 25.9726%, total TER: 12.6509%, progress (thread 0): 88.1408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2091.81_2092.15, WER: 0%, TER: 0%, total WER: 25.9723%, total TER: 12.6508%, progress (thread 0): 88.1487%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2217.62_2217.96, WER: 0%, TER: 0%, total WER: 25.972%, total TER: 12.6506%, progress (thread 0): 88.1566%]
|T|: m m | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2323.41_2323.75, WER: 50%, TER: 42.8571%, total WER: 25.9725%, total TER: 12.6511%, progress (thread 0): 88.1646%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2420.39_2420.73, WER: 0%, TER: 0%, total WER: 25.9722%, total TER: 12.651%, progress (thread 0): 88.1725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_406.15_406.49, WER: 0%, TER: 0%, total WER: 25.9719%, total TER: 12.6509%, progress (thread 0): 88.1804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_601.54_601.88, WER: 0%, TER: 0%, total WER: 25.9716%, total TER: 12.6508%, progress (thread 0): 88.1883%]
|T|: h m m
|P|: h m
[sample: EN2002a_H02_FEO072_713.14_713.48, WER: 100%, TER: 33.3333%, total WER: 25.9725%, total TER: 12.6509%, progress (thread 0): 88.1962%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_859.83_860.17, WER: 100%, TER: 66.6667%, total WER: 25.9733%, total TER: 12.6513%, progress (thread 0): 88.2041%]
|T|: a l r i g h t
|P|: i t
[sample: EN2002a_H01_FEO070_920.16_920.5, WER: 100%, TER: 71.4286%, total WER: 25.9741%, total TER: 12.6523%, progress (thread 0): 88.212%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1586.73_1587.07, WER: 0%, TER: 0%, total WER: 25.9738%, total TER: 12.6522%, progress (thread 0): 88.2199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1927.75_1928.09, WER: 0%, TER: 0%, total WER: 25.9736%, total TER: 12.652%, progress (thread 0): 88.2279%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1929.86_1930.2, WER: 0%, TER: 0%, total WER: 25.9733%, total TER: 12.6519%, progress (thread 0): 88.2358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2048.6_2048.94, WER: 0%, TER: 0%, total WER: 25.973%, total TER: 12.6518%, progress (thread 0): 88.2437%]
|T|: s o | i t ' s
|P|: t w a
[sample: EN2002a_H03_MEE071_2098.66_2099, WER: 100%, TER: 85.7143%, total WER: 25.9746%, total TER: 12.653%, progress (thread 0): 88.2516%]
|T|: o k a y
|P|: o k
[sample: EN2002a_H00_MEE073_2129.89_2130.23, WER: 100%, TER: 50%, total WER: 25.9755%, total TER: 12.6533%, progress (thread 0): 88.2595%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_144.51_144.85, WER: 0%, TER: 0%, total WER: 25.9752%, total TER: 12.6532%, progress (thread 0): 88.2674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_190.18_190.52, WER: 0%, TER: 0%, total WER: 25.9749%, total TER: 12.6531%, progress (thread 0): 88.2753%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_302.27_302.61, WER: 100%, TER: 66.6667%, total WER: 25.9757%, total TER: 12.6535%, progress (thread 0): 88.2832%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_309.52_309.86, WER: 0%, TER: 0%, total WER: 25.9754%, total TER: 12.6534%, progress (thread 0): 88.2911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_363.35_363.69, WER: 0%, TER: 0%, total WER: 25.9751%, total TER: 12.6533%, progress (thread 0): 88.299%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_409.14_409.48, WER: 0%, TER: 0%, total WER: 25.9749%, total TER: 12.6531%, progress (thread 0): 88.307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_736.4_736.74, WER: 0%, TER: 0%, total WER: 25.9746%, total TER: 12.653%, progress (thread 0): 88.3149%]
|T|: y e a h
|P|: y o
[sample: EN2002b_H03_MEE073_935.86_936.2, WER: 100%, TER: 75%, total WER: 25.9754%, total TER: 12.6536%, progress (thread 0): 88.3228%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002b_H03_MEE073_1252.52_1252.86, WER: 0%, TER: 0%, total WER: 25.9748%, total TER: 12.6534%, progress (thread 0): 88.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1563.14_1563.48, WER: 0%, TER: 0%, total WER: 25.9745%, total TER: 12.6533%, progress (thread 0): 88.3386%]
|T|: y e a h
|P|: y e h
[sample: EN2002b_H00_FEO070_1626.33_1626.67, WER: 100%, TER: 25%, total WER: 25.9754%, total TER: 12.6534%, progress (thread 0): 88.3465%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H01_FEO072_67.59_67.93, WER: 0%, TER: 0%, total WER: 25.9751%, total TER: 12.6533%, progress (thread 0): 88.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_273.14_273.48, WER: 0%, TER: 0%, total WER: 25.9748%, total TER: 12.6532%, progress (thread 0): 88.3623%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_444.82_445.16, WER: 100%, TER: 40%, total WER: 25.9756%, total TER: 12.6535%, progress (thread 0): 88.3703%]
|T|: o r
|P|: e
[sample: EN2002c_H01_FEO072_486.4_486.74, WER: 100%, TER: 100%, total WER: 25.9764%, total TER: 12.6539%, progress (thread 0): 88.3782%]
|T|: o h | w e l l
|P|: h o w
[sample: EN2002c_H03_MEE073_497.23_497.57, WER: 100%, TER: 71.4286%, total WER: 25.9781%, total TER: 12.6549%, progress (thread 0): 88.3861%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002c_H01_FEO072_721.91_722.25, WER: 200%, TER: 14.2857%, total WER: 25.9801%, total TER: 12.6549%, progress (thread 0): 88.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1305.34_1305.68, WER: 0%, TER: 0%, total WER: 25.9798%, total TER: 12.6548%, progress (thread 0): 88.4019%]
|T|: o h
|P|: o h
[sample: EN2002c_H01_FEO072_1925.9_1926.24, WER: 0%, TER: 0%, total WER: 25.9795%, total TER: 12.6547%, progress (thread 0): 88.4098%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2040.49_2040.83, WER: 0%, TER: 0%, total WER: 25.9792%, total TER: 12.6546%, progress (thread 0): 88.4177%]
|T|: m m h m m
|P|: m p
[sample: EN2002c_H03_MEE073_2245.81_2246.15, WER: 100%, TER: 80%, total WER: 25.9801%, total TER: 12.6554%, progress (thread 0): 88.4256%]
|T|: y e a h
|P|: r e a l l y
[sample: EN2002d_H03_MEE073_344.02_344.36, WER: 100%, TER: 100%, total WER: 25.9809%, total TER: 12.6562%, progress (thread 0): 88.4335%]
|T|: w h a t | w a s | i t
|P|: o h | w a s | i t
[sample: EN2002d_H03_MEE073_508.22_508.56, WER: 33.3333%, TER: 27.2727%, total WER: 25.9811%, total TER: 12.6566%, progress (thread 0): 88.4415%]
|T|: y e a h
|P|: g o o d
[sample: EN2002d_H02_MEE071_688.4_688.74, WER: 100%, TER: 100%, total WER: 25.982%, total TER: 12.6574%, progress (thread 0): 88.4494%]
|T|: y e a h
|P|: y e s
[sample: EN2002d_H02_MEE071_715.16_715.5, WER: 100%, TER: 50%, total WER: 25.9828%, total TER: 12.6577%, progress (thread 0): 88.4573%]
|T|: u h
|P|:
[sample: EN2002d_H03_MEE073_1125.77_1126.11, WER: 100%, TER: 100%, total WER: 25.9837%, total TER: 12.6582%, progress (thread 0): 88.4652%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1325.04_1325.38, WER: 100%, TER: 33.3333%, total WER: 25.9845%, total TER: 12.6583%, progress (thread 0): 88.4731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1423.14_1423.48, WER: 0%, TER: 0%, total WER: 25.9842%, total TER: 12.6582%, progress (thread 0): 88.481%]
|T|: n o | w e | p
|P|: s o | w e
[sample: EN2002d_H00_FEO070_1437.94_1438.28, WER: 66.6667%, TER: 42.8571%, total WER: 25.9856%, total TER: 12.6587%, progress (thread 0): 88.4889%]
|T|: w i t h
|P|: w i t h
[sample: EN2002d_H02_MEE071_2073.15_2073.49, WER: 0%, TER: 0%, total WER: 25.9853%, total TER: 12.6586%, progress (thread 0): 88.4968%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_258.31_258.64, WER: 100%, TER: 40%, total WER: 25.9861%, total TER: 12.6589%, progress (thread 0): 88.5048%]
|T|: m m | y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_1048.05_1048.38, WER: 50%, TER: 42.8571%, total WER: 25.9867%, total TER: 12.6594%, progress (thread 0): 88.5127%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_736.03_736.36, WER: 0%, TER: 0%, total WER: 25.9864%, total TER: 12.6593%, progress (thread 0): 88.5206%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1313.86_1314.19, WER: 0%, TER: 0%, total WER: 25.9861%, total TER: 12.6591%, progress (thread 0): 88.5285%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H01_FEE013_1711.46_1711.79, WER: 100%, TER: 200%, total WER: 25.9869%, total TER: 12.66%, progress (thread 0): 88.5364%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004b_H03_FEE016_2208.79_2209.12, WER: 200%, TER: 20%, total WER: 25.9889%, total TER: 12.6601%, progress (thread 0): 88.5443%]
|T|: o k a y
|P|: o k a y
[sample: ES2004b_H03_FEE016_2308.52_2308.85, WER: 0%, TER: 0%, total WER: 25.9886%, total TER: 12.66%, progress (thread 0): 88.5522%]
|T|: y e a h
|P|: y u p
[sample: ES2004c_H02_MEE014_172.32_172.65, WER: 100%, TER: 75%, total WER: 25.9895%, total TER: 12.6606%, progress (thread 0): 88.5601%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_560.34_560.67, WER: 100%, TER: 50%, total WER: 25.9903%, total TER: 12.6608%, progress (thread 0): 88.568%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H01_FEE013_873.37_873.7, WER: 100%, TER: 40%, total WER: 25.9911%, total TER: 12.6611%, progress (thread 0): 88.576%]
|T|: w e l l
|P|: w e l l
[sample: ES2004c_H02_MEE014_925.27_925.6, WER: 0%, TER: 0%, total WER: 25.9908%, total TER: 12.661%, progress (thread 0): 88.5839%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1268.11_1268.44, WER: 0%, TER: 0%, total WER: 25.9905%, total TER: 12.6608%, progress (thread 0): 88.5918%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1965.56_1965.89, WER: 100%, TER: 40%, total WER: 25.9914%, total TER: 12.6612%, progress (thread 0): 88.5997%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H01_FEE013_607.94_608.27, WER: 100%, TER: 40%, total WER: 25.9922%, total TER: 12.6615%, progress (thread 0): 88.6076%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_753.08_753.41, WER: 100%, TER: 33.3333%, total WER: 25.9931%, total TER: 12.6616%, progress (thread 0): 88.6155%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H03_FEE016_876.07_876.4, WER: 100%, TER: 40%, total WER: 25.9939%, total TER: 12.6619%, progress (thread 0): 88.6234%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H00_MEO015_1304.99_1305.32, WER: 0%, TER: 0%, total WER: 25.9936%, total TER: 12.6619%, progress (thread 0): 88.6313%]
|T|: s u r e
|P|: s u r e
[sample: ES2004d_H03_FEE016_1499.95_1500.28, WER: 0%, TER: 0%, total WER: 25.9933%, total TER: 12.6617%, progress (thread 0): 88.6392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1845.84_1846.17, WER: 0%, TER: 0%, total WER: 25.993%, total TER: 12.6616%, progress (thread 0): 88.6471%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1856.75_1857.08, WER: 0%, TER: 0%, total WER: 25.9927%, total TER: 12.6615%, progress (thread 0): 88.6551%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1984.68_1985.01, WER: 0%, TER: 0%, total WER: 25.9924%, total TER: 12.6614%, progress (thread 0): 88.663%]
|T|: m m
|P|: o h
[sample: ES2004d_H01_FEE013_2126.04_2126.37, WER: 100%, TER: 100%, total WER: 25.9933%, total TER: 12.6618%, progress (thread 0): 88.6709%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_55.7_56.03, WER: 0%, TER: 0%, total WER: 25.993%, total TER: 12.6617%, progress (thread 0): 88.6788%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_226.94_227.27, WER: 100%, TER: 40%, total WER: 25.9938%, total TER: 12.662%, progress (thread 0): 88.6867%]
|T|: o k a y
|P|: o k a t
[sample: IS1009a_H03_FIO089_318.12_318.45, WER: 100%, TER: 25%, total WER: 25.9946%, total TER: 12.6621%, progress (thread 0): 88.6946%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_652.91_653.24, WER: 100%, TER: 40%, total WER: 25.9955%, total TER: 12.6624%, progress (thread 0): 88.7025%]
|T|: t h e n
|P|: a n d | t h e n
[sample: IS1009a_H00_FIE088_714.21_714.54, WER: 100%, TER: 100%, total WER: 25.9963%, total TER: 12.6633%, progress (thread 0): 88.7104%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_741.37_741.7, WER: 100%, TER: 40%, total WER: 25.9972%, total TER: 12.6636%, progress (thread 0): 88.7184%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_790.95_791.28, WER: 100%, TER: 40%, total WER: 25.998%, total TER: 12.6639%, progress (thread 0): 88.7263%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_787.47_787.8, WER: 100%, TER: 40%, total WER: 25.9988%, total TER: 12.6642%, progress (thread 0): 88.7342%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1063.18_1063.51, WER: 100%, TER: 40%, total WER: 25.9997%, total TER: 12.6645%, progress (thread 0): 88.7421%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1809.47_1809.8, WER: 0%, TER: 0%, total WER: 25.9994%, total TER: 12.6644%, progress (thread 0): 88.75%]
|T|: m m h m m
|P|: u h
[sample: IS1009b_H03_FIO089_1882.22_1882.55, WER: 100%, TER: 80%, total WER: 26.0002%, total TER: 12.6652%, progress (thread 0): 88.7579%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_408.02_408.35, WER: 100%, TER: 40%, total WER: 26.0011%, total TER: 12.6655%, progress (thread 0): 88.7658%]
|T|: m m h m m
|P|: u h
[sample: IS1009c_H00_FIE088_430.74_431.07, WER: 100%, TER: 80%, total WER: 26.0019%, total TER: 12.6663%, progress (thread 0): 88.7737%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1552.21_1552.54, WER: 0%, TER: 0%, total WER: 26.0016%, total TER: 12.6662%, progress (thread 0): 88.7816%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_383.89_384.22, WER: 100%, TER: 40%, total WER: 26.0024%, total TER: 12.6665%, progress (thread 0): 88.7896%]
|T|: m m h m m
|P|: m
[sample: IS1009d_H03_FIO089_388.95_389.28, WER: 100%, TER: 80%, total WER: 26.0033%, total TER: 12.6673%, progress (thread 0): 88.7975%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_483.18_483.51, WER: 100%, TER: 40%, total WER: 26.0041%, total TER: 12.6676%, progress (thread 0): 88.8054%]
|T|: t h a t ' s | r i g h t
|P|:
[sample: IS1009d_H00_FIE088_757.72_758.05, WER: 100%, TER: 100%, total WER: 26.0058%, total TER: 12.6701%, progress (thread 0): 88.8133%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H01_FIO087_784.18_784.51, WER: 100%, TER: 40%, total WER: 26.0066%, total TER: 12.6704%, progress (thread 0): 88.8212%]
|T|: y e a h
|P|: i t
[sample: IS1009d_H02_FIO084_975.71_976.04, WER: 100%, TER: 100%, total WER: 26.0075%, total TER: 12.6713%, progress (thread 0): 88.8291%]
|T|: m m
|P|: h m
[sample: IS1009d_H02_FIO084_1183.1_1183.43, WER: 100%, TER: 50%, total WER: 26.0083%, total TER: 12.6714%, progress (thread 0): 88.837%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H02_FIO084_1185.85_1186.18, WER: 100%, TER: 40%, total WER: 26.0092%, total TER: 12.6717%, progress (thread 0): 88.8449%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1310.32_1310.65, WER: 100%, TER: 100%, total WER: 26.01%, total TER: 12.6728%, progress (thread 0): 88.8528%]
|T|: u h
|P|: u m
[sample: TS3003a_H01_MTD011UID_915.22_915.55, WER: 100%, TER: 50%, total WER: 26.0108%, total TER: 12.6729%, progress (thread 0): 88.8608%]
|T|: o r | w h o
|P|: o | w h o
[sample: TS3003a_H01_MTD011UID_975.03_975.36, WER: 50%, TER: 16.6667%, total WER: 26.0114%, total TER: 12.673%, progress (thread 0): 88.8687%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1261.99_1262.32, WER: 100%, TER: 50%, total WER: 26.0122%, total TER: 12.6732%, progress (thread 0): 88.8766%]
|T|: s o
|P|: s o
[sample: TS3003b_H03_MTD012ME_280.47_280.8, WER: 0%, TER: 0%, total WER: 26.0119%, total TER: 12.6731%, progress (thread 0): 88.8845%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H00_MTD009PM_842.62_842.95, WER: 100%, TER: 50%, total WER: 26.0128%, total TER: 12.6735%, progress (thread 0): 88.8924%]
|T|: o k a y
|P|: h
[sample: TS3003b_H00_MTD009PM_923.51_923.84, WER: 100%, TER: 100%, total WER: 26.0136%, total TER: 12.6743%, progress (thread 0): 88.9003%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1173.66_1173.99, WER: 0%, TER: 0%, total WER: 26.0133%, total TER: 12.6742%, progress (thread 0): 88.9082%]
|T|: n a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1009.07_1009.4, WER: 100%, TER: 66.6667%, total WER: 26.0141%, total TER: 12.6746%, progress (thread 0): 88.9161%]
|T|: ' k a y
|P|: s o
[sample: TS3003c_H03_MTD012ME_1321.92_1322.25, WER: 100%, TER: 100%, total WER: 26.015%, total TER: 12.6754%, progress (thread 0): 88.924%]
|T|: m m
|P|: a n
[sample: TS3003c_H01_MTD011UID_1395.11_1395.44, WER: 100%, TER: 100%, total WER: 26.0158%, total TER: 12.6758%, progress (thread 0): 88.932%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1507.79_1508.12, WER: 0%, TER: 0%, total WER: 26.0155%, total TER: 12.6757%, progress (thread 0): 88.9399%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1540.01_1540.34, WER: 100%, TER: 40%, total WER: 26.0164%, total TER: 12.676%, progress (thread 0): 88.9478%]
|T|: y e p
|P|: e h
[sample: TS3003c_H00_MTD009PM_1653.43_1653.76, WER: 100%, TER: 66.6667%, total WER: 26.0172%, total TER: 12.6764%, progress (thread 0): 88.9557%]
|T|: u h
|P|: m m
[sample: TS3003c_H01_MTD011UID_1692.63_1692.96, WER: 100%, TER: 100%, total WER: 26.018%, total TER: 12.6768%, progress (thread 0): 88.9636%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2074.03_2074.36, WER: 0%, TER: 0%, total WER: 26.0178%, total TER: 12.6767%, progress (thread 0): 88.9715%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2241.48_2241.81, WER: 0%, TER: 0%, total WER: 26.0175%, total TER: 12.6765%, progress (thread 0): 88.9794%]
|T|: y e a h
|P|: y e r e
[sample: TS3003d_H01_MTD011UID_693.96_694.29, WER: 100%, TER: 50%, total WER: 26.0183%, total TER: 12.6769%, progress (thread 0): 88.9873%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_872.34_872.67, WER: 0%, TER: 0%, total WER: 26.018%, total TER: 12.6768%, progress (thread 0): 88.9953%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_889.13_889.46, WER: 0%, TER: 0%, total WER: 26.0177%, total TER: 12.6767%, progress (thread 0): 89.0032%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_1661.82_1662.15, WER: 0%, TER: 0%, total WER: 26.0174%, total TER: 12.6765%, progress (thread 0): 89.0111%]
|T|: y e a h
|P|: h u h
[sample: TS3003d_H01_MTD011UID_2072.21_2072.54, WER: 100%, TER: 75%, total WER: 26.0183%, total TER: 12.6771%, progress (thread 0): 89.019%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_2085.82_2086.15, WER: 100%, TER: 40%, total WER: 26.0191%, total TER: 12.6774%, progress (thread 0): 89.0269%]
|T|: y e a h
|P|: y e s
[sample: TS3003d_H00_MTD009PM_2164.01_2164.34, WER: 100%, TER: 50%, total WER: 26.0199%, total TER: 12.6778%, progress (thread 0): 89.0348%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2179.8_2180.13, WER: 0%, TER: 0%, total WER: 26.0196%, total TER: 12.6777%, progress (thread 0): 89.0427%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_2461.76_2462.09, WER: 100%, TER: 40%, total WER: 26.0205%, total TER: 12.678%, progress (thread 0): 89.0506%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_299.48_299.81, WER: 0%, TER: 0%, total WER: 26.0202%, total TER: 12.6779%, progress (thread 0): 89.0585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_359.5_359.83, WER: 0%, TER: 0%, total WER: 26.0199%, total TER: 12.6778%, progress (thread 0): 89.0665%]
|T|: w a i t
|P|: w a i t
[sample: EN2002a_H02_FEO072_387.53_387.86, WER: 0%, TER: 0%, total WER: 26.0196%, total TER: 12.6776%, progress (thread 0): 89.0744%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_593.65_593.98, WER: 50%, TER: 55.5556%, total WER: 26.0201%, total TER: 12.6785%, progress (thread 0): 89.0823%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_594.32_594.65, WER: 0%, TER: 0%, total WER: 26.0198%, total TER: 12.6784%, progress (thread 0): 89.0902%]
|T|: y e a h
|P|: y n o
[sample: EN2002a_H01_FEO070_690.97_691.3, WER: 100%, TER: 75%, total WER: 26.0207%, total TER: 12.679%, progress (thread 0): 89.0981%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_1018.98_1019.31, WER: 0%, TER: 0%, total WER: 26.0204%, total TER: 12.6789%, progress (thread 0): 89.106%]
|T|: y e a h
|P|: y e s
[sample: EN2002a_H01_FEO070_1028.68_1029.01, WER: 100%, TER: 50%, total WER: 26.0212%, total TER: 12.6792%, progress (thread 0): 89.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1142.23_1142.56, WER: 0%, TER: 0%, total WER: 26.0209%, total TER: 12.6791%, progress (thread 0): 89.1218%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1361.22_1361.55, WER: 0%, TER: 0%, total WER: 26.0206%, total TER: 12.679%, progress (thread 0): 89.1297%]
|T|: r i g h t
|P|: e h
[sample: EN2002a_H03_MEE071_1383.46_1383.79, WER: 100%, TER: 80%, total WER: 26.0215%, total TER: 12.6798%, progress (thread 0): 89.1377%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1864.48_1864.81, WER: 0%, TER: 0%, total WER: 26.0212%, total TER: 12.6797%, progress (thread 0): 89.1456%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1964.31_1964.64, WER: 0%, TER: 0%, total WER: 26.0209%, total TER: 12.6796%, progress (thread 0): 89.1535%]
|T|: u m
|P|: u m
[sample: EN2002b_H03_MEE073_139.44_139.77, WER: 0%, TER: 0%, total WER: 26.0206%, total TER: 12.6795%, progress (thread 0): 89.1614%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_417.94_418.27, WER: 200%, TER: 175%, total WER: 26.0226%, total TER: 12.681%, progress (thread 0): 89.1693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_420.46_420.79, WER: 0%, TER: 0%, total WER: 26.0223%, total TER: 12.6809%, progress (thread 0): 89.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_705.52_705.85, WER: 0%, TER: 0%, total WER: 26.022%, total TER: 12.6808%, progress (thread 0): 89.1851%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_731.78_732.11, WER: 0%, TER: 0%, total WER: 26.0217%, total TER: 12.6807%, progress (thread 0): 89.193%]
|T|: y e a h | t
|P|: y e a h
[sample: EN2002b_H02_FEO072_1235.77_1236.1, WER: 50%, TER: 33.3333%, total WER: 26.0222%, total TER: 12.681%, progress (thread 0): 89.201%]
|T|: h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_1236.72_1237.05, WER: 100%, TER: 66.6667%, total WER: 26.0231%, total TER: 12.6813%, progress (thread 0): 89.2089%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_1256.5_1256.83, WER: 0%, TER: 0%, total WER: 26.0228%, total TER: 12.6812%, progress (thread 0): 89.2168%]
|T|: h m m
|P|: m
[sample: EN2002b_H02_FEO072_1475.51_1475.84, WER: 100%, TER: 66.6667%, total WER: 26.0236%, total TER: 12.6816%, progress (thread 0): 89.2247%]
|T|: o h | r i g h t
|P|: a l l | r i g h t
[sample: EN2002b_H02_FEO072_1611.8_1612.13, WER: 50%, TER: 37.5%, total WER: 26.0241%, total TER: 12.6821%, progress (thread 0): 89.2326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_93.86_94.19, WER: 0%, TER: 0%, total WER: 26.0238%, total TER: 12.6819%, progress (thread 0): 89.2405%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_245.2_245.53, WER: 0%, TER: 0%, total WER: 26.0235%, total TER: 12.6818%, progress (thread 0): 89.2484%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H01_FEO072_447.06_447.39, WER: 100%, TER: 40%, total WER: 26.0244%, total TER: 12.6822%, progress (thread 0): 89.2563%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002c_H03_MEE073_823.26_823.59, WER: 0%, TER: 0%, total WER: 26.0238%, total TER: 12.6819%, progress (thread 0): 89.2642%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_850.62_850.95, WER: 0%, TER: 0%, total WER: 26.0235%, total TER: 12.6818%, progress (thread 0): 89.2722%]
|T|: t h a t ' s | a
|P|: t h a t ' s | a
[sample: EN2002c_H03_MEE073_903.73_904.06, WER: 0%, TER: 0%, total WER: 26.0229%, total TER: 12.6816%, progress (thread 0): 89.2801%]
|T|: o h
|P|: n o w
[sample: EN2002c_H03_MEE073_962_962.33, WER: 100%, TER: 100%, total WER: 26.0237%, total TER: 12.682%, progress (thread 0): 89.288%]
|T|: y e a h | r i g h t
|P|: y a | r i h
[sample: EN2002c_H03_MEE073_1287.41_1287.74, WER: 100%, TER: 40%, total WER: 26.0254%, total TER: 12.6826%, progress (thread 0): 89.2959%]
|T|: t h a t ' s | t r u e
|P|: t h a t ' s | r u e
[sample: EN2002c_H03_MEE073_1299.73_1300.06, WER: 50%, TER: 9.09091%, total WER: 26.026%, total TER: 12.6825%, progress (thread 0): 89.3038%]
|T|: y o u | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2044.96_2045.29, WER: 0%, TER: 0%, total WER: 26.0254%, total TER: 12.6823%, progress (thread 0): 89.3117%]
|T|: m m
|P|: h m
[sample: EN2002c_H02_MEE071_2072_2072.33, WER: 100%, TER: 50%, total WER: 26.0262%, total TER: 12.6825%, progress (thread 0): 89.3196%]
|T|: o o h
|P|: o o
[sample: EN2002c_H01_FEO072_2817.88_2818.21, WER: 100%, TER: 33.3333%, total WER: 26.0271%, total TER: 12.6826%, progress (thread 0): 89.3275%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002d_H01_FEO072_419.86_420.19, WER: 200%, TER: 14.2857%, total WER: 26.029%, total TER: 12.6827%, progress (thread 0): 89.3354%]
|T|: m m
|P|: i
[sample: EN2002d_H03_MEE073_488.3_488.63, WER: 100%, TER: 100%, total WER: 26.0299%, total TER: 12.6831%, progress (thread 0): 89.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1089.65_1089.98, WER: 0%, TER: 0%, total WER: 26.0296%, total TER: 12.6829%, progress (thread 0): 89.3513%]
|T|: w e l l
|P|: n o
[sample: EN2002d_H03_MEE073_1132.11_1132.44, WER: 100%, TER: 100%, total WER: 26.0304%, total TER: 12.6838%, progress (thread 0): 89.3592%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1131.27_1131.6, WER: 100%, TER: 33.3333%, total WER: 26.0312%, total TER: 12.6839%, progress (thread 0): 89.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1257.89_1258.22, WER: 0%, TER: 0%, total WER: 26.031%, total TER: 12.6838%, progress (thread 0): 89.375%]
|T|: r e a l l y
|P|: r e a l l y
[sample: EN2002d_H01_FEO072_1345.97_1346.3, WER: 0%, TER: 0%, total WER: 26.0307%, total TER: 12.6836%, progress (thread 0): 89.3829%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1429.74_1430.07, WER: 100%, TER: 33.3333%, total WER: 26.0315%, total TER: 12.6838%, progress (thread 0): 89.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1957.8_1958.13, WER: 0%, TER: 0%, total WER: 26.0312%, total TER: 12.6836%, progress (thread 0): 89.3987%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_863.21_863.53, WER: 100%, TER: 40%, total WER: 26.032%, total TER: 12.684%, progress (thread 0): 89.4066%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_895.43_895.75, WER: 100%, TER: 40%, total WER: 26.0329%, total TER: 12.6843%, progress (thread 0): 89.4146%]
|T|: m m
|P|: h m
[sample: ES2004b_H01_FEE013_1140.58_1140.9, WER: 100%, TER: 50%, total WER: 26.0337%, total TER: 12.6845%, progress (thread 0): 89.4225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1182.75_1183.07, WER: 0%, TER: 0%, total WER: 26.0334%, total TER: 12.6843%, progress (thread 0): 89.4304%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1471.32_1471.64, WER: 100%, TER: 40%, total WER: 26.0343%, total TER: 12.6847%, progress (thread 0): 89.4383%]
|T|: s
|P|: s h
[sample: ES2004b_H01_FEE013_1922.51_1922.83, WER: 100%, TER: 100%, total WER: 26.0351%, total TER: 12.6849%, progress (thread 0): 89.4462%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H03_FEE016_1953.34_1953.66, WER: 100%, TER: 60%, total WER: 26.0359%, total TER: 12.6854%, progress (thread 0): 89.4541%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1966.24_1966.56, WER: 0%, TER: 0%, total WER: 26.0356%, total TER: 12.6853%, progress (thread 0): 89.462%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_806.95_807.27, WER: 0%, TER: 0%, total WER: 26.0353%, total TER: 12.6852%, progress (thread 0): 89.4699%]
|T|: o k a y
|P|: o k a
[sample: ES2004c_H03_FEE016_1066.11_1066.43, WER: 100%, TER: 25%, total WER: 26.0362%, total TER: 12.6853%, progress (thread 0): 89.4779%]
|T|: m m
|P|: y e m
[sample: ES2004c_H00_MEO015_1890.24_1890.56, WER: 100%, TER: 100%, total WER: 26.037%, total TER: 12.6857%, progress (thread 0): 89.4858%]
|T|: y e a h
|P|: y e p
[sample: ES2004d_H00_MEO015_341.64_341.96, WER: 100%, TER: 50%, total WER: 26.0379%, total TER: 12.6861%, progress (thread 0): 89.4937%]
|T|: n o
|P|: n o
[sample: ES2004d_H00_MEO015_400.96_401.28, WER: 0%, TER: 0%, total WER: 26.0376%, total TER: 12.686%, progress (thread 0): 89.5016%]
|T|: o o p s
|P|:
[sample: ES2004d_H03_FEE016_430.55_430.87, WER: 100%, TER: 100%, total WER: 26.0384%, total TER: 12.6868%, progress (thread 0): 89.5095%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_774.15_774.47, WER: 0%, TER: 0%, total WER: 26.0381%, total TER: 12.6867%, progress (thread 0): 89.5174%]
|T|: o n e
|P|: w e l l
[sample: ES2004d_H02_MEE014_785.53_785.85, WER: 100%, TER: 133.333%, total WER: 26.0389%, total TER: 12.6875%, progress (thread 0): 89.5253%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_840.47_840.79, WER: 0%, TER: 0%, total WER: 26.0386%, total TER: 12.6875%, progress (thread 0): 89.5332%]
|T|: m m h m m
|P|:
[sample: ES2004d_H03_FEE016_1930.95_1931.27, WER: 100%, TER: 100%, total WER: 26.0395%, total TER: 12.6885%, progress (thread 0): 89.5411%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2146.44_2146.76, WER: 0%, TER: 0%, total WER: 26.0392%, total TER: 12.6884%, progress (thread 0): 89.549%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_290.82_291.14, WER: 100%, TER: 40%, total WER: 26.04%, total TER: 12.6887%, progress (thread 0): 89.557%]
|T|: o o p s
|P|: s
[sample: IS1009a_H03_FIO089_420.14_420.46, WER: 100%, TER: 75%, total WER: 26.0409%, total TER: 12.6893%, progress (thread 0): 89.5649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H01_FIO087_494.72_495.04, WER: 0%, TER: 0%, total WER: 26.0406%, total TER: 12.6892%, progress (thread 0): 89.5728%]
|T|: y e a h
|P|:
[sample: IS1009a_H02_FIO084_577.05_577.37, WER: 100%, TER: 100%, total WER: 26.0414%, total TER: 12.69%, progress (thread 0): 89.5807%]
|T|: m m h m m
|P|: u h h u h
[sample: IS1009a_H02_FIO084_641.8_642.12, WER: 100%, TER: 80%, total WER: 26.0422%, total TER: 12.6908%, progress (thread 0): 89.5886%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_197.33_197.65, WER: 0%, TER: 0%, total WER: 26.0419%, total TER: 12.6906%, progress (thread 0): 89.5965%]
|T|: m m h m m
|P|: u
[sample: IS1009b_H00_FIE088_595.82_596.14, WER: 100%, TER: 100%, total WER: 26.0428%, total TER: 12.6917%, progress (thread 0): 89.6044%]
|T|: m m h m m
|P|: r i h
[sample: IS1009b_H03_FIO089_1073.83_1074.15, WER: 100%, TER: 80%, total WER: 26.0436%, total TER: 12.6925%, progress (thread 0): 89.6123%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1139.48_1139.8, WER: 100%, TER: 40%, total WER: 26.0445%, total TER: 12.6928%, progress (thread 0): 89.6202%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1268.25_1268.57, WER: 100%, TER: 40%, total WER: 26.0453%, total TER: 12.6931%, progress (thread 0): 89.6282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1877.89_1878.21, WER: 0%, TER: 0%, total WER: 26.045%, total TER: 12.693%, progress (thread 0): 89.6361%]
|T|: m m h m m
|P|: m e n
[sample: IS1009b_H03_FIO089_1894.73_1895.05, WER: 100%, TER: 80%, total WER: 26.0458%, total TER: 12.6938%, progress (thread 0): 89.644%]
|T|: h i
|P|: k a y
[sample: IS1009c_H01_FIO087_43.77_44.09, WER: 100%, TER: 150%, total WER: 26.0467%, total TER: 12.6944%, progress (thread 0): 89.6519%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009c_H00_FIE088_405.29_405.61, WER: 200%, TER: 100%, total WER: 26.0487%, total TER: 12.6954%, progress (thread 0): 89.6598%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_524.19_524.51, WER: 100%, TER: 40%, total WER: 26.0495%, total TER: 12.6958%, progress (thread 0): 89.6677%]
|T|: y e s
|P|:
[sample: IS1009c_H01_FIO087_1640_1640.32, WER: 100%, TER: 100%, total WER: 26.0503%, total TER: 12.6964%, progress (thread 0): 89.6756%]
|T|: m e n u
|P|: m e n u
[sample: IS1009d_H00_FIE088_523.67_523.99, WER: 0%, TER: 0%, total WER: 26.05%, total TER: 12.6963%, progress (thread 0): 89.6835%]
|T|: p a r d o n | m e
|P|: b u
[sample: IS1009d_H01_FIO087_699.16_699.48, WER: 100%, TER: 100%, total WER: 26.0517%, total TER: 12.6981%, progress (thread 0): 89.6915%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_799.6_799.92, WER: 100%, TER: 100%, total WER: 26.0525%, total TER: 12.6991%, progress (thread 0): 89.6994%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1056.69_1057.01, WER: 0%, TER: 0%, total WER: 26.0522%, total TER: 12.699%, progress (thread 0): 89.7073%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1722.84_1723.16, WER: 100%, TER: 40%, total WER: 26.0531%, total TER: 12.6993%, progress (thread 0): 89.7152%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1741.45_1741.77, WER: 100%, TER: 40%, total WER: 26.0539%, total TER: 12.6997%, progress (thread 0): 89.7231%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1791.05_1791.37, WER: 0%, TER: 0%, total WER: 26.0536%, total TER: 12.6995%, progress (thread 0): 89.731%]
|T|: y e a h
|P|: h m
[sample: TS3003b_H01_MTD011UID_1651.31_1651.63, WER: 100%, TER: 100%, total WER: 26.0545%, total TER: 12.7004%, progress (thread 0): 89.7389%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_1722.53_1722.85, WER: 100%, TER: 25%, total WER: 26.0553%, total TER: 12.7005%, progress (thread 0): 89.7468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2090.25_2090.57, WER: 0%, TER: 0%, total WER: 26.055%, total TER: 12.7004%, progress (thread 0): 89.7547%]
|T|: m m h m m
|P|: a h
[sample: TS3003c_H03_MTD012ME_2074.3_2074.62, WER: 100%, TER: 80%, total WER: 26.0558%, total TER: 12.7011%, progress (thread 0): 89.7627%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_2134.48_2134.8, WER: 100%, TER: 40%, total WER: 26.0567%, total TER: 12.7015%, progress (thread 0): 89.7706%]
|T|: n a y
|P|: n o
[sample: TS3003d_H01_MTD011UID_417.84_418.16, WER: 100%, TER: 66.6667%, total WER: 26.0575%, total TER: 12.7018%, progress (thread 0): 89.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_694.59_694.91, WER: 0%, TER: 0%, total WER: 26.0572%, total TER: 12.7017%, progress (thread 0): 89.7864%]
|T|: o r | b
|P|: r
[sample: TS3003d_H03_MTD012ME_896.59_896.91, WER: 100%, TER: 75%, total WER: 26.0589%, total TER: 12.7023%, progress (thread 0): 89.7943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1403.4_1403.72, WER: 0%, TER: 0%, total WER: 26.0586%, total TER: 12.7022%, progress (thread 0): 89.8022%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1492.24_1492.56, WER: 0%, TER: 0%, total WER: 26.0583%, total TER: 12.7021%, progress (thread 0): 89.8101%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_2024.39_2024.71, WER: 100%, TER: 100%, total WER: 26.0591%, total TER: 12.7029%, progress (thread 0): 89.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2381.84_2382.16, WER: 0%, TER: 0%, total WER: 26.0588%, total TER: 12.7028%, progress (thread 0): 89.826%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_68.23_68.55, WER: 0%, TER: 0%, total WER: 26.0586%, total TER: 12.7026%, progress (thread 0): 89.8339%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_359.54_359.86, WER: 50%, TER: 42.8571%, total WER: 26.0591%, total TER: 12.7031%, progress (thread 0): 89.8418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_496.88_497.2, WER: 0%, TER: 0%, total WER: 26.0588%, total TER: 12.703%, progress (thread 0): 89.8497%]
|T|: h m m
|P|: y n
[sample: EN2002a_H02_FEO072_504.18_504.5, WER: 100%, TER: 100%, total WER: 26.0596%, total TER: 12.7036%, progress (thread 0): 89.8576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_567.01_567.33, WER: 0%, TER: 0%, total WER: 26.0593%, total TER: 12.7036%, progress (thread 0): 89.8655%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_604.34_604.66, WER: 100%, TER: 40%, total WER: 26.0602%, total TER: 12.7039%, progress (thread 0): 89.8734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_729.25_729.57, WER: 0%, TER: 0%, total WER: 26.0599%, total TER: 12.7038%, progress (thread 0): 89.8813%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_800.36_800.68, WER: 0%, TER: 0%, total WER: 26.0596%, total TER: 12.7036%, progress (thread 0): 89.8892%]
|T|: o h
|P|: n o
[sample: EN2002a_H02_FEO072_856.5_856.82, WER: 100%, TER: 100%, total WER: 26.0604%, total TER: 12.704%, progress (thread 0): 89.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1050.6_1050.92, WER: 0%, TER: 0%, total WER: 26.0601%, total TER: 12.7039%, progress (thread 0): 89.9051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1101.18_1101.5, WER: 0%, TER: 0%, total WER: 26.0598%, total TER: 12.7038%, progress (thread 0): 89.913%]
|T|: o h | n o
|P|: y o u | k n o w
[sample: EN2002a_H01_FEO070_1448.67_1448.99, WER: 100%, TER: 80%, total WER: 26.0615%, total TER: 12.7046%, progress (thread 0): 89.9209%]
|T|: h m m
|P|: y e a h
[sample: EN2002a_H00_MEE073_1448.83_1449.15, WER: 100%, TER: 133.333%, total WER: 26.0623%, total TER: 12.7054%, progress (thread 0): 89.9288%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1537.39_1537.71, WER: 0%, TER: 0%, total WER: 26.0621%, total TER: 12.7053%, progress (thread 0): 89.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1862.6_1862.92, WER: 0%, TER: 0%, total WER: 26.0618%, total TER: 12.7052%, progress (thread 0): 89.9446%]
|T|: y e a h
|P|: i
[sample: EN2002b_H03_MEE073_283.05_283.37, WER: 100%, TER: 100%, total WER: 26.0626%, total TER: 12.706%, progress (thread 0): 89.9525%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_363.29_363.61, WER: 200%, TER: 175%, total WER: 26.0646%, total TER: 12.7075%, progress (thread 0): 89.9604%]
|T|: o h | y e a h
|P|:
[sample: EN2002b_H03_MEE073_679.41_679.73, WER: 100%, TER: 100%, total WER: 26.0662%, total TER: 12.709%, progress (thread 0): 89.9684%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_922.82_923.14, WER: 0%, TER: 0%, total WER: 26.0659%, total TER: 12.7088%, progress (thread 0): 89.9763%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H02_FEO072_1100.2_1100.52, WER: 100%, TER: 40%, total WER: 26.0668%, total TER: 12.7092%, progress (thread 0): 89.9842%]
|T|: g o o d | p o i n t
|P|: t h e | p o i n t
[sample: EN2002b_H03_MEE073_1217.83_1218.15, WER: 50%, TER: 40%, total WER: 26.0673%, total TER: 12.7098%, progress (thread 0): 89.9921%]
|T|: y e a h
|P|: o
[sample: EN2002b_H03_MEE073_1261.76_1262.08, WER: 100%, TER: 100%, total WER: 26.0682%, total TER: 12.7106%, progress (thread 0): 90%]
|T|: h m m
|P|: y n
[sample: EN2002b_H03_MEE073_1598.38_1598.7, WER: 100%, TER: 100%, total WER: 26.069%, total TER: 12.7112%, progress (thread 0): 90.0079%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1611.81_1612.13, WER: 50%, TER: 55.5556%, total WER: 26.0695%, total TER: 12.7121%, progress (thread 0): 90.0158%]
|T|: o k a y
|P|:
[sample: EN2002b_H03_MEE073_1705.25_1705.57, WER: 100%, TER: 100%, total WER: 26.0704%, total TER: 12.713%, progress (thread 0): 90.0237%]
|T|: g o o d
|P|: o k a y
[sample: EN2002c_H01_FEO072_489.73_490.05, WER: 100%, TER: 100%, total WER: 26.0712%, total TER: 12.7138%, progress (thread 0): 90.0316%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_653.98_654.3, WER: 0%, TER: 0%, total WER: 26.0709%, total TER: 12.7137%, progress (thread 0): 90.0396%]
|T|: y e a h
|P|: u h
[sample: EN2002c_H01_FEO072_1352.96_1353.28, WER: 100%, TER: 75%, total WER: 26.0718%, total TER: 12.7143%, progress (thread 0): 90.0475%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_2296.14_2296.46, WER: 100%, TER: 50%, total WER: 26.0726%, total TER: 12.7145%, progress (thread 0): 90.0554%]
|T|: i | t h i n k
|P|: i | t h i n k
[sample: EN2002c_H01_FEO072_2336.35_2336.67, WER: 0%, TER: 0%, total WER: 26.072%, total TER: 12.7143%, progress (thread 0): 90.0633%]
|T|: o h
|P|: i
[sample: EN2002c_H01_FEO072_2368.69_2369.01, WER: 100%, TER: 100%, total WER: 26.0728%, total TER: 12.7147%, progress (thread 0): 90.0712%]
|T|: w h a t
|P|: w e l l
[sample: EN2002c_H01_FEO072_2484.6_2484.92, WER: 100%, TER: 75%, total WER: 26.0737%, total TER: 12.7153%, progress (thread 0): 90.0791%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2800.26_2800.58, WER: 100%, TER: 33.3333%, total WER: 26.0745%, total TER: 12.7154%, progress (thread 0): 90.087%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_2829.54_2829.86, WER: 0%, TER: 0%, total WER: 26.0742%, total TER: 12.7153%, progress (thread 0): 90.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_347.79_348.11, WER: 0%, TER: 0%, total WER: 26.0739%, total TER: 12.7152%, progress (thread 0): 90.1028%]
|T|: i t ' s | g o o d
|P|:
[sample: EN2002d_H00_FEO070_420.4_420.72, WER: 100%, TER: 100%, total WER: 26.0756%, total TER: 12.717%, progress (thread 0): 90.1108%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_574.1_574.42, WER: 100%, TER: 40%, total WER: 26.0764%, total TER: 12.7173%, progress (thread 0): 90.1187%]
|T|: b u t
|P|: u h
[sample: EN2002d_H01_FEO072_590.21_590.53, WER: 100%, TER: 66.6667%, total WER: 26.0773%, total TER: 12.7177%, progress (thread 0): 90.1266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_769.35_769.67, WER: 0%, TER: 0%, total WER: 26.077%, total TER: 12.7176%, progress (thread 0): 90.1345%]
|T|: o h
|P|: n
[sample: EN2002d_H03_MEE073_939.78_940.1, WER: 100%, TER: 100%, total WER: 26.0778%, total TER: 12.718%, progress (thread 0): 90.1424%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_954.69_955.01, WER: 0%, TER: 0%, total WER: 26.0775%, total TER: 12.7179%, progress (thread 0): 90.1503%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1189.57_1189.89, WER: 0%, TER: 0%, total WER: 26.0772%, total TER: 12.7178%, progress (thread 0): 90.1582%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1203.2_1203.52, WER: 0%, TER: 0%, total WER: 26.0769%, total TER: 12.7176%, progress (thread 0): 90.1661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1255.64_1255.96, WER: 0%, TER: 0%, total WER: 26.0766%, total TER: 12.7175%, progress (thread 0): 90.174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1754.3_1754.62, WER: 0%, TER: 0%, total WER: 26.0763%, total TER: 12.7174%, progress (thread 0): 90.182%]
|T|: s o
|P|: s o
[sample: EN2002d_H01_FEO072_1974.1_1974.42, WER: 0%, TER: 0%, total WER: 26.076%, total TER: 12.7173%, progress (thread 0): 90.1899%]
|T|: m m
|P|: h m
[sample: EN2002d_H02_MEE071_2190.35_2190.67, WER: 100%, TER: 50%, total WER: 26.0769%, total TER: 12.7175%, progress (thread 0): 90.1978%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_582.6_582.91, WER: 0%, TER: 0%, total WER: 26.0766%, total TER: 12.7174%, progress (thread 0): 90.2057%]
|T|: m m h m m
|P|: u h | h u h
[sample: ES2004a_H00_MEO015_687.25_687.56, WER: 200%, TER: 100%, total WER: 26.0785%, total TER: 12.7184%, progress (thread 0): 90.2136%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_688.13_688.44, WER: 0%, TER: 0%, total WER: 26.0782%, total TER: 12.7183%, progress (thread 0): 90.2215%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_777.34_777.65, WER: 100%, TER: 40%, total WER: 26.0791%, total TER: 12.7186%, progress (thread 0): 90.2294%]
|T|: y
|P|: y
[sample: ES2004a_H02_MEE014_949.9_950.21, WER: 0%, TER: 0%, total WER: 26.0788%, total TER: 12.7186%, progress (thread 0): 90.2373%]
|T|: n o
|P|: t o
[sample: ES2004b_H03_FEE016_607.28_607.59, WER: 100%, TER: 50%, total WER: 26.0796%, total TER: 12.7188%, progress (thread 0): 90.2453%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H00_MEO015_1612.5_1612.81, WER: 100%, TER: 60%, total WER: 26.0805%, total TER: 12.7193%, progress (thread 0): 90.2532%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_2237.11_2237.42, WER: 0%, TER: 0%, total WER: 26.0802%, total TER: 12.7192%, progress (thread 0): 90.2611%]
|T|: b u t
|P|: b u t
[sample: ES2004c_H02_MEE014_571.73_572.04, WER: 0%, TER: 0%, total WER: 26.0799%, total TER: 12.7191%, progress (thread 0): 90.269%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1321.04_1321.35, WER: 0%, TER: 0%, total WER: 26.0796%, total TER: 12.719%, progress (thread 0): 90.2769%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_1582.98_1583.29, WER: 0%, TER: 0%, total WER: 26.0793%, total TER: 12.7189%, progress (thread 0): 90.2848%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2288.73_2289.04, WER: 0%, TER: 0%, total WER: 26.079%, total TER: 12.7188%, progress (thread 0): 90.2927%]
|T|: m m
|P|: h m
[sample: ES2004d_H00_MEO015_846.15_846.46, WER: 100%, TER: 50%, total WER: 26.0798%, total TER: 12.7189%, progress (thread 0): 90.3006%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1669.25_1669.56, WER: 0%, TER: 0%, total WER: 26.0795%, total TER: 12.7188%, progress (thread 0): 90.3085%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1669.66_1669.97, WER: 0%, TER: 0%, total WER: 26.0792%, total TER: 12.7187%, progress (thread 0): 90.3165%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1825.79_1826.1, WER: 0%, TER: 0%, total WER: 26.0789%, total TER: 12.7186%, progress (thread 0): 90.3244%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1901.99_1902.3, WER: 0%, TER: 0%, total WER: 26.0786%, total TER: 12.7185%, progress (thread 0): 90.3323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1967.13_1967.44, WER: 0%, TER: 0%, total WER: 26.0783%, total TER: 12.7183%, progress (thread 0): 90.3402%]
|T|: w e l l
|P|: y e h
[sample: ES2004d_H02_MEE014_2085.81_2086.12, WER: 100%, TER: 75%, total WER: 26.0792%, total TER: 12.7189%, progress (thread 0): 90.3481%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2220.03_2220.34, WER: 0%, TER: 0%, total WER: 26.0789%, total TER: 12.7188%, progress (thread 0): 90.356%]
|T|: o k a y
|P|: k
[sample: IS1009a_H00_FIE088_90.21_90.52, WER: 100%, TER: 75%, total WER: 26.0797%, total TER: 12.7194%, progress (thread 0): 90.3639%]
|T|: u h
|P|:
[sample: IS1009a_H02_FIO084_451.06_451.37, WER: 100%, TER: 100%, total WER: 26.0806%, total TER: 12.7198%, progress (thread 0): 90.3718%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_702.97_703.28, WER: 0%, TER: 0%, total WER: 26.0803%, total TER: 12.7197%, progress (thread 0): 90.3797%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_795.57_795.88, WER: 0%, TER: 0%, total WER: 26.08%, total TER: 12.7196%, progress (thread 0): 90.3877%]
|T|: m m
|P|: e
[sample: IS1009b_H02_FIO084_218.1_218.41, WER: 100%, TER: 100%, total WER: 26.0808%, total TER: 12.72%, progress (thread 0): 90.3956%]
|T|: m m h m m
|P|: u h m
[sample: IS1009b_H00_FIE088_616.32_616.63, WER: 100%, TER: 60%, total WER: 26.0816%, total TER: 12.7205%, progress (thread 0): 90.4035%]
|T|: y e a h
|P|: y e
[sample: IS1009b_H02_FIO084_1692.92_1693.23, WER: 100%, TER: 50%, total WER: 26.0825%, total TER: 12.7209%, progress (thread 0): 90.4114%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1832.99_1833.3, WER: 0%, TER: 0%, total WER: 26.0822%, total TER: 12.7208%, progress (thread 0): 90.4193%]
|T|: y e s
|P|: i s
[sample: IS1009b_H01_FIO087_1844.23_1844.54, WER: 100%, TER: 66.6667%, total WER: 26.083%, total TER: 12.7211%, progress (thread 0): 90.4272%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H03_FIO089_1904.39_1904.7, WER: 0%, TER: 0%, total WER: 26.0827%, total TER: 12.721%, progress (thread 0): 90.4351%]
|T|: m m h m m
|P|: t a m l e
[sample: IS1009c_H00_FIE088_1140.15_1140.46, WER: 100%, TER: 100%, total WER: 26.0836%, total TER: 12.722%, progress (thread 0): 90.443%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1163.03_1163.34, WER: 100%, TER: 40%, total WER: 26.0844%, total TER: 12.7223%, progress (thread 0): 90.451%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1237.53_1237.84, WER: 100%, TER: 40%, total WER: 26.0852%, total TER: 12.7227%, progress (thread 0): 90.4589%]
|T|: ' k a y
|P|: c a n
[sample: IS1009c_H01_FIO087_1429.75_1430.06, WER: 100%, TER: 75%, total WER: 26.0861%, total TER: 12.7232%, progress (thread 0): 90.4668%]
|T|: m m h m m
|P|: h
[sample: IS1009c_H02_FIO084_1554.5_1554.81, WER: 100%, TER: 80%, total WER: 26.0869%, total TER: 12.724%, progress (thread 0): 90.4747%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H01_FIO087_1580.06_1580.37, WER: 100%, TER: 40%, total WER: 26.0877%, total TER: 12.7243%, progress (thread 0): 90.4826%]
|T|: o h
|P|: u m
[sample: IS1009c_H00_FIE088_1694.98_1695.29, WER: 100%, TER: 100%, total WER: 26.0886%, total TER: 12.7248%, progress (thread 0): 90.4905%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_363.39_363.7, WER: 100%, TER: 40%, total WER: 26.0894%, total TER: 12.7251%, progress (thread 0): 90.4984%]
|T|: m m
|P|: h m
[sample: IS1009d_H02_FIO084_894.86_895.17, WER: 100%, TER: 50%, total WER: 26.0903%, total TER: 12.7253%, progress (thread 0): 90.5063%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_903.74_904.05, WER: 0%, TER: 0%, total WER: 26.09%, total TER: 12.7251%, progress (thread 0): 90.5142%]
|T|: n o
|P|: n o
[sample: IS1009d_H00_FIE088_1011.62_1011.93, WER: 0%, TER: 0%, total WER: 26.0897%, total TER: 12.7251%, progress (thread 0): 90.5222%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1012.22_1012.53, WER: 100%, TER: 40%, total WER: 26.0905%, total TER: 12.7254%, progress (thread 0): 90.5301%]
|T|: m m h m m
|P|: m | h m
[sample: IS1009d_H03_FIO089_1105.97_1106.28, WER: 200%, TER: 40%, total WER: 26.0925%, total TER: 12.7257%, progress (thread 0): 90.538%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1171.27_1171.58, WER: 0%, TER: 0%, total WER: 26.0922%, total TER: 12.7256%, progress (thread 0): 90.5459%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1243.74_1244.05, WER: 100%, TER: 66.6667%, total WER: 26.093%, total TER: 12.726%, progress (thread 0): 90.5538%]
|T|: w h y
|P|: w o w
[sample: IS1009d_H00_FIE088_1442.55_1442.86, WER: 100%, TER: 66.6667%, total WER: 26.0938%, total TER: 12.7264%, progress (thread 0): 90.5617%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1681.21_1681.52, WER: 0%, TER: 0%, total WER: 26.0935%, total TER: 12.7262%, progress (thread 0): 90.5696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_216.48_216.79, WER: 0%, TER: 0%, total WER: 26.0932%, total TER: 12.7261%, progress (thread 0): 90.5775%]
|T|: m e a t
|P|: m e a t
[sample: TS3003a_H03_MTD012ME_595.66_595.97, WER: 0%, TER: 0%, total WER: 26.093%, total TER: 12.726%, progress (thread 0): 90.5854%]
|T|: s o
|P|: s o
[sample: TS3003a_H03_MTD012ME_884.45_884.76, WER: 0%, TER: 0%, total WER: 26.0927%, total TER: 12.7259%, progress (thread 0): 90.5934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1418.64_1418.95, WER: 0%, TER: 0%, total WER: 26.0924%, total TER: 12.7258%, progress (thread 0): 90.6013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_71.8_72.11, WER: 0%, TER: 0%, total WER: 26.0921%, total TER: 12.7257%, progress (thread 0): 90.6092%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_538.03_538.34, WER: 0%, TER: 0%, total WER: 26.0918%, total TER: 12.7256%, progress (thread 0): 90.6171%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1469.03_1469.34, WER: 100%, TER: 100%, total WER: 26.0926%, total TER: 12.726%, progress (thread 0): 90.625%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1715.8_1716.11, WER: 0%, TER: 0%, total WER: 26.0923%, total TER: 12.7259%, progress (thread 0): 90.6329%]
|T|: y e a h
|P|: t h a t
[sample: TS3003b_H00_MTD009PM_1822.91_1823.22, WER: 100%, TER: 75%, total WER: 26.0932%, total TER: 12.7265%, progress (thread 0): 90.6408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_794.24_794.55, WER: 0%, TER: 0%, total WER: 26.0929%, total TER: 12.7264%, progress (thread 0): 90.6487%]
|T|: ' k a y
|P|: k a y
[sample: TS3003c_H00_MTD009PM_934.63_934.94, WER: 100%, TER: 25%, total WER: 26.0937%, total TER: 12.7265%, progress (thread 0): 90.6566%]
|T|: m m
|P|: h m
[sample: TS3003c_H01_MTD011UID_1136.65_1136.96, WER: 100%, TER: 50%, total WER: 26.0945%, total TER: 12.7267%, progress (thread 0): 90.6646%]
|T|: y e a h
|P|: a n c e
[sample: TS3003c_H01_MTD011UID_1444.43_1444.74, WER: 100%, TER: 100%, total WER: 26.0954%, total TER: 12.7275%, progress (thread 0): 90.6725%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1813.2_1813.51, WER: 0%, TER: 0%, total WER: 26.0951%, total TER: 12.7274%, progress (thread 0): 90.6804%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_255.36_255.67, WER: 0%, TER: 0%, total WER: 26.0948%, total TER: 12.7273%, progress (thread 0): 90.6883%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_453.54_453.85, WER: 100%, TER: 50%, total WER: 26.0956%, total TER: 12.7274%, progress (thread 0): 90.6962%]
|T|: y e p
|P|: n o
[sample: TS3003d_H02_MTD0010ID_562.3_562.61, WER: 100%, TER: 100%, total WER: 26.0964%, total TER: 12.728%, progress (thread 0): 90.7041%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H02_MTD0010ID_1317.08_1317.39, WER: 100%, TER: 100%, total WER: 26.0973%, total TER: 12.7289%, progress (thread 0): 90.712%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_1546.63_1546.94, WER: 100%, TER: 40%, total WER: 26.0981%, total TER: 12.7292%, progress (thread 0): 90.7199%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2324.36_2324.67, WER: 0%, TER: 0%, total WER: 26.0978%, total TER: 12.7291%, progress (thread 0): 90.7278%]
|T|: y e a h
|P|: e h
[sample: TS3003d_H01_MTD011UID_2373.13_2373.44, WER: 100%, TER: 50%, total WER: 26.0987%, total TER: 12.7294%, progress (thread 0): 90.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_595.97_596.28, WER: 0%, TER: 0%, total WER: 26.0984%, total TER: 12.7293%, progress (thread 0): 90.7437%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_694.14_694.45, WER: 0%, TER: 0%, total WER: 26.0981%, total TER: 12.7292%, progress (thread 0): 90.7516%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_704.35_704.66, WER: 0%, TER: 0%, total WER: 26.0978%, total TER: 12.7291%, progress (thread 0): 90.7595%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_910.51_910.82, WER: 0%, TER: 0%, total WER: 26.0975%, total TER: 12.7289%, progress (thread 0): 90.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1104.75_1105.06, WER: 0%, TER: 0%, total WER: 26.0972%, total TER: 12.7288%, progress (thread 0): 90.7753%]
|T|: h m m
|P|: h
[sample: EN2002a_H00_MEE073_1619.48_1619.79, WER: 100%, TER: 66.6667%, total WER: 26.098%, total TER: 12.7292%, progress (thread 0): 90.7832%]
|T|: o h
|P|:
[sample: EN2002a_H02_FEO072_1674.71_1675.02, WER: 100%, TER: 100%, total WER: 26.0989%, total TER: 12.7296%, progress (thread 0): 90.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1973.2_1973.51, WER: 0%, TER: 0%, total WER: 26.0986%, total TER: 12.7295%, progress (thread 0): 90.799%]
|T|: o h | r i g h t
|P|: a l | r i g h t
[sample: EN2002a_H03_MEE071_1971.01_1971.32, WER: 50%, TER: 25%, total WER: 26.0991%, total TER: 12.7297%, progress (thread 0): 90.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_346.95_347.26, WER: 0%, TER: 0%, total WER: 26.0988%, total TER: 12.7296%, progress (thread 0): 90.8149%]
|T|: y e a h
|P|: w e l l
[sample: EN2002b_H03_MEE073_372.58_372.89, WER: 100%, TER: 75%, total WER: 26.0996%, total TER: 12.7302%, progress (thread 0): 90.8228%]
|T|: m m
|P|:
[sample: EN2002b_H03_MEE073_565.42_565.73, WER: 100%, TER: 100%, total WER: 26.1005%, total TER: 12.7306%, progress (thread 0): 90.8307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_729.79_730.1, WER: 0%, TER: 0%, total WER: 26.1002%, total TER: 12.7305%, progress (thread 0): 90.8386%]
|T|: y e a h
|P|: r i g h
[sample: EN2002b_H03_MEE073_1214.71_1215.02, WER: 100%, TER: 75%, total WER: 26.101%, total TER: 12.7311%, progress (thread 0): 90.8465%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1323.81_1324.12, WER: 0%, TER: 0%, total WER: 26.1007%, total TER: 12.7309%, progress (thread 0): 90.8544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1461.61_1461.92, WER: 0%, TER: 0%, total WER: 26.1004%, total TER: 12.7308%, progress (thread 0): 90.8623%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1750.66_1750.97, WER: 0%, TER: 0%, total WER: 26.1001%, total TER: 12.7307%, progress (thread 0): 90.8703%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_441.29_441.6, WER: 0%, TER: 0%, total WER: 26.0998%, total TER: 12.7306%, progress (thread 0): 90.8782%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_543.24_543.55, WER: 0%, TER: 0%, total WER: 26.0995%, total TER: 12.7306%, progress (thread 0): 90.8861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_543.24_543.55, WER: 0%, TER: 0%, total WER: 26.0992%, total TER: 12.7305%, progress (thread 0): 90.894%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_982.53_982.84, WER: 100%, TER: 33.3333%, total WER: 26.1001%, total TER: 12.7306%, progress (thread 0): 90.9019%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1524.82_1525.13, WER: 0%, TER: 0%, total WER: 26.0998%, total TER: 12.7305%, progress (thread 0): 90.9098%]
|T|: u m
|P|: u m
[sample: EN2002c_H03_MEE073_1904.01_1904.32, WER: 0%, TER: 0%, total WER: 26.0995%, total TER: 12.7304%, progress (thread 0): 90.9177%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1958.34_1958.65, WER: 0%, TER: 0%, total WER: 26.0992%, total TER: 12.7303%, progress (thread 0): 90.9256%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_2240.67_2240.98, WER: 100%, TER: 40%, total WER: 26.1%, total TER: 12.7306%, progress (thread 0): 90.9335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2358.92_2359.23, WER: 0%, TER: 0%, total WER: 26.0997%, total TER: 12.7305%, progress (thread 0): 90.9415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2477.51_2477.82, WER: 0%, TER: 0%, total WER: 26.0994%, total TER: 12.7304%, progress (thread 0): 90.9494%]
|T|: n o
|P|: n i e
[sample: EN2002c_H02_MEE071_2608.15_2608.46, WER: 100%, TER: 100%, total WER: 26.1003%, total TER: 12.7308%, progress (thread 0): 90.9573%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2724.28_2724.59, WER: 0%, TER: 0%, total WER: 26.1%, total TER: 12.7307%, progress (thread 0): 90.9652%]
|T|: t i c k
|P|: t i k
[sample: EN2002c_H01_FEO072_2878.5_2878.81, WER: 100%, TER: 25%, total WER: 26.1008%, total TER: 12.7308%, progress (thread 0): 90.9731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_391.81_392.12, WER: 0%, TER: 0%, total WER: 26.1005%, total TER: 12.7307%, progress (thread 0): 90.981%]
|T|: y e a h | o k a y
|P|: o k a y
[sample: EN2002d_H00_FEO070_1379.88_1380.19, WER: 50%, TER: 55.5556%, total WER: 26.1011%, total TER: 12.7316%, progress (thread 0): 90.9889%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_1512.76_1513.07, WER: 100%, TER: 40%, total WER: 26.1019%, total TER: 12.7319%, progress (thread 0): 90.9968%]
|T|: s o
|P|: t h e
[sample: EN2002d_H00_FEO070_1542.52_1542.83, WER: 100%, TER: 150%, total WER: 26.1027%, total TER: 12.7325%, progress (thread 0): 91.0047%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1546.76_1547.07, WER: 0%, TER: 0%, total WER: 26.1024%, total TER: 12.7324%, progress (thread 0): 91.0127%]
|T|: s o
|P|: s o
[sample: EN2002d_H03_MEE073_1822.41_1822.72, WER: 0%, TER: 0%, total WER: 26.1021%, total TER: 12.7324%, progress (thread 0): 91.0206%]
|T|: m m
|P|: h m
[sample: EN2002d_H00_FEO070_1872.39_1872.7, WER: 100%, TER: 50%, total WER: 26.103%, total TER: 12.7325%, progress (thread 0): 91.0285%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2114.3_2114.61, WER: 0%, TER: 0%, total WER: 26.1027%, total TER: 12.7324%, progress (thread 0): 91.0364%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_819.96_820.26, WER: 0%, TER: 0%, total WER: 26.1024%, total TER: 12.7323%, progress (thread 0): 91.0443%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1232.15_1232.45, WER: 0%, TER: 0%, total WER: 26.1021%, total TER: 12.7322%, progress (thread 0): 91.0522%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1572.74_1573.04, WER: 100%, TER: 40%, total WER: 26.1029%, total TER: 12.7325%, progress (thread 0): 91.0601%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1784.08_1784.38, WER: 100%, TER: 40%, total WER: 26.1038%, total TER: 12.7328%, progress (thread 0): 91.068%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_2252.76_2253.06, WER: 0%, TER: 0%, total WER: 26.1035%, total TER: 12.7327%, progress (thread 0): 91.076%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_849.84_850.14, WER: 0%, TER: 0%, total WER: 26.1032%, total TER: 12.7326%, progress (thread 0): 91.0839%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1555.92_1556.22, WER: 0%, TER: 0%, total WER: 26.1029%, total TER: 12.7325%, progress (thread 0): 91.0918%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1640.82_1641.12, WER: 100%, TER: 40%, total WER: 26.1037%, total TER: 12.7328%, progress (thread 0): 91.0997%]
|T|: ' k a y
|P|: k e
[sample: ES2004d_H00_MEO015_271.77_272.07, WER: 100%, TER: 75%, total WER: 26.1045%, total TER: 12.7334%, progress (thread 0): 91.1076%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_639.49_639.79, WER: 0%, TER: 0%, total WER: 26.1042%, total TER: 12.7332%, progress (thread 0): 91.1155%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1390.02_1390.32, WER: 0%, TER: 0%, total WER: 26.104%, total TER: 12.7331%, progress (thread 0): 91.1234%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1711.3_1711.6, WER: 0%, TER: 0%, total WER: 26.1037%, total TER: 12.733%, progress (thread 0): 91.1313%]
|T|: m m
|P|: h m
[sample: ES2004d_H01_FEE013_2002_2002.3, WER: 100%, TER: 50%, total WER: 26.1045%, total TER: 12.7332%, progress (thread 0): 91.1392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2096.66_2096.96, WER: 0%, TER: 0%, total WER: 26.1042%, total TER: 12.7331%, progress (thread 0): 91.1472%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_617.96_618.26, WER: 100%, TER: 40%, total WER: 26.105%, total TER: 12.7334%, progress (thread 0): 91.1551%]
|T|: o k a y
|P|: c a n
[sample: IS1009a_H01_FIO087_782.9_783.2, WER: 100%, TER: 75%, total WER: 26.1059%, total TER: 12.734%, progress (thread 0): 91.163%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_182.06_182.36, WER: 0%, TER: 0%, total WER: 26.1056%, total TER: 12.7338%, progress (thread 0): 91.1709%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_574.49_574.79, WER: 100%, TER: 40%, total WER: 26.1064%, total TER: 12.7342%, progress (thread 0): 91.1788%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_707.08_707.38, WER: 100%, TER: 40%, total WER: 26.1072%, total TER: 12.7345%, progress (thread 0): 91.1867%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1025.61_1025.91, WER: 0%, TER: 0%, total WER: 26.1069%, total TER: 12.7344%, progress (thread 0): 91.1946%]
|T|: m m
|P|: m e a n
[sample: IS1009b_H02_FIO084_1366.95_1367.25, WER: 100%, TER: 150%, total WER: 26.1078%, total TER: 12.735%, progress (thread 0): 91.2025%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1371.17_1371.47, WER: 100%, TER: 40%, total WER: 26.1086%, total TER: 12.7353%, progress (thread 0): 91.2104%]
|T|: h m m
|P|: k a y
[sample: IS1009b_H02_FIO084_1604.76_1605.06, WER: 100%, TER: 100%, total WER: 26.1095%, total TER: 12.7359%, progress (thread 0): 91.2184%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1617.66_1617.96, WER: 0%, TER: 0%, total WER: 26.1092%, total TER: 12.7358%, progress (thread 0): 91.2263%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_335.7_336, WER: 0%, TER: 0%, total WER: 26.1089%, total TER: 12.7357%, progress (thread 0): 91.2342%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H03_FIO089_1106.15_1106.45, WER: 100%, TER: 40%, total WER: 26.1097%, total TER: 12.736%, progress (thread 0): 91.2421%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1237.83_1238.13, WER: 0%, TER: 0%, total WER: 26.1094%, total TER: 12.7359%, progress (thread 0): 91.25%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1259.97_1260.27, WER: 0%, TER: 0%, total WER: 26.1091%, total TER: 12.7358%, progress (thread 0): 91.2579%]
|T|: o k a y
|P|: k a m
[sample: IS1009c_H01_FIO087_1297.17_1297.47, WER: 100%, TER: 50%, total WER: 26.1099%, total TER: 12.7362%, progress (thread 0): 91.2658%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_607.11_607.41, WER: 100%, TER: 40%, total WER: 26.1108%, total TER: 12.7365%, progress (thread 0): 91.2737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_769.42_769.72, WER: 0%, TER: 0%, total WER: 26.1105%, total TER: 12.7364%, progress (thread 0): 91.2816%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1062.93_1063.23, WER: 0%, TER: 0%, total WER: 26.1102%, total TER: 12.7362%, progress (thread 0): 91.2896%]
|T|: o n e
|P|: b u t
[sample: IS1009d_H01_FIO087_1482_1482.3, WER: 100%, TER: 100%, total WER: 26.111%, total TER: 12.7369%, progress (thread 0): 91.2975%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H00_FIE088_1528.1_1528.4, WER: 0%, TER: 0%, total WER: 26.1107%, total TER: 12.7368%, progress (thread 0): 91.3054%]
|T|: y e a h
|P|: i
[sample: IS1009d_H02_FIO084_1824.41_1824.71, WER: 100%, TER: 100%, total WER: 26.1116%, total TER: 12.7376%, progress (thread 0): 91.3133%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1853.09_1853.39, WER: 0%, TER: 0%, total WER: 26.1113%, total TER: 12.7375%, progress (thread 0): 91.3212%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_238.31_238.61, WER: 0%, TER: 0%, total WER: 26.111%, total TER: 12.7373%, progress (thread 0): 91.3291%]
|T|: s o
|P|: s o
[sample: TS3003a_H02_MTD0010ID_1080.77_1081.07, WER: 0%, TER: 0%, total WER: 26.1107%, total TER: 12.7373%, progress (thread 0): 91.337%]
|T|: h m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1308.88_1309.18, WER: 100%, TER: 33.3333%, total WER: 26.1115%, total TER: 12.7374%, progress (thread 0): 91.3449%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_1423.96_1424.26, WER: 0%, TER: 0%, total WER: 26.1112%, total TER: 12.7373%, progress (thread 0): 91.3529%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H00_MTD009PM_1463.25_1463.55, WER: 100%, TER: 50%, total WER: 26.1121%, total TER: 12.7377%, progress (thread 0): 91.3608%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_1535.95_1536.25, WER: 0%, TER: 0%, total WER: 26.1118%, total TER: 12.7376%, progress (thread 0): 91.3687%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1635.81_1636.11, WER: 100%, TER: 40%, total WER: 26.1126%, total TER: 12.7379%, progress (thread 0): 91.3766%]
|T|: h m
|P|: i s
[sample: TS3003b_H01_MTD011UID_1834.41_1834.71, WER: 100%, TER: 100%, total WER: 26.1134%, total TER: 12.7383%, progress (thread 0): 91.3845%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1871.5_1871.8, WER: 100%, TER: 100%, total WER: 26.1143%, total TER: 12.7387%, progress (thread 0): 91.3924%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2279.98_2280.28, WER: 0%, TER: 0%, total WER: 26.114%, total TER: 12.7386%, progress (thread 0): 91.4003%]
|T|: s
|P|:
[sample: TS3003d_H01_MTD011UID_856.51_856.81, WER: 100%, TER: 100%, total WER: 26.1148%, total TER: 12.7388%, progress (thread 0): 91.4082%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1057.72_1058.02, WER: 0%, TER: 0%, total WER: 26.1145%, total TER: 12.7387%, progress (thread 0): 91.4161%]
|T|: y e a h
|P|: y e a n
[sample: TS3003d_H02_MTD0010ID_1709.82_1710.12, WER: 100%, TER: 25%, total WER: 26.1153%, total TER: 12.7388%, progress (thread 0): 91.424%]
|T|: y e s
|P|: y e s
[sample: TS3003d_H02_MTD0010ID_1732.89_1733.19, WER: 0%, TER: 0%, total WER: 26.115%, total TER: 12.7387%, progress (thread 0): 91.432%]
|T|: y e a h
|P|: y e h
[sample: TS3003d_H00_MTD009PM_1821.73_1822.03, WER: 100%, TER: 25%, total WER: 26.1159%, total TER: 12.7388%, progress (thread 0): 91.4399%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2250.87_2251.17, WER: 0%, TER: 0%, total WER: 26.1156%, total TER: 12.7387%, progress (thread 0): 91.4478%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2482.46_2482.76, WER: 0%, TER: 0%, total WER: 26.1153%, total TER: 12.7386%, progress (thread 0): 91.4557%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_11.83_12.13, WER: 0%, TER: 0%, total WER: 26.115%, total TER: 12.7385%, progress (thread 0): 91.4636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_48.72_49.02, WER: 0%, TER: 0%, total WER: 26.1147%, total TER: 12.7383%, progress (thread 0): 91.4715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_203.25_203.55, WER: 0%, TER: 0%, total WER: 26.1144%, total TER: 12.7382%, progress (thread 0): 91.4794%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_261.14_261.44, WER: 100%, TER: 33.3333%, total WER: 26.1152%, total TER: 12.7384%, progress (thread 0): 91.4873%]
|T|: w h y | n o t
|P|: w h y | d o n ' t
[sample: EN2002a_H03_MEE071_375.98_376.28, WER: 50%, TER: 42.8571%, total WER: 26.1158%, total TER: 12.7389%, progress (thread 0): 91.4953%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_469.41_469.71, WER: 0%, TER: 0%, total WER: 26.1155%, total TER: 12.7387%, progress (thread 0): 91.5032%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_486.08_486.38, WER: 0%, TER: 0%, total WER: 26.1152%, total TER: 12.7386%, progress (thread 0): 91.5111%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_508.71_509.01, WER: 0%, TER: 0%, total WER: 26.1149%, total TER: 12.7385%, progress (thread 0): 91.519%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_521.78_522.08, WER: 0%, TER: 0%, total WER: 26.1146%, total TER: 12.7384%, progress (thread 0): 91.5269%]
|T|: s o
|P|:
[sample: EN2002a_H03_MEE071_738.15_738.45, WER: 100%, TER: 100%, total WER: 26.1154%, total TER: 12.7388%, progress (thread 0): 91.5348%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_747.85_748.15, WER: 100%, TER: 40%, total WER: 26.1163%, total TER: 12.7391%, progress (thread 0): 91.5427%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_795.73_796.03, WER: 100%, TER: 33.3333%, total WER: 26.1171%, total TER: 12.7393%, progress (thread 0): 91.5506%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_877.67_877.97, WER: 100%, TER: 40%, total WER: 26.1179%, total TER: 12.7396%, progress (thread 0): 91.5585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_918.33_918.63, WER: 0%, TER: 0%, total WER: 26.1176%, total TER: 12.7395%, progress (thread 0): 91.5665%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1889.11_1889.41, WER: 0%, TER: 0%, total WER: 26.1173%, total TER: 12.7393%, progress (thread 0): 91.5744%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H02_FEO072_2116.83_2117.13, WER: 0%, TER: 0%, total WER: 26.1171%, total TER: 12.7392%, progress (thread 0): 91.5823%]
|T|: o h | y e a h
|P|: h e l
[sample: EN2002b_H03_MEE073_211.33_211.63, WER: 100%, TER: 71.4286%, total WER: 26.1187%, total TER: 12.7402%, progress (thread 0): 91.5902%]
|T|: w e ' l l | s e e
|P|: w e l l | s | e
[sample: EN2002b_H00_FEO070_255.08_255.38, WER: 150%, TER: 22.2222%, total WER: 26.1215%, total TER: 12.7404%, progress (thread 0): 91.5981%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_368.42_368.72, WER: 0%, TER: 0%, total WER: 26.1212%, total TER: 12.7402%, progress (thread 0): 91.606%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_391.92_392.22, WER: 100%, TER: 40%, total WER: 26.1221%, total TER: 12.7406%, progress (thread 0): 91.6139%]
|T|: m m
|P|: m
[sample: EN2002b_H03_MEE073_526.11_526.41, WER: 100%, TER: 50%, total WER: 26.1229%, total TER: 12.7407%, progress (thread 0): 91.6218%]
|T|: a l r i g h t
|P|: a l l r i g h t
[sample: EN2002b_H02_FEO072_627.98_628.28, WER: 100%, TER: 14.2857%, total WER: 26.1237%, total TER: 12.7408%, progress (thread 0): 91.6298%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1551.52_1551.82, WER: 0%, TER: 0%, total WER: 26.1234%, total TER: 12.7406%, progress (thread 0): 91.6377%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1611.31_1611.61, WER: 0%, TER: 0%, total WER: 26.1231%, total TER: 12.7405%, progress (thread 0): 91.6456%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1729.42_1729.72, WER: 0%, TER: 0%, total WER: 26.1228%, total TER: 12.7404%, progress (thread 0): 91.6535%]
|T|: i | t h i n k
|P|: i
[sample: EN2002c_H01_FEO072_232.37_232.67, WER: 50%, TER: 85.7143%, total WER: 26.1234%, total TER: 12.7416%, progress (thread 0): 91.6614%]
|T|: y e a h
|P|: y e s
[sample: EN2002c_H02_MEE071_355.18_355.48, WER: 100%, TER: 50%, total WER: 26.1242%, total TER: 12.742%, progress (thread 0): 91.6693%]
|T|: n o | n o
|P|:
[sample: EN2002c_H03_MEE073_543.55_543.85, WER: 100%, TER: 100%, total WER: 26.1259%, total TER: 12.743%, progress (thread 0): 91.6772%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_700.09_700.39, WER: 100%, TER: 40%, total WER: 26.1267%, total TER: 12.7433%, progress (thread 0): 91.6851%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H02_MEE071_1131.17_1131.47, WER: 100%, TER: 66.6667%, total WER: 26.1276%, total TER: 12.7437%, progress (thread 0): 91.693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1143.34_1143.64, WER: 0%, TER: 0%, total WER: 26.1273%, total TER: 12.7436%, progress (thread 0): 91.701%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_1321.8_1322.1, WER: 0%, TER: 0%, total WER: 26.127%, total TER: 12.7435%, progress (thread 0): 91.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1600.62_1600.92, WER: 0%, TER: 0%, total WER: 26.1267%, total TER: 12.7434%, progress (thread 0): 91.7168%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2301.16_2301.46, WER: 0%, TER: 0%, total WER: 26.1264%, total TER: 12.7433%, progress (thread 0): 91.7247%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2443.4_2443.7, WER: 0%, TER: 0%, total WER: 26.1261%, total TER: 12.7432%, progress (thread 0): 91.7326%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H02_MEE071_2847.83_2848.13, WER: 100%, TER: 40%, total WER: 26.1269%, total TER: 12.7435%, progress (thread 0): 91.7405%]
|T|: y e a h
|P|: y e
[sample: EN2002d_H02_MEE071_673.96_674.26, WER: 100%, TER: 50%, total WER: 26.1277%, total TER: 12.7438%, progress (thread 0): 91.7484%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_834.35_834.65, WER: 0%, TER: 0%, total WER: 26.1274%, total TER: 12.7437%, progress (thread 0): 91.7563%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_925.24_925.54, WER: 0%, TER: 0%, total WER: 26.1272%, total TER: 12.7436%, progress (thread 0): 91.7642%]
|T|: u m
|P|: u m
[sample: EN2002d_H01_FEO072_1191.17_1191.47, WER: 0%, TER: 0%, total WER: 26.1269%, total TER: 12.7435%, progress (thread 0): 91.7721%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1205.4_1205.7, WER: 0%, TER: 0%, total WER: 26.1266%, total TER: 12.7434%, progress (thread 0): 91.7801%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H03_MEE073_1221.17_1221.47, WER: 100%, TER: 40%, total WER: 26.1274%, total TER: 12.7437%, progress (thread 0): 91.788%]
|T|: y e a h
|P|: h m
[sample: EN2002d_H00_FEO070_1249.7_1250, WER: 100%, TER: 100%, total WER: 26.1282%, total TER: 12.7445%, progress (thread 0): 91.7959%]
|T|: s u r e
|P|: s u r e
[sample: EN2002d_H03_MEE073_1672.53_1672.83, WER: 0%, TER: 0%, total WER: 26.1279%, total TER: 12.7444%, progress (thread 0): 91.8038%]
|T|: m m
|P|: h m
[sample: EN2002d_H02_MEE071_2192.66_2192.96, WER: 100%, TER: 50%, total WER: 26.1288%, total TER: 12.7446%, progress (thread 0): 91.8117%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_747.66_747.95, WER: 0%, TER: 0%, total WER: 26.1285%, total TER: 12.7444%, progress (thread 0): 91.8196%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_784.99_785.28, WER: 100%, TER: 40%, total WER: 26.1293%, total TER: 12.7448%, progress (thread 0): 91.8275%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_826.06_826.35, WER: 0%, TER: 0%, total WER: 26.129%, total TER: 12.7446%, progress (thread 0): 91.8354%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2019.84_2020.13, WER: 0%, TER: 0%, total WER: 26.1287%, total TER: 12.7445%, progress (thread 0): 91.8434%]
|T|: o k a y
|P|: a
[sample: ES2004b_H02_MEE014_2295.26_2295.55, WER: 100%, TER: 75%, total WER: 26.1296%, total TER: 12.7451%, progress (thread 0): 91.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_316.02_316.31, WER: 0%, TER: 0%, total WER: 26.1293%, total TER: 12.745%, progress (thread 0): 91.8592%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004c_H00_MEO015_1261.19_1261.48, WER: 200%, TER: 20%, total WER: 26.1312%, total TER: 12.745%, progress (thread 0): 91.8671%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H01_FEE013_1987.8_1988.09, WER: 100%, TER: 40%, total WER: 26.1321%, total TER: 12.7454%, progress (thread 0): 91.875%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_2244.26_2244.55, WER: 0%, TER: 0%, total WER: 26.1318%, total TER: 12.7452%, progress (thread 0): 91.8829%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_211.03_211.32, WER: 0%, TER: 0%, total WER: 26.1315%, total TER: 12.7451%, progress (thread 0): 91.8908%]
|T|: r i g h t
|P|: i
[sample: ES2004d_H03_FEE016_810.67_810.96, WER: 100%, TER: 80%, total WER: 26.1323%, total TER: 12.7459%, progress (thread 0): 91.8987%]
|T|: m m
|P|: m h m
[sample: ES2004d_H03_FEE016_1204.42_1204.71, WER: 100%, TER: 50%, total WER: 26.1331%, total TER: 12.7461%, progress (thread 0): 91.9066%]
|T|: u m
|P|:
[sample: ES2004d_H01_FEE013_1227.88_1228.17, WER: 100%, TER: 100%, total WER: 26.134%, total TER: 12.7465%, progress (thread 0): 91.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1634.52_1634.81, WER: 0%, TER: 0%, total WER: 26.1337%, total TER: 12.7463%, progress (thread 0): 91.9225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1787.34_1787.63, WER: 0%, TER: 0%, total WER: 26.1334%, total TER: 12.7462%, progress (thread 0): 91.9304%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1964.63_1964.92, WER: 0%, TER: 0%, total WER: 26.1331%, total TER: 12.7461%, progress (thread 0): 91.9383%]
|T|: o k a y
|P|: o k y
[sample: IS1009a_H03_FIO089_118.76_119.05, WER: 100%, TER: 25%, total WER: 26.1339%, total TER: 12.7462%, progress (thread 0): 91.9462%]
|T|: h m m
|P|: c a s e
[sample: IS1009a_H02_FIO084_803.39_803.68, WER: 100%, TER: 133.333%, total WER: 26.1347%, total TER: 12.7471%, progress (thread 0): 91.9541%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_708.64_708.93, WER: 100%, TER: 40%, total WER: 26.1356%, total TER: 12.7474%, progress (thread 0): 91.962%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_998.44_998.73, WER: 0%, TER: 0%, total WER: 26.1353%, total TER: 12.7473%, progress (thread 0): 91.9699%]
|T|: m m h m m
|P|: h
[sample: IS1009b_H03_FIO089_1311.53_1311.82, WER: 100%, TER: 80%, total WER: 26.1361%, total TER: 12.7481%, progress (thread 0): 91.9778%]
|T|: m m h m m
|P|: a m
[sample: IS1009b_H00_FIE088_1356.24_1356.53, WER: 100%, TER: 80%, total WER: 26.137%, total TER: 12.7488%, progress (thread 0): 91.9858%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_1658.74_1659.03, WER: 100%, TER: 60%, total WER: 26.1378%, total TER: 12.7494%, progress (thread 0): 91.9937%]
|T|: h m m
|P|: m h m
[sample: IS1009b_H02_FIO084_1899.66_1899.95, WER: 100%, TER: 66.6667%, total WER: 26.1386%, total TER: 12.7498%, progress (thread 0): 92.0016%]
|T|: m m h m m
|P|: w h m
[sample: IS1009b_H03_FIO089_1999.16_1999.45, WER: 100%, TER: 60%, total WER: 26.1395%, total TER: 12.7503%, progress (thread 0): 92.0095%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_284.27_284.56, WER: 0%, TER: 0%, total WER: 26.1392%, total TER: 12.7502%, progress (thread 0): 92.0174%]
|T|: m m h m m
|P|: u m | h u m
[sample: IS1009c_H00_FIE088_381_381.29, WER: 200%, TER: 60%, total WER: 26.1411%, total TER: 12.7508%, progress (thread 0): 92.0253%]
|T|: w e | c a n
|P|:
[sample: IS1009c_H01_FIO087_755.64_755.93, WER: 100%, TER: 100%, total WER: 26.1428%, total TER: 12.752%, progress (thread 0): 92.0332%]
|T|: y e a h | b u t | w
|P|:
[sample: IS1009c_H02_FIO084_1559.51_1559.8, WER: 100%, TER: 100%, total WER: 26.1453%, total TER: 12.7541%, progress (thread 0): 92.0411%]
|T|: m m h m m
|P|: y
[sample: IS1009c_H02_FIO084_1608.9_1609.19, WER: 100%, TER: 100%, total WER: 26.1461%, total TER: 12.7551%, progress (thread 0): 92.049%]
|T|: h e l l o
|P|: y o u | k n o w
[sample: IS1009d_H01_FIO087_41.27_41.56, WER: 200%, TER: 140%, total WER: 26.1481%, total TER: 12.7566%, progress (thread 0): 92.057%]
|T|: n o
|P|: y o u | k n o w
[sample: IS1009d_H02_FIO084_952.51_952.8, WER: 200%, TER: 300%, total WER: 26.15%, total TER: 12.7579%, progress (thread 0): 92.0649%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1056.71_1057, WER: 100%, TER: 100%, total WER: 26.1509%, total TER: 12.7589%, progress (thread 0): 92.0728%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H03_FIO089_1344.32_1344.61, WER: 100%, TER: 80%, total WER: 26.1517%, total TER: 12.7597%, progress (thread 0): 92.0807%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_1389.87_1390.16, WER: 0%, TER: 0%, total WER: 26.1514%, total TER: 12.7596%, progress (thread 0): 92.0886%]
|T|: m m h m m
|P|: y e a
[sample: IS1009d_H02_FIO084_1790.06_1790.35, WER: 100%, TER: 100%, total WER: 26.1522%, total TER: 12.7606%, progress (thread 0): 92.0965%]
|T|: o k a y
|P|: i
[sample: TS3003a_H01_MTD011UID_704.99_705.28, WER: 100%, TER: 100%, total WER: 26.1531%, total TER: 12.7615%, progress (thread 0): 92.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_977.31_977.6, WER: 0%, TER: 0%, total WER: 26.1528%, total TER: 12.7613%, progress (thread 0): 92.1123%]
|T|: y o u
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1267.17_1267.46, WER: 100%, TER: 100%, total WER: 26.1536%, total TER: 12.7619%, progress (thread 0): 92.1203%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_178.78_179.07, WER: 0%, TER: 0%, total WER: 26.1533%, total TER: 12.7619%, progress (thread 0): 92.1282%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1577.63_1577.92, WER: 0%, TER: 0%, total WER: 26.153%, total TER: 12.7617%, progress (thread 0): 92.1361%]
|T|: w e l l
|P|: w e l l
[sample: TS3003b_H03_MTD012ME_1752.79_1753.08, WER: 0%, TER: 0%, total WER: 26.1527%, total TER: 12.7616%, progress (thread 0): 92.144%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_2033.23_2033.52, WER: 0%, TER: 0%, total WER: 26.1524%, total TER: 12.7615%, progress (thread 0): 92.1519%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2083.17_2083.46, WER: 0%, TER: 0%, total WER: 26.1521%, total TER: 12.7614%, progress (thread 0): 92.1598%]
|T|: y e a h
|P|: n o
[sample: TS3003b_H02_MTD0010ID_2083.37_2083.66, WER: 100%, TER: 100%, total WER: 26.153%, total TER: 12.7622%, progress (thread 0): 92.1677%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H01_MTD011UID_1289.74_1290.03, WER: 100%, TER: 40%, total WER: 26.1538%, total TER: 12.7625%, progress (thread 0): 92.1756%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1452.67_1452.96, WER: 0%, TER: 0%, total WER: 26.1535%, total TER: 12.7624%, progress (thread 0): 92.1835%]
|T|: m m h m m
|P|: h
[sample: TS3003d_H03_MTD012ME_695.88_696.17, WER: 100%, TER: 80%, total WER: 26.1543%, total TER: 12.7632%, progress (thread 0): 92.1915%]
|T|: n a y
|P|: h m
[sample: TS3003d_H01_MTD011UID_835.49_835.78, WER: 100%, TER: 100%, total WER: 26.1552%, total TER: 12.7638%, progress (thread 0): 92.1994%]
|T|: h m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_1010.19_1010.48, WER: 100%, TER: 33.3333%, total WER: 26.156%, total TER: 12.7639%, progress (thread 0): 92.2073%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H02_MTD0010ID_1075.11_1075.4, WER: 100%, TER: 100%, total WER: 26.1568%, total TER: 12.7648%, progress (thread 0): 92.2152%]
|T|: t r u e
|P|: t r u e
[sample: TS3003d_H03_MTD012ME_1129.3_1129.59, WER: 0%, TER: 0%, total WER: 26.1566%, total TER: 12.7646%, progress (thread 0): 92.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1862.62_1862.91, WER: 0%, TER: 0%, total WER: 26.1563%, total TER: 12.7645%, progress (thread 0): 92.231%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_2370.37_2370.66, WER: 100%, TER: 50%, total WER: 26.1571%, total TER: 12.7647%, progress (thread 0): 92.2389%]
|T|: h m m
|P|: h m
[sample: TS3003d_H00_MTD009PM_2517.23_2517.52, WER: 100%, TER: 33.3333%, total WER: 26.1579%, total TER: 12.7648%, progress (thread 0): 92.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2571.69_2571.98, WER: 0%, TER: 0%, total WER: 26.1576%, total TER: 12.7647%, progress (thread 0): 92.2547%]
|T|: h m m
|P|: y e h
[sample: EN2002a_H00_MEE073_218.41_218.7, WER: 100%, TER: 100%, total WER: 26.1585%, total TER: 12.7653%, progress (thread 0): 92.2627%]
|T|: i | d o n ' t | k n o w
|P|: i | d n '
[sample: EN2002a_H00_MEE073_234.63_234.92, WER: 66.6667%, TER: 58.3333%, total WER: 26.1598%, total TER: 12.7666%, progress (thread 0): 92.2706%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_526.91_527.2, WER: 100%, TER: 66.6667%, total WER: 26.1607%, total TER: 12.767%, progress (thread 0): 92.2785%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_646.99_647.28, WER: 0%, TER: 0%, total WER: 26.1604%, total TER: 12.7669%, progress (thread 0): 92.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_723.91_724.2, WER: 0%, TER: 0%, total WER: 26.1601%, total TER: 12.7667%, progress (thread 0): 92.2943%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_749.38_749.67, WER: 0%, TER: 0%, total WER: 26.1598%, total TER: 12.7666%, progress (thread 0): 92.3022%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_856.03_856.32, WER: 0%, TER: 0%, total WER: 26.1595%, total TER: 12.7665%, progress (thread 0): 92.3101%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_872.78_873.07, WER: 0%, TER: 0%, total WER: 26.1592%, total TER: 12.7664%, progress (thread 0): 92.318%]
|T|: h m m
|P|: y e h
[sample: EN2002a_H00_MEE073_1076.05_1076.34, WER: 100%, TER: 100%, total WER: 26.16%, total TER: 12.767%, progress (thread 0): 92.326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1255.44_1255.73, WER: 0%, TER: 0%, total WER: 26.1597%, total TER: 12.7669%, progress (thread 0): 92.3339%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1364.93_1365.22, WER: 0%, TER: 0%, total WER: 26.1594%, total TER: 12.7668%, progress (thread 0): 92.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1593.85_1594.14, WER: 0%, TER: 0%, total WER: 26.1591%, total TER: 12.7666%, progress (thread 0): 92.3497%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1595.68_1595.97, WER: 0%, TER: 0%, total WER: 26.1588%, total TER: 12.7665%, progress (thread 0): 92.3576%]
|T|: m m h m m
|P|: w e
[sample: EN2002a_H02_FEO072_1620.66_1620.95, WER: 100%, TER: 100%, total WER: 26.1597%, total TER: 12.7675%, progress (thread 0): 92.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1960.6_1960.89, WER: 0%, TER: 0%, total WER: 26.1594%, total TER: 12.7674%, progress (thread 0): 92.3734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2127.38_2127.67, WER: 0%, TER: 0%, total WER: 26.1591%, total TER: 12.7673%, progress (thread 0): 92.3813%]
|T|: v e r y | g o o d
|P|: w h e n
[sample: EN2002b_H02_FEO072_142.65_142.94, WER: 100%, TER: 100%, total WER: 26.1608%, total TER: 12.7691%, progress (thread 0): 92.3892%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_382.41_382.7, WER: 0%, TER: 0%, total WER: 26.1605%, total TER: 12.769%, progress (thread 0): 92.3972%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_419.68_419.97, WER: 0%, TER: 0%, total WER: 26.1602%, total TER: 12.7689%, progress (thread 0): 92.4051%]
|T|: o k a y
|P|: o k a
[sample: EN2002b_H00_FEO070_734.04_734.33, WER: 100%, TER: 25%, total WER: 26.161%, total TER: 12.769%, progress (thread 0): 92.413%]
|T|: h m m
|P|: n o
[sample: EN2002b_H03_MEE073_1150.51_1150.8, WER: 100%, TER: 100%, total WER: 26.1618%, total TER: 12.7696%, progress (thread 0): 92.4209%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_1286.29_1286.58, WER: 100%, TER: 40%, total WER: 26.1627%, total TER: 12.77%, progress (thread 0): 92.4288%]
|T|: s o
|P|: s o
[sample: EN2002b_H03_MEE073_1517.43_1517.72, WER: 0%, TER: 0%, total WER: 26.1624%, total TER: 12.7699%, progress (thread 0): 92.4367%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_61.76_62.05, WER: 0%, TER: 0%, total WER: 26.1621%, total TER: 12.7698%, progress (thread 0): 92.4446%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_697.18_697.47, WER: 100%, TER: 50%, total WER: 26.1629%, total TER: 12.7699%, progress (thread 0): 92.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1026.29_1026.58, WER: 0%, TER: 0%, total WER: 26.1626%, total TER: 12.7698%, progress (thread 0): 92.4604%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_1088_1088.29, WER: 100%, TER: 40%, total WER: 26.1634%, total TER: 12.7701%, progress (thread 0): 92.4684%]
|T|: t h a t
|P|: o
[sample: EN2002c_H03_MEE073_1302.84_1303.13, WER: 100%, TER: 100%, total WER: 26.1643%, total TER: 12.771%, progress (thread 0): 92.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1313.99_1314.28, WER: 0%, TER: 0%, total WER: 26.164%, total TER: 12.7708%, progress (thread 0): 92.4842%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_1543.66_1543.95, WER: 100%, TER: 50%, total WER: 26.1648%, total TER: 12.771%, progress (thread 0): 92.4921%]
|T|: ' c a u s e
|P|: t a u s
[sample: EN2002c_H03_MEE073_2609.79_2610.08, WER: 100%, TER: 50%, total WER: 26.1656%, total TER: 12.7715%, progress (thread 0): 92.5%]
|T|: a c t u a l l y
|P|: a c t u a l l y
[sample: EN2002d_H03_MEE073_783.19_783.48, WER: 0%, TER: 0%, total WER: 26.1653%, total TER: 12.7713%, progress (thread 0): 92.5079%]
|T|: r i g h t
|P|: u
[sample: EN2002d_H02_MEE071_833.43_833.72, WER: 100%, TER: 100%, total WER: 26.1662%, total TER: 12.7723%, progress (thread 0): 92.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1031.38_1031.67, WER: 0%, TER: 0%, total WER: 26.1659%, total TER: 12.7722%, progress (thread 0): 92.5237%]
|T|: y e a h
|P|: n o
[sample: EN2002d_H03_MEE073_1490.73_1491.02, WER: 100%, TER: 100%, total WER: 26.1667%, total TER: 12.773%, progress (thread 0): 92.5316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2069.6_2069.89, WER: 0%, TER: 0%, total WER: 26.1664%, total TER: 12.7729%, progress (thread 0): 92.5396%]
|T|: n o
|P|: n
[sample: ES2004b_H03_FEE016_602.63_602.91, WER: 100%, TER: 50%, total WER: 26.1673%, total TER: 12.7731%, progress (thread 0): 92.5475%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1146.25_1146.53, WER: 0%, TER: 0%, total WER: 26.167%, total TER: 12.773%, progress (thread 0): 92.5554%]
|T|: m m
|P|: h m
[sample: ES2004b_H03_FEE016_1316.47_1316.75, WER: 100%, TER: 50%, total WER: 26.1678%, total TER: 12.7731%, progress (thread 0): 92.5633%]
|T|: r i g h t
|P|: r i h t
[sample: ES2004b_H00_MEO015_1939.86_1940.14, WER: 100%, TER: 20%, total WER: 26.1686%, total TER: 12.7732%, progress (thread 0): 92.5712%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2216.73_2217.01, WER: 0%, TER: 0%, total WER: 26.1683%, total TER: 12.7731%, progress (thread 0): 92.5791%]
|T|: t h a n k s
|P|:
[sample: ES2004c_H01_FEE013_54.47_54.75, WER: 100%, TER: 100%, total WER: 26.1692%, total TER: 12.7743%, progress (thread 0): 92.587%]
|T|: u h
|P|: u h
[sample: ES2004c_H02_MEE014_1126.8_1127.08, WER: 0%, TER: 0%, total WER: 26.1689%, total TER: 12.7743%, progress (thread 0): 92.5949%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1180.98_1181.26, WER: 100%, TER: 50%, total WER: 26.1697%, total TER: 12.7744%, progress (thread 0): 92.6029%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1398.69_1398.97, WER: 0%, TER: 0%, total WER: 26.1694%, total TER: 12.7743%, progress (thread 0): 92.6108%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2123.64_2123.92, WER: 0%, TER: 0%, total WER: 26.1691%, total TER: 12.7742%, progress (thread 0): 92.6187%]
|T|: n o
|P|: n o
[sample: ES2004d_H02_MEE014_1050.44_1050.72, WER: 0%, TER: 0%, total WER: 26.1688%, total TER: 12.7741%, progress (thread 0): 92.6266%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_1500.31_1500.59, WER: 100%, TER: 40%, total WER: 26.1696%, total TER: 12.7745%, progress (thread 0): 92.6345%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1843.24_1843.52, WER: 0%, TER: 0%, total WER: 26.1694%, total TER: 12.7743%, progress (thread 0): 92.6424%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_2015.69_2015.97, WER: 0%, TER: 0%, total WER: 26.1691%, total TER: 12.7742%, progress (thread 0): 92.6503%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H01_FEE013_2120.6_2120.88, WER: 0%, TER: 0%, total WER: 26.1688%, total TER: 12.7741%, progress (thread 0): 92.6582%]
|T|: o k a y
|P|: o
[sample: IS1009a_H03_FIO089_188.06_188.34, WER: 100%, TER: 75%, total WER: 26.1696%, total TER: 12.7746%, progress (thread 0): 92.6661%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_793.23_793.51, WER: 100%, TER: 40%, total WER: 26.1704%, total TER: 12.775%, progress (thread 0): 92.674%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_804.79_805.07, WER: 0%, TER: 0%, total WER: 26.1701%, total TER: 12.7748%, progress (thread 0): 92.682%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_894.05_894.33, WER: 0%, TER: 0%, total WER: 26.1698%, total TER: 12.7747%, progress (thread 0): 92.6899%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1764.17_1764.45, WER: 0%, TER: 0%, total WER: 26.1695%, total TER: 12.7746%, progress (thread 0): 92.6978%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009c_H02_FIO084_724.39_724.67, WER: 100%, TER: 100%, total WER: 26.1704%, total TER: 12.7756%, progress (thread 0): 92.7057%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1140.8_1141.08, WER: 0%, TER: 0%, total WER: 26.1701%, total TER: 12.7755%, progress (thread 0): 92.7136%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1619.41_1619.69, WER: 0%, TER: 0%, total WER: 26.1698%, total TER: 12.7754%, progress (thread 0): 92.7215%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_617.29_617.57, WER: 100%, TER: 40%, total WER: 26.1706%, total TER: 12.7757%, progress (thread 0): 92.7294%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_773.28_773.56, WER: 0%, TER: 0%, total WER: 26.1703%, total TER: 12.7756%, progress (thread 0): 92.7373%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_980.1_980.38, WER: 0%, TER: 0%, total WER: 26.17%, total TER: 12.7755%, progress (thread 0): 92.7452%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1792.74_1793.02, WER: 100%, TER: 100%, total WER: 26.1709%, total TER: 12.7765%, progress (thread 0): 92.7532%]
|T|: h m m
|P|: h m
[sample: IS1009d_H02_FIO084_1830.44_1830.72, WER: 100%, TER: 33.3333%, total WER: 26.1717%, total TER: 12.7766%, progress (thread 0): 92.7611%]
|T|: m m h m m
|P|: u h | h u
[sample: IS1009d_H03_FIO089_1876.62_1876.9, WER: 200%, TER: 100%, total WER: 26.1736%, total TER: 12.7777%, progress (thread 0): 92.769%]
|T|: m o r n i n g
|P|: o n t
[sample: TS3003a_H02_MTD0010ID_16.42_16.7, WER: 100%, TER: 71.4286%, total WER: 26.1745%, total TER: 12.7786%, progress (thread 0): 92.7769%]
|T|: s u r e
|P|: s u r e
[sample: TS3003a_H03_MTD012ME_163.72_164, WER: 0%, TER: 0%, total WER: 26.1742%, total TER: 12.7785%, progress (thread 0): 92.7848%]
|T|: h m m
|P|: h m
[sample: TS3003a_H03_MTD012ME_661.22_661.5, WER: 100%, TER: 33.3333%, total WER: 26.175%, total TER: 12.7786%, progress (thread 0): 92.7927%]
|T|: h m m
|P|: h m
[sample: TS3003a_H00_MTD009PM_1235.68_1235.96, WER: 100%, TER: 33.3333%, total WER: 26.1759%, total TER: 12.7788%, progress (thread 0): 92.8006%]
|T|: y e a h
|P|: t h a
[sample: TS3003b_H00_MTD009PM_823.58_823.86, WER: 100%, TER: 75%, total WER: 26.1767%, total TER: 12.7794%, progress (thread 0): 92.8085%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1892.2_1892.48, WER: 0%, TER: 0%, total WER: 26.1764%, total TER: 12.7792%, progress (thread 0): 92.8165%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1860.06_1860.34, WER: 0%, TER: 0%, total WER: 26.1761%, total TER: 12.7791%, progress (thread 0): 92.8244%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1891.82_1892.1, WER: 100%, TER: 40%, total WER: 26.1769%, total TER: 12.7794%, progress (thread 0): 92.8323%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_267.26_267.54, WER: 0%, TER: 0%, total WER: 26.1766%, total TER: 12.7793%, progress (thread 0): 92.8402%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_365.09_365.37, WER: 0%, TER: 0%, total WER: 26.1763%, total TER: 12.7792%, progress (thread 0): 92.8481%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H00_MTD009PM_816.6_816.88, WER: 0%, TER: 0%, total WER: 26.176%, total TER: 12.7791%, progress (thread 0): 92.856%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_860.45_860.73, WER: 0%, TER: 0%, total WER: 26.1757%, total TER: 12.779%, progress (thread 0): 92.8639%]
|T|: a n d | i t
|P|: a n d | i t
[sample: TS3003d_H02_MTD0010ID_1310.28_1310.56, WER: 0%, TER: 0%, total WER: 26.1752%, total TER: 12.7788%, progress (thread 0): 92.8718%]
|T|: s h
|P|: s
[sample: TS3003d_H02_MTD0010ID_1311.62_1311.9, WER: 100%, TER: 50%, total WER: 26.176%, total TER: 12.779%, progress (thread 0): 92.8797%]
|T|: y e p
|P|: y u p
[sample: TS3003d_H02_MTD0010ID_1403.4_1403.68, WER: 100%, TER: 33.3333%, total WER: 26.1768%, total TER: 12.7791%, progress (thread 0): 92.8877%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1945.66_1945.94, WER: 0%, TER: 0%, total WER: 26.1765%, total TER: 12.779%, progress (thread 0): 92.8956%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2142.04_2142.32, WER: 0%, TER: 0%, total WER: 26.1762%, total TER: 12.7789%, progress (thread 0): 92.9035%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_49.78_50.06, WER: 100%, TER: 50%, total WER: 26.1771%, total TER: 12.7792%, progress (thread 0): 92.9114%]
|T|: y e p
|P|: y e p
[sample: EN2002a_H01_FEO070_478.66_478.94, WER: 0%, TER: 0%, total WER: 26.1768%, total TER: 12.7792%, progress (thread 0): 92.9193%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_758.4_758.68, WER: 0%, TER: 0%, total WER: 26.1765%, total TER: 12.779%, progress (thread 0): 92.9272%]
|T|: y e a h
|P|: h m
[sample: EN2002a_H01_FEO070_762.35_762.63, WER: 100%, TER: 100%, total WER: 26.1773%, total TER: 12.7799%, progress (thread 0): 92.9351%]
|T|: b u t
|P|: b u t
[sample: EN2002a_H01_FEO070_1024.58_1024.86, WER: 0%, TER: 0%, total WER: 26.177%, total TER: 12.7798%, progress (thread 0): 92.943%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1306.05_1306.33, WER: 0%, TER: 0%, total WER: 26.1767%, total TER: 12.7796%, progress (thread 0): 92.951%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1392.09_1392.37, WER: 0%, TER: 0%, total WER: 26.1764%, total TER: 12.7795%, progress (thread 0): 92.9589%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002a_H00_MEE073_1638.28_1638.56, WER: 200%, TER: 175%, total WER: 26.1784%, total TER: 12.781%, progress (thread 0): 92.9668%]
|T|: s e e
|P|: s e e
[sample: EN2002a_H01_FEO070_1702.66_1702.94, WER: 0%, TER: 0%, total WER: 26.1781%, total TER: 12.781%, progress (thread 0): 92.9747%]
|T|: y e a h
|P|: n o
[sample: EN2002a_H01_FEO070_1796.46_1796.74, WER: 100%, TER: 100%, total WER: 26.1789%, total TER: 12.7818%, progress (thread 0): 92.9826%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1831.52_1831.8, WER: 0%, TER: 0%, total WER: 26.1786%, total TER: 12.7816%, progress (thread 0): 92.9905%]
|T|: o h
|P|: u h
[sample: EN2002a_H02_FEO072_1981.37_1981.65, WER: 100%, TER: 50%, total WER: 26.1794%, total TER: 12.7818%, progress (thread 0): 92.9984%]
|T|: i | t h
|P|: i
[sample: EN2002a_H00_MEE073_2026.22_2026.5, WER: 50%, TER: 75%, total WER: 26.18%, total TER: 12.7824%, progress (thread 0): 93.0063%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H01_MEE071_16.64_16.92, WER: 0%, TER: 0%, total WER: 26.1797%, total TER: 12.7823%, progress (thread 0): 93.0142%]
|T|: m m h m m
|P|: h m
[sample: EN2002b_H03_MEE073_335.63_335.91, WER: 100%, TER: 60%, total WER: 26.1805%, total TER: 12.7828%, progress (thread 0): 93.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_356.11_356.39, WER: 0%, TER: 0%, total WER: 26.1802%, total TER: 12.7827%, progress (thread 0): 93.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_987.48_987.76, WER: 0%, TER: 0%, total WER: 26.1799%, total TER: 12.7826%, progress (thread 0): 93.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1285.46_1285.74, WER: 0%, TER: 0%, total WER: 26.1796%, total TER: 12.7825%, progress (thread 0): 93.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1450.71_1450.99, WER: 0%, TER: 0%, total WER: 26.1793%, total TER: 12.7824%, progress (thread 0): 93.0538%]
|T|: y e a h
|P|: y e a k n
[sample: EN2002b_H03_MEE073_1588.08_1588.36, WER: 100%, TER: 50%, total WER: 26.1802%, total TER: 12.7827%, progress (thread 0): 93.0617%]
|T|: y e a h
|P|: y o a h
[sample: EN2002c_H03_MEE073_264.61_264.89, WER: 100%, TER: 25%, total WER: 26.181%, total TER: 12.7828%, progress (thread 0): 93.0696%]
|T|: m m h m m
|P|: m
[sample: EN2002c_H03_MEE073_638.33_638.61, WER: 100%, TER: 80%, total WER: 26.1818%, total TER: 12.7836%, progress (thread 0): 93.0775%]
|T|: a h
|P|: u h
[sample: EN2002c_H01_FEO072_1466.83_1467.11, WER: 100%, TER: 50%, total WER: 26.1827%, total TER: 12.7838%, progress (thread 0): 93.0854%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1508.55_1508.83, WER: 100%, TER: 28.5714%, total WER: 26.1835%, total TER: 12.784%, progress (thread 0): 93.0934%]
|T|: i | k n o w
|P|: k i n d | o f
[sample: EN2002c_H03_MEE073_1627.18_1627.46, WER: 100%, TER: 83.3333%, total WER: 26.1852%, total TER: 12.785%, progress (thread 0): 93.1013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2613.98_2614.26, WER: 0%, TER: 0%, total WER: 26.1849%, total TER: 12.7849%, progress (thread 0): 93.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2738.83_2739.11, WER: 0%, TER: 0%, total WER: 26.1846%, total TER: 12.7848%, progress (thread 0): 93.1171%]
|T|: w e l l
|P|: o
[sample: EN2002c_H03_MEE073_2837.32_2837.6, WER: 100%, TER: 100%, total WER: 26.1854%, total TER: 12.7856%, progress (thread 0): 93.125%]
|T|: i | t h
|P|: i
[sample: EN2002d_H03_MEE073_209.34_209.62, WER: 50%, TER: 75%, total WER: 26.1859%, total TER: 12.7862%, progress (thread 0): 93.1329%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H03_MEE073_379.93_380.21, WER: 100%, TER: 40%, total WER: 26.1868%, total TER: 12.7865%, progress (thread 0): 93.1408%]
|T|: y e a h
|P|:
[sample: EN2002d_H03_MEE073_446.25_446.53, WER: 100%, TER: 100%, total WER: 26.1876%, total TER: 12.7873%, progress (thread 0): 93.1487%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_622.33_622.61, WER: 0%, TER: 0%, total WER: 26.1873%, total TER: 12.7872%, progress (thread 0): 93.1566%]
|T|: y e a h
|P|: h m
[sample: EN2002d_H03_MEE073_989.71_989.99, WER: 100%, TER: 100%, total WER: 26.1881%, total TER: 12.788%, progress (thread 0): 93.1646%]
|T|: h m m
|P|: h m
[sample: EN2002d_H02_MEE071_1294.26_1294.54, WER: 100%, TER: 33.3333%, total WER: 26.189%, total TER: 12.7882%, progress (thread 0): 93.1725%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1744.58_1744.86, WER: 0%, TER: 0%, total WER: 26.1887%, total TER: 12.788%, progress (thread 0): 93.1804%]
|T|: t h e
|P|: e
[sample: EN2002d_H03_MEE073_1888.86_1889.14, WER: 100%, TER: 66.6667%, total WER: 26.1895%, total TER: 12.7884%, progress (thread 0): 93.1883%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2067.26_2067.54, WER: 0%, TER: 0%, total WER: 26.1892%, total TER: 12.7883%, progress (thread 0): 93.1962%]
|T|: s u r e
|P|: t r u e
[sample: ES2004b_H00_MEO015_1306.29_1306.56, WER: 100%, TER: 75%, total WER: 26.19%, total TER: 12.7889%, progress (thread 0): 93.2041%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1834.89_1835.16, WER: 0%, TER: 0%, total WER: 26.1898%, total TER: 12.7888%, progress (thread 0): 93.212%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1954.73_1955, WER: 100%, TER: 40%, total WER: 26.1906%, total TER: 12.7891%, progress (thread 0): 93.2199%]
|T|: m m h m m
|P|: h m
[sample: ES2004c_H03_FEE016_652.96_653.23, WER: 100%, TER: 60%, total WER: 26.1914%, total TER: 12.7896%, progress (thread 0): 93.2278%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1141.18_1141.45, WER: 100%, TER: 40%, total WER: 26.1922%, total TER: 12.79%, progress (thread 0): 93.2358%]
|T|: m m
|P|: j u
[sample: ES2004c_H03_FEE016_1281.44_1281.71, WER: 100%, TER: 100%, total WER: 26.1931%, total TER: 12.7904%, progress (thread 0): 93.2437%]
|T|: o k a y
|P|: k a y
[sample: ES2004c_H00_MEO015_1403.89_1404.16, WER: 100%, TER: 25%, total WER: 26.1939%, total TER: 12.7905%, progress (thread 0): 93.2516%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1730.76_1731.03, WER: 0%, TER: 0%, total WER: 26.1936%, total TER: 12.7904%, progress (thread 0): 93.2595%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1835.22_1835.49, WER: 100%, TER: 40%, total WER: 26.1944%, total TER: 12.7907%, progress (thread 0): 93.2674%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1921.81_1922.08, WER: 0%, TER: 0%, total WER: 26.1942%, total TER: 12.7906%, progress (thread 0): 93.2753%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_2149.62_2149.89, WER: 0%, TER: 0%, total WER: 26.1939%, total TER: 12.7904%, progress (thread 0): 93.2832%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_654.95_655.22, WER: 100%, TER: 40%, total WER: 26.1947%, total TER: 12.7908%, progress (thread 0): 93.2911%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_738.4_738.67, WER: 100%, TER: 33.3333%, total WER: 26.1955%, total TER: 12.7909%, progress (thread 0): 93.299%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_793.57_793.84, WER: 0%, TER: 0%, total WER: 26.1952%, total TER: 12.7908%, progress (thread 0): 93.307%]
|T|: n o
|P|: n o
[sample: ES2004d_H03_FEE016_1418.58_1418.85, WER: 0%, TER: 0%, total WER: 26.1949%, total TER: 12.7907%, progress (thread 0): 93.3149%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1497.73_1498, WER: 0%, TER: 0%, total WER: 26.1946%, total TER: 12.7906%, progress (thread 0): 93.3228%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1559.56_1559.83, WER: 0%, TER: 0%, total WER: 26.1943%, total TER: 12.7905%, progress (thread 0): 93.3307%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2099.96_2100.23, WER: 0%, TER: 0%, total WER: 26.194%, total TER: 12.7904%, progress (thread 0): 93.3386%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_159.51_159.78, WER: 100%, TER: 40%, total WER: 26.1949%, total TER: 12.7907%, progress (thread 0): 93.3465%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_495.24_495.51, WER: 0%, TER: 0%, total WER: 26.1946%, total TER: 12.7906%, progress (thread 0): 93.3544%]
|T|: m m h m m
|P|: m h | h m
[sample: IS1009b_H02_FIO084_130.02_130.29, WER: 200%, TER: 60%, total WER: 26.1965%, total TER: 12.7911%, progress (thread 0): 93.3623%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_195.35_195.62, WER: 100%, TER: 60%, total WER: 26.1974%, total TER: 12.7917%, progress (thread 0): 93.3703%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1093_1093.27, WER: 100%, TER: 40%, total WER: 26.1982%, total TER: 12.792%, progress (thread 0): 93.3782%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H01_FIO087_323.83_324.1, WER: 0%, TER: 0%, total WER: 26.1979%, total TER: 12.7918%, progress (thread 0): 93.3861%]
|T|: m m h m m
|P|:
[sample: IS1009c_H00_FIE088_770.63_770.9, WER: 100%, TER: 100%, total WER: 26.1987%, total TER: 12.7929%, progress (thread 0): 93.394%]
|T|: m m h m m
|P|: u h | h
[sample: IS1009c_H03_FIO089_922.16_922.43, WER: 200%, TER: 80%, total WER: 26.2007%, total TER: 12.7936%, progress (thread 0): 93.4019%]
|T|: m m h m m
|P|: u h | h m
[sample: IS1009c_H03_FIO089_1095.56_1095.83, WER: 200%, TER: 80%, total WER: 26.2026%, total TER: 12.7944%, progress (thread 0): 93.4098%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1374.42_1374.69, WER: 0%, TER: 0%, total WER: 26.2024%, total TER: 12.7943%, progress (thread 0): 93.4177%]
|T|: o k a y
|P|: l i c
[sample: IS1009d_H03_FIO089_215.46_215.73, WER: 100%, TER: 100%, total WER: 26.2032%, total TER: 12.7951%, progress (thread 0): 93.4256%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_377.8_378.07, WER: 100%, TER: 100%, total WER: 26.204%, total TER: 12.7961%, progress (thread 0): 93.4335%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_381.16_381.43, WER: 100%, TER: 40%, total WER: 26.2048%, total TER: 12.7965%, progress (thread 0): 93.4415%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_938.34_938.61, WER: 100%, TER: 60%, total WER: 26.2057%, total TER: 12.797%, progress (thread 0): 93.4494%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H03_FIO089_1114.59_1114.86, WER: 100%, TER: 80%, total WER: 26.2065%, total TER: 12.7978%, progress (thread 0): 93.4573%]
|T|: o h
|P|: o h
[sample: IS1009d_H00_FIE088_1436.69_1436.96, WER: 0%, TER: 0%, total WER: 26.2062%, total TER: 12.7977%, progress (thread 0): 93.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1775.25_1775.52, WER: 0%, TER: 0%, total WER: 26.2059%, total TER: 12.7976%, progress (thread 0): 93.4731%]
|T|: o k a y
|P|: o h
[sample: IS1009d_H01_FIO087_1908.84_1909.11, WER: 100%, TER: 75%, total WER: 26.2067%, total TER: 12.7982%, progress (thread 0): 93.481%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_684.57_684.84, WER: 100%, TER: 150%, total WER: 26.2076%, total TER: 12.7988%, progress (thread 0): 93.4889%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_790.17_790.44, WER: 100%, TER: 25%, total WER: 26.2084%, total TER: 12.799%, progress (thread 0): 93.4968%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_1326.95_1327.22, WER: 0%, TER: 0%, total WER: 26.2081%, total TER: 12.7989%, progress (thread 0): 93.5047%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1909.01_1909.28, WER: 0%, TER: 0%, total WER: 26.2078%, total TER: 12.7988%, progress (thread 0): 93.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1979.53_1979.8, WER: 0%, TER: 0%, total WER: 26.2075%, total TER: 12.7987%, progress (thread 0): 93.5206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2145.84_2146.11, WER: 0%, TER: 0%, total WER: 26.2072%, total TER: 12.7985%, progress (thread 0): 93.5285%]
|T|: o h
|P|: h m
[sample: TS3003d_H01_MTD011UID_364.71_364.98, WER: 100%, TER: 100%, total WER: 26.2081%, total TER: 12.7989%, progress (thread 0): 93.5364%]
|T|: t e n
|P|: t e n
[sample: TS3003d_H02_MTD0010ID_864.45_864.72, WER: 0%, TER: 0%, total WER: 26.2078%, total TER: 12.7989%, progress (thread 0): 93.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_958.91_959.18, WER: 0%, TER: 0%, total WER: 26.2075%, total TER: 12.7987%, progress (thread 0): 93.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1378.52_1378.79, WER: 0%, TER: 0%, total WER: 26.2072%, total TER: 12.7986%, progress (thread 0): 93.5601%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_2176.17_2176.44, WER: 0%, TER: 0%, total WER: 26.2069%, total TER: 12.7986%, progress (thread 0): 93.568%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_2345.04_2345.31, WER: 100%, TER: 50%, total WER: 26.2077%, total TER: 12.7987%, progress (thread 0): 93.576%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2364.31_2364.58, WER: 0%, TER: 0%, total WER: 26.2074%, total TER: 12.7986%, progress (thread 0): 93.5839%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_2435.3_2435.57, WER: 100%, TER: 25%, total WER: 26.2082%, total TER: 12.7987%, progress (thread 0): 93.5918%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_501.72_501.99, WER: 100%, TER: 33.3333%, total WER: 26.2091%, total TER: 12.7989%, progress (thread 0): 93.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_727.19_727.46, WER: 0%, TER: 0%, total WER: 26.2088%, total TER: 12.7987%, progress (thread 0): 93.6076%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_806.36_806.63, WER: 0%, TER: 0%, total WER: 26.2085%, total TER: 12.7986%, progress (thread 0): 93.6155%]
|T|: o k a y
|P|:
[sample: EN2002a_H00_MEE073_927.86_928.13, WER: 100%, TER: 100%, total WER: 26.2093%, total TER: 12.7994%, progress (thread 0): 93.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1223.91_1224.18, WER: 0%, TER: 0%, total WER: 26.209%, total TER: 12.7993%, progress (thread 0): 93.6313%]
|T|: y e a h
|P|: y o u n w
[sample: EN2002a_H00_MEE073_1319.85_1320.12, WER: 100%, TER: 100%, total WER: 26.2099%, total TER: 12.8001%, progress (thread 0): 93.6392%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_1452_1452.27, WER: 100%, TER: 50%, total WER: 26.2107%, total TER: 12.8005%, progress (thread 0): 93.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1516.15_1516.42, WER: 0%, TER: 0%, total WER: 26.2104%, total TER: 12.8004%, progress (thread 0): 93.6551%]
|T|: y e a h
|P|: y e s
[sample: EN2002a_H03_MEE071_1533.19_1533.46, WER: 100%, TER: 50%, total WER: 26.2112%, total TER: 12.8007%, progress (thread 0): 93.663%]
|T|: y e a h
|P|: y o n
[sample: EN2002a_H00_MEE073_1556.33_1556.6, WER: 100%, TER: 75%, total WER: 26.2121%, total TER: 12.8013%, progress (thread 0): 93.6709%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1945.11_1945.38, WER: 0%, TER: 0%, total WER: 26.2118%, total TER: 12.8012%, progress (thread 0): 93.6788%]
|T|: o h
|P|: o h
[sample: EN2002a_H00_MEE073_1989_1989.27, WER: 0%, TER: 0%, total WER: 26.2115%, total TER: 12.8011%, progress (thread 0): 93.6867%]
|T|: o h
|P|: u m
[sample: EN2002a_H00_MEE073_2003.83_2004.1, WER: 100%, TER: 100%, total WER: 26.2123%, total TER: 12.8015%, progress (thread 0): 93.6946%]
|T|: n o
|P|: y o u | k n o w
[sample: EN2002a_H01_FEO070_2105.6_2105.87, WER: 200%, TER: 300%, total WER: 26.2142%, total TER: 12.8029%, progress (thread 0): 93.7025%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_355.59_355.86, WER: 0%, TER: 0%, total WER: 26.214%, total TER: 12.8028%, progress (thread 0): 93.7104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_485.61_485.88, WER: 0%, TER: 0%, total WER: 26.2137%, total TER: 12.8027%, progress (thread 0): 93.7184%]
|T|: r i g h t
|P|: g r i a t
[sample: EN2002b_H03_MEE073_1027.72_1027.99, WER: 100%, TER: 60%, total WER: 26.2145%, total TER: 12.8032%, progress (thread 0): 93.7263%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_1296.06_1296.33, WER: 0%, TER: 0%, total WER: 26.2142%, total TER: 12.8032%, progress (thread 0): 93.7342%]
|T|: s o
|P|: s
[sample: EN2002b_H01_MEE071_1333.58_1333.85, WER: 100%, TER: 50%, total WER: 26.215%, total TER: 12.8034%, progress (thread 0): 93.7421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1339.47_1339.74, WER: 0%, TER: 0%, total WER: 26.2147%, total TER: 12.8032%, progress (thread 0): 93.75%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1448.9_1449.17, WER: 0%, TER: 0%, total WER: 26.2144%, total TER: 12.8031%, progress (thread 0): 93.7579%]
|T|: a l r i g h t
|P|: a l r i g h t
[sample: EN2002c_H03_MEE073_558.45_558.72, WER: 0%, TER: 0%, total WER: 26.2141%, total TER: 12.8029%, progress (thread 0): 93.7658%]
|T|: h m m
|P|: h m
[sample: EN2002c_H01_FEO072_655.42_655.69, WER: 100%, TER: 33.3333%, total WER: 26.215%, total TER: 12.803%, progress (thread 0): 93.7737%]
|T|: c o o l
|P|: c o o l
[sample: EN2002c_H01_FEO072_778.13_778.4, WER: 0%, TER: 0%, total WER: 26.2147%, total TER: 12.8029%, progress (thread 0): 93.7816%]
|T|: o r
|P|: o l
[sample: EN2002c_H01_FEO072_798.93_799.2, WER: 100%, TER: 50%, total WER: 26.2155%, total TER: 12.8031%, progress (thread 0): 93.7896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_938.98_939.25, WER: 0%, TER: 0%, total WER: 26.2152%, total TER: 12.803%, progress (thread 0): 93.7975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1122.75_1123.02, WER: 0%, TER: 0%, total WER: 26.2149%, total TER: 12.8029%, progress (thread 0): 93.8054%]
|T|: o k a y
|P|:
[sample: EN2002c_H02_MEE071_1353.05_1353.32, WER: 100%, TER: 100%, total WER: 26.2157%, total TER: 12.8037%, progress (thread 0): 93.8133%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1574.37_1574.64, WER: 0%, TER: 0%, total WER: 26.2154%, total TER: 12.8035%, progress (thread 0): 93.8212%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_1672.35_1672.62, WER: 100%, TER: 25%, total WER: 26.2163%, total TER: 12.8036%, progress (thread 0): 93.8291%]
|T|: y e a h
|P|: n o
[sample: EN2002c_H03_MEE073_1740.26_1740.53, WER: 100%, TER: 100%, total WER: 26.2171%, total TER: 12.8045%, progress (thread 0): 93.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1980.58_1980.85, WER: 0%, TER: 0%, total WER: 26.2168%, total TER: 12.8043%, progress (thread 0): 93.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2155.14_2155.41, WER: 0%, TER: 0%, total WER: 26.2165%, total TER: 12.8042%, progress (thread 0): 93.8528%]
|T|: a l r i g h t
|P|: a h
[sample: EN2002c_H03_MEE073_2202.51_2202.78, WER: 100%, TER: 71.4286%, total WER: 26.2174%, total TER: 12.8052%, progress (thread 0): 93.8608%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2272.51_2272.78, WER: 200%, TER: 175%, total WER: 26.2193%, total TER: 12.8067%, progress (thread 0): 93.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2294.4_2294.67, WER: 0%, TER: 0%, total WER: 26.219%, total TER: 12.8066%, progress (thread 0): 93.8766%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2396.65_2396.92, WER: 0%, TER: 0%, total WER: 26.2187%, total TER: 12.8065%, progress (thread 0): 93.8845%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2568.46_2568.73, WER: 0%, TER: 0%, total WER: 26.2184%, total TER: 12.8063%, progress (thread 0): 93.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2705.6_2705.87, WER: 0%, TER: 0%, total WER: 26.2181%, total TER: 12.8062%, progress (thread 0): 93.9003%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_2790.29_2790.56, WER: 100%, TER: 50%, total WER: 26.219%, total TER: 12.8064%, progress (thread 0): 93.9082%]
|T|: i | k n o w
|P|: n
[sample: EN2002d_H03_MEE073_903.75_904.02, WER: 100%, TER: 83.3333%, total WER: 26.2206%, total TER: 12.8074%, progress (thread 0): 93.9161%]
|T|: b u t
|P|: k h a t
[sample: EN2002d_H01_FEO072_946.69_946.96, WER: 100%, TER: 100%, total WER: 26.2214%, total TER: 12.808%, progress (thread 0): 93.924%]
|T|: s o r r y
|P|: s o
[sample: EN2002d_H00_FEO070_966.35_966.62, WER: 100%, TER: 60%, total WER: 26.2223%, total TER: 12.8085%, progress (thread 0): 93.932%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1127.55_1127.82, WER: 0%, TER: 0%, total WER: 26.222%, total TER: 12.8084%, progress (thread 0): 93.9399%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1281.17_1281.44, WER: 0%, TER: 0%, total WER: 26.2217%, total TER: 12.8083%, progress (thread 0): 93.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1305.04_1305.31, WER: 0%, TER: 0%, total WER: 26.2214%, total TER: 12.8082%, progress (thread 0): 93.9557%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002d_H00_FEO070_1507.8_1508.07, WER: 100%, TER: 100%, total WER: 26.2231%, total TER: 12.8096%, progress (thread 0): 93.9636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_1832.82_1833.09, WER: 0%, TER: 0%, total WER: 26.2228%, total TER: 12.8095%, progress (thread 0): 93.9715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_2000.66_2000.93, WER: 0%, TER: 0%, total WER: 26.2225%, total TER: 12.8094%, progress (thread 0): 93.9794%]
|T|: w h a t
|P|: w e l l
[sample: EN2002d_H02_MEE071_2072.88_2073.15, WER: 100%, TER: 75%, total WER: 26.2233%, total TER: 12.8099%, progress (thread 0): 93.9873%]
|T|: y e p
|P|: y e p
[sample: EN2002d_H03_MEE073_2169.42_2169.69, WER: 0%, TER: 0%, total WER: 26.223%, total TER: 12.8099%, progress (thread 0): 93.9953%]
|T|: y e a h
|P|: y o a | h
[sample: ES2004a_H00_MEO015_17.88_18.14, WER: 200%, TER: 50%, total WER: 26.225%, total TER: 12.8102%, progress (thread 0): 94.0032%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_966.22_966.48, WER: 0%, TER: 0%, total WER: 26.2247%, total TER: 12.8101%, progress (thread 0): 94.0111%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_89.12_89.38, WER: 100%, TER: 40%, total WER: 26.2255%, total TER: 12.8104%, progress (thread 0): 94.019%]
|T|: t h e r e | w e | g o
|P|:
[sample: ES2004b_H02_MEE014_830.72_830.98, WER: 100%, TER: 100%, total WER: 26.228%, total TER: 12.8126%, progress (thread 0): 94.0269%]
|T|: h u h
|P|: a
[sample: ES2004b_H02_MEE014_1039.01_1039.27, WER: 100%, TER: 100%, total WER: 26.2288%, total TER: 12.8132%, progress (thread 0): 94.0348%]
|T|: p e n s
|P|: b e e n
[sample: ES2004b_H00_MEO015_1162.44_1162.7, WER: 100%, TER: 75%, total WER: 26.2296%, total TER: 12.8138%, progress (thread 0): 94.0427%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1848.94_1849.2, WER: 0%, TER: 0%, total WER: 26.2293%, total TER: 12.8137%, progress (thread 0): 94.0506%]
|T|: ' k a y
|P|: t o
[sample: ES2004c_H00_MEO015_188.1_188.36, WER: 100%, TER: 100%, total WER: 26.2302%, total TER: 12.8145%, progress (thread 0): 94.0585%]
|T|: y e p
|P|: y u p
[sample: ES2004c_H00_MEO015_540.57_540.83, WER: 100%, TER: 33.3333%, total WER: 26.231%, total TER: 12.8146%, progress (thread 0): 94.0665%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1192.94_1193.2, WER: 0%, TER: 0%, total WER: 26.2307%, total TER: 12.8145%, progress (thread 0): 94.0744%]
|T|: m m h m m
|P|: u h | h u m
[sample: ES2004d_H00_MEO015_39.85_40.11, WER: 200%, TER: 80%, total WER: 26.2327%, total TER: 12.8153%, progress (thread 0): 94.0823%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_129.84_130.1, WER: 0%, TER: 0%, total WER: 26.2324%, total TER: 12.8152%, progress (thread 0): 94.0902%]
|T|: o h
|P|: o m
[sample: ES2004d_H03_FEE016_1400.02_1400.28, WER: 100%, TER: 50%, total WER: 26.2332%, total TER: 12.8154%, progress (thread 0): 94.0981%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1553.35_1553.61, WER: 0%, TER: 0%, total WER: 26.2329%, total TER: 12.8152%, progress (thread 0): 94.106%]
|T|: n o
|P|: n o
[sample: ES2004d_H01_FEE013_1608.41_1608.67, WER: 0%, TER: 0%, total WER: 26.2326%, total TER: 12.8152%, progress (thread 0): 94.1139%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_1680.41_1680.67, WER: 100%, TER: 40%, total WER: 26.2334%, total TER: 12.8155%, progress (thread 0): 94.1218%]
|T|: m m h m m
|P|: h
[sample: IS1009a_H03_FIO089_195.56_195.82, WER: 100%, TER: 80%, total WER: 26.2343%, total TER: 12.8163%, progress (thread 0): 94.1297%]
|T|: o k a y
|P|: a k
[sample: IS1009b_H01_FIO087_250.63_250.89, WER: 100%, TER: 75%, total WER: 26.2351%, total TER: 12.8169%, progress (thread 0): 94.1377%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_289.01_289.27, WER: 0%, TER: 0%, total WER: 26.2348%, total TER: 12.8168%, progress (thread 0): 94.1456%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_892.83_893.09, WER: 100%, TER: 100%, total WER: 26.2356%, total TER: 12.8178%, progress (thread 0): 94.1535%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_2020.18_2020.44, WER: 0%, TER: 0%, total WER: 26.2353%, total TER: 12.8177%, progress (thread 0): 94.1614%]
|T|: m m h m m
|P|: y e | h
[sample: IS1009c_H00_FIE088_626.71_626.97, WER: 200%, TER: 100%, total WER: 26.2373%, total TER: 12.8187%, progress (thread 0): 94.1693%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_766.43_766.69, WER: 0%, TER: 0%, total WER: 26.237%, total TER: 12.8186%, progress (thread 0): 94.1772%]
|T|: m m
|P|:
[sample: IS1009d_H01_FIO087_656.85_657.11, WER: 100%, TER: 100%, total WER: 26.2378%, total TER: 12.819%, progress (thread 0): 94.1851%]
|T|: ' c a u s e
|P|:
[sample: TS3003a_H03_MTD012ME_1376.63_1376.89, WER: 100%, TER: 100%, total WER: 26.2387%, total TER: 12.8202%, progress (thread 0): 94.193%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H02_MTD0010ID_1474.04_1474.3, WER: 0%, TER: 0%, total WER: 26.2384%, total TER: 12.8201%, progress (thread 0): 94.201%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_206.59_206.85, WER: 100%, TER: 25%, total WER: 26.2392%, total TER: 12.8202%, progress (thread 0): 94.2089%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H01_MTD011UID_581.65_581.91, WER: 0%, TER: 0%, total WER: 26.2389%, total TER: 12.8201%, progress (thread 0): 94.2168%]
|T|: h m m
|P|: h m
[sample: TS3003b_H03_MTD012ME_883.3_883.56, WER: 100%, TER: 33.3333%, total WER: 26.2397%, total TER: 12.8202%, progress (thread 0): 94.2247%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1533.07_1533.33, WER: 100%, TER: 40%, total WER: 26.2406%, total TER: 12.8205%, progress (thread 0): 94.2326%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_2048.3_2048.56, WER: 0%, TER: 0%, total WER: 26.2403%, total TER: 12.8205%, progress (thread 0): 94.2405%]
|T|: m m
|P|: t e
[sample: TS3003c_H01_MTD011UID_1933.15_1933.41, WER: 100%, TER: 100%, total WER: 26.2411%, total TER: 12.8209%, progress (thread 0): 94.2484%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1980.3_1980.56, WER: 0%, TER: 0%, total WER: 26.2408%, total TER: 12.8207%, progress (thread 0): 94.2563%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_694.34_694.6, WER: 0%, TER: 0%, total WER: 26.2405%, total TER: 12.8206%, progress (thread 0): 94.2642%]
|T|: t w o
|P|: t o
[sample: TS3003d_H03_MTD012ME_1897.81_1898.07, WER: 100%, TER: 33.3333%, total WER: 26.2413%, total TER: 12.8208%, progress (thread 0): 94.2722%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2141.52_2141.78, WER: 0%, TER: 0%, total WER: 26.241%, total TER: 12.8206%, progress (thread 0): 94.2801%]
|T|: m m h m m
|P|: m h | h
[sample: EN2002a_H02_FEO072_36.34_36.6, WER: 200%, TER: 60%, total WER: 26.243%, total TER: 12.8212%, progress (thread 0): 94.288%]
|T|: y e a h
|P|: y h
[sample: EN2002a_H00_MEE073_319.23_319.49, WER: 100%, TER: 50%, total WER: 26.2438%, total TER: 12.8215%, progress (thread 0): 94.2959%]
|T|: y e a h
|P|: e h
[sample: EN2002a_H03_MEE071_671.05_671.31, WER: 100%, TER: 50%, total WER: 26.2446%, total TER: 12.8219%, progress (thread 0): 94.3038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_721.89_722.15, WER: 0%, TER: 0%, total WER: 26.2443%, total TER: 12.8218%, progress (thread 0): 94.3117%]
|T|: t r u e
|P|: t r u e
[sample: EN2002a_H00_MEE073_751.8_752.06, WER: 0%, TER: 0%, total WER: 26.2441%, total TER: 12.8217%, progress (thread 0): 94.3196%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1052.1_1052.36, WER: 0%, TER: 0%, total WER: 26.2438%, total TER: 12.8215%, progress (thread 0): 94.3275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1446.67_1446.93, WER: 0%, TER: 0%, total WER: 26.2435%, total TER: 12.8214%, progress (thread 0): 94.3354%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1777.39_1777.65, WER: 0%, TER: 0%, total WER: 26.2432%, total TER: 12.8213%, progress (thread 0): 94.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1868.15_1868.41, WER: 0%, TER: 0%, total WER: 26.2429%, total TER: 12.8212%, progress (thread 0): 94.3513%]
|T|: o k a y
|P|: g
[sample: EN2002b_H02_FEO072_93.22_93.48, WER: 100%, TER: 100%, total WER: 26.2437%, total TER: 12.822%, progress (thread 0): 94.3592%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_252.07_252.33, WER: 200%, TER: 175%, total WER: 26.2457%, total TER: 12.8235%, progress (thread 0): 94.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_460.58_460.84, WER: 0%, TER: 0%, total WER: 26.2454%, total TER: 12.8234%, progress (thread 0): 94.375%]
|T|: o h
|P|: o h
[sample: EN2002b_H03_MEE073_674.06_674.32, WER: 0%, TER: 0%, total WER: 26.2451%, total TER: 12.8233%, progress (thread 0): 94.3829%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_1026.26_1026.52, WER: 200%, TER: 175%, total WER: 26.247%, total TER: 12.8248%, progress (thread 0): 94.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1104.98_1105.24, WER: 0%, TER: 0%, total WER: 26.2467%, total TER: 12.8247%, progress (thread 0): 94.3987%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_1549.41_1549.67, WER: 100%, TER: 33.3333%, total WER: 26.2476%, total TER: 12.8249%, progress (thread 0): 94.4066%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1643.55_1643.81, WER: 0%, TER: 0%, total WER: 26.2473%, total TER: 12.8247%, progress (thread 0): 94.4146%]
|T|: r i g h t
|P|: r i
[sample: EN2002c_H02_MEE071_46.45_46.71, WER: 100%, TER: 60%, total WER: 26.2481%, total TER: 12.8253%, progress (thread 0): 94.4225%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_577.1_577.36, WER: 0%, TER: 0%, total WER: 26.2478%, total TER: 12.8252%, progress (thread 0): 94.4304%]
|T|: y e p
|P|: n o
[sample: EN2002c_H01_FEO072_600.57_600.83, WER: 100%, TER: 100%, total WER: 26.2486%, total TER: 12.8258%, progress (thread 0): 94.4383%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_775.24_775.5, WER: 0%, TER: 0%, total WER: 26.2483%, total TER: 12.8257%, progress (thread 0): 94.4462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1079.16_1079.42, WER: 0%, TER: 0%, total WER: 26.248%, total TER: 12.8256%, progress (thread 0): 94.4541%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_1105.57_1105.83, WER: 100%, TER: 40%, total WER: 26.2489%, total TER: 12.8259%, progress (thread 0): 94.462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1119.48_1119.74, WER: 0%, TER: 0%, total WER: 26.2486%, total TER: 12.8258%, progress (thread 0): 94.4699%]
|T|: m m h m m
|P|: s o
[sample: EN2002c_H03_MEE073_1983.15_1983.41, WER: 100%, TER: 100%, total WER: 26.2494%, total TER: 12.8268%, progress (thread 0): 94.4779%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1999.1_1999.36, WER: 0%, TER: 0%, total WER: 26.2491%, total TER: 12.8266%, progress (thread 0): 94.4858%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2082.11_2082.37, WER: 0%, TER: 0%, total WER: 26.2488%, total TER: 12.8265%, progress (thread 0): 94.4937%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2586.11_2586.37, WER: 0%, TER: 0%, total WER: 26.2485%, total TER: 12.8264%, progress (thread 0): 94.5016%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_347.04_347.3, WER: 0%, TER: 0%, total WER: 26.2482%, total TER: 12.8263%, progress (thread 0): 94.5095%]
|T|: w h o
|P|: h m
[sample: EN2002d_H00_FEO070_501.31_501.57, WER: 100%, TER: 66.6667%, total WER: 26.249%, total TER: 12.8267%, progress (thread 0): 94.5174%]
|T|: y e a h
|P|: y e p
[sample: EN2002d_H02_MEE071_728.46_728.72, WER: 100%, TER: 50%, total WER: 26.2499%, total TER: 12.827%, progress (thread 0): 94.5253%]
|T|: w e l l | i t ' s
|P|: j u s t
[sample: EN2002d_H03_MEE073_882.54_882.8, WER: 100%, TER: 88.8889%, total WER: 26.2515%, total TER: 12.8286%, progress (thread 0): 94.5332%]
|T|: s u p e r
|P|: s u p
[sample: EN2002d_H03_MEE073_953.8_954.06, WER: 100%, TER: 40%, total WER: 26.2524%, total TER: 12.8289%, progress (thread 0): 94.5411%]
|T|: o h | y e a h
|P|:
[sample: EN2002d_H00_FEO070_1517.63_1517.89, WER: 100%, TER: 100%, total WER: 26.254%, total TER: 12.8303%, progress (thread 0): 94.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1704.28_1704.54, WER: 0%, TER: 0%, total WER: 26.2537%, total TER: 12.8302%, progress (thread 0): 94.557%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_2135.36_2135.62, WER: 100%, TER: 33.3333%, total WER: 26.2546%, total TER: 12.8304%, progress (thread 0): 94.5649%]
|T|: m m
|P|: h m
[sample: ES2004a_H03_FEE016_666.69_666.94, WER: 100%, TER: 50%, total WER: 26.2554%, total TER: 12.8305%, progress (thread 0): 94.5728%]
|T|: y e p
|P|: y e p
[sample: ES2004b_H00_MEO015_817.97_818.22, WER: 0%, TER: 0%, total WER: 26.2551%, total TER: 12.8305%, progress (thread 0): 94.5807%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_2175.08_2175.33, WER: 100%, TER: 40%, total WER: 26.2559%, total TER: 12.8308%, progress (thread 0): 94.5886%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_620.36_620.61, WER: 100%, TER: 40%, total WER: 26.2567%, total TER: 12.8311%, progress (thread 0): 94.5965%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1866.87_1867.12, WER: 100%, TER: 100%, total WER: 26.2576%, total TER: 12.8317%, progress (thread 0): 94.6044%]
|T|: s o r r y
|P|: s o r r y
[sample: ES2004d_H01_FEE013_455.38_455.63, WER: 0%, TER: 0%, total WER: 26.2573%, total TER: 12.8315%, progress (thread 0): 94.6123%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_607.67_607.92, WER: 0%, TER: 0%, total WER: 26.257%, total TER: 12.8314%, progress (thread 0): 94.6203%]
|T|: y e s
|P|: y e a h
[sample: ES2004d_H03_FEE016_1135.45_1135.7, WER: 100%, TER: 66.6667%, total WER: 26.2578%, total TER: 12.8318%, progress (thread 0): 94.6282%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1553.38_1553.63, WER: 0%, TER: 0%, total WER: 26.2575%, total TER: 12.8317%, progress (thread 0): 94.6361%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1086.32_1086.57, WER: 100%, TER: 40%, total WER: 26.2583%, total TER: 12.832%, progress (thread 0): 94.644%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1900.42_1900.67, WER: 0%, TER: 0%, total WER: 26.2581%, total TER: 12.8319%, progress (thread 0): 94.6519%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1909.04_1909.29, WER: 0%, TER: 0%, total WER: 26.2578%, total TER: 12.8318%, progress (thread 0): 94.6598%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H00_FIE088_324.58_324.83, WER: 0%, TER: 0%, total WER: 26.2575%, total TER: 12.8316%, progress (thread 0): 94.6677%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1287.33_1287.58, WER: 0%, TER: 0%, total WER: 26.2572%, total TER: 12.8315%, progress (thread 0): 94.6756%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1781.33_1781.58, WER: 0%, TER: 0%, total WER: 26.2569%, total TER: 12.8314%, progress (thread 0): 94.6835%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_351.32_351.57, WER: 0%, TER: 0%, total WER: 26.2566%, total TER: 12.8313%, progress (thread 0): 94.6915%]
|T|: m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_427.43_427.68, WER: 100%, TER: 200%, total WER: 26.2574%, total TER: 12.8322%, progress (thread 0): 94.6994%]
|T|: m m
|P|: a
[sample: IS1009d_H02_FIO084_826.51_826.76, WER: 100%, TER: 100%, total WER: 26.2582%, total TER: 12.8326%, progress (thread 0): 94.7073%]
|T|: n o
|P|: n o
[sample: TS3003a_H03_MTD012ME_1222.74_1222.99, WER: 0%, TER: 0%, total WER: 26.2579%, total TER: 12.8325%, progress (thread 0): 94.7152%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_281.83_282.08, WER: 100%, TER: 50%, total WER: 26.2588%, total TER: 12.8327%, progress (thread 0): 94.7231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1559.52_1559.77, WER: 0%, TER: 0%, total WER: 26.2585%, total TER: 12.8326%, progress (thread 0): 94.731%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_1839.11_1839.36, WER: 0%, TER: 0%, total WER: 26.2582%, total TER: 12.8324%, progress (thread 0): 94.7389%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H02_MTD0010ID_2094.57_2094.82, WER: 100%, TER: 50%, total WER: 26.259%, total TER: 12.8328%, progress (thread 0): 94.7468%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1972.52_1972.77, WER: 100%, TER: 40%, total WER: 26.2598%, total TER: 12.8331%, progress (thread 0): 94.7548%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_303.27_303.52, WER: 100%, TER: 75%, total WER: 26.2607%, total TER: 12.8337%, progress (thread 0): 94.7627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_583.29_583.54, WER: 0%, TER: 0%, total WER: 26.2604%, total TER: 12.8336%, progress (thread 0): 94.7706%]
|T|: y e a h
|P|: y e h
[sample: TS3003d_H02_MTD0010ID_847.16_847.41, WER: 100%, TER: 25%, total WER: 26.2612%, total TER: 12.8337%, progress (thread 0): 94.7785%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1370.99_1371.24, WER: 100%, TER: 75%, total WER: 26.262%, total TER: 12.8343%, progress (thread 0): 94.7864%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1371.48_1371.73, WER: 0%, TER: 0%, total WER: 26.2617%, total TER: 12.8341%, progress (thread 0): 94.7943%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1404.32_1404.57, WER: 100%, TER: 75%, total WER: 26.2626%, total TER: 12.8347%, progress (thread 0): 94.8022%]
|T|: g o o d
|P|: c a u s e
[sample: TS3003d_H03_MTD012ME_1696.35_1696.6, WER: 100%, TER: 125%, total WER: 26.2634%, total TER: 12.8358%, progress (thread 0): 94.8101%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2064.36_2064.61, WER: 0%, TER: 0%, total WER: 26.2631%, total TER: 12.8356%, progress (thread 0): 94.818%]
|T|: y e a h
|P|:
[sample: TS3003d_H02_MTD0010ID_2111_2111.25, WER: 100%, TER: 100%, total WER: 26.2639%, total TER: 12.8365%, progress (thread 0): 94.826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2138.4_2138.65, WER: 0%, TER: 0%, total WER: 26.2636%, total TER: 12.8363%, progress (thread 0): 94.8339%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2216.27_2216.52, WER: 0%, TER: 0%, total WER: 26.2633%, total TER: 12.8362%, progress (thread 0): 94.8418%]
|T|: a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2381.92_2382.17, WER: 100%, TER: 100%, total WER: 26.2642%, total TER: 12.8366%, progress (thread 0): 94.8497%]
|T|: s o
|P|: s o
[sample: EN2002a_H00_MEE073_202.42_202.67, WER: 0%, TER: 0%, total WER: 26.2639%, total TER: 12.8366%, progress (thread 0): 94.8576%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002a_H00_MEE073_749.45_749.7, WER: 200%, TER: 175%, total WER: 26.2658%, total TER: 12.8381%, progress (thread 0): 94.8655%]
|T|: y e a h
|P|: t o
[sample: EN2002a_H00_MEE073_1642_1642.25, WER: 100%, TER: 100%, total WER: 26.2666%, total TER: 12.8389%, progress (thread 0): 94.8734%]
|T|: s o
|P|: s o
[sample: EN2002a_H02_FEO072_1664.11_1664.36, WER: 0%, TER: 0%, total WER: 26.2663%, total TER: 12.8388%, progress (thread 0): 94.8813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1964.06_1964.31, WER: 0%, TER: 0%, total WER: 26.2661%, total TER: 12.8387%, progress (thread 0): 94.8892%]
|T|: y o u ' d | b e | s
|P|: i n p
[sample: EN2002a_H00_MEE073_2096.92_2097.17, WER: 100%, TER: 100%, total WER: 26.2685%, total TER: 12.8408%, progress (thread 0): 94.8971%]
|T|: y e a h
|P|: y e p
[sample: EN2002b_H01_MEE071_93.68_93.93, WER: 100%, TER: 50%, total WER: 26.2694%, total TER: 12.8411%, progress (thread 0): 94.9051%]
|T|: r i g h t
|P|: i
[sample: EN2002b_H03_MEE073_311.61_311.86, WER: 100%, TER: 80%, total WER: 26.2702%, total TER: 12.8419%, progress (thread 0): 94.913%]
|T|: a l r i g h t
|P|: r g h t
[sample: EN2002b_H03_MEE073_1129.86_1130.11, WER: 100%, TER: 42.8571%, total WER: 26.271%, total TER: 12.8424%, progress (thread 0): 94.9209%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1222.15_1222.4, WER: 0%, TER: 0%, total WER: 26.2707%, total TER: 12.8423%, progress (thread 0): 94.9288%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1237.83_1238.08, WER: 0%, TER: 0%, total WER: 26.2704%, total TER: 12.8422%, progress (thread 0): 94.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1245.09_1245.34, WER: 0%, TER: 0%, total WER: 26.2701%, total TER: 12.842%, progress (thread 0): 94.9446%]
|T|: a n d
|P|: t h e n
[sample: EN2002b_H01_MEE071_1309.75_1310, WER: 100%, TER: 133.333%, total WER: 26.271%, total TER: 12.8429%, progress (thread 0): 94.9525%]
|T|: y e p
|P|: n o
[sample: EN2002b_H02_FEO072_1594.84_1595.09, WER: 100%, TER: 100%, total WER: 26.2718%, total TER: 12.8435%, progress (thread 0): 94.9604%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_1643.59_1643.84, WER: 200%, TER: 175%, total WER: 26.2737%, total TER: 12.845%, progress (thread 0): 94.9684%]
|T|: s u r e
|P|: s u e
[sample: EN2002c_H02_MEE071_67.06_67.31, WER: 100%, TER: 25%, total WER: 26.2746%, total TER: 12.8451%, progress (thread 0): 94.9763%]
|T|: o k a y
|P|: n
[sample: EN2002c_H03_MEE073_76.13_76.38, WER: 100%, TER: 100%, total WER: 26.2754%, total TER: 12.8459%, progress (thread 0): 94.9842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_253.63_253.88, WER: 0%, TER: 0%, total WER: 26.2751%, total TER: 12.8458%, progress (thread 0): 94.9921%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_470.85_471.1, WER: 100%, TER: 40%, total WER: 26.2759%, total TER: 12.8461%, progress (thread 0): 95%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_759.06_759.31, WER: 0%, TER: 0%, total WER: 26.2756%, total TER: 12.846%, progress (thread 0): 95.0079%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1000.82_1001.07, WER: 0%, TER: 0%, total WER: 26.2753%, total TER: 12.8459%, progress (thread 0): 95.0158%]
|T|: y e a h
|P|: y e a | h | w
[sample: EN2002c_H03_MEE073_1023.06_1023.31, WER: 300%, TER: 75%, total WER: 26.2784%, total TER: 12.8464%, progress (thread 0): 95.0237%]
|T|: m m h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1102.48_1102.73, WER: 100%, TER: 60%, total WER: 26.2793%, total TER: 12.847%, progress (thread 0): 95.0316%]
|T|: y e a h
|P|: y e s
[sample: EN2002c_H02_MEE071_1122.76_1123.01, WER: 100%, TER: 50%, total WER: 26.2801%, total TER: 12.8473%, progress (thread 0): 95.0396%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_1359.23_1359.48, WER: 100%, TER: 66.6667%, total WER: 26.2809%, total TER: 12.8477%, progress (thread 0): 95.0475%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_1508.23_1508.48, WER: 0%, TER: 0%, total WER: 26.2806%, total TER: 12.8476%, progress (thread 0): 95.0554%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2100.94_2101.19, WER: 0%, TER: 0%, total WER: 26.2803%, total TER: 12.8475%, progress (thread 0): 95.0633%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2354.17_2354.42, WER: 0%, TER: 0%, total WER: 26.28%, total TER: 12.8474%, progress (thread 0): 95.0712%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_146.37_146.62, WER: 100%, TER: 28.5714%, total WER: 26.2808%, total TER: 12.8476%, progress (thread 0): 95.0791%]
|T|: o h
|P|: o
[sample: EN2002d_H00_FEO070_286.77_287.02, WER: 100%, TER: 50%, total WER: 26.2817%, total TER: 12.8478%, progress (thread 0): 95.087%]
|T|: y e a h
|P|: y h m
[sample: EN2002d_H03_MEE073_589.91_590.16, WER: 100%, TER: 75%, total WER: 26.2825%, total TER: 12.8484%, progress (thread 0): 95.0949%]
|T|: y e a h
|P|: c a n c e
[sample: EN2002d_H03_MEE073_850.49_850.74, WER: 100%, TER: 125%, total WER: 26.2833%, total TER: 12.8494%, progress (thread 0): 95.1028%]
|T|: s o
|P|: s o
[sample: EN2002d_H02_MEE071_1251.8_1252.05, WER: 0%, TER: 0%, total WER: 26.283%, total TER: 12.8494%, progress (thread 0): 95.1108%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_1830.85_1831.1, WER: 100%, TER: 33.3333%, total WER: 26.2839%, total TER: 12.8495%, progress (thread 0): 95.1187%]
|T|: o k a y
|P|: c o n
[sample: EN2002d_H03_MEE073_1854.42_1854.67, WER: 100%, TER: 100%, total WER: 26.2847%, total TER: 12.8503%, progress (thread 0): 95.1266%]
|T|: m m
|P|: o
[sample: EN2002d_H01_FEO072_2083.17_2083.42, WER: 100%, TER: 100%, total WER: 26.2855%, total TER: 12.8508%, progress (thread 0): 95.1345%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2192.17_2192.42, WER: 0%, TER: 0%, total WER: 26.2852%, total TER: 12.8506%, progress (thread 0): 95.1424%]
|T|: s
|P|: s
[sample: ES2004a_H02_MEE014_484.48_484.72, WER: 0%, TER: 0%, total WER: 26.2849%, total TER: 12.8506%, progress (thread 0): 95.1503%]
|T|: m m
|P|:
[sample: ES2004a_H03_FEE016_775.6_775.84, WER: 100%, TER: 100%, total WER: 26.2858%, total TER: 12.851%, progress (thread 0): 95.1582%]
|T|: s o r r y
|P|: t
[sample: ES2004b_H00_MEO015_100.7_100.94, WER: 100%, TER: 100%, total WER: 26.2866%, total TER: 12.852%, progress (thread 0): 95.1661%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1311.92_1312.16, WER: 0%, TER: 0%, total WER: 26.2863%, total TER: 12.8519%, progress (thread 0): 95.174%]
|T|: m m h m m
|P|: m h u m
[sample: ES2004b_H00_MEO015_1636.65_1636.89, WER: 100%, TER: 40%, total WER: 26.2871%, total TER: 12.8522%, progress (thread 0): 95.182%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1305.92_1306.16, WER: 100%, TER: 25%, total WER: 26.2879%, total TER: 12.8523%, progress (thread 0): 95.1899%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1345.16_1345.4, WER: 100%, TER: 50%, total WER: 26.2888%, total TER: 12.8525%, progress (thread 0): 95.1978%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_1364.93_1365.17, WER: 100%, TER: 100%, total WER: 26.2896%, total TER: 12.8529%, progress (thread 0): 95.2057%]
|T|: f i n e
|P|: p o i n t
[sample: ES2004c_H00_MEO015_2156.57_2156.81, WER: 100%, TER: 75%, total WER: 26.2904%, total TER: 12.8535%, progress (thread 0): 95.2136%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_973.73_973.97, WER: 0%, TER: 0%, total WER: 26.2901%, total TER: 12.8534%, progress (thread 0): 95.2215%]
|T|: m m
|P|: h
[sample: ES2004d_H03_FEE016_1286.7_1286.94, WER: 100%, TER: 100%, total WER: 26.291%, total TER: 12.8538%, progress (thread 0): 95.2294%]
|T|: o h
|P|: o h
[sample: ES2004d_H01_FEE013_2176.67_2176.91, WER: 0%, TER: 0%, total WER: 26.2907%, total TER: 12.8537%, progress (thread 0): 95.2373%]
|T|: n o
|P|: h
[sample: IS1009a_H03_FIO089_145.55_145.79, WER: 100%, TER: 100%, total WER: 26.2915%, total TER: 12.8541%, progress (thread 0): 95.2453%]
|T|: m m h m m
|P|:
[sample: IS1009a_H00_FIE088_466.21_466.45, WER: 100%, TER: 100%, total WER: 26.2923%, total TER: 12.8551%, progress (thread 0): 95.2532%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_556.58_556.82, WER: 0%, TER: 0%, total WER: 26.292%, total TER: 12.855%, progress (thread 0): 95.2611%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1033.16_1033.4, WER: 0%, TER: 0%, total WER: 26.2917%, total TER: 12.8549%, progress (thread 0): 95.269%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_1099.19_1099.43, WER: 0%, TER: 0%, total WER: 26.2914%, total TER: 12.8548%, progress (thread 0): 95.2769%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1247.84_1248.08, WER: 100%, TER: 60%, total WER: 26.2923%, total TER: 12.8553%, progress (thread 0): 95.2848%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H03_FIO089_752.07_752.31, WER: 100%, TER: 40%, total WER: 26.2931%, total TER: 12.8557%, progress (thread 0): 95.2927%]
|T|: m m | r i g h t
|P|: r i g t
[sample: IS1009c_H00_FIE088_764.18_764.42, WER: 100%, TER: 50%, total WER: 26.2947%, total TER: 12.8563%, progress (thread 0): 95.3006%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1110.14_1110.38, WER: 100%, TER: 40%, total WER: 26.2956%, total TER: 12.8567%, progress (thread 0): 95.3085%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1123.59_1123.83, WER: 100%, TER: 40%, total WER: 26.2964%, total TER: 12.857%, progress (thread 0): 95.3165%]
|T|: h e l l o
|P|: n o
[sample: IS1009d_H02_FIO084_41.31_41.55, WER: 100%, TER: 80%, total WER: 26.2972%, total TER: 12.8578%, progress (thread 0): 95.3244%]
|T|: m m
|P|: m m
[sample: IS1009d_H03_FIO089_516.52_516.76, WER: 0%, TER: 0%, total WER: 26.2969%, total TER: 12.8577%, progress (thread 0): 95.3323%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_656.9_657.14, WER: 100%, TER: 60%, total WER: 26.2978%, total TER: 12.8583%, progress (thread 0): 95.3402%]
|T|: y e a h
|P|: y o n o
[sample: IS1009d_H02_FIO084_1011.72_1011.96, WER: 100%, TER: 75%, total WER: 26.2986%, total TER: 12.8588%, progress (thread 0): 95.3481%]
|T|: o k a y
|P|: o k a
[sample: IS1009d_H03_FIO089_1883.01_1883.25, WER: 100%, TER: 25%, total WER: 26.2994%, total TER: 12.8589%, progress (thread 0): 95.356%]
|T|: w e
|P|: w
[sample: TS3003a_H00_MTD009PM_1109.9_1110.14, WER: 100%, TER: 50%, total WER: 26.3002%, total TER: 12.8591%, progress (thread 0): 95.3639%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1420.32_1420.56, WER: 100%, TER: 50%, total WER: 26.3011%, total TER: 12.8593%, progress (thread 0): 95.3718%]
|T|: n o
|P|: n o
[sample: TS3003b_H02_MTD0010ID_1474.4_1474.64, WER: 0%, TER: 0%, total WER: 26.3008%, total TER: 12.8592%, progress (thread 0): 95.3797%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_1677.79_1678.03, WER: 0%, TER: 0%, total WER: 26.3005%, total TER: 12.8591%, progress (thread 0): 95.3877%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1975.67_1975.91, WER: 100%, TER: 100%, total WER: 26.3013%, total TER: 12.8595%, progress (thread 0): 95.3956%]
|T|: y e a h
|P|: h u m
[sample: TS3003b_H01_MTD011UID_2083.9_2084.14, WER: 100%, TER: 100%, total WER: 26.3021%, total TER: 12.8603%, progress (thread 0): 95.4035%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_1240.02_1240.26, WER: 100%, TER: 75%, total WER: 26.303%, total TER: 12.8609%, progress (thread 0): 95.4114%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1254.68_1254.92, WER: 0%, TER: 0%, total WER: 26.3027%, total TER: 12.8608%, progress (thread 0): 95.4193%]
|T|: s o
|P|: s o
[sample: TS3003c_H01_MTD011UID_1417.83_1418.07, WER: 0%, TER: 0%, total WER: 26.3024%, total TER: 12.8607%, progress (thread 0): 95.4272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1477.96_1478.2, WER: 0%, TER: 0%, total WER: 26.3021%, total TER: 12.8606%, progress (thread 0): 95.4351%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1566.64_1566.88, WER: 100%, TER: 25%, total WER: 26.3029%, total TER: 12.8607%, progress (thread 0): 95.443%]
|T|: y e a h
|P|: a h
[sample: TS3003c_H01_MTD011UID_1716.46_1716.7, WER: 100%, TER: 50%, total WER: 26.3037%, total TER: 12.8611%, progress (thread 0): 95.451%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2217.59_2217.83, WER: 0%, TER: 0%, total WER: 26.3034%, total TER: 12.861%, progress (thread 0): 95.4589%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2243.54_2243.78, WER: 0%, TER: 0%, total WER: 26.3031%, total TER: 12.8608%, progress (thread 0): 95.4668%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1049.33_1049.57, WER: 0%, TER: 0%, total WER: 26.3028%, total TER: 12.8607%, progress (thread 0): 95.4747%]
|T|: y e a h
|P|: o h
[sample: TS3003d_H01_MTD011UID_1496.68_1496.92, WER: 100%, TER: 75%, total WER: 26.3037%, total TER: 12.8613%, progress (thread 0): 95.4826%]
|T|: d | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_106.38_106.62, WER: 50%, TER: 33.3333%, total WER: 26.3042%, total TER: 12.8616%, progress (thread 0): 95.4905%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_140.88_141.12, WER: 0%, TER: 0%, total WER: 26.3039%, total TER: 12.8615%, progress (thread 0): 95.4984%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_333.3_333.54, WER: 0%, TER: 0%, total WER: 26.3036%, total TER: 12.8613%, progress (thread 0): 95.5063%]
|T|: y e a h
|P|: h m
[sample: EN2002a_H01_FEO070_398.18_398.42, WER: 100%, TER: 100%, total WER: 26.3044%, total TER: 12.8621%, progress (thread 0): 95.5142%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_481.62_481.86, WER: 100%, TER: 50%, total WER: 26.3053%, total TER: 12.8625%, progress (thread 0): 95.5222%]
|T|: y e a h
|P|: e m
[sample: EN2002a_H00_MEE073_682.97_683.21, WER: 100%, TER: 75%, total WER: 26.3061%, total TER: 12.863%, progress (thread 0): 95.5301%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H02_FEO072_1009.94_1010.18, WER: 100%, TER: 40%, total WER: 26.3069%, total TER: 12.8634%, progress (thread 0): 95.538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1041.03_1041.27, WER: 0%, TER: 0%, total WER: 26.3066%, total TER: 12.8632%, progress (thread 0): 95.5459%]
|T|: i | s e e
|P|: a s
[sample: EN2002a_H00_MEE073_1171.72_1171.96, WER: 100%, TER: 80%, total WER: 26.3083%, total TER: 12.864%, progress (thread 0): 95.5538%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002a_H00_MEE073_1209.32_1209.56, WER: 100%, TER: 85.7143%, total WER: 26.3099%, total TER: 12.8652%, progress (thread 0): 95.5617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1310.69_1310.93, WER: 0%, TER: 0%, total WER: 26.3096%, total TER: 12.8651%, progress (thread 0): 95.5696%]
|T|: o p e n
|P|:
[sample: EN2002a_H02_FEO072_1350.04_1350.28, WER: 100%, TER: 100%, total WER: 26.3105%, total TER: 12.8659%, progress (thread 0): 95.5775%]
|T|: m m
|P|: h m
[sample: EN2002a_H03_MEE071_1405.73_1405.97, WER: 100%, TER: 50%, total WER: 26.3113%, total TER: 12.8661%, progress (thread 0): 95.5854%]
|T|: r i g h t
|P|: r i g t
[sample: EN2002a_H00_MEE073_1791.17_1791.41, WER: 100%, TER: 20%, total WER: 26.3121%, total TER: 12.8662%, progress (thread 0): 95.5934%]
|T|: y e a h
|P|: t e h
[sample: EN2002a_H00_MEE073_1834.42_1834.66, WER: 100%, TER: 50%, total WER: 26.313%, total TER: 12.8665%, progress (thread 0): 95.6013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2028.12_2028.36, WER: 0%, TER: 0%, total WER: 26.3127%, total TER: 12.8664%, progress (thread 0): 95.6092%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_2040.58_2040.82, WER: 0%, TER: 0%, total WER: 26.3124%, total TER: 12.8663%, progress (thread 0): 95.6171%]
|T|: o h
|P|: o h
[sample: EN2002a_H01_FEO070_2098.91_2099.15, WER: 0%, TER: 0%, total WER: 26.3121%, total TER: 12.8662%, progress (thread 0): 95.625%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_522.48_522.72, WER: 0%, TER: 0%, total WER: 26.3118%, total TER: 12.8661%, progress (thread 0): 95.6329%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_612.23_612.47, WER: 0%, TER: 0%, total WER: 26.3115%, total TER: 12.866%, progress (thread 0): 95.6408%]
|T|: m m
|P|:
[sample: EN2002b_H02_FEO072_1475.84_1476.08, WER: 100%, TER: 100%, total WER: 26.3123%, total TER: 12.8664%, progress (thread 0): 95.6487%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_235.84_236.08, WER: 100%, TER: 33.3333%, total WER: 26.3131%, total TER: 12.8665%, progress (thread 0): 95.6566%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_571.47_571.71, WER: 0%, TER: 0%, total WER: 26.3128%, total TER: 12.8664%, progress (thread 0): 95.6646%]
|T|: h m m
|P|: y o u h
[sample: EN2002c_H03_MEE073_574.34_574.58, WER: 100%, TER: 133.333%, total WER: 26.3137%, total TER: 12.8672%, progress (thread 0): 95.6725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_598.8_599.04, WER: 0%, TER: 0%, total WER: 26.3134%, total TER: 12.8671%, progress (thread 0): 95.6804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1009.87_1010.11, WER: 0%, TER: 0%, total WER: 26.3131%, total TER: 12.867%, progress (thread 0): 95.6883%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1018.66_1018.9, WER: 0%, TER: 0%, total WER: 26.3128%, total TER: 12.8669%, progress (thread 0): 95.6962%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1281.36_1281.6, WER: 100%, TER: 33.3333%, total WER: 26.3136%, total TER: 12.867%, progress (thread 0): 95.7041%]
|T|: y e a h
|P|: y e a | k n | w
[sample: EN2002c_H03_MEE073_1396.34_1396.58, WER: 300%, TER: 125%, total WER: 26.3167%, total TER: 12.868%, progress (thread 0): 95.712%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1906.84_1907.08, WER: 0%, TER: 0%, total WER: 26.3164%, total TER: 12.8679%, progress (thread 0): 95.7199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1913.84_1914.08, WER: 0%, TER: 0%, total WER: 26.3161%, total TER: 12.8678%, progress (thread 0): 95.7279%]
|T|: ' c a u s e
|P|: a s
[sample: EN2002c_H03_MEE073_2264.19_2264.43, WER: 100%, TER: 66.6667%, total WER: 26.3169%, total TER: 12.8686%, progress (thread 0): 95.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2309.3_2309.54, WER: 0%, TER: 0%, total WER: 26.3166%, total TER: 12.8684%, progress (thread 0): 95.7437%]
|T|: r i g h t
|P|: i
[sample: EN2002c_H03_MEE073_2341.41_2341.65, WER: 100%, TER: 80%, total WER: 26.3174%, total TER: 12.8692%, progress (thread 0): 95.7516%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2370.76_2371, WER: 0%, TER: 0%, total WER: 26.3171%, total TER: 12.8691%, progress (thread 0): 95.7595%]
|T|: g o o d
|P|: g o o d
[sample: EN2002d_H01_FEO072_337.97_338.21, WER: 0%, TER: 0%, total WER: 26.3169%, total TER: 12.869%, progress (thread 0): 95.7674%]
|T|: y e a h
|P|: t o
[sample: EN2002d_H03_MEE073_390.81_391.05, WER: 100%, TER: 100%, total WER: 26.3177%, total TER: 12.8698%, progress (thread 0): 95.7753%]
|T|: r i g h t
|P|: r i g h
[sample: EN2002d_H01_FEO072_496.78_497.02, WER: 100%, TER: 20%, total WER: 26.3185%, total TER: 12.8698%, progress (thread 0): 95.7832%]
|T|: o k a y
|P|: u h
[sample: EN2002d_H00_FEO070_513.66_513.9, WER: 100%, TER: 100%, total WER: 26.3193%, total TER: 12.8707%, progress (thread 0): 95.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1190.34_1190.58, WER: 0%, TER: 0%, total WER: 26.319%, total TER: 12.8705%, progress (thread 0): 95.799%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1371.83_1372.07, WER: 0%, TER: 0%, total WER: 26.3187%, total TER: 12.8704%, progress (thread 0): 95.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1477.13_1477.37, WER: 0%, TER: 0%, total WER: 26.3184%, total TER: 12.8703%, progress (thread 0): 95.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1706.39_1706.63, WER: 0%, TER: 0%, total WER: 26.3182%, total TER: 12.8702%, progress (thread 0): 95.8228%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H03_FEE016_1717.78_1718.01, WER: 100%, TER: 200%, total WER: 26.319%, total TER: 12.8711%, progress (thread 0): 95.8307%]
|T|: m m h m m
|P|: h
[sample: ES2004b_H00_MEO015_1860.91_1861.14, WER: 100%, TER: 80%, total WER: 26.3198%, total TER: 12.8718%, progress (thread 0): 95.8386%]
|T|: t r u e
|P|: j u
[sample: ES2004b_H00_MEO015_2294.79_2295.02, WER: 100%, TER: 75%, total WER: 26.3206%, total TER: 12.8724%, progress (thread 0): 95.8465%]
|T|: y e a h
|P|: a n d
[sample: ES2004c_H00_MEO015_85.13_85.36, WER: 100%, TER: 100%, total WER: 26.3215%, total TER: 12.8732%, progress (thread 0): 95.8544%]
|T|: ' k a y
|P|: k a
[sample: ES2004c_H00_MEO015_356.94_357.17, WER: 100%, TER: 50%, total WER: 26.3223%, total TER: 12.8736%, progress (thread 0): 95.8623%]
|T|: s o
|P|: a
[sample: ES2004c_H02_MEE014_748.27_748.5, WER: 100%, TER: 100%, total WER: 26.3231%, total TER: 12.874%, progress (thread 0): 95.8702%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_1121.33_1121.56, WER: 100%, TER: 100%, total WER: 26.3239%, total TER: 12.8744%, progress (thread 0): 95.8782%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1169.98_1170.21, WER: 0%, TER: 0%, total WER: 26.3236%, total TER: 12.8743%, progress (thread 0): 95.8861%]
|T|: o k a y
|P|: o
[sample: ES2004d_H03_FEE016_711.82_712.05, WER: 100%, TER: 75%, total WER: 26.3245%, total TER: 12.8748%, progress (thread 0): 95.894%]
|T|: t w o
|P|: d o
[sample: ES2004d_H00_MEO015_732.46_732.69, WER: 100%, TER: 66.6667%, total WER: 26.3253%, total TER: 12.8752%, progress (thread 0): 95.9019%]
|T|: m m h m m
|P|: h
[sample: ES2004d_H00_MEO015_822.98_823.21, WER: 100%, TER: 80%, total WER: 26.3261%, total TER: 12.876%, progress (thread 0): 95.9098%]
|T|: ' k a y
|P|: s c h o o
[sample: ES2004d_H00_MEO015_1813.16_1813.39, WER: 100%, TER: 125%, total WER: 26.3269%, total TER: 12.877%, progress (thread 0): 95.9177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_195.04_195.27, WER: 0%, TER: 0%, total WER: 26.3267%, total TER: 12.8769%, progress (thread 0): 95.9256%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_532.46_532.69, WER: 0%, TER: 0%, total WER: 26.3264%, total TER: 12.8768%, progress (thread 0): 95.9335%]
|T|: m m
|P|: h m
[sample: IS1009b_H02_FIO084_55.21_55.44, WER: 100%, TER: 50%, total WER: 26.3272%, total TER: 12.877%, progress (thread 0): 95.9415%]
|T|: y e a h
|P|: y e
[sample: IS1009b_H02_FIO084_1919.19_1919.42, WER: 100%, TER: 50%, total WER: 26.328%, total TER: 12.8774%, progress (thread 0): 95.9494%]
|T|: m m h m m
|P|: h m
[sample: IS1009c_H00_FIE088_554.54_554.77, WER: 100%, TER: 60%, total WER: 26.3288%, total TER: 12.8779%, progress (thread 0): 95.9573%]
|T|: m m | y e s
|P|: y e a h
[sample: IS1009c_H01_FIO087_728.73_728.96, WER: 100%, TER: 83.3333%, total WER: 26.3305%, total TER: 12.8789%, progress (thread 0): 95.9652%]
|T|: y e p
|P|: y e a h
[sample: IS1009c_H03_FIO089_1639.78_1640.01, WER: 100%, TER: 66.6667%, total WER: 26.3313%, total TER: 12.8793%, progress (thread 0): 95.9731%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_747.97_748.2, WER: 100%, TER: 66.6667%, total WER: 26.3321%, total TER: 12.8796%, progress (thread 0): 95.981%]
|T|: h m m
|P|:
[sample: IS1009d_H02_FIO084_748.42_748.65, WER: 100%, TER: 100%, total WER: 26.333%, total TER: 12.8803%, progress (thread 0): 95.9889%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_758.1_758.33, WER: 100%, TER: 100%, total WER: 26.3338%, total TER: 12.8813%, progress (thread 0): 95.9968%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_1695.75_1695.98, WER: 0%, TER: 0%, total WER: 26.3335%, total TER: 12.8812%, progress (thread 0): 96.0047%]
|T|: o k a y
|P|: y e
[sample: IS1009d_H03_FIO089_1889.3_1889.53, WER: 100%, TER: 100%, total WER: 26.3343%, total TER: 12.882%, progress (thread 0): 96.0127%]
|T|: ' k a y
|P|: o k a
[sample: TS3003a_H03_MTD012ME_843.57_843.8, WER: 100%, TER: 50%, total WER: 26.3352%, total TER: 12.8823%, progress (thread 0): 96.0206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1334.2_1334.43, WER: 0%, TER: 0%, total WER: 26.3349%, total TER: 12.8822%, progress (thread 0): 96.0285%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1346.08_1346.31, WER: 0%, TER: 0%, total WER: 26.3346%, total TER: 12.8821%, progress (thread 0): 96.0364%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1078.3_1078.53, WER: 100%, TER: 50%, total WER: 26.3354%, total TER: 12.8823%, progress (thread 0): 96.0443%]
|T|: n o
|P|: n o
[sample: TS3003b_H01_MTD011UID_1716.14_1716.37, WER: 0%, TER: 0%, total WER: 26.3351%, total TER: 12.8822%, progress (thread 0): 96.0522%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_1250.59_1250.82, WER: 0%, TER: 0%, total WER: 26.3348%, total TER: 12.8822%, progress (thread 0): 96.0601%]
|T|: y e s
|P|: j u s t
[sample: TS3003c_H02_MTD0010ID_1518.39_1518.62, WER: 100%, TER: 100%, total WER: 26.3356%, total TER: 12.8828%, progress (thread 0): 96.068%]
|T|: m m
|P|: a m
[sample: TS3003c_H01_MTD011UID_1654.26_1654.49, WER: 100%, TER: 50%, total WER: 26.3365%, total TER: 12.8829%, progress (thread 0): 96.076%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_2049.46_2049.69, WER: 100%, TER: 75%, total WER: 26.3373%, total TER: 12.8835%, progress (thread 0): 96.0839%]
|T|: s o
|P|: s o
[sample: TS3003d_H01_MTD011UID_261.45_261.68, WER: 0%, TER: 0%, total WER: 26.337%, total TER: 12.8835%, progress (thread 0): 96.0918%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H02_MTD0010ID_1021.27_1021.5, WER: 0%, TER: 0%, total WER: 26.3367%, total TER: 12.8834%, progress (thread 0): 96.0997%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_1945.96_1946.19, WER: 100%, TER: 100%, total WER: 26.3375%, total TER: 12.8842%, progress (thread 0): 96.1076%]
|T|: y e a h
|P|: e a h
[sample: TS3003d_H01_MTD011UID_2064.77_2065, WER: 100%, TER: 25%, total WER: 26.3383%, total TER: 12.8843%, progress (thread 0): 96.1155%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_424.58_424.81, WER: 0%, TER: 0%, total WER: 26.338%, total TER: 12.8841%, progress (thread 0): 96.1234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_781.91_782.14, WER: 0%, TER: 0%, total WER: 26.3377%, total TER: 12.884%, progress (thread 0): 96.1313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_855.85_856.08, WER: 0%, TER: 0%, total WER: 26.3375%, total TER: 12.8839%, progress (thread 0): 96.1392%]
|T|: h m m
|P|:
[sample: EN2002a_H02_FEO072_877.24_877.47, WER: 100%, TER: 100%, total WER: 26.3383%, total TER: 12.8845%, progress (thread 0): 96.1471%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1019.4_1019.63, WER: 100%, TER: 33.3333%, total WER: 26.3391%, total TER: 12.8847%, progress (thread 0): 96.1551%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_1188.61_1188.84, WER: 100%, TER: 66.6667%, total WER: 26.3399%, total TER: 12.885%, progress (thread 0): 96.163%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1295.18_1295.41, WER: 0%, TER: 0%, total WER: 26.3396%, total TER: 12.8849%, progress (thread 0): 96.1709%]
|T|: y e a h
|P|: t o
[sample: EN2002a_H01_FEO070_1408.29_1408.52, WER: 100%, TER: 100%, total WER: 26.3405%, total TER: 12.8857%, progress (thread 0): 96.1788%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1571.85_1572.08, WER: 0%, TER: 0%, total WER: 26.3402%, total TER: 12.8857%, progress (thread 0): 96.1867%]
|T|: o h
|P|: o
[sample: EN2002b_H03_MEE073_29.06_29.29, WER: 100%, TER: 50%, total WER: 26.341%, total TER: 12.8858%, progress (thread 0): 96.1946%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_194.27_194.5, WER: 0%, TER: 0%, total WER: 26.3407%, total TER: 12.8857%, progress (thread 0): 96.2025%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_211.63_211.86, WER: 0%, TER: 0%, total WER: 26.3404%, total TER: 12.8856%, progress (thread 0): 96.2104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_493.43_493.66, WER: 0%, TER: 0%, total WER: 26.3401%, total TER: 12.8855%, progress (thread 0): 96.2184%]
|T|: h m m
|P|: h m
[sample: EN2002b_H00_FEO070_534.29_534.52, WER: 100%, TER: 33.3333%, total WER: 26.3409%, total TER: 12.8856%, progress (thread 0): 96.2263%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_672.54_672.77, WER: 100%, TER: 33.3333%, total WER: 26.3418%, total TER: 12.8858%, progress (thread 0): 96.2342%]
|T|: m m h m m
|P|: y e
[sample: EN2002b_H03_MEE073_1361.79_1362.02, WER: 100%, TER: 100%, total WER: 26.3426%, total TER: 12.8868%, progress (thread 0): 96.2421%]
|T|: y e a h
|P|: e a h
[sample: EN2002c_H03_MEE073_555.02_555.25, WER: 100%, TER: 25%, total WER: 26.3434%, total TER: 12.8869%, progress (thread 0): 96.25%]
|T|: h m m
|P|: b u h
[sample: EN2002c_H01_FEO072_699.88_700.11, WER: 100%, TER: 100%, total WER: 26.3442%, total TER: 12.8875%, progress (thread 0): 96.2579%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1900.45_1900.68, WER: 0%, TER: 0%, total WER: 26.3439%, total TER: 12.8874%, progress (thread 0): 96.2658%]
|T|: d o h
|P|: d o
[sample: EN2002c_H02_MEE071_2395.6_2395.83, WER: 100%, TER: 33.3333%, total WER: 26.3448%, total TER: 12.8875%, progress (thread 0): 96.2737%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2817.5_2817.73, WER: 0%, TER: 0%, total WER: 26.3445%, total TER: 12.8874%, progress (thread 0): 96.2816%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2846.79_2847.02, WER: 0%, TER: 0%, total WER: 26.3442%, total TER: 12.8873%, progress (thread 0): 96.2896%]
|T|: o k a y
|P|: k a
[sample: EN2002d_H03_MEE073_107.69_107.92, WER: 100%, TER: 50%, total WER: 26.345%, total TER: 12.8876%, progress (thread 0): 96.2975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_503.45_503.68, WER: 0%, TER: 0%, total WER: 26.3447%, total TER: 12.8875%, progress (thread 0): 96.3054%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_548.74_548.97, WER: 100%, TER: 40%, total WER: 26.3455%, total TER: 12.8878%, progress (thread 0): 96.3133%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_767.8_768.03, WER: 0%, TER: 0%, total WER: 26.3452%, total TER: 12.8877%, progress (thread 0): 96.3212%]
|T|: n o
|P|: n o
[sample: EN2002d_H00_FEO070_1427.88_1428.11, WER: 0%, TER: 0%, total WER: 26.3449%, total TER: 12.8876%, progress (thread 0): 96.3291%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1760.63_1760.86, WER: 100%, TER: 33.3333%, total WER: 26.3458%, total TER: 12.8878%, progress (thread 0): 96.337%]
|T|: o k a y
|P|:
[sample: EN2002d_H00_FEO070_2207.62_2207.85, WER: 100%, TER: 100%, total WER: 26.3466%, total TER: 12.8886%, progress (thread 0): 96.3449%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_1022.74_1022.96, WER: 0%, TER: 0%, total WER: 26.3463%, total TER: 12.8885%, progress (thread 0): 96.3528%]
|T|: m m
|P|: h m
[sample: ES2004b_H02_MEE014_1537.12_1537.34, WER: 100%, TER: 50%, total WER: 26.3471%, total TER: 12.8886%, progress (thread 0): 96.3608%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004c_H00_MEO015_575.7_575.92, WER: 0%, TER: 0%, total WER: 26.3468%, total TER: 12.8885%, progress (thread 0): 96.3687%]
|T|: m m h m m
|P|: y m | h
[sample: ES2004c_H03_FEE016_1311.86_1312.08, WER: 200%, TER: 80%, total WER: 26.3488%, total TER: 12.8893%, progress (thread 0): 96.3766%]
|T|: r i g h t
|P|: i
[sample: ES2004c_H00_MEO015_2116.51_2116.73, WER: 100%, TER: 80%, total WER: 26.3496%, total TER: 12.8901%, progress (thread 0): 96.3845%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_2159.26_2159.48, WER: 0%, TER: 0%, total WER: 26.3493%, total TER: 12.8899%, progress (thread 0): 96.3924%]
|T|: y e a h
|P|: e h
[sample: ES2004d_H02_MEE014_968.07_968.29, WER: 100%, TER: 50%, total WER: 26.3501%, total TER: 12.8903%, progress (thread 0): 96.4003%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1220.58_1220.8, WER: 0%, TER: 0%, total WER: 26.3498%, total TER: 12.8902%, progress (thread 0): 96.4082%]
|T|: o k a y
|P|:
[sample: ES2004d_H03_FEE016_1281.25_1281.47, WER: 100%, TER: 100%, total WER: 26.3507%, total TER: 12.891%, progress (thread 0): 96.4161%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1684.26_1684.48, WER: 0%, TER: 0%, total WER: 26.3504%, total TER: 12.8909%, progress (thread 0): 96.424%]
|T|: o k a y
|P|:
[sample: IS1009b_H03_FIO089_45.13_45.35, WER: 100%, TER: 100%, total WER: 26.3512%, total TER: 12.8917%, progress (thread 0): 96.432%]
|T|: ' k a y
|P|: c o
[sample: IS1009b_H02_FIO084_155.16_155.38, WER: 100%, TER: 100%, total WER: 26.352%, total TER: 12.8925%, progress (thread 0): 96.4399%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_404.12_404.34, WER: 100%, TER: 60%, total WER: 26.3528%, total TER: 12.893%, progress (thread 0): 96.4478%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_961.79_962.01, WER: 0%, TER: 0%, total WER: 26.3525%, total TER: 12.8929%, progress (thread 0): 96.4557%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1007.66_1007.88, WER: 0%, TER: 0%, total WER: 26.3523%, total TER: 12.8928%, progress (thread 0): 96.4636%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H00_FIE088_337.53_337.75, WER: 0%, TER: 0%, total WER: 26.352%, total TER: 12.8927%, progress (thread 0): 96.4715%]
|T|: o k a y
|P|: o
[sample: IS1009c_H01_FIO087_1263.61_1263.83, WER: 100%, TER: 75%, total WER: 26.3528%, total TER: 12.8933%, progress (thread 0): 96.4794%]
|T|: m m
|P|: e
[sample: IS1009d_H03_FIO089_590.61_590.83, WER: 100%, TER: 100%, total WER: 26.3536%, total TER: 12.8937%, progress (thread 0): 96.4873%]
|T|: m m
|P|:
[sample: IS1009d_H02_FIO084_801.03_801.25, WER: 100%, TER: 100%, total WER: 26.3544%, total TER: 12.8941%, progress (thread 0): 96.4953%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H02_FIO084_1687.52_1687.74, WER: 100%, TER: 80%, total WER: 26.3553%, total TER: 12.8948%, progress (thread 0): 96.5032%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1775.76_1775.98, WER: 0%, TER: 0%, total WER: 26.355%, total TER: 12.8947%, progress (thread 0): 96.5111%]
|T|: o k a y
|P|:
[sample: IS1009d_H03_FIO089_1846.51_1846.73, WER: 100%, TER: 100%, total WER: 26.3558%, total TER: 12.8955%, progress (thread 0): 96.519%]
|T|: m o r n i n g
|P|: m o n e y
[sample: TS3003a_H01_MTD011UID_15.34_15.56, WER: 100%, TER: 57.1429%, total WER: 26.3566%, total TER: 12.8963%, progress (thread 0): 96.5269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1316.76_1316.98, WER: 0%, TER: 0%, total WER: 26.3563%, total TER: 12.8961%, progress (thread 0): 96.5348%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1357.36_1357.58, WER: 100%, TER: 50%, total WER: 26.3571%, total TER: 12.8963%, progress (thread 0): 96.5427%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_888.56_888.78, WER: 0%, TER: 0%, total WER: 26.3568%, total TER: 12.8962%, progress (thread 0): 96.5506%]
|T|: h m m
|P|: h m
[sample: TS3003b_H03_MTD012ME_1371.93_1372.15, WER: 100%, TER: 33.3333%, total WER: 26.3577%, total TER: 12.8963%, progress (thread 0): 96.5585%]
|T|: y e a h
|P|: a n d
[sample: TS3003b_H01_MTD011UID_1600.07_1600.29, WER: 100%, TER: 100%, total WER: 26.3585%, total TER: 12.8971%, progress (thread 0): 96.5665%]
|T|: n o
|P|: n
[sample: TS3003c_H01_MTD011UID_109.75_109.97, WER: 100%, TER: 50%, total WER: 26.3593%, total TER: 12.8973%, progress (thread 0): 96.5744%]
|T|: m a y b
|P|: i
[sample: TS3003c_H00_MTD009PM_437.36_437.58, WER: 100%, TER: 100%, total WER: 26.3602%, total TER: 12.8981%, progress (thread 0): 96.5823%]
|T|: m m h m m
|P|:
[sample: TS3003c_H01_MTD011UID_1100.48_1100.7, WER: 100%, TER: 100%, total WER: 26.361%, total TER: 12.8991%, progress (thread 0): 96.5902%]
|T|: y e a h
|P|: h m
[sample: TS3003c_H01_MTD011UID_1304.78_1305, WER: 100%, TER: 100%, total WER: 26.3618%, total TER: 12.9%, progress (thread 0): 96.5981%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_2263.6_2263.82, WER: 0%, TER: 0%, total WER: 26.3615%, total TER: 12.8998%, progress (thread 0): 96.606%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_563.58_563.8, WER: 100%, TER: 75%, total WER: 26.3623%, total TER: 12.9004%, progress (thread 0): 96.6139%]
|T|: y e p
|P|: y u p
[sample: TS3003d_H02_MTD0010ID_577.11_577.33, WER: 100%, TER: 33.3333%, total WER: 26.3632%, total TER: 12.9006%, progress (thread 0): 96.6218%]
|T|: n o
|P|: u h
[sample: TS3003d_H01_MTD011UID_1458.81_1459.03, WER: 100%, TER: 100%, total WER: 26.364%, total TER: 12.901%, progress (thread 0): 96.6297%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_1674.77_1674.99, WER: 100%, TER: 50%, total WER: 26.3648%, total TER: 12.9011%, progress (thread 0): 96.6377%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1736.91_1737.13, WER: 0%, TER: 0%, total WER: 26.3645%, total TER: 12.901%, progress (thread 0): 96.6456%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_1751.36_1751.58, WER: 0%, TER: 0%, total WER: 26.3642%, total TER: 12.901%, progress (thread 0): 96.6535%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1991.39_1991.61, WER: 0%, TER: 0%, total WER: 26.3639%, total TER: 12.9008%, progress (thread 0): 96.6614%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H03_MTD012ME_1998.36_1998.58, WER: 100%, TER: 25%, total WER: 26.3647%, total TER: 12.9009%, progress (thread 0): 96.6693%]
|T|: y e a h
|P|: y o u | k n o
[sample: EN2002a_H00_MEE073_482.53_482.75, WER: 200%, TER: 150%, total WER: 26.3667%, total TER: 12.9022%, progress (thread 0): 96.6772%]
|T|: h m m
|P|: y m
[sample: EN2002a_H01_FEO070_706.29_706.51, WER: 100%, TER: 66.6667%, total WER: 26.3675%, total TER: 12.9026%, progress (thread 0): 96.6851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_805.91_806.13, WER: 0%, TER: 0%, total WER: 26.3672%, total TER: 12.9025%, progress (thread 0): 96.693%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1103.91_1104.13, WER: 0%, TER: 0%, total WER: 26.3669%, total TER: 12.9023%, progress (thread 0): 96.701%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_1216.04_1216.26, WER: 100%, TER: 66.6667%, total WER: 26.3678%, total TER: 12.9027%, progress (thread 0): 96.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1537.4_1537.62, WER: 0%, TER: 0%, total WER: 26.3675%, total TER: 12.9026%, progress (thread 0): 96.7168%]
|T|: j u s t
|P|: j u s t
[sample: EN2002a_H01_FEO070_1871.92_1872.14, WER: 0%, TER: 0%, total WER: 26.3672%, total TER: 12.9025%, progress (thread 0): 96.7247%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1963.02_1963.24, WER: 0%, TER: 0%, total WER: 26.3669%, total TER: 12.9023%, progress (thread 0): 96.7326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_639.41_639.63, WER: 0%, TER: 0%, total WER: 26.3666%, total TER: 12.9022%, progress (thread 0): 96.7405%]
|T|: r i g h t
|P|: w u t
[sample: EN2002b_H03_MEE073_1397.63_1397.85, WER: 100%, TER: 80%, total WER: 26.3674%, total TER: 12.903%, progress (thread 0): 96.7484%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_1547.56_1547.78, WER: 100%, TER: 33.3333%, total WER: 26.3682%, total TER: 12.9032%, progress (thread 0): 96.7563%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_575.92_576.14, WER: 100%, TER: 28.5714%, total WER: 26.369%, total TER: 12.9034%, progress (thread 0): 96.7642%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H02_MEE071_589.02_589.24, WER: 100%, TER: 80%, total WER: 26.3699%, total TER: 12.9042%, progress (thread 0): 96.7722%]
|T|: ' k a y
|P|: y e h
[sample: EN2002c_H03_MEE073_684.52_684.74, WER: 100%, TER: 100%, total WER: 26.3707%, total TER: 12.905%, progress (thread 0): 96.7801%]
|T|: o h | y e a h
|P|: e
[sample: EN2002c_H03_MEE073_1506.68_1506.9, WER: 100%, TER: 85.7143%, total WER: 26.3723%, total TER: 12.9062%, progress (thread 0): 96.788%]
|T|: y e a h
|P|: t o
[sample: EN2002c_H03_MEE073_1602.82_1603.04, WER: 100%, TER: 100%, total WER: 26.3732%, total TER: 12.907%, progress (thread 0): 96.7959%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1707.51_1707.73, WER: 100%, TER: 33.3333%, total WER: 26.374%, total TER: 12.9071%, progress (thread 0): 96.8038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2260.94_2261.16, WER: 0%, TER: 0%, total WER: 26.3737%, total TER: 12.907%, progress (thread 0): 96.8117%]
|T|: y e a h
|P|: e m
[sample: EN2002c_H03_MEE073_2697.66_2697.88, WER: 100%, TER: 75%, total WER: 26.3745%, total TER: 12.9076%, progress (thread 0): 96.8196%]
|T|: o k a y
|P|:
[sample: EN2002d_H03_MEE073_402.37_402.59, WER: 100%, TER: 100%, total WER: 26.3754%, total TER: 12.9084%, progress (thread 0): 96.8275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_736.63_736.85, WER: 0%, TER: 0%, total WER: 26.3751%, total TER: 12.9083%, progress (thread 0): 96.8354%]
|T|: o h
|P|: h o m
[sample: EN2002d_H00_FEO070_909.94_910.16, WER: 100%, TER: 100%, total WER: 26.3759%, total TER: 12.9087%, progress (thread 0): 96.8434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1061.24_1061.46, WER: 0%, TER: 0%, total WER: 26.3756%, total TER: 12.9086%, progress (thread 0): 96.8513%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1400.88_1401.1, WER: 0%, TER: 0%, total WER: 26.3753%, total TER: 12.9085%, progress (thread 0): 96.8592%]
|T|: b u t
|P|: b u t
[sample: EN2002d_H00_FEO070_1658_1658.22, WER: 0%, TER: 0%, total WER: 26.375%, total TER: 12.9084%, progress (thread 0): 96.8671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1732.66_1732.88, WER: 0%, TER: 0%, total WER: 26.3747%, total TER: 12.9082%, progress (thread 0): 96.875%]
|T|: m m
|P|: h m
[sample: ES2004a_H03_FEE016_622.02_622.23, WER: 100%, TER: 50%, total WER: 26.3755%, total TER: 12.9084%, progress (thread 0): 96.8829%]
|T|: y e a h
|P|: y u p
[sample: ES2004b_H00_MEO015_1323.05_1323.26, WER: 100%, TER: 75%, total WER: 26.3763%, total TER: 12.909%, progress (thread 0): 96.8908%]
|T|: m m h m m
|P|: u | h m
[sample: ES2004b_H00_MEO015_1464.74_1464.95, WER: 200%, TER: 60%, total WER: 26.3783%, total TER: 12.9095%, progress (thread 0): 96.8987%]
|T|: m m h m m
|P|: u h u
[sample: ES2004c_H00_MEO015_96.42_96.63, WER: 100%, TER: 80%, total WER: 26.3791%, total TER: 12.9103%, progress (thread 0): 96.9066%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1385.07_1385.28, WER: 0%, TER: 0%, total WER: 26.3788%, total TER: 12.9102%, progress (thread 0): 96.9146%]
|T|: h m m
|P|: h m
[sample: ES2004d_H00_MEO015_26.63_26.84, WER: 100%, TER: 33.3333%, total WER: 26.3796%, total TER: 12.9103%, progress (thread 0): 96.9225%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_627.79_628, WER: 100%, TER: 66.6667%, total WER: 26.3805%, total TER: 12.9107%, progress (thread 0): 96.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_39.06_39.27, WER: 0%, TER: 0%, total WER: 26.3802%, total TER: 12.9106%, progress (thread 0): 96.9383%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_169.39_169.6, WER: 100%, TER: 100%, total WER: 26.381%, total TER: 12.9116%, progress (thread 0): 96.9462%]
|T|: m m h m m
|P|:
[sample: IS1009b_H00_FIE088_1286.43_1286.64, WER: 100%, TER: 100%, total WER: 26.3818%, total TER: 12.9126%, progress (thread 0): 96.9541%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H00_FIE088_1289.44_1289.65, WER: 0%, TER: 0%, total WER: 26.3815%, total TER: 12.9125%, progress (thread 0): 96.962%]
|T|: m m
|P|: a n
[sample: IS1009b_H02_FIO084_1632.09_1632.3, WER: 100%, TER: 100%, total WER: 26.3824%, total TER: 12.9129%, progress (thread 0): 96.9699%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_284.08_284.29, WER: 0%, TER: 0%, total WER: 26.3821%, total TER: 12.9128%, progress (thread 0): 96.9778%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1563.53_1563.74, WER: 0%, TER: 0%, total WER: 26.3818%, total TER: 12.9126%, progress (thread 0): 96.9858%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1707.23_1707.44, WER: 0%, TER: 0%, total WER: 26.3815%, total TER: 12.9125%, progress (thread 0): 96.9937%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_741.38_741.59, WER: 0%, TER: 0%, total WER: 26.3812%, total TER: 12.9124%, progress (thread 0): 97.0016%]
|T|: y e a h
|P|:
[sample: IS1009d_H02_FIO084_1371.67_1371.88, WER: 100%, TER: 100%, total WER: 26.382%, total TER: 12.9132%, progress (thread 0): 97.0095%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1880.74_1880.95, WER: 100%, TER: 100%, total WER: 26.3828%, total TER: 12.9142%, progress (thread 0): 97.0174%]
|T|: y e a h
|P|: y e s
[sample: TS3003a_H01_MTD011UID_1295.8_1296.01, WER: 100%, TER: 50%, total WER: 26.3836%, total TER: 12.9146%, progress (thread 0): 97.0253%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_901.66_901.87, WER: 0%, TER: 0%, total WER: 26.3834%, total TER: 12.9145%, progress (thread 0): 97.0332%]
|T|: y e a h
|P|: h u h
[sample: TS3003b_H01_MTD011UID_1547.15_1547.36, WER: 100%, TER: 75%, total WER: 26.3842%, total TER: 12.915%, progress (thread 0): 97.0411%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1091.82_1092.03, WER: 0%, TER: 0%, total WER: 26.3839%, total TER: 12.9149%, progress (thread 0): 97.049%]
|T|: y e a h
|P|: h u h
[sample: TS3003c_H01_MTD011UID_1210.7_1210.91, WER: 100%, TER: 75%, total WER: 26.3847%, total TER: 12.9155%, progress (thread 0): 97.057%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_530.79_531, WER: 100%, TER: 75%, total WER: 26.3855%, total TER: 12.9161%, progress (thread 0): 97.0649%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1465.64_1465.85, WER: 0%, TER: 0%, total WER: 26.3852%, total TER: 12.916%, progress (thread 0): 97.0728%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1695.15_1695.36, WER: 0%, TER: 0%, total WER: 26.3849%, total TER: 12.9158%, progress (thread 0): 97.0807%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1711.66_1711.87, WER: 0%, TER: 0%, total WER: 26.3846%, total TER: 12.9157%, progress (thread 0): 97.0886%]
|T|: y e a h
|P|: y e p
[sample: TS3003d_H00_MTD009PM_1789.64_1789.85, WER: 100%, TER: 50%, total WER: 26.3855%, total TER: 12.9161%, progress (thread 0): 97.0965%]
|T|: n o
|P|: n o
[sample: TS3003d_H03_MTD012ME_2014.88_2015.09, WER: 0%, TER: 0%, total WER: 26.3852%, total TER: 12.916%, progress (thread 0): 97.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2064.37_2064.58, WER: 0%, TER: 0%, total WER: 26.3849%, total TER: 12.9159%, progress (thread 0): 97.1123%]
|T|: y e a h
|P|: e h
[sample: TS3003d_H01_MTD011UID_2107.64_2107.85, WER: 100%, TER: 50%, total WER: 26.3857%, total TER: 12.9162%, progress (thread 0): 97.1203%]
|T|: y e a h
|P|: h m
[sample: TS3003d_H01_MTD011UID_2462.55_2462.76, WER: 100%, TER: 100%, total WER: 26.3865%, total TER: 12.917%, progress (thread 0): 97.1282%]
|T|: s o r r y
|P|: s o r r
[sample: EN2002a_H02_FEO072_377.93_378.14, WER: 100%, TER: 20%, total WER: 26.3873%, total TER: 12.9171%, progress (thread 0): 97.1361%]
|T|: o h
|P|:
[sample: EN2002a_H03_MEE071_483.42_483.63, WER: 100%, TER: 100%, total WER: 26.3882%, total TER: 12.9175%, progress (thread 0): 97.144%]
|T|: y e a h
|P|: y a h
[sample: EN2002a_H03_MEE071_648.41_648.62, WER: 100%, TER: 25%, total WER: 26.389%, total TER: 12.9176%, progress (thread 0): 97.1519%]
|T|: y e a h
|P|: c o m
[sample: EN2002a_H01_FEO070_1319.66_1319.87, WER: 100%, TER: 100%, total WER: 26.3898%, total TER: 12.9184%, progress (thread 0): 97.1598%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1429.36_1429.57, WER: 0%, TER: 0%, total WER: 26.3895%, total TER: 12.9183%, progress (thread 0): 97.1677%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1896.08_1896.29, WER: 0%, TER: 0%, total WER: 26.3892%, total TER: 12.9182%, progress (thread 0): 97.1756%]
|T|: y e a h
|P|: y e
[sample: EN2002a_H03_MEE071_2122.73_2122.94, WER: 100%, TER: 50%, total WER: 26.3901%, total TER: 12.9185%, progress (thread 0): 97.1835%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_266.76_266.97, WER: 0%, TER: 0%, total WER: 26.3898%, total TER: 12.9184%, progress (thread 0): 97.1915%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_291.79_292, WER: 0%, TER: 0%, total WER: 26.3895%, total TER: 12.9183%, progress (thread 0): 97.1994%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1335.23_1335.44, WER: 0%, TER: 0%, total WER: 26.3892%, total TER: 12.9182%, progress (thread 0): 97.2073%]
|T|: ' k a y
|P|: k u
[sample: EN2002c_H03_MEE073_21.49_21.7, WER: 100%, TER: 75%, total WER: 26.39%, total TER: 12.9188%, progress (thread 0): 97.2152%]
|T|: o k a y
|P|:
[sample: EN2002c_H03_MEE073_183.87_184.08, WER: 100%, TER: 100%, total WER: 26.3908%, total TER: 12.9196%, progress (thread 0): 97.2231%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1748.27_1748.48, WER: 0%, TER: 0%, total WER: 26.3905%, total TER: 12.9194%, progress (thread 0): 97.231%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002c_H03_MEE073_1810.36_1810.57, WER: 100%, TER: 85.7143%, total WER: 26.3922%, total TER: 12.9206%, progress (thread 0): 97.2389%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1861.15_1861.36, WER: 0%, TER: 0%, total WER: 26.3919%, total TER: 12.9205%, progress (thread 0): 97.2468%]
|T|: y e a h
|P|: y o u | k n
[sample: EN2002c_H03_MEE073_2180.5_2180.71, WER: 200%, TER: 125%, total WER: 26.3938%, total TER: 12.9215%, progress (thread 0): 97.2547%]
|T|: t r u e
|P|: t r u e
[sample: EN2002c_H03_MEE073_2453.86_2454.07, WER: 0%, TER: 0%, total WER: 26.3935%, total TER: 12.9214%, progress (thread 0): 97.2627%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2819.2_2819.41, WER: 0%, TER: 0%, total WER: 26.3932%, total TER: 12.9213%, progress (thread 0): 97.2706%]
|T|: m m h m m
|P|: u h m
[sample: EN2002d_H01_FEO072_164.51_164.72, WER: 100%, TER: 60%, total WER: 26.3941%, total TER: 12.9218%, progress (thread 0): 97.2785%]
|T|: r i g h t
|P|: t o
[sample: EN2002d_H03_MEE073_338.56_338.77, WER: 100%, TER: 100%, total WER: 26.3949%, total TER: 12.9229%, progress (thread 0): 97.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_647.49_647.7, WER: 0%, TER: 0%, total WER: 26.3946%, total TER: 12.9227%, progress (thread 0): 97.2943%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_782.98_783.19, WER: 100%, TER: 100%, total WER: 26.3954%, total TER: 12.9231%, progress (thread 0): 97.3022%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_998.01_998.22, WER: 0%, TER: 0%, total WER: 26.3951%, total TER: 12.923%, progress (thread 0): 97.3101%]
|T|: o h | y e a h
|P|: c
[sample: EN2002d_H00_FEO070_1377.89_1378.1, WER: 100%, TER: 100%, total WER: 26.3968%, total TER: 12.9244%, progress (thread 0): 97.318%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1391.57_1391.78, WER: 0%, TER: 0%, total WER: 26.3965%, total TER: 12.9243%, progress (thread 0): 97.326%]
|T|: o h | y e a h
|P|: y e
[sample: EN2002d_H03_MEE073_1912.21_1912.42, WER: 100%, TER: 71.4286%, total WER: 26.3981%, total TER: 12.9252%, progress (thread 0): 97.3339%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_2147.17_2147.38, WER: 100%, TER: 33.3333%, total WER: 26.3989%, total TER: 12.9254%, progress (thread 0): 97.3418%]
|T|: o k a y
|P|: j
[sample: ES2004b_H00_MEO015_338.55_338.75, WER: 100%, TER: 100%, total WER: 26.3998%, total TER: 12.9262%, progress (thread 0): 97.3497%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1514.44_1514.64, WER: 0%, TER: 0%, total WER: 26.3995%, total TER: 12.9261%, progress (thread 0): 97.3576%]
|T|: o o p s
|P|:
[sample: ES2004c_H00_MEO015_54.23_54.43, WER: 100%, TER: 100%, total WER: 26.4003%, total TER: 12.9269%, progress (thread 0): 97.3655%]
|T|: m m h m m
|P|: h
[sample: ES2004c_H00_MEO015_1395.98_1396.18, WER: 100%, TER: 80%, total WER: 26.4011%, total TER: 12.9277%, progress (thread 0): 97.3734%]
|T|: ' k a y
|P|: t
[sample: ES2004d_H00_MEO015_562.82_563.02, WER: 100%, TER: 100%, total WER: 26.4019%, total TER: 12.9285%, progress (thread 0): 97.3813%]
|T|: y e p
|P|: y e a h
[sample: ES2004d_H02_MEE014_922.35_922.55, WER: 100%, TER: 66.6667%, total WER: 26.4028%, total TER: 12.9289%, progress (thread 0): 97.3892%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1214.04_1214.24, WER: 0%, TER: 0%, total WER: 26.4025%, total TER: 12.9287%, progress (thread 0): 97.3972%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1952.42_1952.62, WER: 0%, TER: 0%, total WER: 26.4022%, total TER: 12.9286%, progress (thread 0): 97.4051%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_784.26_784.46, WER: 0%, TER: 0%, total WER: 26.4019%, total TER: 12.9285%, progress (thread 0): 97.413%]
|T|: ' k a y
|P|: t o
[sample: IS1009a_H03_FIO089_794.48_794.68, WER: 100%, TER: 100%, total WER: 26.4027%, total TER: 12.9293%, progress (thread 0): 97.4209%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H02_FIO084_179.12_179.32, WER: 100%, TER: 200%, total WER: 26.4035%, total TER: 12.9302%, progress (thread 0): 97.4288%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1111.83_1112.03, WER: 0%, TER: 0%, total WER: 26.4032%, total TER: 12.9301%, progress (thread 0): 97.4367%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_2011.99_2012.19, WER: 100%, TER: 100%, total WER: 26.404%, total TER: 12.9311%, progress (thread 0): 97.4446%]
|T|: y e a h
|P|: y e a
[sample: IS1009c_H03_FIO089_285.99_286.19, WER: 100%, TER: 25%, total WER: 26.4049%, total TER: 12.9312%, progress (thread 0): 97.4525%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1689.26_1689.46, WER: 0%, TER: 0%, total WER: 26.4046%, total TER: 12.9311%, progress (thread 0): 97.4604%]
|T|: m m
|P|:
[sample: IS1009d_H01_FIO087_964.59_964.79, WER: 100%, TER: 100%, total WER: 26.4054%, total TER: 12.9315%, progress (thread 0): 97.4684%]
|T|: y e a h
|P|: a n d
[sample: TS3003a_H01_MTD011UID_1286.22_1286.42, WER: 100%, TER: 100%, total WER: 26.4062%, total TER: 12.9323%, progress (thread 0): 97.4763%]
|T|: m m
|P|: y
[sample: TS3003a_H01_MTD011UID_1431.86_1432.06, WER: 100%, TER: 100%, total WER: 26.407%, total TER: 12.9327%, progress (thread 0): 97.4842%]
|T|: ' k a y
|P|: i n g
[sample: TS3003b_H03_MTD012ME_550.33_550.53, WER: 100%, TER: 100%, total WER: 26.4079%, total TER: 12.9335%, progress (thread 0): 97.4921%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_576.96_577.16, WER: 100%, TER: 50%, total WER: 26.4087%, total TER: 12.9337%, progress (thread 0): 97.5%]
|T|: y e a h
|P|: u h
[sample: TS3003b_H01_MTD011UID_1340.71_1340.91, WER: 100%, TER: 75%, total WER: 26.4095%, total TER: 12.9342%, progress (thread 0): 97.5079%]
|T|: m m
|P|: y e
[sample: TS3003b_H01_MTD011UID_1635.98_1636.18, WER: 100%, TER: 100%, total WER: 26.4103%, total TER: 12.9346%, progress (thread 0): 97.5158%]
|T|: m m
|P|: h m
[sample: TS3003c_H03_MTD012ME_1936.99_1937.19, WER: 100%, TER: 50%, total WER: 26.4112%, total TER: 12.9348%, progress (thread 0): 97.5237%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_214.36_214.56, WER: 0%, TER: 0%, total WER: 26.4109%, total TER: 12.9347%, progress (thread 0): 97.5316%]
|T|: y e p
|P|: y e
[sample: TS3003d_H00_MTD009PM_418.75_418.95, WER: 100%, TER: 33.3333%, total WER: 26.4117%, total TER: 12.9348%, progress (thread 0): 97.5396%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1236.15_1236.35, WER: 0%, TER: 0%, total WER: 26.4114%, total TER: 12.9347%, progress (thread 0): 97.5475%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1334.1_1334.3, WER: 0%, TER: 0%, total WER: 26.4111%, total TER: 12.9346%, progress (thread 0): 97.5554%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1736.65_1736.85, WER: 0%, TER: 0%, total WER: 26.4108%, total TER: 12.9345%, progress (thread 0): 97.5633%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1862.16_1862.36, WER: 0%, TER: 0%, total WER: 26.4105%, total TER: 12.9344%, progress (thread 0): 97.5712%]
|T|: h m m
|P|: w h e n
[sample: TS3003d_H01_MTD011UID_2423.71_2423.91, WER: 100%, TER: 100%, total WER: 26.4113%, total TER: 12.935%, progress (thread 0): 97.5791%]
|T|: y e a h
|P|: h
[sample: EN2002a_H00_MEE073_6.65_6.85, WER: 100%, TER: 75%, total WER: 26.4122%, total TER: 12.9355%, progress (thread 0): 97.587%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_207.6_207.8, WER: 0%, TER: 0%, total WER: 26.4119%, total TER: 12.9354%, progress (thread 0): 97.5949%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_325.97_326.17, WER: 100%, TER: 33.3333%, total WER: 26.4127%, total TER: 12.9356%, progress (thread 0): 97.6029%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_517.59_517.79, WER: 0%, TER: 0%, total WER: 26.4124%, total TER: 12.9354%, progress (thread 0): 97.6108%]
|T|: m m h m m
|P|: e h
[sample: EN2002a_H00_MEE073_625.1_625.3, WER: 100%, TER: 80%, total WER: 26.4132%, total TER: 12.9362%, progress (thread 0): 97.6187%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1082.64_1082.84, WER: 0%, TER: 0%, total WER: 26.4129%, total TER: 12.9361%, progress (thread 0): 97.6266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1296.98_1297.18, WER: 0%, TER: 0%, total WER: 26.4126%, total TER: 12.936%, progress (thread 0): 97.6345%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1544.23_1544.43, WER: 100%, TER: 33.3333%, total WER: 26.4134%, total TER: 12.9361%, progress (thread 0): 97.6424%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_1988.65_1988.85, WER: 0%, TER: 0%, total WER: 26.4132%, total TER: 12.936%, progress (thread 0): 97.6503%]
|T|: y e p
|P|: y e p
[sample: EN2002b_H00_FEO070_58.73_58.93, WER: 0%, TER: 0%, total WER: 26.4129%, total TER: 12.9359%, progress (thread 0): 97.6582%]
|T|: d o | w e | d
|P|: d w e
[sample: EN2002b_H01_MEE071_223.55_223.75, WER: 100%, TER: 57.1429%, total WER: 26.4153%, total TER: 12.9366%, progress (thread 0): 97.6661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_233.29_233.49, WER: 0%, TER: 0%, total WER: 26.415%, total TER: 12.9365%, progress (thread 0): 97.674%]
|T|: h m m
|P|: y e h
[sample: EN2002b_H03_MEE073_259.41_259.61, WER: 100%, TER: 100%, total WER: 26.4159%, total TER: 12.9371%, progress (thread 0): 97.682%]
|T|: y e a h
|P|: t o
[sample: EN2002b_H03_MEE073_498.07_498.27, WER: 100%, TER: 100%, total WER: 26.4167%, total TER: 12.9379%, progress (thread 0): 97.6899%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1591.2_1591.4, WER: 0%, TER: 0%, total WER: 26.4164%, total TER: 12.9378%, progress (thread 0): 97.6978%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H00_FEO070_1723.38_1723.58, WER: 0%, TER: 0%, total WER: 26.4161%, total TER: 12.9377%, progress (thread 0): 97.7057%]
|T|: o k a y
|P|: g o
[sample: EN2002c_H03_MEE073_241.42_241.62, WER: 100%, TER: 100%, total WER: 26.4169%, total TER: 12.9385%, progress (thread 0): 97.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_404.9_405.1, WER: 0%, TER: 0%, total WER: 26.4166%, total TER: 12.9384%, progress (thread 0): 97.7215%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_817.73_817.93, WER: 100%, TER: 33.3333%, total WER: 26.4174%, total TER: 12.9385%, progress (thread 0): 97.7294%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1319.11_1319.31, WER: 0%, TER: 0%, total WER: 26.4171%, total TER: 12.9384%, progress (thread 0): 97.7373%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1392.95_1393.15, WER: 0%, TER: 0%, total WER: 26.4168%, total TER: 12.9383%, progress (thread 0): 97.7453%]
|T|: r i g h t
|P|: f r i r t
[sample: EN2002c_H03_MEE073_1395.54_1395.74, WER: 100%, TER: 60%, total WER: 26.4177%, total TER: 12.9388%, progress (thread 0): 97.7532%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1637.64_1637.84, WER: 0%, TER: 0%, total WER: 26.4174%, total TER: 12.9387%, progress (thread 0): 97.7611%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1911.7_1911.9, WER: 0%, TER: 0%, total WER: 26.4171%, total TER: 12.9385%, progress (thread 0): 97.769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1939.05_1939.25, WER: 0%, TER: 0%, total WER: 26.4168%, total TER: 12.9384%, progress (thread 0): 97.7769%]
|T|: y e a h
|P|: t o
[sample: EN2002d_H03_MEE073_327.41_327.61, WER: 100%, TER: 100%, total WER: 26.4176%, total TER: 12.9392%, progress (thread 0): 97.7848%]
|T|: y e a h
|P|: s h n g
[sample: EN2002d_H03_MEE073_967.12_967.32, WER: 100%, TER: 100%, total WER: 26.4184%, total TER: 12.94%, progress (thread 0): 97.7927%]
|T|: y e p
|P|: y u p
[sample: ES2004a_H03_FEE016_1048.29_1048.48, WER: 100%, TER: 33.3333%, total WER: 26.4193%, total TER: 12.9401%, progress (thread 0): 97.8006%]
|T|: o o p s
|P|:
[sample: ES2004b_H00_MEO015_572.79_572.98, WER: 100%, TER: 100%, total WER: 26.4201%, total TER: 12.941%, progress (thread 0): 97.8085%]
|T|: a l r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1308.81_1309, WER: 100%, TER: 28.5714%, total WER: 26.4209%, total TER: 12.9412%, progress (thread 0): 97.8165%]
|T|: h u h
|P|: h
[sample: ES2004b_H02_MEE014_1454.52_1454.71, WER: 100%, TER: 66.6667%, total WER: 26.4217%, total TER: 12.9416%, progress (thread 0): 97.8244%]
|T|: m m
|P|: y o u | k
[sample: ES2004b_H03_FEE016_1635.41_1635.6, WER: 200%, TER: 250%, total WER: 26.4237%, total TER: 12.9427%, progress (thread 0): 97.8323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1436.81_1437, WER: 0%, TER: 0%, total WER: 26.4234%, total TER: 12.9426%, progress (thread 0): 97.8402%]
|T|: t h i n g
|P|: t h i n g
[sample: ES2004d_H02_MEE014_2209.98_2210.17, WER: 0%, TER: 0%, total WER: 26.4231%, total TER: 12.9424%, progress (thread 0): 97.8481%]
|T|: y e a h
|P|: t h e r
[sample: IS1009a_H02_FIO084_805.49_805.68, WER: 100%, TER: 100%, total WER: 26.4239%, total TER: 12.9432%, progress (thread 0): 97.856%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_690.57_690.76, WER: 0%, TER: 0%, total WER: 26.4236%, total TER: 12.9431%, progress (thread 0): 97.8639%]
|T|: m m h m m
|P|:
[sample: IS1009c_H00_FIE088_808.35_808.54, WER: 100%, TER: 100%, total WER: 26.4244%, total TER: 12.9441%, progress (thread 0): 97.8718%]
|T|: b a t t e r y
|P|: t h a t
[sample: IS1009c_H01_FIO087_1631.84_1632.03, WER: 100%, TER: 85.7143%, total WER: 26.4252%, total TER: 12.9453%, progress (thread 0): 97.8798%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1314.07_1314.26, WER: 0%, TER: 0%, total WER: 26.425%, total TER: 12.9452%, progress (thread 0): 97.8877%]
|T|: ' k a y
|P|: k a n
[sample: TS3003a_H03_MTD012ME_506.74_506.93, WER: 100%, TER: 50%, total WER: 26.4258%, total TER: 12.9455%, progress (thread 0): 97.8956%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1214.55_1214.74, WER: 100%, TER: 50%, total WER: 26.4266%, total TER: 12.9457%, progress (thread 0): 97.9035%]
|T|: ' k a y
|P|: g a y
[sample: TS3003a_H01_MTD011UID_1401.33_1401.52, WER: 100%, TER: 50%, total WER: 26.4274%, total TER: 12.946%, progress (thread 0): 97.9114%]
|T|: y e a h
|P|: n o
[sample: TS3003b_H01_MTD011UID_2147.67_2147.86, WER: 100%, TER: 100%, total WER: 26.4282%, total TER: 12.9468%, progress (thread 0): 97.9193%]
|T|: y e a h
|P|: i
[sample: TS3003c_H01_MTD011UID_1090.92_1091.11, WER: 100%, TER: 100%, total WER: 26.4291%, total TER: 12.9476%, progress (thread 0): 97.9272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1632.95_1633.14, WER: 0%, TER: 0%, total WER: 26.4288%, total TER: 12.9475%, progress (thread 0): 97.9351%]
|T|: y e a h
|P|: a n
[sample: TS3003d_H01_MTD011UID_375.66_375.85, WER: 100%, TER: 75%, total WER: 26.4296%, total TER: 12.9481%, progress (thread 0): 97.943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_507.6_507.79, WER: 0%, TER: 0%, total WER: 26.4293%, total TER: 12.948%, progress (thread 0): 97.951%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_816.71_816.9, WER: 0%, TER: 0%, total WER: 26.429%, total TER: 12.9479%, progress (thread 0): 97.9589%]
|T|: m m
|P|:
[sample: TS3003d_H01_MTD011UID_966.74_966.93, WER: 100%, TER: 100%, total WER: 26.4298%, total TER: 12.9483%, progress (thread 0): 97.9668%]
|T|: m m
|P|: h m
[sample: TS3003d_H00_MTD009PM_1884.46_1884.65, WER: 100%, TER: 50%, total WER: 26.4306%, total TER: 12.9484%, progress (thread 0): 97.9747%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1975.04_1975.23, WER: 100%, TER: 66.6667%, total WER: 26.4315%, total TER: 12.9488%, progress (thread 0): 97.9826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1998.6_1998.79, WER: 0%, TER: 0%, total WER: 26.4312%, total TER: 12.9487%, progress (thread 0): 97.9905%]
|T|: y e p
|P|: y u p
[sample: EN2002a_H00_MEE073_399.03_399.22, WER: 100%, TER: 33.3333%, total WER: 26.432%, total TER: 12.9488%, progress (thread 0): 97.9984%]
|T|: y e a h
|P|: r i g h t
[sample: EN2002a_H03_MEE071_741.39_741.58, WER: 100%, TER: 100%, total WER: 26.4328%, total TER: 12.9496%, progress (thread 0): 98.0063%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1037.67_1037.86, WER: 0%, TER: 0%, total WER: 26.4325%, total TER: 12.9495%, progress (thread 0): 98.0142%]
|T|: w h
|P|:
[sample: EN2002a_H03_MEE071_1305.67_1305.86, WER: 100%, TER: 100%, total WER: 26.4333%, total TER: 12.9499%, progress (thread 0): 98.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1781.11_1781.3, WER: 0%, TER: 0%, total WER: 26.4331%, total TER: 12.9498%, progress (thread 0): 98.0301%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H02_FEO072_1823.51_1823.7, WER: 100%, TER: 50%, total WER: 26.4339%, total TER: 12.9501%, progress (thread 0): 98.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1836.03_1836.22, WER: 0%, TER: 0%, total WER: 26.4336%, total TER: 12.95%, progress (thread 0): 98.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1886.9_1887.09, WER: 0%, TER: 0%, total WER: 26.4333%, total TER: 12.9499%, progress (thread 0): 98.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1917.91_1918.1, WER: 0%, TER: 0%, total WER: 26.433%, total TER: 12.9498%, progress (thread 0): 98.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_142.44_142.63, WER: 0%, TER: 0%, total WER: 26.4327%, total TER: 12.9497%, progress (thread 0): 98.0696%]
|T|: u h h u h
|P|: w
[sample: EN2002c_H03_MEE073_1527.18_1527.37, WER: 100%, TER: 100%, total WER: 26.4335%, total TER: 12.9507%, progress (thread 0): 98.0775%]
|T|: h m m
|P|: y e a
[sample: EN2002c_H03_MEE073_1630_1630.19, WER: 100%, TER: 100%, total WER: 26.4343%, total TER: 12.9513%, progress (thread 0): 98.0854%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2672.39_2672.58, WER: 100%, TER: 33.3333%, total WER: 26.4352%, total TER: 12.9514%, progress (thread 0): 98.0934%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H03_MEE073_2679.33_2679.52, WER: 100%, TER: 80%, total WER: 26.436%, total TER: 12.9522%, progress (thread 0): 98.1013%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_647.1_647.29, WER: 0%, TER: 0%, total WER: 26.4357%, total TER: 12.9521%, progress (thread 0): 98.1092%]
|T|: y e a h
|P|: t h
[sample: EN2002d_H03_MEE073_649.08_649.27, WER: 100%, TER: 75%, total WER: 26.4365%, total TER: 12.9526%, progress (thread 0): 98.1171%]
|T|: o k
|P|: i ' m
[sample: EN2002d_H03_MEE073_909.71_909.9, WER: 100%, TER: 150%, total WER: 26.4373%, total TER: 12.9533%, progress (thread 0): 98.125%]
|T|: s u r e
|P|: t r e
[sample: EN2002d_H03_MEE073_1568.39_1568.58, WER: 100%, TER: 50%, total WER: 26.4382%, total TER: 12.9536%, progress (thread 0): 98.1329%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_1708.21_1708.4, WER: 100%, TER: 66.6667%, total WER: 26.439%, total TER: 12.954%, progress (thread 0): 98.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2037.78_2037.97, WER: 0%, TER: 0%, total WER: 26.4387%, total TER: 12.9539%, progress (thread 0): 98.1487%]
|T|: s o
|P|: s h
[sample: ES2004a_H03_FEE016_605.84_606.02, WER: 100%, TER: 50%, total WER: 26.4395%, total TER: 12.954%, progress (thread 0): 98.1566%]
|T|: ' k a y
|P|:
[sample: ES2004b_H00_MEO015_1027.78_1027.96, WER: 100%, TER: 100%, total WER: 26.4403%, total TER: 12.9549%, progress (thread 0): 98.1646%]
|T|: m m h m m
|P|:
[sample: ES2004c_H03_FEE016_1360.96_1361.14, WER: 100%, TER: 100%, total WER: 26.4412%, total TER: 12.9559%, progress (thread 0): 98.1725%]
|T|: m m
|P|: h m
[sample: ES2004d_H02_MEE014_2221.15_2221.33, WER: 100%, TER: 50%, total WER: 26.442%, total TER: 12.956%, progress (thread 0): 98.1804%]
|T|: m m h m m
|P|: a
[sample: IS1009b_H02_FIO084_361.5_361.68, WER: 100%, TER: 100%, total WER: 26.4428%, total TER: 12.957%, progress (thread 0): 98.1883%]
|T|: u m
|P|: i | m a
[sample: IS1009c_H03_FIO089_1368.34_1368.52, WER: 200%, TER: 150%, total WER: 26.4447%, total TER: 12.9577%, progress (thread 0): 98.1962%]
|T|: a n d
|P|: a n d
[sample: IS1009c_H01_FIO087_1687.91_1688.09, WER: 0%, TER: 0%, total WER: 26.4444%, total TER: 12.9576%, progress (thread 0): 98.2041%]
|T|: m m
|P|: h m
[sample: IS1009d_H00_FIE088_1039.16_1039.34, WER: 100%, TER: 50%, total WER: 26.4453%, total TER: 12.9578%, progress (thread 0): 98.212%]
|T|: y e s
|P|: i t ' s
[sample: IS1009d_H01_FIO087_1532.02_1532.2, WER: 100%, TER: 100%, total WER: 26.4461%, total TER: 12.9584%, progress (thread 0): 98.2199%]
|T|: n o
|P|: y o u | k
[sample: TS3003a_H00_MTD009PM_1017.51_1017.69, WER: 200%, TER: 200%, total WER: 26.448%, total TER: 12.9592%, progress (thread 0): 98.2278%]
|T|: y e a h
|P|: y e a
[sample: TS3003b_H00_MTD009PM_583.19_583.37, WER: 100%, TER: 25%, total WER: 26.4488%, total TER: 12.9594%, progress (thread 0): 98.2358%]
|T|: m m
|P|: h h
[sample: TS3003b_H01_MTD011UID_1188.77_1188.95, WER: 100%, TER: 100%, total WER: 26.4497%, total TER: 12.9598%, progress (thread 0): 98.2437%]
|T|: y e a h
|P|: y o
[sample: TS3003b_H02_MTD0010ID_1801.24_1801.42, WER: 100%, TER: 75%, total WER: 26.4505%, total TER: 12.9603%, progress (thread 0): 98.2516%]
|T|: m m
|P|:
[sample: TS3003b_H01_MTD011UID_1801.82_1802, WER: 100%, TER: 100%, total WER: 26.4513%, total TER: 12.9607%, progress (thread 0): 98.2595%]
|T|: h m m
|P|: r i g h t
[sample: TS3003d_H01_MTD011UID_1131_1131.18, WER: 100%, TER: 166.667%, total WER: 26.4521%, total TER: 12.9618%, progress (thread 0): 98.2674%]
|T|: a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_2394.1_2394.28, WER: 100%, TER: 50%, total WER: 26.453%, total TER: 12.962%, progress (thread 0): 98.2753%]
|T|: n o
|P|: h m
[sample: EN2002a_H00_MEE073_23.11_23.29, WER: 100%, TER: 100%, total WER: 26.4538%, total TER: 12.9624%, progress (thread 0): 98.2832%]
|T|: y e a h
|P|: y o
[sample: EN2002a_H01_FEO070_32.53_32.71, WER: 100%, TER: 75%, total WER: 26.4546%, total TER: 12.963%, progress (thread 0): 98.2911%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_106.69_106.87, WER: 100%, TER: 33.3333%, total WER: 26.4554%, total TER: 12.9631%, progress (thread 0): 98.299%]
|T|: y e a h
|P|: a n
[sample: EN2002a_H00_MEE073_1441.15_1441.33, WER: 100%, TER: 75%, total WER: 26.4563%, total TER: 12.9637%, progress (thread 0): 98.307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1717.2_1717.38, WER: 0%, TER: 0%, total WER: 26.456%, total TER: 12.9636%, progress (thread 0): 98.3149%]
|T|: o k a y
|P|:
[sample: EN2002b_H03_MEE073_530.52_530.7, WER: 100%, TER: 100%, total WER: 26.4568%, total TER: 12.9644%, progress (thread 0): 98.3228%]
|T|: i s | i t
|P|: i s
[sample: EN2002b_H01_MEE071_1142.07_1142.25, WER: 50%, TER: 60%, total WER: 26.4573%, total TER: 12.9649%, progress (thread 0): 98.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1339.32_1339.5, WER: 0%, TER: 0%, total WER: 26.457%, total TER: 12.9648%, progress (thread 0): 98.3386%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_186.7_186.88, WER: 0%, TER: 0%, total WER: 26.4567%, total TER: 12.9647%, progress (thread 0): 98.3465%]
|T|: a l r i g h t
|P|: r
[sample: EN2002c_H03_MEE073_536.86_537.04, WER: 100%, TER: 85.7143%, total WER: 26.4575%, total TER: 12.9658%, progress (thread 0): 98.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1667.09_1667.27, WER: 0%, TER: 0%, total WER: 26.4572%, total TER: 12.9657%, progress (thread 0): 98.3623%]
|T|: h m m
|P|:
[sample: EN2002d_H01_FEO072_363.49_363.67, WER: 100%, TER: 100%, total WER: 26.4581%, total TER: 12.9663%, progress (thread 0): 98.3703%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_500.44_500.62, WER: 0%, TER: 0%, total WER: 26.4578%, total TER: 12.9662%, progress (thread 0): 98.3782%]
|T|: w h a t
|P|: w h i t
[sample: EN2002d_H00_FEO070_508.48_508.66, WER: 100%, TER: 25%, total WER: 26.4586%, total TER: 12.9663%, progress (thread 0): 98.3861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_512.59_512.77, WER: 0%, TER: 0%, total WER: 26.4583%, total TER: 12.9662%, progress (thread 0): 98.394%]
|T|: y e a h
|P|: i t s
[sample: EN2002d_H02_MEE071_518.66_518.84, WER: 100%, TER: 100%, total WER: 26.4591%, total TER: 12.967%, progress (thread 0): 98.4019%]
|T|: y e a h
|P|: h u h
[sample: EN2002d_H00_FEO070_882.18_882.36, WER: 100%, TER: 75%, total WER: 26.4599%, total TER: 12.9676%, progress (thread 0): 98.4098%]
|T|: m m
|P|:
[sample: EN2002d_H02_MEE071_1451.31_1451.49, WER: 100%, TER: 100%, total WER: 26.4608%, total TER: 12.968%, progress (thread 0): 98.4177%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1581.55_1581.73, WER: 0%, TER: 0%, total WER: 26.4605%, total TER: 12.9678%, progress (thread 0): 98.4256%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_2062.62_2062.8, WER: 0%, TER: 0%, total WER: 26.4602%, total TER: 12.9677%, progress (thread 0): 98.4335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2166.41_2166.59, WER: 0%, TER: 0%, total WER: 26.4599%, total TER: 12.9676%, progress (thread 0): 98.4415%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_2172.96_2173.14, WER: 0%, TER: 0%, total WER: 26.4596%, total TER: 12.9674%, progress (thread 0): 98.4494%]
|T|: a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_843.75_843.92, WER: 100%, TER: 100%, total WER: 26.4604%, total TER: 12.9678%, progress (thread 0): 98.4573%]
|T|: y e p
|P|: y e a h
[sample: ES2004c_H00_MEO015_2220.58_2220.75, WER: 100%, TER: 66.6667%, total WER: 26.4612%, total TER: 12.9682%, progress (thread 0): 98.4652%]
|T|: y e a h
|P|:
[sample: IS1009b_H02_FIO084_381.27_381.44, WER: 100%, TER: 100%, total WER: 26.462%, total TER: 12.969%, progress (thread 0): 98.4731%]
|T|: h i
|P|: i n
[sample: IS1009c_H02_FIO084_43.25_43.42, WER: 100%, TER: 100%, total WER: 26.4629%, total TER: 12.9694%, progress (thread 0): 98.481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_347.27_347.44, WER: 0%, TER: 0%, total WER: 26.4626%, total TER: 12.9693%, progress (thread 0): 98.4889%]
|T|: m m
|P|: h
[sample: IS1009d_H02_FIO084_784.48_784.65, WER: 100%, TER: 100%, total WER: 26.4634%, total TER: 12.9697%, progress (thread 0): 98.4968%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_182.69_182.86, WER: 100%, TER: 66.6667%, total WER: 26.4642%, total TER: 12.9701%, progress (thread 0): 98.5047%]
|T|: h m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_748.2_748.37, WER: 100%, TER: 33.3333%, total WER: 26.465%, total TER: 12.9702%, progress (thread 0): 98.5127%]
|T|: y e a h
|P|: a n d
[sample: TS3003a_H01_MTD011UID_1432.6_1432.77, WER: 100%, TER: 100%, total WER: 26.4659%, total TER: 12.971%, progress (thread 0): 98.5206%]
|T|: n o
|P|: t h e t
[sample: TS3003a_H00_MTD009PM_1442.45_1442.62, WER: 100%, TER: 200%, total WER: 26.4667%, total TER: 12.9719%, progress (thread 0): 98.5285%]
|T|: y e a h
|P|:
[sample: TS3003b_H01_MTD011UID_1584.22_1584.39, WER: 100%, TER: 100%, total WER: 26.4675%, total TER: 12.9727%, progress (thread 0): 98.5364%]
|T|: n o
|P|: u h
[sample: TS3003b_H01_MTD011UID_1779.4_1779.57, WER: 100%, TER: 100%, total WER: 26.4683%, total TER: 12.9731%, progress (thread 0): 98.5443%]
|T|: y e a h
|P|: o p
[sample: TS3003b_H03_MTD012ME_2009.37_2009.54, WER: 100%, TER: 100%, total WER: 26.4691%, total TER: 12.9739%, progress (thread 0): 98.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1183.13_1183.3, WER: 0%, TER: 0%, total WER: 26.4688%, total TER: 12.9738%, progress (thread 0): 98.5601%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2272.21_2272.38, WER: 0%, TER: 0%, total WER: 26.4686%, total TER: 12.9737%, progress (thread 0): 98.568%]
|T|: y e a h
|P|: e a h
[sample: TS3003d_H01_MTD011UID_306.52_306.69, WER: 100%, TER: 25%, total WER: 26.4694%, total TER: 12.9738%, progress (thread 0): 98.576%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_1283.14_1283.31, WER: 100%, TER: 100%, total WER: 26.4702%, total TER: 12.9746%, progress (thread 0): 98.5839%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_549.83_550, WER: 100%, TER: 100%, total WER: 26.471%, total TER: 12.9752%, progress (thread 0): 98.5918%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_1153.54_1153.71, WER: 100%, TER: 100%, total WER: 26.4718%, total TER: 12.9758%, progress (thread 0): 98.5997%]
|T|: y e a h
|P|:
[sample: EN2002a_H00_MEE073_1180.31_1180.48, WER: 100%, TER: 100%, total WER: 26.4727%, total TER: 12.9766%, progress (thread 0): 98.6076%]
|T|: h m m
|P|: h m
[sample: EN2002a_H02_FEO072_2053.69_2053.86, WER: 100%, TER: 33.3333%, total WER: 26.4735%, total TER: 12.9768%, progress (thread 0): 98.6155%]
|T|: y e a h
|P|: j u s
[sample: EN2002a_H01_FEO070_2086.54_2086.71, WER: 100%, TER: 100%, total WER: 26.4743%, total TER: 12.9776%, progress (thread 0): 98.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_423.51_423.68, WER: 0%, TER: 0%, total WER: 26.474%, total TER: 12.9774%, progress (thread 0): 98.6313%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1344.27_1344.44, WER: 100%, TER: 100%, total WER: 26.4748%, total TER: 12.9783%, progress (thread 0): 98.6392%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1400.92_1401.09, WER: 0%, TER: 0%, total WER: 26.4745%, total TER: 12.9781%, progress (thread 0): 98.6472%]
|T|: y e a h
|P|: e s t
[sample: EN2002b_H01_MEE071_1518.9_1519.07, WER: 100%, TER: 75%, total WER: 26.4754%, total TER: 12.9787%, progress (thread 0): 98.6551%]
|T|: y e a h
|P|: i n
[sample: EN2002c_H03_MEE073_55.6_55.77, WER: 100%, TER: 100%, total WER: 26.4762%, total TER: 12.9795%, progress (thread 0): 98.663%]
|T|: y e a h
|P|:
[sample: EN2002c_H03_MEE073_711.52_711.69, WER: 100%, TER: 100%, total WER: 26.477%, total TER: 12.9803%, progress (thread 0): 98.6709%]
|T|: b u t
|P|: t a e
[sample: EN2002d_H02_MEE071_431.16_431.33, WER: 100%, TER: 100%, total WER: 26.4778%, total TER: 12.9809%, progress (thread 0): 98.6788%]
|T|: y e a h
|P|: i s
[sample: EN2002d_H02_MEE071_649.62_649.79, WER: 100%, TER: 100%, total WER: 26.4786%, total TER: 12.9817%, progress (thread 0): 98.6867%]
|T|: y e a h
|P|: h
[sample: EN2002d_H03_MEE073_831.51_831.68, WER: 100%, TER: 75%, total WER: 26.4795%, total TER: 12.9823%, progress (thread 0): 98.6946%]
|T|: s o
|P|:
[sample: EN2002d_H03_MEE073_1058.82_1058.99, WER: 100%, TER: 100%, total WER: 26.4803%, total TER: 12.9827%, progress (thread 0): 98.7025%]
|T|: o h
|P|: i
[sample: EN2002d_H00_FEO070_1459.48_1459.65, WER: 100%, TER: 100%, total WER: 26.4811%, total TER: 12.9831%, progress (thread 0): 98.7104%]
|T|: r i g h t
|P|: r i h t
[sample: EN2002d_H03_MEE073_1858.1_1858.27, WER: 100%, TER: 20%, total WER: 26.4819%, total TER: 12.9832%, progress (thread 0): 98.7184%]
|T|: i | m e a n
|P|: i | m n
[sample: ES2004c_H03_FEE016_691.32_691.48, WER: 50%, TER: 33.3333%, total WER: 26.4825%, total TER: 12.9835%, progress (thread 0): 98.7263%]
|T|: o k a y
|P|:
[sample: ES2004c_H03_FEE016_1560.36_1560.52, WER: 100%, TER: 100%, total WER: 26.4833%, total TER: 12.9843%, progress (thread 0): 98.7342%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H00_FIE088_757.84_758, WER: 0%, TER: 0%, total WER: 26.483%, total TER: 12.9842%, progress (thread 0): 98.7421%]
|T|: n o
|P|: n o
[sample: IS1009d_H01_FIO087_584.12_584.28, WER: 0%, TER: 0%, total WER: 26.4827%, total TER: 12.9841%, progress (thread 0): 98.75%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_740.48_740.64, WER: 0%, TER: 0%, total WER: 26.4824%, total TER: 12.984%, progress (thread 0): 98.7579%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_826.94_827.1, WER: 0%, TER: 0%, total WER: 26.4821%, total TER: 12.9839%, progress (thread 0): 98.7658%]
|T|: n o
|P|: n o
[sample: IS1009d_H03_FIO089_862.59_862.75, WER: 0%, TER: 0%, total WER: 26.4818%, total TER: 12.9838%, progress (thread 0): 98.7737%]
|T|: y e a h
|P|: y e a
[sample: IS1009d_H02_FIO084_1025.48_1025.64, WER: 100%, TER: 25%, total WER: 26.4826%, total TER: 12.9839%, progress (thread 0): 98.7816%]
|T|: y e a h
|P|: a n
[sample: TS3003a_H01_MTD011UID_580.47_580.63, WER: 100%, TER: 75%, total WER: 26.4834%, total TER: 12.9845%, progress (thread 0): 98.7896%]
|T|: u h
|P|: r i h
[sample: TS3003a_H00_MTD009PM_617.55_617.71, WER: 100%, TER: 100%, total WER: 26.4843%, total TER: 12.9849%, progress (thread 0): 98.7975%]
|T|: u h
|P|: e a h
[sample: TS3003a_H00_MTD009PM_1421.57_1421.73, WER: 100%, TER: 100%, total WER: 26.4851%, total TER: 12.9853%, progress (thread 0): 98.8054%]
|T|: h m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1789.09_1789.25, WER: 100%, TER: 33.3333%, total WER: 26.4859%, total TER: 12.9855%, progress (thread 0): 98.8133%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2009.54_2009.7, WER: 0%, TER: 0%, total WER: 26.4856%, total TER: 12.9853%, progress (thread 0): 98.8212%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1865.26_1865.42, WER: 100%, TER: 66.6667%, total WER: 26.4864%, total TER: 12.9857%, progress (thread 0): 98.8291%]
|T|: o h
|P|: t o
[sample: TS3003d_H00_MTD009PM_2587.23_2587.39, WER: 100%, TER: 100%, total WER: 26.4872%, total TER: 12.9861%, progress (thread 0): 98.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_376.27_376.43, WER: 0%, TER: 0%, total WER: 26.487%, total TER: 12.986%, progress (thread 0): 98.8449%]
|T|: y e a h
|P|: s i n e
[sample: EN2002a_H00_MEE073_720.58_720.74, WER: 100%, TER: 100%, total WER: 26.4878%, total TER: 12.9868%, progress (thread 0): 98.8529%]
|T|: y e a h
|P|: b u t
[sample: EN2002a_H00_MEE073_737.85_738.01, WER: 100%, TER: 100%, total WER: 26.4886%, total TER: 12.9876%, progress (thread 0): 98.8608%]
|T|: y e a h
|P|: y o
[sample: EN2002a_H00_MEE073_844.91_845.07, WER: 100%, TER: 75%, total WER: 26.4894%, total TER: 12.9882%, progress (thread 0): 98.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1041.33_1041.49, WER: 0%, TER: 0%, total WER: 26.4891%, total TER: 12.9881%, progress (thread 0): 98.8766%]
|T|: y e a h
|P|: b u t
[sample: EN2002a_H00_MEE073_1050.03_1050.19, WER: 100%, TER: 100%, total WER: 26.4899%, total TER: 12.9889%, progress (thread 0): 98.8845%]
|T|: w h y
|P|: w
[sample: EN2002a_H00_MEE073_1237.91_1238.07, WER: 100%, TER: 66.6667%, total WER: 26.4908%, total TER: 12.9893%, progress (thread 0): 98.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1596.11_1596.27, WER: 0%, TER: 0%, total WER: 26.4905%, total TER: 12.9891%, progress (thread 0): 98.9003%]
|T|: s o r r y
|P|: t u e
[sample: EN2002a_H00_MEE073_1813.61_1813.77, WER: 100%, TER: 100%, total WER: 26.4913%, total TER: 12.9901%, progress (thread 0): 98.9082%]
|T|: s u r e
|P|:
[sample: EN2002a_H00_MEE073_1881.96_1882.12, WER: 100%, TER: 100%, total WER: 26.4921%, total TER: 12.991%, progress (thread 0): 98.9161%]
|T|: y e a h
|P|: y o u
[sample: EN2002b_H02_FEO072_39.76_39.92, WER: 100%, TER: 75%, total WER: 26.4929%, total TER: 12.9915%, progress (thread 0): 98.924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_317.94_318.1, WER: 0%, TER: 0%, total WER: 26.4926%, total TER: 12.9914%, progress (thread 0): 98.932%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1075.72_1075.88, WER: 0%, TER: 0%, total WER: 26.4923%, total TER: 12.9913%, progress (thread 0): 98.9399%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1155.04_1155.2, WER: 100%, TER: 100%, total WER: 26.4932%, total TER: 12.9921%, progress (thread 0): 98.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_713.06_713.22, WER: 0%, TER: 0%, total WER: 26.4929%, total TER: 12.9919%, progress (thread 0): 98.9557%]
|T|: o h
|P|:
[sample: EN2002c_H03_MEE073_1210.59_1210.75, WER: 100%, TER: 100%, total WER: 26.4937%, total TER: 12.9924%, progress (thread 0): 98.9636%]
|T|: r i g h t
|P|: o r
[sample: EN2002c_H03_MEE073_1484.69_1484.85, WER: 100%, TER: 100%, total WER: 26.4945%, total TER: 12.9934%, progress (thread 0): 98.9715%]
|T|: y e a h
|P|: e
[sample: EN2002c_H03_MEE073_2554.98_2555.14, WER: 100%, TER: 75%, total WER: 26.4953%, total TER: 12.9939%, progress (thread 0): 98.9794%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2776.86_2777.02, WER: 100%, TER: 33.3333%, total WER: 26.4961%, total TER: 12.9941%, progress (thread 0): 98.9873%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_645.29_645.45, WER: 100%, TER: 100%, total WER: 26.497%, total TER: 12.9945%, progress (thread 0): 98.9952%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1397.11_1397.27, WER: 0%, TER: 0%, total WER: 26.4967%, total TER: 12.9944%, progress (thread 0): 99.0032%]
|T|: o o p s
|P|:
[sample: ES2004a_H00_MEO015_188.28_188.43, WER: 100%, TER: 100%, total WER: 26.4975%, total TER: 12.9952%, progress (thread 0): 99.0111%]
|T|: o o h
|P|: i t
[sample: ES2004b_H02_MEE014_534.45_534.6, WER: 100%, TER: 100%, total WER: 26.4983%, total TER: 12.9958%, progress (thread 0): 99.019%]
|T|: ' k a y
|P|:
[sample: ES2004c_H01_FEE013_472.18_472.33, WER: 100%, TER: 100%, total WER: 26.4991%, total TER: 12.9966%, progress (thread 0): 99.0269%]
|T|: y e s
|P|:
[sample: IS1009a_H01_FIO087_693.82_693.97, WER: 100%, TER: 100%, total WER: 26.5%, total TER: 12.9972%, progress (thread 0): 99.0348%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1647.59_1647.74, WER: 0%, TER: 0%, total WER: 26.4997%, total TER: 12.9971%, progress (thread 0): 99.0427%]
|T|: y e a h
|P|: s a m e
[sample: IS1009d_H02_FIO084_1392_1392.15, WER: 100%, TER: 100%, total WER: 26.5005%, total TER: 12.9979%, progress (thread 0): 99.0506%]
|T|: t w o
|P|: d o
[sample: IS1009d_H01_FIO087_1586.28_1586.43, WER: 100%, TER: 66.6667%, total WER: 26.5013%, total TER: 12.9983%, progress (thread 0): 99.0585%]
|T|: n o
|P|:
[sample: IS1009d_H01_FIO087_1824.98_1825.13, WER: 100%, TER: 100%, total WER: 26.5021%, total TER: 12.9987%, progress (thread 0): 99.0665%]
|T|: y e a h
|P|: n
[sample: IS1009d_H02_FIO084_1883.61_1883.76, WER: 100%, TER: 100%, total WER: 26.5029%, total TER: 12.9995%, progress (thread 0): 99.0744%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_567.51_567.66, WER: 0%, TER: 0%, total WER: 26.5026%, total TER: 12.9993%, progress (thread 0): 99.0823%]
|T|: y e a h
|P|:
[sample: TS3003c_H02_MTD0010ID_2049.54_2049.69, WER: 100%, TER: 100%, total WER: 26.5035%, total TER: 13.0002%, progress (thread 0): 99.0902%]
|T|: m m
|P|: m
[sample: TS3003d_H01_MTD011UID_2041.85_2042, WER: 100%, TER: 50%, total WER: 26.5043%, total TER: 13.0003%, progress (thread 0): 99.0981%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_639.89_640.04, WER: 100%, TER: 66.6667%, total WER: 26.5051%, total TER: 13.0007%, progress (thread 0): 99.106%]
|T|: y e a h
|P|: a n
[sample: EN2002a_H00_MEE073_967.58_967.73, WER: 100%, TER: 75%, total WER: 26.5059%, total TER: 13.0013%, progress (thread 0): 99.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1030.96_1031.11, WER: 0%, TER: 0%, total WER: 26.5056%, total TER: 13.0012%, progress (thread 0): 99.1218%]
|T|: y e a h
|P|: h o
[sample: EN2002a_H01_FEO070_1104.83_1104.98, WER: 100%, TER: 100%, total WER: 26.5065%, total TER: 13.002%, progress (thread 0): 99.1297%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1527.69_1527.84, WER: 100%, TER: 33.3333%, total WER: 26.5073%, total TER: 13.0021%, progress (thread 0): 99.1377%]
|T|: y e a h
|P|:
[sample: EN2002a_H00_MEE073_1539.77_1539.92, WER: 100%, TER: 100%, total WER: 26.5081%, total TER: 13.0029%, progress (thread 0): 99.1456%]
|T|: y e a h
|P|: r e
[sample: EN2002a_H00_MEE073_1744.01_1744.16, WER: 100%, TER: 75%, total WER: 26.5089%, total TER: 13.0035%, progress (thread 0): 99.1535%]
|T|: y e a h
|P|: y o a
[sample: EN2002a_H01_FEO070_1785.21_1785.36, WER: 100%, TER: 50%, total WER: 26.5097%, total TER: 13.0038%, progress (thread 0): 99.1614%]
|T|: y e p
|P|: y e p
[sample: EN2002b_H03_MEE073_382.32_382.47, WER: 0%, TER: 0%, total WER: 26.5094%, total TER: 13.0037%, progress (thread 0): 99.1693%]
|T|: u h
|P|: y e a h
[sample: EN2002b_H02_FEO072_424.35_424.5, WER: 100%, TER: 150%, total WER: 26.5103%, total TER: 13.0044%, progress (thread 0): 99.1772%]
|T|: y e a h
|P|:
[sample: EN2002b_H00_FEO070_496.03_496.18, WER: 100%, TER: 100%, total WER: 26.5111%, total TER: 13.0052%, progress (thread 0): 99.1851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_897.7_897.85, WER: 0%, TER: 0%, total WER: 26.5108%, total TER: 13.0051%, progress (thread 0): 99.193%]
|T|: y e a h
|P|: t h
[sample: EN2002c_H03_MEE073_2198.65_2198.8, WER: 100%, TER: 75%, total WER: 26.5116%, total TER: 13.0056%, progress (thread 0): 99.201%]
|T|: y e a h
|P|: i t
[sample: EN2002d_H00_FEO070_1290.13_1290.28, WER: 100%, TER: 100%, total WER: 26.5124%, total TER: 13.0065%, progress (thread 0): 99.2089%]
|T|: n o
|P|:
[sample: EN2002d_H03_MEE073_1424.03_1424.18, WER: 100%, TER: 100%, total WER: 26.5132%, total TER: 13.0069%, progress (thread 0): 99.2168%]
|T|: r i g h t
|P|:
[sample: EN2002d_H03_MEE073_1909.61_1909.76, WER: 100%, TER: 100%, total WER: 26.5141%, total TER: 13.0079%, progress (thread 0): 99.2247%]
|T|: o k a y
|P|:
[sample: EN2002d_H03_MEE073_1985.35_1985.5, WER: 100%, TER: 100%, total WER: 26.5149%, total TER: 13.0087%, progress (thread 0): 99.2326%]
|T|: i | t h
|P|: i
[sample: ES2004b_H01_FEE013_2305.5_2305.64, WER: 50%, TER: 75%, total WER: 26.5154%, total TER: 13.0093%, progress (thread 0): 99.2405%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_777.13_777.27, WER: 100%, TER: 100%, total WER: 26.5162%, total TER: 13.0097%, progress (thread 0): 99.2484%]
|T|: y e a h
|P|:
[sample: IS1009a_H02_FIO084_56.9_57.04, WER: 100%, TER: 100%, total WER: 26.5171%, total TER: 13.0105%, progress (thread 0): 99.2563%]
|T|: y e s
|P|: y e a
[sample: IS1009d_H01_FIO087_645.27_645.41, WER: 100%, TER: 33.3333%, total WER: 26.5179%, total TER: 13.0106%, progress (thread 0): 99.2642%]
|T|: m m h m m
|P|:
[sample: IS1009d_H00_FIE088_770.97_771.11, WER: 100%, TER: 100%, total WER: 26.5187%, total TER: 13.0116%, progress (thread 0): 99.2721%]
|T|: h m m
|P|:
[sample: TS3003a_H01_MTD011UID_189.69_189.83, WER: 100%, TER: 100%, total WER: 26.5195%, total TER: 13.0122%, progress (thread 0): 99.2801%]
|T|: m m
|P|:
[sample: TS3003a_H01_MTD011UID_903.08_903.22, WER: 100%, TER: 100%, total WER: 26.5203%, total TER: 13.0126%, progress (thread 0): 99.288%]
|T|: m m
|P|:
[sample: TS3003b_H01_MTD011UID_2047.35_2047.49, WER: 100%, TER: 100%, total WER: 26.5212%, total TER: 13.013%, progress (thread 0): 99.2959%]
|T|: ' k a y
|P|:
[sample: TS3003b_H03_MTD012ME_2140.48_2140.62, WER: 100%, TER: 100%, total WER: 26.522%, total TER: 13.0138%, progress (thread 0): 99.3038%]
|T|: y e a h
|P|:
[sample: TS3003c_H03_MTD012ME_1869.23_1869.37, WER: 100%, TER: 100%, total WER: 26.5228%, total TER: 13.0147%, progress (thread 0): 99.3117%]
|T|: y e a h
|P|:
[sample: TS3003d_H00_MTD009PM_1390.61_1390.75, WER: 100%, TER: 100%, total WER: 26.5236%, total TER: 13.0155%, progress (thread 0): 99.3196%]
|T|: y e a h
|P|:
[sample: TS3003d_H00_MTD009PM_2021.71_2021.85, WER: 100%, TER: 100%, total WER: 26.5244%, total TER: 13.0163%, progress (thread 0): 99.3275%]
|T|: r i g h t
|P|: i k
[sample: EN2002a_H00_MEE073_1184.15_1184.29, WER: 100%, TER: 80%, total WER: 26.5253%, total TER: 13.017%, progress (thread 0): 99.3354%]
|T|: h m m
|P|: i h m
[sample: EN2002a_H00_MEE073_1193.94_1194.08, WER: 100%, TER: 66.6667%, total WER: 26.5261%, total TER: 13.0174%, progress (thread 0): 99.3434%]
|T|: y e p
|P|: y e h
[sample: EN2002a_H00_MEE073_2048.07_2048.21, WER: 100%, TER: 33.3333%, total WER: 26.5269%, total TER: 13.0176%, progress (thread 0): 99.3513%]
|T|: ' k a y
|P|:
[sample: EN2002c_H03_MEE073_103.84_103.98, WER: 100%, TER: 100%, total WER: 26.5277%, total TER: 13.0184%, progress (thread 0): 99.3592%]
|T|: y e a h
|P|: g e
[sample: EN2002c_H02_MEE071_531.32_531.46, WER: 100%, TER: 75%, total WER: 26.5285%, total TER: 13.0189%, progress (thread 0): 99.3671%]
|T|: y e a h
|P|: s o
[sample: EN2002c_H03_MEE073_667.22_667.36, WER: 100%, TER: 100%, total WER: 26.5294%, total TER: 13.0198%, progress (thread 0): 99.375%]
|T|: y e a h
|P|: y e
[sample: EN2002c_H02_MEE071_1476.08_1476.22, WER: 100%, TER: 50%, total WER: 26.5302%, total TER: 13.0201%, progress (thread 0): 99.3829%]
|T|: m m
|P|:
[sample: EN2002c_H03_MEE073_2096.91_2097.05, WER: 100%, TER: 100%, total WER: 26.531%, total TER: 13.0205%, progress (thread 0): 99.3908%]
|T|: o k a y
|P|: e
[sample: EN2002d_H00_FEO070_1251.9_1252.04, WER: 100%, TER: 100%, total WER: 26.5318%, total TER: 13.0213%, progress (thread 0): 99.3987%]
|T|: m m
|P|: y e a
[sample: ES2004c_H03_FEE016_98.28_98.41, WER: 100%, TER: 150%, total WER: 26.5326%, total TER: 13.0219%, progress (thread 0): 99.4066%]
|T|: s
|P|:
[sample: ES2004d_H03_FEE016_1370.74_1370.87, WER: 100%, TER: 100%, total WER: 26.5335%, total TER: 13.0221%, progress (thread 0): 99.4146%]
|T|: o r | a | b
|P|:
[sample: IS1009a_H01_FIO087_573.12_573.25, WER: 100%, TER: 100%, total WER: 26.5359%, total TER: 13.0234%, progress (thread 0): 99.4225%]
|T|: h m m
|P|: y e
[sample: IS1009c_H02_FIO084_144.08_144.21, WER: 100%, TER: 100%, total WER: 26.5367%, total TER: 13.024%, progress (thread 0): 99.4304%]
|T|: o k a y
|P|: j u s
[sample: IS1009d_H00_FIE088_604.16_604.29, WER: 100%, TER: 100%, total WER: 26.5376%, total TER: 13.0248%, progress (thread 0): 99.4383%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_942.55_942.68, WER: 100%, TER: 100%, total WER: 26.5384%, total TER: 13.0254%, progress (thread 0): 99.4462%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_392.63_392.76, WER: 100%, TER: 66.6667%, total WER: 26.5392%, total TER: 13.0258%, progress (thread 0): 99.4541%]
|T|: h u h
|P|:
[sample: TS3003a_H01_MTD011UID_1266.08_1266.21, WER: 100%, TER: 100%, total WER: 26.54%, total TER: 13.0264%, progress (thread 0): 99.462%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1335.11_1335.24, WER: 100%, TER: 100%, total WER: 26.5408%, total TER: 13.0268%, progress (thread 0): 99.4699%]
|T|: y e p
|P|: y h a
[sample: TS3003a_H01_MTD011UID_1475.42_1475.55, WER: 100%, TER: 66.6667%, total WER: 26.5417%, total TER: 13.0271%, progress (thread 0): 99.4778%]
|T|: y e a h
|P|:
[sample: TS3003b_H01_MTD011UID_1528.52_1528.65, WER: 100%, TER: 100%, total WER: 26.5425%, total TER: 13.0279%, progress (thread 0): 99.4858%]
|T|: m m
|P|:
[sample: TS3003c_H01_MTD011UID_1173.23_1173.36, WER: 100%, TER: 100%, total WER: 26.5433%, total TER: 13.0284%, progress (thread 0): 99.4937%]
|T|: y e a h
|P|: t h
[sample: TS3003d_H00_MTD009PM_1693.45_1693.58, WER: 100%, TER: 75%, total WER: 26.5441%, total TER: 13.0289%, progress (thread 0): 99.5016%]
|T|: ' k a y
|P|:
[sample: EN2002a_H00_MEE073_32.58_32.71, WER: 100%, TER: 100%, total WER: 26.5449%, total TER: 13.0297%, progress (thread 0): 99.5095%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_984.87_985, WER: 100%, TER: 100%, total WER: 26.5458%, total TER: 13.0303%, progress (thread 0): 99.5174%]
|T|: w e l l | f
|P|: b u h
[sample: EN2002a_H00_MEE073_1458.73_1458.86, WER: 100%, TER: 100%, total WER: 26.5474%, total TER: 13.0316%, progress (thread 0): 99.5253%]
|T|: y e p
|P|: y e a
[sample: EN2002b_H01_MEE071_493.19_493.32, WER: 100%, TER: 33.3333%, total WER: 26.5482%, total TER: 13.0317%, progress (thread 0): 99.5332%]
|T|: y e a h
|P|:
[sample: EN2002c_H03_MEE073_665.01_665.14, WER: 100%, TER: 100%, total WER: 26.549%, total TER: 13.0325%, progress (thread 0): 99.5411%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_135.16_135.29, WER: 100%, TER: 100%, total WER: 26.5499%, total TER: 13.0329%, progress (thread 0): 99.549%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H01_FEO072_726.53_726.66, WER: 100%, TER: 25%, total WER: 26.5507%, total TER: 13.033%, progress (thread 0): 99.557%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H01_FEO072_794.79_794.92, WER: 100%, TER: 25%, total WER: 26.5515%, total TER: 13.0331%, progress (thread 0): 99.5649%]
|T|: y e a h
|P|: g o
[sample: EN2002d_H00_FEO070_1595.77_1595.9, WER: 100%, TER: 100%, total WER: 26.5523%, total TER: 13.0339%, progress (thread 0): 99.5728%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_1641.64_1641.77, WER: 100%, TER: 100%, total WER: 26.5531%, total TER: 13.0343%, progress (thread 0): 99.5807%]
|T|: y e a h
|P|:
[sample: ES2004b_H00_MEO015_922.62_922.74, WER: 100%, TER: 100%, total WER: 26.554%, total TER: 13.0351%, progress (thread 0): 99.5886%]
|T|: o k a y
|P|: j u
[sample: ES2004b_H00_MEO015_1977.87_1977.99, WER: 100%, TER: 100%, total WER: 26.5548%, total TER: 13.036%, progress (thread 0): 99.5965%]
|T|: y e a h
|P|: i
[sample: IS1009b_H02_FIO084_173.86_173.98, WER: 100%, TER: 100%, total WER: 26.5556%, total TER: 13.0368%, progress (thread 0): 99.6044%]
|T|: h m m
|P|:
[sample: IS1009c_H02_FIO084_1703.14_1703.26, WER: 100%, TER: 100%, total WER: 26.5564%, total TER: 13.0374%, progress (thread 0): 99.6123%]
|T|: m m | m m
|P|:
[sample: IS1009d_H03_FIO089_870.76_870.88, WER: 100%, TER: 100%, total WER: 26.5581%, total TER: 13.0384%, progress (thread 0): 99.6203%]
|T|: y e p
|P|: i f
[sample: TS3003a_H01_MTD011UID_257.2_257.32, WER: 100%, TER: 100%, total WER: 26.5589%, total TER: 13.039%, progress (thread 0): 99.6282%]
|T|: y e a h
|P|: j u s
[sample: EN2002a_H03_MEE071_170.81_170.93, WER: 100%, TER: 100%, total WER: 26.5597%, total TER: 13.0398%, progress (thread 0): 99.6361%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1468.26_1468.38, WER: 100%, TER: 33.3333%, total WER: 26.5605%, total TER: 13.0399%, progress (thread 0): 99.644%]
|T|: n o
|P|: n h
[sample: EN2002b_H03_MEE073_4.83_4.95, WER: 100%, TER: 50%, total WER: 26.5613%, total TER: 13.0401%, progress (thread 0): 99.6519%]
|T|: a l t e r
|P|: u
[sample: EN2002b_H03_MEE073_170.46_170.58, WER: 100%, TER: 100%, total WER: 26.5622%, total TER: 13.0411%, progress (thread 0): 99.6598%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1641.92_1642.04, WER: 100%, TER: 100%, total WER: 26.563%, total TER: 13.0419%, progress (thread 0): 99.6677%]
|T|: y e a h
|P|: y e
[sample: EN2002d_H03_MEE073_806.78_806.9, WER: 100%, TER: 50%, total WER: 26.5638%, total TER: 13.0423%, progress (thread 0): 99.6756%]
|T|: o k a y
|P|:
[sample: ES2004a_H00_MEO015_71.95_72.06, WER: 100%, TER: 100%, total WER: 26.5646%, total TER: 13.0431%, progress (thread 0): 99.6835%]
|T|: o
|P|:
[sample: ES2004a_H03_FEE016_403.24_403.35, WER: 100%, TER: 100%, total WER: 26.5654%, total TER: 13.0433%, progress (thread 0): 99.6915%]
|T|: ' k a y
|P|:
[sample: ES2004a_H03_FEE016_1042.19_1042.3, WER: 100%, TER: 100%, total WER: 26.5662%, total TER: 13.0441%, progress (thread 0): 99.6994%]
|T|: m m
|P|:
[sample: IS1009c_H02_FIO084_620.84_620.95, WER: 100%, TER: 100%, total WER: 26.5671%, total TER: 13.0445%, progress (thread 0): 99.7073%]
|T|: y e a h
|P|:
[sample: TS3003a_H01_MTD011UID_436.27_436.38, WER: 100%, TER: 100%, total WER: 26.5679%, total TER: 13.0453%, progress (thread 0): 99.7152%]
|T|: y e a h
|P|: o
[sample: EN2002a_H00_MEE073_669.47_669.58, WER: 100%, TER: 100%, total WER: 26.5687%, total TER: 13.0461%, progress (thread 0): 99.7231%]
|T|: y e a h
|P|:
[sample: EN2002a_H01_FEO070_1169.61_1169.72, WER: 100%, TER: 100%, total WER: 26.5695%, total TER: 13.0469%, progress (thread 0): 99.731%]
|T|: n o
|P|:
[sample: EN2002b_H02_FEO072_4.48_4.59, WER: 100%, TER: 100%, total WER: 26.5703%, total TER: 13.0473%, progress (thread 0): 99.7389%]
|T|: y e p
|P|:
[sample: EN2002b_H03_MEE073_307.97_308.08, WER: 100%, TER: 100%, total WER: 26.5712%, total TER: 13.0479%, progress (thread 0): 99.7468%]
|T|: h m m
|P|:
[sample: EN2002b_H03_MEE073_1578.82_1578.93, WER: 100%, TER: 100%, total WER: 26.572%, total TER: 13.0485%, progress (thread 0): 99.7547%]
|T|: h m m
|P|:
[sample: EN2002c_H03_MEE073_1446.36_1446.47, WER: 100%, TER: 100%, total WER: 26.5728%, total TER: 13.0491%, progress (thread 0): 99.7627%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_118.11_118.22, WER: 100%, TER: 100%, total WER: 26.5736%, total TER: 13.0495%, progress (thread 0): 99.7706%]
|T|: s o
|P|:
[sample: EN2002d_H02_MEE071_2107.05_2107.16, WER: 100%, TER: 100%, total WER: 26.5744%, total TER: 13.0499%, progress (thread 0): 99.7785%]
|T|: t
|P|:
[sample: ES2004c_H02_MEE014_1184.81_1184.91, WER: 100%, TER: 100%, total WER: 26.5753%, total TER: 13.0501%, progress (thread 0): 99.7864%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_1744.56_1744.66, WER: 100%, TER: 100%, total WER: 26.5761%, total TER: 13.0507%, progress (thread 0): 99.7943%]
|T|: o h
|P|:
[sample: TS3003c_H02_MTD0010ID_1023.66_1023.76, WER: 100%, TER: 100%, total WER: 26.5769%, total TER: 13.0512%, progress (thread 0): 99.8022%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_2013_2013.1, WER: 100%, TER: 100%, total WER: 26.5777%, total TER: 13.0518%, progress (thread 0): 99.8101%]
|T|: y e a h
|P|:
[sample: EN2002b_H00_FEO070_43.63_43.73, WER: 100%, TER: 100%, total WER: 26.5785%, total TER: 13.0526%, progress (thread 0): 99.818%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_293.52_293.62, WER: 100%, TER: 100%, total WER: 26.5794%, total TER: 13.0534%, progress (thread 0): 99.826%]
|T|: y e p
|P|: e
[sample: EN2002c_H03_MEE073_763_763.1, WER: 100%, TER: 66.6667%, total WER: 26.5802%, total TER: 13.0537%, progress (thread 0): 99.8339%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_2099.51_2099.61, WER: 100%, TER: 100%, total WER: 26.581%, total TER: 13.0541%, progress (thread 0): 99.8418%]
|T|: ' k a y
|P|:
[sample: ES2004a_H00_MEO015_168.79_168.88, WER: 100%, TER: 100%, total WER: 26.5818%, total TER: 13.055%, progress (thread 0): 99.8497%]
|T|: y e p
|P|:
[sample: ES2004c_H00_MEO015_360.73_360.82, WER: 100%, TER: 100%, total WER: 26.5826%, total TER: 13.0556%, progress (thread 0): 99.8576%]
|T|: m m h m m
|P|:
[sample: ES2004c_H03_FEE016_1132.2_1132.29, WER: 100%, TER: 100%, total WER: 26.5835%, total TER: 13.0566%, progress (thread 0): 99.8655%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1651.84_1651.93, WER: 100%, TER: 100%, total WER: 26.5843%, total TER: 13.0572%, progress (thread 0): 99.8734%]
|T|: m m h m m
|P|:
[sample: IS1009c_H02_FIO084_1753.49_1753.58, WER: 100%, TER: 100%, total WER: 26.5851%, total TER: 13.0582%, progress (thread 0): 99.8813%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_749.88_749.97, WER: 100%, TER: 100%, total WER: 26.5859%, total TER: 13.0588%, progress (thread 0): 99.8892%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1718.32_1718.41, WER: 100%, TER: 100%, total WER: 26.5867%, total TER: 13.0598%, progress (thread 0): 99.8972%]
|T|: w h a t
|P|:
[sample: TS3003d_H00_MTD009PM_1597.33_1597.42, WER: 100%, TER: 100%, total WER: 26.5875%, total TER: 13.0606%, progress (thread 0): 99.9051%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_887.36_887.45, WER: 100%, TER: 100%, total WER: 26.5884%, total TER: 13.0612%, progress (thread 0): 99.913%]
|T|: d a m n
|P|:
[sample: EN2002a_H00_MEE073_1500.91_1501, WER: 100%, TER: 100%, total WER: 26.5892%, total TER: 13.062%, progress (thread 0): 99.9209%]
|T|: h m m
|P|:
[sample: EN2002c_H03_MEE073_429.63_429.72, WER: 100%, TER: 100%, total WER: 26.59%, total TER: 13.0626%, progress (thread 0): 99.9288%]
|T|: y e a h
|P|: a
[sample: EN2002c_H02_MEE071_2578.94_2579.03, WER: 100%, TER: 75%, total WER: 26.5908%, total TER: 13.0632%, progress (thread 0): 99.9367%]
|T|: d a m n
|P|:
[sample: EN2002d_H03_MEE073_999.94_1000.03, WER: 100%, TER: 100%, total WER: 26.5916%, total TER: 13.064%, progress (thread 0): 99.9446%]
|T|: m m
|P|:
[sample: ES2004a_H00_MEO015_252.48_252.56, WER: 100%, TER: 100%, total WER: 26.5925%, total TER: 13.0644%, progress (thread 0): 99.9525%]
|T|: m m
|P|:
[sample: ES2004c_H00_MEO015_1885.03_1885.11, WER: 100%, TER: 100%, total WER: 26.5933%, total TER: 13.0648%, progress (thread 0): 99.9604%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1313.86_1313.94, WER: 100%, TER: 100%, total WER: 26.5941%, total TER: 13.0652%, progress (thread 0): 99.9684%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_185.9_185.98, WER: 100%, TER: 100%, total WER: 26.5949%, total TER: 13.0658%, progress (thread 0): 99.9763%]
|T|: g
|P|:
[sample: EN2002d_H03_MEE073_241.62_241.69, WER: 100%, TER: 100%, total WER: 26.5957%, total TER: 13.066%, progress (thread 0): 99.9842%]
|T|: h m m
|P|:
[sample: IS1009a_H02_FIO084_490.4_490.46, WER: 100%, TER: 100%, total WER: 26.5966%, total TER: 13.0666%, progress (thread 0): 99.9921%]
|T|: ' k a y
|P|:
[sample: EN2002b_H03_MEE073_613.94_614, WER: 100%, TER: 100%, total WER: 26.5974%, total TER: 13.0674%, progress (thread 0): 100%]
I1224 05:05:23.029392 9187 Test.cpp:418] ------
I1224 05:05:23.029417 9187 Test.cpp:419] [Test ami_limited_supervision/test.lst (12640 samples) in 363.179s (actual decoding time 0.0287s/sample) -- WER: 26.5974%, TER: 13.0674%]
###Markdown
We can see that the viterbi WER is 26.6% before finetuning. Step 3: Run FinetuningNow, let's run finetuning with the AMI Corpus to see if we can improve the WER. Important parameters for `fl_asr_finetune_ctc`:`--train`, `--valid` - list files for training and validation sets respectively. Use comma to separate multiple files`--datadir` - [optional] base path to be used for `--train`, `--valid` flags`--lr` - learning rate for SGD`--momentum` - SGD momentum `--lr_decay` - epoch at which learning decay starts `--lr_decay_step` - learning rate halves after this epoch interval starting from epoch given by `lr_decay` `--arch` - architecture file. Tune droupout if necessary. `--tokens` - tokens file `--batchsize` - batchsize per process`--lexicon` - lexicon file `--rundir` - path to store checkpoint logs`--reportiters` - Number of updates after which we will run evaluation on validation data and save model, if 0 we only do this at end of each epoch>Amount of train data | Config to use >---|---|> 10 min| --train train_10min_0.lst> 1 hr| --train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst> 10 hr| --train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lstLet's run finetuning with 10hr AMI data (**~7min** for 1000 updates with evaluation on dev set)
###Code
! ./flashlight/build/bin/asr/fl_asr_tutorial_finetune_ctc model.bin \
--datadir ami_limited_supervision \
--train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lst \
--valid dev:dev.lst \
--arch arch.txt \
--tokens tokens.txt \
--lexicon lexicon.txt \
--rundir checkpoint \
--lr 0.025 \
--netoptim sgd \
--momentum 0.8 \
--reportiters 1000 \
--lr_decay 100 \
--lr_decay_step 50 \
--iter 25000 \
--batchsize 4 \
--warmup 0
###Output
I1224 06:39:48.599629 11517 FinetuneCTC.cpp:76] Parsing command line flags
Initialized NCCL 2.7.8 successfully!
I1224 06:39:49.002488 11517 FinetuneCTC.cpp:106] Gflags after parsing
--flagfile=; --fromenv=; --tryfromenv=; --undefok=; --tab_completion_columns=80; --tab_completion_word=; --help=false; --helpfull=false; --helpmatch=; --helpon=; --helppackage=false; --helpshort=false; --helpxml=false; --version=false; --adambeta1=0.94999999999999996; --adambeta2=0.98999999999999999; --am=; --am_decoder_tr_dropout=0.20000000000000001; --am_decoder_tr_layerdrop=0.20000000000000001; --am_decoder_tr_layers=6; --arch=arch.txt; --attention=keyvalue; --attentionthreshold=2147483647; --attnWindow=softPretrain; --attnconvchannel=0; --attnconvkernel=0; --attndim=0; --batching_max_duration=0; --batching_strategy=none; --batchsize=4; --beamsize=2500; --beamsizetoken=250000; --beamthreshold=25; --channels=1; --criterion=ctc; --critoptim=adagrad; --datadir=ami_limited_supervision; --decoderattnround=1; --decoderdropout=0; --decoderrnnlayer=1; --decodertype=wrd; --devwin=0; --emission_dir=; --emission_queue_size=3000; --enable_distributed=true; --encoderdim=256; --eosscore=0; --eostoken=false; --everstoredb=false; --fftcachesize=1; --filterbanks=80; --fl_amp_max_scale_factor=32000; --fl_amp_scale_factor=4096; --fl_amp_scale_factor_update_interval=2000; --fl_amp_use_mixed_precision=false; --fl_benchmark_mode=true; --fl_log_level=; --fl_optim_mode=; --fl_vlog_level=0; --flagsfile=; --framesizems=25; --framestridems=10; --gamma=1; --gumbeltemperature=1; --highfreqfilterbank=-1; --inputfeeding=false; --isbeamdump=false; --iter=25000; --itersave=false; --labelsmooth=0.050000000000000003; --leftWindowSize=50; --lexicon=lexicon.txt; --linlr=-1; --linlrcrit=-1; --linseg=0; --lm=; --lm_memory=5000; --lm_vocab=; --lmtype=kenlm; --lmweight=0; --lmweight_high=4; --lmweight_low=0; --lmweight_step=0.20000000000000001; --localnrmlleftctx=0; --localnrmlrightctx=0; --logadd=false; --lowfreqfilterbank=0; --lr=0.025000000000000001; --lr_decay=100; --lr_decay_step=50; --lrcosine=false; --lrcrit=0.02; --max_devices_per_node=8; --maxdecoderoutputlen=400; --maxgradnorm=0.10000000000000001; --maxload=-1; --maxrate=10; --maxsil=50; --maxword=-1; --melfloor=1; --mfcc=false; --mfcccoeffs=13; --mfsc=true; --minrate=3; --minsil=0; --momentum=0.80000000000000004; --netoptim=sgd; --nthread=6; --nthread_decoder=1; --nthread_decoder_am_forward=1; --numattnhead=8; --onorm=target; --optimepsilon=1e-08; --optimrho=0.90000000000000002; --pctteacherforcing=99; --pcttraineval=1; --pow=false; --pretrainWindow=0; --replabel=0; --reportiters=1000; --rightWindowSize=50; --rndv_filepath=; --rundir=checkpoint; --samplerate=16000; --sampletarget=0.01; --samplingstrategy=rand; --saug_fmaskf=30; --saug_fmaskn=2; --saug_start_update=24000; --saug_tmaskn=10; --saug_tmaskp=0.050000000000000003; --saug_tmaskt=30; --sclite=; --seed=0; --sfx_config=; --show=false; --showletters=false; --silscore=0; --smearing=none; --smoothingtemperature=1; --softwoffset=10; --softwrate=5; --softwstd=4; --sqnorm=true; --stepsize=9223372036854775807; --surround=; --test=; --tokens=tokens.txt; --train=train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lst; --trainWithWindow=true; --transdiag=0; --unkscore=-inf; --use_memcache=false; --uselexicon=true; --usewordpiece=false; --valid=dev:dev.lst; --validbatchsize=-1; --warmup=0; --weightdecay=0; --wordscore=0; --wordseparator=|; --world_rank=0; --world_size=1; --alsologtoemail=; --alsologtostderr=false; --colorlogtostderr=false; --drop_log_memory=true; --log_backtrace_at=; --log_dir=; --log_link=; --log_prefix=true; --logbuflevel=0; --logbufsecs=30; --logemaillevel=999; --logfile_mode=436; --logmailer=/bin/mail; --logtostderr=true; --max_log_size=1800; --minloglevel=0; --stderrthreshold=2; --stop_logging_if_full_disk=false; --symbolize_stacktrace=true; --v=0; --vmodule=;
I1224 06:39:49.002910 11517 FinetuneCTC.cpp:107] Experiment path: checkpoint
I1224 06:39:49.002919 11517 FinetuneCTC.cpp:108] Experiment runidx: 1
I1224 06:39:49.003252 11517 FinetuneCTC.cpp:153] Number of classes (network): 29
I1224 06:39:49.248888 11517 FinetuneCTC.cpp:160] Number of words: 200001
I1224 06:39:50.344347 11517 FinetuneCTC.cpp:248] Loading architecture file from arch.txt
I1224 06:39:50.868463 11517 FinetuneCTC.cpp:277] [Network] Sequential [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> (19) -> (20) -> (21) -> (22) -> (23) -> (24) -> (25) -> (26) -> (27) -> (28) -> (29) -> (30) -> (31) -> (32) -> (33) -> (34) -> (35) -> (36) -> (37) -> (38) -> (39) -> (40) -> (41) -> (42) -> output]
(0): View (-1 1 80 0)
(1): LayerNorm ( axis : { 0 1 2 } , size : -1)
(2): Conv2D (80->768, 7x1, 3,1, SAME,0, 1, 1) (with bias)
(3): GatedLinearUnit (2)
(4): Dropout (0.050000)
(5): Reorder (2,0,3,1)
(6): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(7): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(8): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(9): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(10): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(11): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(12): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(13): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(14): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(15): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(16): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(17): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(18): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(19): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(20): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(21): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(22): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(23): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(24): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(25): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(26): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(27): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(28): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(29): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(30): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(31): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(32): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(33): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(34): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(35): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(36): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(37): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(38): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(39): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(40): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(41): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(42): Linear (384->29) (with bias)
I1224 06:39:50.868662 11517 FinetuneCTC.cpp:278] [Network Params: 70498735]
I1224 06:39:50.868705 11517 FinetuneCTC.cpp:283] [Criterion] ConnectionistTemporalClassificationCriterion
I1224 06:39:51.004284 11517 FinetuneCTC.cpp:287] [Network Optimizer] SGD (momentum=0.8)
I1224 06:39:51.005266 11517 FinetuneCTC.cpp:547] Shuffling trainset
I1224 06:39:51.005805 11517 FinetuneCTC.cpp:554] Epoch 1 started!
I1224 06:46:52.988443 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 1000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:32 | bch(ms): 212.42 | smp(ms): 3.21 | fwd(ms): 82.29 | crit-fwd(ms): 2.90 | bwd(ms): 94.35 | optim(ms): 31.26 | loss: 2.78453 | train-TER: 9.68 | train-WER: 21.00 | dev-loss: 2.59365 | dev-TER: 10.09 | dev-WER: 19.50 | avg-isz: 287 | avg-tsz: 040 | max-tsz: 339 | avr-batchsz: 4.00 | hrs: 3.20 | thrpt(sec/sec): 54.18 | timestamp: 2020-12-24 06:46:52
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 06:53:31.970283 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 2000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:06 | bch(ms): 186.76 | smp(ms): 0.04 | fwd(ms): 69.67 | crit-fwd(ms): 2.02 | bwd(ms): 86.59 | optim(ms): 30.22 | loss: 2.63802 | train-TER: 9.86 | train-WER: 22.43 | dev-loss: 2.54714 | dev-TER: 9.84 | dev-WER: 18.86 | avg-isz: 259 | avg-tsz: 036 | max-tsz: 345 | avr-batchsz: 4.00 | hrs: 2.88 | thrpt(sec/sec): 55.57 | timestamp: 2020-12-24 06:53:31
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 07:00:07.246326 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 3000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:02 | bch(ms): 182.40 | smp(ms): 0.03 | fwd(ms): 67.86 | crit-fwd(ms): 1.92 | bwd(ms): 84.27 | optim(ms): 30.00 | loss: 2.57714 | train-TER: 12.22 | train-WER: 24.38 | dev-loss: 2.45296 | dev-TER: 9.55 | dev-WER: 18.37 | avg-isz: 248 | avg-tsz: 035 | max-tsz: 257 | avr-batchsz: 4.00 | hrs: 2.76 | thrpt(sec/sec): 54.53 | timestamp: 2020-12-24 07:00:07
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 07:01:30.886020 11517 FinetuneCTC.cpp:547] Shuffling trainset
I1224 07:01:30.886448 11517 FinetuneCTC.cpp:554] Epoch 2 started!
[5e8e495af856:11519] *** Process received signal ***
[5e8e495af856:11519] Signal: Segmentation fault (11)
[5e8e495af856:11519] Signal code: Address not mapped (1)
[5e8e495af856:11519] Failing at address: 0x7f848b62120d
[5e8e495af856:11519] [ 0] /lib/x86_64-linux-gnu/libpthread.so.0(+0x12980)[0x7f848e2cd980]
[5e8e495af856:11519] [ 1] /lib/x86_64-linux-gnu/libc.so.6(getenv+0xa5)[0x7f848df0c8a5]
[5e8e495af856:11519] [ 2] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(_ZN13TCMallocGuardD1Ev+0x34)[0x7f848e777e44]
[5e8e495af856:11519] [ 3] /lib/x86_64-linux-gnu/libc.so.6(__cxa_finalize+0xf5)[0x7f848df0d735]
[5e8e495af856:11519] [ 4] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(+0x13cb3)[0x7f848e775cb3]
[5e8e495af856:11519] *** End of error message ***
^C
###Markdown
Step 4: Run Decoding Viterbi decoding
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am checkpoint/001_model_dev.bin --datadir '' --emission_dir '' --uselexicon false \
--test ami_limited_supervision/test.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1046.72_1047.07, WER: 0%, TER: 0%, total WER: 18.9985%, total TER: 8.47729%, progress (thread 0): 86.8275%]
|T|: m m
|P|: m m
[sample: ES2004c_H01_FEE013_1294.34_1294.69, WER: 0%, TER: 0%, total WER: 18.9983%, total TER: 8.47725%, progress (thread 0): 86.8354%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1302.15_1302.5, WER: 100%, TER: 0%, total WER: 18.9992%, total TER: 8.47715%, progress (thread 0): 86.8434%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1515.72_1516.07, WER: 0%, TER: 0%, total WER: 18.999%, total TER: 8.47707%, progress (thread 0): 86.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1690.13_1690.48, WER: 0%, TER: 0%, total WER: 18.9988%, total TER: 8.47699%, progress (thread 0): 86.8592%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2078.48_2078.83, WER: 100%, TER: 0%, total WER: 18.9997%, total TER: 8.47689%, progress (thread 0): 86.8671%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H02_MEE014_2291.54_2291.89, WER: 0%, TER: 0%, total WER: 18.9995%, total TER: 8.47681%, progress (thread 0): 86.875%]
|T|: o h
|P|: u m
[sample: ES2004d_H00_MEO015_127.17_127.52, WER: 100%, TER: 100%, total WER: 19.0004%, total TER: 8.47725%, progress (thread 0): 86.8829%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_561.86_562.21, WER: 0%, TER: 0%, total WER: 19.0002%, total TER: 8.47717%, progress (thread 0): 86.8908%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004d_H00_MEO015_640.16_640.51, WER: 100%, TER: 25%, total WER: 19.0011%, total TER: 8.47732%, progress (thread 0): 86.8987%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_821.44_821.79, WER: 0%, TER: 0%, total WER: 19.0009%, total TER: 8.47722%, progress (thread 0): 86.9066%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H01_FEE013_822.51_822.86, WER: 100%, TER: 0%, total WER: 19.0019%, total TER: 8.47712%, progress (thread 0): 86.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1778.02_1778.37, WER: 0%, TER: 0%, total WER: 19.0016%, total TER: 8.47704%, progress (thread 0): 86.9225%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_76.08_76.43, WER: 100%, TER: 0%, total WER: 19.0026%, total TER: 8.47694%, progress (thread 0): 86.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_431.19_431.54, WER: 0%, TER: 0%, total WER: 19.0023%, total TER: 8.47686%, progress (thread 0): 86.9383%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_492.57_492.92, WER: 100%, TER: 66.6667%, total WER: 19.0033%, total TER: 8.47727%, progress (thread 0): 86.9462%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_232.67_233.02, WER: 0%, TER: 0%, total WER: 19.003%, total TER: 8.47719%, progress (thread 0): 86.9541%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_361.11_361.46, WER: 0%, TER: 0%, total WER: 19.0028%, total TER: 8.47711%, progress (thread 0): 86.962%]
|T|: y e p
|P|: y e a h
[sample: IS1009b_H00_FIE088_723.57_723.92, WER: 100%, TER: 66.6667%, total WER: 19.0038%, total TER: 8.47752%, progress (thread 0): 86.9699%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_993.71_994.06, WER: 100%, TER: 60%, total WER: 19.0047%, total TER: 8.47813%, progress (thread 0): 86.9778%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1007.39_1007.74, WER: 0%, TER: 0%, total WER: 19.0045%, total TER: 8.47807%, progress (thread 0): 86.9858%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1079.18_1079.53, WER: 100%, TER: 0%, total WER: 19.0054%, total TER: 8.47797%, progress (thread 0): 86.9937%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1082.71_1083.06, WER: 100%, TER: 0%, total WER: 19.0063%, total TER: 8.47787%, progress (thread 0): 87.0016%]
|T|: t h i s | o n e
|P|: t h i s | o n e
[sample: IS1009b_H00_FIE088_1179.44_1179.79, WER: 0%, TER: 0%, total WER: 19.0059%, total TER: 8.47771%, progress (thread 0): 87.0095%]
|T|: y o u ' r e | t h r e e
|P|: y o u | t h r e e
[sample: IS1009b_H00_FIE088_1203.06_1203.41, WER: 50%, TER: 25%, total WER: 19.0066%, total TER: 8.47818%, progress (thread 0): 87.0174%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1699.07_1699.42, WER: 0%, TER: 0%, total WER: 19.0064%, total TER: 8.4781%, progress (thread 0): 87.0253%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1821.76_1822.11, WER: 0%, TER: 0%, total WER: 19.0061%, total TER: 8.47802%, progress (thread 0): 87.0332%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_527.63_527.98, WER: 100%, TER: 0%, total WER: 19.0071%, total TER: 8.47792%, progress (thread 0): 87.0411%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_546.82_547.17, WER: 100%, TER: 0%, total WER: 19.008%, total TER: 8.47782%, progress (thread 0): 87.049%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_609.9_610.25, WER: 100%, TER: 0%, total WER: 19.0089%, total TER: 8.47772%, progress (thread 0): 87.057%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_688.49_688.84, WER: 100%, TER: 20%, total WER: 19.0098%, total TER: 8.47786%, progress (thread 0): 87.0649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1303.58_1303.93, WER: 0%, TER: 0%, total WER: 19.0096%, total TER: 8.47778%, progress (thread 0): 87.0728%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H01_FIO087_1514.93_1515.28, WER: 100%, TER: 0%, total WER: 19.0105%, total TER: 8.47768%, progress (thread 0): 87.0807%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1737.76_1738.11, WER: 100%, TER: 0%, total WER: 19.0115%, total TER: 8.47758%, progress (thread 0): 87.0886%]
|T|: a h
|P|: m m
[sample: IS1009d_H02_FIO084_734.11_734.46, WER: 100%, TER: 100%, total WER: 19.0124%, total TER: 8.47801%, progress (thread 0): 87.0965%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_742.93_743.28, WER: 0%, TER: 0%, total WER: 19.0122%, total TER: 8.47795%, progress (thread 0): 87.1044%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_743.52_743.87, WER: 0%, TER: 0%, total WER: 19.0119%, total TER: 8.47787%, progress (thread 0): 87.1123%]
|T|: y e a h
|P|: w h a t
[sample: IS1009d_H02_FIO084_953.71_954.06, WER: 100%, TER: 75%, total WER: 19.0129%, total TER: 8.4785%, progress (thread 0): 87.1203%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1243.4_1243.75, WER: 0%, TER: 0%, total WER: 19.0126%, total TER: 8.47842%, progress (thread 0): 87.1282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1323.32_1323.67, WER: 0%, TER: 0%, total WER: 19.0124%, total TER: 8.47834%, progress (thread 0): 87.1361%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_1417.8_1418.15, WER: 100%, TER: 0%, total WER: 19.0133%, total TER: 8.47824%, progress (thread 0): 87.144%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1674.44_1674.79, WER: 100%, TER: 0%, total WER: 19.0143%, total TER: 8.47814%, progress (thread 0): 87.1519%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_1745.56_1745.91, WER: 100%, TER: 40%, total WER: 19.0152%, total TER: 8.47851%, progress (thread 0): 87.1598%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_506.21_506.56, WER: 0%, TER: 0%, total WER: 19.015%, total TER: 8.47843%, progress (thread 0): 87.1677%]
|T|: t h e | p e n
|P|: t e
[sample: TS3003a_H02_MTD0010ID_1074.9_1075.25, WER: 100%, TER: 71.4286%, total WER: 19.0168%, total TER: 8.47947%, progress (thread 0): 87.1756%]
|T|: r i g h t
|P|: r i g h t
[sample: TS3003a_H03_MTD012ME_1219.55_1219.9, WER: 0%, TER: 0%, total WER: 19.0166%, total TER: 8.47937%, progress (thread 0): 87.1835%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003a_H01_MTD011UID_1371.45_1371.8, WER: 100%, TER: 20%, total WER: 19.0175%, total TER: 8.4795%, progress (thread 0): 87.1915%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_1453.73_1454.08, WER: 0%, TER: 0%, total WER: 19.0173%, total TER: 8.47942%, progress (thread 0): 87.1994%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1295.49_1295.84, WER: 0%, TER: 0%, total WER: 19.0171%, total TER: 8.47938%, progress (thread 0): 87.2073%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1351.56_1351.91, WER: 0%, TER: 0%, total WER: 19.0169%, total TER: 8.47934%, progress (thread 0): 87.2152%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_180.89_181.24, WER: 0%, TER: 0%, total WER: 19.0167%, total TER: 8.47926%, progress (thread 0): 87.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1380.5_1380.85, WER: 0%, TER: 0%, total WER: 19.0164%, total TER: 8.47918%, progress (thread 0): 87.231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1531.23_1531.58, WER: 0%, TER: 0%, total WER: 19.0162%, total TER: 8.4791%, progress (thread 0): 87.2389%]
|T|: t h a t ' s | g o o d
|P|: t h a t ' s g o
[sample: TS3003c_H03_MTD012ME_1567.88_1568.23, WER: 100%, TER: 27.2727%, total WER: 19.0181%, total TER: 8.47959%, progress (thread 0): 87.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1713.34_1713.69, WER: 0%, TER: 0%, total WER: 19.0178%, total TER: 8.47951%, progress (thread 0): 87.2547%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_2280.01_2280.36, WER: 0%, TER: 0%, total WER: 19.0176%, total TER: 8.47943%, progress (thread 0): 87.2627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_306.6_306.95, WER: 0%, TER: 0%, total WER: 19.0174%, total TER: 8.47935%, progress (thread 0): 87.2706%]
|T|: n o
|P|: n o
[sample: TS3003d_H00_MTD009PM_819.47_819.82, WER: 0%, TER: 0%, total WER: 19.0172%, total TER: 8.47931%, progress (thread 0): 87.2785%]
|T|: a l r i g h t
|P|: i
[sample: TS3003d_H00_MTD009PM_965.64_965.99, WER: 100%, TER: 85.7143%, total WER: 19.0181%, total TER: 8.48058%, progress (thread 0): 87.2864%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1373.9_1374.25, WER: 100%, TER: 66.6667%, total WER: 19.019%, total TER: 8.48099%, progress (thread 0): 87.2943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1847.37_1847.72, WER: 0%, TER: 0%, total WER: 19.0188%, total TER: 8.48091%, progress (thread 0): 87.3022%]
|T|: n i n e
|P|: n i n e
[sample: TS3003d_H03_MTD012ME_1899.7_1900.05, WER: 0%, TER: 0%, total WER: 19.0186%, total TER: 8.48083%, progress (thread 0): 87.3101%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H01_MTD011UID_2218.91_2219.26, WER: 0%, TER: 0%, total WER: 19.0184%, total TER: 8.48075%, progress (thread 0): 87.318%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_142.16_142.51, WER: 0%, TER: 0%, total WER: 19.0182%, total TER: 8.48065%, progress (thread 0): 87.326%]
|T|: h m m
|P|: m m m m
[sample: EN2002a_H00_MEE073_203.92_204.27, WER: 100%, TER: 66.6667%, total WER: 19.0191%, total TER: 8.48107%, progress (thread 0): 87.3339%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_220.21_220.56, WER: 0%, TER: 0%, total WER: 19.0189%, total TER: 8.48099%, progress (thread 0): 87.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1393.11_1393.46, WER: 0%, TER: 0%, total WER: 19.0187%, total TER: 8.48091%, progress (thread 0): 87.3497%]
|T|: n o
|P|: n o
[sample: EN2002a_H02_FEO072_1494.01_1494.36, WER: 0%, TER: 0%, total WER: 19.0184%, total TER: 8.48087%, progress (thread 0): 87.3576%]
|T|: n o
|P|: y e a h
[sample: EN2002a_H01_FEO070_1616.41_1616.76, WER: 100%, TER: 200%, total WER: 19.0194%, total TER: 8.48177%, progress (thread 0): 87.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2062.28_2062.63, WER: 0%, TER: 0%, total WER: 19.0192%, total TER: 8.48169%, progress (thread 0): 87.3734%]
|T|: t h e n | u m
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_306.19_306.54, WER: 100%, TER: 114.286%, total WER: 19.021%, total TER: 8.48343%, progress (thread 0): 87.3813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_679.83_680.18, WER: 0%, TER: 0%, total WER: 19.0208%, total TER: 8.48335%, progress (thread 0): 87.3892%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1132.98_1133.33, WER: 0%, TER: 0%, total WER: 19.0206%, total TER: 8.48327%, progress (thread 0): 87.3972%]
|T|: s h o u l d n ' t | n o
|P|: s h o u d n ' n
[sample: EN2002b_H01_MEE071_1400.16_1400.51, WER: 100%, TER: 33.3333%, total WER: 19.0224%, total TER: 8.48398%, progress (thread 0): 87.4051%]
|T|: n o
|P|: y e a h
[sample: EN2002b_H02_FEO072_1650.79_1651.14, WER: 100%, TER: 200%, total WER: 19.0233%, total TER: 8.48488%, progress (thread 0): 87.413%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_550.49_550.84, WER: 0%, TER: 0%, total WER: 19.0231%, total TER: 8.4848%, progress (thread 0): 87.4209%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_586.84_587.19, WER: 0%, TER: 0%, total WER: 19.0229%, total TER: 8.48472%, progress (thread 0): 87.4288%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_725.66_726.01, WER: 0%, TER: 0%, total WER: 19.0227%, total TER: 8.48468%, progress (thread 0): 87.4367%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_743.92_744.27, WER: 0%, TER: 0%, total WER: 19.0225%, total TER: 8.48464%, progress (thread 0): 87.4446%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_975_975.35, WER: 0%, TER: 0%, total WER: 19.0222%, total TER: 8.48456%, progress (thread 0): 87.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1520.32_1520.67, WER: 0%, TER: 0%, total WER: 19.022%, total TER: 8.48448%, progress (thread 0): 87.4604%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_1667.21_1667.56, WER: 0%, TER: 0%, total WER: 19.0218%, total TER: 8.48444%, progress (thread 0): 87.4684%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_2075.98_2076.33, WER: 0%, TER: 0%, total WER: 19.0216%, total TER: 8.4844%, progress (thread 0): 87.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2182.63_2182.98, WER: 0%, TER: 0%, total WER: 19.0214%, total TER: 8.48432%, progress (thread 0): 87.4842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_352.2_352.55, WER: 0%, TER: 0%, total WER: 19.0212%, total TER: 8.48424%, progress (thread 0): 87.4921%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002d_H03_MEE073_387.18_387.53, WER: 0%, TER: 0%, total WER: 19.0207%, total TER: 8.4841%, progress (thread 0): 87.5%]
|T|: m m
|P|: m m
[sample: EN2002d_H01_FEO072_831.78_832.13, WER: 0%, TER: 0%, total WER: 19.0205%, total TER: 8.48406%, progress (thread 0): 87.5079%]
|T|: n o
|P|: n o
[sample: EN2002d_H03_MEE073_909.36_909.71, WER: 0%, TER: 0%, total WER: 19.0203%, total TER: 8.48402%, progress (thread 0): 87.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1113.08_1113.43, WER: 0%, TER: 0%, total WER: 19.0201%, total TER: 8.48394%, progress (thread 0): 87.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1233.84_1234.19, WER: 0%, TER: 0%, total WER: 19.0199%, total TER: 8.48386%, progress (thread 0): 87.5316%]
|T|: i | d o n ' t | k n o w
|P|: i d
[sample: EN2002d_H01_FEO072_1245.04_1245.39, WER: 100%, TER: 83.3333%, total WER: 19.0226%, total TER: 8.48597%, progress (thread 0): 87.5396%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1798.12_1798.47, WER: 0%, TER: 0%, total WER: 19.0224%, total TER: 8.48589%, progress (thread 0): 87.5475%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1926.51_1926.86, WER: 0%, TER: 0%, total WER: 19.0222%, total TER: 8.48581%, progress (thread 0): 87.5554%]
|T|: u h h u h
|P|: u m
[sample: EN2002d_H00_FEO070_2027.85_2028.2, WER: 100%, TER: 80%, total WER: 19.0231%, total TER: 8.48666%, progress (thread 0): 87.5633%]
|T|: o h | s o r r y
|P|: o k a y
[sample: ES2004a_H00_MEO015_255.91_256.25, WER: 100%, TER: 75%, total WER: 19.025%, total TER: 8.48791%, progress (thread 0): 87.5712%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H01_FEE013_545.3_545.64, WER: 100%, TER: 0%, total WER: 19.0259%, total TER: 8.48781%, progress (thread 0): 87.5791%]
|T|: r e a l l y
|P|: r e a l l y
[sample: ES2004a_H01_FEE013_603.88_604.22, WER: 0%, TER: 0%, total WER: 19.0257%, total TER: 8.48769%, progress (thread 0): 87.587%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_635.54_635.88, WER: 100%, TER: 0%, total WER: 19.0266%, total TER: 8.48759%, progress (thread 0): 87.5949%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H03_FEE016_811.15_811.49, WER: 0%, TER: 0%, total WER: 19.0264%, total TER: 8.48751%, progress (thread 0): 87.6028%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_954.93_955.27, WER: 100%, TER: 0%, total WER: 19.0273%, total TER: 8.48741%, progress (thread 0): 87.6108%]
|T|: m m h m m
|P|: m m
[sample: ES2004b_H03_FEE016_1445.71_1446.05, WER: 100%, TER: 60%, total WER: 19.0282%, total TER: 8.48802%, progress (thread 0): 87.6187%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1995.5_1995.84, WER: 100%, TER: 0%, total WER: 19.0291%, total TER: 8.48792%, progress (thread 0): 87.6266%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H03_FEE016_22.85_23.19, WER: 100%, TER: 25%, total WER: 19.03%, total TER: 8.48807%, progress (thread 0): 87.6345%]
|T|: t h a n k | y o u
|P|: o k a y
[sample: ES2004c_H03_FEE016_201.1_201.44, WER: 100%, TER: 77.7778%, total WER: 19.0319%, total TER: 8.48954%, progress (thread 0): 87.6424%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_451.06_451.4, WER: 100%, TER: 25%, total WER: 19.0328%, total TER: 8.4897%, progress (thread 0): 87.6503%]
|T|: n o
|P|: n o t
[sample: ES2004c_H02_MEE014_535.19_535.53, WER: 100%, TER: 50%, total WER: 19.0337%, total TER: 8.48989%, progress (thread 0): 87.6582%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1178.32_1178.66, WER: 0%, TER: 0%, total WER: 19.0335%, total TER: 8.48981%, progress (thread 0): 87.6661%]
|T|: h m m
|P|: m m
[sample: ES2004c_H02_MEE014_1181.26_1181.6, WER: 100%, TER: 33.3333%, total WER: 19.0344%, total TER: 8.48999%, progress (thread 0): 87.674%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1478.47_1478.81, WER: 0%, TER: 0%, total WER: 19.0342%, total TER: 8.48995%, progress (thread 0): 87.682%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1640.61_1640.95, WER: 100%, TER: 0%, total WER: 19.0351%, total TER: 8.48985%, progress (thread 0): 87.6899%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2025.11_2025.45, WER: 100%, TER: 0%, total WER: 19.036%, total TER: 8.48975%, progress (thread 0): 87.6978%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2072.31_2072.65, WER: 100%, TER: 0%, total WER: 19.037%, total TER: 8.48965%, progress (thread 0): 87.7057%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_100.76_101.1, WER: 0%, TER: 0%, total WER: 19.0367%, total TER: 8.48957%, progress (thread 0): 87.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H01_FEE013_139.4_139.74, WER: 0%, TER: 0%, total WER: 19.0365%, total TER: 8.48947%, progress (thread 0): 87.7215%]
|T|: y e p
|P|: y e p
[sample: ES2004d_H00_MEO015_155.57_155.91, WER: 0%, TER: 0%, total WER: 19.0363%, total TER: 8.48941%, progress (thread 0): 87.7294%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_178.12_178.46, WER: 0%, TER: 0%, total WER: 19.0361%, total TER: 8.48933%, progress (thread 0): 87.7373%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_405.44_405.78, WER: 0%, TER: 0%, total WER: 19.0359%, total TER: 8.48925%, progress (thread 0): 87.7453%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H00_MEO015_548.5_548.84, WER: 100%, TER: 0%, total WER: 19.0368%, total TER: 8.48915%, progress (thread 0): 87.7532%]
|T|: t h r e e
|P|: t h r e e
[sample: ES2004d_H02_MEE014_646.42_646.76, WER: 0%, TER: 0%, total WER: 19.0366%, total TER: 8.48905%, progress (thread 0): 87.7611%]
|T|: f o u r
|P|: f o u r
[sample: ES2004d_H01_FEE013_911.53_911.87, WER: 0%, TER: 0%, total WER: 19.0364%, total TER: 8.48897%, progress (thread 0): 87.769%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1135.09_1135.43, WER: 0%, TER: 0%, total WER: 19.0362%, total TER: 8.48889%, progress (thread 0): 87.7769%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_1953.89_1954.23, WER: 0%, TER: 0%, total WER: 19.0359%, total TER: 8.48885%, progress (thread 0): 87.7848%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_792.79_793.13, WER: 0%, TER: 0%, total WER: 19.0357%, total TER: 8.48879%, progress (thread 0): 87.7927%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_43.16_43.5, WER: 100%, TER: 0%, total WER: 19.0366%, total TER: 8.48869%, progress (thread 0): 87.8006%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_260.98_261.32, WER: 0%, TER: 0%, total WER: 19.0364%, total TER: 8.48861%, progress (thread 0): 87.8085%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_425.4_425.74, WER: 0%, TER: 0%, total WER: 19.0362%, total TER: 8.48853%, progress (thread 0): 87.8165%]
|T|: y e p
|P|: y o h
[sample: IS1009b_H00_FIE088_598.28_598.62, WER: 100%, TER: 66.6667%, total WER: 19.0371%, total TER: 8.48894%, progress (thread 0): 87.8244%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_613.29_613.63, WER: 0%, TER: 0%, total WER: 19.0369%, total TER: 8.48886%, progress (thread 0): 87.8323%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1002.09_1002.43, WER: 0%, TER: 0%, total WER: 19.0367%, total TER: 8.4888%, progress (thread 0): 87.8402%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H00_FIE088_1223.66_1224, WER: 100%, TER: 20%, total WER: 19.0376%, total TER: 8.48894%, progress (thread 0): 87.8481%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1225.44_1225.78, WER: 100%, TER: 0%, total WER: 19.0385%, total TER: 8.48884%, progress (thread 0): 87.856%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H03_FIO089_1734.63_1734.97, WER: 100%, TER: 200%, total WER: 19.0395%, total TER: 8.48974%, progress (thread 0): 87.8639%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1834.47_1834.81, WER: 0%, TER: 0%, total WER: 19.0392%, total TER: 8.48966%, progress (thread 0): 87.8718%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_1882.63_1882.97, WER: 0%, TER: 0%, total WER: 19.039%, total TER: 8.48958%, progress (thread 0): 87.8797%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H01_FIO087_1960.82_1961.16, WER: 100%, TER: 0%, total WER: 19.0399%, total TER: 8.48948%, progress (thread 0): 87.8877%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_535.66_536, WER: 100%, TER: 0%, total WER: 19.0409%, total TER: 8.48938%, progress (thread 0): 87.8956%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_575.77_576.11, WER: 100%, TER: 0%, total WER: 19.0418%, total TER: 8.48928%, progress (thread 0): 87.9035%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_950.04_950.38, WER: 100%, TER: 0%, total WER: 19.0427%, total TER: 8.48918%, progress (thread 0): 87.9114%]
|T|: i t | w o r k s
|P|: b u t
[sample: IS1009c_H01_FIO087_1083.82_1084.16, WER: 100%, TER: 100%, total WER: 19.0445%, total TER: 8.4909%, progress (thread 0): 87.9193%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_1109.75_1110.09, WER: 100%, TER: 0%, total WER: 19.0455%, total TER: 8.4908%, progress (thread 0): 87.9272%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1603.38_1603.72, WER: 100%, TER: 0%, total WER: 19.0464%, total TER: 8.4907%, progress (thread 0): 87.9351%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1613.06_1613.4, WER: 100%, TER: 0%, total WER: 19.0473%, total TER: 8.4906%, progress (thread 0): 87.943%]
|T|: y e s
|P|: y e a s
[sample: IS1009c_H01_FIO087_1633.76_1634.1, WER: 100%, TER: 33.3333%, total WER: 19.0482%, total TER: 8.49078%, progress (thread 0): 87.951%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_434.93_435.27, WER: 100%, TER: 0%, total WER: 19.0491%, total TER: 8.49068%, progress (thread 0): 87.9589%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_637.66_638, WER: 100%, TER: 40%, total WER: 19.0501%, total TER: 8.49105%, progress (thread 0): 87.9668%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_649.7_650.04, WER: 0%, TER: 0%, total WER: 19.0498%, total TER: 8.49095%, progress (thread 0): 87.9747%]
|T|: h m m
|P|: m h t
[sample: IS1009d_H02_FIO084_674.88_675.22, WER: 100%, TER: 100%, total WER: 19.0508%, total TER: 8.49159%, progress (thread 0): 87.9826%]
|T|: o o p s
|P|: u p s
[sample: IS1009d_H00_FIE088_1068.99_1069.33, WER: 100%, TER: 50%, total WER: 19.0517%, total TER: 8.49199%, progress (thread 0): 87.9905%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H01_FIO087_1509.75_1510.09, WER: 0%, TER: 0%, total WER: 19.0515%, total TER: 8.49193%, progress (thread 0): 87.9984%]
|T|: t h a n k | y o u
|P|: t h a n k | y o u
[sample: TS3003a_H03_MTD012ME_48.1_48.44, WER: 0%, TER: 0%, total WER: 19.051%, total TER: 8.49175%, progress (thread 0): 88.0063%]
|T|: g u e s s
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_983.96_984.3, WER: 100%, TER: 80%, total WER: 19.0519%, total TER: 8.49259%, progress (thread 0): 88.0142%]
|T|: t h e | p e n
|P|: m m h
[sample: TS3003a_H02_MTD0010ID_1090.49_1090.83, WER: 100%, TER: 100%, total WER: 19.0538%, total TER: 8.49409%, progress (thread 0): 88.0222%]
|T|: t h i n g
|P|: t h i n g
[sample: TS3003a_H00_MTD009PM_1320.19_1320.53, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49399%, progress (thread 0): 88.0301%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_132.84_133.18, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49395%, progress (thread 0): 88.038%]
|T|: r i g h t
|P|: b u h
[sample: TS3003b_H01_MTD011UID_2086.12_2086.46, WER: 100%, TER: 80%, total WER: 19.0543%, total TER: 8.4948%, progress (thread 0): 88.0459%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2089.85_2090.19, WER: 0%, TER: 0%, total WER: 19.0541%, total TER: 8.49472%, progress (thread 0): 88.0538%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_982.32_982.66, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49464%, progress (thread 0): 88.0617%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_1007.54_1007.88, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49456%, progress (thread 0): 88.0696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1240.35_1240.69, WER: 0%, TER: 0%, total WER: 19.0534%, total TER: 8.49448%, progress (thread 0): 88.0775%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1413.87_1414.21, WER: 0%, TER: 0%, total WER: 19.0532%, total TER: 8.4944%, progress (thread 0): 88.0854%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1849.58_1849.92, WER: 0%, TER: 0%, total WER: 19.053%, total TER: 8.49432%, progress (thread 0): 88.0934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_595.75_596.09, WER: 0%, TER: 0%, total WER: 19.0528%, total TER: 8.49424%, progress (thread 0): 88.1013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_921.62_921.96, WER: 0%, TER: 0%, total WER: 19.0525%, total TER: 8.49416%, progress (thread 0): 88.1092%]
|T|: u h | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1308.11_1308.45, WER: 50%, TER: 42.8571%, total WER: 19.0532%, total TER: 8.49472%, progress (thread 0): 88.1171%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1388.52_1388.86, WER: 0%, TER: 0%, total WER: 19.053%, total TER: 8.49464%, progress (thread 0): 88.125%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1802.71_1803.05, WER: 0%, TER: 0%, total WER: 19.0528%, total TER: 8.49456%, progress (thread 0): 88.1329%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1835.63_1835.97, WER: 100%, TER: 25%, total WER: 19.0537%, total TER: 8.49472%, progress (thread 0): 88.1408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2091.81_2092.15, WER: 0%, TER: 0%, total WER: 19.0535%, total TER: 8.49464%, progress (thread 0): 88.1487%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2217.62_2217.96, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49456%, progress (thread 0): 88.1566%]
|T|: m m | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2323.41_2323.75, WER: 50%, TER: 42.8571%, total WER: 19.054%, total TER: 8.49512%, progress (thread 0): 88.1646%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2420.39_2420.73, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49504%, progress (thread 0): 88.1725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_406.15_406.49, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49496%, progress (thread 0): 88.1804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_601.54_601.88, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49488%, progress (thread 0): 88.1883%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H02_FEO072_713.14_713.48, WER: 0%, TER: 0%, total WER: 19.0531%, total TER: 8.49482%, progress (thread 0): 88.1962%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_859.83_860.17, WER: 100%, TER: 66.6667%, total WER: 19.0541%, total TER: 8.49524%, progress (thread 0): 88.2041%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002a_H01_FEO070_920.16_920.5, WER: 100%, TER: 28.5714%, total WER: 19.055%, total TER: 8.49557%, progress (thread 0): 88.212%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1586.73_1587.07, WER: 0%, TER: 0%, total WER: 19.0548%, total TER: 8.49549%, progress (thread 0): 88.2199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1927.75_1928.09, WER: 0%, TER: 0%, total WER: 19.0545%, total TER: 8.49541%, progress (thread 0): 88.2279%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1929.86_1930.2, WER: 0%, TER: 0%, total WER: 19.0543%, total TER: 8.49533%, progress (thread 0): 88.2358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2048.6_2048.94, WER: 0%, TER: 0%, total WER: 19.0541%, total TER: 8.49525%, progress (thread 0): 88.2437%]
|T|: s o | i t ' s
|P|: t m
[sample: EN2002a_H03_MEE071_2098.66_2099, WER: 100%, TER: 85.7143%, total WER: 19.0559%, total TER: 8.49652%, progress (thread 0): 88.2516%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_2129.89_2130.23, WER: 0%, TER: 0%, total WER: 19.0557%, total TER: 8.49644%, progress (thread 0): 88.2595%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_144.51_144.85, WER: 0%, TER: 0%, total WER: 19.0555%, total TER: 8.49636%, progress (thread 0): 88.2674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_190.18_190.52, WER: 0%, TER: 0%, total WER: 19.0553%, total TER: 8.49628%, progress (thread 0): 88.2753%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_302.27_302.61, WER: 100%, TER: 66.6667%, total WER: 19.0562%, total TER: 8.49669%, progress (thread 0): 88.2832%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_309.52_309.86, WER: 0%, TER: 0%, total WER: 19.056%, total TER: 8.49661%, progress (thread 0): 88.2911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_363.35_363.69, WER: 0%, TER: 0%, total WER: 19.0558%, total TER: 8.49653%, progress (thread 0): 88.299%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_409.14_409.48, WER: 0%, TER: 0%, total WER: 19.0556%, total TER: 8.49645%, progress (thread 0): 88.307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_736.4_736.74, WER: 0%, TER: 0%, total WER: 19.0553%, total TER: 8.49637%, progress (thread 0): 88.3149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_935.86_936.2, WER: 0%, TER: 0%, total WER: 19.0551%, total TER: 8.49629%, progress (thread 0): 88.3228%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002b_H03_MEE073_1252.52_1252.86, WER: 0%, TER: 0%, total WER: 19.0547%, total TER: 8.49615%, progress (thread 0): 88.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1563.14_1563.48, WER: 0%, TER: 0%, total WER: 19.0545%, total TER: 8.49607%, progress (thread 0): 88.3386%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1626.33_1626.67, WER: 0%, TER: 0%, total WER: 19.0543%, total TER: 8.49599%, progress (thread 0): 88.3465%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H01_FEO072_67.59_67.93, WER: 0%, TER: 0%, total WER: 19.054%, total TER: 8.49591%, progress (thread 0): 88.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_273.14_273.48, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49583%, progress (thread 0): 88.3623%]
|T|: m m h m m
|P|: m m m m
[sample: EN2002c_H03_MEE073_444.82_445.16, WER: 100%, TER: 20%, total WER: 19.0548%, total TER: 8.49596%, progress (thread 0): 88.3703%]
|T|: o r
|P|: m h
[sample: EN2002c_H01_FEO072_486.4_486.74, WER: 100%, TER: 100%, total WER: 19.0557%, total TER: 8.4964%, progress (thread 0): 88.3782%]
|T|: o h | w e l l
|P|: o h
[sample: EN2002c_H03_MEE073_497.23_497.57, WER: 50%, TER: 71.4286%, total WER: 19.0564%, total TER: 8.49743%, progress (thread 0): 88.3861%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002c_H01_FEO072_721.91_722.25, WER: 200%, TER: 14.2857%, total WER: 19.0584%, total TER: 8.49753%, progress (thread 0): 88.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1305.34_1305.68, WER: 0%, TER: 0%, total WER: 19.0582%, total TER: 8.49745%, progress (thread 0): 88.4019%]
|T|: o h
|P|: o h
[sample: EN2002c_H01_FEO072_1925.9_1926.24, WER: 0%, TER: 0%, total WER: 19.058%, total TER: 8.49741%, progress (thread 0): 88.4098%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2040.49_2040.83, WER: 0%, TER: 0%, total WER: 19.0578%, total TER: 8.49733%, progress (thread 0): 88.4177%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002c_H03_MEE073_2245.81_2246.15, WER: 100%, TER: 20%, total WER: 19.0587%, total TER: 8.49746%, progress (thread 0): 88.4256%]
|T|: y e a h
|P|: r e a h
[sample: EN2002d_H03_MEE073_344.02_344.36, WER: 100%, TER: 25%, total WER: 19.0596%, total TER: 8.49762%, progress (thread 0): 88.4335%]
|T|: w h a t | w a s | i t
|P|: w h a | w a s | i t
[sample: EN2002d_H03_MEE073_508.22_508.56, WER: 33.3333%, TER: 9.09091%, total WER: 19.0601%, total TER: 8.49763%, progress (thread 0): 88.4415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_688.4_688.74, WER: 0%, TER: 0%, total WER: 19.0599%, total TER: 8.49755%, progress (thread 0): 88.4494%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_715.16_715.5, WER: 0%, TER: 0%, total WER: 19.0597%, total TER: 8.49747%, progress (thread 0): 88.4573%]
|T|: u h
|P|: m m
[sample: EN2002d_H03_MEE073_1125.77_1126.11, WER: 100%, TER: 100%, total WER: 19.0606%, total TER: 8.4979%, progress (thread 0): 88.4652%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1325.04_1325.38, WER: 100%, TER: 33.3333%, total WER: 19.0615%, total TER: 8.49808%, progress (thread 0): 88.4731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1423.14_1423.48, WER: 0%, TER: 0%, total WER: 19.0613%, total TER: 8.498%, progress (thread 0): 88.481%]
|T|: n o | w e | p
|P|: n o | w e
[sample: EN2002d_H00_FEO070_1437.94_1438.28, WER: 33.3333%, TER: 28.5714%, total WER: 19.0618%, total TER: 8.49833%, progress (thread 0): 88.4889%]
|T|: w i t h
|P|: w i t h
[sample: EN2002d_H02_MEE071_2073.15_2073.49, WER: 0%, TER: 0%, total WER: 19.0616%, total TER: 8.49825%, progress (thread 0): 88.4968%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_258.31_258.64, WER: 100%, TER: 0%, total WER: 19.0625%, total TER: 8.49815%, progress (thread 0): 88.5048%]
|T|: m m | y e a h
|P|: m y e a h
[sample: ES2004a_H00_MEO015_1048.05_1048.38, WER: 100%, TER: 28.5714%, total WER: 19.0643%, total TER: 8.49848%, progress (thread 0): 88.5127%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_736.03_736.36, WER: 0%, TER: 0%, total WER: 19.0641%, total TER: 8.4984%, progress (thread 0): 88.5206%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1313.86_1314.19, WER: 0%, TER: 0%, total WER: 19.0639%, total TER: 8.49832%, progress (thread 0): 88.5285%]
|T|: m m
|P|: m m
[sample: ES2004b_H01_FEE013_1711.46_1711.79, WER: 0%, TER: 0%, total WER: 19.0637%, total TER: 8.49828%, progress (thread 0): 88.5364%]
|T|: u h h u h
|P|: m h | h u h
[sample: ES2004b_H03_FEE016_2208.79_2209.12, WER: 200%, TER: 40%, total WER: 19.0657%, total TER: 8.49865%, progress (thread 0): 88.5443%]
|T|: o k a y
|P|: o k a y
[sample: ES2004b_H03_FEE016_2308.52_2308.85, WER: 0%, TER: 0%, total WER: 19.0655%, total TER: 8.49857%, progress (thread 0): 88.5522%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_172.32_172.65, WER: 0%, TER: 0%, total WER: 19.0653%, total TER: 8.49849%, progress (thread 0): 88.5601%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_560.34_560.67, WER: 0%, TER: 0%, total WER: 19.0651%, total TER: 8.49845%, progress (thread 0): 88.568%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H01_FEE013_873.37_873.7, WER: 100%, TER: 0%, total WER: 19.066%, total TER: 8.49835%, progress (thread 0): 88.576%]
|T|: w e l l
|P|: w e l l
[sample: ES2004c_H02_MEE014_925.27_925.6, WER: 0%, TER: 0%, total WER: 19.0658%, total TER: 8.49827%, progress (thread 0): 88.5839%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1268.11_1268.44, WER: 0%, TER: 0%, total WER: 19.0656%, total TER: 8.49819%, progress (thread 0): 88.5918%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1965.56_1965.89, WER: 100%, TER: 0%, total WER: 19.0665%, total TER: 8.49809%, progress (thread 0): 88.5997%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H01_FEE013_607.94_608.27, WER: 100%, TER: 0%, total WER: 19.0674%, total TER: 8.49799%, progress (thread 0): 88.6076%]
|T|: t w o
|P|: t w o
[sample: ES2004d_H02_MEE014_753.08_753.41, WER: 0%, TER: 0%, total WER: 19.0672%, total TER: 8.49793%, progress (thread 0): 88.6155%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H03_FEE016_876.07_876.4, WER: 100%, TER: 0%, total WER: 19.0681%, total TER: 8.49783%, progress (thread 0): 88.6234%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H00_MEO015_1304.99_1305.32, WER: 0%, TER: 0%, total WER: 19.0679%, total TER: 8.49777%, progress (thread 0): 88.6313%]
|T|: s u r e
|P|: s u r e
[sample: ES2004d_H03_FEE016_1499.95_1500.28, WER: 0%, TER: 0%, total WER: 19.0677%, total TER: 8.49769%, progress (thread 0): 88.6392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1845.84_1846.17, WER: 0%, TER: 0%, total WER: 19.0674%, total TER: 8.49761%, progress (thread 0): 88.6471%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1856.75_1857.08, WER: 0%, TER: 0%, total WER: 19.0672%, total TER: 8.49753%, progress (thread 0): 88.6551%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1984.68_1985.01, WER: 0%, TER: 0%, total WER: 19.067%, total TER: 8.49745%, progress (thread 0): 88.663%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_2126.04_2126.37, WER: 0%, TER: 0%, total WER: 19.0668%, total TER: 8.49741%, progress (thread 0): 88.6709%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_55.7_56.03, WER: 0%, TER: 0%, total WER: 19.0666%, total TER: 8.49733%, progress (thread 0): 88.6788%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_226.94_227.27, WER: 100%, TER: 0%, total WER: 19.0675%, total TER: 8.49723%, progress (thread 0): 88.6867%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_318.12_318.45, WER: 0%, TER: 0%, total WER: 19.0673%, total TER: 8.49715%, progress (thread 0): 88.6946%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_652.91_653.24, WER: 100%, TER: 0%, total WER: 19.0682%, total TER: 8.49705%, progress (thread 0): 88.7025%]
|T|: t h e n
|P|: a n d | t h e n
[sample: IS1009a_H00_FIE088_714.21_714.54, WER: 100%, TER: 100%, total WER: 19.0691%, total TER: 8.49791%, progress (thread 0): 88.7104%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_741.37_741.7, WER: 100%, TER: 0%, total WER: 19.07%, total TER: 8.49781%, progress (thread 0): 88.7184%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_790.95_791.28, WER: 100%, TER: 0%, total WER: 19.0709%, total TER: 8.49771%, progress (thread 0): 88.7263%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_787.47_787.8, WER: 100%, TER: 0%, total WER: 19.0719%, total TER: 8.49761%, progress (thread 0): 88.7342%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1063.18_1063.51, WER: 100%, TER: 0%, total WER: 19.0728%, total TER: 8.49751%, progress (thread 0): 88.7421%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1809.47_1809.8, WER: 0%, TER: 0%, total WER: 19.0726%, total TER: 8.49743%, progress (thread 0): 88.75%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1882.22_1882.55, WER: 100%, TER: 0%, total WER: 19.0735%, total TER: 8.49733%, progress (thread 0): 88.7579%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_408.02_408.35, WER: 100%, TER: 0%, total WER: 19.0744%, total TER: 8.49723%, progress (thread 0): 88.7658%]
|T|: m m h m m
|P|: m m
[sample: IS1009c_H00_FIE088_430.74_431.07, WER: 100%, TER: 60%, total WER: 19.0753%, total TER: 8.49784%, progress (thread 0): 88.7737%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1552.21_1552.54, WER: 0%, TER: 0%, total WER: 19.0751%, total TER: 8.49776%, progress (thread 0): 88.7816%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_383.89_384.22, WER: 100%, TER: 0%, total WER: 19.076%, total TER: 8.49766%, progress (thread 0): 88.7896%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_388.95_389.28, WER: 100%, TER: 0%, total WER: 19.0769%, total TER: 8.49756%, progress (thread 0): 88.7975%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_483.18_483.51, WER: 100%, TER: 0%, total WER: 19.0779%, total TER: 8.49746%, progress (thread 0): 88.8054%]
|T|: t h a t ' s | r i g h t
|P|: t h a t ' s | r g h t
[sample: IS1009d_H00_FIE088_757.72_758.05, WER: 50%, TER: 8.33333%, total WER: 19.0786%, total TER: 8.49745%, progress (thread 0): 88.8133%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H01_FIO087_784.18_784.51, WER: 100%, TER: 0%, total WER: 19.0795%, total TER: 8.49735%, progress (thread 0): 88.8212%]
|T|: y e a h
|P|: m m
[sample: IS1009d_H02_FIO084_975.71_976.04, WER: 100%, TER: 100%, total WER: 19.0804%, total TER: 8.49822%, progress (thread 0): 88.8291%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_1183.1_1183.43, WER: 0%, TER: 0%, total WER: 19.0802%, total TER: 8.49818%, progress (thread 0): 88.837%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H02_FIO084_1185.85_1186.18, WER: 100%, TER: 0%, total WER: 19.0811%, total TER: 8.49808%, progress (thread 0): 88.8449%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_1310.32_1310.65, WER: 100%, TER: 40%, total WER: 19.082%, total TER: 8.49845%, progress (thread 0): 88.8528%]
|T|: u h
|P|: u m
[sample: TS3003a_H01_MTD011UID_915.22_915.55, WER: 100%, TER: 50%, total WER: 19.0829%, total TER: 8.49864%, progress (thread 0): 88.8608%]
|T|: o r | w h o
|P|: o h | w h o
[sample: TS3003a_H01_MTD011UID_975.03_975.36, WER: 50%, TER: 16.6667%, total WER: 19.0836%, total TER: 8.49876%, progress (thread 0): 88.8687%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1261.99_1262.32, WER: 0%, TER: 0%, total WER: 19.0834%, total TER: 8.49872%, progress (thread 0): 88.8766%]
|T|: s o
|P|: s o
[sample: TS3003b_H03_MTD012ME_280.47_280.8, WER: 0%, TER: 0%, total WER: 19.0832%, total TER: 8.49868%, progress (thread 0): 88.8845%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_842.62_842.95, WER: 0%, TER: 0%, total WER: 19.083%, total TER: 8.4986%, progress (thread 0): 88.8924%]
|T|: o k a y
|P|: h m h
[sample: TS3003b_H00_MTD009PM_923.51_923.84, WER: 100%, TER: 100%, total WER: 19.0839%, total TER: 8.49946%, progress (thread 0): 88.9003%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1173.66_1173.99, WER: 0%, TER: 0%, total WER: 19.0837%, total TER: 8.49938%, progress (thread 0): 88.9082%]
|T|: n a h
|P|: n e a h
[sample: TS3003c_H01_MTD011UID_1009.07_1009.4, WER: 100%, TER: 33.3333%, total WER: 19.0846%, total TER: 8.49955%, progress (thread 0): 88.9161%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1321.92_1322.25, WER: 100%, TER: 25%, total WER: 19.0855%, total TER: 8.49971%, progress (thread 0): 88.924%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1395.11_1395.44, WER: 0%, TER: 0%, total WER: 19.0853%, total TER: 8.49967%, progress (thread 0): 88.932%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1507.79_1508.12, WER: 0%, TER: 0%, total WER: 19.0851%, total TER: 8.49959%, progress (thread 0): 88.9399%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_1540.01_1540.34, WER: 100%, TER: 0%, total WER: 19.086%, total TER: 8.49949%, progress (thread 0): 88.9478%]
|T|: y e p
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1653.43_1653.76, WER: 100%, TER: 66.6667%, total WER: 19.0869%, total TER: 8.4999%, progress (thread 0): 88.9557%]
|T|: u h
|P|: m h m m
[sample: TS3003c_H01_MTD011UID_1692.63_1692.96, WER: 100%, TER: 150%, total WER: 19.0878%, total TER: 8.50056%, progress (thread 0): 88.9636%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2074.03_2074.36, WER: 0%, TER: 0%, total WER: 19.0876%, total TER: 8.50048%, progress (thread 0): 88.9715%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2241.48_2241.81, WER: 0%, TER: 0%, total WER: 19.0874%, total TER: 8.5004%, progress (thread 0): 88.9794%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_693.96_694.29, WER: 0%, TER: 0%, total WER: 19.0872%, total TER: 8.50032%, progress (thread 0): 88.9873%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_872.34_872.67, WER: 0%, TER: 0%, total WER: 19.087%, total TER: 8.50024%, progress (thread 0): 88.9953%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_889.13_889.46, WER: 0%, TER: 0%, total WER: 19.0868%, total TER: 8.50016%, progress (thread 0): 89.0032%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_1661.82_1662.15, WER: 0%, TER: 0%, total WER: 19.0865%, total TER: 8.50008%, progress (thread 0): 89.0111%]
|T|: y e a h
|P|: n a h
[sample: TS3003d_H01_MTD011UID_2072.21_2072.54, WER: 100%, TER: 50%, total WER: 19.0875%, total TER: 8.50047%, progress (thread 0): 89.019%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_2085.82_2086.15, WER: 100%, TER: 0%, total WER: 19.0884%, total TER: 8.50037%, progress (thread 0): 89.0269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2164.01_2164.34, WER: 0%, TER: 0%, total WER: 19.0882%, total TER: 8.50029%, progress (thread 0): 89.0348%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2179.8_2180.13, WER: 0%, TER: 0%, total WER: 19.0879%, total TER: 8.50021%, progress (thread 0): 89.0427%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_2461.76_2462.09, WER: 100%, TER: 0%, total WER: 19.0889%, total TER: 8.50011%, progress (thread 0): 89.0506%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_299.48_299.81, WER: 0%, TER: 0%, total WER: 19.0886%, total TER: 8.50003%, progress (thread 0): 89.0585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_359.5_359.83, WER: 0%, TER: 0%, total WER: 19.0884%, total TER: 8.49995%, progress (thread 0): 89.0665%]
|T|: w a i t
|P|: w a i t
[sample: EN2002a_H02_FEO072_387.53_387.86, WER: 0%, TER: 0%, total WER: 19.0882%, total TER: 8.49987%, progress (thread 0): 89.0744%]
|T|: y e a h | y e a h
|P|: e h | y e a h
[sample: EN2002a_H03_MEE071_593.65_593.98, WER: 50%, TER: 22.2222%, total WER: 19.0889%, total TER: 8.50016%, progress (thread 0): 89.0823%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_594.32_594.65, WER: 0%, TER: 0%, total WER: 19.0887%, total TER: 8.50008%, progress (thread 0): 89.0902%]
|T|: y e a h
|P|: n e a h
[sample: EN2002a_H01_FEO070_690.97_691.3, WER: 100%, TER: 25%, total WER: 19.0896%, total TER: 8.50024%, progress (thread 0): 89.0981%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_1018.98_1019.31, WER: 0%, TER: 0%, total WER: 19.0894%, total TER: 8.50016%, progress (thread 0): 89.106%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1028.68_1029.01, WER: 0%, TER: 0%, total WER: 19.0892%, total TER: 8.50008%, progress (thread 0): 89.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1142.23_1142.56, WER: 0%, TER: 0%, total WER: 19.089%, total TER: 8.5%, progress (thread 0): 89.1218%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1361.22_1361.55, WER: 0%, TER: 0%, total WER: 19.0887%, total TER: 8.49992%, progress (thread 0): 89.1297%]
|T|: r i g h t
|P|: i h
[sample: EN2002a_H03_MEE071_1383.46_1383.79, WER: 100%, TER: 60%, total WER: 19.0897%, total TER: 8.50053%, progress (thread 0): 89.1377%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1864.48_1864.81, WER: 0%, TER: 0%, total WER: 19.0894%, total TER: 8.50045%, progress (thread 0): 89.1456%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1964.31_1964.64, WER: 0%, TER: 0%, total WER: 19.0892%, total TER: 8.50037%, progress (thread 0): 89.1535%]
|T|: u m
|P|: m m
[sample: EN2002b_H03_MEE073_139.44_139.77, WER: 100%, TER: 50%, total WER: 19.0901%, total TER: 8.50056%, progress (thread 0): 89.1614%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_417.94_418.27, WER: 0%, TER: 0%, total WER: 19.0899%, total TER: 8.50048%, progress (thread 0): 89.1693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_420.46_420.79, WER: 0%, TER: 0%, total WER: 19.0897%, total TER: 8.5004%, progress (thread 0): 89.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_705.52_705.85, WER: 0%, TER: 0%, total WER: 19.0895%, total TER: 8.50032%, progress (thread 0): 89.1851%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_731.78_732.11, WER: 0%, TER: 0%, total WER: 19.0893%, total TER: 8.50024%, progress (thread 0): 89.193%]
|T|: y e a h | t
|P|: y e a h
[sample: EN2002b_H02_FEO072_1235.77_1236.1, WER: 50%, TER: 33.3333%, total WER: 19.09%, total TER: 8.50059%, progress (thread 0): 89.201%]
|T|: h m m
|P|: m m m
[sample: EN2002b_H03_MEE073_1236.72_1237.05, WER: 100%, TER: 33.3333%, total WER: 19.0909%, total TER: 8.50077%, progress (thread 0): 89.2089%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_1256.5_1256.83, WER: 0%, TER: 0%, total WER: 19.0907%, total TER: 8.50069%, progress (thread 0): 89.2168%]
|T|: h m m
|P|: m m m
[sample: EN2002b_H02_FEO072_1475.51_1475.84, WER: 100%, TER: 33.3333%, total WER: 19.0916%, total TER: 8.50086%, progress (thread 0): 89.2247%]
|T|: o h | r i g h t
|P|: a l | r i g h t
[sample: EN2002b_H02_FEO072_1611.8_1612.13, WER: 50%, TER: 25%, total WER: 19.0923%, total TER: 8.50117%, progress (thread 0): 89.2326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_93.86_94.19, WER: 0%, TER: 0%, total WER: 19.0921%, total TER: 8.50109%, progress (thread 0): 89.2405%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_245.2_245.53, WER: 0%, TER: 0%, total WER: 19.0919%, total TER: 8.50101%, progress (thread 0): 89.2484%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H01_FEO072_447.06_447.39, WER: 100%, TER: 0%, total WER: 19.0928%, total TER: 8.50091%, progress (thread 0): 89.2563%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002c_H03_MEE073_823.26_823.59, WER: 0%, TER: 0%, total WER: 19.0924%, total TER: 8.50077%, progress (thread 0): 89.2642%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_850.62_850.95, WER: 0%, TER: 0%, total WER: 19.0921%, total TER: 8.50069%, progress (thread 0): 89.2722%]
|T|: t h a t ' s | a
|P|: o s a y
[sample: EN2002c_H03_MEE073_903.73_904.06, WER: 100%, TER: 87.5%, total WER: 19.094%, total TER: 8.50218%, progress (thread 0): 89.2801%]
|T|: o h
|P|: o h
[sample: EN2002c_H03_MEE073_962_962.33, WER: 0%, TER: 0%, total WER: 19.0938%, total TER: 8.50214%, progress (thread 0): 89.288%]
|T|: y e a h | r i g h t
|P|: y e a h | r g h t
[sample: EN2002c_H03_MEE073_1287.41_1287.74, WER: 50%, TER: 10%, total WER: 19.0945%, total TER: 8.50217%, progress (thread 0): 89.2959%]
|T|: t h a t ' s | t r u e
|P|: t h a t ' s | t r e
[sample: EN2002c_H03_MEE073_1299.73_1300.06, WER: 50%, TER: 9.09091%, total WER: 19.0952%, total TER: 8.50219%, progress (thread 0): 89.3038%]
|T|: y o u | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2044.96_2045.29, WER: 0%, TER: 0%, total WER: 19.0947%, total TER: 8.50203%, progress (thread 0): 89.3117%]
|T|: m m
|P|: m m
[sample: EN2002c_H02_MEE071_2072_2072.33, WER: 0%, TER: 0%, total WER: 19.0945%, total TER: 8.50199%, progress (thread 0): 89.3196%]
|T|: o o h
|P|: o o h
[sample: EN2002c_H01_FEO072_2817.88_2818.21, WER: 0%, TER: 0%, total WER: 19.0943%, total TER: 8.50193%, progress (thread 0): 89.3275%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002d_H01_FEO072_419.86_420.19, WER: 200%, TER: 14.2857%, total WER: 19.0963%, total TER: 8.50202%, progress (thread 0): 89.3354%]
|T|: m m
|P|: u m
[sample: EN2002d_H03_MEE073_488.3_488.63, WER: 100%, TER: 50%, total WER: 19.0973%, total TER: 8.50222%, progress (thread 0): 89.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1089.65_1089.98, WER: 0%, TER: 0%, total WER: 19.097%, total TER: 8.50214%, progress (thread 0): 89.3513%]
|T|: w e l l
|P|: n o
[sample: EN2002d_H03_MEE073_1132.11_1132.44, WER: 100%, TER: 100%, total WER: 19.098%, total TER: 8.503%, progress (thread 0): 89.3592%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1131.27_1131.6, WER: 100%, TER: 33.3333%, total WER: 19.0989%, total TER: 8.50317%, progress (thread 0): 89.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1257.89_1258.22, WER: 0%, TER: 0%, total WER: 19.0987%, total TER: 8.50309%, progress (thread 0): 89.375%]
|T|: r e a l l y
|P|: r e a l l y
[sample: EN2002d_H01_FEO072_1345.97_1346.3, WER: 0%, TER: 0%, total WER: 19.0984%, total TER: 8.50297%, progress (thread 0): 89.3829%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1429.74_1430.07, WER: 100%, TER: 33.3333%, total WER: 19.0994%, total TER: 8.50315%, progress (thread 0): 89.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1957.8_1958.13, WER: 0%, TER: 0%, total WER: 19.0991%, total TER: 8.50307%, progress (thread 0): 89.3987%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_863.21_863.53, WER: 100%, TER: 0%, total WER: 19.1001%, total TER: 8.50297%, progress (thread 0): 89.4066%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_895.43_895.75, WER: 100%, TER: 0%, total WER: 19.101%, total TER: 8.50287%, progress (thread 0): 89.4146%]
|T|: m m
|P|: m m
[sample: ES2004b_H01_FEE013_1140.58_1140.9, WER: 0%, TER: 0%, total WER: 19.1008%, total TER: 8.50283%, progress (thread 0): 89.4225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1182.75_1183.07, WER: 0%, TER: 0%, total WER: 19.1005%, total TER: 8.50275%, progress (thread 0): 89.4304%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1471.32_1471.64, WER: 100%, TER: 0%, total WER: 19.1015%, total TER: 8.50265%, progress (thread 0): 89.4383%]
|T|: s
|P|: m m
[sample: ES2004b_H01_FEE013_1922.51_1922.83, WER: 100%, TER: 200%, total WER: 19.1024%, total TER: 8.5031%, progress (thread 0): 89.4462%]
|T|: m m h m m
|P|: m m
[sample: ES2004b_H03_FEE016_1953.34_1953.66, WER: 100%, TER: 60%, total WER: 19.1033%, total TER: 8.5037%, progress (thread 0): 89.4541%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1966.24_1966.56, WER: 0%, TER: 0%, total WER: 19.1031%, total TER: 8.50362%, progress (thread 0): 89.462%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_806.95_807.27, WER: 0%, TER: 0%, total WER: 19.1029%, total TER: 8.50354%, progress (thread 0): 89.4699%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H03_FEE016_1066.11_1066.43, WER: 0%, TER: 0%, total WER: 19.1026%, total TER: 8.50346%, progress (thread 0): 89.4779%]
|T|: m m
|P|: m m
[sample: ES2004c_H00_MEO015_1890.24_1890.56, WER: 0%, TER: 0%, total WER: 19.1024%, total TER: 8.50342%, progress (thread 0): 89.4858%]
|T|: y e a h
|P|: y e a p
[sample: ES2004d_H00_MEO015_341.64_341.96, WER: 100%, TER: 25%, total WER: 19.1033%, total TER: 8.50358%, progress (thread 0): 89.4937%]
|T|: n o
|P|: n o
[sample: ES2004d_H00_MEO015_400.96_401.28, WER: 0%, TER: 0%, total WER: 19.1031%, total TER: 8.50354%, progress (thread 0): 89.5016%]
|T|: o o p s
|P|: i p s
[sample: ES2004d_H03_FEE016_430.55_430.87, WER: 100%, TER: 50%, total WER: 19.104%, total TER: 8.50393%, progress (thread 0): 89.5095%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_774.15_774.47, WER: 0%, TER: 0%, total WER: 19.1038%, total TER: 8.50385%, progress (thread 0): 89.5174%]
|T|: o n e
|P|: w e l l
[sample: ES2004d_H02_MEE014_785.53_785.85, WER: 100%, TER: 133.333%, total WER: 19.1047%, total TER: 8.50473%, progress (thread 0): 89.5253%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_840.47_840.79, WER: 0%, TER: 0%, total WER: 19.1045%, total TER: 8.50467%, progress (thread 0): 89.5332%]
|T|: m m h m m
|P|: m m m m
[sample: ES2004d_H03_FEE016_1930.95_1931.27, WER: 100%, TER: 20%, total WER: 19.1054%, total TER: 8.5048%, progress (thread 0): 89.5411%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2146.44_2146.76, WER: 0%, TER: 0%, total WER: 19.1052%, total TER: 8.50472%, progress (thread 0): 89.549%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_290.82_291.14, WER: 100%, TER: 0%, total WER: 19.1061%, total TER: 8.50462%, progress (thread 0): 89.557%]
|T|: o o p s
|P|: s o
[sample: IS1009a_H03_FIO089_420.14_420.46, WER: 100%, TER: 75%, total WER: 19.1071%, total TER: 8.50525%, progress (thread 0): 89.5649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H01_FIO087_494.72_495.04, WER: 0%, TER: 0%, total WER: 19.1068%, total TER: 8.50517%, progress (thread 0): 89.5728%]
|T|: y e a h
|P|: h
[sample: IS1009a_H02_FIO084_577.05_577.37, WER: 100%, TER: 75%, total WER: 19.1078%, total TER: 8.50579%, progress (thread 0): 89.5807%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H02_FIO084_641.8_642.12, WER: 100%, TER: 20%, total WER: 19.1087%, total TER: 8.50593%, progress (thread 0): 89.5886%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_197.33_197.65, WER: 0%, TER: 0%, total WER: 19.1085%, total TER: 8.50585%, progress (thread 0): 89.5965%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_595.82_596.14, WER: 100%, TER: 60%, total WER: 19.1094%, total TER: 8.50645%, progress (thread 0): 89.6044%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1073.83_1074.15, WER: 100%, TER: 0%, total WER: 19.1103%, total TER: 8.50635%, progress (thread 0): 89.6123%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1139.48_1139.8, WER: 100%, TER: 0%, total WER: 19.1112%, total TER: 8.50625%, progress (thread 0): 89.6202%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1268.25_1268.57, WER: 100%, TER: 0%, total WER: 19.1121%, total TER: 8.50615%, progress (thread 0): 89.6282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1877.89_1878.21, WER: 0%, TER: 0%, total WER: 19.1119%, total TER: 8.50607%, progress (thread 0): 89.6361%]
|T|: m m h m m
|P|: m e n
[sample: IS1009b_H03_FIO089_1894.73_1895.05, WER: 100%, TER: 80%, total WER: 19.1128%, total TER: 8.50691%, progress (thread 0): 89.644%]
|T|: h i
|P|: k a y
[sample: IS1009c_H01_FIO087_43.77_44.09, WER: 100%, TER: 150%, total WER: 19.1137%, total TER: 8.50758%, progress (thread 0): 89.6519%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_405.29_405.61, WER: 100%, TER: 0%, total WER: 19.1146%, total TER: 8.50748%, progress (thread 0): 89.6598%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_524.19_524.51, WER: 100%, TER: 0%, total WER: 19.1156%, total TER: 8.50738%, progress (thread 0): 89.6677%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_1640_1640.32, WER: 0%, TER: 0%, total WER: 19.1153%, total TER: 8.50732%, progress (thread 0): 89.6756%]
|T|: m e n u
|P|: m e n u
[sample: IS1009d_H00_FIE088_523.67_523.99, WER: 0%, TER: 0%, total WER: 19.1151%, total TER: 8.50724%, progress (thread 0): 89.6835%]
|T|: p a r d o n | m e
|P|: b u d d o n
[sample: IS1009d_H01_FIO087_699.16_699.48, WER: 100%, TER: 66.6667%, total WER: 19.117%, total TER: 8.50847%, progress (thread 0): 89.6915%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_799.6_799.92, WER: 100%, TER: 60%, total WER: 19.1179%, total TER: 8.50907%, progress (thread 0): 89.6994%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1056.69_1057.01, WER: 0%, TER: 0%, total WER: 19.1177%, total TER: 8.50899%, progress (thread 0): 89.7073%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1722.84_1723.16, WER: 100%, TER: 0%, total WER: 19.1186%, total TER: 8.50889%, progress (thread 0): 89.7152%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1741.45_1741.77, WER: 100%, TER: 0%, total WER: 19.1195%, total TER: 8.50879%, progress (thread 0): 89.7231%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1791.05_1791.37, WER: 0%, TER: 0%, total WER: 19.1193%, total TER: 8.50871%, progress (thread 0): 89.731%]
|T|: y e a h
|P|: m m
[sample: TS3003b_H01_MTD011UID_1651.31_1651.63, WER: 100%, TER: 100%, total WER: 19.1202%, total TER: 8.50957%, progress (thread 0): 89.7389%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_1722.53_1722.85, WER: 100%, TER: 25%, total WER: 19.1211%, total TER: 8.50973%, progress (thread 0): 89.7468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2090.25_2090.57, WER: 0%, TER: 0%, total WER: 19.1209%, total TER: 8.50965%, progress (thread 0): 89.7547%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_2074.3_2074.62, WER: 100%, TER: 0%, total WER: 19.1218%, total TER: 8.50955%, progress (thread 0): 89.7627%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_2134.48_2134.8, WER: 100%, TER: 0%, total WER: 19.1227%, total TER: 8.50945%, progress (thread 0): 89.7706%]
|T|: n a y
|P|: n o
[sample: TS3003d_H01_MTD011UID_417.84_418.16, WER: 100%, TER: 66.6667%, total WER: 19.1236%, total TER: 8.50986%, progress (thread 0): 89.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_694.59_694.91, WER: 0%, TER: 0%, total WER: 19.1234%, total TER: 8.50978%, progress (thread 0): 89.7864%]
|T|: o r | b
|P|: o r
[sample: TS3003d_H03_MTD012ME_896.59_896.91, WER: 50%, TER: 50%, total WER: 19.1241%, total TER: 8.51017%, progress (thread 0): 89.7943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1403.4_1403.72, WER: 0%, TER: 0%, total WER: 19.1239%, total TER: 8.51009%, progress (thread 0): 89.8022%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1492.24_1492.56, WER: 0%, TER: 0%, total WER: 19.1237%, total TER: 8.51001%, progress (thread 0): 89.8101%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_2024.39_2024.71, WER: 100%, TER: 100%, total WER: 19.1246%, total TER: 8.51087%, progress (thread 0): 89.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2381.84_2382.16, WER: 0%, TER: 0%, total WER: 19.1244%, total TER: 8.51079%, progress (thread 0): 89.826%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_68.23_68.55, WER: 0%, TER: 0%, total WER: 19.1242%, total TER: 8.51069%, progress (thread 0): 89.8339%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_359.54_359.86, WER: 50%, TER: 42.8571%, total WER: 19.1249%, total TER: 8.51125%, progress (thread 0): 89.8418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_496.88_497.2, WER: 0%, TER: 0%, total WER: 19.1247%, total TER: 8.51117%, progress (thread 0): 89.8497%]
|T|: h m m
|P|: n m m
[sample: EN2002a_H02_FEO072_504.18_504.5, WER: 100%, TER: 33.3333%, total WER: 19.1256%, total TER: 8.51135%, progress (thread 0): 89.8576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_567.01_567.33, WER: 0%, TER: 0%, total WER: 19.1254%, total TER: 8.51131%, progress (thread 0): 89.8655%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_604.34_604.66, WER: 100%, TER: 0%, total WER: 19.1263%, total TER: 8.51121%, progress (thread 0): 89.8734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_729.25_729.57, WER: 0%, TER: 0%, total WER: 19.1261%, total TER: 8.51113%, progress (thread 0): 89.8813%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_800.36_800.68, WER: 0%, TER: 0%, total WER: 19.1258%, total TER: 8.51103%, progress (thread 0): 89.8892%]
|T|: o h
|P|: n o
[sample: EN2002a_H02_FEO072_856.5_856.82, WER: 100%, TER: 100%, total WER: 19.1268%, total TER: 8.51146%, progress (thread 0): 89.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1050.6_1050.92, WER: 0%, TER: 0%, total WER: 19.1265%, total TER: 8.51138%, progress (thread 0): 89.9051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1101.18_1101.5, WER: 0%, TER: 0%, total WER: 19.1263%, total TER: 8.5113%, progress (thread 0): 89.913%]
|T|: o h | n o
|P|: o o
[sample: EN2002a_H01_FEO070_1448.67_1448.99, WER: 100%, TER: 60%, total WER: 19.1282%, total TER: 8.5119%, progress (thread 0): 89.9209%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1448.83_1449.15, WER: 100%, TER: 33.3333%, total WER: 19.1291%, total TER: 8.51208%, progress (thread 0): 89.9288%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1537.39_1537.71, WER: 0%, TER: 0%, total WER: 19.1289%, total TER: 8.512%, progress (thread 0): 89.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1862.6_1862.92, WER: 0%, TER: 0%, total WER: 19.1286%, total TER: 8.51192%, progress (thread 0): 89.9446%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_283.05_283.37, WER: 100%, TER: 100%, total WER: 19.1295%, total TER: 8.51278%, progress (thread 0): 89.9525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_363.29_363.61, WER: 0%, TER: 0%, total WER: 19.1293%, total TER: 8.5127%, progress (thread 0): 89.9604%]
|T|: o h | y e a h
|P|: m e a h
[sample: EN2002b_H03_MEE073_679.41_679.73, WER: 100%, TER: 57.1429%, total WER: 19.1312%, total TER: 8.5135%, progress (thread 0): 89.9684%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_922.82_923.14, WER: 0%, TER: 0%, total WER: 19.1309%, total TER: 8.51342%, progress (thread 0): 89.9763%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H02_FEO072_1100.2_1100.52, WER: 100%, TER: 0%, total WER: 19.1319%, total TER: 8.51332%, progress (thread 0): 89.9842%]
|T|: g o o d | p o i n t
|P|: t o e | p o i n t
[sample: EN2002b_H03_MEE073_1217.83_1218.15, WER: 50%, TER: 30%, total WER: 19.1326%, total TER: 8.51382%, progress (thread 0): 89.9921%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1261.76_1262.08, WER: 0%, TER: 0%, total WER: 19.1323%, total TER: 8.51374%, progress (thread 0): 90%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1598.38_1598.7, WER: 100%, TER: 33.3333%, total WER: 19.1333%, total TER: 8.51392%, progress (thread 0): 90.0079%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1611.81_1612.13, WER: 50%, TER: 55.5556%, total WER: 19.134%, total TER: 8.51491%, progress (thread 0): 90.0158%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1705.25_1705.57, WER: 0%, TER: 0%, total WER: 19.1337%, total TER: 8.51483%, progress (thread 0): 90.0237%]
|T|: g o o d
|P|: o k a y
[sample: EN2002c_H01_FEO072_489.73_490.05, WER: 100%, TER: 100%, total WER: 19.1347%, total TER: 8.51569%, progress (thread 0): 90.0316%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_653.98_654.3, WER: 0%, TER: 0%, total WER: 19.1344%, total TER: 8.51565%, progress (thread 0): 90.0396%]
|T|: y e a h
|P|: e h
[sample: EN2002c_H01_FEO072_1352.96_1353.28, WER: 100%, TER: 50%, total WER: 19.1354%, total TER: 8.51604%, progress (thread 0): 90.0475%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_2296.14_2296.46, WER: 0%, TER: 0%, total WER: 19.1351%, total TER: 8.516%, progress (thread 0): 90.0554%]
|T|: i | t h i n k
|P|: i | t h i n k
[sample: EN2002c_H01_FEO072_2336.35_2336.67, WER: 0%, TER: 0%, total WER: 19.1347%, total TER: 8.51586%, progress (thread 0): 90.0633%]
|T|: o h
|P|: e h
[sample: EN2002c_H01_FEO072_2368.69_2369.01, WER: 100%, TER: 50%, total WER: 19.1356%, total TER: 8.51605%, progress (thread 0): 90.0712%]
|T|: w h a t
|P|: w e l l
[sample: EN2002c_H01_FEO072_2484.6_2484.92, WER: 100%, TER: 75%, total WER: 19.1365%, total TER: 8.51668%, progress (thread 0): 90.0791%]
|T|: h m m
|P|: m m m
[sample: EN2002c_H03_MEE073_2800.26_2800.58, WER: 100%, TER: 33.3333%, total WER: 19.1375%, total TER: 8.51685%, progress (thread 0): 90.087%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_2829.54_2829.86, WER: 0%, TER: 0%, total WER: 19.1372%, total TER: 8.51677%, progress (thread 0): 90.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_347.79_348.11, WER: 0%, TER: 0%, total WER: 19.137%, total TER: 8.51669%, progress (thread 0): 90.1028%]
|T|: i t ' s | g o o d
|P|: t h a ' s | g o
[sample: EN2002d_H00_FEO070_420.4_420.72, WER: 100%, TER: 55.5556%, total WER: 19.1388%, total TER: 8.51769%, progress (thread 0): 90.1108%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002d_H01_FEO072_574.1_574.42, WER: 100%, TER: 20%, total WER: 19.1398%, total TER: 8.51782%, progress (thread 0): 90.1187%]
|T|: b u t
|P|: b u t
[sample: EN2002d_H01_FEO072_590.21_590.53, WER: 0%, TER: 0%, total WER: 19.1395%, total TER: 8.51776%, progress (thread 0): 90.1266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_769.35_769.67, WER: 0%, TER: 0%, total WER: 19.1393%, total TER: 8.51768%, progress (thread 0): 90.1345%]
|T|: o h
|P|: o h
[sample: EN2002d_H03_MEE073_939.78_940.1, WER: 0%, TER: 0%, total WER: 19.1391%, total TER: 8.51764%, progress (thread 0): 90.1424%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_954.69_955.01, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.51756%, progress (thread 0): 90.1503%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1189.57_1189.89, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51748%, progress (thread 0): 90.1582%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1203.2_1203.52, WER: 0%, TER: 0%, total WER: 19.1385%, total TER: 8.5174%, progress (thread 0): 90.1661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1255.64_1255.96, WER: 0%, TER: 0%, total WER: 19.1382%, total TER: 8.51732%, progress (thread 0): 90.174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1754.3_1754.62, WER: 0%, TER: 0%, total WER: 19.138%, total TER: 8.51724%, progress (thread 0): 90.182%]
|T|: s o
|P|: s o
[sample: EN2002d_H01_FEO072_1974.1_1974.42, WER: 0%, TER: 0%, total WER: 19.1378%, total TER: 8.5172%, progress (thread 0): 90.1899%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_2190.35_2190.67, WER: 0%, TER: 0%, total WER: 19.1376%, total TER: 8.51716%, progress (thread 0): 90.1978%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_582.6_582.91, WER: 0%, TER: 0%, total WER: 19.1374%, total TER: 8.51708%, progress (thread 0): 90.2057%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_687.25_687.56, WER: 100%, TER: 0%, total WER: 19.1383%, total TER: 8.51698%, progress (thread 0): 90.2136%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_688.13_688.44, WER: 0%, TER: 0%, total WER: 19.1381%, total TER: 8.5169%, progress (thread 0): 90.2215%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_777.34_777.65, WER: 100%, TER: 0%, total WER: 19.139%, total TER: 8.5168%, progress (thread 0): 90.2294%]
|T|: y
|P|: y
[sample: ES2004a_H02_MEE014_949.9_950.21, WER: 0%, TER: 0%, total WER: 19.1388%, total TER: 8.51678%, progress (thread 0): 90.2373%]
|T|: n o
|P|: n o
[sample: ES2004b_H03_FEE016_607.28_607.59, WER: 0%, TER: 0%, total WER: 19.1386%, total TER: 8.51674%, progress (thread 0): 90.2453%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1612.5_1612.81, WER: 100%, TER: 0%, total WER: 19.1395%, total TER: 8.51664%, progress (thread 0): 90.2532%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_2237.11_2237.42, WER: 0%, TER: 0%, total WER: 19.1393%, total TER: 8.51656%, progress (thread 0): 90.2611%]
|T|: b u t
|P|: b u t
[sample: ES2004c_H02_MEE014_571.73_572.04, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.5165%, progress (thread 0): 90.269%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1321.04_1321.35, WER: 0%, TER: 0%, total WER: 19.1388%, total TER: 8.51642%, progress (thread 0): 90.2769%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_1582.98_1583.29, WER: 0%, TER: 0%, total WER: 19.1386%, total TER: 8.51634%, progress (thread 0): 90.2848%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2288.73_2289.04, WER: 0%, TER: 0%, total WER: 19.1384%, total TER: 8.51626%, progress (thread 0): 90.2927%]
|T|: m m
|P|: h m m
[sample: ES2004d_H00_MEO015_846.15_846.46, WER: 100%, TER: 50%, total WER: 19.1393%, total TER: 8.51646%, progress (thread 0): 90.3006%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1669.25_1669.56, WER: 0%, TER: 0%, total WER: 19.1391%, total TER: 8.51638%, progress (thread 0): 90.3085%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1669.66_1669.97, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.5163%, progress (thread 0): 90.3165%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1825.79_1826.1, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51622%, progress (thread 0): 90.3244%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1901.99_1902.3, WER: 0%, TER: 0%, total WER: 19.1384%, total TER: 8.51614%, progress (thread 0): 90.3323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1967.13_1967.44, WER: 0%, TER: 0%, total WER: 19.1382%, total TER: 8.51606%, progress (thread 0): 90.3402%]
|T|: w e l l
|P|: o h
[sample: ES2004d_H02_MEE014_2085.81_2086.12, WER: 100%, TER: 100%, total WER: 19.1391%, total TER: 8.51692%, progress (thread 0): 90.3481%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2220.03_2220.34, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.51684%, progress (thread 0): 90.356%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_90.21_90.52, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51676%, progress (thread 0): 90.3639%]
|T|: u h
|P|: m m
[sample: IS1009a_H02_FIO084_451.06_451.37, WER: 100%, TER: 100%, total WER: 19.1396%, total TER: 8.51719%, progress (thread 0): 90.3718%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_702.97_703.28, WER: 0%, TER: 0%, total WER: 19.1394%, total TER: 8.51711%, progress (thread 0): 90.3797%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_795.57_795.88, WER: 0%, TER: 0%, total WER: 19.1392%, total TER: 8.51703%, progress (thread 0): 90.3877%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_218.1_218.41, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.51699%, progress (thread 0): 90.3956%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_616.32_616.63, WER: 100%, TER: 0%, total WER: 19.1399%, total TER: 8.51689%, progress (thread 0): 90.4035%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1692.92_1693.23, WER: 0%, TER: 0%, total WER: 19.1397%, total TER: 8.51681%, progress (thread 0): 90.4114%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1832.99_1833.3, WER: 0%, TER: 0%, total WER: 19.1395%, total TER: 8.51673%, progress (thread 0): 90.4193%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1844.23_1844.54, WER: 0%, TER: 0%, total WER: 19.1392%, total TER: 8.51667%, progress (thread 0): 90.4272%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H03_FIO089_1904.39_1904.7, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.51657%, progress (thread 0): 90.4351%]
|T|: m m h m m
|P|: m e m
[sample: IS1009c_H00_FIE088_1140.15_1140.46, WER: 100%, TER: 60%, total WER: 19.1399%, total TER: 8.51717%, progress (thread 0): 90.443%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_1163.03_1163.34, WER: 100%, TER: 0%, total WER: 19.1409%, total TER: 8.51707%, progress (thread 0): 90.451%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_1237.53_1237.84, WER: 100%, TER: 0%, total WER: 19.1418%, total TER: 8.51697%, progress (thread 0): 90.4589%]
|T|: ' k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1429.75_1430.06, WER: 100%, TER: 25%, total WER: 19.1427%, total TER: 8.51712%, progress (thread 0): 90.4668%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1554.5_1554.81, WER: 100%, TER: 0%, total WER: 19.1436%, total TER: 8.51702%, progress (thread 0): 90.4747%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H01_FIO087_1580.06_1580.37, WER: 100%, TER: 0%, total WER: 19.1445%, total TER: 8.51692%, progress (thread 0): 90.4826%]
|T|: o h
|P|: o h
[sample: IS1009c_H00_FIE088_1694.98_1695.29, WER: 0%, TER: 0%, total WER: 19.1443%, total TER: 8.51688%, progress (thread 0): 90.4905%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_363.39_363.7, WER: 100%, TER: 0%, total WER: 19.1452%, total TER: 8.51678%, progress (thread 0): 90.4984%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_894.86_895.17, WER: 0%, TER: 0%, total WER: 19.145%, total TER: 8.51674%, progress (thread 0): 90.5063%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_903.74_904.05, WER: 0%, TER: 0%, total WER: 19.1448%, total TER: 8.51666%, progress (thread 0): 90.5142%]
|T|: n o
|P|: n o
[sample: IS1009d_H00_FIE088_1011.62_1011.93, WER: 0%, TER: 0%, total WER: 19.1446%, total TER: 8.51662%, progress (thread 0): 90.5222%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1012.22_1012.53, WER: 100%, TER: 0%, total WER: 19.1455%, total TER: 8.51652%, progress (thread 0): 90.5301%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1105.97_1106.28, WER: 100%, TER: 0%, total WER: 19.1464%, total TER: 8.51642%, progress (thread 0): 90.538%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1171.27_1171.58, WER: 0%, TER: 0%, total WER: 19.1462%, total TER: 8.51634%, progress (thread 0): 90.5459%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1243.74_1244.05, WER: 100%, TER: 66.6667%, total WER: 19.1471%, total TER: 8.51675%, progress (thread 0): 90.5538%]
|T|: w h y
|P|: w o w
[sample: IS1009d_H00_FIE088_1442.55_1442.86, WER: 100%, TER: 66.6667%, total WER: 19.148%, total TER: 8.51716%, progress (thread 0): 90.5617%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1681.21_1681.52, WER: 0%, TER: 0%, total WER: 19.1478%, total TER: 8.51708%, progress (thread 0): 90.5696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_216.48_216.79, WER: 0%, TER: 0%, total WER: 19.1476%, total TER: 8.517%, progress (thread 0): 90.5775%]
|T|: m e a t
|P|: n e a t
[sample: TS3003a_H03_MTD012ME_595.66_595.97, WER: 100%, TER: 25%, total WER: 19.1485%, total TER: 8.51716%, progress (thread 0): 90.5854%]
|T|: s o
|P|: s o
[sample: TS3003a_H03_MTD012ME_884.45_884.76, WER: 0%, TER: 0%, total WER: 19.1483%, total TER: 8.51712%, progress (thread 0): 90.5934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1418.64_1418.95, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51704%, progress (thread 0): 90.6013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_71.8_72.11, WER: 0%, TER: 0%, total WER: 19.1478%, total TER: 8.51696%, progress (thread 0): 90.6092%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_538.03_538.34, WER: 0%, TER: 0%, total WER: 19.1476%, total TER: 8.51688%, progress (thread 0): 90.6171%]
|T|: m m
|P|: m h
[sample: TS3003b_H01_MTD011UID_1469.03_1469.34, WER: 100%, TER: 50%, total WER: 19.1485%, total TER: 8.51707%, progress (thread 0): 90.625%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1715.8_1716.11, WER: 0%, TER: 0%, total WER: 19.1483%, total TER: 8.51703%, progress (thread 0): 90.6329%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1822.91_1823.22, WER: 0%, TER: 0%, total WER: 19.1481%, total TER: 8.51695%, progress (thread 0): 90.6408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_794.24_794.55, WER: 0%, TER: 0%, total WER: 19.1479%, total TER: 8.51687%, progress (thread 0): 90.6487%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_934.63_934.94, WER: 100%, TER: 25%, total WER: 19.1488%, total TER: 8.51703%, progress (thread 0): 90.6566%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1136.65_1136.96, WER: 0%, TER: 0%, total WER: 19.1486%, total TER: 8.51699%, progress (thread 0): 90.6646%]
|T|: y e a h
|P|: y e m
[sample: TS3003c_H01_MTD011UID_1444.43_1444.74, WER: 100%, TER: 50%, total WER: 19.1495%, total TER: 8.51738%, progress (thread 0): 90.6725%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1813.2_1813.51, WER: 0%, TER: 0%, total WER: 19.1493%, total TER: 8.5173%, progress (thread 0): 90.6804%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_255.36_255.67, WER: 0%, TER: 0%, total WER: 19.1491%, total TER: 8.51722%, progress (thread 0): 90.6883%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_453.54_453.85, WER: 0%, TER: 0%, total WER: 19.1488%, total TER: 8.51718%, progress (thread 0): 90.6962%]
|T|: y e p
|P|: o h
[sample: TS3003d_H02_MTD0010ID_562.3_562.61, WER: 100%, TER: 100%, total WER: 19.1498%, total TER: 8.51782%, progress (thread 0): 90.7041%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1317.08_1317.39, WER: 0%, TER: 0%, total WER: 19.1495%, total TER: 8.51774%, progress (thread 0): 90.712%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_1546.63_1546.94, WER: 100%, TER: 0%, total WER: 19.1505%, total TER: 8.51764%, progress (thread 0): 90.7199%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2324.36_2324.67, WER: 0%, TER: 0%, total WER: 19.1502%, total TER: 8.51756%, progress (thread 0): 90.7278%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2373.13_2373.44, WER: 0%, TER: 0%, total WER: 19.15%, total TER: 8.51748%, progress (thread 0): 90.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_595.97_596.28, WER: 0%, TER: 0%, total WER: 19.1498%, total TER: 8.5174%, progress (thread 0): 90.7437%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_694.14_694.45, WER: 0%, TER: 0%, total WER: 19.1496%, total TER: 8.51732%, progress (thread 0): 90.7516%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_704.35_704.66, WER: 0%, TER: 0%, total WER: 19.1494%, total TER: 8.51724%, progress (thread 0): 90.7595%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_910.51_910.82, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51716%, progress (thread 0): 90.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1104.75_1105.06, WER: 0%, TER: 0%, total WER: 19.1489%, total TER: 8.51708%, progress (thread 0): 90.7753%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1619.48_1619.79, WER: 100%, TER: 33.3333%, total WER: 19.1499%, total TER: 8.51726%, progress (thread 0): 90.7832%]
|T|: o h
|P|: m m
[sample: EN2002a_H02_FEO072_1674.71_1675.02, WER: 100%, TER: 100%, total WER: 19.1508%, total TER: 8.51769%, progress (thread 0): 90.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1973.2_1973.51, WER: 0%, TER: 0%, total WER: 19.1505%, total TER: 8.51761%, progress (thread 0): 90.799%]
|T|: o h | r i g h t
|P|: o h | r i g h t
[sample: EN2002a_H03_MEE071_1971.01_1971.32, WER: 0%, TER: 0%, total WER: 19.1501%, total TER: 8.51745%, progress (thread 0): 90.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_346.95_347.26, WER: 0%, TER: 0%, total WER: 19.1499%, total TER: 8.51737%, progress (thread 0): 90.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_372.58_372.89, WER: 0%, TER: 0%, total WER: 19.1497%, total TER: 8.51729%, progress (thread 0): 90.8228%]
|T|: m m
|P|: m m
[sample: EN2002b_H03_MEE073_565.42_565.73, WER: 0%, TER: 0%, total WER: 19.1495%, total TER: 8.51725%, progress (thread 0): 90.8307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_729.79_730.1, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51717%, progress (thread 0): 90.8386%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1214.71_1215.02, WER: 0%, TER: 0%, total WER: 19.149%, total TER: 8.51709%, progress (thread 0): 90.8465%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1323.81_1324.12, WER: 0%, TER: 0%, total WER: 19.1488%, total TER: 8.51701%, progress (thread 0): 90.8544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1461.61_1461.92, WER: 0%, TER: 0%, total WER: 19.1486%, total TER: 8.51693%, progress (thread 0): 90.8623%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1750.66_1750.97, WER: 0%, TER: 0%, total WER: 19.1484%, total TER: 8.51685%, progress (thread 0): 90.8703%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_441.29_441.6, WER: 0%, TER: 0%, total WER: 19.1482%, total TER: 8.51681%, progress (thread 0): 90.8782%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_543.24_543.55, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51677%, progress (thread 0): 90.8861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_543.24_543.55, WER: 0%, TER: 0%, total WER: 19.1477%, total TER: 8.51669%, progress (thread 0): 90.894%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_982.53_982.84, WER: 100%, TER: 33.3333%, total WER: 19.1486%, total TER: 8.51686%, progress (thread 0): 90.9019%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1524.82_1525.13, WER: 0%, TER: 0%, total WER: 19.1484%, total TER: 8.51678%, progress (thread 0): 90.9098%]
|T|: u m
|P|: u m
[sample: EN2002c_H03_MEE073_1904.01_1904.32, WER: 0%, TER: 0%, total WER: 19.1482%, total TER: 8.51674%, progress (thread 0): 90.9177%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1958.34_1958.65, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51666%, progress (thread 0): 90.9256%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_2240.67_2240.98, WER: 100%, TER: 0%, total WER: 19.1489%, total TER: 8.51656%, progress (thread 0): 90.9335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2358.92_2359.23, WER: 0%, TER: 0%, total WER: 19.1487%, total TER: 8.51648%, progress (thread 0): 90.9415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2477.51_2477.82, WER: 0%, TER: 0%, total WER: 19.1485%, total TER: 8.5164%, progress (thread 0): 90.9494%]
|T|: n o
|P|: n i c
[sample: EN2002c_H02_MEE071_2608.15_2608.46, WER: 100%, TER: 100%, total WER: 19.1494%, total TER: 8.51683%, progress (thread 0): 90.9573%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2724.28_2724.59, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51675%, progress (thread 0): 90.9652%]
|T|: t i c k
|P|: t i c k
[sample: EN2002c_H01_FEO072_2878.5_2878.81, WER: 0%, TER: 0%, total WER: 19.149%, total TER: 8.51667%, progress (thread 0): 90.9731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_391.81_392.12, WER: 0%, TER: 0%, total WER: 19.1487%, total TER: 8.51659%, progress (thread 0): 90.981%]
|T|: y e a h | o k a y
|P|: o k a y
[sample: EN2002d_H00_FEO070_1379.88_1380.19, WER: 50%, TER: 55.5556%, total WER: 19.1494%, total TER: 8.51758%, progress (thread 0): 90.9889%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H01_FEO072_1512.76_1513.07, WER: 100%, TER: 0%, total WER: 19.1504%, total TER: 8.51748%, progress (thread 0): 90.9968%]
|T|: s o
|P|: y e a h
[sample: EN2002d_H00_FEO070_1542.52_1542.83, WER: 100%, TER: 200%, total WER: 19.1513%, total TER: 8.51838%, progress (thread 0): 91.0047%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1546.76_1547.07, WER: 0%, TER: 0%, total WER: 19.1511%, total TER: 8.5183%, progress (thread 0): 91.0127%]
|T|: s o
|P|: s o
[sample: EN2002d_H03_MEE073_1822.41_1822.72, WER: 0%, TER: 0%, total WER: 19.1508%, total TER: 8.51826%, progress (thread 0): 91.0206%]
|T|: m m
|P|: m m
[sample: EN2002d_H00_FEO070_1872.39_1872.7, WER: 0%, TER: 0%, total WER: 19.1506%, total TER: 8.51822%, progress (thread 0): 91.0285%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2114.3_2114.61, WER: 0%, TER: 0%, total WER: 19.1504%, total TER: 8.51814%, progress (thread 0): 91.0364%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_819.96_820.26, WER: 0%, TER: 0%, total WER: 19.1502%, total TER: 8.51806%, progress (thread 0): 91.0443%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1232.15_1232.45, WER: 0%, TER: 0%, total WER: 19.15%, total TER: 8.51798%, progress (thread 0): 91.0522%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1572.74_1573.04, WER: 100%, TER: 0%, total WER: 19.1509%, total TER: 8.51788%, progress (thread 0): 91.0601%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1784.08_1784.38, WER: 100%, TER: 0%, total WER: 19.1518%, total TER: 8.51778%, progress (thread 0): 91.068%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_2252.76_2253.06, WER: 0%, TER: 0%, total WER: 19.1516%, total TER: 8.5177%, progress (thread 0): 91.076%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_849.84_850.14, WER: 0%, TER: 0%, total WER: 19.1514%, total TER: 8.51762%, progress (thread 0): 91.0839%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1555.92_1556.22, WER: 0%, TER: 0%, total WER: 19.1511%, total TER: 8.51754%, progress (thread 0): 91.0918%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H00_MEO015_1640.82_1641.12, WER: 100%, TER: 0%, total WER: 19.1521%, total TER: 8.51744%, progress (thread 0): 91.0997%]
|T|: ' k a y
|P|: k e y
[sample: ES2004d_H00_MEO015_271.77_272.07, WER: 100%, TER: 50%, total WER: 19.153%, total TER: 8.51783%, progress (thread 0): 91.1076%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_639.49_639.79, WER: 0%, TER: 0%, total WER: 19.1528%, total TER: 8.51775%, progress (thread 0): 91.1155%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1390.02_1390.32, WER: 0%, TER: 0%, total WER: 19.1525%, total TER: 8.51767%, progress (thread 0): 91.1234%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1711.3_1711.6, WER: 0%, TER: 0%, total WER: 19.1523%, total TER: 8.51759%, progress (thread 0): 91.1313%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_2002_2002.3, WER: 0%, TER: 0%, total WER: 19.1521%, total TER: 8.51755%, progress (thread 0): 91.1392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2096.66_2096.96, WER: 0%, TER: 0%, total WER: 19.1519%, total TER: 8.51747%, progress (thread 0): 91.1472%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_617.96_618.26, WER: 100%, TER: 0%, total WER: 19.1528%, total TER: 8.51737%, progress (thread 0): 91.1551%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H01_FIO087_782.9_783.2, WER: 0%, TER: 0%, total WER: 19.1526%, total TER: 8.51729%, progress (thread 0): 91.163%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_182.06_182.36, WER: 0%, TER: 0%, total WER: 19.1524%, total TER: 8.51721%, progress (thread 0): 91.1709%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_574.49_574.79, WER: 100%, TER: 0%, total WER: 19.1533%, total TER: 8.51711%, progress (thread 0): 91.1788%]
|T|: m m h m m
|P|: m m m
[sample: IS1009b_H03_FIO089_707.08_707.38, WER: 100%, TER: 40%, total WER: 19.1542%, total TER: 8.51748%, progress (thread 0): 91.1867%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1025.61_1025.91, WER: 0%, TER: 0%, total WER: 19.154%, total TER: 8.5174%, progress (thread 0): 91.1946%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_1366.95_1367.25, WER: 0%, TER: 0%, total WER: 19.1538%, total TER: 8.51736%, progress (thread 0): 91.2025%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1371.17_1371.47, WER: 100%, TER: 0%, total WER: 19.1547%, total TER: 8.51726%, progress (thread 0): 91.2104%]
|T|: h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1604.76_1605.06, WER: 100%, TER: 33.3333%, total WER: 19.1556%, total TER: 8.51744%, progress (thread 0): 91.2184%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1617.66_1617.96, WER: 0%, TER: 0%, total WER: 19.1554%, total TER: 8.51736%, progress (thread 0): 91.2263%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_335.7_336, WER: 0%, TER: 0%, total WER: 19.1552%, total TER: 8.51728%, progress (thread 0): 91.2342%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_1106.15_1106.45, WER: 100%, TER: 0%, total WER: 19.1561%, total TER: 8.51718%, progress (thread 0): 91.2421%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1237.83_1238.13, WER: 0%, TER: 0%, total WER: 19.1559%, total TER: 8.51712%, progress (thread 0): 91.25%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1259.97_1260.27, WER: 0%, TER: 0%, total WER: 19.1556%, total TER: 8.51704%, progress (thread 0): 91.2579%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1297.17_1297.47, WER: 0%, TER: 0%, total WER: 19.1554%, total TER: 8.51696%, progress (thread 0): 91.2658%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_607.11_607.41, WER: 100%, TER: 0%, total WER: 19.1563%, total TER: 8.51686%, progress (thread 0): 91.2737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_769.42_769.72, WER: 0%, TER: 0%, total WER: 19.1561%, total TER: 8.51678%, progress (thread 0): 91.2816%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1062.93_1063.23, WER: 0%, TER: 0%, total WER: 19.1559%, total TER: 8.5167%, progress (thread 0): 91.2896%]
|T|: o n e
|P|: b u t
[sample: IS1009d_H01_FIO087_1482_1482.3, WER: 100%, TER: 100%, total WER: 19.1568%, total TER: 8.51734%, progress (thread 0): 91.2975%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H00_FIE088_1528.1_1528.4, WER: 0%, TER: 0%, total WER: 19.1566%, total TER: 8.51728%, progress (thread 0): 91.3054%]
|T|: y e a h
|P|: i
[sample: IS1009d_H02_FIO084_1824.41_1824.71, WER: 100%, TER: 100%, total WER: 19.1575%, total TER: 8.51814%, progress (thread 0): 91.3133%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1853.09_1853.39, WER: 0%, TER: 0%, total WER: 19.1573%, total TER: 8.51806%, progress (thread 0): 91.3212%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_238.31_238.61, WER: 0%, TER: 0%, total WER: 19.1571%, total TER: 8.51798%, progress (thread 0): 91.3291%]
|T|: s o
|P|: s o
[sample: TS3003a_H02_MTD0010ID_1080.77_1081.07, WER: 0%, TER: 0%, total WER: 19.1569%, total TER: 8.51794%, progress (thread 0): 91.337%]
|T|: h m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1308.88_1309.18, WER: 100%, TER: 33.3333%, total WER: 19.1578%, total TER: 8.51812%, progress (thread 0): 91.3449%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_1423.96_1424.26, WER: 0%, TER: 0%, total WER: 19.1576%, total TER: 8.51806%, progress (thread 0): 91.3529%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1463.25_1463.55, WER: 0%, TER: 0%, total WER: 19.1573%, total TER: 8.51798%, progress (thread 0): 91.3608%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_1535.95_1536.25, WER: 0%, TER: 0%, total WER: 19.1571%, total TER: 8.51792%, progress (thread 0): 91.3687%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003b_H03_MTD012ME_1635.81_1636.11, WER: 100%, TER: 0%, total WER: 19.158%, total TER: 8.51782%, progress (thread 0): 91.3766%]
|T|: h m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1834.41_1834.71, WER: 100%, TER: 50%, total WER: 19.159%, total TER: 8.51801%, progress (thread 0): 91.3845%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1871.5_1871.8, WER: 0%, TER: 0%, total WER: 19.1587%, total TER: 8.51797%, progress (thread 0): 91.3924%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2279.98_2280.28, WER: 0%, TER: 0%, total WER: 19.1585%, total TER: 8.51789%, progress (thread 0): 91.4003%]
|T|: s
|P|: y m
[sample: TS3003d_H01_MTD011UID_856.51_856.81, WER: 100%, TER: 200%, total WER: 19.1594%, total TER: 8.51834%, progress (thread 0): 91.4082%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1057.72_1058.02, WER: 0%, TER: 0%, total WER: 19.1592%, total TER: 8.51826%, progress (thread 0): 91.4161%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1709.82_1710.12, WER: 0%, TER: 0%, total WER: 19.159%, total TER: 8.51818%, progress (thread 0): 91.424%]
|T|: y e s
|P|: y e s
[sample: TS3003d_H02_MTD0010ID_1732.89_1733.19, WER: 0%, TER: 0%, total WER: 19.1588%, total TER: 8.51812%, progress (thread 0): 91.432%]
|T|: y e a h
|P|: y m a m
[sample: TS3003d_H00_MTD009PM_1821.73_1822.03, WER: 100%, TER: 50%, total WER: 19.1597%, total TER: 8.51851%, progress (thread 0): 91.4399%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2250.87_2251.17, WER: 0%, TER: 0%, total WER: 19.1595%, total TER: 8.51843%, progress (thread 0): 91.4478%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2482.46_2482.76, WER: 0%, TER: 0%, total WER: 19.1593%, total TER: 8.51835%, progress (thread 0): 91.4557%]
|T|: r i g h t
|P|: y o u r e r i g h t
[sample: EN2002a_H03_MEE071_11.83_12.13, WER: 100%, TER: 100%, total WER: 19.1602%, total TER: 8.51942%, progress (thread 0): 91.4636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_48.72_49.02, WER: 0%, TER: 0%, total WER: 19.16%, total TER: 8.51934%, progress (thread 0): 91.4715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_203.25_203.55, WER: 0%, TER: 0%, total WER: 19.1597%, total TER: 8.51926%, progress (thread 0): 91.4794%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_261.14_261.44, WER: 100%, TER: 33.3333%, total WER: 19.1607%, total TER: 8.51944%, progress (thread 0): 91.4873%]
|T|: w h y | n o t
|P|: w h y | d o n ' t
[sample: EN2002a_H03_MEE071_375.98_376.28, WER: 50%, TER: 42.8571%, total WER: 19.1614%, total TER: 8.52%, progress (thread 0): 91.4953%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_469.41_469.71, WER: 0%, TER: 0%, total WER: 19.1611%, total TER: 8.51992%, progress (thread 0): 91.5032%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_486.08_486.38, WER: 0%, TER: 0%, total WER: 19.1609%, total TER: 8.51984%, progress (thread 0): 91.5111%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_508.71_509.01, WER: 0%, TER: 0%, total WER: 19.1607%, total TER: 8.51976%, progress (thread 0): 91.519%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_521.78_522.08, WER: 0%, TER: 0%, total WER: 19.1605%, total TER: 8.51968%, progress (thread 0): 91.5269%]
|T|: s o
|P|: s o
[sample: EN2002a_H03_MEE071_738.15_738.45, WER: 0%, TER: 0%, total WER: 19.1603%, total TER: 8.51964%, progress (thread 0): 91.5348%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_747.85_748.15, WER: 100%, TER: 0%, total WER: 19.1612%, total TER: 8.51954%, progress (thread 0): 91.5427%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_795.73_796.03, WER: 100%, TER: 33.3333%, total WER: 19.1621%, total TER: 8.51971%, progress (thread 0): 91.5506%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_877.67_877.97, WER: 100%, TER: 0%, total WER: 19.163%, total TER: 8.51961%, progress (thread 0): 91.5585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_918.33_918.63, WER: 0%, TER: 0%, total WER: 19.1628%, total TER: 8.51954%, progress (thread 0): 91.5665%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1889.11_1889.41, WER: 0%, TER: 0%, total WER: 19.1626%, total TER: 8.51946%, progress (thread 0): 91.5744%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H02_FEO072_2116.83_2117.13, WER: 0%, TER: 0%, total WER: 19.1624%, total TER: 8.51936%, progress (thread 0): 91.5823%]
|T|: o h | y e a h
|P|: h m h
[sample: EN2002b_H03_MEE073_211.33_211.63, WER: 100%, TER: 71.4286%, total WER: 19.1642%, total TER: 8.52039%, progress (thread 0): 91.5902%]
|T|: w e ' l l | s e e
|P|: w e l l | s e
[sample: EN2002b_H00_FEO070_255.08_255.38, WER: 100%, TER: 22.2222%, total WER: 19.166%, total TER: 8.52068%, progress (thread 0): 91.5981%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_368.42_368.72, WER: 0%, TER: 0%, total WER: 19.1658%, total TER: 8.5206%, progress (thread 0): 91.606%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H03_MEE073_391.92_392.22, WER: 100%, TER: 0%, total WER: 19.1667%, total TER: 8.5205%, progress (thread 0): 91.6139%]
|T|: m m
|P|: m m
[sample: EN2002b_H03_MEE073_526.11_526.41, WER: 0%, TER: 0%, total WER: 19.1665%, total TER: 8.52046%, progress (thread 0): 91.6218%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002b_H02_FEO072_627.98_628.28, WER: 200%, TER: 14.2857%, total WER: 19.1685%, total TER: 8.52055%, progress (thread 0): 91.6298%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1551.52_1551.82, WER: 0%, TER: 0%, total WER: 19.1683%, total TER: 8.52047%, progress (thread 0): 91.6377%]
|T|: b u t
|P|: b h u t
[sample: EN2002b_H01_MEE071_1611.31_1611.61, WER: 100%, TER: 33.3333%, total WER: 19.1692%, total TER: 8.52065%, progress (thread 0): 91.6456%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1729.42_1729.72, WER: 0%, TER: 0%, total WER: 19.169%, total TER: 8.52057%, progress (thread 0): 91.6535%]
|T|: i | t h i n k
|P|: i
[sample: EN2002c_H01_FEO072_232.37_232.67, WER: 50%, TER: 85.7143%, total WER: 19.1697%, total TER: 8.52183%, progress (thread 0): 91.6614%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_355.18_355.48, WER: 0%, TER: 0%, total WER: 19.1695%, total TER: 8.52175%, progress (thread 0): 91.6693%]
|T|: n o | n o
|P|: m m
[sample: EN2002c_H03_MEE073_543.55_543.85, WER: 100%, TER: 100%, total WER: 19.1713%, total TER: 8.52282%, progress (thread 0): 91.6772%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_700.09_700.39, WER: 100%, TER: 0%, total WER: 19.1722%, total TER: 8.52272%, progress (thread 0): 91.6851%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H02_MEE071_1131.17_1131.47, WER: 100%, TER: 66.6667%, total WER: 19.1731%, total TER: 8.52313%, progress (thread 0): 91.693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1143.34_1143.64, WER: 0%, TER: 0%, total WER: 19.1729%, total TER: 8.52305%, progress (thread 0): 91.701%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_1321.8_1322.1, WER: 0%, TER: 0%, total WER: 19.1727%, total TER: 8.52301%, progress (thread 0): 91.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1600.62_1600.92, WER: 0%, TER: 0%, total WER: 19.1725%, total TER: 8.52293%, progress (thread 0): 91.7168%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2301.16_2301.46, WER: 0%, TER: 0%, total WER: 19.1723%, total TER: 8.52285%, progress (thread 0): 91.7247%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2443.4_2443.7, WER: 0%, TER: 0%, total WER: 19.1721%, total TER: 8.52277%, progress (thread 0): 91.7326%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H02_MEE071_2847.83_2848.13, WER: 100%, TER: 0%, total WER: 19.173%, total TER: 8.52267%, progress (thread 0): 91.7405%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_673.96_674.26, WER: 0%, TER: 0%, total WER: 19.1728%, total TER: 8.52259%, progress (thread 0): 91.7484%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_834.35_834.65, WER: 0%, TER: 0%, total WER: 19.1725%, total TER: 8.52251%, progress (thread 0): 91.7563%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_925.24_925.54, WER: 0%, TER: 0%, total WER: 19.1723%, total TER: 8.52243%, progress (thread 0): 91.7642%]
|T|: u m
|P|: u m
[sample: EN2002d_H01_FEO072_1191.17_1191.47, WER: 0%, TER: 0%, total WER: 19.1721%, total TER: 8.52239%, progress (thread 0): 91.7721%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1205.4_1205.7, WER: 0%, TER: 0%, total WER: 19.1719%, total TER: 8.52229%, progress (thread 0): 91.7801%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H03_MEE073_1221.17_1221.47, WER: 100%, TER: 0%, total WER: 19.1728%, total TER: 8.52219%, progress (thread 0): 91.788%]
|T|: y e a h
|P|: m m
[sample: EN2002d_H00_FEO070_1249.7_1250, WER: 100%, TER: 100%, total WER: 19.1737%, total TER: 8.52305%, progress (thread 0): 91.7959%]
|T|: s u r e
|P|: s u r e
[sample: EN2002d_H03_MEE073_1672.53_1672.83, WER: 0%, TER: 0%, total WER: 19.1735%, total TER: 8.52297%, progress (thread 0): 91.8038%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_2192.66_2192.96, WER: 0%, TER: 0%, total WER: 19.1733%, total TER: 8.52293%, progress (thread 0): 91.8117%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_747.66_747.95, WER: 0%, TER: 0%, total WER: 19.1731%, total TER: 8.52285%, progress (thread 0): 91.8196%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004a_H00_MEO015_784.99_785.28, WER: 100%, TER: 20%, total WER: 19.174%, total TER: 8.52299%, progress (thread 0): 91.8275%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_826.06_826.35, WER: 0%, TER: 0%, total WER: 19.1738%, total TER: 8.52289%, progress (thread 0): 91.8354%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2019.84_2020.13, WER: 0%, TER: 0%, total WER: 19.1736%, total TER: 8.52281%, progress (thread 0): 91.8434%]
|T|: o k a y
|P|: m m
[sample: ES2004b_H02_MEE014_2295.26_2295.55, WER: 100%, TER: 100%, total WER: 19.1745%, total TER: 8.52366%, progress (thread 0): 91.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_316.02_316.31, WER: 0%, TER: 0%, total WER: 19.1742%, total TER: 8.52358%, progress (thread 0): 91.8592%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004c_H00_MEO015_1261.19_1261.48, WER: 200%, TER: 20%, total WER: 19.1763%, total TER: 8.52372%, progress (thread 0): 91.8671%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H01_FEE013_1987.8_1988.09, WER: 100%, TER: 0%, total WER: 19.1772%, total TER: 8.52362%, progress (thread 0): 91.875%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_2244.26_2244.55, WER: 0%, TER: 0%, total WER: 19.177%, total TER: 8.52354%, progress (thread 0): 91.8829%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_211.03_211.32, WER: 0%, TER: 0%, total WER: 19.1768%, total TER: 8.52344%, progress (thread 0): 91.8908%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H03_FEE016_810.67_810.96, WER: 0%, TER: 0%, total WER: 19.1766%, total TER: 8.52334%, progress (thread 0): 91.8987%]
|T|: m m
|P|: m m
[sample: ES2004d_H03_FEE016_1204.42_1204.71, WER: 0%, TER: 0%, total WER: 19.1763%, total TER: 8.5233%, progress (thread 0): 91.9066%]
|T|: u m
|P|: u m
[sample: ES2004d_H01_FEE013_1227.88_1228.17, WER: 0%, TER: 0%, total WER: 19.1761%, total TER: 8.52326%, progress (thread 0): 91.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1634.52_1634.81, WER: 0%, TER: 0%, total WER: 19.1759%, total TER: 8.52318%, progress (thread 0): 91.9225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1787.34_1787.63, WER: 0%, TER: 0%, total WER: 19.1757%, total TER: 8.5231%, progress (thread 0): 91.9304%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1964.63_1964.92, WER: 0%, TER: 0%, total WER: 19.1755%, total TER: 8.52302%, progress (thread 0): 91.9383%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_118.76_119.05, WER: 0%, TER: 0%, total WER: 19.1753%, total TER: 8.52294%, progress (thread 0): 91.9462%]
|T|: h m m
|P|: m m
[sample: IS1009a_H02_FIO084_803.39_803.68, WER: 100%, TER: 33.3333%, total WER: 19.1762%, total TER: 8.52311%, progress (thread 0): 91.9541%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_708.64_708.93, WER: 100%, TER: 0%, total WER: 19.1771%, total TER: 8.52301%, progress (thread 0): 91.962%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_998.44_998.73, WER: 0%, TER: 0%, total WER: 19.1769%, total TER: 8.52293%, progress (thread 0): 91.9699%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1311.53_1311.82, WER: 100%, TER: 0%, total WER: 19.1778%, total TER: 8.52284%, progress (thread 0): 91.9778%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1356.24_1356.53, WER: 100%, TER: 60%, total WER: 19.1787%, total TER: 8.52344%, progress (thread 0): 91.9858%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1658.74_1659.03, WER: 100%, TER: 60%, total WER: 19.1796%, total TER: 8.52404%, progress (thread 0): 91.9937%]
|T|: h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1899.66_1899.95, WER: 100%, TER: 33.3333%, total WER: 19.1805%, total TER: 8.52422%, progress (thread 0): 92.0016%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H03_FIO089_1999.16_1999.45, WER: 100%, TER: 20%, total WER: 19.1814%, total TER: 8.52435%, progress (thread 0): 92.0095%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_284.27_284.56, WER: 0%, TER: 0%, total WER: 19.1812%, total TER: 8.52429%, progress (thread 0): 92.0174%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_381_381.29, WER: 100%, TER: 20%, total WER: 19.1821%, total TER: 8.52442%, progress (thread 0): 92.0253%]
|T|: w e | c a n
|P|: w e g e t
[sample: IS1009c_H01_FIO087_755.64_755.93, WER: 100%, TER: 66.6667%, total WER: 19.1839%, total TER: 8.52524%, progress (thread 0): 92.0332%]
|T|: y e a h | b u t | w
|P|: y e a h
[sample: IS1009c_H02_FIO084_1559.51_1559.8, WER: 66.6667%, TER: 60%, total WER: 19.1855%, total TER: 8.52645%, progress (thread 0): 92.0411%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H02_FIO084_1608.9_1609.19, WER: 100%, TER: 20%, total WER: 19.1865%, total TER: 8.52658%, progress (thread 0): 92.049%]
|T|: h e l l o
|P|: o
[sample: IS1009d_H01_FIO087_41.27_41.56, WER: 100%, TER: 80%, total WER: 19.1874%, total TER: 8.52742%, progress (thread 0): 92.057%]
|T|: n o
|P|: n
[sample: IS1009d_H02_FIO084_952.51_952.8, WER: 100%, TER: 50%, total WER: 19.1883%, total TER: 8.52761%, progress (thread 0): 92.0649%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1056.71_1057, WER: 100%, TER: 0%, total WER: 19.1892%, total TER: 8.52751%, progress (thread 0): 92.0728%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1344.32_1344.61, WER: 100%, TER: 0%, total WER: 19.1901%, total TER: 8.52741%, progress (thread 0): 92.0807%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_1389.87_1390.16, WER: 0%, TER: 0%, total WER: 19.1899%, total TER: 8.52733%, progress (thread 0): 92.0886%]
|T|: m m h m m
|P|: y e a m
[sample: IS1009d_H02_FIO084_1790.06_1790.35, WER: 100%, TER: 80%, total WER: 19.1908%, total TER: 8.52817%, progress (thread 0): 92.0965%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_704.99_705.28, WER: 0%, TER: 0%, total WER: 19.1906%, total TER: 8.52809%, progress (thread 0): 92.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_977.31_977.6, WER: 0%, TER: 0%, total WER: 19.1904%, total TER: 8.52801%, progress (thread 0): 92.1123%]
|T|: y o u
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1267.17_1267.46, WER: 100%, TER: 100%, total WER: 19.1913%, total TER: 8.52865%, progress (thread 0): 92.1203%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_178.78_179.07, WER: 0%, TER: 0%, total WER: 19.1911%, total TER: 8.52859%, progress (thread 0): 92.1282%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1577.63_1577.92, WER: 0%, TER: 0%, total WER: 19.1908%, total TER: 8.52851%, progress (thread 0): 92.1361%]
|T|: w e l l
|P|: w e l l
[sample: TS3003b_H03_MTD012ME_1752.79_1753.08, WER: 0%, TER: 0%, total WER: 19.1906%, total TER: 8.52843%, progress (thread 0): 92.144%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_2033.23_2033.52, WER: 0%, TER: 0%, total WER: 19.1904%, total TER: 8.52835%, progress (thread 0): 92.1519%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2083.17_2083.46, WER: 0%, TER: 0%, total WER: 19.1902%, total TER: 8.52827%, progress (thread 0): 92.1598%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_2083.37_2083.66, WER: 0%, TER: 0%, total WER: 19.19%, total TER: 8.52819%, progress (thread 0): 92.1677%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H01_MTD011UID_1289.74_1290.03, WER: 100%, TER: 0%, total WER: 19.1909%, total TER: 8.52809%, progress (thread 0): 92.1756%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1452.67_1452.96, WER: 0%, TER: 0%, total WER: 19.1907%, total TER: 8.52801%, progress (thread 0): 92.1835%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003d_H03_MTD012ME_695.88_696.17, WER: 100%, TER: 20%, total WER: 19.1916%, total TER: 8.52815%, progress (thread 0): 92.1915%]
|T|: n a y
|P|: n m h
[sample: TS3003d_H01_MTD011UID_835.49_835.78, WER: 100%, TER: 66.6667%, total WER: 19.1925%, total TER: 8.52856%, progress (thread 0): 92.1994%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1010.19_1010.48, WER: 100%, TER: 33.3333%, total WER: 19.1934%, total TER: 8.52873%, progress (thread 0): 92.2073%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H02_MTD0010ID_1075.11_1075.4, WER: 100%, TER: 75%, total WER: 19.1943%, total TER: 8.52935%, progress (thread 0): 92.2152%]
|T|: t r u e
|P|: t r u e
[sample: TS3003d_H03_MTD012ME_1129.3_1129.59, WER: 0%, TER: 0%, total WER: 19.1941%, total TER: 8.52927%, progress (thread 0): 92.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1862.62_1862.91, WER: 0%, TER: 0%, total WER: 19.1939%, total TER: 8.52919%, progress (thread 0): 92.231%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2370.37_2370.66, WER: 0%, TER: 0%, total WER: 19.1937%, total TER: 8.52915%, progress (thread 0): 92.2389%]
|T|: h m m
|P|: m m
[sample: TS3003d_H00_MTD009PM_2517.23_2517.52, WER: 100%, TER: 33.3333%, total WER: 19.1946%, total TER: 8.52933%, progress (thread 0): 92.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2571.69_2571.98, WER: 0%, TER: 0%, total WER: 19.1944%, total TER: 8.52925%, progress (thread 0): 92.2547%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_218.41_218.7, WER: 100%, TER: 33.3333%, total WER: 19.1953%, total TER: 8.52942%, progress (thread 0): 92.2627%]
|T|: i | d o n ' t | k n o w
|P|: i | d o n t
[sample: EN2002a_H00_MEE073_234.63_234.92, WER: 66.6667%, TER: 50%, total WER: 19.1969%, total TER: 8.53059%, progress (thread 0): 92.2706%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_526.91_527.2, WER: 100%, TER: 66.6667%, total WER: 19.1978%, total TER: 8.531%, progress (thread 0): 92.2785%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_646.99_647.28, WER: 0%, TER: 0%, total WER: 19.1976%, total TER: 8.53092%, progress (thread 0): 92.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_723.91_724.2, WER: 0%, TER: 0%, total WER: 19.1974%, total TER: 8.53084%, progress (thread 0): 92.2943%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_749.38_749.67, WER: 0%, TER: 0%, total WER: 19.1971%, total TER: 8.53076%, progress (thread 0): 92.3022%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_856.03_856.32, WER: 0%, TER: 0%, total WER: 19.1969%, total TER: 8.53068%, progress (thread 0): 92.3101%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_872.78_873.07, WER: 0%, TER: 0%, total WER: 19.1967%, total TER: 8.5306%, progress (thread 0): 92.318%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1076.05_1076.34, WER: 100%, TER: 33.3333%, total WER: 19.1976%, total TER: 8.53077%, progress (thread 0): 92.326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1255.44_1255.73, WER: 0%, TER: 0%, total WER: 19.1974%, total TER: 8.53069%, progress (thread 0): 92.3339%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1364.93_1365.22, WER: 0%, TER: 0%, total WER: 19.1972%, total TER: 8.53061%, progress (thread 0): 92.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1593.85_1594.14, WER: 0%, TER: 0%, total WER: 19.197%, total TER: 8.53053%, progress (thread 0): 92.3497%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1595.68_1595.97, WER: 0%, TER: 0%, total WER: 19.1968%, total TER: 8.53045%, progress (thread 0): 92.3576%]
|T|: m m h m m
|P|: m m
[sample: EN2002a_H02_FEO072_1620.66_1620.95, WER: 100%, TER: 60%, total WER: 19.1977%, total TER: 8.53105%, progress (thread 0): 92.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1960.6_1960.89, WER: 0%, TER: 0%, total WER: 19.1975%, total TER: 8.53097%, progress (thread 0): 92.3734%]
|T|: y e a h
|P|: o m
[sample: EN2002a_H00_MEE073_2127.38_2127.67, WER: 100%, TER: 100%, total WER: 19.1984%, total TER: 8.53183%, progress (thread 0): 92.3813%]
|T|: v e r y | g o o d
|P|: m o n
[sample: EN2002b_H02_FEO072_142.65_142.94, WER: 100%, TER: 88.8889%, total WER: 19.2002%, total TER: 8.53352%, progress (thread 0): 92.3892%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_382.41_382.7, WER: 0%, TER: 0%, total WER: 19.2%, total TER: 8.53344%, progress (thread 0): 92.3972%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_419.68_419.97, WER: 0%, TER: 0%, total WER: 19.1998%, total TER: 8.53336%, progress (thread 0): 92.4051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_734.04_734.33, WER: 0%, TER: 0%, total WER: 19.1995%, total TER: 8.53328%, progress (thread 0): 92.413%]
|T|: h m m
|P|: n o
[sample: EN2002b_H03_MEE073_1150.51_1150.8, WER: 100%, TER: 100%, total WER: 19.2005%, total TER: 8.53393%, progress (thread 0): 92.4209%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H03_MEE073_1286.29_1286.58, WER: 100%, TER: 0%, total WER: 19.2014%, total TER: 8.53383%, progress (thread 0): 92.4288%]
|T|: s o
|P|: s o
[sample: EN2002b_H03_MEE073_1517.43_1517.72, WER: 0%, TER: 0%, total WER: 19.2011%, total TER: 8.53379%, progress (thread 0): 92.4367%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_61.76_62.05, WER: 0%, TER: 0%, total WER: 19.2009%, total TER: 8.53371%, progress (thread 0): 92.4446%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_697.18_697.47, WER: 0%, TER: 0%, total WER: 19.2007%, total TER: 8.53367%, progress (thread 0): 92.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1026.29_1026.58, WER: 0%, TER: 0%, total WER: 19.2005%, total TER: 8.53359%, progress (thread 0): 92.4604%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_1088_1088.29, WER: 100%, TER: 0%, total WER: 19.2014%, total TER: 8.53349%, progress (thread 0): 92.4684%]
|T|: t h a t
|P|: o k y
[sample: EN2002c_H03_MEE073_1302.84_1303.13, WER: 100%, TER: 100%, total WER: 19.2023%, total TER: 8.53434%, progress (thread 0): 92.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1313.99_1314.28, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53426%, progress (thread 0): 92.4842%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_1543.66_1543.95, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53422%, progress (thread 0): 92.4921%]
|T|: ' c a u s e
|P|: t h e s
[sample: EN2002c_H03_MEE073_2609.79_2610.08, WER: 100%, TER: 83.3333%, total WER: 19.2028%, total TER: 8.53527%, progress (thread 0): 92.5%]
|T|: a c t u a l l y
|P|: a c t u a l l y
[sample: EN2002d_H03_MEE073_783.19_783.48, WER: 0%, TER: 0%, total WER: 19.2026%, total TER: 8.53512%, progress (thread 0): 92.5079%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H02_MEE071_833.43_833.72, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53502%, progress (thread 0): 92.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1031.38_1031.67, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53494%, progress (thread 0): 92.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1490.73_1491.02, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53486%, progress (thread 0): 92.5316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2069.6_2069.89, WER: 0%, TER: 0%, total WER: 19.2017%, total TER: 8.53478%, progress (thread 0): 92.5396%]
|T|: n o
|P|: n m m
[sample: ES2004b_H03_FEE016_602.63_602.91, WER: 100%, TER: 100%, total WER: 19.2026%, total TER: 8.5352%, progress (thread 0): 92.5475%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1146.25_1146.53, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53512%, progress (thread 0): 92.5554%]
|T|: m m
|P|: m m
[sample: ES2004b_H03_FEE016_1316.47_1316.75, WER: 0%, TER: 0%, total WER: 19.2022%, total TER: 8.53508%, progress (thread 0): 92.5633%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1939.86_1940.14, WER: 0%, TER: 0%, total WER: 19.202%, total TER: 8.53498%, progress (thread 0): 92.5712%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2216.73_2217.01, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.5349%, progress (thread 0): 92.5791%]
|T|: t h a n k s
|P|: m
[sample: ES2004c_H01_FEE013_54.47_54.75, WER: 100%, TER: 100%, total WER: 19.2027%, total TER: 8.53619%, progress (thread 0): 92.587%]
|T|: u h
|P|: u h
[sample: ES2004c_H02_MEE014_1126.8_1127.08, WER: 0%, TER: 0%, total WER: 19.2025%, total TER: 8.53615%, progress (thread 0): 92.5949%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1180.98_1181.26, WER: 0%, TER: 0%, total WER: 19.2022%, total TER: 8.53611%, progress (thread 0): 92.6029%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1398.69_1398.97, WER: 0%, TER: 0%, total WER: 19.202%, total TER: 8.53603%, progress (thread 0): 92.6108%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2123.64_2123.92, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.53595%, progress (thread 0): 92.6187%]
|T|: n o
|P|: n o
[sample: ES2004d_H02_MEE014_1050.44_1050.72, WER: 0%, TER: 0%, total WER: 19.2016%, total TER: 8.53591%, progress (thread 0): 92.6266%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H00_MEO015_1500.31_1500.59, WER: 100%, TER: 0%, total WER: 19.2025%, total TER: 8.53581%, progress (thread 0): 92.6345%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1843.24_1843.52, WER: 0%, TER: 0%, total WER: 19.2023%, total TER: 8.53573%, progress (thread 0): 92.6424%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_2015.69_2015.97, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53563%, progress (thread 0): 92.6503%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H01_FEE013_2120.6_2120.88, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.53555%, progress (thread 0): 92.6582%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_188.06_188.34, WER: 0%, TER: 0%, total WER: 19.2016%, total TER: 8.53547%, progress (thread 0): 92.6661%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_793.23_793.51, WER: 100%, TER: 0%, total WER: 19.2025%, total TER: 8.53537%, progress (thread 0): 92.674%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_804.79_805.07, WER: 0%, TER: 0%, total WER: 19.2023%, total TER: 8.53529%, progress (thread 0): 92.682%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_894.05_894.33, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53521%, progress (thread 0): 92.6899%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1764.17_1764.45, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53513%, progress (thread 0): 92.6978%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009c_H02_FIO084_724.39_724.67, WER: 100%, TER: 100%, total WER: 19.2028%, total TER: 8.5362%, progress (thread 0): 92.7057%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1140.8_1141.08, WER: 0%, TER: 0%, total WER: 19.2026%, total TER: 8.53614%, progress (thread 0): 92.7136%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1619.41_1619.69, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53606%, progress (thread 0): 92.7215%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_617.29_617.57, WER: 100%, TER: 0%, total WER: 19.2033%, total TER: 8.53596%, progress (thread 0): 92.7294%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_773.28_773.56, WER: 0%, TER: 0%, total WER: 19.2031%, total TER: 8.53586%, progress (thread 0): 92.7373%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_980.1_980.38, WER: 0%, TER: 0%, total WER: 19.2028%, total TER: 8.53578%, progress (thread 0): 92.7452%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1792.74_1793.02, WER: 100%, TER: 20%, total WER: 19.2038%, total TER: 8.53591%, progress (thread 0): 92.7532%]
|T|: h m m
|P|: m m
[sample: IS1009d_H02_FIO084_1830.44_1830.72, WER: 100%, TER: 33.3333%, total WER: 19.2047%, total TER: 8.53609%, progress (thread 0): 92.7611%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1876.62_1876.9, WER: 100%, TER: 20%, total WER: 19.2056%, total TER: 8.53622%, progress (thread 0): 92.769%]
|T|: m o r n i n g
|P|: o
[sample: TS3003a_H02_MTD0010ID_16.42_16.7, WER: 100%, TER: 85.7143%, total WER: 19.2065%, total TER: 8.53749%, progress (thread 0): 92.7769%]
|T|: s u r e
|P|: s u r e
[sample: TS3003a_H03_MTD012ME_163.72_164, WER: 0%, TER: 0%, total WER: 19.2063%, total TER: 8.53741%, progress (thread 0): 92.7848%]
|T|: h m m
|P|: h m m
[sample: TS3003a_H03_MTD012ME_661.22_661.5, WER: 0%, TER: 0%, total WER: 19.2061%, total TER: 8.53735%, progress (thread 0): 92.7927%]
|T|: h m m
|P|: m m
[sample: TS3003a_H00_MTD009PM_1235.68_1235.96, WER: 100%, TER: 33.3333%, total WER: 19.207%, total TER: 8.53752%, progress (thread 0): 92.8006%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_823.58_823.86, WER: 0%, TER: 0%, total WER: 19.2068%, total TER: 8.53744%, progress (thread 0): 92.8085%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1892.2_1892.48, WER: 0%, TER: 0%, total WER: 19.2065%, total TER: 8.53736%, progress (thread 0): 92.8165%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1860.06_1860.34, WER: 0%, TER: 0%, total WER: 19.2063%, total TER: 8.53728%, progress (thread 0): 92.8244%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_1891.82_1892.1, WER: 100%, TER: 0%, total WER: 19.2072%, total TER: 8.53718%, progress (thread 0): 92.8323%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_267.26_267.54, WER: 0%, TER: 0%, total WER: 19.207%, total TER: 8.5371%, progress (thread 0): 92.8402%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_365.09_365.37, WER: 0%, TER: 0%, total WER: 19.2068%, total TER: 8.53702%, progress (thread 0): 92.8481%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_816.6_816.88, WER: 100%, TER: 66.6667%, total WER: 19.2077%, total TER: 8.53743%, progress (thread 0): 92.856%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_860.45_860.73, WER: 0%, TER: 0%, total WER: 19.2075%, total TER: 8.53735%, progress (thread 0): 92.8639%]
|T|: a n d | i t
|P|: a n d | i t
[sample: TS3003d_H02_MTD0010ID_1310.28_1310.56, WER: 0%, TER: 0%, total WER: 19.2071%, total TER: 8.53723%, progress (thread 0): 92.8718%]
|T|: s h
|P|: s
[sample: TS3003d_H02_MTD0010ID_1311.62_1311.9, WER: 100%, TER: 50%, total WER: 19.208%, total TER: 8.53742%, progress (thread 0): 92.8797%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1403.4_1403.68, WER: 100%, TER: 66.6667%, total WER: 19.2089%, total TER: 8.53783%, progress (thread 0): 92.8877%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1945.66_1945.94, WER: 0%, TER: 0%, total WER: 19.2087%, total TER: 8.53775%, progress (thread 0): 92.8956%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2142.04_2142.32, WER: 0%, TER: 0%, total WER: 19.2084%, total TER: 8.53767%, progress (thread 0): 92.9035%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_49.78_50.06, WER: 0%, TER: 0%, total WER: 19.2082%, total TER: 8.53759%, progress (thread 0): 92.9114%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_478.66_478.94, WER: 100%, TER: 66.6667%, total WER: 19.2091%, total TER: 8.538%, progress (thread 0): 92.9193%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_758.4_758.68, WER: 0%, TER: 0%, total WER: 19.2089%, total TER: 8.53792%, progress (thread 0): 92.9272%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H01_FEO070_762.35_762.63, WER: 100%, TER: 100%, total WER: 19.2098%, total TER: 8.53878%, progress (thread 0): 92.9351%]
|T|: b u t
|P|: b u t
[sample: EN2002a_H01_FEO070_1024.58_1024.86, WER: 0%, TER: 0%, total WER: 19.2096%, total TER: 8.53872%, progress (thread 0): 92.943%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1306.05_1306.33, WER: 0%, TER: 0%, total WER: 19.2094%, total TER: 8.53864%, progress (thread 0): 92.951%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1392.09_1392.37, WER: 0%, TER: 0%, total WER: 19.2092%, total TER: 8.53856%, progress (thread 0): 92.9589%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1638.28_1638.56, WER: 0%, TER: 0%, total WER: 19.209%, total TER: 8.53848%, progress (thread 0): 92.9668%]
|T|: s e e
|P|: s e e
[sample: EN2002a_H01_FEO070_1702.66_1702.94, WER: 0%, TER: 0%, total WER: 19.2088%, total TER: 8.53842%, progress (thread 0): 92.9747%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1796.46_1796.74, WER: 0%, TER: 0%, total WER: 19.2085%, total TER: 8.53834%, progress (thread 0): 92.9826%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1831.52_1831.8, WER: 0%, TER: 0%, total WER: 19.2083%, total TER: 8.53826%, progress (thread 0): 92.9905%]
|T|: o h
|P|: a h
[sample: EN2002a_H02_FEO072_1981.37_1981.65, WER: 100%, TER: 50%, total WER: 19.2092%, total TER: 8.53845%, progress (thread 0): 92.9984%]
|T|: i | t h
|P|: i
[sample: EN2002a_H00_MEE073_2026.22_2026.5, WER: 50%, TER: 75%, total WER: 19.2099%, total TER: 8.53907%, progress (thread 0): 93.0063%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H01_MEE071_16.64_16.92, WER: 0%, TER: 0%, total WER: 19.2097%, total TER: 8.53899%, progress (thread 0): 93.0142%]
|T|: m m h m m
|P|: m m
[sample: EN2002b_H03_MEE073_335.63_335.91, WER: 100%, TER: 60%, total WER: 19.2106%, total TER: 8.5396%, progress (thread 0): 93.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_356.11_356.39, WER: 0%, TER: 0%, total WER: 19.2104%, total TER: 8.53952%, progress (thread 0): 93.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_987.48_987.76, WER: 0%, TER: 0%, total WER: 19.2102%, total TER: 8.53944%, progress (thread 0): 93.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1285.46_1285.74, WER: 0%, TER: 0%, total WER: 19.21%, total TER: 8.53936%, progress (thread 0): 93.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1450.71_1450.99, WER: 0%, TER: 0%, total WER: 19.2098%, total TER: 8.53928%, progress (thread 0): 93.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1588.08_1588.36, WER: 0%, TER: 0%, total WER: 19.2095%, total TER: 8.5392%, progress (thread 0): 93.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_264.61_264.89, WER: 0%, TER: 0%, total WER: 19.2093%, total TER: 8.53912%, progress (thread 0): 93.0696%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_638.33_638.61, WER: 100%, TER: 0%, total WER: 19.2102%, total TER: 8.53902%, progress (thread 0): 93.0775%]
|T|: a h
|P|: o h
[sample: EN2002c_H01_FEO072_1466.83_1467.11, WER: 100%, TER: 50%, total WER: 19.2111%, total TER: 8.53921%, progress (thread 0): 93.0854%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1508.55_1508.83, WER: 100%, TER: 28.5714%, total WER: 19.2121%, total TER: 8.53954%, progress (thread 0): 93.0934%]
|T|: i | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_1627.18_1627.46, WER: 50%, TER: 50%, total WER: 19.2127%, total TER: 8.54012%, progress (thread 0): 93.1013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2613.98_2614.26, WER: 0%, TER: 0%, total WER: 19.2125%, total TER: 8.54004%, progress (thread 0): 93.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2738.83_2739.11, WER: 0%, TER: 0%, total WER: 19.2123%, total TER: 8.53996%, progress (thread 0): 93.1171%]
|T|: w e l l
|P|: m m
[sample: EN2002c_H03_MEE073_2837.32_2837.6, WER: 100%, TER: 100%, total WER: 19.2132%, total TER: 8.54082%, progress (thread 0): 93.125%]
|T|: i | t h
|P|: i
[sample: EN2002d_H03_MEE073_209.34_209.62, WER: 50%, TER: 75%, total WER: 19.2139%, total TER: 8.54144%, progress (thread 0): 93.1329%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H03_MEE073_379.93_380.21, WER: 100%, TER: 0%, total WER: 19.2148%, total TER: 8.54134%, progress (thread 0): 93.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_446.25_446.53, WER: 0%, TER: 0%, total WER: 19.2146%, total TER: 8.54126%, progress (thread 0): 93.1487%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_622.33_622.61, WER: 0%, TER: 0%, total WER: 19.2144%, total TER: 8.54118%, progress (thread 0): 93.1566%]
|T|: y e a h
|P|: y m a m
[sample: EN2002d_H03_MEE073_989.71_989.99, WER: 100%, TER: 50%, total WER: 19.2153%, total TER: 8.54157%, progress (thread 0): 93.1646%]
|T|: h m m
|P|: m m
[sample: EN2002d_H02_MEE071_1294.26_1294.54, WER: 100%, TER: 33.3333%, total WER: 19.2162%, total TER: 8.54174%, progress (thread 0): 93.1725%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1744.58_1744.86, WER: 0%, TER: 0%, total WER: 19.216%, total TER: 8.54166%, progress (thread 0): 93.1804%]
|T|: t h e
|P|: y m a h
[sample: EN2002d_H03_MEE073_1888.86_1889.14, WER: 100%, TER: 133.333%, total WER: 19.2169%, total TER: 8.54254%, progress (thread 0): 93.1883%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2067.26_2067.54, WER: 0%, TER: 0%, total WER: 19.2167%, total TER: 8.54246%, progress (thread 0): 93.1962%]
|T|: s u r e
|P|: s r u e
[sample: ES2004b_H00_MEO015_1306.29_1306.56, WER: 100%, TER: 50%, total WER: 19.2176%, total TER: 8.54284%, progress (thread 0): 93.2041%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1834.89_1835.16, WER: 0%, TER: 0%, total WER: 19.2174%, total TER: 8.54276%, progress (thread 0): 93.212%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1954.73_1955, WER: 100%, TER: 0%, total WER: 19.2183%, total TER: 8.54266%, progress (thread 0): 93.2199%]
|T|: m m h m m
|P|: m m m
[sample: ES2004c_H03_FEE016_652.96_653.23, WER: 100%, TER: 40%, total WER: 19.2192%, total TER: 8.54303%, progress (thread 0): 93.2278%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004c_H00_MEO015_1141.18_1141.45, WER: 100%, TER: 20%, total WER: 19.2201%, total TER: 8.54317%, progress (thread 0): 93.2358%]
|T|: m m
|P|: s o
[sample: ES2004c_H03_FEE016_1281.44_1281.71, WER: 100%, TER: 100%, total WER: 19.221%, total TER: 8.54359%, progress (thread 0): 93.2437%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1403.89_1404.16, WER: 0%, TER: 0%, total WER: 19.2208%, total TER: 8.54351%, progress (thread 0): 93.2516%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1730.76_1731.03, WER: 0%, TER: 0%, total WER: 19.2206%, total TER: 8.54343%, progress (thread 0): 93.2595%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H00_MEO015_1835.22_1835.49, WER: 100%, TER: 0%, total WER: 19.2215%, total TER: 8.54333%, progress (thread 0): 93.2674%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1921.81_1922.08, WER: 0%, TER: 0%, total WER: 19.2213%, total TER: 8.54325%, progress (thread 0): 93.2753%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_2149.62_2149.89, WER: 0%, TER: 0%, total WER: 19.2211%, total TER: 8.54317%, progress (thread 0): 93.2832%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_654.95_655.22, WER: 100%, TER: 20%, total WER: 19.222%, total TER: 8.54331%, progress (thread 0): 93.2911%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_738.4_738.67, WER: 100%, TER: 33.3333%, total WER: 19.2229%, total TER: 8.54348%, progress (thread 0): 93.299%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_793.57_793.84, WER: 0%, TER: 0%, total WER: 19.2227%, total TER: 8.5434%, progress (thread 0): 93.307%]
|T|: n o
|P|: n o
[sample: ES2004d_H03_FEE016_1418.58_1418.85, WER: 0%, TER: 0%, total WER: 19.2225%, total TER: 8.54336%, progress (thread 0): 93.3149%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1497.73_1498, WER: 0%, TER: 0%, total WER: 19.2222%, total TER: 8.54328%, progress (thread 0): 93.3228%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1559.56_1559.83, WER: 0%, TER: 0%, total WER: 19.222%, total TER: 8.5432%, progress (thread 0): 93.3307%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2099.96_2100.23, WER: 0%, TER: 0%, total WER: 19.2218%, total TER: 8.54312%, progress (thread 0): 93.3386%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_159.51_159.78, WER: 100%, TER: 0%, total WER: 19.2227%, total TER: 8.54302%, progress (thread 0): 93.3465%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_495.24_495.51, WER: 0%, TER: 0%, total WER: 19.2225%, total TER: 8.54294%, progress (thread 0): 93.3544%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H02_FIO084_130.02_130.29, WER: 100%, TER: 20%, total WER: 19.2234%, total TER: 8.54308%, progress (thread 0): 93.3623%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_195.35_195.62, WER: 100%, TER: 60%, total WER: 19.2243%, total TER: 8.54368%, progress (thread 0): 93.3703%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H00_FIE088_1093_1093.27, WER: 100%, TER: 20%, total WER: 19.2252%, total TER: 8.54381%, progress (thread 0): 93.3782%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H01_FIO087_323.83_324.1, WER: 0%, TER: 0%, total WER: 19.225%, total TER: 8.54371%, progress (thread 0): 93.3861%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_770.63_770.9, WER: 100%, TER: 0%, total WER: 19.2259%, total TER: 8.54361%, progress (thread 0): 93.394%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_922.16_922.43, WER: 100%, TER: 20%, total WER: 19.2268%, total TER: 8.54375%, progress (thread 0): 93.4019%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_1095.56_1095.83, WER: 100%, TER: 20%, total WER: 19.2277%, total TER: 8.54388%, progress (thread 0): 93.4098%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1374.42_1374.69, WER: 0%, TER: 0%, total WER: 19.2275%, total TER: 8.5438%, progress (thread 0): 93.4177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_215.46_215.73, WER: 0%, TER: 0%, total WER: 19.2273%, total TER: 8.54372%, progress (thread 0): 93.4256%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_377.8_378.07, WER: 100%, TER: 0%, total WER: 19.2282%, total TER: 8.54362%, progress (thread 0): 93.4335%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_381.16_381.43, WER: 100%, TER: 0%, total WER: 19.2291%, total TER: 8.54352%, progress (thread 0): 93.4415%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H02_FIO084_938.34_938.61, WER: 100%, TER: 20%, total WER: 19.23%, total TER: 8.54365%, progress (thread 0): 93.4494%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1114.59_1114.86, WER: 100%, TER: 20%, total WER: 19.2309%, total TER: 8.54379%, progress (thread 0): 93.4573%]
|T|: o h
|P|: o h
[sample: IS1009d_H00_FIE088_1436.69_1436.96, WER: 0%, TER: 0%, total WER: 19.2307%, total TER: 8.54375%, progress (thread 0): 93.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1775.25_1775.52, WER: 0%, TER: 0%, total WER: 19.2305%, total TER: 8.54367%, progress (thread 0): 93.4731%]
|T|: o k a y
|P|: o h
[sample: IS1009d_H01_FIO087_1908.84_1909.11, WER: 100%, TER: 75%, total WER: 19.2314%, total TER: 8.54429%, progress (thread 0): 93.481%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_684.57_684.84, WER: 100%, TER: 150%, total WER: 19.2323%, total TER: 8.54495%, progress (thread 0): 93.4889%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_790.17_790.44, WER: 100%, TER: 25%, total WER: 19.2332%, total TER: 8.54511%, progress (thread 0): 93.4968%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_1326.95_1327.22, WER: 0%, TER: 0%, total WER: 19.233%, total TER: 8.54507%, progress (thread 0): 93.5047%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1909.01_1909.28, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54499%, progress (thread 0): 93.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1979.53_1979.8, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54491%, progress (thread 0): 93.5206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2145.84_2146.11, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54483%, progress (thread 0): 93.5285%]
|T|: o h
|P|: m m
[sample: TS3003d_H01_MTD011UID_364.71_364.98, WER: 100%, TER: 100%, total WER: 19.2333%, total TER: 8.54525%, progress (thread 0): 93.5364%]
|T|: t e n
|P|: t e n
[sample: TS3003d_H02_MTD0010ID_864.45_864.72, WER: 0%, TER: 0%, total WER: 19.2331%, total TER: 8.54519%, progress (thread 0): 93.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_958.91_959.18, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54511%, progress (thread 0): 93.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1378.52_1378.79, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54503%, progress (thread 0): 93.5601%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_2176.17_2176.44, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54499%, progress (thread 0): 93.568%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2345.04_2345.31, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54495%, progress (thread 0): 93.576%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2364.31_2364.58, WER: 0%, TER: 0%, total WER: 19.232%, total TER: 8.54487%, progress (thread 0): 93.5839%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_2435.3_2435.57, WER: 100%, TER: 25%, total WER: 19.2329%, total TER: 8.54503%, progress (thread 0): 93.5918%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_501.72_501.99, WER: 100%, TER: 33.3333%, total WER: 19.2338%, total TER: 8.5452%, progress (thread 0): 93.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_727.19_727.46, WER: 0%, TER: 0%, total WER: 19.2336%, total TER: 8.54512%, progress (thread 0): 93.6076%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_806.36_806.63, WER: 0%, TER: 0%, total WER: 19.2334%, total TER: 8.54504%, progress (thread 0): 93.6155%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_927.86_928.13, WER: 0%, TER: 0%, total WER: 19.2332%, total TER: 8.54496%, progress (thread 0): 93.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1223.91_1224.18, WER: 0%, TER: 0%, total WER: 19.2329%, total TER: 8.54488%, progress (thread 0): 93.6313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1319.85_1320.12, WER: 0%, TER: 0%, total WER: 19.2327%, total TER: 8.5448%, progress (thread 0): 93.6392%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1452_1452.27, WER: 0%, TER: 0%, total WER: 19.2325%, total TER: 8.54472%, progress (thread 0): 93.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1516.15_1516.42, WER: 0%, TER: 0%, total WER: 19.2323%, total TER: 8.54464%, progress (thread 0): 93.6551%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1533.19_1533.46, WER: 0%, TER: 0%, total WER: 19.2321%, total TER: 8.54456%, progress (thread 0): 93.663%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1556.33_1556.6, WER: 0%, TER: 0%, total WER: 19.2319%, total TER: 8.54448%, progress (thread 0): 93.6709%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1945.11_1945.38, WER: 0%, TER: 0%, total WER: 19.2316%, total TER: 8.5444%, progress (thread 0): 93.6788%]
|T|: o h
|P|: o h
[sample: EN2002a_H00_MEE073_1989_1989.27, WER: 0%, TER: 0%, total WER: 19.2314%, total TER: 8.54436%, progress (thread 0): 93.6867%]
|T|: o h
|P|: u m
[sample: EN2002a_H00_MEE073_2003.83_2004.1, WER: 100%, TER: 100%, total WER: 19.2323%, total TER: 8.54479%, progress (thread 0): 93.6946%]
|T|: n o
|P|: n
[sample: EN2002a_H01_FEO070_2105.6_2105.87, WER: 100%, TER: 50%, total WER: 19.2332%, total TER: 8.54498%, progress (thread 0): 93.7025%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_355.59_355.86, WER: 0%, TER: 0%, total WER: 19.233%, total TER: 8.54494%, progress (thread 0): 93.7104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_485.61_485.88, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54486%, progress (thread 0): 93.7184%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1027.72_1027.99, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54476%, progress (thread 0): 93.7263%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_1296.06_1296.33, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54472%, progress (thread 0): 93.7342%]
|T|: s o
|P|: s o
[sample: EN2002b_H01_MEE071_1333.58_1333.85, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54468%, progress (thread 0): 93.7421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1339.47_1339.74, WER: 0%, TER: 0%, total WER: 19.2319%, total TER: 8.5446%, progress (thread 0): 93.75%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1448.9_1449.17, WER: 0%, TER: 0%, total WER: 19.2317%, total TER: 8.54452%, progress (thread 0): 93.7579%]
|T|: a l r i g h t
|P|: a l r i g h t
[sample: EN2002c_H03_MEE073_558.45_558.72, WER: 0%, TER: 0%, total WER: 19.2315%, total TER: 8.54439%, progress (thread 0): 93.7658%]
|T|: h m m
|P|: m m
[sample: EN2002c_H01_FEO072_655.42_655.69, WER: 100%, TER: 33.3333%, total WER: 19.2324%, total TER: 8.54456%, progress (thread 0): 93.7737%]
|T|: c o o l
|P|: c o o l
[sample: EN2002c_H01_FEO072_778.13_778.4, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54448%, progress (thread 0): 93.7816%]
|T|: o r
|P|: o h
[sample: EN2002c_H01_FEO072_798.93_799.2, WER: 100%, TER: 50%, total WER: 19.2331%, total TER: 8.54467%, progress (thread 0): 93.7896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_938.98_939.25, WER: 0%, TER: 0%, total WER: 19.2329%, total TER: 8.54459%, progress (thread 0): 93.7975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1122.75_1123.02, WER: 0%, TER: 0%, total WER: 19.2327%, total TER: 8.54451%, progress (thread 0): 93.8054%]
|T|: o k a y
|P|: m h m m
[sample: EN2002c_H02_MEE071_1353.05_1353.32, WER: 100%, TER: 100%, total WER: 19.2336%, total TER: 8.54537%, progress (thread 0): 93.8133%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1574.37_1574.64, WER: 0%, TER: 0%, total WER: 19.2334%, total TER: 8.54527%, progress (thread 0): 93.8212%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_1672.35_1672.62, WER: 100%, TER: 25%, total WER: 19.2343%, total TER: 8.54542%, progress (thread 0): 93.8291%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1740.26_1740.53, WER: 0%, TER: 0%, total WER: 19.2341%, total TER: 8.54534%, progress (thread 0): 93.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1980.58_1980.85, WER: 0%, TER: 0%, total WER: 19.2338%, total TER: 8.54526%, progress (thread 0): 93.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2155.14_2155.41, WER: 0%, TER: 0%, total WER: 19.2336%, total TER: 8.54518%, progress (thread 0): 93.8528%]
|T|: a l r i g h t
|P|: o h r i
[sample: EN2002c_H03_MEE073_2202.51_2202.78, WER: 100%, TER: 71.4286%, total WER: 19.2345%, total TER: 8.54621%, progress (thread 0): 93.8608%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2272.51_2272.78, WER: 0%, TER: 0%, total WER: 19.2343%, total TER: 8.54613%, progress (thread 0): 93.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2294.4_2294.67, WER: 0%, TER: 0%, total WER: 19.2341%, total TER: 8.54605%, progress (thread 0): 93.8766%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2396.65_2396.92, WER: 0%, TER: 0%, total WER: 19.2339%, total TER: 8.54597%, progress (thread 0): 93.8845%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2568.46_2568.73, WER: 0%, TER: 0%, total WER: 19.2337%, total TER: 8.54589%, progress (thread 0): 93.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2705.6_2705.87, WER: 0%, TER: 0%, total WER: 19.2335%, total TER: 8.54581%, progress (thread 0): 93.9003%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_2790.29_2790.56, WER: 0%, TER: 0%, total WER: 19.2332%, total TER: 8.54577%, progress (thread 0): 93.9082%]
|T|: i | k n o w
|P|: y o u | k n o w
[sample: EN2002d_H03_MEE073_903.75_904.02, WER: 50%, TER: 50%, total WER: 19.2339%, total TER: 8.54635%, progress (thread 0): 93.9161%]
|T|: b u t
|P|: w h l
[sample: EN2002d_H01_FEO072_946.69_946.96, WER: 100%, TER: 100%, total WER: 19.2348%, total TER: 8.54699%, progress (thread 0): 93.924%]
|T|: s o r r y
|P|: s o r r y
[sample: EN2002d_H00_FEO070_966.35_966.62, WER: 0%, TER: 0%, total WER: 19.2346%, total TER: 8.54689%, progress (thread 0): 93.932%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1127.55_1127.82, WER: 0%, TER: 0%, total WER: 19.2344%, total TER: 8.54681%, progress (thread 0): 93.9399%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1281.17_1281.44, WER: 0%, TER: 0%, total WER: 19.2342%, total TER: 8.54673%, progress (thread 0): 93.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1305.04_1305.31, WER: 0%, TER: 0%, total WER: 19.234%, total TER: 8.54665%, progress (thread 0): 93.9557%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002d_H00_FEO070_1507.8_1508.07, WER: 100%, TER: 100%, total WER: 19.2358%, total TER: 8.54815%, progress (thread 0): 93.9636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_1832.82_1833.09, WER: 0%, TER: 0%, total WER: 19.2356%, total TER: 8.54807%, progress (thread 0): 93.9715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_2000.66_2000.93, WER: 0%, TER: 0%, total WER: 19.2354%, total TER: 8.54799%, progress (thread 0): 93.9794%]
|T|: w h a t
|P|: w e a l
[sample: EN2002d_H02_MEE071_2072.88_2073.15, WER: 100%, TER: 50%, total WER: 19.2363%, total TER: 8.54838%, progress (thread 0): 93.9873%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_2169.42_2169.69, WER: 100%, TER: 66.6667%, total WER: 19.2372%, total TER: 8.54879%, progress (thread 0): 93.9953%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_17.88_18.14, WER: 0%, TER: 0%, total WER: 19.237%, total TER: 8.54871%, progress (thread 0): 94.0032%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_966.22_966.48, WER: 0%, TER: 0%, total WER: 19.2367%, total TER: 8.54861%, progress (thread 0): 94.0111%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_89.12_89.38, WER: 100%, TER: 20%, total WER: 19.2376%, total TER: 8.54874%, progress (thread 0): 94.019%]
|T|: t h e r e | w e | g o
|P|: o k a y
[sample: ES2004b_H02_MEE014_830.72_830.98, WER: 100%, TER: 100%, total WER: 19.2404%, total TER: 8.55109%, progress (thread 0): 94.0269%]
|T|: h u h
|P|: m m
[sample: ES2004b_H02_MEE014_1039.01_1039.27, WER: 100%, TER: 100%, total WER: 19.2413%, total TER: 8.55173%, progress (thread 0): 94.0348%]
|T|: p e n s
|P|: i n
[sample: ES2004b_H00_MEO015_1162.44_1162.7, WER: 100%, TER: 75%, total WER: 19.2422%, total TER: 8.55235%, progress (thread 0): 94.0427%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1848.94_1849.2, WER: 0%, TER: 0%, total WER: 19.242%, total TER: 8.55227%, progress (thread 0): 94.0506%]
|T|: ' k a y
|P|: k a y
[sample: ES2004c_H00_MEO015_188.1_188.36, WER: 100%, TER: 25%, total WER: 19.2429%, total TER: 8.55243%, progress (thread 0): 94.0585%]
|T|: y e p
|P|: y e p
[sample: ES2004c_H00_MEO015_540.57_540.83, WER: 0%, TER: 0%, total WER: 19.2427%, total TER: 8.55237%, progress (thread 0): 94.0665%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1192.94_1193.2, WER: 0%, TER: 0%, total WER: 19.2425%, total TER: 8.55229%, progress (thread 0): 94.0744%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_39.85_40.11, WER: 100%, TER: 20%, total WER: 19.2434%, total TER: 8.55242%, progress (thread 0): 94.0823%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_129.84_130.1, WER: 0%, TER: 0%, total WER: 19.2431%, total TER: 8.55234%, progress (thread 0): 94.0902%]
|T|: o h
|P|: m h
[sample: ES2004d_H03_FEE016_1400.02_1400.28, WER: 100%, TER: 50%, total WER: 19.2441%, total TER: 8.55253%, progress (thread 0): 94.0981%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1553.35_1553.61, WER: 0%, TER: 0%, total WER: 19.2438%, total TER: 8.55245%, progress (thread 0): 94.106%]
|T|: n o
|P|: y e a h
[sample: ES2004d_H01_FEE013_1608.41_1608.67, WER: 100%, TER: 200%, total WER: 19.2447%, total TER: 8.55335%, progress (thread 0): 94.1139%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_1680.41_1680.67, WER: 100%, TER: 20%, total WER: 19.2457%, total TER: 8.55348%, progress (thread 0): 94.1218%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H03_FIO089_195.56_195.82, WER: 100%, TER: 20%, total WER: 19.2466%, total TER: 8.55362%, progress (thread 0): 94.1297%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_250.63_250.89, WER: 0%, TER: 0%, total WER: 19.2463%, total TER: 8.55354%, progress (thread 0): 94.1377%]
|T|: y e s
|P|: y e a h
[sample: IS1009b_H01_FIO087_289.01_289.27, WER: 100%, TER: 66.6667%, total WER: 19.2473%, total TER: 8.55394%, progress (thread 0): 94.1456%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_892.83_893.09, WER: 100%, TER: 60%, total WER: 19.2482%, total TER: 8.55455%, progress (thread 0): 94.1535%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_2020.18_2020.44, WER: 0%, TER: 0%, total WER: 19.2479%, total TER: 8.55447%, progress (thread 0): 94.1614%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_626.71_626.97, WER: 100%, TER: 40%, total WER: 19.2489%, total TER: 8.55483%, progress (thread 0): 94.1693%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_766.43_766.69, WER: 0%, TER: 0%, total WER: 19.2486%, total TER: 8.55475%, progress (thread 0): 94.1772%]
|T|: m m
|P|: y m m
[sample: IS1009d_H01_FIO087_656.85_657.11, WER: 100%, TER: 50%, total WER: 19.2495%, total TER: 8.55495%, progress (thread 0): 94.1851%]
|T|: ' c a u s e
|P|: s
[sample: TS3003a_H03_MTD012ME_1376.63_1376.89, WER: 100%, TER: 83.3333%, total WER: 19.2505%, total TER: 8.55599%, progress (thread 0): 94.193%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H02_MTD0010ID_1474.04_1474.3, WER: 0%, TER: 0%, total WER: 19.2502%, total TER: 8.55591%, progress (thread 0): 94.201%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_206.59_206.85, WER: 100%, TER: 25%, total WER: 19.2511%, total TER: 8.55607%, progress (thread 0): 94.2089%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H01_MTD011UID_581.65_581.91, WER: 0%, TER: 0%, total WER: 19.2509%, total TER: 8.55599%, progress (thread 0): 94.2168%]
|T|: h m m
|P|: m m
[sample: TS3003b_H03_MTD012ME_883.3_883.56, WER: 100%, TER: 33.3333%, total WER: 19.2518%, total TER: 8.55616%, progress (thread 0): 94.2247%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1533.07_1533.33, WER: 100%, TER: 40%, total WER: 19.2527%, total TER: 8.55653%, progress (thread 0): 94.2326%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_2048.3_2048.56, WER: 0%, TER: 0%, total WER: 19.2525%, total TER: 8.55647%, progress (thread 0): 94.2405%]
|T|: m m
|P|: h m m
[sample: TS3003c_H01_MTD011UID_1933.15_1933.41, WER: 100%, TER: 50%, total WER: 19.2534%, total TER: 8.55666%, progress (thread 0): 94.2484%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1980.3_1980.56, WER: 0%, TER: 0%, total WER: 19.2532%, total TER: 8.55658%, progress (thread 0): 94.2563%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_694.34_694.6, WER: 0%, TER: 0%, total WER: 19.253%, total TER: 8.5565%, progress (thread 0): 94.2642%]
|T|: t w o
|P|: t w o
[sample: TS3003d_H03_MTD012ME_1897.81_1898.07, WER: 0%, TER: 0%, total WER: 19.2528%, total TER: 8.55644%, progress (thread 0): 94.2722%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2141.52_2141.78, WER: 0%, TER: 0%, total WER: 19.2526%, total TER: 8.55636%, progress (thread 0): 94.2801%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002a_H02_FEO072_36.34_36.6, WER: 100%, TER: 20%, total WER: 19.2535%, total TER: 8.5565%, progress (thread 0): 94.288%]
|T|: y e a h
|P|: y m a m
[sample: EN2002a_H00_MEE073_319.23_319.49, WER: 100%, TER: 50%, total WER: 19.2544%, total TER: 8.55688%, progress (thread 0): 94.2959%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_671.05_671.31, WER: 0%, TER: 0%, total WER: 19.2542%, total TER: 8.5568%, progress (thread 0): 94.3038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_721.89_722.15, WER: 0%, TER: 0%, total WER: 19.254%, total TER: 8.55672%, progress (thread 0): 94.3117%]
|T|: t r u e
|P|: t r u e
[sample: EN2002a_H00_MEE073_751.8_752.06, WER: 0%, TER: 0%, total WER: 19.2537%, total TER: 8.55664%, progress (thread 0): 94.3196%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1052.1_1052.36, WER: 0%, TER: 0%, total WER: 19.2535%, total TER: 8.55656%, progress (thread 0): 94.3275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1446.67_1446.93, WER: 0%, TER: 0%, total WER: 19.2533%, total TER: 8.55648%, progress (thread 0): 94.3354%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1777.39_1777.65, WER: 0%, TER: 0%, total WER: 19.2531%, total TER: 8.5564%, progress (thread 0): 94.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1868.15_1868.41, WER: 0%, TER: 0%, total WER: 19.2529%, total TER: 8.55632%, progress (thread 0): 94.3513%]
|T|: o k a y
|P|: o g a y
[sample: EN2002b_H02_FEO072_93.22_93.48, WER: 100%, TER: 25%, total WER: 19.2538%, total TER: 8.55648%, progress (thread 0): 94.3592%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_252.07_252.33, WER: 0%, TER: 0%, total WER: 19.2536%, total TER: 8.5564%, progress (thread 0): 94.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_460.58_460.84, WER: 0%, TER: 0%, total WER: 19.2533%, total TER: 8.55632%, progress (thread 0): 94.375%]
|T|: o h
|P|: o h
[sample: EN2002b_H03_MEE073_674.06_674.32, WER: 0%, TER: 0%, total WER: 19.2531%, total TER: 8.55628%, progress (thread 0): 94.3829%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1026.26_1026.52, WER: 0%, TER: 0%, total WER: 19.2529%, total TER: 8.5562%, progress (thread 0): 94.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1104.98_1105.24, WER: 0%, TER: 0%, total WER: 19.2527%, total TER: 8.55612%, progress (thread 0): 94.3987%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1549.41_1549.67, WER: 100%, TER: 33.3333%, total WER: 19.2536%, total TER: 8.55629%, progress (thread 0): 94.4066%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1643.55_1643.81, WER: 0%, TER: 0%, total WER: 19.2534%, total TER: 8.55621%, progress (thread 0): 94.4146%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H02_MEE071_46.45_46.71, WER: 0%, TER: 0%, total WER: 19.2532%, total TER: 8.55611%, progress (thread 0): 94.4225%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_577.1_577.36, WER: 0%, TER: 0%, total WER: 19.253%, total TER: 8.55605%, progress (thread 0): 94.4304%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_600.57_600.83, WER: 100%, TER: 66.6667%, total WER: 19.2539%, total TER: 8.55646%, progress (thread 0): 94.4383%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_775.24_775.5, WER: 0%, TER: 0%, total WER: 19.2536%, total TER: 8.55638%, progress (thread 0): 94.4462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1079.16_1079.42, WER: 0%, TER: 0%, total WER: 19.2534%, total TER: 8.5563%, progress (thread 0): 94.4541%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002c_H03_MEE073_1105.57_1105.83, WER: 100%, TER: 20%, total WER: 19.2543%, total TER: 8.55643%, progress (thread 0): 94.462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1119.48_1119.74, WER: 0%, TER: 0%, total WER: 19.2541%, total TER: 8.55635%, progress (thread 0): 94.4699%]
|T|: m m h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1983.15_1983.41, WER: 100%, TER: 60%, total WER: 19.255%, total TER: 8.55695%, progress (thread 0): 94.4779%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1999.1_1999.36, WER: 0%, TER: 0%, total WER: 19.2548%, total TER: 8.55685%, progress (thread 0): 94.4858%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2082.11_2082.37, WER: 0%, TER: 0%, total WER: 19.2546%, total TER: 8.55677%, progress (thread 0): 94.4937%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2586.11_2586.37, WER: 0%, TER: 0%, total WER: 19.2544%, total TER: 8.55669%, progress (thread 0): 94.5016%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_347.04_347.3, WER: 0%, TER: 0%, total WER: 19.2542%, total TER: 8.55661%, progress (thread 0): 94.5095%]
|T|: w h o
|P|: h m m
[sample: EN2002d_H00_FEO070_501.31_501.57, WER: 100%, TER: 100%, total WER: 19.2551%, total TER: 8.55726%, progress (thread 0): 94.5174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_728.46_728.72, WER: 0%, TER: 0%, total WER: 19.2549%, total TER: 8.55718%, progress (thread 0): 94.5253%]
|T|: w e l l | i t ' s
|P|: j u s t
[sample: EN2002d_H03_MEE073_882.54_882.8, WER: 100%, TER: 88.8889%, total WER: 19.2567%, total TER: 8.55886%, progress (thread 0): 94.5332%]
|T|: s u p e r
|P|: s u p e
[sample: EN2002d_H03_MEE073_953.8_954.06, WER: 100%, TER: 20%, total WER: 19.2576%, total TER: 8.559%, progress (thread 0): 94.5411%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1517.63_1517.89, WER: 50%, TER: 42.8571%, total WER: 19.2583%, total TER: 8.55956%, progress (thread 0): 94.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1704.28_1704.54, WER: 0%, TER: 0%, total WER: 19.2581%, total TER: 8.55948%, progress (thread 0): 94.557%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_2135.36_2135.62, WER: 100%, TER: 33.3333%, total WER: 19.259%, total TER: 8.55965%, progress (thread 0): 94.5649%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_666.69_666.94, WER: 0%, TER: 0%, total WER: 19.2587%, total TER: 8.55961%, progress (thread 0): 94.5728%]
|T|: y e p
|P|: y e a h
[sample: ES2004b_H00_MEO015_817.97_818.22, WER: 100%, TER: 66.6667%, total WER: 19.2597%, total TER: 8.56002%, progress (thread 0): 94.5807%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_2175.08_2175.33, WER: 100%, TER: 20%, total WER: 19.2606%, total TER: 8.56015%, progress (thread 0): 94.5886%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004c_H00_MEO015_620.36_620.61, WER: 100%, TER: 20%, total WER: 19.2615%, total TER: 8.56029%, progress (thread 0): 94.5965%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1866.87_1867.12, WER: 100%, TER: 100%, total WER: 19.2624%, total TER: 8.56093%, progress (thread 0): 94.6044%]
|T|: s o r r y
|P|: s o r r y
[sample: ES2004d_H01_FEE013_455.38_455.63, WER: 0%, TER: 0%, total WER: 19.2622%, total TER: 8.56083%, progress (thread 0): 94.6123%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_607.67_607.92, WER: 0%, TER: 0%, total WER: 19.2619%, total TER: 8.56075%, progress (thread 0): 94.6203%]
|T|: y e s
|P|: y e a h
[sample: ES2004d_H03_FEE016_1135.45_1135.7, WER: 100%, TER: 66.6667%, total WER: 19.2628%, total TER: 8.56115%, progress (thread 0): 94.6282%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1553.38_1553.63, WER: 0%, TER: 0%, total WER: 19.2626%, total TER: 8.56107%, progress (thread 0): 94.6361%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H03_FIO089_1086.32_1086.57, WER: 100%, TER: 20%, total WER: 19.2635%, total TER: 8.56121%, progress (thread 0): 94.644%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1900.42_1900.67, WER: 0%, TER: 0%, total WER: 19.2633%, total TER: 8.56113%, progress (thread 0): 94.6519%]
|T|: y e s
|P|: y e a h
[sample: IS1009b_H01_FIO087_1909.04_1909.29, WER: 100%, TER: 66.6667%, total WER: 19.2642%, total TER: 8.56153%, progress (thread 0): 94.6598%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H00_FIE088_324.58_324.83, WER: 0%, TER: 0%, total WER: 19.264%, total TER: 8.56143%, progress (thread 0): 94.6677%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1287.33_1287.58, WER: 0%, TER: 0%, total WER: 19.2638%, total TER: 8.56135%, progress (thread 0): 94.6756%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1781.33_1781.58, WER: 0%, TER: 0%, total WER: 19.2636%, total TER: 8.56127%, progress (thread 0): 94.6835%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_351.32_351.57, WER: 0%, TER: 0%, total WER: 19.2634%, total TER: 8.56119%, progress (thread 0): 94.6915%]
|T|: m m
|P|: m m m
[sample: IS1009d_H03_FIO089_427.43_427.68, WER: 100%, TER: 50%, total WER: 19.2643%, total TER: 8.56139%, progress (thread 0): 94.6994%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_826.51_826.76, WER: 0%, TER: 0%, total WER: 19.2641%, total TER: 8.56135%, progress (thread 0): 94.7073%]
|T|: n o
|P|: n o
[sample: TS3003a_H03_MTD012ME_1222.74_1222.99, WER: 0%, TER: 0%, total WER: 19.2638%, total TER: 8.56131%, progress (thread 0): 94.7152%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_281.83_282.08, WER: 0%, TER: 0%, total WER: 19.2636%, total TER: 8.56127%, progress (thread 0): 94.7231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1559.52_1559.77, WER: 0%, TER: 0%, total WER: 19.2634%, total TER: 8.56119%, progress (thread 0): 94.731%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_1839.11_1839.36, WER: 0%, TER: 0%, total WER: 19.2632%, total TER: 8.56111%, progress (thread 0): 94.7389%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H02_MTD0010ID_2094.57_2094.82, WER: 100%, TER: 50%, total WER: 19.2641%, total TER: 8.5615%, progress (thread 0): 94.7468%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003c_H03_MTD012ME_1972.52_1972.77, WER: 100%, TER: 20%, total WER: 19.265%, total TER: 8.56163%, progress (thread 0): 94.7548%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_303.27_303.52, WER: 100%, TER: 75%, total WER: 19.2659%, total TER: 8.56225%, progress (thread 0): 94.7627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_583.29_583.54, WER: 0%, TER: 0%, total WER: 19.2657%, total TER: 8.56217%, progress (thread 0): 94.7706%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_847.16_847.41, WER: 0%, TER: 0%, total WER: 19.2655%, total TER: 8.56209%, progress (thread 0): 94.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1370.99_1371.24, WER: 0%, TER: 0%, total WER: 19.2653%, total TER: 8.56201%, progress (thread 0): 94.7864%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1371.48_1371.73, WER: 0%, TER: 0%, total WER: 19.265%, total TER: 8.56193%, progress (thread 0): 94.7943%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_1404.32_1404.57, WER: 100%, TER: 75%, total WER: 19.266%, total TER: 8.56255%, progress (thread 0): 94.8022%]
|T|: g o o d
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1696.35_1696.6, WER: 100%, TER: 100%, total WER: 19.2669%, total TER: 8.5634%, progress (thread 0): 94.8101%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2064.36_2064.61, WER: 0%, TER: 0%, total WER: 19.2666%, total TER: 8.56332%, progress (thread 0): 94.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_2111_2111.25, WER: 0%, TER: 0%, total WER: 19.2664%, total TER: 8.56324%, progress (thread 0): 94.826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2138.4_2138.65, WER: 0%, TER: 0%, total WER: 19.2662%, total TER: 8.56316%, progress (thread 0): 94.8339%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2216.27_2216.52, WER: 0%, TER: 0%, total WER: 19.266%, total TER: 8.56308%, progress (thread 0): 94.8418%]
|T|: a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2381.92_2382.17, WER: 100%, TER: 100%, total WER: 19.2669%, total TER: 8.56351%, progress (thread 0): 94.8497%]
|T|: s o
|P|: s o
[sample: EN2002a_H00_MEE073_202.42_202.67, WER: 0%, TER: 0%, total WER: 19.2667%, total TER: 8.56347%, progress (thread 0): 94.8576%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_749.45_749.7, WER: 0%, TER: 0%, total WER: 19.2665%, total TER: 8.56339%, progress (thread 0): 94.8655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1642_1642.25, WER: 0%, TER: 0%, total WER: 19.2663%, total TER: 8.56331%, progress (thread 0): 94.8734%]
|T|: s o
|P|: s o
[sample: EN2002a_H02_FEO072_1664.11_1664.36, WER: 0%, TER: 0%, total WER: 19.266%, total TER: 8.56327%, progress (thread 0): 94.8813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1964.06_1964.31, WER: 0%, TER: 0%, total WER: 19.2658%, total TER: 8.56319%, progress (thread 0): 94.8892%]
|T|: y o u ' d | b e | s
|P|: c i m p e
[sample: EN2002a_H00_MEE073_2096.92_2097.17, WER: 100%, TER: 90%, total WER: 19.2685%, total TER: 8.56509%, progress (thread 0): 94.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_93.68_93.93, WER: 0%, TER: 0%, total WER: 19.2683%, total TER: 8.56501%, progress (thread 0): 94.9051%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_311.61_311.86, WER: 0%, TER: 0%, total WER: 19.2681%, total TER: 8.56491%, progress (thread 0): 94.913%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1129.86_1130.11, WER: 100%, TER: 28.5714%, total WER: 19.269%, total TER: 8.56524%, progress (thread 0): 94.9209%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1222.15_1222.4, WER: 0%, TER: 0%, total WER: 19.2688%, total TER: 8.56518%, progress (thread 0): 94.9288%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1237.83_1238.08, WER: 0%, TER: 0%, total WER: 19.2686%, total TER: 8.5651%, progress (thread 0): 94.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1245.09_1245.34, WER: 0%, TER: 0%, total WER: 19.2684%, total TER: 8.56502%, progress (thread 0): 94.9446%]
|T|: a n d
|P|: t h e n
[sample: EN2002b_H01_MEE071_1309.75_1310, WER: 100%, TER: 133.333%, total WER: 19.2693%, total TER: 8.56589%, progress (thread 0): 94.9525%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H02_FEO072_1594.84_1595.09, WER: 100%, TER: 66.6667%, total WER: 19.2702%, total TER: 8.5663%, progress (thread 0): 94.9604%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1643.59_1643.84, WER: 0%, TER: 0%, total WER: 19.27%, total TER: 8.56622%, progress (thread 0): 94.9684%]
|T|: s u r e
|P|: s u r e
[sample: EN2002c_H02_MEE071_67.06_67.31, WER: 0%, TER: 0%, total WER: 19.2697%, total TER: 8.56614%, progress (thread 0): 94.9763%]
|T|: o k a y
|P|: m m
[sample: EN2002c_H03_MEE073_76.13_76.38, WER: 100%, TER: 100%, total WER: 19.2707%, total TER: 8.56699%, progress (thread 0): 94.9842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_253.63_253.88, WER: 0%, TER: 0%, total WER: 19.2704%, total TER: 8.56691%, progress (thread 0): 94.9921%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_470.85_471.1, WER: 100%, TER: 0%, total WER: 19.2713%, total TER: 8.56681%, progress (thread 0): 95%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_759.06_759.31, WER: 0%, TER: 0%, total WER: 19.2711%, total TER: 8.56671%, progress (thread 0): 95.0079%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1000.82_1001.07, WER: 0%, TER: 0%, total WER: 19.2709%, total TER: 8.56663%, progress (thread 0): 95.0158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1023.06_1023.31, WER: 0%, TER: 0%, total WER: 19.2707%, total TER: 8.56655%, progress (thread 0): 95.0237%]
|T|: m m h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1102.48_1102.73, WER: 100%, TER: 60%, total WER: 19.2716%, total TER: 8.56715%, progress (thread 0): 95.0316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1122.76_1123.01, WER: 0%, TER: 0%, total WER: 19.2714%, total TER: 8.56707%, progress (thread 0): 95.0396%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_1359.23_1359.48, WER: 100%, TER: 66.6667%, total WER: 19.2723%, total TER: 8.56748%, progress (thread 0): 95.0475%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_1508.23_1508.48, WER: 0%, TER: 0%, total WER: 19.2721%, total TER: 8.56742%, progress (thread 0): 95.0554%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2100.94_2101.19, WER: 0%, TER: 0%, total WER: 19.2719%, total TER: 8.56734%, progress (thread 0): 95.0633%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2354.17_2354.42, WER: 0%, TER: 0%, total WER: 19.2716%, total TER: 8.56726%, progress (thread 0): 95.0712%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_146.37_146.62, WER: 100%, TER: 28.5714%, total WER: 19.2725%, total TER: 8.56759%, progress (thread 0): 95.0791%]
|T|: o h
|P|: m m
[sample: EN2002d_H00_FEO070_286.77_287.02, WER: 100%, TER: 100%, total WER: 19.2735%, total TER: 8.56801%, progress (thread 0): 95.087%]
|T|: y e a h
|P|: m m
[sample: EN2002d_H03_MEE073_589.91_590.16, WER: 100%, TER: 100%, total WER: 19.2744%, total TER: 8.56887%, progress (thread 0): 95.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_850.49_850.74, WER: 0%, TER: 0%, total WER: 19.2741%, total TER: 8.56879%, progress (thread 0): 95.1028%]
|T|: s o
|P|: h m h
[sample: EN2002d_H02_MEE071_1251.8_1252.05, WER: 100%, TER: 150%, total WER: 19.2751%, total TER: 8.56945%, progress (thread 0): 95.1108%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_1830.85_1831.1, WER: 100%, TER: 33.3333%, total WER: 19.276%, total TER: 8.56962%, progress (thread 0): 95.1187%]
|T|: o k a y
|P|: c o y
[sample: EN2002d_H03_MEE073_1854.42_1854.67, WER: 100%, TER: 75%, total WER: 19.2769%, total TER: 8.57024%, progress (thread 0): 95.1266%]
|T|: m m
|P|: h m m
[sample: EN2002d_H01_FEO072_2083.17_2083.42, WER: 100%, TER: 50%, total WER: 19.2778%, total TER: 8.57044%, progress (thread 0): 95.1345%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2192.17_2192.42, WER: 0%, TER: 0%, total WER: 19.2776%, total TER: 8.57036%, progress (thread 0): 95.1424%]
|T|: s
|P|: s
[sample: ES2004a_H02_MEE014_484.48_484.72, WER: 0%, TER: 0%, total WER: 19.2773%, total TER: 8.57033%, progress (thread 0): 95.1503%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_775.6_775.84, WER: 0%, TER: 0%, total WER: 19.2771%, total TER: 8.57029%, progress (thread 0): 95.1582%]
|T|: s o r r y
|P|: m m
[sample: ES2004b_H00_MEO015_100.7_100.94, WER: 100%, TER: 100%, total WER: 19.278%, total TER: 8.57136%, progress (thread 0): 95.1661%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1311.92_1312.16, WER: 0%, TER: 0%, total WER: 19.2778%, total TER: 8.57126%, progress (thread 0): 95.174%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_1636.65_1636.89, WER: 100%, TER: 20%, total WER: 19.2787%, total TER: 8.5714%, progress (thread 0): 95.182%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1305.92_1306.16, WER: 100%, TER: 25%, total WER: 19.2796%, total TER: 8.57155%, progress (thread 0): 95.1899%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1345.16_1345.4, WER: 0%, TER: 0%, total WER: 19.2794%, total TER: 8.57151%, progress (thread 0): 95.1978%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1364.93_1365.17, WER: 0%, TER: 0%, total WER: 19.2792%, total TER: 8.57147%, progress (thread 0): 95.2057%]
|T|: f i n e
|P|: i n y
[sample: ES2004c_H00_MEO015_2156.57_2156.81, WER: 100%, TER: 50%, total WER: 19.2801%, total TER: 8.57186%, progress (thread 0): 95.2136%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_973.73_973.97, WER: 0%, TER: 0%, total WER: 19.2799%, total TER: 8.5718%, progress (thread 0): 95.2215%]
|T|: m m
|P|: m m
[sample: ES2004d_H03_FEE016_1286.7_1286.94, WER: 0%, TER: 0%, total WER: 19.2797%, total TER: 8.57176%, progress (thread 0): 95.2294%]
|T|: o h
|P|: o h
[sample: ES2004d_H01_FEE013_2176.67_2176.91, WER: 0%, TER: 0%, total WER: 19.2794%, total TER: 8.57172%, progress (thread 0): 95.2373%]
|T|: n o
|P|: m m
[sample: IS1009a_H03_FIO089_145.55_145.79, WER: 100%, TER: 100%, total WER: 19.2804%, total TER: 8.57214%, progress (thread 0): 95.2453%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H00_FIE088_466.21_466.45, WER: 100%, TER: 20%, total WER: 19.2813%, total TER: 8.57228%, progress (thread 0): 95.2532%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_556.58_556.82, WER: 0%, TER: 0%, total WER: 19.281%, total TER: 8.5722%, progress (thread 0): 95.2611%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1033.16_1033.4, WER: 0%, TER: 0%, total WER: 19.2808%, total TER: 8.57212%, progress (thread 0): 95.269%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_1099.19_1099.43, WER: 0%, TER: 0%, total WER: 19.2806%, total TER: 8.57204%, progress (thread 0): 95.2769%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1247.84_1248.08, WER: 100%, TER: 0%, total WER: 19.2815%, total TER: 8.57194%, progress (thread 0): 95.2848%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_752.07_752.31, WER: 100%, TER: 20%, total WER: 19.2824%, total TER: 8.57207%, progress (thread 0): 95.2927%]
|T|: m m | r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_764.18_764.42, WER: 50%, TER: 37.5%, total WER: 19.2831%, total TER: 8.57261%, progress (thread 0): 95.3006%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1110.14_1110.38, WER: 100%, TER: 40%, total WER: 19.284%, total TER: 8.57298%, progress (thread 0): 95.3085%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_1123.59_1123.83, WER: 100%, TER: 20%, total WER: 19.2849%, total TER: 8.57311%, progress (thread 0): 95.3165%]
|T|: h e l l o
|P|: n o
[sample: IS1009d_H02_FIO084_41.31_41.55, WER: 100%, TER: 80%, total WER: 19.2858%, total TER: 8.57394%, progress (thread 0): 95.3244%]
|T|: m m
|P|: m m
[sample: IS1009d_H03_FIO089_516.52_516.76, WER: 0%, TER: 0%, total WER: 19.2856%, total TER: 8.5739%, progress (thread 0): 95.3323%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_656.9_657.14, WER: 100%, TER: 60%, total WER: 19.2865%, total TER: 8.5745%, progress (thread 0): 95.3402%]
|T|: y e a h
|P|: n o
[sample: IS1009d_H02_FIO084_1011.72_1011.96, WER: 100%, TER: 100%, total WER: 19.2874%, total TER: 8.57536%, progress (thread 0): 95.3481%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_1883.01_1883.25, WER: 0%, TER: 0%, total WER: 19.2872%, total TER: 8.57528%, progress (thread 0): 95.356%]
|T|: w e
|P|: w e
[sample: TS3003a_H00_MTD009PM_1109.9_1110.14, WER: 0%, TER: 0%, total WER: 19.287%, total TER: 8.57524%, progress (thread 0): 95.3639%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1420.32_1420.56, WER: 0%, TER: 0%, total WER: 19.2868%, total TER: 8.5752%, progress (thread 0): 95.3718%]
|T|: n o
|P|: n o
[sample: TS3003b_H02_MTD0010ID_1474.4_1474.64, WER: 0%, TER: 0%, total WER: 19.2866%, total TER: 8.57516%, progress (thread 0): 95.3797%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_1677.79_1678.03, WER: 0%, TER: 0%, total WER: 19.2863%, total TER: 8.57508%, progress (thread 0): 95.3877%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1975.67_1975.91, WER: 100%, TER: 100%, total WER: 19.2873%, total TER: 8.5755%, progress (thread 0): 95.3956%]
|T|: y e a h
|P|: h m m
[sample: TS3003b_H01_MTD011UID_2083.9_2084.14, WER: 100%, TER: 100%, total WER: 19.2882%, total TER: 8.57635%, progress (thread 0): 95.4035%]
|T|: y e a h
|P|: a h
[sample: TS3003c_H01_MTD011UID_1240.02_1240.26, WER: 100%, TER: 50%, total WER: 19.2891%, total TER: 8.57674%, progress (thread 0): 95.4114%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1254.68_1254.92, WER: 0%, TER: 0%, total WER: 19.2888%, total TER: 8.57666%, progress (thread 0): 95.4193%]
|T|: s o
|P|: s o
[sample: TS3003c_H01_MTD011UID_1417.83_1418.07, WER: 0%, TER: 0%, total WER: 19.2886%, total TER: 8.57662%, progress (thread 0): 95.4272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1477.96_1478.2, WER: 0%, TER: 0%, total WER: 19.2884%, total TER: 8.57654%, progress (thread 0): 95.4351%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1566.64_1566.88, WER: 100%, TER: 25%, total WER: 19.2893%, total TER: 8.57669%, progress (thread 0): 95.443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1716.46_1716.7, WER: 0%, TER: 0%, total WER: 19.2891%, total TER: 8.57661%, progress (thread 0): 95.451%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2217.59_2217.83, WER: 0%, TER: 0%, total WER: 19.2889%, total TER: 8.57653%, progress (thread 0): 95.4589%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2243.54_2243.78, WER: 0%, TER: 0%, total WER: 19.2887%, total TER: 8.57645%, progress (thread 0): 95.4668%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1049.33_1049.57, WER: 0%, TER: 0%, total WER: 19.2885%, total TER: 8.57637%, progress (thread 0): 95.4747%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1496.68_1496.92, WER: 100%, TER: 75%, total WER: 19.2894%, total TER: 8.57699%, progress (thread 0): 95.4826%]
|T|: d | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_106.38_106.62, WER: 50%, TER: 33.3333%, total WER: 19.2901%, total TER: 8.57734%, progress (thread 0): 95.4905%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_140.88_141.12, WER: 0%, TER: 0%, total WER: 19.2898%, total TER: 8.57726%, progress (thread 0): 95.4984%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_333.3_333.54, WER: 0%, TER: 0%, total WER: 19.2896%, total TER: 8.57716%, progress (thread 0): 95.5063%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H01_FEO070_398.18_398.42, WER: 100%, TER: 100%, total WER: 19.2905%, total TER: 8.57801%, progress (thread 0): 95.5142%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_481.62_481.86, WER: 0%, TER: 0%, total WER: 19.2903%, total TER: 8.57793%, progress (thread 0): 95.5222%]
|T|: y e a h
|P|: y e a m
[sample: EN2002a_H00_MEE073_682.97_683.21, WER: 100%, TER: 25%, total WER: 19.2912%, total TER: 8.57809%, progress (thread 0): 95.5301%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002a_H02_FEO072_1009.94_1010.18, WER: 100%, TER: 20%, total WER: 19.2921%, total TER: 8.57822%, progress (thread 0): 95.538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1041.03_1041.27, WER: 0%, TER: 0%, total WER: 19.2919%, total TER: 8.57814%, progress (thread 0): 95.5459%]
|T|: i | s e e
|P|: a t ' s
[sample: EN2002a_H00_MEE073_1171.72_1171.96, WER: 100%, TER: 100%, total WER: 19.2937%, total TER: 8.57921%, progress (thread 0): 95.5538%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002a_H00_MEE073_1209.32_1209.56, WER: 100%, TER: 85.7143%, total WER: 19.2955%, total TER: 8.58047%, progress (thread 0): 95.5617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1310.69_1310.93, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.58039%, progress (thread 0): 95.5696%]
|T|: o p e n
|P|: o p a y
[sample: EN2002a_H02_FEO072_1350.04_1350.28, WER: 100%, TER: 50%, total WER: 19.2962%, total TER: 8.58077%, progress (thread 0): 95.5775%]
|T|: m m
|P|: m m
[sample: EN2002a_H03_MEE071_1405.73_1405.97, WER: 0%, TER: 0%, total WER: 19.296%, total TER: 8.58073%, progress (thread 0): 95.5854%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1791.17_1791.41, WER: 0%, TER: 0%, total WER: 19.2958%, total TER: 8.58063%, progress (thread 0): 95.5934%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1834.42_1834.66, WER: 0%, TER: 0%, total WER: 19.2956%, total TER: 8.58055%, progress (thread 0): 95.6013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2028.12_2028.36, WER: 0%, TER: 0%, total WER: 19.2954%, total TER: 8.58047%, progress (thread 0): 95.6092%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_2040.58_2040.82, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.58039%, progress (thread 0): 95.6171%]
|T|: o h
|P|: o h
[sample: EN2002a_H01_FEO070_2098.91_2099.15, WER: 0%, TER: 0%, total WER: 19.2949%, total TER: 8.58035%, progress (thread 0): 95.625%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_522.48_522.72, WER: 0%, TER: 0%, total WER: 19.2947%, total TER: 8.58027%, progress (thread 0): 95.6329%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_612.23_612.47, WER: 0%, TER: 0%, total WER: 19.2945%, total TER: 8.58019%, progress (thread 0): 95.6408%]
|T|: m m
|P|: m m
[sample: EN2002b_H02_FEO072_1475.84_1476.08, WER: 0%, TER: 0%, total WER: 19.2943%, total TER: 8.58015%, progress (thread 0): 95.6487%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_235.84_236.08, WER: 100%, TER: 33.3333%, total WER: 19.2952%, total TER: 8.58033%, progress (thread 0): 95.6566%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_571.47_571.71, WER: 0%, TER: 0%, total WER: 19.295%, total TER: 8.58025%, progress (thread 0): 95.6646%]
|T|: h m m
|P|: y e a h
[sample: EN2002c_H03_MEE073_574.34_574.58, WER: 100%, TER: 133.333%, total WER: 19.2959%, total TER: 8.58112%, progress (thread 0): 95.6725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_598.8_599.04, WER: 0%, TER: 0%, total WER: 19.2956%, total TER: 8.58104%, progress (thread 0): 95.6804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1009.87_1010.11, WER: 0%, TER: 0%, total WER: 19.2954%, total TER: 8.58096%, progress (thread 0): 95.6883%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1018.66_1018.9, WER: 0%, TER: 0%, total WER: 19.2952%, total TER: 8.58086%, progress (thread 0): 95.6962%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1281.36_1281.6, WER: 100%, TER: 33.3333%, total WER: 19.2961%, total TER: 8.58103%, progress (thread 0): 95.7041%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1396.34_1396.58, WER: 0%, TER: 0%, total WER: 19.2959%, total TER: 8.58095%, progress (thread 0): 95.712%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1906.84_1907.08, WER: 0%, TER: 0%, total WER: 19.2957%, total TER: 8.58087%, progress (thread 0): 95.7199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1913.84_1914.08, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58079%, progress (thread 0): 95.7279%]
|T|: ' c a u s e
|P|: t h e ' s
[sample: EN2002c_H03_MEE073_2264.19_2264.43, WER: 100%, TER: 83.3333%, total WER: 19.2964%, total TER: 8.58184%, progress (thread 0): 95.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2309.3_2309.54, WER: 0%, TER: 0%, total WER: 19.2962%, total TER: 8.58176%, progress (thread 0): 95.7437%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2341.41_2341.65, WER: 0%, TER: 0%, total WER: 19.2959%, total TER: 8.58166%, progress (thread 0): 95.7516%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2370.76_2371, WER: 0%, TER: 0%, total WER: 19.2957%, total TER: 8.58156%, progress (thread 0): 95.7595%]
|T|: g o o d
|P|: g o o d
[sample: EN2002d_H01_FEO072_337.97_338.21, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58148%, progress (thread 0): 95.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_390.81_391.05, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.5814%, progress (thread 0): 95.7753%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_496.78_497.02, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.5813%, progress (thread 0): 95.7832%]
|T|: o k a y
|P|: h u h
[sample: EN2002d_H00_FEO070_513.66_513.9, WER: 100%, TER: 100%, total WER: 19.296%, total TER: 8.58215%, progress (thread 0): 95.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1190.34_1190.58, WER: 0%, TER: 0%, total WER: 19.2958%, total TER: 8.58207%, progress (thread 0): 95.799%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1371.83_1372.07, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58199%, progress (thread 0): 95.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1477.13_1477.37, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.58191%, progress (thread 0): 95.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1706.39_1706.63, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.58183%, progress (thread 0): 95.8228%]
|T|: m m
|P|: m m
[sample: ES2004b_H03_FEE016_1717.78_1718.01, WER: 0%, TER: 0%, total WER: 19.2949%, total TER: 8.58179%, progress (thread 0): 95.8307%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1860.91_1861.14, WER: 100%, TER: 40%, total WER: 19.2958%, total TER: 8.58216%, progress (thread 0): 95.8386%]
|T|: t r u e
|P|: s u e
[sample: ES2004b_H00_MEO015_2294.79_2295.02, WER: 100%, TER: 50%, total WER: 19.2967%, total TER: 8.58254%, progress (thread 0): 95.8465%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_85.13_85.36, WER: 0%, TER: 0%, total WER: 19.2965%, total TER: 8.58246%, progress (thread 0): 95.8544%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_356.94_357.17, WER: 100%, TER: 25%, total WER: 19.2974%, total TER: 8.58262%, progress (thread 0): 95.8623%]
|T|: s o
|P|: s a e
[sample: ES2004c_H02_MEE014_748.27_748.5, WER: 100%, TER: 100%, total WER: 19.2983%, total TER: 8.58304%, progress (thread 0): 95.8702%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1121.33_1121.56, WER: 0%, TER: 0%, total WER: 19.2981%, total TER: 8.583%, progress (thread 0): 95.8782%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1169.98_1170.21, WER: 0%, TER: 0%, total WER: 19.2979%, total TER: 8.58292%, progress (thread 0): 95.8861%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H03_FEE016_711.82_712.05, WER: 0%, TER: 0%, total WER: 19.2977%, total TER: 8.58284%, progress (thread 0): 95.894%]
|T|: t w o
|P|: t o
[sample: ES2004d_H00_MEO015_732.46_732.69, WER: 100%, TER: 33.3333%, total WER: 19.2986%, total TER: 8.58301%, progress (thread 0): 95.9019%]
|T|: m m h m m
|P|: m e h m
[sample: ES2004d_H00_MEO015_822.98_823.21, WER: 100%, TER: 40%, total WER: 19.2995%, total TER: 8.58338%, progress (thread 0): 95.9098%]
|T|: ' k a y
|P|: s o
[sample: ES2004d_H00_MEO015_1813.16_1813.39, WER: 100%, TER: 100%, total WER: 19.3004%, total TER: 8.58423%, progress (thread 0): 95.9177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_195.04_195.27, WER: 0%, TER: 0%, total WER: 19.3002%, total TER: 8.58415%, progress (thread 0): 95.9256%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_532.46_532.69, WER: 100%, TER: 66.6667%, total WER: 19.3011%, total TER: 8.58456%, progress (thread 0): 95.9335%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_55.21_55.44, WER: 0%, TER: 0%, total WER: 19.3008%, total TER: 8.58452%, progress (thread 0): 95.9415%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1919.19_1919.42, WER: 0%, TER: 0%, total WER: 19.3006%, total TER: 8.58444%, progress (thread 0): 95.9494%]
|T|: m m h m m
|P|: m m
[sample: IS1009c_H00_FIE088_554.54_554.77, WER: 100%, TER: 60%, total WER: 19.3015%, total TER: 8.58504%, progress (thread 0): 95.9573%]
|T|: m m | y e s
|P|: y e a h
[sample: IS1009c_H01_FIO087_728.73_728.96, WER: 100%, TER: 83.3333%, total WER: 19.3033%, total TER: 8.58608%, progress (thread 0): 95.9652%]
|T|: y e p
|P|: y e a h
[sample: IS1009c_H03_FIO089_1639.78_1640.01, WER: 100%, TER: 66.6667%, total WER: 19.3042%, total TER: 8.58649%, progress (thread 0): 95.9731%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_747.97_748.2, WER: 100%, TER: 66.6667%, total WER: 19.3052%, total TER: 8.5869%, progress (thread 0): 95.981%]
|T|: h m m
|P|: m m
[sample: IS1009d_H02_FIO084_748.42_748.65, WER: 100%, TER: 33.3333%, total WER: 19.3061%, total TER: 8.58707%, progress (thread 0): 95.9889%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_758.1_758.33, WER: 100%, TER: 100%, total WER: 19.307%, total TER: 8.58814%, progress (thread 0): 95.9968%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1695.75_1695.98, WER: 100%, TER: 66.6667%, total WER: 19.3079%, total TER: 8.58854%, progress (thread 0): 96.0047%]
|T|: o k a y
|P|: y e a h
[sample: IS1009d_H03_FIO089_1889.3_1889.53, WER: 100%, TER: 75%, total WER: 19.3088%, total TER: 8.58916%, progress (thread 0): 96.0127%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_843.57_843.8, WER: 100%, TER: 25%, total WER: 19.3097%, total TER: 8.58931%, progress (thread 0): 96.0206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1334.2_1334.43, WER: 0%, TER: 0%, total WER: 19.3095%, total TER: 8.58923%, progress (thread 0): 96.0285%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1346.08_1346.31, WER: 0%, TER: 0%, total WER: 19.3092%, total TER: 8.58915%, progress (thread 0): 96.0364%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1078.3_1078.53, WER: 0%, TER: 0%, total WER: 19.309%, total TER: 8.58911%, progress (thread 0): 96.0443%]
|T|: n o
|P|: n o
[sample: TS3003b_H01_MTD011UID_1716.14_1716.37, WER: 0%, TER: 0%, total WER: 19.3088%, total TER: 8.58907%, progress (thread 0): 96.0522%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_1250.59_1250.82, WER: 0%, TER: 0%, total WER: 19.3086%, total TER: 8.58903%, progress (thread 0): 96.0601%]
|T|: y e s
|P|: j u s t
[sample: TS3003c_H02_MTD0010ID_1518.39_1518.62, WER: 100%, TER: 100%, total WER: 19.3095%, total TER: 8.58967%, progress (thread 0): 96.068%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1654.26_1654.49, WER: 0%, TER: 0%, total WER: 19.3093%, total TER: 8.58963%, progress (thread 0): 96.076%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_2049.46_2049.69, WER: 100%, TER: 75%, total WER: 19.3102%, total TER: 8.59025%, progress (thread 0): 96.0839%]
|T|: s o
|P|: s o
[sample: TS3003d_H01_MTD011UID_261.45_261.68, WER: 0%, TER: 0%, total WER: 19.31%, total TER: 8.59021%, progress (thread 0): 96.0918%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1021.27_1021.5, WER: 100%, TER: 66.6667%, total WER: 19.3109%, total TER: 8.59062%, progress (thread 0): 96.0997%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1945.96_1946.19, WER: 100%, TER: 75%, total WER: 19.3118%, total TER: 8.59124%, progress (thread 0): 96.1076%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2064.77_2065, WER: 0%, TER: 0%, total WER: 19.3116%, total TER: 8.59116%, progress (thread 0): 96.1155%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_424.58_424.81, WER: 0%, TER: 0%, total WER: 19.3114%, total TER: 8.59106%, progress (thread 0): 96.1234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_781.91_782.14, WER: 0%, TER: 0%, total WER: 19.3111%, total TER: 8.59098%, progress (thread 0): 96.1313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_855.85_856.08, WER: 0%, TER: 0%, total WER: 19.3109%, total TER: 8.5909%, progress (thread 0): 96.1392%]
|T|: h m m
|P|: m m
[sample: EN2002a_H02_FEO072_877.24_877.47, WER: 100%, TER: 33.3333%, total WER: 19.3118%, total TER: 8.59107%, progress (thread 0): 96.1471%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H00_MEE073_1019.4_1019.63, WER: 0%, TER: 0%, total WER: 19.3116%, total TER: 8.59101%, progress (thread 0): 96.1551%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_1188.61_1188.84, WER: 100%, TER: 66.6667%, total WER: 19.3125%, total TER: 8.59142%, progress (thread 0): 96.163%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1295.18_1295.41, WER: 0%, TER: 0%, total WER: 19.3123%, total TER: 8.59134%, progress (thread 0): 96.1709%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1408.29_1408.52, WER: 0%, TER: 0%, total WER: 19.3121%, total TER: 8.59126%, progress (thread 0): 96.1788%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1571.85_1572.08, WER: 0%, TER: 0%, total WER: 19.3119%, total TER: 8.59122%, progress (thread 0): 96.1867%]
|T|: o h
|P|: o
[sample: EN2002b_H03_MEE073_29.06_29.29, WER: 100%, TER: 50%, total WER: 19.3128%, total TER: 8.59141%, progress (thread 0): 96.1946%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_194.27_194.5, WER: 0%, TER: 0%, total WER: 19.3125%, total TER: 8.59133%, progress (thread 0): 96.2025%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_211.63_211.86, WER: 0%, TER: 0%, total WER: 19.3123%, total TER: 8.59125%, progress (thread 0): 96.2104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_493.43_493.66, WER: 0%, TER: 0%, total WER: 19.3121%, total TER: 8.59117%, progress (thread 0): 96.2184%]
|T|: h m m
|P|: m m
[sample: EN2002b_H00_FEO070_534.29_534.52, WER: 100%, TER: 33.3333%, total WER: 19.313%, total TER: 8.59134%, progress (thread 0): 96.2263%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_672.54_672.77, WER: 100%, TER: 33.3333%, total WER: 19.3139%, total TER: 8.59152%, progress (thread 0): 96.2342%]
|T|: m m h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1361.79_1362.02, WER: 100%, TER: 60%, total WER: 19.3148%, total TER: 8.59211%, progress (thread 0): 96.2421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_555.02_555.25, WER: 0%, TER: 0%, total WER: 19.3146%, total TER: 8.59203%, progress (thread 0): 96.25%]
|T|: h m m
|P|: h m m
[sample: EN2002c_H01_FEO072_699.88_700.11, WER: 0%, TER: 0%, total WER: 19.3144%, total TER: 8.59197%, progress (thread 0): 96.2579%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1900.45_1900.68, WER: 0%, TER: 0%, total WER: 19.3142%, total TER: 8.59189%, progress (thread 0): 96.2658%]
|T|: d o h
|P|: s o
[sample: EN2002c_H02_MEE071_2395.6_2395.83, WER: 100%, TER: 66.6667%, total WER: 19.3151%, total TER: 8.5923%, progress (thread 0): 96.2737%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2817.5_2817.73, WER: 0%, TER: 0%, total WER: 19.3149%, total TER: 8.59222%, progress (thread 0): 96.2816%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2846.79_2847.02, WER: 0%, TER: 0%, total WER: 19.3147%, total TER: 8.59214%, progress (thread 0): 96.2896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_107.69_107.92, WER: 0%, TER: 0%, total WER: 19.3144%, total TER: 8.59206%, progress (thread 0): 96.2975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_503.45_503.68, WER: 0%, TER: 0%, total WER: 19.3142%, total TER: 8.59198%, progress (thread 0): 96.3054%]
|T|: m m h m m
|P|: m m
[sample: EN2002d_H01_FEO072_548.74_548.97, WER: 100%, TER: 60%, total WER: 19.3151%, total TER: 8.59258%, progress (thread 0): 96.3133%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_767.8_768.03, WER: 0%, TER: 0%, total WER: 19.3149%, total TER: 8.5925%, progress (thread 0): 96.3212%]
|T|: n o
|P|: n o
[sample: EN2002d_H00_FEO070_1427.88_1428.11, WER: 0%, TER: 0%, total WER: 19.3147%, total TER: 8.59246%, progress (thread 0): 96.3291%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1760.63_1760.86, WER: 100%, TER: 33.3333%, total WER: 19.3156%, total TER: 8.59263%, progress (thread 0): 96.337%]
|T|: o k a y
|P|: y e h
[sample: EN2002d_H00_FEO070_2207.62_2207.85, WER: 100%, TER: 100%, total WER: 19.3165%, total TER: 8.59348%, progress (thread 0): 96.3449%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_1022.74_1022.96, WER: 0%, TER: 0%, total WER: 19.3163%, total TER: 8.5934%, progress (thread 0): 96.3528%]
|T|: m m
|P|: m m
[sample: ES2004b_H02_MEE014_1537.12_1537.34, WER: 0%, TER: 0%, total WER: 19.3161%, total TER: 8.59336%, progress (thread 0): 96.3608%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004c_H00_MEO015_575.7_575.92, WER: 0%, TER: 0%, total WER: 19.3159%, total TER: 8.59326%, progress (thread 0): 96.3687%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1311.86_1312.08, WER: 100%, TER: 40%, total WER: 19.3168%, total TER: 8.59363%, progress (thread 0): 96.3766%]
|T|: r i g h t
|P|: i k
[sample: ES2004c_H00_MEO015_2116.51_2116.73, WER: 100%, TER: 80%, total WER: 19.3177%, total TER: 8.59446%, progress (thread 0): 96.3845%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_2159.26_2159.48, WER: 0%, TER: 0%, total WER: 19.3174%, total TER: 8.59438%, progress (thread 0): 96.3924%]
|T|: y e a h
|P|: y e m
[sample: ES2004d_H02_MEE014_968.07_968.29, WER: 100%, TER: 50%, total WER: 19.3183%, total TER: 8.59477%, progress (thread 0): 96.4003%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1220.58_1220.8, WER: 0%, TER: 0%, total WER: 19.3181%, total TER: 8.59469%, progress (thread 0): 96.4082%]
|T|: o k a y
|P|: y o u t
[sample: ES2004d_H03_FEE016_1281.25_1281.47, WER: 100%, TER: 100%, total WER: 19.319%, total TER: 8.59554%, progress (thread 0): 96.4161%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1684.26_1684.48, WER: 0%, TER: 0%, total WER: 19.3188%, total TER: 8.59546%, progress (thread 0): 96.424%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_45.13_45.35, WER: 0%, TER: 0%, total WER: 19.3186%, total TER: 8.59538%, progress (thread 0): 96.432%]
|T|: ' k a y
|P|: m m
[sample: IS1009b_H02_FIO084_155.16_155.38, WER: 100%, TER: 100%, total WER: 19.3195%, total TER: 8.59623%, progress (thread 0): 96.4399%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_404.12_404.34, WER: 100%, TER: 60%, total WER: 19.3204%, total TER: 8.59683%, progress (thread 0): 96.4478%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_961.79_962.01, WER: 0%, TER: 0%, total WER: 19.3202%, total TER: 8.59675%, progress (thread 0): 96.4557%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1007.66_1007.88, WER: 0%, TER: 0%, total WER: 19.32%, total TER: 8.59667%, progress (thread 0): 96.4636%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H00_FIE088_337.53_337.75, WER: 0%, TER: 0%, total WER: 19.3198%, total TER: 8.59659%, progress (thread 0): 96.4715%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1263.61_1263.83, WER: 0%, TER: 0%, total WER: 19.3195%, total TER: 8.59651%, progress (thread 0): 96.4794%]
|T|: m m
|P|: m m m
[sample: IS1009d_H03_FIO089_590.61_590.83, WER: 100%, TER: 50%, total WER: 19.3204%, total TER: 8.5967%, progress (thread 0): 96.4873%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_801.03_801.25, WER: 0%, TER: 0%, total WER: 19.3202%, total TER: 8.59666%, progress (thread 0): 96.4953%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_1687.52_1687.74, WER: 100%, TER: 60%, total WER: 19.3211%, total TER: 8.59726%, progress (thread 0): 96.5032%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1775.76_1775.98, WER: 0%, TER: 0%, total WER: 19.3209%, total TER: 8.59718%, progress (thread 0): 96.5111%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_1846.51_1846.73, WER: 0%, TER: 0%, total WER: 19.3207%, total TER: 8.5971%, progress (thread 0): 96.519%]
|T|: m o r n i n g
|P|: m o n e y
[sample: TS3003a_H01_MTD011UID_15.34_15.56, WER: 100%, TER: 57.1429%, total WER: 19.3216%, total TER: 8.59789%, progress (thread 0): 96.5269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1316.76_1316.98, WER: 0%, TER: 0%, total WER: 19.3214%, total TER: 8.59781%, progress (thread 0): 96.5348%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1357.36_1357.58, WER: 0%, TER: 0%, total WER: 19.3212%, total TER: 8.59777%, progress (thread 0): 96.5427%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_888.56_888.78, WER: 0%, TER: 0%, total WER: 19.321%, total TER: 8.59769%, progress (thread 0): 96.5506%]
|T|: h m m
|P|: m m
[sample: TS3003b_H03_MTD012ME_1371.93_1372.15, WER: 100%, TER: 33.3333%, total WER: 19.3219%, total TER: 8.59787%, progress (thread 0): 96.5585%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1600.07_1600.29, WER: 0%, TER: 0%, total WER: 19.3216%, total TER: 8.59779%, progress (thread 0): 96.5665%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_109.75_109.97, WER: 0%, TER: 0%, total WER: 19.3214%, total TER: 8.59775%, progress (thread 0): 96.5744%]
|T|: m a y b
|P|: i
[sample: TS3003c_H00_MTD009PM_437.36_437.58, WER: 100%, TER: 100%, total WER: 19.3223%, total TER: 8.5986%, progress (thread 0): 96.5823%]
|T|: m m h m m
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_1100.48_1100.7, WER: 100%, TER: 100%, total WER: 19.3232%, total TER: 8.59966%, progress (thread 0): 96.5902%]
|T|: y e a h
|P|: m m
[sample: TS3003c_H01_MTD011UID_1304.78_1305, WER: 100%, TER: 100%, total WER: 19.3241%, total TER: 8.60051%, progress (thread 0): 96.5981%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_2263.6_2263.82, WER: 0%, TER: 0%, total WER: 19.3239%, total TER: 8.60043%, progress (thread 0): 96.606%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_563.58_563.8, WER: 0%, TER: 0%, total WER: 19.3237%, total TER: 8.60035%, progress (thread 0): 96.6139%]
|T|: y e p
|P|: y e a p
[sample: TS3003d_H02_MTD0010ID_577.11_577.33, WER: 100%, TER: 33.3333%, total WER: 19.3246%, total TER: 8.60053%, progress (thread 0): 96.6218%]
|T|: n o
|P|: o h
[sample: TS3003d_H01_MTD011UID_1458.81_1459.03, WER: 100%, TER: 100%, total WER: 19.3255%, total TER: 8.60095%, progress (thread 0): 96.6297%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1674.77_1674.99, WER: 0%, TER: 0%, total WER: 19.3253%, total TER: 8.60091%, progress (thread 0): 96.6377%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1736.91_1737.13, WER: 0%, TER: 0%, total WER: 19.3251%, total TER: 8.60083%, progress (thread 0): 96.6456%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_1751.36_1751.58, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60079%, progress (thread 0): 96.6535%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1991.39_1991.61, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60071%, progress (thread 0): 96.6614%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1998.36_1998.58, WER: 0%, TER: 0%, total WER: 19.3244%, total TER: 8.60063%, progress (thread 0): 96.6693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_482.53_482.75, WER: 0%, TER: 0%, total WER: 19.3242%, total TER: 8.60055%, progress (thread 0): 96.6772%]
|T|: h m m
|P|: m m
[sample: EN2002a_H01_FEO070_706.29_706.51, WER: 100%, TER: 33.3333%, total WER: 19.3251%, total TER: 8.60073%, progress (thread 0): 96.6851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_805.91_806.13, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60064%, progress (thread 0): 96.693%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1103.91_1104.13, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60054%, progress (thread 0): 96.701%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_1216.04_1216.26, WER: 100%, TER: 66.6667%, total WER: 19.3256%, total TER: 8.60095%, progress (thread 0): 96.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1537.4_1537.62, WER: 0%, TER: 0%, total WER: 19.3254%, total TER: 8.60087%, progress (thread 0): 96.7168%]
|T|: j u s t
|P|: j u s t
[sample: EN2002a_H01_FEO070_1871.92_1872.14, WER: 0%, TER: 0%, total WER: 19.3252%, total TER: 8.60079%, progress (thread 0): 96.7247%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1963.02_1963.24, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60071%, progress (thread 0): 96.7326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_639.41_639.63, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60063%, progress (thread 0): 96.7405%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1397.63_1397.85, WER: 0%, TER: 0%, total WER: 19.3245%, total TER: 8.60053%, progress (thread 0): 96.7484%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1547.56_1547.78, WER: 100%, TER: 33.3333%, total WER: 19.3254%, total TER: 8.6007%, progress (thread 0): 96.7563%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_575.92_576.14, WER: 100%, TER: 28.5714%, total WER: 19.3263%, total TER: 8.60103%, progress (thread 0): 96.7642%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H02_MEE071_589.02_589.24, WER: 100%, TER: 80%, total WER: 19.3272%, total TER: 8.60186%, progress (thread 0): 96.7722%]
|T|: ' k a y
|P|: y e a h
[sample: EN2002c_H03_MEE073_684.52_684.74, WER: 100%, TER: 75%, total WER: 19.3281%, total TER: 8.60248%, progress (thread 0): 96.7801%]
|T|: o h | y e a h
|P|: m h
[sample: EN2002c_H03_MEE073_1506.68_1506.9, WER: 100%, TER: 85.7143%, total WER: 19.3299%, total TER: 8.60374%, progress (thread 0): 96.788%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1602.82_1603.04, WER: 0%, TER: 0%, total WER: 19.3297%, total TER: 8.60366%, progress (thread 0): 96.7959%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1707.51_1707.73, WER: 100%, TER: 33.3333%, total WER: 19.3306%, total TER: 8.60383%, progress (thread 0): 96.8038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2260.94_2261.16, WER: 0%, TER: 0%, total WER: 19.3304%, total TER: 8.60375%, progress (thread 0): 96.8117%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2697.66_2697.88, WER: 0%, TER: 0%, total WER: 19.3302%, total TER: 8.60367%, progress (thread 0): 96.8196%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_402.37_402.59, WER: 0%, TER: 0%, total WER: 19.33%, total TER: 8.60359%, progress (thread 0): 96.8275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_736.63_736.85, WER: 0%, TER: 0%, total WER: 19.3298%, total TER: 8.60351%, progress (thread 0): 96.8354%]
|T|: o h
|P|: h u h
[sample: EN2002d_H00_FEO070_909.94_910.16, WER: 100%, TER: 100%, total WER: 19.3307%, total TER: 8.60393%, progress (thread 0): 96.8434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1061.24_1061.46, WER: 0%, TER: 0%, total WER: 19.3304%, total TER: 8.60385%, progress (thread 0): 96.8513%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1400.88_1401.1, WER: 0%, TER: 0%, total WER: 19.3302%, total TER: 8.60377%, progress (thread 0): 96.8592%]
|T|: b u t
|P|: b u h
[sample: EN2002d_H00_FEO070_1658_1658.22, WER: 100%, TER: 33.3333%, total WER: 19.3311%, total TER: 8.60395%, progress (thread 0): 96.8671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1732.66_1732.88, WER: 0%, TER: 0%, total WER: 19.3309%, total TER: 8.60387%, progress (thread 0): 96.875%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_622.02_622.23, WER: 0%, TER: 0%, total WER: 19.3307%, total TER: 8.60383%, progress (thread 0): 96.8829%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_1323.05_1323.26, WER: 0%, TER: 0%, total WER: 19.3305%, total TER: 8.60375%, progress (thread 0): 96.8908%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1464.74_1464.95, WER: 100%, TER: 40%, total WER: 19.3314%, total TER: 8.60411%, progress (thread 0): 96.8987%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_96.42_96.63, WER: 100%, TER: 40%, total WER: 19.3323%, total TER: 8.60448%, progress (thread 0): 96.9066%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1385.07_1385.28, WER: 0%, TER: 0%, total WER: 19.3321%, total TER: 8.6044%, progress (thread 0): 96.9146%]
|T|: h m m
|P|: m m
[sample: ES2004d_H00_MEO015_26.63_26.84, WER: 100%, TER: 33.3333%, total WER: 19.333%, total TER: 8.60457%, progress (thread 0): 96.9225%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_627.79_628, WER: 100%, TER: 66.6667%, total WER: 19.3339%, total TER: 8.60498%, progress (thread 0): 96.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_39.06_39.27, WER: 0%, TER: 0%, total WER: 19.3337%, total TER: 8.6049%, progress (thread 0): 96.9383%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_169.39_169.6, WER: 100%, TER: 60%, total WER: 19.3346%, total TER: 8.60549%, progress (thread 0): 96.9462%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1286.43_1286.64, WER: 100%, TER: 60%, total WER: 19.3355%, total TER: 8.60609%, progress (thread 0): 96.9541%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H00_FIE088_1289.44_1289.65, WER: 0%, TER: 0%, total WER: 19.3353%, total TER: 8.60599%, progress (thread 0): 96.962%]
|T|: m m
|P|: o n
[sample: IS1009b_H02_FIO084_1632.09_1632.3, WER: 100%, TER: 100%, total WER: 19.3362%, total TER: 8.60642%, progress (thread 0): 96.9699%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_284.08_284.29, WER: 0%, TER: 0%, total WER: 19.3359%, total TER: 8.60634%, progress (thread 0): 96.9778%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1563.53_1563.74, WER: 0%, TER: 0%, total WER: 19.3357%, total TER: 8.60626%, progress (thread 0): 96.9858%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1707.23_1707.44, WER: 0%, TER: 0%, total WER: 19.3355%, total TER: 8.60618%, progress (thread 0): 96.9937%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_741.38_741.59, WER: 0%, TER: 0%, total WER: 19.3353%, total TER: 8.6061%, progress (thread 0): 97.0016%]
|T|: y e a h
|P|:
[sample: IS1009d_H02_FIO084_1371.67_1371.88, WER: 100%, TER: 100%, total WER: 19.3362%, total TER: 8.60695%, progress (thread 0): 97.0095%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1880.74_1880.95, WER: 100%, TER: 40%, total WER: 19.3371%, total TER: 8.60731%, progress (thread 0): 97.0174%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1295.8_1296.01, WER: 0%, TER: 0%, total WER: 19.3369%, total TER: 8.60723%, progress (thread 0): 97.0253%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_901.66_901.87, WER: 0%, TER: 0%, total WER: 19.3367%, total TER: 8.60715%, progress (thread 0): 97.0332%]
|T|: y e a h
|P|: h u h
[sample: TS3003b_H01_MTD011UID_1547.15_1547.36, WER: 100%, TER: 75%, total WER: 19.3376%, total TER: 8.60777%, progress (thread 0): 97.0411%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1091.82_1092.03, WER: 0%, TER: 0%, total WER: 19.3374%, total TER: 8.60769%, progress (thread 0): 97.049%]
|T|: y e a h
|P|: h u h
[sample: TS3003c_H01_MTD011UID_1210.7_1210.91, WER: 100%, TER: 75%, total WER: 19.3383%, total TER: 8.60831%, progress (thread 0): 97.057%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_530.79_531, WER: 100%, TER: 75%, total WER: 19.3392%, total TER: 8.60893%, progress (thread 0): 97.0649%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1465.64_1465.85, WER: 0%, TER: 0%, total WER: 19.3389%, total TER: 8.60885%, progress (thread 0): 97.0728%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1695.15_1695.36, WER: 0%, TER: 0%, total WER: 19.3387%, total TER: 8.60877%, progress (thread 0): 97.0807%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1711.66_1711.87, WER: 0%, TER: 0%, total WER: 19.3385%, total TER: 8.60869%, progress (thread 0): 97.0886%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1789.64_1789.85, WER: 0%, TER: 0%, total WER: 19.3383%, total TER: 8.60861%, progress (thread 0): 97.0965%]
|T|: n o
|P|: n o
[sample: TS3003d_H03_MTD012ME_2014.88_2015.09, WER: 0%, TER: 0%, total WER: 19.3381%, total TER: 8.60857%, progress (thread 0): 97.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2064.37_2064.58, WER: 0%, TER: 0%, total WER: 19.3379%, total TER: 8.60849%, progress (thread 0): 97.1123%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2107.64_2107.85, WER: 0%, TER: 0%, total WER: 19.3376%, total TER: 8.60841%, progress (thread 0): 97.1203%]
|T|: y e a h
|P|: y m
[sample: TS3003d_H01_MTD011UID_2462.55_2462.76, WER: 100%, TER: 75%, total WER: 19.3385%, total TER: 8.60903%, progress (thread 0): 97.1282%]
|T|: s o r r y
|P|: s o r r y
[sample: EN2002a_H02_FEO072_377.93_378.14, WER: 0%, TER: 0%, total WER: 19.3383%, total TER: 8.60893%, progress (thread 0): 97.1361%]
|T|: o h
|P|: u h
[sample: EN2002a_H03_MEE071_483.42_483.63, WER: 100%, TER: 50%, total WER: 19.3392%, total TER: 8.60912%, progress (thread 0): 97.144%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_648.41_648.62, WER: 0%, TER: 0%, total WER: 19.339%, total TER: 8.60904%, progress (thread 0): 97.1519%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1319.66_1319.87, WER: 0%, TER: 0%, total WER: 19.3388%, total TER: 8.60896%, progress (thread 0): 97.1598%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1429.36_1429.57, WER: 0%, TER: 0%, total WER: 19.3386%, total TER: 8.60888%, progress (thread 0): 97.1677%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1896.08_1896.29, WER: 0%, TER: 0%, total WER: 19.3384%, total TER: 8.6088%, progress (thread 0): 97.1756%]
|T|: y e a h
|P|: h e h
[sample: EN2002a_H03_MEE071_2122.73_2122.94, WER: 100%, TER: 50%, total WER: 19.3393%, total TER: 8.60918%, progress (thread 0): 97.1835%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_266.76_266.97, WER: 0%, TER: 0%, total WER: 19.3391%, total TER: 8.6091%, progress (thread 0): 97.1915%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_291.79_292, WER: 0%, TER: 0%, total WER: 19.3388%, total TER: 8.60902%, progress (thread 0): 97.1994%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1335.23_1335.44, WER: 0%, TER: 0%, total WER: 19.3386%, total TER: 8.60894%, progress (thread 0): 97.2073%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_21.49_21.7, WER: 100%, TER: 25%, total WER: 19.3395%, total TER: 8.6091%, progress (thread 0): 97.2152%]
|T|: o k a y
|P|: o m a y
[sample: EN2002c_H03_MEE073_183.87_184.08, WER: 100%, TER: 25%, total WER: 19.3404%, total TER: 8.60925%, progress (thread 0): 97.2231%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1748.27_1748.48, WER: 0%, TER: 0%, total WER: 19.3402%, total TER: 8.60915%, progress (thread 0): 97.231%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1810.36_1810.57, WER: 100%, TER: 100%, total WER: 19.342%, total TER: 8.61064%, progress (thread 0): 97.2389%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1861.15_1861.36, WER: 0%, TER: 0%, total WER: 19.3418%, total TER: 8.61056%, progress (thread 0): 97.2468%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2180.5_2180.71, WER: 0%, TER: 0%, total WER: 19.3416%, total TER: 8.61048%, progress (thread 0): 97.2547%]
|T|: t r u e
|P|: t r u e
[sample: EN2002c_H03_MEE073_2453.86_2454.07, WER: 0%, TER: 0%, total WER: 19.3414%, total TER: 8.6104%, progress (thread 0): 97.2627%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2819.2_2819.41, WER: 0%, TER: 0%, total WER: 19.3411%, total TER: 8.61032%, progress (thread 0): 97.2706%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_164.51_164.72, WER: 100%, TER: 40%, total WER: 19.3421%, total TER: 8.61068%, progress (thread 0): 97.2785%]
|T|: r i g h t
|P|: y e a h
[sample: EN2002d_H03_MEE073_338.56_338.77, WER: 100%, TER: 80%, total WER: 19.343%, total TER: 8.61151%, progress (thread 0): 97.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_647.49_647.7, WER: 0%, TER: 0%, total WER: 19.3427%, total TER: 8.61143%, progress (thread 0): 97.2943%]
|T|: m m
|P|: y e a h
[sample: EN2002d_H03_MEE073_782.98_783.19, WER: 100%, TER: 200%, total WER: 19.3436%, total TER: 8.61232%, progress (thread 0): 97.3022%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_998.01_998.22, WER: 0%, TER: 0%, total WER: 19.3434%, total TER: 8.61222%, progress (thread 0): 97.3101%]
|T|: o h | y e a h
|P|: o
[sample: EN2002d_H00_FEO070_1377.89_1378.1, WER: 100%, TER: 85.7143%, total WER: 19.3452%, total TER: 8.61348%, progress (thread 0): 97.318%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1391.57_1391.78, WER: 0%, TER: 0%, total WER: 19.345%, total TER: 8.6134%, progress (thread 0): 97.326%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1912.21_1912.42, WER: 50%, TER: 42.8571%, total WER: 19.3457%, total TER: 8.61396%, progress (thread 0): 97.3339%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_2147.17_2147.38, WER: 100%, TER: 33.3333%, total WER: 19.3466%, total TER: 8.61413%, progress (thread 0): 97.3418%]
|T|: o k a y
|P|: h m m
[sample: ES2004b_H00_MEO015_338.55_338.75, WER: 100%, TER: 100%, total WER: 19.3475%, total TER: 8.61498%, progress (thread 0): 97.3497%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1514.44_1514.64, WER: 0%, TER: 0%, total WER: 19.3473%, total TER: 8.6149%, progress (thread 0): 97.3576%]
|T|: o o p s
|P|: o p
[sample: ES2004c_H00_MEO015_54.23_54.43, WER: 100%, TER: 50%, total WER: 19.3482%, total TER: 8.61529%, progress (thread 0): 97.3655%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1395.98_1396.18, WER: 100%, TER: 40%, total WER: 19.3491%, total TER: 8.61565%, progress (thread 0): 97.3734%]
|T|: ' k a y
|P|: t
[sample: ES2004d_H00_MEO015_562.82_563.02, WER: 100%, TER: 100%, total WER: 19.35%, total TER: 8.6165%, progress (thread 0): 97.3813%]
|T|: y e p
|P|: y e a h
[sample: ES2004d_H02_MEE014_922.35_922.55, WER: 100%, TER: 66.6667%, total WER: 19.3509%, total TER: 8.61691%, progress (thread 0): 97.3892%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1214.04_1214.24, WER: 0%, TER: 0%, total WER: 19.3507%, total TER: 8.61683%, progress (thread 0): 97.3972%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1952.42_1952.62, WER: 0%, TER: 0%, total WER: 19.3505%, total TER: 8.61675%, progress (thread 0): 97.4051%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_784.26_784.46, WER: 0%, TER: 0%, total WER: 19.3503%, total TER: 8.61667%, progress (thread 0): 97.413%]
|T|: ' k a y
|P|: k e a y
[sample: IS1009a_H03_FIO089_794.48_794.68, WER: 100%, TER: 50%, total WER: 19.3512%, total TER: 8.61705%, progress (thread 0): 97.4209%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H02_FIO084_179.12_179.32, WER: 100%, TER: 200%, total WER: 19.3521%, total TER: 8.61794%, progress (thread 0): 97.4288%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1111.83_1112.03, WER: 0%, TER: 0%, total WER: 19.3518%, total TER: 8.61786%, progress (thread 0): 97.4367%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_2011.99_2012.19, WER: 100%, TER: 60%, total WER: 19.3527%, total TER: 8.61846%, progress (thread 0): 97.4446%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_285.99_286.19, WER: 0%, TER: 0%, total WER: 19.3525%, total TER: 8.61838%, progress (thread 0): 97.4525%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1689.26_1689.46, WER: 0%, TER: 0%, total WER: 19.3523%, total TER: 8.6183%, progress (thread 0): 97.4604%]
|T|: m m
|P|: m m
[sample: IS1009d_H01_FIO087_964.59_964.79, WER: 0%, TER: 0%, total WER: 19.3521%, total TER: 8.61826%, progress (thread 0): 97.4684%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1286.22_1286.42, WER: 0%, TER: 0%, total WER: 19.3519%, total TER: 8.61818%, progress (thread 0): 97.4763%]
|T|: m m
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1431.86_1432.06, WER: 100%, TER: 200%, total WER: 19.3528%, total TER: 8.61907%, progress (thread 0): 97.4842%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_550.33_550.53, WER: 100%, TER: 25%, total WER: 19.3537%, total TER: 8.61922%, progress (thread 0): 97.4921%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_576.96_577.16, WER: 0%, TER: 0%, total WER: 19.3535%, total TER: 8.61918%, progress (thread 0): 97.5%]
|T|: y e a h
|P|: e h
[sample: TS3003b_H01_MTD011UID_1340.71_1340.91, WER: 100%, TER: 50%, total WER: 19.3544%, total TER: 8.61957%, progress (thread 0): 97.5079%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1635.98_1636.18, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61953%, progress (thread 0): 97.5158%]
|T|: m m
|P|: m m
[sample: TS3003c_H03_MTD012ME_1936.99_1937.19, WER: 0%, TER: 0%, total WER: 19.3539%, total TER: 8.61949%, progress (thread 0): 97.5237%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_214.36_214.56, WER: 0%, TER: 0%, total WER: 19.3537%, total TER: 8.61941%, progress (thread 0): 97.5316%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_418.75_418.95, WER: 100%, TER: 66.6667%, total WER: 19.3546%, total TER: 8.61981%, progress (thread 0): 97.5396%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1236.15_1236.35, WER: 0%, TER: 0%, total WER: 19.3544%, total TER: 8.61973%, progress (thread 0): 97.5475%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1334.1_1334.3, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61965%, progress (thread 0): 97.5554%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1736.65_1736.85, WER: 0%, TER: 0%, total WER: 19.354%, total TER: 8.61957%, progress (thread 0): 97.5633%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1862.16_1862.36, WER: 0%, TER: 0%, total WER: 19.3538%, total TER: 8.61949%, progress (thread 0): 97.5712%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2423.71_2423.91, WER: 100%, TER: 33.3333%, total WER: 19.3547%, total TER: 8.61967%, progress (thread 0): 97.5791%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_6.65_6.85, WER: 0%, TER: 0%, total WER: 19.3544%, total TER: 8.61959%, progress (thread 0): 97.587%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_207.6_207.8, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61951%, progress (thread 0): 97.5949%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H00_MEE073_325.97_326.17, WER: 0%, TER: 0%, total WER: 19.354%, total TER: 8.61945%, progress (thread 0): 97.6029%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_517.59_517.79, WER: 0%, TER: 0%, total WER: 19.3538%, total TER: 8.61937%, progress (thread 0): 97.6108%]
|T|: m m h m m
|P|: m m
[sample: EN2002a_H00_MEE073_625.1_625.3, WER: 100%, TER: 60%, total WER: 19.3547%, total TER: 8.61996%, progress (thread 0): 97.6187%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1082.64_1082.84, WER: 0%, TER: 0%, total WER: 19.3545%, total TER: 8.61988%, progress (thread 0): 97.6266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1296.98_1297.18, WER: 0%, TER: 0%, total WER: 19.3543%, total TER: 8.6198%, progress (thread 0): 97.6345%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1544.23_1544.43, WER: 100%, TER: 33.3333%, total WER: 19.3552%, total TER: 8.61998%, progress (thread 0): 97.6424%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_1988.65_1988.85, WER: 0%, TER: 0%, total WER: 19.3549%, total TER: 8.6199%, progress (thread 0): 97.6503%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_58.73_58.93, WER: 100%, TER: 66.6667%, total WER: 19.3558%, total TER: 8.6203%, progress (thread 0): 97.6582%]
|T|: d o | w e | d
|P|: d o m e
[sample: EN2002b_H01_MEE071_223.55_223.75, WER: 100%, TER: 57.1429%, total WER: 19.3586%, total TER: 8.62109%, progress (thread 0): 97.6661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_233.29_233.49, WER: 0%, TER: 0%, total WER: 19.3583%, total TER: 8.62101%, progress (thread 0): 97.674%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_259.41_259.61, WER: 100%, TER: 33.3333%, total WER: 19.3592%, total TER: 8.62118%, progress (thread 0): 97.682%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_498.07_498.27, WER: 0%, TER: 0%, total WER: 19.359%, total TER: 8.6211%, progress (thread 0): 97.6899%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1591.2_1591.4, WER: 0%, TER: 0%, total WER: 19.3588%, total TER: 8.621%, progress (thread 0): 97.6978%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H00_FEO070_1723.38_1723.58, WER: 0%, TER: 0%, total WER: 19.3586%, total TER: 8.62094%, progress (thread 0): 97.7057%]
|T|: o k a y
|P|: y e a y
[sample: EN2002c_H03_MEE073_241.42_241.62, WER: 100%, TER: 50%, total WER: 19.3595%, total TER: 8.62133%, progress (thread 0): 97.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_404.9_405.1, WER: 0%, TER: 0%, total WER: 19.3593%, total TER: 8.62123%, progress (thread 0): 97.7215%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_817.73_817.93, WER: 100%, TER: 33.3333%, total WER: 19.3602%, total TER: 8.6214%, progress (thread 0): 97.7294%]
|T|: y e a h
|P|: m e m
[sample: EN2002c_H02_MEE071_1319.11_1319.31, WER: 100%, TER: 75%, total WER: 19.3611%, total TER: 8.62202%, progress (thread 0): 97.7373%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1392.95_1393.15, WER: 0%, TER: 0%, total WER: 19.3609%, total TER: 8.62194%, progress (thread 0): 97.7453%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1395.54_1395.74, WER: 0%, TER: 0%, total WER: 19.3606%, total TER: 8.62184%, progress (thread 0): 97.7532%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1637.64_1637.84, WER: 0%, TER: 0%, total WER: 19.3604%, total TER: 8.62174%, progress (thread 0): 97.7611%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1911.7_1911.9, WER: 0%, TER: 0%, total WER: 19.3602%, total TER: 8.62164%, progress (thread 0): 97.769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1939.05_1939.25, WER: 0%, TER: 0%, total WER: 19.36%, total TER: 8.62156%, progress (thread 0): 97.7769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_327.41_327.61, WER: 0%, TER: 0%, total WER: 19.3598%, total TER: 8.62148%, progress (thread 0): 97.7848%]
|T|: y e a h
|P|: y e m m
[sample: EN2002d_H03_MEE073_967.12_967.32, WER: 100%, TER: 50%, total WER: 19.3607%, total TER: 8.62186%, progress (thread 0): 97.7927%]
|T|: y e p
|P|: y e a p
[sample: ES2004a_H03_FEE016_1048.29_1048.48, WER: 100%, TER: 33.3333%, total WER: 19.3616%, total TER: 8.62203%, progress (thread 0): 97.8006%]
|T|: o o p s
|P|: m
[sample: ES2004b_H00_MEO015_572.79_572.98, WER: 100%, TER: 100%, total WER: 19.3625%, total TER: 8.62288%, progress (thread 0): 97.8085%]
|T|: a l r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1308.81_1309, WER: 100%, TER: 28.5714%, total WER: 19.3634%, total TER: 8.62321%, progress (thread 0): 97.8165%]
|T|: h u h
|P|: m m
[sample: ES2004b_H02_MEE014_1454.52_1454.71, WER: 100%, TER: 100%, total WER: 19.3643%, total TER: 8.62385%, progress (thread 0): 97.8244%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H03_FEE016_1635.41_1635.6, WER: 100%, TER: 200%, total WER: 19.3652%, total TER: 8.62474%, progress (thread 0): 97.8323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1436.81_1437, WER: 0%, TER: 0%, total WER: 19.365%, total TER: 8.62466%, progress (thread 0): 97.8402%]
|T|: t h i n g
|P|: t h i n g
[sample: ES2004d_H02_MEE014_2209.98_2210.17, WER: 0%, TER: 0%, total WER: 19.3648%, total TER: 8.62456%, progress (thread 0): 97.8481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_805.49_805.68, WER: 0%, TER: 0%, total WER: 19.3645%, total TER: 8.62448%, progress (thread 0): 97.856%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_690.57_690.76, WER: 0%, TER: 0%, total WER: 19.3643%, total TER: 8.62438%, progress (thread 0): 97.8639%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H00_FIE088_808.35_808.54, WER: 100%, TER: 80%, total WER: 19.3652%, total TER: 8.62521%, progress (thread 0): 97.8718%]
|T|: b a t t e r y
|P|: t h a t
[sample: IS1009c_H01_FIO087_1631.84_1632.03, WER: 100%, TER: 85.7143%, total WER: 19.3661%, total TER: 8.62646%, progress (thread 0): 97.8798%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1314.07_1314.26, WER: 0%, TER: 0%, total WER: 19.3659%, total TER: 8.62638%, progress (thread 0): 97.8877%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_506.74_506.93, WER: 100%, TER: 25%, total WER: 19.3668%, total TER: 8.62653%, progress (thread 0): 97.8956%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1214.55_1214.74, WER: 0%, TER: 0%, total WER: 19.3666%, total TER: 8.62649%, progress (thread 0): 97.9035%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_1401.33_1401.52, WER: 100%, TER: 25%, total WER: 19.3675%, total TER: 8.62665%, progress (thread 0): 97.9114%]
|T|: y e a h
|P|: n o h
[sample: TS3003b_H01_MTD011UID_2147.67_2147.86, WER: 100%, TER: 75%, total WER: 19.3684%, total TER: 8.62726%, progress (thread 0): 97.9193%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1090.92_1091.11, WER: 0%, TER: 0%, total WER: 19.3682%, total TER: 8.62718%, progress (thread 0): 97.9272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1632.95_1633.14, WER: 0%, TER: 0%, total WER: 19.368%, total TER: 8.6271%, progress (thread 0): 97.9351%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_375.66_375.85, WER: 100%, TER: 75%, total WER: 19.3689%, total TER: 8.62772%, progress (thread 0): 97.943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_507.6_507.79, WER: 0%, TER: 0%, total WER: 19.3687%, total TER: 8.62764%, progress (thread 0): 97.951%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_816.71_816.9, WER: 0%, TER: 0%, total WER: 19.3684%, total TER: 8.62756%, progress (thread 0): 97.9589%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_966.74_966.93, WER: 0%, TER: 0%, total WER: 19.3682%, total TER: 8.62752%, progress (thread 0): 97.9668%]
|T|: m m
|P|: m m
[sample: TS3003d_H00_MTD009PM_1884.46_1884.65, WER: 0%, TER: 0%, total WER: 19.368%, total TER: 8.62748%, progress (thread 0): 97.9747%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1975.04_1975.23, WER: 100%, TER: 66.6667%, total WER: 19.3689%, total TER: 8.62789%, progress (thread 0): 97.9826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1998.6_1998.79, WER: 0%, TER: 0%, total WER: 19.3687%, total TER: 8.6278%, progress (thread 0): 97.9905%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H00_MEE073_399.03_399.22, WER: 100%, TER: 66.6667%, total WER: 19.3696%, total TER: 8.62821%, progress (thread 0): 97.9984%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H03_MEE071_741.39_741.58, WER: 100%, TER: 100%, total WER: 19.3705%, total TER: 8.62906%, progress (thread 0): 98.0063%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1037.67_1037.86, WER: 0%, TER: 0%, total WER: 19.3703%, total TER: 8.62898%, progress (thread 0): 98.0142%]
|T|: w h
|P|: m m
[sample: EN2002a_H03_MEE071_1305.67_1305.86, WER: 100%, TER: 100%, total WER: 19.3712%, total TER: 8.62941%, progress (thread 0): 98.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1781.11_1781.3, WER: 0%, TER: 0%, total WER: 19.371%, total TER: 8.62932%, progress (thread 0): 98.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1823.51_1823.7, WER: 0%, TER: 0%, total WER: 19.3707%, total TER: 8.62924%, progress (thread 0): 98.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1836.03_1836.22, WER: 0%, TER: 0%, total WER: 19.3705%, total TER: 8.62916%, progress (thread 0): 98.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1886.9_1887.09, WER: 0%, TER: 0%, total WER: 19.3703%, total TER: 8.62908%, progress (thread 0): 98.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1917.91_1918.1, WER: 0%, TER: 0%, total WER: 19.3701%, total TER: 8.629%, progress (thread 0): 98.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_142.44_142.63, WER: 0%, TER: 0%, total WER: 19.3699%, total TER: 8.62892%, progress (thread 0): 98.0696%]
|T|: u h h u h
|P|: m m
[sample: EN2002c_H03_MEE073_1527.18_1527.37, WER: 100%, TER: 100%, total WER: 19.3708%, total TER: 8.62999%, progress (thread 0): 98.0775%]
|T|: h m m
|P|: m e h
[sample: EN2002c_H03_MEE073_1630_1630.19, WER: 100%, TER: 100%, total WER: 19.3717%, total TER: 8.63062%, progress (thread 0): 98.0854%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_2672.39_2672.58, WER: 100%, TER: 33.3333%, total WER: 19.3726%, total TER: 8.6308%, progress (thread 0): 98.0934%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2679.33_2679.52, WER: 0%, TER: 0%, total WER: 19.3724%, total TER: 8.6307%, progress (thread 0): 98.1013%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_647.1_647.29, WER: 0%, TER: 0%, total WER: 19.3722%, total TER: 8.6306%, progress (thread 0): 98.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_649.08_649.27, WER: 0%, TER: 0%, total WER: 19.3719%, total TER: 8.63051%, progress (thread 0): 98.1171%]
|T|: o k
|P|: o k a y
[sample: EN2002d_H03_MEE073_909.71_909.9, WER: 100%, TER: 100%, total WER: 19.3728%, total TER: 8.63094%, progress (thread 0): 98.125%]
|T|: s u r e
|P|: t r u e
[sample: EN2002d_H03_MEE073_1568.39_1568.58, WER: 100%, TER: 75%, total WER: 19.3737%, total TER: 8.63156%, progress (thread 0): 98.1329%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_1708.21_1708.4, WER: 100%, TER: 66.6667%, total WER: 19.3746%, total TER: 8.63196%, progress (thread 0): 98.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2037.78_2037.97, WER: 0%, TER: 0%, total WER: 19.3744%, total TER: 8.63188%, progress (thread 0): 98.1487%]
|T|: s o
|P|: s o
[sample: ES2004a_H03_FEE016_605.84_606.02, WER: 0%, TER: 0%, total WER: 19.3742%, total TER: 8.63184%, progress (thread 0): 98.1566%]
|T|: ' k a y
|P|:
[sample: ES2004b_H00_MEO015_1027.78_1027.96, WER: 100%, TER: 100%, total WER: 19.3751%, total TER: 8.63269%, progress (thread 0): 98.1646%]
|T|: m m h m m
|P|: m m
[sample: ES2004c_H03_FEE016_1360.96_1361.14, WER: 100%, TER: 60%, total WER: 19.376%, total TER: 8.63329%, progress (thread 0): 98.1725%]
|T|: m m
|P|: h m m
[sample: ES2004d_H02_MEE014_2221.15_2221.33, WER: 100%, TER: 50%, total WER: 19.3769%, total TER: 8.63348%, progress (thread 0): 98.1804%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_361.5_361.68, WER: 100%, TER: 60%, total WER: 19.3778%, total TER: 8.63408%, progress (thread 0): 98.1883%]
|T|: u m
|P|: i m a n
[sample: IS1009c_H03_FIO089_1368.34_1368.52, WER: 100%, TER: 150%, total WER: 19.3787%, total TER: 8.63474%, progress (thread 0): 98.1962%]
|T|: a n d
|P|: a n d
[sample: IS1009c_H01_FIO087_1687.91_1688.09, WER: 0%, TER: 0%, total WER: 19.3785%, total TER: 8.63468%, progress (thread 0): 98.2041%]
|T|: m m
|P|: m m
[sample: IS1009d_H00_FIE088_1039.16_1039.34, WER: 0%, TER: 0%, total WER: 19.3783%, total TER: 8.63464%, progress (thread 0): 98.212%]
|T|: y e s
|P|: y e a
[sample: IS1009d_H01_FIO087_1532.02_1532.2, WER: 100%, TER: 33.3333%, total WER: 19.3792%, total TER: 8.63481%, progress (thread 0): 98.2199%]
|T|: n o
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1017.51_1017.69, WER: 100%, TER: 200%, total WER: 19.3801%, total TER: 8.6357%, progress (thread 0): 98.2278%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_583.19_583.37, WER: 0%, TER: 0%, total WER: 19.3799%, total TER: 8.63562%, progress (thread 0): 98.2358%]
|T|: m m
|P|: h m h
[sample: TS3003b_H01_MTD011UID_1188.77_1188.95, WER: 100%, TER: 100%, total WER: 19.3808%, total TER: 8.63604%, progress (thread 0): 98.2437%]
|T|: y e a h
|P|: y e m
[sample: TS3003b_H02_MTD0010ID_1801.24_1801.42, WER: 100%, TER: 50%, total WER: 19.3817%, total TER: 8.63643%, progress (thread 0): 98.2516%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1801.82_1802, WER: 0%, TER: 0%, total WER: 19.3815%, total TER: 8.63639%, progress (thread 0): 98.2595%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1131_1131.18, WER: 100%, TER: 33.3333%, total WER: 19.3824%, total TER: 8.63656%, progress (thread 0): 98.2674%]
|T|: a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_2394.1_2394.28, WER: 100%, TER: 50%, total WER: 19.3833%, total TER: 8.63675%, progress (thread 0): 98.2753%]
|T|: n o
|P|: m h
[sample: EN2002a_H00_MEE073_23.11_23.29, WER: 100%, TER: 100%, total WER: 19.3842%, total TER: 8.63718%, progress (thread 0): 98.2832%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_32.53_32.71, WER: 0%, TER: 0%, total WER: 19.3839%, total TER: 8.6371%, progress (thread 0): 98.2911%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_106.69_106.87, WER: 100%, TER: 33.3333%, total WER: 19.3848%, total TER: 8.63727%, progress (thread 0): 98.299%]
|T|: y e a h
|P|: m h
[sample: EN2002a_H00_MEE073_1441.15_1441.33, WER: 100%, TER: 75%, total WER: 19.3857%, total TER: 8.63789%, progress (thread 0): 98.307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1717.2_1717.38, WER: 0%, TER: 0%, total WER: 19.3855%, total TER: 8.63781%, progress (thread 0): 98.3149%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_530.52_530.7, WER: 0%, TER: 0%, total WER: 19.3853%, total TER: 8.63773%, progress (thread 0): 98.3228%]
|T|: i s | i t
|P|: i s | i t
[sample: EN2002b_H01_MEE071_1142.07_1142.25, WER: 0%, TER: 0%, total WER: 19.3849%, total TER: 8.63763%, progress (thread 0): 98.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1339.32_1339.5, WER: 0%, TER: 0%, total WER: 19.3847%, total TER: 8.63755%, progress (thread 0): 98.3386%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_186.7_186.88, WER: 0%, TER: 0%, total WER: 19.3844%, total TER: 8.63744%, progress (thread 0): 98.3465%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_536.86_537.04, WER: 100%, TER: 28.5714%, total WER: 19.3853%, total TER: 8.63777%, progress (thread 0): 98.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1667.09_1667.27, WER: 0%, TER: 0%, total WER: 19.3851%, total TER: 8.63769%, progress (thread 0): 98.3623%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_363.49_363.67, WER: 100%, TER: 33.3333%, total WER: 19.386%, total TER: 8.63786%, progress (thread 0): 98.3703%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_500.44_500.62, WER: 0%, TER: 0%, total WER: 19.3858%, total TER: 8.63776%, progress (thread 0): 98.3782%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_508.48_508.66, WER: 0%, TER: 0%, total WER: 19.3856%, total TER: 8.63768%, progress (thread 0): 98.3861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_512.59_512.77, WER: 0%, TER: 0%, total WER: 19.3854%, total TER: 8.6376%, progress (thread 0): 98.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_518.66_518.84, WER: 0%, TER: 0%, total WER: 19.3852%, total TER: 8.63752%, progress (thread 0): 98.4019%]
|T|: y e a h
|P|: h u h
[sample: EN2002d_H00_FEO070_882.18_882.36, WER: 100%, TER: 75%, total WER: 19.3861%, total TER: 8.63814%, progress (thread 0): 98.4098%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_1451.31_1451.49, WER: 0%, TER: 0%, total WER: 19.3859%, total TER: 8.6381%, progress (thread 0): 98.4177%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1581.55_1581.73, WER: 0%, TER: 0%, total WER: 19.3856%, total TER: 8.638%, progress (thread 0): 98.4256%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_2062.62_2062.8, WER: 0%, TER: 0%, total WER: 19.3854%, total TER: 8.63792%, progress (thread 0): 98.4335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2166.41_2166.59, WER: 0%, TER: 0%, total WER: 19.3852%, total TER: 8.63784%, progress (thread 0): 98.4415%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_2172.96_2173.14, WER: 0%, TER: 0%, total WER: 19.385%, total TER: 8.63774%, progress (thread 0): 98.4494%]
|T|: a h
|P|: e h
[sample: ES2004b_H01_FEE013_843.75_843.92, WER: 100%, TER: 50%, total WER: 19.3859%, total TER: 8.63793%, progress (thread 0): 98.4573%]
|T|: y e p
|P|: y e a h
[sample: ES2004c_H00_MEO015_2220.58_2220.75, WER: 100%, TER: 66.6667%, total WER: 19.3868%, total TER: 8.63833%, progress (thread 0): 98.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_381.27_381.44, WER: 0%, TER: 0%, total WER: 19.3866%, total TER: 8.63825%, progress (thread 0): 98.4731%]
|T|: h i
|P|: h i
[sample: IS1009c_H02_FIO084_43.25_43.42, WER: 0%, TER: 0%, total WER: 19.3864%, total TER: 8.63821%, progress (thread 0): 98.481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_347.27_347.44, WER: 0%, TER: 0%, total WER: 19.3861%, total TER: 8.63813%, progress (thread 0): 98.4889%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_784.48_784.65, WER: 0%, TER: 0%, total WER: 19.3859%, total TER: 8.63809%, progress (thread 0): 98.4968%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_182.69_182.86, WER: 100%, TER: 66.6667%, total WER: 19.3868%, total TER: 8.6385%, progress (thread 0): 98.5047%]
|T|: h m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_748.2_748.37, WER: 100%, TER: 33.3333%, total WER: 19.3877%, total TER: 8.63867%, progress (thread 0): 98.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1432.6_1432.77, WER: 0%, TER: 0%, total WER: 19.3875%, total TER: 8.63859%, progress (thread 0): 98.5206%]
|T|: n o
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1442.45_1442.62, WER: 100%, TER: 200%, total WER: 19.3884%, total TER: 8.63948%, progress (thread 0): 98.5285%]
|T|: y e a h
|P|: a h
[sample: TS3003b_H01_MTD011UID_1584.22_1584.39, WER: 100%, TER: 50%, total WER: 19.3893%, total TER: 8.63986%, progress (thread 0): 98.5364%]
|T|: n o
|P|: u h
[sample: TS3003b_H01_MTD011UID_1779.4_1779.57, WER: 100%, TER: 100%, total WER: 19.3902%, total TER: 8.64029%, progress (thread 0): 98.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2009.37_2009.54, WER: 0%, TER: 0%, total WER: 19.39%, total TER: 8.64021%, progress (thread 0): 98.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1183.13_1183.3, WER: 0%, TER: 0%, total WER: 19.3898%, total TER: 8.64013%, progress (thread 0): 98.5601%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2272.21_2272.38, WER: 0%, TER: 0%, total WER: 19.3896%, total TER: 8.64005%, progress (thread 0): 98.568%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_306.52_306.69, WER: 0%, TER: 0%, total WER: 19.3893%, total TER: 8.63997%, progress (thread 0): 98.576%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1283.14_1283.31, WER: 100%, TER: 75%, total WER: 19.3902%, total TER: 8.64058%, progress (thread 0): 98.5839%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_549.83_550, WER: 100%, TER: 33.3333%, total WER: 19.3911%, total TER: 8.64076%, progress (thread 0): 98.5918%]
|T|: h m m
|P|: m h
[sample: EN2002a_H00_MEE073_1153.54_1153.71, WER: 100%, TER: 66.6667%, total WER: 19.392%, total TER: 8.64116%, progress (thread 0): 98.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1180.31_1180.48, WER: 0%, TER: 0%, total WER: 19.3918%, total TER: 8.64108%, progress (thread 0): 98.6076%]
|T|: h m m
|P|: m m
[sample: EN2002a_H02_FEO072_2053.69_2053.86, WER: 100%, TER: 33.3333%, total WER: 19.3927%, total TER: 8.64125%, progress (thread 0): 98.6155%]
|T|: y e a h
|P|: y e a m
[sample: EN2002a_H01_FEO070_2086.54_2086.71, WER: 100%, TER: 25%, total WER: 19.3936%, total TER: 8.6414%, progress (thread 0): 98.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_423.51_423.68, WER: 0%, TER: 0%, total WER: 19.3934%, total TER: 8.64132%, progress (thread 0): 98.6313%]
|T|: y e a h
|P|: m h
[sample: EN2002b_H03_MEE073_1344.27_1344.44, WER: 100%, TER: 75%, total WER: 19.3943%, total TER: 8.64194%, progress (thread 0): 98.6392%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_1400.92_1401.09, WER: 100%, TER: 100%, total WER: 19.3952%, total TER: 8.64279%, progress (thread 0): 98.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1518.9_1519.07, WER: 0%, TER: 0%, total WER: 19.395%, total TER: 8.64271%, progress (thread 0): 98.6551%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_55.6_55.77, WER: 0%, TER: 0%, total WER: 19.3948%, total TER: 8.64263%, progress (thread 0): 98.663%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_711.52_711.69, WER: 0%, TER: 0%, total WER: 19.3946%, total TER: 8.64255%, progress (thread 0): 98.6709%]
|T|: b u t
|P|: m
[sample: EN2002d_H02_MEE071_431.16_431.33, WER: 100%, TER: 100%, total WER: 19.3955%, total TER: 8.64319%, progress (thread 0): 98.6788%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_649.62_649.79, WER: 0%, TER: 0%, total WER: 19.3952%, total TER: 8.64311%, progress (thread 0): 98.6867%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_831.51_831.68, WER: 0%, TER: 0%, total WER: 19.395%, total TER: 8.64303%, progress (thread 0): 98.6946%]
|T|: s o
|P|: m
[sample: EN2002d_H03_MEE073_1058.82_1058.99, WER: 100%, TER: 100%, total WER: 19.3959%, total TER: 8.64345%, progress (thread 0): 98.7025%]
|T|: o h
|P|: o h
[sample: EN2002d_H00_FEO070_1459.48_1459.65, WER: 0%, TER: 0%, total WER: 19.3957%, total TER: 8.64341%, progress (thread 0): 98.7104%]
|T|: r i g h t
|P|: r i g t
[sample: EN2002d_H03_MEE073_1858.1_1858.27, WER: 100%, TER: 20%, total WER: 19.3966%, total TER: 8.64354%, progress (thread 0): 98.7184%]
|T|: i | m e a n
|P|: a n y
[sample: ES2004c_H03_FEE016_691.32_691.48, WER: 100%, TER: 83.3333%, total WER: 19.3984%, total TER: 8.64458%, progress (thread 0): 98.7263%]
|T|: o k a y
|P|: m
[sample: ES2004c_H03_FEE016_1560.36_1560.52, WER: 100%, TER: 100%, total WER: 19.3993%, total TER: 8.64543%, progress (thread 0): 98.7342%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H00_FIE088_757.84_758, WER: 0%, TER: 0%, total WER: 19.3991%, total TER: 8.64535%, progress (thread 0): 98.7421%]
|T|: n o
|P|: n o
[sample: IS1009d_H01_FIO087_584.12_584.28, WER: 0%, TER: 0%, total WER: 19.3989%, total TER: 8.64531%, progress (thread 0): 98.75%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_740.48_740.64, WER: 0%, TER: 0%, total WER: 19.3987%, total TER: 8.64523%, progress (thread 0): 98.7579%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_826.94_827.1, WER: 0%, TER: 0%, total WER: 19.3984%, total TER: 8.64515%, progress (thread 0): 98.7658%]
|T|: n o
|P|: n o
[sample: IS1009d_H03_FIO089_862.59_862.75, WER: 0%, TER: 0%, total WER: 19.3982%, total TER: 8.64511%, progress (thread 0): 98.7737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1025.48_1025.64, WER: 0%, TER: 0%, total WER: 19.398%, total TER: 8.64503%, progress (thread 0): 98.7816%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_580.47_580.63, WER: 0%, TER: 0%, total WER: 19.3978%, total TER: 8.64495%, progress (thread 0): 98.7896%]
|T|: u h
|P|: i h
[sample: TS3003a_H00_MTD009PM_617.55_617.71, WER: 100%, TER: 50%, total WER: 19.3987%, total TER: 8.64514%, progress (thread 0): 98.7975%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1421.57_1421.73, WER: 100%, TER: 150%, total WER: 19.3996%, total TER: 8.6458%, progress (thread 0): 98.8054%]
|T|: h m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1789.09_1789.25, WER: 100%, TER: 33.3333%, total WER: 19.4005%, total TER: 8.64597%, progress (thread 0): 98.8133%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2009.54_2009.7, WER: 0%, TER: 0%, total WER: 19.4003%, total TER: 8.64589%, progress (thread 0): 98.8212%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1865.26_1865.42, WER: 100%, TER: 66.6667%, total WER: 19.4012%, total TER: 8.6463%, progress (thread 0): 98.8291%]
|T|: o h
|P|: t o
[sample: TS3003d_H00_MTD009PM_2587.23_2587.39, WER: 100%, TER: 100%, total WER: 19.4021%, total TER: 8.64672%, progress (thread 0): 98.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_376.27_376.43, WER: 0%, TER: 0%, total WER: 19.4019%, total TER: 8.64664%, progress (thread 0): 98.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_720.58_720.74, WER: 0%, TER: 0%, total WER: 19.4017%, total TER: 8.64656%, progress (thread 0): 98.8529%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H00_MEE073_737.85_738.01, WER: 100%, TER: 100%, total WER: 19.4026%, total TER: 8.64741%, progress (thread 0): 98.8608%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_844.91_845.07, WER: 0%, TER: 0%, total WER: 19.4023%, total TER: 8.64733%, progress (thread 0): 98.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1041.33_1041.49, WER: 0%, TER: 0%, total WER: 19.4021%, total TER: 8.64725%, progress (thread 0): 98.8766%]
|T|: y e a h
|P|: m e h
[sample: EN2002a_H00_MEE073_1050.03_1050.19, WER: 100%, TER: 50%, total WER: 19.403%, total TER: 8.64763%, progress (thread 0): 98.8845%]
|T|: w h y
|P|: m
[sample: EN2002a_H00_MEE073_1237.91_1238.07, WER: 100%, TER: 100%, total WER: 19.4039%, total TER: 8.64827%, progress (thread 0): 98.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1596.11_1596.27, WER: 0%, TER: 0%, total WER: 19.4037%, total TER: 8.64819%, progress (thread 0): 98.9003%]
|T|: s o r r y
|P|: s u r e
[sample: EN2002a_H00_MEE073_1813.61_1813.77, WER: 100%, TER: 60%, total WER: 19.4046%, total TER: 8.64879%, progress (thread 0): 98.9082%]
|T|: s u r e
|P|: s u e
[sample: EN2002a_H00_MEE073_1881.96_1882.12, WER: 100%, TER: 25%, total WER: 19.4055%, total TER: 8.64894%, progress (thread 0): 98.9161%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_39.76_39.92, WER: 0%, TER: 0%, total WER: 19.4053%, total TER: 8.64886%, progress (thread 0): 98.924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_317.94_318.1, WER: 0%, TER: 0%, total WER: 19.4051%, total TER: 8.64878%, progress (thread 0): 98.932%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1075.72_1075.88, WER: 0%, TER: 0%, total WER: 19.4049%, total TER: 8.64868%, progress (thread 0): 98.9399%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_1155.04_1155.2, WER: 100%, TER: 100%, total WER: 19.4058%, total TER: 8.64953%, progress (thread 0): 98.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_713.06_713.22, WER: 0%, TER: 0%, total WER: 19.4055%, total TER: 8.64945%, progress (thread 0): 98.9557%]
|T|: o h
|P|: m m
[sample: EN2002c_H03_MEE073_1210.59_1210.75, WER: 100%, TER: 100%, total WER: 19.4064%, total TER: 8.64987%, progress (thread 0): 98.9636%]
|T|: r i g h t
|P|: o r h
[sample: EN2002c_H03_MEE073_1484.69_1484.85, WER: 100%, TER: 80%, total WER: 19.4073%, total TER: 8.6507%, progress (thread 0): 98.9715%]
|T|: y e a h
|P|: m m
[sample: EN2002c_H03_MEE073_2554.98_2555.14, WER: 100%, TER: 100%, total WER: 19.4082%, total TER: 8.65155%, progress (thread 0): 98.9794%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_2776.86_2777.02, WER: 100%, TER: 33.3333%, total WER: 19.4091%, total TER: 8.65172%, progress (thread 0): 98.9873%]
|T|: m m
|P|: m m
[sample: EN2002d_H03_MEE073_645.29_645.45, WER: 0%, TER: 0%, total WER: 19.4089%, total TER: 8.65168%, progress (thread 0): 98.9952%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1397.11_1397.27, WER: 0%, TER: 0%, total WER: 19.4087%, total TER: 8.6516%, progress (thread 0): 99.0032%]
|T|: o o p s
|P|: m
[sample: ES2004a_H00_MEO015_188.28_188.43, WER: 100%, TER: 100%, total WER: 19.4096%, total TER: 8.65245%, progress (thread 0): 99.0111%]
|T|: o o h
|P|: m m
[sample: ES2004b_H02_MEE014_534.45_534.6, WER: 100%, TER: 100%, total WER: 19.4105%, total TER: 8.65309%, progress (thread 0): 99.019%]
|T|: ' k a y
|P|: y e a h
[sample: ES2004c_H01_FEE013_472.18_472.33, WER: 100%, TER: 75%, total WER: 19.4114%, total TER: 8.6537%, progress (thread 0): 99.0269%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_693.82_693.97, WER: 100%, TER: 66.6667%, total WER: 19.4123%, total TER: 8.65411%, progress (thread 0): 99.0348%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1647.59_1647.74, WER: 0%, TER: 0%, total WER: 19.4121%, total TER: 8.65403%, progress (thread 0): 99.0427%]
|T|: y e a h
|P|: s o m
[sample: IS1009d_H02_FIO084_1392_1392.15, WER: 100%, TER: 100%, total WER: 19.413%, total TER: 8.65488%, progress (thread 0): 99.0506%]
|T|: t w o
|P|: d o
[sample: IS1009d_H01_FIO087_1586.28_1586.43, WER: 100%, TER: 66.6667%, total WER: 19.4139%, total TER: 8.65528%, progress (thread 0): 99.0585%]
|T|: n o
|P|: y e h
[sample: IS1009d_H01_FIO087_1824.98_1825.13, WER: 100%, TER: 150%, total WER: 19.4148%, total TER: 8.65594%, progress (thread 0): 99.0665%]
|T|: y e a h
|P|: m e a h
[sample: IS1009d_H02_FIO084_1883.61_1883.76, WER: 100%, TER: 25%, total WER: 19.4157%, total TER: 8.65609%, progress (thread 0): 99.0744%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_567.51_567.66, WER: 0%, TER: 0%, total WER: 19.4155%, total TER: 8.65601%, progress (thread 0): 99.0823%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_2049.54_2049.69, WER: 0%, TER: 0%, total WER: 19.4153%, total TER: 8.65593%, progress (thread 0): 99.0902%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2041.85_2042, WER: 0%, TER: 0%, total WER: 19.415%, total TER: 8.65589%, progress (thread 0): 99.0981%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_639.89_640.04, WER: 100%, TER: 66.6667%, total WER: 19.4159%, total TER: 8.65629%, progress (thread 0): 99.106%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_967.58_967.73, WER: 0%, TER: 0%, total WER: 19.4157%, total TER: 8.65621%, progress (thread 0): 99.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1030.96_1031.11, WER: 0%, TER: 0%, total WER: 19.4155%, total TER: 8.65613%, progress (thread 0): 99.1218%]
|T|: y e a h
|P|: o h
[sample: EN2002a_H01_FEO070_1104.83_1104.98, WER: 100%, TER: 75%, total WER: 19.4164%, total TER: 8.65675%, progress (thread 0): 99.1297%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1527.69_1527.84, WER: 100%, TER: 33.3333%, total WER: 19.4173%, total TER: 8.65692%, progress (thread 0): 99.1377%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H00_MEE073_1539.77_1539.92, WER: 100%, TER: 100%, total WER: 19.4182%, total TER: 8.65777%, progress (thread 0): 99.1456%]
|T|: y e a h
|P|: m h
[sample: EN2002a_H00_MEE073_1744.01_1744.16, WER: 100%, TER: 75%, total WER: 19.4191%, total TER: 8.65839%, progress (thread 0): 99.1535%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1785.21_1785.36, WER: 0%, TER: 0%, total WER: 19.4189%, total TER: 8.65831%, progress (thread 0): 99.1614%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H03_MEE073_382.32_382.47, WER: 100%, TER: 66.6667%, total WER: 19.4198%, total TER: 8.65871%, progress (thread 0): 99.1693%]
|T|: u h
|P|: y e a h
[sample: EN2002b_H02_FEO072_424.35_424.5, WER: 100%, TER: 150%, total WER: 19.4207%, total TER: 8.65937%, progress (thread 0): 99.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_496.03_496.18, WER: 0%, TER: 0%, total WER: 19.4205%, total TER: 8.65929%, progress (thread 0): 99.1851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_897.7_897.85, WER: 0%, TER: 0%, total WER: 19.4203%, total TER: 8.65921%, progress (thread 0): 99.193%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2198.65_2198.8, WER: 0%, TER: 0%, total WER: 19.42%, total TER: 8.65913%, progress (thread 0): 99.201%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1290.13_1290.28, WER: 0%, TER: 0%, total WER: 19.4198%, total TER: 8.65904%, progress (thread 0): 99.2089%]
|T|: n o
|P|: o h
[sample: EN2002d_H03_MEE073_1424.03_1424.18, WER: 100%, TER: 100%, total WER: 19.4207%, total TER: 8.65947%, progress (thread 0): 99.2168%]
|T|: r i g h t
|P|: h
[sample: EN2002d_H03_MEE073_1909.61_1909.76, WER: 100%, TER: 80%, total WER: 19.4216%, total TER: 8.6603%, progress (thread 0): 99.2247%]
|T|: o k a y
|P|: m
[sample: EN2002d_H03_MEE073_1985.35_1985.5, WER: 100%, TER: 100%, total WER: 19.4225%, total TER: 8.66115%, progress (thread 0): 99.2326%]
|T|: i | t h
|P|: i
[sample: ES2004b_H01_FEE013_2305.5_2305.64, WER: 50%, TER: 75%, total WER: 19.4232%, total TER: 8.66176%, progress (thread 0): 99.2405%]
|T|: m m
|P|: m
[sample: ES2004c_H03_FEE016_777.13_777.27, WER: 100%, TER: 50%, total WER: 19.4241%, total TER: 8.66196%, progress (thread 0): 99.2484%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_56.9_57.04, WER: 0%, TER: 0%, total WER: 19.4239%, total TER: 8.66187%, progress (thread 0): 99.2563%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_645.27_645.41, WER: 100%, TER: 66.6667%, total WER: 19.4248%, total TER: 8.66228%, progress (thread 0): 99.2642%]
|T|: m m h m m
|P|: s o
[sample: IS1009d_H00_FIE088_770.97_771.11, WER: 100%, TER: 100%, total WER: 19.4257%, total TER: 8.66334%, progress (thread 0): 99.2721%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_189.69_189.83, WER: 100%, TER: 66.6667%, total WER: 19.4266%, total TER: 8.66374%, progress (thread 0): 99.2801%]
|T|: m m
|P|: m
[sample: TS3003a_H01_MTD011UID_903.08_903.22, WER: 100%, TER: 50%, total WER: 19.4275%, total TER: 8.66394%, progress (thread 0): 99.288%]
|T|: m m
|P|: m
[sample: TS3003b_H01_MTD011UID_2047.35_2047.49, WER: 100%, TER: 50%, total WER: 19.4284%, total TER: 8.66413%, progress (thread 0): 99.2959%]
|T|: ' k a y
|P|: y h
[sample: TS3003b_H03_MTD012ME_2140.48_2140.62, WER: 100%, TER: 100%, total WER: 19.4293%, total TER: 8.66498%, progress (thread 0): 99.3038%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1869.23_1869.37, WER: 0%, TER: 0%, total WER: 19.4291%, total TER: 8.6649%, progress (thread 0): 99.3117%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_1390.61_1390.75, WER: 100%, TER: 25%, total WER: 19.43%, total TER: 8.66505%, progress (thread 0): 99.3196%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_2021.71_2021.85, WER: 100%, TER: 25%, total WER: 19.4309%, total TER: 8.6652%, progress (thread 0): 99.3275%]
|T|: r i g h t
|P|: i h
[sample: EN2002a_H00_MEE073_1184.15_1184.29, WER: 100%, TER: 60%, total WER: 19.4318%, total TER: 8.6658%, progress (thread 0): 99.3354%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_1193.94_1194.08, WER: 100%, TER: 66.6667%, total WER: 19.4327%, total TER: 8.6662%, progress (thread 0): 99.3434%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H00_MEE073_2048.07_2048.21, WER: 100%, TER: 66.6667%, total WER: 19.4336%, total TER: 8.6666%, progress (thread 0): 99.3513%]
|T|: ' k a y
|P|:
[sample: EN2002c_H03_MEE073_103.84_103.98, WER: 100%, TER: 100%, total WER: 19.4345%, total TER: 8.66745%, progress (thread 0): 99.3592%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_531.32_531.46, WER: 0%, TER: 0%, total WER: 19.4342%, total TER: 8.66737%, progress (thread 0): 99.3671%]
|T|: y e a h
|P|: y e h
[sample: EN2002c_H03_MEE073_667.22_667.36, WER: 100%, TER: 25%, total WER: 19.4351%, total TER: 8.66752%, progress (thread 0): 99.375%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1476.08_1476.22, WER: 0%, TER: 0%, total WER: 19.4349%, total TER: 8.66744%, progress (thread 0): 99.3829%]
|T|: m m
|P|: m
[sample: EN2002c_H03_MEE073_2096.91_2097.05, WER: 100%, TER: 50%, total WER: 19.4358%, total TER: 8.66764%, progress (thread 0): 99.3908%]
|T|: o k a y
|P|: m e m
[sample: EN2002d_H00_FEO070_1251.9_1252.04, WER: 100%, TER: 100%, total WER: 19.4367%, total TER: 8.66848%, progress (thread 0): 99.3987%]
|T|: m m
|P|: m e a
[sample: ES2004c_H03_FEE016_98.28_98.41, WER: 100%, TER: 100%, total WER: 19.4376%, total TER: 8.66891%, progress (thread 0): 99.4066%]
|T|: s
|P|: m
[sample: ES2004d_H03_FEE016_1370.74_1370.87, WER: 100%, TER: 100%, total WER: 19.4385%, total TER: 8.66912%, progress (thread 0): 99.4146%]
|T|: o r | a | b
|P|: m a
[sample: IS1009a_H01_FIO087_573.12_573.25, WER: 100%, TER: 83.3333%, total WER: 19.4412%, total TER: 8.67016%, progress (thread 0): 99.4225%]
|T|: h m m
|P|: m m
[sample: IS1009c_H02_FIO084_144.08_144.21, WER: 100%, TER: 33.3333%, total WER: 19.4421%, total TER: 8.67033%, progress (thread 0): 99.4304%]
|T|: o k a y
|P|: m
[sample: IS1009d_H00_FIE088_604.16_604.29, WER: 100%, TER: 100%, total WER: 19.443%, total TER: 8.67118%, progress (thread 0): 99.4383%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_942.55_942.68, WER: 100%, TER: 66.6667%, total WER: 19.4439%, total TER: 8.67159%, progress (thread 0): 99.4462%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_392.63_392.76, WER: 100%, TER: 66.6667%, total WER: 19.4448%, total TER: 8.67199%, progress (thread 0): 99.4541%]
|T|: h u h
|P|: m
[sample: TS3003a_H01_MTD011UID_1266.08_1266.21, WER: 100%, TER: 100%, total WER: 19.4457%, total TER: 8.67263%, progress (thread 0): 99.462%]
|T|: o h
|P|: m h
[sample: TS3003a_H01_MTD011UID_1335.11_1335.24, WER: 100%, TER: 50%, total WER: 19.4466%, total TER: 8.67282%, progress (thread 0): 99.4699%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1475.42_1475.55, WER: 100%, TER: 66.6667%, total WER: 19.4475%, total TER: 8.67322%, progress (thread 0): 99.4778%]
|T|: y e a h
|P|: m m
[sample: TS3003b_H01_MTD011UID_1528.52_1528.65, WER: 100%, TER: 100%, total WER: 19.4484%, total TER: 8.67407%, progress (thread 0): 99.4858%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1173.23_1173.36, WER: 0%, TER: 0%, total WER: 19.4482%, total TER: 8.67403%, progress (thread 0): 99.4937%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_1693.45_1693.58, WER: 100%, TER: 25%, total WER: 19.4491%, total TER: 8.67418%, progress (thread 0): 99.5016%]
|T|: ' k a y
|P|: m e m
[sample: EN2002a_H00_MEE073_32.58_32.71, WER: 100%, TER: 100%, total WER: 19.45%, total TER: 8.67503%, progress (thread 0): 99.5095%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_984.87_985, WER: 100%, TER: 66.6667%, total WER: 19.4509%, total TER: 8.67544%, progress (thread 0): 99.5174%]
|T|: w e l l | f
|P|: h
[sample: EN2002a_H00_MEE073_1458.73_1458.86, WER: 100%, TER: 100%, total WER: 19.4527%, total TER: 8.67671%, progress (thread 0): 99.5253%]
|T|: y e p
|P|: y e a
[sample: EN2002b_H01_MEE071_493.19_493.32, WER: 100%, TER: 33.3333%, total WER: 19.4536%, total TER: 8.67688%, progress (thread 0): 99.5332%]
|T|: y e a h
|P|: y e a
[sample: EN2002c_H03_MEE073_665.01_665.14, WER: 100%, TER: 25%, total WER: 19.4545%, total TER: 8.67703%, progress (thread 0): 99.5411%]
|T|: o h
|P|: m
[sample: EN2002d_H01_FEO072_135.16_135.29, WER: 100%, TER: 100%, total WER: 19.4554%, total TER: 8.67746%, progress (thread 0): 99.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_726.53_726.66, WER: 0%, TER: 0%, total WER: 19.4552%, total TER: 8.67738%, progress (thread 0): 99.557%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_794.79_794.92, WER: 0%, TER: 0%, total WER: 19.455%, total TER: 8.67729%, progress (thread 0): 99.5649%]
|T|: y e a h
|P|: m e a
[sample: EN2002d_H00_FEO070_1595.77_1595.9, WER: 100%, TER: 50%, total WER: 19.4559%, total TER: 8.67768%, progress (thread 0): 99.5728%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_1641.64_1641.77, WER: 100%, TER: 100%, total WER: 19.4568%, total TER: 8.6781%, progress (thread 0): 99.5807%]
|T|: y e a h
|P|: m m
[sample: ES2004b_H00_MEO015_922.62_922.74, WER: 100%, TER: 100%, total WER: 19.4576%, total TER: 8.67895%, progress (thread 0): 99.5886%]
|T|: o k a y
|P|: m h
[sample: ES2004b_H00_MEO015_1977.87_1977.99, WER: 100%, TER: 100%, total WER: 19.4585%, total TER: 8.6798%, progress (thread 0): 99.5965%]
|T|: y e a h
|P|: m e m
[sample: IS1009b_H02_FIO084_173.86_173.98, WER: 100%, TER: 75%, total WER: 19.4594%, total TER: 8.68042%, progress (thread 0): 99.6044%]
|T|: h m m
|P|: m
[sample: IS1009c_H02_FIO084_1703.14_1703.26, WER: 100%, TER: 66.6667%, total WER: 19.4603%, total TER: 8.68082%, progress (thread 0): 99.6123%]
|T|: m m | m m
|P|:
[sample: IS1009d_H03_FIO089_870.76_870.88, WER: 100%, TER: 100%, total WER: 19.4621%, total TER: 8.68188%, progress (thread 0): 99.6203%]
|T|: y e p
|P|: y e a
[sample: TS3003a_H01_MTD011UID_257.2_257.32, WER: 100%, TER: 33.3333%, total WER: 19.463%, total TER: 8.68205%, progress (thread 0): 99.6282%]
|T|: y e a h
|P|: m
[sample: EN2002a_H03_MEE071_170.81_170.93, WER: 100%, TER: 100%, total WER: 19.4639%, total TER: 8.6829%, progress (thread 0): 99.6361%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_1468.26_1468.38, WER: 100%, TER: 66.6667%, total WER: 19.4648%, total TER: 8.6833%, progress (thread 0): 99.644%]
|T|: n o
|P|: m h
[sample: EN2002b_H03_MEE073_4.83_4.95, WER: 100%, TER: 100%, total WER: 19.4657%, total TER: 8.68373%, progress (thread 0): 99.6519%]
|T|: a l t e r
|P|: y e a
[sample: EN2002b_H03_MEE073_170.46_170.58, WER: 100%, TER: 80%, total WER: 19.4666%, total TER: 8.68456%, progress (thread 0): 99.6598%]
|T|: y e a h
|P|: m
[sample: EN2002b_H03_MEE073_1641.92_1642.04, WER: 100%, TER: 100%, total WER: 19.4675%, total TER: 8.6854%, progress (thread 0): 99.6677%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H03_MEE073_806.78_806.9, WER: 100%, TER: 25%, total WER: 19.4684%, total TER: 8.68556%, progress (thread 0): 99.6756%]
|T|: o k a y
|P|: y e a
[sample: ES2004a_H00_MEO015_71.95_72.06, WER: 100%, TER: 75%, total WER: 19.4693%, total TER: 8.68617%, progress (thread 0): 99.6835%]
|T|: o
|P|: y a
[sample: ES2004a_H03_FEE016_403.24_403.35, WER: 100%, TER: 200%, total WER: 19.4702%, total TER: 8.68662%, progress (thread 0): 99.6915%]
|T|: ' k a y
|P|:
[sample: ES2004a_H03_FEE016_1042.19_1042.3, WER: 100%, TER: 100%, total WER: 19.4711%, total TER: 8.68746%, progress (thread 0): 99.6994%]
|T|: m m
|P|: m m
[sample: IS1009c_H02_FIO084_620.84_620.95, WER: 0%, TER: 0%, total WER: 19.4709%, total TER: 8.68742%, progress (thread 0): 99.7073%]
|T|: y e a h
|P|: m
[sample: TS3003a_H01_MTD011UID_436.27_436.38, WER: 100%, TER: 100%, total WER: 19.4718%, total TER: 8.68827%, progress (thread 0): 99.7152%]
|T|: y e a h
|P|: m
[sample: EN2002a_H00_MEE073_669.47_669.58, WER: 100%, TER: 100%, total WER: 19.4727%, total TER: 8.68912%, progress (thread 0): 99.7231%]
|T|: y e a h
|P|: y a
[sample: EN2002a_H01_FEO070_1169.61_1169.72, WER: 100%, TER: 50%, total WER: 19.4736%, total TER: 8.6895%, progress (thread 0): 99.731%]
|T|: n o
|P|: y e
[sample: EN2002b_H02_FEO072_4.48_4.59, WER: 100%, TER: 100%, total WER: 19.4745%, total TER: 8.68993%, progress (thread 0): 99.7389%]
|T|: y e p
|P|: m
[sample: EN2002b_H03_MEE073_307.97_308.08, WER: 100%, TER: 100%, total WER: 19.4754%, total TER: 8.69056%, progress (thread 0): 99.7468%]
|T|: h m m
|P|: m
[sample: EN2002b_H03_MEE073_1578.82_1578.93, WER: 100%, TER: 66.6667%, total WER: 19.4763%, total TER: 8.69097%, progress (thread 0): 99.7547%]
|T|: h m m
|P|: m e
[sample: EN2002c_H03_MEE073_1446.36_1446.47, WER: 100%, TER: 66.6667%, total WER: 19.4772%, total TER: 8.69137%, progress (thread 0): 99.7627%]
|T|: o h
|P|: h
[sample: EN2002d_H01_FEO072_118.11_118.22, WER: 100%, TER: 50%, total WER: 19.4781%, total TER: 8.69156%, progress (thread 0): 99.7706%]
|T|: s o
|P|: m
[sample: EN2002d_H02_MEE071_2107.05_2107.16, WER: 100%, TER: 100%, total WER: 19.479%, total TER: 8.69199%, progress (thread 0): 99.7785%]
|T|: t
|P|: m
[sample: ES2004c_H02_MEE014_1184.81_1184.91, WER: 100%, TER: 100%, total WER: 19.4799%, total TER: 8.6922%, progress (thread 0): 99.7864%]
|T|: y e s
|P|: m a
[sample: IS1009d_H01_FIO087_1744.56_1744.66, WER: 100%, TER: 100%, total WER: 19.4808%, total TER: 8.69284%, progress (thread 0): 99.7943%]
|T|: o h
|P|: h
[sample: TS3003c_H02_MTD0010ID_1023.66_1023.76, WER: 100%, TER: 50%, total WER: 19.4817%, total TER: 8.69303%, progress (thread 0): 99.8022%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_2013_2013.1, WER: 100%, TER: 66.6667%, total WER: 19.4826%, total TER: 8.69343%, progress (thread 0): 99.8101%]
|T|: y e a h
|P|: m
[sample: EN2002b_H00_FEO070_43.63_43.73, WER: 100%, TER: 100%, total WER: 19.4835%, total TER: 8.69428%, progress (thread 0): 99.818%]
|T|: y e a h
|P|: y e a
[sample: EN2002b_H03_MEE073_293.52_293.62, WER: 100%, TER: 25%, total WER: 19.4844%, total TER: 8.69443%, progress (thread 0): 99.826%]
|T|: y e p
|P|: y e
[sample: EN2002c_H03_MEE073_763_763.1, WER: 100%, TER: 33.3333%, total WER: 19.4853%, total TER: 8.6946%, progress (thread 0): 99.8339%]
|T|: m m
|P|: m
[sample: EN2002d_H03_MEE073_2099.51_2099.61, WER: 100%, TER: 50%, total WER: 19.4862%, total TER: 8.69479%, progress (thread 0): 99.8418%]
|T|: ' k a y
|P|: m
[sample: ES2004a_H00_MEO015_168.79_168.88, WER: 100%, TER: 100%, total WER: 19.4871%, total TER: 8.69564%, progress (thread 0): 99.8497%]
|T|: y e p
|P|: y e
[sample: ES2004c_H00_MEO015_360.73_360.82, WER: 100%, TER: 33.3333%, total WER: 19.488%, total TER: 8.69581%, progress (thread 0): 99.8576%]
|T|: m m h m m
|P|: m
[sample: ES2004c_H03_FEE016_1132.2_1132.29, WER: 100%, TER: 80%, total WER: 19.4889%, total TER: 8.69664%, progress (thread 0): 99.8655%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1651.84_1651.93, WER: 100%, TER: 100%, total WER: 19.4898%, total TER: 8.69728%, progress (thread 0): 99.8734%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H02_FIO084_1753.49_1753.58, WER: 100%, TER: 80%, total WER: 19.4907%, total TER: 8.69811%, progress (thread 0): 99.8813%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_749.88_749.97, WER: 100%, TER: 100%, total WER: 19.4916%, total TER: 8.69874%, progress (thread 0): 99.8892%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1718.32_1718.41, WER: 100%, TER: 100%, total WER: 19.4925%, total TER: 8.6998%, progress (thread 0): 99.8972%]
|T|: w h a t
|P|: m
[sample: TS3003d_H00_MTD009PM_1597.33_1597.42, WER: 100%, TER: 100%, total WER: 19.4934%, total TER: 8.70065%, progress (thread 0): 99.9051%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_887.36_887.45, WER: 100%, TER: 66.6667%, total WER: 19.4943%, total TER: 8.70105%, progress (thread 0): 99.913%]
|T|: d a m n
|P|: m
[sample: EN2002a_H00_MEE073_1500.91_1501, WER: 100%, TER: 75%, total WER: 19.4952%, total TER: 8.70167%, progress (thread 0): 99.9209%]
|T|: h m m
|P|: m
[sample: EN2002c_H03_MEE073_429.63_429.72, WER: 100%, TER: 66.6667%, total WER: 19.4961%, total TER: 8.70207%, progress (thread 0): 99.9288%]
|T|: y e a h
|P|: y a
[sample: EN2002c_H02_MEE071_2578.94_2579.03, WER: 100%, TER: 50%, total WER: 19.497%, total TER: 8.70246%, progress (thread 0): 99.9367%]
|T|: d a m n
|P|: y e a
[sample: EN2002d_H03_MEE073_999.94_1000.03, WER: 100%, TER: 100%, total WER: 19.4979%, total TER: 8.7033%, progress (thread 0): 99.9446%]
|T|: m m
|P|:
[sample: ES2004a_H00_MEO015_252.48_252.56, WER: 100%, TER: 100%, total WER: 19.4988%, total TER: 8.70373%, progress (thread 0): 99.9525%]
|T|: m m
|P|:
[sample: ES2004c_H00_MEO015_1885.03_1885.11, WER: 100%, TER: 100%, total WER: 19.4997%, total TER: 8.70415%, progress (thread 0): 99.9604%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1313.86_1313.94, WER: 100%, TER: 100%, total WER: 19.5006%, total TER: 8.70458%, progress (thread 0): 99.9684%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_185.9_185.98, WER: 100%, TER: 100%, total WER: 19.5014%, total TER: 8.70521%, progress (thread 0): 99.9763%]
|T|: g
|P|: m
[sample: EN2002d_H03_MEE073_241.62_241.69, WER: 100%, TER: 100%, total WER: 19.5023%, total TER: 8.70542%, progress (thread 0): 99.9842%]
|T|: h m m
|P|:
[sample: IS1009a_H02_FIO084_490.4_490.46, WER: 100%, TER: 100%, total WER: 19.5032%, total TER: 8.70606%, progress (thread 0): 99.9921%]
|T|: ' k a y
|P|:
[sample: EN2002b_H03_MEE073_613.94_614, WER: 100%, TER: 100%, total WER: 19.5041%, total TER: 8.70691%, progress (thread 0): 100%]
I1224 07:09:02.842401 11830 Test.cpp:418] ------
I1224 07:09:02.842427 11830 Test.cpp:419] [Test ami_limited_supervision/test.lst (12640 samples) in 361.478s (actual decoding time 0.0286s/sample) -- WER: 19.5041%, TER: 8.70691%]
###Markdown
Viterbi WER improved from 26.6% to 19.5% after 1 epoch with finetuning... Beam Search decoding with a language model To do this, download the finetuned model and use the [Inference CTC tutorial](https://colab.research.google.com/github/flashlight/flashlight/blob/master/flashlight/app/asr/tutorial/notebooks/InferenceAndAlignmentCTC.ipynb) Step 5: Running with your own data To finetune on your own data, create `train`, `dev` and `test` list files and run the finetuning step. Each list file consists of multiple lines with each line describing one sample in the following format : ``` ```For example, let's take a look at the `dev.lst` file from AMI corpus.
###Code
! head ami_limited_supervision/dev.lst
###Output
ES2011a_H00_FEE041_34.27_37.14 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_34.27_37.14.flac 2870.0 here we go
ES2011a_H00_FEE041_37.14_39.15 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_37.14_39.15.flac 2010.0 welcome everybody
ES2011a_H00_FEE041_43.32_44.39 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_43.32_44.39.flac 1070.0 you can call me abbie
ES2011a_H00_FEE041_39.15_43.32 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_39.15_43.32.flac 4170.0 um i'm abigail claflin
ES2011a_H00_FEE041_46.43_47.63 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_46.43_47.63.flac 1200.0 's see
ES2011a_H00_FEE041_51.33_55.53 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_51.33_55.53.flac 4200.0 so this is our kick off meeting
ES2011a_H00_FEE041_55.53_56.85 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_55.53_56.85.flac 1320.0 um
ES2011a_H00_FEE041_47.63_50.2 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_47.63_50.2.flac 2570.0 powerpoint that's not it
ES2011a_H00_FEE041_50.2_51.33 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_50.2_51.33.flac 1130.0 there we go
ES2011a_H00_FEE041_62.17_64.28 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_62.17_64.28.flac 2110.0 let's shall we all introduce ourselves
###Markdown
Recording your own audioFor example, you can record your own audio and finetune the model...Installing a few packages first...
###Code
!apt-get install sox
!pip install ffmpeg-python sox
from flashlight.scripts.colab.record import record_audio
###Output
_____no_output_____
###Markdown
**Let's record now the following sentences:****1:** A flashlight or torch is a small, portable spotlight.
###Code
record_audio("recorded_audio_1")
###Output
_____no_output_____
###Markdown
**2:** Its function is a beam of light which helps to see and it usually requires batteries.
###Code
record_audio("recorded_audio_2")
###Output
_____no_output_____
###Markdown
**3:** In 1896, the first dry cell battery was invented.
###Code
record_audio("recorded_audio_3")
###Output
_____no_output_____
###Markdown
**4:** Unlike previous batteries, it used a paste electrolyte instead of a liquid.
###Code
record_audio("recorded_audio_4")
###Output
_____no_output_____
###Markdown
**5** This was the first battery suitable for portable electrical devices, as it did not spill or break easily and worked in any orientation.
###Code
record_audio("recorded_audio_5")
###Output
_____no_output_____
###Markdown
Create now new training/dev lists:(yes, you need to edit transcriptions below to your recordings)
###Code
import sox
transcriptions = [
"a flashlight or torch is a small portable spotlight",
"its function is a beam of light which helps to see and it usually requires batteries",
"in eighteen ninthy six the first dry cell battery was invented",
"unlike previous batteries it used a paste electrolyte instead of a liquid",
"this was the first battery suitable for portable electrical devices, as it did not spill or break easily and worked in any orientation"
]
with open("own_train.lst", "w") as f_train, open("own_dev.lst", "w") as f_dev:
for index, transcription in enumerate(transcriptions):
fname = "recorded_audio_" + str(index + 1) + ".wav"
duration_ms = sox.file_info.duration(fname) * 1000
if index % 2 == 0:
f_train.write("{}\t{}\t{}\t{}\n".format(
index + 1, fname, duration_ms, transcription))
else:
f_dev.write("{}\t{}\t{}\t{}\n".format(
index + 1, fname, duration_ms, transcription))
###Output
_____no_output_____
###Markdown
Check at first model quality on dev before finetuning
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am model.bin --datadir '' --emission_dir '' --uselexicon false \
--test own_dev.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
_____no_output_____
###Markdown
Finetune on recorded audio samplesPlay with parameters if needed.
###Code
! ./flashlight/build/bin/asr/fl_asr_tutorial_finetune_ctc model.bin \
--datadir= \
--train own_train.lst \
--valid dev:own_dev.lst \
--arch arch.txt \
--tokens tokens.txt \
--lexicon lexicon.txt \
--rundir own_checkpoint \
--lr 0.025 \
--netoptim sgd \
--momentum 0.8 \
--reportiters 1000 \
--lr_decay 100 \
--lr_decay_step 50 \
--iter 25000 \
--batchsize 4 \
--warmup 0
###Output
_____no_output_____
###Markdown
Test finetuned model(unlikely you get significant improvement with just five phrases, but let's check!)
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am own_checkpoint/001_model_dev.bin --datadir '' --emission_dir '' --uselexicon false \
--test own_dev.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
_____no_output_____
###Markdown
Tutorial on ASR Finetuning with CTC model Let's finetune a pretrained ASR model!Here we provide pre-trained speech recognition model with CTC loss that is trained on many open-sourced datasets. Details can be found in [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://arxiv.org/abs/2010.11745) Step 1: Install `Flashlight`First we install `Flashlight` and its dependencies. Flashlight is built from source with either CPU/CUDA backend and installation takes **~16 minutes**. For installation out of colab notebook please use [link](https://github.com/fairinternal/flashlightbuilding).
###Code
# First, choose backend to build with
backend = 'CUDA' #@param ["CPU", "CUDA"]
# Clone Flashlight
!git clone https://github.com/facebookresearch/flashlight.git
# install all dependencies for colab notebook
!source flashlight/scripts/colab/colab_install_deps.sh
###Output
_____no_output_____
###Markdown
Build CPU/CUDA Backend of `Flashlight`:- Build from current master. - Builds the ASR app. - Resulting binaries in `/content/flashlight/build/bin/asr`.If using a GPU Colab runtime, build the CUDA backend; else build the CPU backend.
###Code
# export necessary env variables
%env MKLROOT=/opt/intel/mkl
%env ArrayFire_DIR=/opt/arrayfire/share/ArrayFire/cmake
%env DNNL_DIR=/opt/dnnl/dnnl_lnx_2.0.0_cpu_iomp/lib/cmake/dnnl
if backend == "CUDA":
# Total time: ~13 minutes
!cd flashlight && mkdir -p build && cd build && \
cmake .. -DCMAKE_BUILD_TYPE=Release \
-DFL_BUILD_TESTS=OFF \
-DFL_BUILD_EXAMPLES=OFF \
-DFL_BUILD_APP_IMGCLASS=OFF \
-DFL_BUILD_APP_OBJDET=OFF \
-DFL_BUILD_APP_LM=OFF && \
make -j$(nproc)
elif backend == "CPU":
# Total time: ~14 minutes
!cd flashlight && mkdir -p build && cd build && \
cmake .. -DFL_BACKEND=CPU \
-DCMAKE_BUILD_TYPE=Release \
-DFL_BUILD_TESTS=OFF \
-DFL_BUILD_EXAMPLES=OFF \
-DFL_BUILD_APP_IMGCLASS=OFF \
-DFL_BUILD_APP_OBJDET=OFF \
-DFL_BUILD_APP_LM=OFF && \
make -j$(nproc)
else:
raise ValueError(f"Unknown backend {backend}")
###Output
_____no_output_____
###Markdown
Let's take a look around.
###Code
# Binaries are located in
!ls flashlight/build/bin/asr
###Output
fl_asr_align fl_asr_tutorial_finetune_ctc
fl_asr_decode fl_asr_tutorial_inference_ctc
fl_asr_test fl_asr_voice_activity_detection_ctc
fl_asr_train
###Markdown
Step 2: Setup Finetuning Downloading the model filesFirst, let's download the pretrained models for finetuning. For acoustic model, you can choose from >Architecture | Params | Criterion | Model Name | Arch Name >---|---|:---|:---:|:---:> Transformer|70Mil|CTC|am_transformer_ctc_stride3_letters_70Mparams.bin |am_transformer_ctc_stride3_letters_70Mparams.arch> Transformer|300Mil|CTC|am_transformer_ctc_stride3_letters_300Mparams.bin | am_transformer_ctc_stride3_letters_300Mparams.arch> Conformer|25Mil|CTC|am_conformer_ctc_stride3_letters_25Mparams.bin|am_conformer_ctc_stride3_letters_25Mparams.arch> Conformer|87Mil|CTC|am_conformer_ctc_stride3_letters_87Mparams.bin|am_conformer_ctc_stride3_letters_87Mparams.arch> Conformer|300Mil|CTC|am_conformer_ctc_stride3_letters_300Mparams.bin| am_conformer_ctc_stride3_letters_300Mparams.archFor demonstration, we will use the model in first row and download the model and its arch file.
###Code
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/am_transformer_ctc_stride3_letters_70Mparams.bin -O model.bin # acoustic model
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/am_transformer_ctc_stride3_letters_70Mparams.arch -O arch.txt # model architecture file
###Output
_____no_output_____
###Markdown
Along with the acoustic model, we will also download the tokens file, lexicon file
###Code
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/tokens.txt -O tokens.txt # tokens (defines predicted tokens)
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/lexicon.txt -O lexicon.txt # lexicon files (defines mapping between words)
###Output
_____no_output_____
###Markdown
Downloading the datasetFor finetuning the model, we provide a limited supervision dataset based on [AMI Corpus](http://groups.inf.ed.ac.uk/ami/corpus/). It consists of 10m, 1hr and 10hr subsets organized as follows. ```dev.lst development set test.lst test set train_10min_0.lst first 10 min foldtrain_10min_1.lsttrain_10min_2.lsttrain_10min_3.lsttrain_10min_4.lsttrain_10min_5.lsttrain_9hr.lst remaining data of the 10h split (10h=1h+9h)```The 10h split is created by combining the data from the 9h split and the 1h split. The 1h split is itself made of 6 folds of 10 min splits.The recipe used for preparing this corpus can be found [here](https://github.com/facebookresearch/wav2letter/tree/master/data/ami). **You can also use your own dataset to finetune the model instead of AMI Corpus.**
###Code
!rm ami_limited_supervision.tar.gz
!wget -nv --continue -o /dev/null https://dl.fbaipublicfiles.com/wav2letter/rasr/tutorial/ami_limited_supervision.tar.gz -O ami_limited_supervision.tar.gz
!tar -xf ami_limited_supervision.tar.gz
!ls ami_limited_supervision
###Output
rm: cannot remove 'ami_limited_supervision.tar.gz': No such file or directory
audio train_10min_0.lst train_10min_3.lst train_9hr.lst
dev.lst train_10min_1.lst train_10min_4.lst
test.lst train_10min_2.lst train_10min_5.lst
###Markdown
Get baseline WER before finetuningBefore proceeding to finetuning, let's test (viterbi) WER on AMI dataset to we have something to compare results after finetuning.
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am model.bin --datadir '' --emission_dir '' --uselexicon false \
--test ami_limited_supervision/test.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1046.72_1047.07, WER: 100%, TER: 50%, total WER: 25.9066%, total TER: 12.6062%, progress (thread 0): 86.8275%]
|T|: m m
|P|: m m
[sample: ES2004c_H01_FEE013_1294.34_1294.69, WER: 0%, TER: 0%, total WER: 25.9063%, total TER: 12.6061%, progress (thread 0): 86.8354%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1302.15_1302.5, WER: 100%, TER: 40%, total WER: 25.9071%, total TER: 12.6065%, progress (thread 0): 86.8434%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1515.72_1516.07, WER: 0%, TER: 0%, total WER: 25.9068%, total TER: 12.6063%, progress (thread 0): 86.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1690.13_1690.48, WER: 0%, TER: 0%, total WER: 25.9065%, total TER: 12.6062%, progress (thread 0): 86.8592%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_2078.48_2078.83, WER: 100%, TER: 40%, total WER: 25.9074%, total TER: 12.6065%, progress (thread 0): 86.8671%]
|T|: o k a y
|P|: i t
[sample: ES2004c_H02_MEE014_2291.54_2291.89, WER: 100%, TER: 100%, total WER: 25.9082%, total TER: 12.6074%, progress (thread 0): 86.875%]
|T|: o h
|P|:
[sample: ES2004d_H00_MEO015_127.17_127.52, WER: 100%, TER: 100%, total WER: 25.9091%, total TER: 12.6078%, progress (thread 0): 86.8829%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_561.86_562.21, WER: 0%, TER: 0%, total WER: 25.9088%, total TER: 12.6077%, progress (thread 0): 86.8908%]
|T|: ' k a y
|P|: k a y
[sample: ES2004d_H00_MEO015_640.16_640.51, WER: 100%, TER: 25%, total WER: 25.9096%, total TER: 12.6078%, progress (thread 0): 86.8987%]
|T|: r i g h t
|P|: l i k e
[sample: ES2004d_H00_MEO015_821.44_821.79, WER: 100%, TER: 80%, total WER: 25.9105%, total TER: 12.6086%, progress (thread 0): 86.9066%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H01_FEE013_822.51_822.86, WER: 100%, TER: 40%, total WER: 25.9113%, total TER: 12.6089%, progress (thread 0): 86.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1778.02_1778.37, WER: 0%, TER: 0%, total WER: 25.911%, total TER: 12.6088%, progress (thread 0): 86.9225%]
|T|: m m h m m
|P|: u | h u h
[sample: IS1009a_H00_FIE088_76.08_76.43, WER: 200%, TER: 80%, total WER: 25.913%, total TER: 12.6096%, progress (thread 0): 86.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_431.19_431.54, WER: 0%, TER: 0%, total WER: 25.9127%, total TER: 12.6094%, progress (thread 0): 86.9383%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_492.57_492.92, WER: 0%, TER: 0%, total WER: 25.9124%, total TER: 12.6094%, progress (thread 0): 86.9462%]
|T|: y e a h
|P|: h e | w a s
[sample: IS1009b_H02_FIO084_232.67_233.02, WER: 200%, TER: 100%, total WER: 25.9144%, total TER: 12.6102%, progress (thread 0): 86.9541%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_361.11_361.46, WER: 0%, TER: 0%, total WER: 25.9141%, total TER: 12.6101%, progress (thread 0): 86.962%]
|T|: y e p
|P|: y e a h
[sample: IS1009b_H00_FIE088_723.57_723.92, WER: 100%, TER: 66.6667%, total WER: 25.9149%, total TER: 12.6104%, progress (thread 0): 86.9699%]
|T|: m m h m m
|P|: e
[sample: IS1009b_H02_FIO084_993.71_994.06, WER: 100%, TER: 100%, total WER: 25.9158%, total TER: 12.6115%, progress (thread 0): 86.9778%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1007.39_1007.74, WER: 0%, TER: 0%, total WER: 25.9155%, total TER: 12.6114%, progress (thread 0): 86.9858%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1079.18_1079.53, WER: 100%, TER: 40%, total WER: 25.9163%, total TER: 12.6117%, progress (thread 0): 86.9937%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1082.71_1083.06, WER: 100%, TER: 40%, total WER: 25.9172%, total TER: 12.612%, progress (thread 0): 87.0016%]
|T|: t h i s | o n e
|P|: t h i s | o n e
[sample: IS1009b_H00_FIE088_1179.44_1179.79, WER: 0%, TER: 0%, total WER: 25.9166%, total TER: 12.6118%, progress (thread 0): 87.0095%]
|T|: y o u ' r e | t h r e e
|P|: i t h r i
[sample: IS1009b_H00_FIE088_1203.06_1203.41, WER: 100%, TER: 75%, total WER: 25.9183%, total TER: 12.6136%, progress (thread 0): 87.0174%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1699.07_1699.42, WER: 0%, TER: 0%, total WER: 25.918%, total TER: 12.6134%, progress (thread 0): 87.0253%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1821.76_1822.11, WER: 0%, TER: 0%, total WER: 25.9177%, total TER: 12.6133%, progress (thread 0): 87.0332%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_527.63_527.98, WER: 100%, TER: 40%, total WER: 25.9185%, total TER: 12.6136%, progress (thread 0): 87.0411%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_546.82_547.17, WER: 100%, TER: 40%, total WER: 25.9194%, total TER: 12.614%, progress (thread 0): 87.049%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_609.9_610.25, WER: 100%, TER: 40%, total WER: 25.9202%, total TER: 12.6143%, progress (thread 0): 87.057%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009c_H00_FIE088_688.49_688.84, WER: 200%, TER: 100%, total WER: 25.9222%, total TER: 12.6153%, progress (thread 0): 87.0649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1303.58_1303.93, WER: 0%, TER: 0%, total WER: 25.9219%, total TER: 12.6152%, progress (thread 0): 87.0728%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H01_FIO087_1514.93_1515.28, WER: 100%, TER: 40%, total WER: 25.9227%, total TER: 12.6155%, progress (thread 0): 87.0807%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1737.76_1738.11, WER: 100%, TER: 40%, total WER: 25.9236%, total TER: 12.6158%, progress (thread 0): 87.0886%]
|T|: a h
|P|: i
[sample: IS1009d_H02_FIO084_734.11_734.46, WER: 100%, TER: 100%, total WER: 25.9244%, total TER: 12.6163%, progress (thread 0): 87.0965%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_742.93_743.28, WER: 0%, TER: 0%, total WER: 25.9241%, total TER: 12.6162%, progress (thread 0): 87.1044%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_743.52_743.87, WER: 0%, TER: 0%, total WER: 25.9238%, total TER: 12.616%, progress (thread 0): 87.1123%]
|T|: y e a h
|P|: w h e r e
[sample: IS1009d_H02_FIO084_953.71_954.06, WER: 100%, TER: 100%, total WER: 25.9247%, total TER: 12.6169%, progress (thread 0): 87.1203%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1243.4_1243.75, WER: 0%, TER: 0%, total WER: 25.9244%, total TER: 12.6168%, progress (thread 0): 87.1282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1323.32_1323.67, WER: 0%, TER: 0%, total WER: 25.9241%, total TER: 12.6166%, progress (thread 0): 87.1361%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_1417.8_1418.15, WER: 100%, TER: 40%, total WER: 25.9249%, total TER: 12.617%, progress (thread 0): 87.144%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_1674.44_1674.79, WER: 100%, TER: 100%, total WER: 25.9258%, total TER: 12.618%, progress (thread 0): 87.1519%]
|T|: m m h m m
|P|: m h
[sample: IS1009d_H02_FIO084_1745.56_1745.91, WER: 100%, TER: 60%, total WER: 25.9266%, total TER: 12.6185%, progress (thread 0): 87.1598%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_506.21_506.56, WER: 0%, TER: 0%, total WER: 25.9263%, total TER: 12.6184%, progress (thread 0): 87.1677%]
|T|: t h e | p e n
|P|: b
[sample: TS3003a_H02_MTD0010ID_1074.9_1075.25, WER: 100%, TER: 100%, total WER: 25.928%, total TER: 12.6199%, progress (thread 0): 87.1756%]
|T|: r i g h t
|P|: r i g h t
[sample: TS3003a_H03_MTD012ME_1219.55_1219.9, WER: 0%, TER: 0%, total WER: 25.9277%, total TER: 12.6197%, progress (thread 0): 87.1835%]
|T|: m m h m m
|P|: m h m
[sample: TS3003a_H01_MTD011UID_1371.45_1371.8, WER: 100%, TER: 40%, total WER: 25.9285%, total TER: 12.62%, progress (thread 0): 87.1915%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_1453.73_1454.08, WER: 0%, TER: 0%, total WER: 25.9282%, total TER: 12.6199%, progress (thread 0): 87.1994%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1295.49_1295.84, WER: 0%, TER: 0%, total WER: 25.9279%, total TER: 12.6199%, progress (thread 0): 87.2073%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1351.56_1351.91, WER: 100%, TER: 50%, total WER: 25.9288%, total TER: 12.62%, progress (thread 0): 87.2152%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_180.89_181.24, WER: 0%, TER: 0%, total WER: 25.9285%, total TER: 12.6199%, progress (thread 0): 87.2231%]
|T|: y e a h
|P|: y e a p
[sample: TS3003c_H00_MTD009PM_1380.5_1380.85, WER: 100%, TER: 25%, total WER: 25.9293%, total TER: 12.62%, progress (thread 0): 87.231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1531.23_1531.58, WER: 0%, TER: 0%, total WER: 25.929%, total TER: 12.6199%, progress (thread 0): 87.2389%]
|T|: t h a t ' s | g o o d
|P|: t h a t ' s g o
[sample: TS3003c_H03_MTD012ME_1567.88_1568.23, WER: 100%, TER: 27.2727%, total WER: 25.9307%, total TER: 12.6203%, progress (thread 0): 87.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1713.34_1713.69, WER: 0%, TER: 0%, total WER: 25.9304%, total TER: 12.6202%, progress (thread 0): 87.2547%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_2280.01_2280.36, WER: 0%, TER: 0%, total WER: 25.9301%, total TER: 12.6201%, progress (thread 0): 87.2627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_306.6_306.95, WER: 0%, TER: 0%, total WER: 25.9298%, total TER: 12.6199%, progress (thread 0): 87.2706%]
|T|: n o
|P|: o
[sample: TS3003d_H00_MTD009PM_819.47_819.82, WER: 100%, TER: 50%, total WER: 25.9307%, total TER: 12.6201%, progress (thread 0): 87.2785%]
|T|: a l r i g h t
|P|:
[sample: TS3003d_H00_MTD009PM_965.64_965.99, WER: 100%, TER: 100%, total WER: 25.9315%, total TER: 12.6216%, progress (thread 0): 87.2864%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H00_MTD009PM_1373.9_1374.25, WER: 0%, TER: 0%, total WER: 25.9312%, total TER: 12.6215%, progress (thread 0): 87.2943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1847.37_1847.72, WER: 0%, TER: 0%, total WER: 25.9309%, total TER: 12.6213%, progress (thread 0): 87.3022%]
|T|: n i n e
|P|: n i n e t h
[sample: TS3003d_H03_MTD012ME_1899.7_1900.05, WER: 100%, TER: 50%, total WER: 25.9318%, total TER: 12.6217%, progress (thread 0): 87.3101%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H01_MTD011UID_2218.91_2219.26, WER: 0%, TER: 0%, total WER: 25.9315%, total TER: 12.6216%, progress (thread 0): 87.318%]
|T|: r i g h t
|P|: b u t
[sample: EN2002a_H03_MEE071_142.16_142.51, WER: 100%, TER: 80%, total WER: 25.9323%, total TER: 12.6224%, progress (thread 0): 87.326%]
|T|: h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_203.92_204.27, WER: 100%, TER: 66.6667%, total WER: 25.9332%, total TER: 12.6228%, progress (thread 0): 87.3339%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_220.21_220.56, WER: 0%, TER: 0%, total WER: 25.9329%, total TER: 12.6226%, progress (thread 0): 87.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1393.11_1393.46, WER: 0%, TER: 0%, total WER: 25.9326%, total TER: 12.6225%, progress (thread 0): 87.3497%]
|T|: n o
|P|: n o
[sample: EN2002a_H02_FEO072_1494.01_1494.36, WER: 0%, TER: 0%, total WER: 25.9323%, total TER: 12.6225%, progress (thread 0): 87.3576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1616.41_1616.76, WER: 0%, TER: 0%, total WER: 25.932%, total TER: 12.6224%, progress (thread 0): 87.3655%]
|T|: y e a h
|P|: y e h
[sample: EN2002a_H01_FEO070_2062.28_2062.63, WER: 100%, TER: 25%, total WER: 25.9328%, total TER: 12.6225%, progress (thread 0): 87.3734%]
|T|: t h e n | u m
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_306.19_306.54, WER: 100%, TER: 114.286%, total WER: 25.9345%, total TER: 12.6242%, progress (thread 0): 87.3813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_679.83_680.18, WER: 0%, TER: 0%, total WER: 25.9342%, total TER: 12.6241%, progress (thread 0): 87.3892%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1132.98_1133.33, WER: 0%, TER: 0%, total WER: 25.9339%, total TER: 12.624%, progress (thread 0): 87.3972%]
|T|: s h o u l d n ' t | n o
|P|: n i
[sample: EN2002b_H01_MEE071_1400.16_1400.51, WER: 100%, TER: 91.6667%, total WER: 25.9356%, total TER: 12.6262%, progress (thread 0): 87.4051%]
|T|: n o
|P|: k n o w
[sample: EN2002b_H02_FEO072_1650.79_1651.14, WER: 100%, TER: 100%, total WER: 25.9364%, total TER: 12.6266%, progress (thread 0): 87.413%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_550.49_550.84, WER: 0%, TER: 0%, total WER: 25.9362%, total TER: 12.6265%, progress (thread 0): 87.4209%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_586.84_587.19, WER: 0%, TER: 0%, total WER: 25.9359%, total TER: 12.6264%, progress (thread 0): 87.4288%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_725.66_726.01, WER: 0%, TER: 0%, total WER: 25.9356%, total TER: 12.6263%, progress (thread 0): 87.4367%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_743.92_744.27, WER: 0%, TER: 0%, total WER: 25.9353%, total TER: 12.6262%, progress (thread 0): 87.4446%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_975_975.35, WER: 0%, TER: 0%, total WER: 25.935%, total TER: 12.6261%, progress (thread 0): 87.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1520.32_1520.67, WER: 0%, TER: 0%, total WER: 25.9347%, total TER: 12.626%, progress (thread 0): 87.4604%]
|T|: s o
|P|:
[sample: EN2002c_H02_MEE071_1667.21_1667.56, WER: 100%, TER: 100%, total WER: 25.9355%, total TER: 12.6264%, progress (thread 0): 87.4684%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_2075.98_2076.33, WER: 0%, TER: 0%, total WER: 25.9352%, total TER: 12.6264%, progress (thread 0): 87.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2182.63_2182.98, WER: 0%, TER: 0%, total WER: 25.9349%, total TER: 12.6262%, progress (thread 0): 87.4842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_352.2_352.55, WER: 0%, TER: 0%, total WER: 25.9346%, total TER: 12.6261%, progress (thread 0): 87.4921%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002d_H03_MEE073_387.18_387.53, WER: 0%, TER: 0%, total WER: 25.934%, total TER: 12.6259%, progress (thread 0): 87.5%]
|T|: m m
|P|:
[sample: EN2002d_H01_FEO072_831.78_832.13, WER: 100%, TER: 100%, total WER: 25.9349%, total TER: 12.6263%, progress (thread 0): 87.5079%]
|T|: n o
|P|: n o
[sample: EN2002d_H03_MEE073_909.36_909.71, WER: 0%, TER: 0%, total WER: 25.9346%, total TER: 12.6263%, progress (thread 0): 87.5158%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002d_H03_MEE073_1113.08_1113.43, WER: 200%, TER: 175%, total WER: 25.9366%, total TER: 12.6278%, progress (thread 0): 87.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1233.84_1234.19, WER: 0%, TER: 0%, total WER: 25.9363%, total TER: 12.6277%, progress (thread 0): 87.5316%]
|T|: i | d o n ' t | k n o w
|P|:
[sample: EN2002d_H01_FEO072_1245.04_1245.39, WER: 100%, TER: 100%, total WER: 25.9388%, total TER: 12.6301%, progress (thread 0): 87.5396%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1798.12_1798.47, WER: 0%, TER: 0%, total WER: 25.9385%, total TER: 12.63%, progress (thread 0): 87.5475%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1926.51_1926.86, WER: 0%, TER: 0%, total WER: 25.9382%, total TER: 12.6299%, progress (thread 0): 87.5554%]
|T|: u h h u h
|P|: u h u h
[sample: EN2002d_H00_FEO070_2027.85_2028.2, WER: 100%, TER: 20%, total WER: 25.9391%, total TER: 12.63%, progress (thread 0): 87.5633%]
|T|: o h | s o r r y
|P|:
[sample: ES2004a_H00_MEO015_255.91_256.25, WER: 100%, TER: 100%, total WER: 25.9407%, total TER: 12.6316%, progress (thread 0): 87.5712%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H01_FEE013_545.3_545.64, WER: 100%, TER: 40%, total WER: 25.9416%, total TER: 12.632%, progress (thread 0): 87.5791%]
|T|: r e a l l y
|P|: r e a l l y
[sample: ES2004a_H01_FEE013_603.88_604.22, WER: 0%, TER: 0%, total WER: 25.9413%, total TER: 12.6318%, progress (thread 0): 87.587%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_635.54_635.88, WER: 100%, TER: 40%, total WER: 25.9421%, total TER: 12.6321%, progress (thread 0): 87.5949%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H03_FEE016_811.15_811.49, WER: 0%, TER: 0%, total WER: 25.9418%, total TER: 12.632%, progress (thread 0): 87.6028%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_954.93_955.27, WER: 100%, TER: 40%, total WER: 25.9427%, total TER: 12.6323%, progress (thread 0): 87.6108%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H03_FEE016_1445.71_1446.05, WER: 100%, TER: 60%, total WER: 25.9435%, total TER: 12.6329%, progress (thread 0): 87.6187%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1995.5_1995.84, WER: 100%, TER: 40%, total WER: 25.9444%, total TER: 12.6332%, progress (thread 0): 87.6266%]
|T|: ' k a y
|P|: t a n
[sample: ES2004c_H03_FEE016_22.85_23.19, WER: 100%, TER: 75%, total WER: 25.9452%, total TER: 12.6338%, progress (thread 0): 87.6345%]
|T|: t h a n k | y o u
|P|:
[sample: ES2004c_H03_FEE016_201.1_201.44, WER: 100%, TER: 100%, total WER: 25.9469%, total TER: 12.6356%, progress (thread 0): 87.6424%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_451.06_451.4, WER: 100%, TER: 25%, total WER: 25.9477%, total TER: 12.6357%, progress (thread 0): 87.6503%]
|T|: n o
|P|: n o t
[sample: ES2004c_H02_MEE014_535.19_535.53, WER: 100%, TER: 50%, total WER: 25.9486%, total TER: 12.6359%, progress (thread 0): 87.6582%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1178.32_1178.66, WER: 0%, TER: 0%, total WER: 25.9483%, total TER: 12.6358%, progress (thread 0): 87.6661%]
|T|: h m m
|P|: m m
[sample: ES2004c_H02_MEE014_1181.26_1181.6, WER: 100%, TER: 33.3333%, total WER: 25.9491%, total TER: 12.6359%, progress (thread 0): 87.674%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1478.47_1478.81, WER: 100%, TER: 50%, total WER: 25.9499%, total TER: 12.6361%, progress (thread 0): 87.682%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1640.61_1640.95, WER: 100%, TER: 40%, total WER: 25.9508%, total TER: 12.6364%, progress (thread 0): 87.6899%]
|T|: m m h m m
|P|: h e
[sample: ES2004c_H03_FEE016_2025.11_2025.45, WER: 100%, TER: 80%, total WER: 25.9516%, total TER: 12.6372%, progress (thread 0): 87.6978%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_2072.31_2072.65, WER: 100%, TER: 40%, total WER: 25.9525%, total TER: 12.6376%, progress (thread 0): 87.7057%]
|T|: y e a h
|P|: y e s
[sample: ES2004d_H00_MEO015_100.76_101.1, WER: 100%, TER: 50%, total WER: 25.9533%, total TER: 12.6379%, progress (thread 0): 87.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H01_FEE013_139.4_139.74, WER: 0%, TER: 0%, total WER: 25.953%, total TER: 12.6378%, progress (thread 0): 87.7215%]
|T|: y e p
|P|: y u p
[sample: ES2004d_H00_MEO015_155.57_155.91, WER: 100%, TER: 33.3333%, total WER: 25.9539%, total TER: 12.6379%, progress (thread 0): 87.7294%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_178.12_178.46, WER: 0%, TER: 0%, total WER: 25.9536%, total TER: 12.6378%, progress (thread 0): 87.7373%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_405.44_405.78, WER: 0%, TER: 0%, total WER: 25.9533%, total TER: 12.6377%, progress (thread 0): 87.7453%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_548.5_548.84, WER: 100%, TER: 40%, total WER: 25.9541%, total TER: 12.638%, progress (thread 0): 87.7532%]
|T|: t h r e e
|P|: t h r e e
[sample: ES2004d_H02_MEE014_646.42_646.76, WER: 0%, TER: 0%, total WER: 25.9538%, total TER: 12.6378%, progress (thread 0): 87.7611%]
|T|: f o u r
|P|: f o r
[sample: ES2004d_H01_FEE013_911.53_911.87, WER: 100%, TER: 25%, total WER: 25.9547%, total TER: 12.638%, progress (thread 0): 87.769%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1135.09_1135.43, WER: 0%, TER: 0%, total WER: 25.9544%, total TER: 12.6378%, progress (thread 0): 87.7769%]
|T|: m m
|P|: h m
[sample: ES2004d_H01_FEE013_1953.89_1954.23, WER: 100%, TER: 50%, total WER: 25.9552%, total TER: 12.638%, progress (thread 0): 87.7848%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_792.79_793.13, WER: 0%, TER: 0%, total WER: 25.9549%, total TER: 12.6379%, progress (thread 0): 87.7927%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_43.16_43.5, WER: 100%, TER: 40%, total WER: 25.9558%, total TER: 12.6383%, progress (thread 0): 87.8006%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_260.98_261.32, WER: 0%, TER: 0%, total WER: 25.9555%, total TER: 12.6381%, progress (thread 0): 87.8085%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_425.4_425.74, WER: 0%, TER: 0%, total WER: 25.9552%, total TER: 12.638%, progress (thread 0): 87.8165%]
|T|: y e p
|P|: o
[sample: IS1009b_H00_FIE088_598.28_598.62, WER: 100%, TER: 100%, total WER: 25.956%, total TER: 12.6386%, progress (thread 0): 87.8244%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_613.29_613.63, WER: 0%, TER: 0%, total WER: 25.9557%, total TER: 12.6385%, progress (thread 0): 87.8323%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1002.09_1002.43, WER: 0%, TER: 0%, total WER: 25.9554%, total TER: 12.6384%, progress (thread 0): 87.8402%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009b_H00_FIE088_1223.66_1224, WER: 200%, TER: 100%, total WER: 25.9574%, total TER: 12.6395%, progress (thread 0): 87.8481%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1225.44_1225.78, WER: 100%, TER: 40%, total WER: 25.9582%, total TER: 12.6398%, progress (thread 0): 87.856%]
|T|: m m
|P|: e a h
[sample: IS1009b_H03_FIO089_1734.63_1734.97, WER: 100%, TER: 150%, total WER: 25.9591%, total TER: 12.6404%, progress (thread 0): 87.8639%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1834.47_1834.81, WER: 0%, TER: 0%, total WER: 25.9588%, total TER: 12.6403%, progress (thread 0): 87.8718%]
|T|: o k a y
|P|: k
[sample: IS1009b_H01_FIO087_1882.63_1882.97, WER: 100%, TER: 75%, total WER: 25.9596%, total TER: 12.6409%, progress (thread 0): 87.8797%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H01_FIO087_1960.82_1961.16, WER: 100%, TER: 40%, total WER: 25.9605%, total TER: 12.6412%, progress (thread 0): 87.8877%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_535.66_536, WER: 100%, TER: 40%, total WER: 25.9613%, total TER: 12.6415%, progress (thread 0): 87.8956%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_575.77_576.11, WER: 100%, TER: 40%, total WER: 25.9621%, total TER: 12.6419%, progress (thread 0): 87.9035%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H03_FIO089_950.04_950.38, WER: 100%, TER: 80%, total WER: 25.963%, total TER: 12.6426%, progress (thread 0): 87.9114%]
|T|: i t | w o r k s
|P|: w u t
[sample: IS1009c_H01_FIO087_1083.82_1084.16, WER: 100%, TER: 87.5%, total WER: 25.9647%, total TER: 12.6441%, progress (thread 0): 87.9193%]
|T|: m m h m m
|P|: y e h
[sample: IS1009c_H03_FIO089_1109.75_1110.09, WER: 100%, TER: 80%, total WER: 25.9655%, total TER: 12.6449%, progress (thread 0): 87.9272%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1603.38_1603.72, WER: 100%, TER: 40%, total WER: 25.9663%, total TER: 12.6452%, progress (thread 0): 87.9351%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H02_FIO084_1613.06_1613.4, WER: 100%, TER: 40%, total WER: 25.9672%, total TER: 12.6455%, progress (thread 0): 87.943%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_1633.76_1634.1, WER: 0%, TER: 0%, total WER: 25.9669%, total TER: 12.6454%, progress (thread 0): 87.951%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_434.93_435.27, WER: 100%, TER: 40%, total WER: 25.9677%, total TER: 12.6457%, progress (thread 0): 87.9589%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_637.66_638, WER: 100%, TER: 60%, total WER: 25.9686%, total TER: 12.6463%, progress (thread 0): 87.9668%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_649.7_650.04, WER: 0%, TER: 0%, total WER: 25.9683%, total TER: 12.6461%, progress (thread 0): 87.9747%]
|T|: h m m
|P|: r i g h t
[sample: IS1009d_H02_FIO084_674.88_675.22, WER: 100%, TER: 166.667%, total WER: 25.9691%, total TER: 12.6472%, progress (thread 0): 87.9826%]
|T|: o o p s
|P|: u p
[sample: IS1009d_H00_FIE088_1068.99_1069.33, WER: 100%, TER: 75%, total WER: 25.97%, total TER: 12.6478%, progress (thread 0): 87.9905%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H01_FIO087_1509.75_1510.09, WER: 0%, TER: 0%, total WER: 25.9697%, total TER: 12.6477%, progress (thread 0): 87.9984%]
|T|: t h a n k | y o u
|P|: t h a n k | y o u
[sample: TS3003a_H03_MTD012ME_48.1_48.44, WER: 0%, TER: 0%, total WER: 25.9691%, total TER: 12.6475%, progress (thread 0): 88.0063%]
|T|: g u e s s
|P|: y e a f
[sample: TS3003a_H01_MTD011UID_983.96_984.3, WER: 100%, TER: 80%, total WER: 25.9699%, total TER: 12.6482%, progress (thread 0): 88.0142%]
|T|: t h e | p e n
|P|:
[sample: TS3003a_H02_MTD0010ID_1090.49_1090.83, WER: 100%, TER: 100%, total WER: 25.9716%, total TER: 12.6497%, progress (thread 0): 88.0222%]
|T|: t h i n g
|P|: t h i n g
[sample: TS3003a_H00_MTD009PM_1320.19_1320.53, WER: 0%, TER: 0%, total WER: 25.9713%, total TER: 12.6495%, progress (thread 0): 88.0301%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_132.84_133.18, WER: 0%, TER: 0%, total WER: 25.971%, total TER: 12.6495%, progress (thread 0): 88.038%]
|T|: r i g h t
|P|:
[sample: TS3003b_H01_MTD011UID_2086.12_2086.46, WER: 100%, TER: 100%, total WER: 25.9718%, total TER: 12.6505%, progress (thread 0): 88.0459%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2089.85_2090.19, WER: 0%, TER: 0%, total WER: 25.9715%, total TER: 12.6504%, progress (thread 0): 88.0538%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_982.32_982.66, WER: 0%, TER: 0%, total WER: 25.9713%, total TER: 12.6503%, progress (thread 0): 88.0617%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_1007.54_1007.88, WER: 0%, TER: 0%, total WER: 25.971%, total TER: 12.6502%, progress (thread 0): 88.0696%]
|T|: y e a h
|P|: y e h
[sample: TS3003c_H00_MTD009PM_1240.35_1240.69, WER: 100%, TER: 25%, total WER: 25.9718%, total TER: 12.6503%, progress (thread 0): 88.0775%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1413.87_1414.21, WER: 0%, TER: 0%, total WER: 25.9715%, total TER: 12.6501%, progress (thread 0): 88.0854%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1849.58_1849.92, WER: 0%, TER: 0%, total WER: 25.9712%, total TER: 12.65%, progress (thread 0): 88.0934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_595.75_596.09, WER: 0%, TER: 0%, total WER: 25.9709%, total TER: 12.6499%, progress (thread 0): 88.1013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_921.62_921.96, WER: 0%, TER: 0%, total WER: 25.9706%, total TER: 12.6498%, progress (thread 0): 88.1092%]
|T|: u h | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1308.11_1308.45, WER: 50%, TER: 42.8571%, total WER: 25.9712%, total TER: 12.6503%, progress (thread 0): 88.1171%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1388.52_1388.86, WER: 0%, TER: 0%, total WER: 25.9709%, total TER: 12.6502%, progress (thread 0): 88.125%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1802.71_1803.05, WER: 100%, TER: 75%, total WER: 25.9717%, total TER: 12.6508%, progress (thread 0): 88.1329%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1835.63_1835.97, WER: 100%, TER: 25%, total WER: 25.9726%, total TER: 12.6509%, progress (thread 0): 88.1408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2091.81_2092.15, WER: 0%, TER: 0%, total WER: 25.9723%, total TER: 12.6508%, progress (thread 0): 88.1487%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2217.62_2217.96, WER: 0%, TER: 0%, total WER: 25.972%, total TER: 12.6506%, progress (thread 0): 88.1566%]
|T|: m m | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2323.41_2323.75, WER: 50%, TER: 42.8571%, total WER: 25.9725%, total TER: 12.6511%, progress (thread 0): 88.1646%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2420.39_2420.73, WER: 0%, TER: 0%, total WER: 25.9722%, total TER: 12.651%, progress (thread 0): 88.1725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_406.15_406.49, WER: 0%, TER: 0%, total WER: 25.9719%, total TER: 12.6509%, progress (thread 0): 88.1804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_601.54_601.88, WER: 0%, TER: 0%, total WER: 25.9716%, total TER: 12.6508%, progress (thread 0): 88.1883%]
|T|: h m m
|P|: h m
[sample: EN2002a_H02_FEO072_713.14_713.48, WER: 100%, TER: 33.3333%, total WER: 25.9725%, total TER: 12.6509%, progress (thread 0): 88.1962%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_859.83_860.17, WER: 100%, TER: 66.6667%, total WER: 25.9733%, total TER: 12.6513%, progress (thread 0): 88.2041%]
|T|: a l r i g h t
|P|: i t
[sample: EN2002a_H01_FEO070_920.16_920.5, WER: 100%, TER: 71.4286%, total WER: 25.9741%, total TER: 12.6523%, progress (thread 0): 88.212%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1586.73_1587.07, WER: 0%, TER: 0%, total WER: 25.9738%, total TER: 12.6522%, progress (thread 0): 88.2199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1927.75_1928.09, WER: 0%, TER: 0%, total WER: 25.9736%, total TER: 12.652%, progress (thread 0): 88.2279%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1929.86_1930.2, WER: 0%, TER: 0%, total WER: 25.9733%, total TER: 12.6519%, progress (thread 0): 88.2358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2048.6_2048.94, WER: 0%, TER: 0%, total WER: 25.973%, total TER: 12.6518%, progress (thread 0): 88.2437%]
|T|: s o | i t ' s
|P|: t w a
[sample: EN2002a_H03_MEE071_2098.66_2099, WER: 100%, TER: 85.7143%, total WER: 25.9746%, total TER: 12.653%, progress (thread 0): 88.2516%]
|T|: o k a y
|P|: o k
[sample: EN2002a_H00_MEE073_2129.89_2130.23, WER: 100%, TER: 50%, total WER: 25.9755%, total TER: 12.6533%, progress (thread 0): 88.2595%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_144.51_144.85, WER: 0%, TER: 0%, total WER: 25.9752%, total TER: 12.6532%, progress (thread 0): 88.2674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_190.18_190.52, WER: 0%, TER: 0%, total WER: 25.9749%, total TER: 12.6531%, progress (thread 0): 88.2753%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_302.27_302.61, WER: 100%, TER: 66.6667%, total WER: 25.9757%, total TER: 12.6535%, progress (thread 0): 88.2832%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_309.52_309.86, WER: 0%, TER: 0%, total WER: 25.9754%, total TER: 12.6534%, progress (thread 0): 88.2911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_363.35_363.69, WER: 0%, TER: 0%, total WER: 25.9751%, total TER: 12.6533%, progress (thread 0): 88.299%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_409.14_409.48, WER: 0%, TER: 0%, total WER: 25.9749%, total TER: 12.6531%, progress (thread 0): 88.307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_736.4_736.74, WER: 0%, TER: 0%, total WER: 25.9746%, total TER: 12.653%, progress (thread 0): 88.3149%]
|T|: y e a h
|P|: y o
[sample: EN2002b_H03_MEE073_935.86_936.2, WER: 100%, TER: 75%, total WER: 25.9754%, total TER: 12.6536%, progress (thread 0): 88.3228%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002b_H03_MEE073_1252.52_1252.86, WER: 0%, TER: 0%, total WER: 25.9748%, total TER: 12.6534%, progress (thread 0): 88.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1563.14_1563.48, WER: 0%, TER: 0%, total WER: 25.9745%, total TER: 12.6533%, progress (thread 0): 88.3386%]
|T|: y e a h
|P|: y e h
[sample: EN2002b_H00_FEO070_1626.33_1626.67, WER: 100%, TER: 25%, total WER: 25.9754%, total TER: 12.6534%, progress (thread 0): 88.3465%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H01_FEO072_67.59_67.93, WER: 0%, TER: 0%, total WER: 25.9751%, total TER: 12.6533%, progress (thread 0): 88.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_273.14_273.48, WER: 0%, TER: 0%, total WER: 25.9748%, total TER: 12.6532%, progress (thread 0): 88.3623%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_444.82_445.16, WER: 100%, TER: 40%, total WER: 25.9756%, total TER: 12.6535%, progress (thread 0): 88.3703%]
|T|: o r
|P|: e
[sample: EN2002c_H01_FEO072_486.4_486.74, WER: 100%, TER: 100%, total WER: 25.9764%, total TER: 12.6539%, progress (thread 0): 88.3782%]
|T|: o h | w e l l
|P|: h o w
[sample: EN2002c_H03_MEE073_497.23_497.57, WER: 100%, TER: 71.4286%, total WER: 25.9781%, total TER: 12.6549%, progress (thread 0): 88.3861%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002c_H01_FEO072_721.91_722.25, WER: 200%, TER: 14.2857%, total WER: 25.9801%, total TER: 12.6549%, progress (thread 0): 88.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1305.34_1305.68, WER: 0%, TER: 0%, total WER: 25.9798%, total TER: 12.6548%, progress (thread 0): 88.4019%]
|T|: o h
|P|: o h
[sample: EN2002c_H01_FEO072_1925.9_1926.24, WER: 0%, TER: 0%, total WER: 25.9795%, total TER: 12.6547%, progress (thread 0): 88.4098%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2040.49_2040.83, WER: 0%, TER: 0%, total WER: 25.9792%, total TER: 12.6546%, progress (thread 0): 88.4177%]
|T|: m m h m m
|P|: m p
[sample: EN2002c_H03_MEE073_2245.81_2246.15, WER: 100%, TER: 80%, total WER: 25.9801%, total TER: 12.6554%, progress (thread 0): 88.4256%]
|T|: y e a h
|P|: r e a l l y
[sample: EN2002d_H03_MEE073_344.02_344.36, WER: 100%, TER: 100%, total WER: 25.9809%, total TER: 12.6562%, progress (thread 0): 88.4335%]
|T|: w h a t | w a s | i t
|P|: o h | w a s | i t
[sample: EN2002d_H03_MEE073_508.22_508.56, WER: 33.3333%, TER: 27.2727%, total WER: 25.9811%, total TER: 12.6566%, progress (thread 0): 88.4415%]
|T|: y e a h
|P|: g o o d
[sample: EN2002d_H02_MEE071_688.4_688.74, WER: 100%, TER: 100%, total WER: 25.982%, total TER: 12.6574%, progress (thread 0): 88.4494%]
|T|: y e a h
|P|: y e s
[sample: EN2002d_H02_MEE071_715.16_715.5, WER: 100%, TER: 50%, total WER: 25.9828%, total TER: 12.6577%, progress (thread 0): 88.4573%]
|T|: u h
|P|:
[sample: EN2002d_H03_MEE073_1125.77_1126.11, WER: 100%, TER: 100%, total WER: 25.9837%, total TER: 12.6582%, progress (thread 0): 88.4652%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1325.04_1325.38, WER: 100%, TER: 33.3333%, total WER: 25.9845%, total TER: 12.6583%, progress (thread 0): 88.4731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1423.14_1423.48, WER: 0%, TER: 0%, total WER: 25.9842%, total TER: 12.6582%, progress (thread 0): 88.481%]
|T|: n o | w e | p
|P|: s o | w e
[sample: EN2002d_H00_FEO070_1437.94_1438.28, WER: 66.6667%, TER: 42.8571%, total WER: 25.9856%, total TER: 12.6587%, progress (thread 0): 88.4889%]
|T|: w i t h
|P|: w i t h
[sample: EN2002d_H02_MEE071_2073.15_2073.49, WER: 0%, TER: 0%, total WER: 25.9853%, total TER: 12.6586%, progress (thread 0): 88.4968%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_258.31_258.64, WER: 100%, TER: 40%, total WER: 25.9861%, total TER: 12.6589%, progress (thread 0): 88.5048%]
|T|: m m | y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_1048.05_1048.38, WER: 50%, TER: 42.8571%, total WER: 25.9867%, total TER: 12.6594%, progress (thread 0): 88.5127%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_736.03_736.36, WER: 0%, TER: 0%, total WER: 25.9864%, total TER: 12.6593%, progress (thread 0): 88.5206%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1313.86_1314.19, WER: 0%, TER: 0%, total WER: 25.9861%, total TER: 12.6591%, progress (thread 0): 88.5285%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H01_FEE013_1711.46_1711.79, WER: 100%, TER: 200%, total WER: 25.9869%, total TER: 12.66%, progress (thread 0): 88.5364%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004b_H03_FEE016_2208.79_2209.12, WER: 200%, TER: 20%, total WER: 25.9889%, total TER: 12.6601%, progress (thread 0): 88.5443%]
|T|: o k a y
|P|: o k a y
[sample: ES2004b_H03_FEE016_2308.52_2308.85, WER: 0%, TER: 0%, total WER: 25.9886%, total TER: 12.66%, progress (thread 0): 88.5522%]
|T|: y e a h
|P|: y u p
[sample: ES2004c_H02_MEE014_172.32_172.65, WER: 100%, TER: 75%, total WER: 25.9895%, total TER: 12.6606%, progress (thread 0): 88.5601%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_560.34_560.67, WER: 100%, TER: 50%, total WER: 25.9903%, total TER: 12.6608%, progress (thread 0): 88.568%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H01_FEE013_873.37_873.7, WER: 100%, TER: 40%, total WER: 25.9911%, total TER: 12.6611%, progress (thread 0): 88.576%]
|T|: w e l l
|P|: w e l l
[sample: ES2004c_H02_MEE014_925.27_925.6, WER: 0%, TER: 0%, total WER: 25.9908%, total TER: 12.661%, progress (thread 0): 88.5839%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1268.11_1268.44, WER: 0%, TER: 0%, total WER: 25.9905%, total TER: 12.6608%, progress (thread 0): 88.5918%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1965.56_1965.89, WER: 100%, TER: 40%, total WER: 25.9914%, total TER: 12.6612%, progress (thread 0): 88.5997%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H01_FEE013_607.94_608.27, WER: 100%, TER: 40%, total WER: 25.9922%, total TER: 12.6615%, progress (thread 0): 88.6076%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_753.08_753.41, WER: 100%, TER: 33.3333%, total WER: 25.9931%, total TER: 12.6616%, progress (thread 0): 88.6155%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H03_FEE016_876.07_876.4, WER: 100%, TER: 40%, total WER: 25.9939%, total TER: 12.6619%, progress (thread 0): 88.6234%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H00_MEO015_1304.99_1305.32, WER: 0%, TER: 0%, total WER: 25.9936%, total TER: 12.6619%, progress (thread 0): 88.6313%]
|T|: s u r e
|P|: s u r e
[sample: ES2004d_H03_FEE016_1499.95_1500.28, WER: 0%, TER: 0%, total WER: 25.9933%, total TER: 12.6617%, progress (thread 0): 88.6392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1845.84_1846.17, WER: 0%, TER: 0%, total WER: 25.993%, total TER: 12.6616%, progress (thread 0): 88.6471%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1856.75_1857.08, WER: 0%, TER: 0%, total WER: 25.9927%, total TER: 12.6615%, progress (thread 0): 88.6551%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1984.68_1985.01, WER: 0%, TER: 0%, total WER: 25.9924%, total TER: 12.6614%, progress (thread 0): 88.663%]
|T|: m m
|P|: o h
[sample: ES2004d_H01_FEE013_2126.04_2126.37, WER: 100%, TER: 100%, total WER: 25.9933%, total TER: 12.6618%, progress (thread 0): 88.6709%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_55.7_56.03, WER: 0%, TER: 0%, total WER: 25.993%, total TER: 12.6617%, progress (thread 0): 88.6788%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_226.94_227.27, WER: 100%, TER: 40%, total WER: 25.9938%, total TER: 12.662%, progress (thread 0): 88.6867%]
|T|: o k a y
|P|: o k a t
[sample: IS1009a_H03_FIO089_318.12_318.45, WER: 100%, TER: 25%, total WER: 25.9946%, total TER: 12.6621%, progress (thread 0): 88.6946%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_652.91_653.24, WER: 100%, TER: 40%, total WER: 25.9955%, total TER: 12.6624%, progress (thread 0): 88.7025%]
|T|: t h e n
|P|: a n d | t h e n
[sample: IS1009a_H00_FIE088_714.21_714.54, WER: 100%, TER: 100%, total WER: 25.9963%, total TER: 12.6633%, progress (thread 0): 88.7104%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_741.37_741.7, WER: 100%, TER: 40%, total WER: 25.9972%, total TER: 12.6636%, progress (thread 0): 88.7184%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_790.95_791.28, WER: 100%, TER: 40%, total WER: 25.998%, total TER: 12.6639%, progress (thread 0): 88.7263%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_787.47_787.8, WER: 100%, TER: 40%, total WER: 25.9988%, total TER: 12.6642%, progress (thread 0): 88.7342%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1063.18_1063.51, WER: 100%, TER: 40%, total WER: 25.9997%, total TER: 12.6645%, progress (thread 0): 88.7421%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1809.47_1809.8, WER: 0%, TER: 0%, total WER: 25.9994%, total TER: 12.6644%, progress (thread 0): 88.75%]
|T|: m m h m m
|P|: u h
[sample: IS1009b_H03_FIO089_1882.22_1882.55, WER: 100%, TER: 80%, total WER: 26.0002%, total TER: 12.6652%, progress (thread 0): 88.7579%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_408.02_408.35, WER: 100%, TER: 40%, total WER: 26.0011%, total TER: 12.6655%, progress (thread 0): 88.7658%]
|T|: m m h m m
|P|: u h
[sample: IS1009c_H00_FIE088_430.74_431.07, WER: 100%, TER: 80%, total WER: 26.0019%, total TER: 12.6663%, progress (thread 0): 88.7737%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1552.21_1552.54, WER: 0%, TER: 0%, total WER: 26.0016%, total TER: 12.6662%, progress (thread 0): 88.7816%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_383.89_384.22, WER: 100%, TER: 40%, total WER: 26.0024%, total TER: 12.6665%, progress (thread 0): 88.7896%]
|T|: m m h m m
|P|: m
[sample: IS1009d_H03_FIO089_388.95_389.28, WER: 100%, TER: 80%, total WER: 26.0033%, total TER: 12.6673%, progress (thread 0): 88.7975%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_483.18_483.51, WER: 100%, TER: 40%, total WER: 26.0041%, total TER: 12.6676%, progress (thread 0): 88.8054%]
|T|: t h a t ' s | r i g h t
|P|:
[sample: IS1009d_H00_FIE088_757.72_758.05, WER: 100%, TER: 100%, total WER: 26.0058%, total TER: 12.6701%, progress (thread 0): 88.8133%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H01_FIO087_784.18_784.51, WER: 100%, TER: 40%, total WER: 26.0066%, total TER: 12.6704%, progress (thread 0): 88.8212%]
|T|: y e a h
|P|: i t
[sample: IS1009d_H02_FIO084_975.71_976.04, WER: 100%, TER: 100%, total WER: 26.0075%, total TER: 12.6713%, progress (thread 0): 88.8291%]
|T|: m m
|P|: h m
[sample: IS1009d_H02_FIO084_1183.1_1183.43, WER: 100%, TER: 50%, total WER: 26.0083%, total TER: 12.6714%, progress (thread 0): 88.837%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H02_FIO084_1185.85_1186.18, WER: 100%, TER: 40%, total WER: 26.0092%, total TER: 12.6717%, progress (thread 0): 88.8449%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1310.32_1310.65, WER: 100%, TER: 100%, total WER: 26.01%, total TER: 12.6728%, progress (thread 0): 88.8528%]
|T|: u h
|P|: u m
[sample: TS3003a_H01_MTD011UID_915.22_915.55, WER: 100%, TER: 50%, total WER: 26.0108%, total TER: 12.6729%, progress (thread 0): 88.8608%]
|T|: o r | w h o
|P|: o | w h o
[sample: TS3003a_H01_MTD011UID_975.03_975.36, WER: 50%, TER: 16.6667%, total WER: 26.0114%, total TER: 12.673%, progress (thread 0): 88.8687%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1261.99_1262.32, WER: 100%, TER: 50%, total WER: 26.0122%, total TER: 12.6732%, progress (thread 0): 88.8766%]
|T|: s o
|P|: s o
[sample: TS3003b_H03_MTD012ME_280.47_280.8, WER: 0%, TER: 0%, total WER: 26.0119%, total TER: 12.6731%, progress (thread 0): 88.8845%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H00_MTD009PM_842.62_842.95, WER: 100%, TER: 50%, total WER: 26.0128%, total TER: 12.6735%, progress (thread 0): 88.8924%]
|T|: o k a y
|P|: h
[sample: TS3003b_H00_MTD009PM_923.51_923.84, WER: 100%, TER: 100%, total WER: 26.0136%, total TER: 12.6743%, progress (thread 0): 88.9003%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1173.66_1173.99, WER: 0%, TER: 0%, total WER: 26.0133%, total TER: 12.6742%, progress (thread 0): 88.9082%]
|T|: n a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1009.07_1009.4, WER: 100%, TER: 66.6667%, total WER: 26.0141%, total TER: 12.6746%, progress (thread 0): 88.9161%]
|T|: ' k a y
|P|: s o
[sample: TS3003c_H03_MTD012ME_1321.92_1322.25, WER: 100%, TER: 100%, total WER: 26.015%, total TER: 12.6754%, progress (thread 0): 88.924%]
|T|: m m
|P|: a n
[sample: TS3003c_H01_MTD011UID_1395.11_1395.44, WER: 100%, TER: 100%, total WER: 26.0158%, total TER: 12.6758%, progress (thread 0): 88.932%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1507.79_1508.12, WER: 0%, TER: 0%, total WER: 26.0155%, total TER: 12.6757%, progress (thread 0): 88.9399%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1540.01_1540.34, WER: 100%, TER: 40%, total WER: 26.0164%, total TER: 12.676%, progress (thread 0): 88.9478%]
|T|: y e p
|P|: e h
[sample: TS3003c_H00_MTD009PM_1653.43_1653.76, WER: 100%, TER: 66.6667%, total WER: 26.0172%, total TER: 12.6764%, progress (thread 0): 88.9557%]
|T|: u h
|P|: m m
[sample: TS3003c_H01_MTD011UID_1692.63_1692.96, WER: 100%, TER: 100%, total WER: 26.018%, total TER: 12.6768%, progress (thread 0): 88.9636%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2074.03_2074.36, WER: 0%, TER: 0%, total WER: 26.0178%, total TER: 12.6767%, progress (thread 0): 88.9715%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2241.48_2241.81, WER: 0%, TER: 0%, total WER: 26.0175%, total TER: 12.6765%, progress (thread 0): 88.9794%]
|T|: y e a h
|P|: y e r e
[sample: TS3003d_H01_MTD011UID_693.96_694.29, WER: 100%, TER: 50%, total WER: 26.0183%, total TER: 12.6769%, progress (thread 0): 88.9873%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_872.34_872.67, WER: 0%, TER: 0%, total WER: 26.018%, total TER: 12.6768%, progress (thread 0): 88.9953%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_889.13_889.46, WER: 0%, TER: 0%, total WER: 26.0177%, total TER: 12.6767%, progress (thread 0): 89.0032%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_1661.82_1662.15, WER: 0%, TER: 0%, total WER: 26.0174%, total TER: 12.6765%, progress (thread 0): 89.0111%]
|T|: y e a h
|P|: h u h
[sample: TS3003d_H01_MTD011UID_2072.21_2072.54, WER: 100%, TER: 75%, total WER: 26.0183%, total TER: 12.6771%, progress (thread 0): 89.019%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_2085.82_2086.15, WER: 100%, TER: 40%, total WER: 26.0191%, total TER: 12.6774%, progress (thread 0): 89.0269%]
|T|: y e a h
|P|: y e s
[sample: TS3003d_H00_MTD009PM_2164.01_2164.34, WER: 100%, TER: 50%, total WER: 26.0199%, total TER: 12.6778%, progress (thread 0): 89.0348%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2179.8_2180.13, WER: 0%, TER: 0%, total WER: 26.0196%, total TER: 12.6777%, progress (thread 0): 89.0427%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_2461.76_2462.09, WER: 100%, TER: 40%, total WER: 26.0205%, total TER: 12.678%, progress (thread 0): 89.0506%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_299.48_299.81, WER: 0%, TER: 0%, total WER: 26.0202%, total TER: 12.6779%, progress (thread 0): 89.0585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_359.5_359.83, WER: 0%, TER: 0%, total WER: 26.0199%, total TER: 12.6778%, progress (thread 0): 89.0665%]
|T|: w a i t
|P|: w a i t
[sample: EN2002a_H02_FEO072_387.53_387.86, WER: 0%, TER: 0%, total WER: 26.0196%, total TER: 12.6776%, progress (thread 0): 89.0744%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_593.65_593.98, WER: 50%, TER: 55.5556%, total WER: 26.0201%, total TER: 12.6785%, progress (thread 0): 89.0823%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_594.32_594.65, WER: 0%, TER: 0%, total WER: 26.0198%, total TER: 12.6784%, progress (thread 0): 89.0902%]
|T|: y e a h
|P|: y n o
[sample: EN2002a_H01_FEO070_690.97_691.3, WER: 100%, TER: 75%, total WER: 26.0207%, total TER: 12.679%, progress (thread 0): 89.0981%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_1018.98_1019.31, WER: 0%, TER: 0%, total WER: 26.0204%, total TER: 12.6789%, progress (thread 0): 89.106%]
|T|: y e a h
|P|: y e s
[sample: EN2002a_H01_FEO070_1028.68_1029.01, WER: 100%, TER: 50%, total WER: 26.0212%, total TER: 12.6792%, progress (thread 0): 89.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1142.23_1142.56, WER: 0%, TER: 0%, total WER: 26.0209%, total TER: 12.6791%, progress (thread 0): 89.1218%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1361.22_1361.55, WER: 0%, TER: 0%, total WER: 26.0206%, total TER: 12.679%, progress (thread 0): 89.1297%]
|T|: r i g h t
|P|: e h
[sample: EN2002a_H03_MEE071_1383.46_1383.79, WER: 100%, TER: 80%, total WER: 26.0215%, total TER: 12.6798%, progress (thread 0): 89.1377%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1864.48_1864.81, WER: 0%, TER: 0%, total WER: 26.0212%, total TER: 12.6797%, progress (thread 0): 89.1456%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1964.31_1964.64, WER: 0%, TER: 0%, total WER: 26.0209%, total TER: 12.6796%, progress (thread 0): 89.1535%]
|T|: u m
|P|: u m
[sample: EN2002b_H03_MEE073_139.44_139.77, WER: 0%, TER: 0%, total WER: 26.0206%, total TER: 12.6795%, progress (thread 0): 89.1614%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_417.94_418.27, WER: 200%, TER: 175%, total WER: 26.0226%, total TER: 12.681%, progress (thread 0): 89.1693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_420.46_420.79, WER: 0%, TER: 0%, total WER: 26.0223%, total TER: 12.6809%, progress (thread 0): 89.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_705.52_705.85, WER: 0%, TER: 0%, total WER: 26.022%, total TER: 12.6808%, progress (thread 0): 89.1851%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_731.78_732.11, WER: 0%, TER: 0%, total WER: 26.0217%, total TER: 12.6807%, progress (thread 0): 89.193%]
|T|: y e a h | t
|P|: y e a h
[sample: EN2002b_H02_FEO072_1235.77_1236.1, WER: 50%, TER: 33.3333%, total WER: 26.0222%, total TER: 12.681%, progress (thread 0): 89.201%]
|T|: h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_1236.72_1237.05, WER: 100%, TER: 66.6667%, total WER: 26.0231%, total TER: 12.6813%, progress (thread 0): 89.2089%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_1256.5_1256.83, WER: 0%, TER: 0%, total WER: 26.0228%, total TER: 12.6812%, progress (thread 0): 89.2168%]
|T|: h m m
|P|: m
[sample: EN2002b_H02_FEO072_1475.51_1475.84, WER: 100%, TER: 66.6667%, total WER: 26.0236%, total TER: 12.6816%, progress (thread 0): 89.2247%]
|T|: o h | r i g h t
|P|: a l l | r i g h t
[sample: EN2002b_H02_FEO072_1611.8_1612.13, WER: 50%, TER: 37.5%, total WER: 26.0241%, total TER: 12.6821%, progress (thread 0): 89.2326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_93.86_94.19, WER: 0%, TER: 0%, total WER: 26.0238%, total TER: 12.6819%, progress (thread 0): 89.2405%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_245.2_245.53, WER: 0%, TER: 0%, total WER: 26.0235%, total TER: 12.6818%, progress (thread 0): 89.2484%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H01_FEO072_447.06_447.39, WER: 100%, TER: 40%, total WER: 26.0244%, total TER: 12.6822%, progress (thread 0): 89.2563%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002c_H03_MEE073_823.26_823.59, WER: 0%, TER: 0%, total WER: 26.0238%, total TER: 12.6819%, progress (thread 0): 89.2642%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_850.62_850.95, WER: 0%, TER: 0%, total WER: 26.0235%, total TER: 12.6818%, progress (thread 0): 89.2722%]
|T|: t h a t ' s | a
|P|: t h a t ' s | a
[sample: EN2002c_H03_MEE073_903.73_904.06, WER: 0%, TER: 0%, total WER: 26.0229%, total TER: 12.6816%, progress (thread 0): 89.2801%]
|T|: o h
|P|: n o w
[sample: EN2002c_H03_MEE073_962_962.33, WER: 100%, TER: 100%, total WER: 26.0237%, total TER: 12.682%, progress (thread 0): 89.288%]
|T|: y e a h | r i g h t
|P|: y a | r i h
[sample: EN2002c_H03_MEE073_1287.41_1287.74, WER: 100%, TER: 40%, total WER: 26.0254%, total TER: 12.6826%, progress (thread 0): 89.2959%]
|T|: t h a t ' s | t r u e
|P|: t h a t ' s | r u e
[sample: EN2002c_H03_MEE073_1299.73_1300.06, WER: 50%, TER: 9.09091%, total WER: 26.026%, total TER: 12.6825%, progress (thread 0): 89.3038%]
|T|: y o u | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2044.96_2045.29, WER: 0%, TER: 0%, total WER: 26.0254%, total TER: 12.6823%, progress (thread 0): 89.3117%]
|T|: m m
|P|: h m
[sample: EN2002c_H02_MEE071_2072_2072.33, WER: 100%, TER: 50%, total WER: 26.0262%, total TER: 12.6825%, progress (thread 0): 89.3196%]
|T|: o o h
|P|: o o
[sample: EN2002c_H01_FEO072_2817.88_2818.21, WER: 100%, TER: 33.3333%, total WER: 26.0271%, total TER: 12.6826%, progress (thread 0): 89.3275%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002d_H01_FEO072_419.86_420.19, WER: 200%, TER: 14.2857%, total WER: 26.029%, total TER: 12.6827%, progress (thread 0): 89.3354%]
|T|: m m
|P|: i
[sample: EN2002d_H03_MEE073_488.3_488.63, WER: 100%, TER: 100%, total WER: 26.0299%, total TER: 12.6831%, progress (thread 0): 89.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1089.65_1089.98, WER: 0%, TER: 0%, total WER: 26.0296%, total TER: 12.6829%, progress (thread 0): 89.3513%]
|T|: w e l l
|P|: n o
[sample: EN2002d_H03_MEE073_1132.11_1132.44, WER: 100%, TER: 100%, total WER: 26.0304%, total TER: 12.6838%, progress (thread 0): 89.3592%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1131.27_1131.6, WER: 100%, TER: 33.3333%, total WER: 26.0312%, total TER: 12.6839%, progress (thread 0): 89.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1257.89_1258.22, WER: 0%, TER: 0%, total WER: 26.031%, total TER: 12.6838%, progress (thread 0): 89.375%]
|T|: r e a l l y
|P|: r e a l l y
[sample: EN2002d_H01_FEO072_1345.97_1346.3, WER: 0%, TER: 0%, total WER: 26.0307%, total TER: 12.6836%, progress (thread 0): 89.3829%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1429.74_1430.07, WER: 100%, TER: 33.3333%, total WER: 26.0315%, total TER: 12.6838%, progress (thread 0): 89.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1957.8_1958.13, WER: 0%, TER: 0%, total WER: 26.0312%, total TER: 12.6836%, progress (thread 0): 89.3987%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_863.21_863.53, WER: 100%, TER: 40%, total WER: 26.032%, total TER: 12.684%, progress (thread 0): 89.4066%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_895.43_895.75, WER: 100%, TER: 40%, total WER: 26.0329%, total TER: 12.6843%, progress (thread 0): 89.4146%]
|T|: m m
|P|: h m
[sample: ES2004b_H01_FEE013_1140.58_1140.9, WER: 100%, TER: 50%, total WER: 26.0337%, total TER: 12.6845%, progress (thread 0): 89.4225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1182.75_1183.07, WER: 0%, TER: 0%, total WER: 26.0334%, total TER: 12.6843%, progress (thread 0): 89.4304%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1471.32_1471.64, WER: 100%, TER: 40%, total WER: 26.0343%, total TER: 12.6847%, progress (thread 0): 89.4383%]
|T|: s
|P|: s h
[sample: ES2004b_H01_FEE013_1922.51_1922.83, WER: 100%, TER: 100%, total WER: 26.0351%, total TER: 12.6849%, progress (thread 0): 89.4462%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H03_FEE016_1953.34_1953.66, WER: 100%, TER: 60%, total WER: 26.0359%, total TER: 12.6854%, progress (thread 0): 89.4541%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1966.24_1966.56, WER: 0%, TER: 0%, total WER: 26.0356%, total TER: 12.6853%, progress (thread 0): 89.462%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_806.95_807.27, WER: 0%, TER: 0%, total WER: 26.0353%, total TER: 12.6852%, progress (thread 0): 89.4699%]
|T|: o k a y
|P|: o k a
[sample: ES2004c_H03_FEE016_1066.11_1066.43, WER: 100%, TER: 25%, total WER: 26.0362%, total TER: 12.6853%, progress (thread 0): 89.4779%]
|T|: m m
|P|: y e m
[sample: ES2004c_H00_MEO015_1890.24_1890.56, WER: 100%, TER: 100%, total WER: 26.037%, total TER: 12.6857%, progress (thread 0): 89.4858%]
|T|: y e a h
|P|: y e p
[sample: ES2004d_H00_MEO015_341.64_341.96, WER: 100%, TER: 50%, total WER: 26.0379%, total TER: 12.6861%, progress (thread 0): 89.4937%]
|T|: n o
|P|: n o
[sample: ES2004d_H00_MEO015_400.96_401.28, WER: 0%, TER: 0%, total WER: 26.0376%, total TER: 12.686%, progress (thread 0): 89.5016%]
|T|: o o p s
|P|:
[sample: ES2004d_H03_FEE016_430.55_430.87, WER: 100%, TER: 100%, total WER: 26.0384%, total TER: 12.6868%, progress (thread 0): 89.5095%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_774.15_774.47, WER: 0%, TER: 0%, total WER: 26.0381%, total TER: 12.6867%, progress (thread 0): 89.5174%]
|T|: o n e
|P|: w e l l
[sample: ES2004d_H02_MEE014_785.53_785.85, WER: 100%, TER: 133.333%, total WER: 26.0389%, total TER: 12.6875%, progress (thread 0): 89.5253%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_840.47_840.79, WER: 0%, TER: 0%, total WER: 26.0386%, total TER: 12.6875%, progress (thread 0): 89.5332%]
|T|: m m h m m
|P|:
[sample: ES2004d_H03_FEE016_1930.95_1931.27, WER: 100%, TER: 100%, total WER: 26.0395%, total TER: 12.6885%, progress (thread 0): 89.5411%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2146.44_2146.76, WER: 0%, TER: 0%, total WER: 26.0392%, total TER: 12.6884%, progress (thread 0): 89.549%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_290.82_291.14, WER: 100%, TER: 40%, total WER: 26.04%, total TER: 12.6887%, progress (thread 0): 89.557%]
|T|: o o p s
|P|: s
[sample: IS1009a_H03_FIO089_420.14_420.46, WER: 100%, TER: 75%, total WER: 26.0409%, total TER: 12.6893%, progress (thread 0): 89.5649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H01_FIO087_494.72_495.04, WER: 0%, TER: 0%, total WER: 26.0406%, total TER: 12.6892%, progress (thread 0): 89.5728%]
|T|: y e a h
|P|:
[sample: IS1009a_H02_FIO084_577.05_577.37, WER: 100%, TER: 100%, total WER: 26.0414%, total TER: 12.69%, progress (thread 0): 89.5807%]
|T|: m m h m m
|P|: u h h u h
[sample: IS1009a_H02_FIO084_641.8_642.12, WER: 100%, TER: 80%, total WER: 26.0422%, total TER: 12.6908%, progress (thread 0): 89.5886%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_197.33_197.65, WER: 0%, TER: 0%, total WER: 26.0419%, total TER: 12.6906%, progress (thread 0): 89.5965%]
|T|: m m h m m
|P|: u
[sample: IS1009b_H00_FIE088_595.82_596.14, WER: 100%, TER: 100%, total WER: 26.0428%, total TER: 12.6917%, progress (thread 0): 89.6044%]
|T|: m m h m m
|P|: r i h
[sample: IS1009b_H03_FIO089_1073.83_1074.15, WER: 100%, TER: 80%, total WER: 26.0436%, total TER: 12.6925%, progress (thread 0): 89.6123%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1139.48_1139.8, WER: 100%, TER: 40%, total WER: 26.0445%, total TER: 12.6928%, progress (thread 0): 89.6202%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1268.25_1268.57, WER: 100%, TER: 40%, total WER: 26.0453%, total TER: 12.6931%, progress (thread 0): 89.6282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1877.89_1878.21, WER: 0%, TER: 0%, total WER: 26.045%, total TER: 12.693%, progress (thread 0): 89.6361%]
|T|: m m h m m
|P|: m e n
[sample: IS1009b_H03_FIO089_1894.73_1895.05, WER: 100%, TER: 80%, total WER: 26.0458%, total TER: 12.6938%, progress (thread 0): 89.644%]
|T|: h i
|P|: k a y
[sample: IS1009c_H01_FIO087_43.77_44.09, WER: 100%, TER: 150%, total WER: 26.0467%, total TER: 12.6944%, progress (thread 0): 89.6519%]
|T|: m m h m m
|P|: u h | h u h
[sample: IS1009c_H00_FIE088_405.29_405.61, WER: 200%, TER: 100%, total WER: 26.0487%, total TER: 12.6954%, progress (thread 0): 89.6598%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_524.19_524.51, WER: 100%, TER: 40%, total WER: 26.0495%, total TER: 12.6958%, progress (thread 0): 89.6677%]
|T|: y e s
|P|:
[sample: IS1009c_H01_FIO087_1640_1640.32, WER: 100%, TER: 100%, total WER: 26.0503%, total TER: 12.6964%, progress (thread 0): 89.6756%]
|T|: m e n u
|P|: m e n u
[sample: IS1009d_H00_FIE088_523.67_523.99, WER: 0%, TER: 0%, total WER: 26.05%, total TER: 12.6963%, progress (thread 0): 89.6835%]
|T|: p a r d o n | m e
|P|: b u
[sample: IS1009d_H01_FIO087_699.16_699.48, WER: 100%, TER: 100%, total WER: 26.0517%, total TER: 12.6981%, progress (thread 0): 89.6915%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_799.6_799.92, WER: 100%, TER: 100%, total WER: 26.0525%, total TER: 12.6991%, progress (thread 0): 89.6994%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1056.69_1057.01, WER: 0%, TER: 0%, total WER: 26.0522%, total TER: 12.699%, progress (thread 0): 89.7073%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1722.84_1723.16, WER: 100%, TER: 40%, total WER: 26.0531%, total TER: 12.6993%, progress (thread 0): 89.7152%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1741.45_1741.77, WER: 100%, TER: 40%, total WER: 26.0539%, total TER: 12.6997%, progress (thread 0): 89.7231%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1791.05_1791.37, WER: 0%, TER: 0%, total WER: 26.0536%, total TER: 12.6995%, progress (thread 0): 89.731%]
|T|: y e a h
|P|: h m
[sample: TS3003b_H01_MTD011UID_1651.31_1651.63, WER: 100%, TER: 100%, total WER: 26.0545%, total TER: 12.7004%, progress (thread 0): 89.7389%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_1722.53_1722.85, WER: 100%, TER: 25%, total WER: 26.0553%, total TER: 12.7005%, progress (thread 0): 89.7468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2090.25_2090.57, WER: 0%, TER: 0%, total WER: 26.055%, total TER: 12.7004%, progress (thread 0): 89.7547%]
|T|: m m h m m
|P|: a h
[sample: TS3003c_H03_MTD012ME_2074.3_2074.62, WER: 100%, TER: 80%, total WER: 26.0558%, total TER: 12.7011%, progress (thread 0): 89.7627%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_2134.48_2134.8, WER: 100%, TER: 40%, total WER: 26.0567%, total TER: 12.7015%, progress (thread 0): 89.7706%]
|T|: n a y
|P|: n o
[sample: TS3003d_H01_MTD011UID_417.84_418.16, WER: 100%, TER: 66.6667%, total WER: 26.0575%, total TER: 12.7018%, progress (thread 0): 89.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_694.59_694.91, WER: 0%, TER: 0%, total WER: 26.0572%, total TER: 12.7017%, progress (thread 0): 89.7864%]
|T|: o r | b
|P|: r
[sample: TS3003d_H03_MTD012ME_896.59_896.91, WER: 100%, TER: 75%, total WER: 26.0589%, total TER: 12.7023%, progress (thread 0): 89.7943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1403.4_1403.72, WER: 0%, TER: 0%, total WER: 26.0586%, total TER: 12.7022%, progress (thread 0): 89.8022%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1492.24_1492.56, WER: 0%, TER: 0%, total WER: 26.0583%, total TER: 12.7021%, progress (thread 0): 89.8101%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_2024.39_2024.71, WER: 100%, TER: 100%, total WER: 26.0591%, total TER: 12.7029%, progress (thread 0): 89.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2381.84_2382.16, WER: 0%, TER: 0%, total WER: 26.0588%, total TER: 12.7028%, progress (thread 0): 89.826%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_68.23_68.55, WER: 0%, TER: 0%, total WER: 26.0586%, total TER: 12.7026%, progress (thread 0): 89.8339%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_359.54_359.86, WER: 50%, TER: 42.8571%, total WER: 26.0591%, total TER: 12.7031%, progress (thread 0): 89.8418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_496.88_497.2, WER: 0%, TER: 0%, total WER: 26.0588%, total TER: 12.703%, progress (thread 0): 89.8497%]
|T|: h m m
|P|: y n
[sample: EN2002a_H02_FEO072_504.18_504.5, WER: 100%, TER: 100%, total WER: 26.0596%, total TER: 12.7036%, progress (thread 0): 89.8576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_567.01_567.33, WER: 0%, TER: 0%, total WER: 26.0593%, total TER: 12.7036%, progress (thread 0): 89.8655%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_604.34_604.66, WER: 100%, TER: 40%, total WER: 26.0602%, total TER: 12.7039%, progress (thread 0): 89.8734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_729.25_729.57, WER: 0%, TER: 0%, total WER: 26.0599%, total TER: 12.7038%, progress (thread 0): 89.8813%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_800.36_800.68, WER: 0%, TER: 0%, total WER: 26.0596%, total TER: 12.7036%, progress (thread 0): 89.8892%]
|T|: o h
|P|: n o
[sample: EN2002a_H02_FEO072_856.5_856.82, WER: 100%, TER: 100%, total WER: 26.0604%, total TER: 12.704%, progress (thread 0): 89.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1050.6_1050.92, WER: 0%, TER: 0%, total WER: 26.0601%, total TER: 12.7039%, progress (thread 0): 89.9051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1101.18_1101.5, WER: 0%, TER: 0%, total WER: 26.0598%, total TER: 12.7038%, progress (thread 0): 89.913%]
|T|: o h | n o
|P|: y o u | k n o w
[sample: EN2002a_H01_FEO070_1448.67_1448.99, WER: 100%, TER: 80%, total WER: 26.0615%, total TER: 12.7046%, progress (thread 0): 89.9209%]
|T|: h m m
|P|: y e a h
[sample: EN2002a_H00_MEE073_1448.83_1449.15, WER: 100%, TER: 133.333%, total WER: 26.0623%, total TER: 12.7054%, progress (thread 0): 89.9288%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1537.39_1537.71, WER: 0%, TER: 0%, total WER: 26.0621%, total TER: 12.7053%, progress (thread 0): 89.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1862.6_1862.92, WER: 0%, TER: 0%, total WER: 26.0618%, total TER: 12.7052%, progress (thread 0): 89.9446%]
|T|: y e a h
|P|: i
[sample: EN2002b_H03_MEE073_283.05_283.37, WER: 100%, TER: 100%, total WER: 26.0626%, total TER: 12.706%, progress (thread 0): 89.9525%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_363.29_363.61, WER: 200%, TER: 175%, total WER: 26.0646%, total TER: 12.7075%, progress (thread 0): 89.9604%]
|T|: o h | y e a h
|P|:
[sample: EN2002b_H03_MEE073_679.41_679.73, WER: 100%, TER: 100%, total WER: 26.0662%, total TER: 12.709%, progress (thread 0): 89.9684%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_922.82_923.14, WER: 0%, TER: 0%, total WER: 26.0659%, total TER: 12.7088%, progress (thread 0): 89.9763%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H02_FEO072_1100.2_1100.52, WER: 100%, TER: 40%, total WER: 26.0668%, total TER: 12.7092%, progress (thread 0): 89.9842%]
|T|: g o o d | p o i n t
|P|: t h e | p o i n t
[sample: EN2002b_H03_MEE073_1217.83_1218.15, WER: 50%, TER: 40%, total WER: 26.0673%, total TER: 12.7098%, progress (thread 0): 89.9921%]
|T|: y e a h
|P|: o
[sample: EN2002b_H03_MEE073_1261.76_1262.08, WER: 100%, TER: 100%, total WER: 26.0682%, total TER: 12.7106%, progress (thread 0): 90%]
|T|: h m m
|P|: y n
[sample: EN2002b_H03_MEE073_1598.38_1598.7, WER: 100%, TER: 100%, total WER: 26.069%, total TER: 12.7112%, progress (thread 0): 90.0079%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1611.81_1612.13, WER: 50%, TER: 55.5556%, total WER: 26.0695%, total TER: 12.7121%, progress (thread 0): 90.0158%]
|T|: o k a y
|P|:
[sample: EN2002b_H03_MEE073_1705.25_1705.57, WER: 100%, TER: 100%, total WER: 26.0704%, total TER: 12.713%, progress (thread 0): 90.0237%]
|T|: g o o d
|P|: o k a y
[sample: EN2002c_H01_FEO072_489.73_490.05, WER: 100%, TER: 100%, total WER: 26.0712%, total TER: 12.7138%, progress (thread 0): 90.0316%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_653.98_654.3, WER: 0%, TER: 0%, total WER: 26.0709%, total TER: 12.7137%, progress (thread 0): 90.0396%]
|T|: y e a h
|P|: u h
[sample: EN2002c_H01_FEO072_1352.96_1353.28, WER: 100%, TER: 75%, total WER: 26.0718%, total TER: 12.7143%, progress (thread 0): 90.0475%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_2296.14_2296.46, WER: 100%, TER: 50%, total WER: 26.0726%, total TER: 12.7145%, progress (thread 0): 90.0554%]
|T|: i | t h i n k
|P|: i | t h i n k
[sample: EN2002c_H01_FEO072_2336.35_2336.67, WER: 0%, TER: 0%, total WER: 26.072%, total TER: 12.7143%, progress (thread 0): 90.0633%]
|T|: o h
|P|: i
[sample: EN2002c_H01_FEO072_2368.69_2369.01, WER: 100%, TER: 100%, total WER: 26.0728%, total TER: 12.7147%, progress (thread 0): 90.0712%]
|T|: w h a t
|P|: w e l l
[sample: EN2002c_H01_FEO072_2484.6_2484.92, WER: 100%, TER: 75%, total WER: 26.0737%, total TER: 12.7153%, progress (thread 0): 90.0791%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2800.26_2800.58, WER: 100%, TER: 33.3333%, total WER: 26.0745%, total TER: 12.7154%, progress (thread 0): 90.087%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_2829.54_2829.86, WER: 0%, TER: 0%, total WER: 26.0742%, total TER: 12.7153%, progress (thread 0): 90.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_347.79_348.11, WER: 0%, TER: 0%, total WER: 26.0739%, total TER: 12.7152%, progress (thread 0): 90.1028%]
|T|: i t ' s | g o o d
|P|:
[sample: EN2002d_H00_FEO070_420.4_420.72, WER: 100%, TER: 100%, total WER: 26.0756%, total TER: 12.717%, progress (thread 0): 90.1108%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_574.1_574.42, WER: 100%, TER: 40%, total WER: 26.0764%, total TER: 12.7173%, progress (thread 0): 90.1187%]
|T|: b u t
|P|: u h
[sample: EN2002d_H01_FEO072_590.21_590.53, WER: 100%, TER: 66.6667%, total WER: 26.0773%, total TER: 12.7177%, progress (thread 0): 90.1266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_769.35_769.67, WER: 0%, TER: 0%, total WER: 26.077%, total TER: 12.7176%, progress (thread 0): 90.1345%]
|T|: o h
|P|: n
[sample: EN2002d_H03_MEE073_939.78_940.1, WER: 100%, TER: 100%, total WER: 26.0778%, total TER: 12.718%, progress (thread 0): 90.1424%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_954.69_955.01, WER: 0%, TER: 0%, total WER: 26.0775%, total TER: 12.7179%, progress (thread 0): 90.1503%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1189.57_1189.89, WER: 0%, TER: 0%, total WER: 26.0772%, total TER: 12.7178%, progress (thread 0): 90.1582%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1203.2_1203.52, WER: 0%, TER: 0%, total WER: 26.0769%, total TER: 12.7176%, progress (thread 0): 90.1661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1255.64_1255.96, WER: 0%, TER: 0%, total WER: 26.0766%, total TER: 12.7175%, progress (thread 0): 90.174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1754.3_1754.62, WER: 0%, TER: 0%, total WER: 26.0763%, total TER: 12.7174%, progress (thread 0): 90.182%]
|T|: s o
|P|: s o
[sample: EN2002d_H01_FEO072_1974.1_1974.42, WER: 0%, TER: 0%, total WER: 26.076%, total TER: 12.7173%, progress (thread 0): 90.1899%]
|T|: m m
|P|: h m
[sample: EN2002d_H02_MEE071_2190.35_2190.67, WER: 100%, TER: 50%, total WER: 26.0769%, total TER: 12.7175%, progress (thread 0): 90.1978%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_582.6_582.91, WER: 0%, TER: 0%, total WER: 26.0766%, total TER: 12.7174%, progress (thread 0): 90.2057%]
|T|: m m h m m
|P|: u h | h u h
[sample: ES2004a_H00_MEO015_687.25_687.56, WER: 200%, TER: 100%, total WER: 26.0785%, total TER: 12.7184%, progress (thread 0): 90.2136%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_688.13_688.44, WER: 0%, TER: 0%, total WER: 26.0782%, total TER: 12.7183%, progress (thread 0): 90.2215%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H03_FEE016_777.34_777.65, WER: 100%, TER: 40%, total WER: 26.0791%, total TER: 12.7186%, progress (thread 0): 90.2294%]
|T|: y
|P|: y
[sample: ES2004a_H02_MEE014_949.9_950.21, WER: 0%, TER: 0%, total WER: 26.0788%, total TER: 12.7186%, progress (thread 0): 90.2373%]
|T|: n o
|P|: t o
[sample: ES2004b_H03_FEE016_607.28_607.59, WER: 100%, TER: 50%, total WER: 26.0796%, total TER: 12.7188%, progress (thread 0): 90.2453%]
|T|: m m h m m
|P|: h m
[sample: ES2004b_H00_MEO015_1612.5_1612.81, WER: 100%, TER: 60%, total WER: 26.0805%, total TER: 12.7193%, progress (thread 0): 90.2532%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_2237.11_2237.42, WER: 0%, TER: 0%, total WER: 26.0802%, total TER: 12.7192%, progress (thread 0): 90.2611%]
|T|: b u t
|P|: b u t
[sample: ES2004c_H02_MEE014_571.73_572.04, WER: 0%, TER: 0%, total WER: 26.0799%, total TER: 12.7191%, progress (thread 0): 90.269%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1321.04_1321.35, WER: 0%, TER: 0%, total WER: 26.0796%, total TER: 12.719%, progress (thread 0): 90.2769%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_1582.98_1583.29, WER: 0%, TER: 0%, total WER: 26.0793%, total TER: 12.7189%, progress (thread 0): 90.2848%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2288.73_2289.04, WER: 0%, TER: 0%, total WER: 26.079%, total TER: 12.7188%, progress (thread 0): 90.2927%]
|T|: m m
|P|: h m
[sample: ES2004d_H00_MEO015_846.15_846.46, WER: 100%, TER: 50%, total WER: 26.0798%, total TER: 12.7189%, progress (thread 0): 90.3006%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1669.25_1669.56, WER: 0%, TER: 0%, total WER: 26.0795%, total TER: 12.7188%, progress (thread 0): 90.3085%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1669.66_1669.97, WER: 0%, TER: 0%, total WER: 26.0792%, total TER: 12.7187%, progress (thread 0): 90.3165%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1825.79_1826.1, WER: 0%, TER: 0%, total WER: 26.0789%, total TER: 12.7186%, progress (thread 0): 90.3244%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1901.99_1902.3, WER: 0%, TER: 0%, total WER: 26.0786%, total TER: 12.7185%, progress (thread 0): 90.3323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1967.13_1967.44, WER: 0%, TER: 0%, total WER: 26.0783%, total TER: 12.7183%, progress (thread 0): 90.3402%]
|T|: w e l l
|P|: y e h
[sample: ES2004d_H02_MEE014_2085.81_2086.12, WER: 100%, TER: 75%, total WER: 26.0792%, total TER: 12.7189%, progress (thread 0): 90.3481%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2220.03_2220.34, WER: 0%, TER: 0%, total WER: 26.0789%, total TER: 12.7188%, progress (thread 0): 90.356%]
|T|: o k a y
|P|: k
[sample: IS1009a_H00_FIE088_90.21_90.52, WER: 100%, TER: 75%, total WER: 26.0797%, total TER: 12.7194%, progress (thread 0): 90.3639%]
|T|: u h
|P|:
[sample: IS1009a_H02_FIO084_451.06_451.37, WER: 100%, TER: 100%, total WER: 26.0806%, total TER: 12.7198%, progress (thread 0): 90.3718%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_702.97_703.28, WER: 0%, TER: 0%, total WER: 26.0803%, total TER: 12.7197%, progress (thread 0): 90.3797%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_795.57_795.88, WER: 0%, TER: 0%, total WER: 26.08%, total TER: 12.7196%, progress (thread 0): 90.3877%]
|T|: m m
|P|: e
[sample: IS1009b_H02_FIO084_218.1_218.41, WER: 100%, TER: 100%, total WER: 26.0808%, total TER: 12.72%, progress (thread 0): 90.3956%]
|T|: m m h m m
|P|: u h m
[sample: IS1009b_H00_FIE088_616.32_616.63, WER: 100%, TER: 60%, total WER: 26.0816%, total TER: 12.7205%, progress (thread 0): 90.4035%]
|T|: y e a h
|P|: y e
[sample: IS1009b_H02_FIO084_1692.92_1693.23, WER: 100%, TER: 50%, total WER: 26.0825%, total TER: 12.7209%, progress (thread 0): 90.4114%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1832.99_1833.3, WER: 0%, TER: 0%, total WER: 26.0822%, total TER: 12.7208%, progress (thread 0): 90.4193%]
|T|: y e s
|P|: i s
[sample: IS1009b_H01_FIO087_1844.23_1844.54, WER: 100%, TER: 66.6667%, total WER: 26.083%, total TER: 12.7211%, progress (thread 0): 90.4272%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H03_FIO089_1904.39_1904.7, WER: 0%, TER: 0%, total WER: 26.0827%, total TER: 12.721%, progress (thread 0): 90.4351%]
|T|: m m h m m
|P|: t a m l e
[sample: IS1009c_H00_FIE088_1140.15_1140.46, WER: 100%, TER: 100%, total WER: 26.0836%, total TER: 12.722%, progress (thread 0): 90.443%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1163.03_1163.34, WER: 100%, TER: 40%, total WER: 26.0844%, total TER: 12.7223%, progress (thread 0): 90.451%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1237.53_1237.84, WER: 100%, TER: 40%, total WER: 26.0852%, total TER: 12.7227%, progress (thread 0): 90.4589%]
|T|: ' k a y
|P|: c a n
[sample: IS1009c_H01_FIO087_1429.75_1430.06, WER: 100%, TER: 75%, total WER: 26.0861%, total TER: 12.7232%, progress (thread 0): 90.4668%]
|T|: m m h m m
|P|: h
[sample: IS1009c_H02_FIO084_1554.5_1554.81, WER: 100%, TER: 80%, total WER: 26.0869%, total TER: 12.724%, progress (thread 0): 90.4747%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H01_FIO087_1580.06_1580.37, WER: 100%, TER: 40%, total WER: 26.0877%, total TER: 12.7243%, progress (thread 0): 90.4826%]
|T|: o h
|P|: u m
[sample: IS1009c_H00_FIE088_1694.98_1695.29, WER: 100%, TER: 100%, total WER: 26.0886%, total TER: 12.7248%, progress (thread 0): 90.4905%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_363.39_363.7, WER: 100%, TER: 40%, total WER: 26.0894%, total TER: 12.7251%, progress (thread 0): 90.4984%]
|T|: m m
|P|: h m
[sample: IS1009d_H02_FIO084_894.86_895.17, WER: 100%, TER: 50%, total WER: 26.0903%, total TER: 12.7253%, progress (thread 0): 90.5063%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_903.74_904.05, WER: 0%, TER: 0%, total WER: 26.09%, total TER: 12.7251%, progress (thread 0): 90.5142%]
|T|: n o
|P|: n o
[sample: IS1009d_H00_FIE088_1011.62_1011.93, WER: 0%, TER: 0%, total WER: 26.0897%, total TER: 12.7251%, progress (thread 0): 90.5222%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1012.22_1012.53, WER: 100%, TER: 40%, total WER: 26.0905%, total TER: 12.7254%, progress (thread 0): 90.5301%]
|T|: m m h m m
|P|: m | h m
[sample: IS1009d_H03_FIO089_1105.97_1106.28, WER: 200%, TER: 40%, total WER: 26.0925%, total TER: 12.7257%, progress (thread 0): 90.538%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1171.27_1171.58, WER: 0%, TER: 0%, total WER: 26.0922%, total TER: 12.7256%, progress (thread 0): 90.5459%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1243.74_1244.05, WER: 100%, TER: 66.6667%, total WER: 26.093%, total TER: 12.726%, progress (thread 0): 90.5538%]
|T|: w h y
|P|: w o w
[sample: IS1009d_H00_FIE088_1442.55_1442.86, WER: 100%, TER: 66.6667%, total WER: 26.0938%, total TER: 12.7264%, progress (thread 0): 90.5617%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1681.21_1681.52, WER: 0%, TER: 0%, total WER: 26.0935%, total TER: 12.7262%, progress (thread 0): 90.5696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_216.48_216.79, WER: 0%, TER: 0%, total WER: 26.0932%, total TER: 12.7261%, progress (thread 0): 90.5775%]
|T|: m e a t
|P|: m e a t
[sample: TS3003a_H03_MTD012ME_595.66_595.97, WER: 0%, TER: 0%, total WER: 26.093%, total TER: 12.726%, progress (thread 0): 90.5854%]
|T|: s o
|P|: s o
[sample: TS3003a_H03_MTD012ME_884.45_884.76, WER: 0%, TER: 0%, total WER: 26.0927%, total TER: 12.7259%, progress (thread 0): 90.5934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1418.64_1418.95, WER: 0%, TER: 0%, total WER: 26.0924%, total TER: 12.7258%, progress (thread 0): 90.6013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_71.8_72.11, WER: 0%, TER: 0%, total WER: 26.0921%, total TER: 12.7257%, progress (thread 0): 90.6092%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_538.03_538.34, WER: 0%, TER: 0%, total WER: 26.0918%, total TER: 12.7256%, progress (thread 0): 90.6171%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1469.03_1469.34, WER: 100%, TER: 100%, total WER: 26.0926%, total TER: 12.726%, progress (thread 0): 90.625%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1715.8_1716.11, WER: 0%, TER: 0%, total WER: 26.0923%, total TER: 12.7259%, progress (thread 0): 90.6329%]
|T|: y e a h
|P|: t h a t
[sample: TS3003b_H00_MTD009PM_1822.91_1823.22, WER: 100%, TER: 75%, total WER: 26.0932%, total TER: 12.7265%, progress (thread 0): 90.6408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_794.24_794.55, WER: 0%, TER: 0%, total WER: 26.0929%, total TER: 12.7264%, progress (thread 0): 90.6487%]
|T|: ' k a y
|P|: k a y
[sample: TS3003c_H00_MTD009PM_934.63_934.94, WER: 100%, TER: 25%, total WER: 26.0937%, total TER: 12.7265%, progress (thread 0): 90.6566%]
|T|: m m
|P|: h m
[sample: TS3003c_H01_MTD011UID_1136.65_1136.96, WER: 100%, TER: 50%, total WER: 26.0945%, total TER: 12.7267%, progress (thread 0): 90.6646%]
|T|: y e a h
|P|: a n c e
[sample: TS3003c_H01_MTD011UID_1444.43_1444.74, WER: 100%, TER: 100%, total WER: 26.0954%, total TER: 12.7275%, progress (thread 0): 90.6725%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1813.2_1813.51, WER: 0%, TER: 0%, total WER: 26.0951%, total TER: 12.7274%, progress (thread 0): 90.6804%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_255.36_255.67, WER: 0%, TER: 0%, total WER: 26.0948%, total TER: 12.7273%, progress (thread 0): 90.6883%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_453.54_453.85, WER: 100%, TER: 50%, total WER: 26.0956%, total TER: 12.7274%, progress (thread 0): 90.6962%]
|T|: y e p
|P|: n o
[sample: TS3003d_H02_MTD0010ID_562.3_562.61, WER: 100%, TER: 100%, total WER: 26.0964%, total TER: 12.728%, progress (thread 0): 90.7041%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H02_MTD0010ID_1317.08_1317.39, WER: 100%, TER: 100%, total WER: 26.0973%, total TER: 12.7289%, progress (thread 0): 90.712%]
|T|: m m h m m
|P|: m h m
[sample: TS3003d_H03_MTD012ME_1546.63_1546.94, WER: 100%, TER: 40%, total WER: 26.0981%, total TER: 12.7292%, progress (thread 0): 90.7199%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2324.36_2324.67, WER: 0%, TER: 0%, total WER: 26.0978%, total TER: 12.7291%, progress (thread 0): 90.7278%]
|T|: y e a h
|P|: e h
[sample: TS3003d_H01_MTD011UID_2373.13_2373.44, WER: 100%, TER: 50%, total WER: 26.0987%, total TER: 12.7294%, progress (thread 0): 90.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_595.97_596.28, WER: 0%, TER: 0%, total WER: 26.0984%, total TER: 12.7293%, progress (thread 0): 90.7437%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_694.14_694.45, WER: 0%, TER: 0%, total WER: 26.0981%, total TER: 12.7292%, progress (thread 0): 90.7516%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_704.35_704.66, WER: 0%, TER: 0%, total WER: 26.0978%, total TER: 12.7291%, progress (thread 0): 90.7595%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_910.51_910.82, WER: 0%, TER: 0%, total WER: 26.0975%, total TER: 12.7289%, progress (thread 0): 90.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1104.75_1105.06, WER: 0%, TER: 0%, total WER: 26.0972%, total TER: 12.7288%, progress (thread 0): 90.7753%]
|T|: h m m
|P|: h
[sample: EN2002a_H00_MEE073_1619.48_1619.79, WER: 100%, TER: 66.6667%, total WER: 26.098%, total TER: 12.7292%, progress (thread 0): 90.7832%]
|T|: o h
|P|:
[sample: EN2002a_H02_FEO072_1674.71_1675.02, WER: 100%, TER: 100%, total WER: 26.0989%, total TER: 12.7296%, progress (thread 0): 90.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1973.2_1973.51, WER: 0%, TER: 0%, total WER: 26.0986%, total TER: 12.7295%, progress (thread 0): 90.799%]
|T|: o h | r i g h t
|P|: a l | r i g h t
[sample: EN2002a_H03_MEE071_1971.01_1971.32, WER: 50%, TER: 25%, total WER: 26.0991%, total TER: 12.7297%, progress (thread 0): 90.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_346.95_347.26, WER: 0%, TER: 0%, total WER: 26.0988%, total TER: 12.7296%, progress (thread 0): 90.8149%]
|T|: y e a h
|P|: w e l l
[sample: EN2002b_H03_MEE073_372.58_372.89, WER: 100%, TER: 75%, total WER: 26.0996%, total TER: 12.7302%, progress (thread 0): 90.8228%]
|T|: m m
|P|:
[sample: EN2002b_H03_MEE073_565.42_565.73, WER: 100%, TER: 100%, total WER: 26.1005%, total TER: 12.7306%, progress (thread 0): 90.8307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_729.79_730.1, WER: 0%, TER: 0%, total WER: 26.1002%, total TER: 12.7305%, progress (thread 0): 90.8386%]
|T|: y e a h
|P|: r i g h
[sample: EN2002b_H03_MEE073_1214.71_1215.02, WER: 100%, TER: 75%, total WER: 26.101%, total TER: 12.7311%, progress (thread 0): 90.8465%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1323.81_1324.12, WER: 0%, TER: 0%, total WER: 26.1007%, total TER: 12.7309%, progress (thread 0): 90.8544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1461.61_1461.92, WER: 0%, TER: 0%, total WER: 26.1004%, total TER: 12.7308%, progress (thread 0): 90.8623%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1750.66_1750.97, WER: 0%, TER: 0%, total WER: 26.1001%, total TER: 12.7307%, progress (thread 0): 90.8703%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_441.29_441.6, WER: 0%, TER: 0%, total WER: 26.0998%, total TER: 12.7306%, progress (thread 0): 90.8782%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_543.24_543.55, WER: 0%, TER: 0%, total WER: 26.0995%, total TER: 12.7306%, progress (thread 0): 90.8861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_543.24_543.55, WER: 0%, TER: 0%, total WER: 26.0992%, total TER: 12.7305%, progress (thread 0): 90.894%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_982.53_982.84, WER: 100%, TER: 33.3333%, total WER: 26.1001%, total TER: 12.7306%, progress (thread 0): 90.9019%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1524.82_1525.13, WER: 0%, TER: 0%, total WER: 26.0998%, total TER: 12.7305%, progress (thread 0): 90.9098%]
|T|: u m
|P|: u m
[sample: EN2002c_H03_MEE073_1904.01_1904.32, WER: 0%, TER: 0%, total WER: 26.0995%, total TER: 12.7304%, progress (thread 0): 90.9177%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1958.34_1958.65, WER: 0%, TER: 0%, total WER: 26.0992%, total TER: 12.7303%, progress (thread 0): 90.9256%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_2240.67_2240.98, WER: 100%, TER: 40%, total WER: 26.1%, total TER: 12.7306%, progress (thread 0): 90.9335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2358.92_2359.23, WER: 0%, TER: 0%, total WER: 26.0997%, total TER: 12.7305%, progress (thread 0): 90.9415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2477.51_2477.82, WER: 0%, TER: 0%, total WER: 26.0994%, total TER: 12.7304%, progress (thread 0): 90.9494%]
|T|: n o
|P|: n i e
[sample: EN2002c_H02_MEE071_2608.15_2608.46, WER: 100%, TER: 100%, total WER: 26.1003%, total TER: 12.7308%, progress (thread 0): 90.9573%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2724.28_2724.59, WER: 0%, TER: 0%, total WER: 26.1%, total TER: 12.7307%, progress (thread 0): 90.9652%]
|T|: t i c k
|P|: t i k
[sample: EN2002c_H01_FEO072_2878.5_2878.81, WER: 100%, TER: 25%, total WER: 26.1008%, total TER: 12.7308%, progress (thread 0): 90.9731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_391.81_392.12, WER: 0%, TER: 0%, total WER: 26.1005%, total TER: 12.7307%, progress (thread 0): 90.981%]
|T|: y e a h | o k a y
|P|: o k a y
[sample: EN2002d_H00_FEO070_1379.88_1380.19, WER: 50%, TER: 55.5556%, total WER: 26.1011%, total TER: 12.7316%, progress (thread 0): 90.9889%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_1512.76_1513.07, WER: 100%, TER: 40%, total WER: 26.1019%, total TER: 12.7319%, progress (thread 0): 90.9968%]
|T|: s o
|P|: t h e
[sample: EN2002d_H00_FEO070_1542.52_1542.83, WER: 100%, TER: 150%, total WER: 26.1027%, total TER: 12.7325%, progress (thread 0): 91.0047%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1546.76_1547.07, WER: 0%, TER: 0%, total WER: 26.1024%, total TER: 12.7324%, progress (thread 0): 91.0127%]
|T|: s o
|P|: s o
[sample: EN2002d_H03_MEE073_1822.41_1822.72, WER: 0%, TER: 0%, total WER: 26.1021%, total TER: 12.7324%, progress (thread 0): 91.0206%]
|T|: m m
|P|: h m
[sample: EN2002d_H00_FEO070_1872.39_1872.7, WER: 100%, TER: 50%, total WER: 26.103%, total TER: 12.7325%, progress (thread 0): 91.0285%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2114.3_2114.61, WER: 0%, TER: 0%, total WER: 26.1027%, total TER: 12.7324%, progress (thread 0): 91.0364%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_819.96_820.26, WER: 0%, TER: 0%, total WER: 26.1024%, total TER: 12.7323%, progress (thread 0): 91.0443%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1232.15_1232.45, WER: 0%, TER: 0%, total WER: 26.1021%, total TER: 12.7322%, progress (thread 0): 91.0522%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1572.74_1573.04, WER: 100%, TER: 40%, total WER: 26.1029%, total TER: 12.7325%, progress (thread 0): 91.0601%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H03_FEE016_1784.08_1784.38, WER: 100%, TER: 40%, total WER: 26.1038%, total TER: 12.7328%, progress (thread 0): 91.068%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_2252.76_2253.06, WER: 0%, TER: 0%, total WER: 26.1035%, total TER: 12.7327%, progress (thread 0): 91.076%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_849.84_850.14, WER: 0%, TER: 0%, total WER: 26.1032%, total TER: 12.7326%, progress (thread 0): 91.0839%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1555.92_1556.22, WER: 0%, TER: 0%, total WER: 26.1029%, total TER: 12.7325%, progress (thread 0): 91.0918%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1640.82_1641.12, WER: 100%, TER: 40%, total WER: 26.1037%, total TER: 12.7328%, progress (thread 0): 91.0997%]
|T|: ' k a y
|P|: k e
[sample: ES2004d_H00_MEO015_271.77_272.07, WER: 100%, TER: 75%, total WER: 26.1045%, total TER: 12.7334%, progress (thread 0): 91.1076%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_639.49_639.79, WER: 0%, TER: 0%, total WER: 26.1042%, total TER: 12.7332%, progress (thread 0): 91.1155%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1390.02_1390.32, WER: 0%, TER: 0%, total WER: 26.104%, total TER: 12.7331%, progress (thread 0): 91.1234%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1711.3_1711.6, WER: 0%, TER: 0%, total WER: 26.1037%, total TER: 12.733%, progress (thread 0): 91.1313%]
|T|: m m
|P|: h m
[sample: ES2004d_H01_FEE013_2002_2002.3, WER: 100%, TER: 50%, total WER: 26.1045%, total TER: 12.7332%, progress (thread 0): 91.1392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2096.66_2096.96, WER: 0%, TER: 0%, total WER: 26.1042%, total TER: 12.7331%, progress (thread 0): 91.1472%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_617.96_618.26, WER: 100%, TER: 40%, total WER: 26.105%, total TER: 12.7334%, progress (thread 0): 91.1551%]
|T|: o k a y
|P|: c a n
[sample: IS1009a_H01_FIO087_782.9_783.2, WER: 100%, TER: 75%, total WER: 26.1059%, total TER: 12.734%, progress (thread 0): 91.163%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_182.06_182.36, WER: 0%, TER: 0%, total WER: 26.1056%, total TER: 12.7338%, progress (thread 0): 91.1709%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_574.49_574.79, WER: 100%, TER: 40%, total WER: 26.1064%, total TER: 12.7342%, progress (thread 0): 91.1788%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_707.08_707.38, WER: 100%, TER: 40%, total WER: 26.1072%, total TER: 12.7345%, progress (thread 0): 91.1867%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1025.61_1025.91, WER: 0%, TER: 0%, total WER: 26.1069%, total TER: 12.7344%, progress (thread 0): 91.1946%]
|T|: m m
|P|: m e a n
[sample: IS1009b_H02_FIO084_1366.95_1367.25, WER: 100%, TER: 150%, total WER: 26.1078%, total TER: 12.735%, progress (thread 0): 91.2025%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1371.17_1371.47, WER: 100%, TER: 40%, total WER: 26.1086%, total TER: 12.7353%, progress (thread 0): 91.2104%]
|T|: h m m
|P|: k a y
[sample: IS1009b_H02_FIO084_1604.76_1605.06, WER: 100%, TER: 100%, total WER: 26.1095%, total TER: 12.7359%, progress (thread 0): 91.2184%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1617.66_1617.96, WER: 0%, TER: 0%, total WER: 26.1092%, total TER: 12.7358%, progress (thread 0): 91.2263%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_335.7_336, WER: 0%, TER: 0%, total WER: 26.1089%, total TER: 12.7357%, progress (thread 0): 91.2342%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H03_FIO089_1106.15_1106.45, WER: 100%, TER: 40%, total WER: 26.1097%, total TER: 12.736%, progress (thread 0): 91.2421%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1237.83_1238.13, WER: 0%, TER: 0%, total WER: 26.1094%, total TER: 12.7359%, progress (thread 0): 91.25%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1259.97_1260.27, WER: 0%, TER: 0%, total WER: 26.1091%, total TER: 12.7358%, progress (thread 0): 91.2579%]
|T|: o k a y
|P|: k a m
[sample: IS1009c_H01_FIO087_1297.17_1297.47, WER: 100%, TER: 50%, total WER: 26.1099%, total TER: 12.7362%, progress (thread 0): 91.2658%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H00_FIE088_607.11_607.41, WER: 100%, TER: 40%, total WER: 26.1108%, total TER: 12.7365%, progress (thread 0): 91.2737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_769.42_769.72, WER: 0%, TER: 0%, total WER: 26.1105%, total TER: 12.7364%, progress (thread 0): 91.2816%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1062.93_1063.23, WER: 0%, TER: 0%, total WER: 26.1102%, total TER: 12.7362%, progress (thread 0): 91.2896%]
|T|: o n e
|P|: b u t
[sample: IS1009d_H01_FIO087_1482_1482.3, WER: 100%, TER: 100%, total WER: 26.111%, total TER: 12.7369%, progress (thread 0): 91.2975%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H00_FIE088_1528.1_1528.4, WER: 0%, TER: 0%, total WER: 26.1107%, total TER: 12.7368%, progress (thread 0): 91.3054%]
|T|: y e a h
|P|: i
[sample: IS1009d_H02_FIO084_1824.41_1824.71, WER: 100%, TER: 100%, total WER: 26.1116%, total TER: 12.7376%, progress (thread 0): 91.3133%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1853.09_1853.39, WER: 0%, TER: 0%, total WER: 26.1113%, total TER: 12.7375%, progress (thread 0): 91.3212%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_238.31_238.61, WER: 0%, TER: 0%, total WER: 26.111%, total TER: 12.7373%, progress (thread 0): 91.3291%]
|T|: s o
|P|: s o
[sample: TS3003a_H02_MTD0010ID_1080.77_1081.07, WER: 0%, TER: 0%, total WER: 26.1107%, total TER: 12.7373%, progress (thread 0): 91.337%]
|T|: h m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1308.88_1309.18, WER: 100%, TER: 33.3333%, total WER: 26.1115%, total TER: 12.7374%, progress (thread 0): 91.3449%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_1423.96_1424.26, WER: 0%, TER: 0%, total WER: 26.1112%, total TER: 12.7373%, progress (thread 0): 91.3529%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H00_MTD009PM_1463.25_1463.55, WER: 100%, TER: 50%, total WER: 26.1121%, total TER: 12.7377%, progress (thread 0): 91.3608%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_1535.95_1536.25, WER: 0%, TER: 0%, total WER: 26.1118%, total TER: 12.7376%, progress (thread 0): 91.3687%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1635.81_1636.11, WER: 100%, TER: 40%, total WER: 26.1126%, total TER: 12.7379%, progress (thread 0): 91.3766%]
|T|: h m
|P|: i s
[sample: TS3003b_H01_MTD011UID_1834.41_1834.71, WER: 100%, TER: 100%, total WER: 26.1134%, total TER: 12.7383%, progress (thread 0): 91.3845%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1871.5_1871.8, WER: 100%, TER: 100%, total WER: 26.1143%, total TER: 12.7387%, progress (thread 0): 91.3924%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2279.98_2280.28, WER: 0%, TER: 0%, total WER: 26.114%, total TER: 12.7386%, progress (thread 0): 91.4003%]
|T|: s
|P|:
[sample: TS3003d_H01_MTD011UID_856.51_856.81, WER: 100%, TER: 100%, total WER: 26.1148%, total TER: 12.7388%, progress (thread 0): 91.4082%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1057.72_1058.02, WER: 0%, TER: 0%, total WER: 26.1145%, total TER: 12.7387%, progress (thread 0): 91.4161%]
|T|: y e a h
|P|: y e a n
[sample: TS3003d_H02_MTD0010ID_1709.82_1710.12, WER: 100%, TER: 25%, total WER: 26.1153%, total TER: 12.7388%, progress (thread 0): 91.424%]
|T|: y e s
|P|: y e s
[sample: TS3003d_H02_MTD0010ID_1732.89_1733.19, WER: 0%, TER: 0%, total WER: 26.115%, total TER: 12.7387%, progress (thread 0): 91.432%]
|T|: y e a h
|P|: y e h
[sample: TS3003d_H00_MTD009PM_1821.73_1822.03, WER: 100%, TER: 25%, total WER: 26.1159%, total TER: 12.7388%, progress (thread 0): 91.4399%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2250.87_2251.17, WER: 0%, TER: 0%, total WER: 26.1156%, total TER: 12.7387%, progress (thread 0): 91.4478%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2482.46_2482.76, WER: 0%, TER: 0%, total WER: 26.1153%, total TER: 12.7386%, progress (thread 0): 91.4557%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_11.83_12.13, WER: 0%, TER: 0%, total WER: 26.115%, total TER: 12.7385%, progress (thread 0): 91.4636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_48.72_49.02, WER: 0%, TER: 0%, total WER: 26.1147%, total TER: 12.7383%, progress (thread 0): 91.4715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_203.25_203.55, WER: 0%, TER: 0%, total WER: 26.1144%, total TER: 12.7382%, progress (thread 0): 91.4794%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_261.14_261.44, WER: 100%, TER: 33.3333%, total WER: 26.1152%, total TER: 12.7384%, progress (thread 0): 91.4873%]
|T|: w h y | n o t
|P|: w h y | d o n ' t
[sample: EN2002a_H03_MEE071_375.98_376.28, WER: 50%, TER: 42.8571%, total WER: 26.1158%, total TER: 12.7389%, progress (thread 0): 91.4953%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_469.41_469.71, WER: 0%, TER: 0%, total WER: 26.1155%, total TER: 12.7387%, progress (thread 0): 91.5032%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_486.08_486.38, WER: 0%, TER: 0%, total WER: 26.1152%, total TER: 12.7386%, progress (thread 0): 91.5111%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_508.71_509.01, WER: 0%, TER: 0%, total WER: 26.1149%, total TER: 12.7385%, progress (thread 0): 91.519%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_521.78_522.08, WER: 0%, TER: 0%, total WER: 26.1146%, total TER: 12.7384%, progress (thread 0): 91.5269%]
|T|: s o
|P|:
[sample: EN2002a_H03_MEE071_738.15_738.45, WER: 100%, TER: 100%, total WER: 26.1154%, total TER: 12.7388%, progress (thread 0): 91.5348%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_747.85_748.15, WER: 100%, TER: 40%, total WER: 26.1163%, total TER: 12.7391%, progress (thread 0): 91.5427%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_795.73_796.03, WER: 100%, TER: 33.3333%, total WER: 26.1171%, total TER: 12.7393%, progress (thread 0): 91.5506%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H00_MEE073_877.67_877.97, WER: 100%, TER: 40%, total WER: 26.1179%, total TER: 12.7396%, progress (thread 0): 91.5585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_918.33_918.63, WER: 0%, TER: 0%, total WER: 26.1176%, total TER: 12.7395%, progress (thread 0): 91.5665%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1889.11_1889.41, WER: 0%, TER: 0%, total WER: 26.1173%, total TER: 12.7393%, progress (thread 0): 91.5744%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H02_FEO072_2116.83_2117.13, WER: 0%, TER: 0%, total WER: 26.1171%, total TER: 12.7392%, progress (thread 0): 91.5823%]
|T|: o h | y e a h
|P|: h e l
[sample: EN2002b_H03_MEE073_211.33_211.63, WER: 100%, TER: 71.4286%, total WER: 26.1187%, total TER: 12.7402%, progress (thread 0): 91.5902%]
|T|: w e ' l l | s e e
|P|: w e l l | s | e
[sample: EN2002b_H00_FEO070_255.08_255.38, WER: 150%, TER: 22.2222%, total WER: 26.1215%, total TER: 12.7404%, progress (thread 0): 91.5981%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_368.42_368.72, WER: 0%, TER: 0%, total WER: 26.1212%, total TER: 12.7402%, progress (thread 0): 91.606%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_391.92_392.22, WER: 100%, TER: 40%, total WER: 26.1221%, total TER: 12.7406%, progress (thread 0): 91.6139%]
|T|: m m
|P|: m
[sample: EN2002b_H03_MEE073_526.11_526.41, WER: 100%, TER: 50%, total WER: 26.1229%, total TER: 12.7407%, progress (thread 0): 91.6218%]
|T|: a l r i g h t
|P|: a l l r i g h t
[sample: EN2002b_H02_FEO072_627.98_628.28, WER: 100%, TER: 14.2857%, total WER: 26.1237%, total TER: 12.7408%, progress (thread 0): 91.6298%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1551.52_1551.82, WER: 0%, TER: 0%, total WER: 26.1234%, total TER: 12.7406%, progress (thread 0): 91.6377%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1611.31_1611.61, WER: 0%, TER: 0%, total WER: 26.1231%, total TER: 12.7405%, progress (thread 0): 91.6456%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1729.42_1729.72, WER: 0%, TER: 0%, total WER: 26.1228%, total TER: 12.7404%, progress (thread 0): 91.6535%]
|T|: i | t h i n k
|P|: i
[sample: EN2002c_H01_FEO072_232.37_232.67, WER: 50%, TER: 85.7143%, total WER: 26.1234%, total TER: 12.7416%, progress (thread 0): 91.6614%]
|T|: y e a h
|P|: y e s
[sample: EN2002c_H02_MEE071_355.18_355.48, WER: 100%, TER: 50%, total WER: 26.1242%, total TER: 12.742%, progress (thread 0): 91.6693%]
|T|: n o | n o
|P|:
[sample: EN2002c_H03_MEE073_543.55_543.85, WER: 100%, TER: 100%, total WER: 26.1259%, total TER: 12.743%, progress (thread 0): 91.6772%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_700.09_700.39, WER: 100%, TER: 40%, total WER: 26.1267%, total TER: 12.7433%, progress (thread 0): 91.6851%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H02_MEE071_1131.17_1131.47, WER: 100%, TER: 66.6667%, total WER: 26.1276%, total TER: 12.7437%, progress (thread 0): 91.693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1143.34_1143.64, WER: 0%, TER: 0%, total WER: 26.1273%, total TER: 12.7436%, progress (thread 0): 91.701%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_1321.8_1322.1, WER: 0%, TER: 0%, total WER: 26.127%, total TER: 12.7435%, progress (thread 0): 91.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1600.62_1600.92, WER: 0%, TER: 0%, total WER: 26.1267%, total TER: 12.7434%, progress (thread 0): 91.7168%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2301.16_2301.46, WER: 0%, TER: 0%, total WER: 26.1264%, total TER: 12.7433%, progress (thread 0): 91.7247%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2443.4_2443.7, WER: 0%, TER: 0%, total WER: 26.1261%, total TER: 12.7432%, progress (thread 0): 91.7326%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H02_MEE071_2847.83_2848.13, WER: 100%, TER: 40%, total WER: 26.1269%, total TER: 12.7435%, progress (thread 0): 91.7405%]
|T|: y e a h
|P|: y e
[sample: EN2002d_H02_MEE071_673.96_674.26, WER: 100%, TER: 50%, total WER: 26.1277%, total TER: 12.7438%, progress (thread 0): 91.7484%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_834.35_834.65, WER: 0%, TER: 0%, total WER: 26.1274%, total TER: 12.7437%, progress (thread 0): 91.7563%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_925.24_925.54, WER: 0%, TER: 0%, total WER: 26.1272%, total TER: 12.7436%, progress (thread 0): 91.7642%]
|T|: u m
|P|: u m
[sample: EN2002d_H01_FEO072_1191.17_1191.47, WER: 0%, TER: 0%, total WER: 26.1269%, total TER: 12.7435%, progress (thread 0): 91.7721%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1205.4_1205.7, WER: 0%, TER: 0%, total WER: 26.1266%, total TER: 12.7434%, progress (thread 0): 91.7801%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H03_MEE073_1221.17_1221.47, WER: 100%, TER: 40%, total WER: 26.1274%, total TER: 12.7437%, progress (thread 0): 91.788%]
|T|: y e a h
|P|: h m
[sample: EN2002d_H00_FEO070_1249.7_1250, WER: 100%, TER: 100%, total WER: 26.1282%, total TER: 12.7445%, progress (thread 0): 91.7959%]
|T|: s u r e
|P|: s u r e
[sample: EN2002d_H03_MEE073_1672.53_1672.83, WER: 0%, TER: 0%, total WER: 26.1279%, total TER: 12.7444%, progress (thread 0): 91.8038%]
|T|: m m
|P|: h m
[sample: EN2002d_H02_MEE071_2192.66_2192.96, WER: 100%, TER: 50%, total WER: 26.1288%, total TER: 12.7446%, progress (thread 0): 91.8117%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_747.66_747.95, WER: 0%, TER: 0%, total WER: 26.1285%, total TER: 12.7444%, progress (thread 0): 91.8196%]
|T|: m m h m m
|P|: m h m
[sample: ES2004a_H00_MEO015_784.99_785.28, WER: 100%, TER: 40%, total WER: 26.1293%, total TER: 12.7448%, progress (thread 0): 91.8275%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_826.06_826.35, WER: 0%, TER: 0%, total WER: 26.129%, total TER: 12.7446%, progress (thread 0): 91.8354%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2019.84_2020.13, WER: 0%, TER: 0%, total WER: 26.1287%, total TER: 12.7445%, progress (thread 0): 91.8434%]
|T|: o k a y
|P|: a
[sample: ES2004b_H02_MEE014_2295.26_2295.55, WER: 100%, TER: 75%, total WER: 26.1296%, total TER: 12.7451%, progress (thread 0): 91.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_316.02_316.31, WER: 0%, TER: 0%, total WER: 26.1293%, total TER: 12.745%, progress (thread 0): 91.8592%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004c_H00_MEO015_1261.19_1261.48, WER: 200%, TER: 20%, total WER: 26.1312%, total TER: 12.745%, progress (thread 0): 91.8671%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H01_FEE013_1987.8_1988.09, WER: 100%, TER: 40%, total WER: 26.1321%, total TER: 12.7454%, progress (thread 0): 91.875%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_2244.26_2244.55, WER: 0%, TER: 0%, total WER: 26.1318%, total TER: 12.7452%, progress (thread 0): 91.8829%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_211.03_211.32, WER: 0%, TER: 0%, total WER: 26.1315%, total TER: 12.7451%, progress (thread 0): 91.8908%]
|T|: r i g h t
|P|: i
[sample: ES2004d_H03_FEE016_810.67_810.96, WER: 100%, TER: 80%, total WER: 26.1323%, total TER: 12.7459%, progress (thread 0): 91.8987%]
|T|: m m
|P|: m h m
[sample: ES2004d_H03_FEE016_1204.42_1204.71, WER: 100%, TER: 50%, total WER: 26.1331%, total TER: 12.7461%, progress (thread 0): 91.9066%]
|T|: u m
|P|:
[sample: ES2004d_H01_FEE013_1227.88_1228.17, WER: 100%, TER: 100%, total WER: 26.134%, total TER: 12.7465%, progress (thread 0): 91.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1634.52_1634.81, WER: 0%, TER: 0%, total WER: 26.1337%, total TER: 12.7463%, progress (thread 0): 91.9225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1787.34_1787.63, WER: 0%, TER: 0%, total WER: 26.1334%, total TER: 12.7462%, progress (thread 0): 91.9304%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1964.63_1964.92, WER: 0%, TER: 0%, total WER: 26.1331%, total TER: 12.7461%, progress (thread 0): 91.9383%]
|T|: o k a y
|P|: o k y
[sample: IS1009a_H03_FIO089_118.76_119.05, WER: 100%, TER: 25%, total WER: 26.1339%, total TER: 12.7462%, progress (thread 0): 91.9462%]
|T|: h m m
|P|: c a s e
[sample: IS1009a_H02_FIO084_803.39_803.68, WER: 100%, TER: 133.333%, total WER: 26.1347%, total TER: 12.7471%, progress (thread 0): 91.9541%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_708.64_708.93, WER: 100%, TER: 40%, total WER: 26.1356%, total TER: 12.7474%, progress (thread 0): 91.962%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_998.44_998.73, WER: 0%, TER: 0%, total WER: 26.1353%, total TER: 12.7473%, progress (thread 0): 91.9699%]
|T|: m m h m m
|P|: h
[sample: IS1009b_H03_FIO089_1311.53_1311.82, WER: 100%, TER: 80%, total WER: 26.1361%, total TER: 12.7481%, progress (thread 0): 91.9778%]
|T|: m m h m m
|P|: a m
[sample: IS1009b_H00_FIE088_1356.24_1356.53, WER: 100%, TER: 80%, total WER: 26.137%, total TER: 12.7488%, progress (thread 0): 91.9858%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_1658.74_1659.03, WER: 100%, TER: 60%, total WER: 26.1378%, total TER: 12.7494%, progress (thread 0): 91.9937%]
|T|: h m m
|P|: m h m
[sample: IS1009b_H02_FIO084_1899.66_1899.95, WER: 100%, TER: 66.6667%, total WER: 26.1386%, total TER: 12.7498%, progress (thread 0): 92.0016%]
|T|: m m h m m
|P|: w h m
[sample: IS1009b_H03_FIO089_1999.16_1999.45, WER: 100%, TER: 60%, total WER: 26.1395%, total TER: 12.7503%, progress (thread 0): 92.0095%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_284.27_284.56, WER: 0%, TER: 0%, total WER: 26.1392%, total TER: 12.7502%, progress (thread 0): 92.0174%]
|T|: m m h m m
|P|: u m | h u m
[sample: IS1009c_H00_FIE088_381_381.29, WER: 200%, TER: 60%, total WER: 26.1411%, total TER: 12.7508%, progress (thread 0): 92.0253%]
|T|: w e | c a n
|P|:
[sample: IS1009c_H01_FIO087_755.64_755.93, WER: 100%, TER: 100%, total WER: 26.1428%, total TER: 12.752%, progress (thread 0): 92.0332%]
|T|: y e a h | b u t | w
|P|:
[sample: IS1009c_H02_FIO084_1559.51_1559.8, WER: 100%, TER: 100%, total WER: 26.1453%, total TER: 12.7541%, progress (thread 0): 92.0411%]
|T|: m m h m m
|P|: y
[sample: IS1009c_H02_FIO084_1608.9_1609.19, WER: 100%, TER: 100%, total WER: 26.1461%, total TER: 12.7551%, progress (thread 0): 92.049%]
|T|: h e l l o
|P|: y o u | k n o w
[sample: IS1009d_H01_FIO087_41.27_41.56, WER: 200%, TER: 140%, total WER: 26.1481%, total TER: 12.7566%, progress (thread 0): 92.057%]
|T|: n o
|P|: y o u | k n o w
[sample: IS1009d_H02_FIO084_952.51_952.8, WER: 200%, TER: 300%, total WER: 26.15%, total TER: 12.7579%, progress (thread 0): 92.0649%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1056.71_1057, WER: 100%, TER: 100%, total WER: 26.1509%, total TER: 12.7589%, progress (thread 0): 92.0728%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H03_FIO089_1344.32_1344.61, WER: 100%, TER: 80%, total WER: 26.1517%, total TER: 12.7597%, progress (thread 0): 92.0807%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_1389.87_1390.16, WER: 0%, TER: 0%, total WER: 26.1514%, total TER: 12.7596%, progress (thread 0): 92.0886%]
|T|: m m h m m
|P|: y e a
[sample: IS1009d_H02_FIO084_1790.06_1790.35, WER: 100%, TER: 100%, total WER: 26.1522%, total TER: 12.7606%, progress (thread 0): 92.0965%]
|T|: o k a y
|P|: i
[sample: TS3003a_H01_MTD011UID_704.99_705.28, WER: 100%, TER: 100%, total WER: 26.1531%, total TER: 12.7615%, progress (thread 0): 92.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_977.31_977.6, WER: 0%, TER: 0%, total WER: 26.1528%, total TER: 12.7613%, progress (thread 0): 92.1123%]
|T|: y o u
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1267.17_1267.46, WER: 100%, TER: 100%, total WER: 26.1536%, total TER: 12.7619%, progress (thread 0): 92.1203%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_178.78_179.07, WER: 0%, TER: 0%, total WER: 26.1533%, total TER: 12.7619%, progress (thread 0): 92.1282%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1577.63_1577.92, WER: 0%, TER: 0%, total WER: 26.153%, total TER: 12.7617%, progress (thread 0): 92.1361%]
|T|: w e l l
|P|: w e l l
[sample: TS3003b_H03_MTD012ME_1752.79_1753.08, WER: 0%, TER: 0%, total WER: 26.1527%, total TER: 12.7616%, progress (thread 0): 92.144%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_2033.23_2033.52, WER: 0%, TER: 0%, total WER: 26.1524%, total TER: 12.7615%, progress (thread 0): 92.1519%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2083.17_2083.46, WER: 0%, TER: 0%, total WER: 26.1521%, total TER: 12.7614%, progress (thread 0): 92.1598%]
|T|: y e a h
|P|: n o
[sample: TS3003b_H02_MTD0010ID_2083.37_2083.66, WER: 100%, TER: 100%, total WER: 26.153%, total TER: 12.7622%, progress (thread 0): 92.1677%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H01_MTD011UID_1289.74_1290.03, WER: 100%, TER: 40%, total WER: 26.1538%, total TER: 12.7625%, progress (thread 0): 92.1756%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1452.67_1452.96, WER: 0%, TER: 0%, total WER: 26.1535%, total TER: 12.7624%, progress (thread 0): 92.1835%]
|T|: m m h m m
|P|: h
[sample: TS3003d_H03_MTD012ME_695.88_696.17, WER: 100%, TER: 80%, total WER: 26.1543%, total TER: 12.7632%, progress (thread 0): 92.1915%]
|T|: n a y
|P|: h m
[sample: TS3003d_H01_MTD011UID_835.49_835.78, WER: 100%, TER: 100%, total WER: 26.1552%, total TER: 12.7638%, progress (thread 0): 92.1994%]
|T|: h m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_1010.19_1010.48, WER: 100%, TER: 33.3333%, total WER: 26.156%, total TER: 12.7639%, progress (thread 0): 92.2073%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H02_MTD0010ID_1075.11_1075.4, WER: 100%, TER: 100%, total WER: 26.1568%, total TER: 12.7648%, progress (thread 0): 92.2152%]
|T|: t r u e
|P|: t r u e
[sample: TS3003d_H03_MTD012ME_1129.3_1129.59, WER: 0%, TER: 0%, total WER: 26.1566%, total TER: 12.7646%, progress (thread 0): 92.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1862.62_1862.91, WER: 0%, TER: 0%, total WER: 26.1563%, total TER: 12.7645%, progress (thread 0): 92.231%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_2370.37_2370.66, WER: 100%, TER: 50%, total WER: 26.1571%, total TER: 12.7647%, progress (thread 0): 92.2389%]
|T|: h m m
|P|: h m
[sample: TS3003d_H00_MTD009PM_2517.23_2517.52, WER: 100%, TER: 33.3333%, total WER: 26.1579%, total TER: 12.7648%, progress (thread 0): 92.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2571.69_2571.98, WER: 0%, TER: 0%, total WER: 26.1576%, total TER: 12.7647%, progress (thread 0): 92.2547%]
|T|: h m m
|P|: y e h
[sample: EN2002a_H00_MEE073_218.41_218.7, WER: 100%, TER: 100%, total WER: 26.1585%, total TER: 12.7653%, progress (thread 0): 92.2627%]
|T|: i | d o n ' t | k n o w
|P|: i | d n '
[sample: EN2002a_H00_MEE073_234.63_234.92, WER: 66.6667%, TER: 58.3333%, total WER: 26.1598%, total TER: 12.7666%, progress (thread 0): 92.2706%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_526.91_527.2, WER: 100%, TER: 66.6667%, total WER: 26.1607%, total TER: 12.767%, progress (thread 0): 92.2785%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_646.99_647.28, WER: 0%, TER: 0%, total WER: 26.1604%, total TER: 12.7669%, progress (thread 0): 92.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_723.91_724.2, WER: 0%, TER: 0%, total WER: 26.1601%, total TER: 12.7667%, progress (thread 0): 92.2943%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_749.38_749.67, WER: 0%, TER: 0%, total WER: 26.1598%, total TER: 12.7666%, progress (thread 0): 92.3022%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_856.03_856.32, WER: 0%, TER: 0%, total WER: 26.1595%, total TER: 12.7665%, progress (thread 0): 92.3101%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_872.78_873.07, WER: 0%, TER: 0%, total WER: 26.1592%, total TER: 12.7664%, progress (thread 0): 92.318%]
|T|: h m m
|P|: y e h
[sample: EN2002a_H00_MEE073_1076.05_1076.34, WER: 100%, TER: 100%, total WER: 26.16%, total TER: 12.767%, progress (thread 0): 92.326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1255.44_1255.73, WER: 0%, TER: 0%, total WER: 26.1597%, total TER: 12.7669%, progress (thread 0): 92.3339%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1364.93_1365.22, WER: 0%, TER: 0%, total WER: 26.1594%, total TER: 12.7668%, progress (thread 0): 92.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1593.85_1594.14, WER: 0%, TER: 0%, total WER: 26.1591%, total TER: 12.7666%, progress (thread 0): 92.3497%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1595.68_1595.97, WER: 0%, TER: 0%, total WER: 26.1588%, total TER: 12.7665%, progress (thread 0): 92.3576%]
|T|: m m h m m
|P|: w e
[sample: EN2002a_H02_FEO072_1620.66_1620.95, WER: 100%, TER: 100%, total WER: 26.1597%, total TER: 12.7675%, progress (thread 0): 92.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1960.6_1960.89, WER: 0%, TER: 0%, total WER: 26.1594%, total TER: 12.7674%, progress (thread 0): 92.3734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2127.38_2127.67, WER: 0%, TER: 0%, total WER: 26.1591%, total TER: 12.7673%, progress (thread 0): 92.3813%]
|T|: v e r y | g o o d
|P|: w h e n
[sample: EN2002b_H02_FEO072_142.65_142.94, WER: 100%, TER: 100%, total WER: 26.1608%, total TER: 12.7691%, progress (thread 0): 92.3892%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_382.41_382.7, WER: 0%, TER: 0%, total WER: 26.1605%, total TER: 12.769%, progress (thread 0): 92.3972%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_419.68_419.97, WER: 0%, TER: 0%, total WER: 26.1602%, total TER: 12.7689%, progress (thread 0): 92.4051%]
|T|: o k a y
|P|: o k a
[sample: EN2002b_H00_FEO070_734.04_734.33, WER: 100%, TER: 25%, total WER: 26.161%, total TER: 12.769%, progress (thread 0): 92.413%]
|T|: h m m
|P|: n o
[sample: EN2002b_H03_MEE073_1150.51_1150.8, WER: 100%, TER: 100%, total WER: 26.1618%, total TER: 12.7696%, progress (thread 0): 92.4209%]
|T|: m m h m m
|P|: m h m
[sample: EN2002b_H03_MEE073_1286.29_1286.58, WER: 100%, TER: 40%, total WER: 26.1627%, total TER: 12.77%, progress (thread 0): 92.4288%]
|T|: s o
|P|: s o
[sample: EN2002b_H03_MEE073_1517.43_1517.72, WER: 0%, TER: 0%, total WER: 26.1624%, total TER: 12.7699%, progress (thread 0): 92.4367%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_61.76_62.05, WER: 0%, TER: 0%, total WER: 26.1621%, total TER: 12.7698%, progress (thread 0): 92.4446%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_697.18_697.47, WER: 100%, TER: 50%, total WER: 26.1629%, total TER: 12.7699%, progress (thread 0): 92.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1026.29_1026.58, WER: 0%, TER: 0%, total WER: 26.1626%, total TER: 12.7698%, progress (thread 0): 92.4604%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_1088_1088.29, WER: 100%, TER: 40%, total WER: 26.1634%, total TER: 12.7701%, progress (thread 0): 92.4684%]
|T|: t h a t
|P|: o
[sample: EN2002c_H03_MEE073_1302.84_1303.13, WER: 100%, TER: 100%, total WER: 26.1643%, total TER: 12.771%, progress (thread 0): 92.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1313.99_1314.28, WER: 0%, TER: 0%, total WER: 26.164%, total TER: 12.7708%, progress (thread 0): 92.4842%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_1543.66_1543.95, WER: 100%, TER: 50%, total WER: 26.1648%, total TER: 12.771%, progress (thread 0): 92.4921%]
|T|: ' c a u s e
|P|: t a u s
[sample: EN2002c_H03_MEE073_2609.79_2610.08, WER: 100%, TER: 50%, total WER: 26.1656%, total TER: 12.7715%, progress (thread 0): 92.5%]
|T|: a c t u a l l y
|P|: a c t u a l l y
[sample: EN2002d_H03_MEE073_783.19_783.48, WER: 0%, TER: 0%, total WER: 26.1653%, total TER: 12.7713%, progress (thread 0): 92.5079%]
|T|: r i g h t
|P|: u
[sample: EN2002d_H02_MEE071_833.43_833.72, WER: 100%, TER: 100%, total WER: 26.1662%, total TER: 12.7723%, progress (thread 0): 92.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1031.38_1031.67, WER: 0%, TER: 0%, total WER: 26.1659%, total TER: 12.7722%, progress (thread 0): 92.5237%]
|T|: y e a h
|P|: n o
[sample: EN2002d_H03_MEE073_1490.73_1491.02, WER: 100%, TER: 100%, total WER: 26.1667%, total TER: 12.773%, progress (thread 0): 92.5316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2069.6_2069.89, WER: 0%, TER: 0%, total WER: 26.1664%, total TER: 12.7729%, progress (thread 0): 92.5396%]
|T|: n o
|P|: n
[sample: ES2004b_H03_FEE016_602.63_602.91, WER: 100%, TER: 50%, total WER: 26.1673%, total TER: 12.7731%, progress (thread 0): 92.5475%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1146.25_1146.53, WER: 0%, TER: 0%, total WER: 26.167%, total TER: 12.773%, progress (thread 0): 92.5554%]
|T|: m m
|P|: h m
[sample: ES2004b_H03_FEE016_1316.47_1316.75, WER: 100%, TER: 50%, total WER: 26.1678%, total TER: 12.7731%, progress (thread 0): 92.5633%]
|T|: r i g h t
|P|: r i h t
[sample: ES2004b_H00_MEO015_1939.86_1940.14, WER: 100%, TER: 20%, total WER: 26.1686%, total TER: 12.7732%, progress (thread 0): 92.5712%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2216.73_2217.01, WER: 0%, TER: 0%, total WER: 26.1683%, total TER: 12.7731%, progress (thread 0): 92.5791%]
|T|: t h a n k s
|P|:
[sample: ES2004c_H01_FEE013_54.47_54.75, WER: 100%, TER: 100%, total WER: 26.1692%, total TER: 12.7743%, progress (thread 0): 92.587%]
|T|: u h
|P|: u h
[sample: ES2004c_H02_MEE014_1126.8_1127.08, WER: 0%, TER: 0%, total WER: 26.1689%, total TER: 12.7743%, progress (thread 0): 92.5949%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1180.98_1181.26, WER: 100%, TER: 50%, total WER: 26.1697%, total TER: 12.7744%, progress (thread 0): 92.6029%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1398.69_1398.97, WER: 0%, TER: 0%, total WER: 26.1694%, total TER: 12.7743%, progress (thread 0): 92.6108%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2123.64_2123.92, WER: 0%, TER: 0%, total WER: 26.1691%, total TER: 12.7742%, progress (thread 0): 92.6187%]
|T|: n o
|P|: n o
[sample: ES2004d_H02_MEE014_1050.44_1050.72, WER: 0%, TER: 0%, total WER: 26.1688%, total TER: 12.7741%, progress (thread 0): 92.6266%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_1500.31_1500.59, WER: 100%, TER: 40%, total WER: 26.1696%, total TER: 12.7745%, progress (thread 0): 92.6345%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1843.24_1843.52, WER: 0%, TER: 0%, total WER: 26.1694%, total TER: 12.7743%, progress (thread 0): 92.6424%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_2015.69_2015.97, WER: 0%, TER: 0%, total WER: 26.1691%, total TER: 12.7742%, progress (thread 0): 92.6503%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H01_FEE013_2120.6_2120.88, WER: 0%, TER: 0%, total WER: 26.1688%, total TER: 12.7741%, progress (thread 0): 92.6582%]
|T|: o k a y
|P|: o
[sample: IS1009a_H03_FIO089_188.06_188.34, WER: 100%, TER: 75%, total WER: 26.1696%, total TER: 12.7746%, progress (thread 0): 92.6661%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H00_FIE088_793.23_793.51, WER: 100%, TER: 40%, total WER: 26.1704%, total TER: 12.775%, progress (thread 0): 92.674%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_804.79_805.07, WER: 0%, TER: 0%, total WER: 26.1701%, total TER: 12.7748%, progress (thread 0): 92.682%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_894.05_894.33, WER: 0%, TER: 0%, total WER: 26.1698%, total TER: 12.7747%, progress (thread 0): 92.6899%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1764.17_1764.45, WER: 0%, TER: 0%, total WER: 26.1695%, total TER: 12.7746%, progress (thread 0): 92.6978%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009c_H02_FIO084_724.39_724.67, WER: 100%, TER: 100%, total WER: 26.1704%, total TER: 12.7756%, progress (thread 0): 92.7057%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1140.8_1141.08, WER: 0%, TER: 0%, total WER: 26.1701%, total TER: 12.7755%, progress (thread 0): 92.7136%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1619.41_1619.69, WER: 0%, TER: 0%, total WER: 26.1698%, total TER: 12.7754%, progress (thread 0): 92.7215%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_617.29_617.57, WER: 100%, TER: 40%, total WER: 26.1706%, total TER: 12.7757%, progress (thread 0): 92.7294%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_773.28_773.56, WER: 0%, TER: 0%, total WER: 26.1703%, total TER: 12.7756%, progress (thread 0): 92.7373%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_980.1_980.38, WER: 0%, TER: 0%, total WER: 26.17%, total TER: 12.7755%, progress (thread 0): 92.7452%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1792.74_1793.02, WER: 100%, TER: 100%, total WER: 26.1709%, total TER: 12.7765%, progress (thread 0): 92.7532%]
|T|: h m m
|P|: h m
[sample: IS1009d_H02_FIO084_1830.44_1830.72, WER: 100%, TER: 33.3333%, total WER: 26.1717%, total TER: 12.7766%, progress (thread 0): 92.7611%]
|T|: m m h m m
|P|: u h | h u
[sample: IS1009d_H03_FIO089_1876.62_1876.9, WER: 200%, TER: 100%, total WER: 26.1736%, total TER: 12.7777%, progress (thread 0): 92.769%]
|T|: m o r n i n g
|P|: o n t
[sample: TS3003a_H02_MTD0010ID_16.42_16.7, WER: 100%, TER: 71.4286%, total WER: 26.1745%, total TER: 12.7786%, progress (thread 0): 92.7769%]
|T|: s u r e
|P|: s u r e
[sample: TS3003a_H03_MTD012ME_163.72_164, WER: 0%, TER: 0%, total WER: 26.1742%, total TER: 12.7785%, progress (thread 0): 92.7848%]
|T|: h m m
|P|: h m
[sample: TS3003a_H03_MTD012ME_661.22_661.5, WER: 100%, TER: 33.3333%, total WER: 26.175%, total TER: 12.7786%, progress (thread 0): 92.7927%]
|T|: h m m
|P|: h m
[sample: TS3003a_H00_MTD009PM_1235.68_1235.96, WER: 100%, TER: 33.3333%, total WER: 26.1759%, total TER: 12.7788%, progress (thread 0): 92.8006%]
|T|: y e a h
|P|: t h a
[sample: TS3003b_H00_MTD009PM_823.58_823.86, WER: 100%, TER: 75%, total WER: 26.1767%, total TER: 12.7794%, progress (thread 0): 92.8085%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1892.2_1892.48, WER: 0%, TER: 0%, total WER: 26.1764%, total TER: 12.7792%, progress (thread 0): 92.8165%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1860.06_1860.34, WER: 0%, TER: 0%, total WER: 26.1761%, total TER: 12.7791%, progress (thread 0): 92.8244%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1891.82_1892.1, WER: 100%, TER: 40%, total WER: 26.1769%, total TER: 12.7794%, progress (thread 0): 92.8323%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_267.26_267.54, WER: 0%, TER: 0%, total WER: 26.1766%, total TER: 12.7793%, progress (thread 0): 92.8402%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_365.09_365.37, WER: 0%, TER: 0%, total WER: 26.1763%, total TER: 12.7792%, progress (thread 0): 92.8481%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H00_MTD009PM_816.6_816.88, WER: 0%, TER: 0%, total WER: 26.176%, total TER: 12.7791%, progress (thread 0): 92.856%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_860.45_860.73, WER: 0%, TER: 0%, total WER: 26.1757%, total TER: 12.779%, progress (thread 0): 92.8639%]
|T|: a n d | i t
|P|: a n d | i t
[sample: TS3003d_H02_MTD0010ID_1310.28_1310.56, WER: 0%, TER: 0%, total WER: 26.1752%, total TER: 12.7788%, progress (thread 0): 92.8718%]
|T|: s h
|P|: s
[sample: TS3003d_H02_MTD0010ID_1311.62_1311.9, WER: 100%, TER: 50%, total WER: 26.176%, total TER: 12.779%, progress (thread 0): 92.8797%]
|T|: y e p
|P|: y u p
[sample: TS3003d_H02_MTD0010ID_1403.4_1403.68, WER: 100%, TER: 33.3333%, total WER: 26.1768%, total TER: 12.7791%, progress (thread 0): 92.8877%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1945.66_1945.94, WER: 0%, TER: 0%, total WER: 26.1765%, total TER: 12.779%, progress (thread 0): 92.8956%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2142.04_2142.32, WER: 0%, TER: 0%, total WER: 26.1762%, total TER: 12.7789%, progress (thread 0): 92.9035%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_49.78_50.06, WER: 100%, TER: 50%, total WER: 26.1771%, total TER: 12.7792%, progress (thread 0): 92.9114%]
|T|: y e p
|P|: y e p
[sample: EN2002a_H01_FEO070_478.66_478.94, WER: 0%, TER: 0%, total WER: 26.1768%, total TER: 12.7792%, progress (thread 0): 92.9193%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_758.4_758.68, WER: 0%, TER: 0%, total WER: 26.1765%, total TER: 12.779%, progress (thread 0): 92.9272%]
|T|: y e a h
|P|: h m
[sample: EN2002a_H01_FEO070_762.35_762.63, WER: 100%, TER: 100%, total WER: 26.1773%, total TER: 12.7799%, progress (thread 0): 92.9351%]
|T|: b u t
|P|: b u t
[sample: EN2002a_H01_FEO070_1024.58_1024.86, WER: 0%, TER: 0%, total WER: 26.177%, total TER: 12.7798%, progress (thread 0): 92.943%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1306.05_1306.33, WER: 0%, TER: 0%, total WER: 26.1767%, total TER: 12.7796%, progress (thread 0): 92.951%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1392.09_1392.37, WER: 0%, TER: 0%, total WER: 26.1764%, total TER: 12.7795%, progress (thread 0): 92.9589%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002a_H00_MEE073_1638.28_1638.56, WER: 200%, TER: 175%, total WER: 26.1784%, total TER: 12.781%, progress (thread 0): 92.9668%]
|T|: s e e
|P|: s e e
[sample: EN2002a_H01_FEO070_1702.66_1702.94, WER: 0%, TER: 0%, total WER: 26.1781%, total TER: 12.781%, progress (thread 0): 92.9747%]
|T|: y e a h
|P|: n o
[sample: EN2002a_H01_FEO070_1796.46_1796.74, WER: 100%, TER: 100%, total WER: 26.1789%, total TER: 12.7818%, progress (thread 0): 92.9826%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1831.52_1831.8, WER: 0%, TER: 0%, total WER: 26.1786%, total TER: 12.7816%, progress (thread 0): 92.9905%]
|T|: o h
|P|: u h
[sample: EN2002a_H02_FEO072_1981.37_1981.65, WER: 100%, TER: 50%, total WER: 26.1794%, total TER: 12.7818%, progress (thread 0): 92.9984%]
|T|: i | t h
|P|: i
[sample: EN2002a_H00_MEE073_2026.22_2026.5, WER: 50%, TER: 75%, total WER: 26.18%, total TER: 12.7824%, progress (thread 0): 93.0063%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H01_MEE071_16.64_16.92, WER: 0%, TER: 0%, total WER: 26.1797%, total TER: 12.7823%, progress (thread 0): 93.0142%]
|T|: m m h m m
|P|: h m
[sample: EN2002b_H03_MEE073_335.63_335.91, WER: 100%, TER: 60%, total WER: 26.1805%, total TER: 12.7828%, progress (thread 0): 93.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_356.11_356.39, WER: 0%, TER: 0%, total WER: 26.1802%, total TER: 12.7827%, progress (thread 0): 93.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_987.48_987.76, WER: 0%, TER: 0%, total WER: 26.1799%, total TER: 12.7826%, progress (thread 0): 93.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1285.46_1285.74, WER: 0%, TER: 0%, total WER: 26.1796%, total TER: 12.7825%, progress (thread 0): 93.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1450.71_1450.99, WER: 0%, TER: 0%, total WER: 26.1793%, total TER: 12.7824%, progress (thread 0): 93.0538%]
|T|: y e a h
|P|: y e a k n
[sample: EN2002b_H03_MEE073_1588.08_1588.36, WER: 100%, TER: 50%, total WER: 26.1802%, total TER: 12.7827%, progress (thread 0): 93.0617%]
|T|: y e a h
|P|: y o a h
[sample: EN2002c_H03_MEE073_264.61_264.89, WER: 100%, TER: 25%, total WER: 26.181%, total TER: 12.7828%, progress (thread 0): 93.0696%]
|T|: m m h m m
|P|: m
[sample: EN2002c_H03_MEE073_638.33_638.61, WER: 100%, TER: 80%, total WER: 26.1818%, total TER: 12.7836%, progress (thread 0): 93.0775%]
|T|: a h
|P|: u h
[sample: EN2002c_H01_FEO072_1466.83_1467.11, WER: 100%, TER: 50%, total WER: 26.1827%, total TER: 12.7838%, progress (thread 0): 93.0854%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1508.55_1508.83, WER: 100%, TER: 28.5714%, total WER: 26.1835%, total TER: 12.784%, progress (thread 0): 93.0934%]
|T|: i | k n o w
|P|: k i n d | o f
[sample: EN2002c_H03_MEE073_1627.18_1627.46, WER: 100%, TER: 83.3333%, total WER: 26.1852%, total TER: 12.785%, progress (thread 0): 93.1013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2613.98_2614.26, WER: 0%, TER: 0%, total WER: 26.1849%, total TER: 12.7849%, progress (thread 0): 93.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2738.83_2739.11, WER: 0%, TER: 0%, total WER: 26.1846%, total TER: 12.7848%, progress (thread 0): 93.1171%]
|T|: w e l l
|P|: o
[sample: EN2002c_H03_MEE073_2837.32_2837.6, WER: 100%, TER: 100%, total WER: 26.1854%, total TER: 12.7856%, progress (thread 0): 93.125%]
|T|: i | t h
|P|: i
[sample: EN2002d_H03_MEE073_209.34_209.62, WER: 50%, TER: 75%, total WER: 26.1859%, total TER: 12.7862%, progress (thread 0): 93.1329%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H03_MEE073_379.93_380.21, WER: 100%, TER: 40%, total WER: 26.1868%, total TER: 12.7865%, progress (thread 0): 93.1408%]
|T|: y e a h
|P|:
[sample: EN2002d_H03_MEE073_446.25_446.53, WER: 100%, TER: 100%, total WER: 26.1876%, total TER: 12.7873%, progress (thread 0): 93.1487%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_622.33_622.61, WER: 0%, TER: 0%, total WER: 26.1873%, total TER: 12.7872%, progress (thread 0): 93.1566%]
|T|: y e a h
|P|: h m
[sample: EN2002d_H03_MEE073_989.71_989.99, WER: 100%, TER: 100%, total WER: 26.1881%, total TER: 12.788%, progress (thread 0): 93.1646%]
|T|: h m m
|P|: h m
[sample: EN2002d_H02_MEE071_1294.26_1294.54, WER: 100%, TER: 33.3333%, total WER: 26.189%, total TER: 12.7882%, progress (thread 0): 93.1725%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1744.58_1744.86, WER: 0%, TER: 0%, total WER: 26.1887%, total TER: 12.788%, progress (thread 0): 93.1804%]
|T|: t h e
|P|: e
[sample: EN2002d_H03_MEE073_1888.86_1889.14, WER: 100%, TER: 66.6667%, total WER: 26.1895%, total TER: 12.7884%, progress (thread 0): 93.1883%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2067.26_2067.54, WER: 0%, TER: 0%, total WER: 26.1892%, total TER: 12.7883%, progress (thread 0): 93.1962%]
|T|: s u r e
|P|: t r u e
[sample: ES2004b_H00_MEO015_1306.29_1306.56, WER: 100%, TER: 75%, total WER: 26.19%, total TER: 12.7889%, progress (thread 0): 93.2041%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1834.89_1835.16, WER: 0%, TER: 0%, total WER: 26.1898%, total TER: 12.7888%, progress (thread 0): 93.212%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1954.73_1955, WER: 100%, TER: 40%, total WER: 26.1906%, total TER: 12.7891%, progress (thread 0): 93.2199%]
|T|: m m h m m
|P|: h m
[sample: ES2004c_H03_FEE016_652.96_653.23, WER: 100%, TER: 60%, total WER: 26.1914%, total TER: 12.7896%, progress (thread 0): 93.2278%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1141.18_1141.45, WER: 100%, TER: 40%, total WER: 26.1922%, total TER: 12.79%, progress (thread 0): 93.2358%]
|T|: m m
|P|: j u
[sample: ES2004c_H03_FEE016_1281.44_1281.71, WER: 100%, TER: 100%, total WER: 26.1931%, total TER: 12.7904%, progress (thread 0): 93.2437%]
|T|: o k a y
|P|: k a y
[sample: ES2004c_H00_MEO015_1403.89_1404.16, WER: 100%, TER: 25%, total WER: 26.1939%, total TER: 12.7905%, progress (thread 0): 93.2516%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1730.76_1731.03, WER: 0%, TER: 0%, total WER: 26.1936%, total TER: 12.7904%, progress (thread 0): 93.2595%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1835.22_1835.49, WER: 100%, TER: 40%, total WER: 26.1944%, total TER: 12.7907%, progress (thread 0): 93.2674%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1921.81_1922.08, WER: 0%, TER: 0%, total WER: 26.1942%, total TER: 12.7906%, progress (thread 0): 93.2753%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_2149.62_2149.89, WER: 0%, TER: 0%, total WER: 26.1939%, total TER: 12.7904%, progress (thread 0): 93.2832%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_654.95_655.22, WER: 100%, TER: 40%, total WER: 26.1947%, total TER: 12.7908%, progress (thread 0): 93.2911%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_738.4_738.67, WER: 100%, TER: 33.3333%, total WER: 26.1955%, total TER: 12.7909%, progress (thread 0): 93.299%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_793.57_793.84, WER: 0%, TER: 0%, total WER: 26.1952%, total TER: 12.7908%, progress (thread 0): 93.307%]
|T|: n o
|P|: n o
[sample: ES2004d_H03_FEE016_1418.58_1418.85, WER: 0%, TER: 0%, total WER: 26.1949%, total TER: 12.7907%, progress (thread 0): 93.3149%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1497.73_1498, WER: 0%, TER: 0%, total WER: 26.1946%, total TER: 12.7906%, progress (thread 0): 93.3228%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1559.56_1559.83, WER: 0%, TER: 0%, total WER: 26.1943%, total TER: 12.7905%, progress (thread 0): 93.3307%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2099.96_2100.23, WER: 0%, TER: 0%, total WER: 26.194%, total TER: 12.7904%, progress (thread 0): 93.3386%]
|T|: m m h m m
|P|: m h m
[sample: IS1009a_H03_FIO089_159.51_159.78, WER: 100%, TER: 40%, total WER: 26.1949%, total TER: 12.7907%, progress (thread 0): 93.3465%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_495.24_495.51, WER: 0%, TER: 0%, total WER: 26.1946%, total TER: 12.7906%, progress (thread 0): 93.3544%]
|T|: m m h m m
|P|: m h | h m
[sample: IS1009b_H02_FIO084_130.02_130.29, WER: 200%, TER: 60%, total WER: 26.1965%, total TER: 12.7911%, progress (thread 0): 93.3623%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_195.35_195.62, WER: 100%, TER: 60%, total WER: 26.1974%, total TER: 12.7917%, progress (thread 0): 93.3703%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H00_FIE088_1093_1093.27, WER: 100%, TER: 40%, total WER: 26.1982%, total TER: 12.792%, progress (thread 0): 93.3782%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H01_FIO087_323.83_324.1, WER: 0%, TER: 0%, total WER: 26.1979%, total TER: 12.7918%, progress (thread 0): 93.3861%]
|T|: m m h m m
|P|:
[sample: IS1009c_H00_FIE088_770.63_770.9, WER: 100%, TER: 100%, total WER: 26.1987%, total TER: 12.7929%, progress (thread 0): 93.394%]
|T|: m m h m m
|P|: u h | h
[sample: IS1009c_H03_FIO089_922.16_922.43, WER: 200%, TER: 80%, total WER: 26.2007%, total TER: 12.7936%, progress (thread 0): 93.4019%]
|T|: m m h m m
|P|: u h | h m
[sample: IS1009c_H03_FIO089_1095.56_1095.83, WER: 200%, TER: 80%, total WER: 26.2026%, total TER: 12.7944%, progress (thread 0): 93.4098%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1374.42_1374.69, WER: 0%, TER: 0%, total WER: 26.2024%, total TER: 12.7943%, progress (thread 0): 93.4177%]
|T|: o k a y
|P|: l i c
[sample: IS1009d_H03_FIO089_215.46_215.73, WER: 100%, TER: 100%, total WER: 26.2032%, total TER: 12.7951%, progress (thread 0): 93.4256%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_377.8_378.07, WER: 100%, TER: 100%, total WER: 26.204%, total TER: 12.7961%, progress (thread 0): 93.4335%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_381.16_381.43, WER: 100%, TER: 40%, total WER: 26.2048%, total TER: 12.7965%, progress (thread 0): 93.4415%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_938.34_938.61, WER: 100%, TER: 60%, total WER: 26.2057%, total TER: 12.797%, progress (thread 0): 93.4494%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H03_FIO089_1114.59_1114.86, WER: 100%, TER: 80%, total WER: 26.2065%, total TER: 12.7978%, progress (thread 0): 93.4573%]
|T|: o h
|P|: o h
[sample: IS1009d_H00_FIE088_1436.69_1436.96, WER: 0%, TER: 0%, total WER: 26.2062%, total TER: 12.7977%, progress (thread 0): 93.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1775.25_1775.52, WER: 0%, TER: 0%, total WER: 26.2059%, total TER: 12.7976%, progress (thread 0): 93.4731%]
|T|: o k a y
|P|: o h
[sample: IS1009d_H01_FIO087_1908.84_1909.11, WER: 100%, TER: 75%, total WER: 26.2067%, total TER: 12.7982%, progress (thread 0): 93.481%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_684.57_684.84, WER: 100%, TER: 150%, total WER: 26.2076%, total TER: 12.7988%, progress (thread 0): 93.4889%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_790.17_790.44, WER: 100%, TER: 25%, total WER: 26.2084%, total TER: 12.799%, progress (thread 0): 93.4968%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_1326.95_1327.22, WER: 0%, TER: 0%, total WER: 26.2081%, total TER: 12.7989%, progress (thread 0): 93.5047%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1909.01_1909.28, WER: 0%, TER: 0%, total WER: 26.2078%, total TER: 12.7988%, progress (thread 0): 93.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1979.53_1979.8, WER: 0%, TER: 0%, total WER: 26.2075%, total TER: 12.7987%, progress (thread 0): 93.5206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2145.84_2146.11, WER: 0%, TER: 0%, total WER: 26.2072%, total TER: 12.7985%, progress (thread 0): 93.5285%]
|T|: o h
|P|: h m
[sample: TS3003d_H01_MTD011UID_364.71_364.98, WER: 100%, TER: 100%, total WER: 26.2081%, total TER: 12.7989%, progress (thread 0): 93.5364%]
|T|: t e n
|P|: t e n
[sample: TS3003d_H02_MTD0010ID_864.45_864.72, WER: 0%, TER: 0%, total WER: 26.2078%, total TER: 12.7989%, progress (thread 0): 93.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_958.91_959.18, WER: 0%, TER: 0%, total WER: 26.2075%, total TER: 12.7987%, progress (thread 0): 93.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1378.52_1378.79, WER: 0%, TER: 0%, total WER: 26.2072%, total TER: 12.7986%, progress (thread 0): 93.5601%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_2176.17_2176.44, WER: 0%, TER: 0%, total WER: 26.2069%, total TER: 12.7986%, progress (thread 0): 93.568%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_2345.04_2345.31, WER: 100%, TER: 50%, total WER: 26.2077%, total TER: 12.7987%, progress (thread 0): 93.576%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2364.31_2364.58, WER: 0%, TER: 0%, total WER: 26.2074%, total TER: 12.7986%, progress (thread 0): 93.5839%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_2435.3_2435.57, WER: 100%, TER: 25%, total WER: 26.2082%, total TER: 12.7987%, progress (thread 0): 93.5918%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_501.72_501.99, WER: 100%, TER: 33.3333%, total WER: 26.2091%, total TER: 12.7989%, progress (thread 0): 93.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_727.19_727.46, WER: 0%, TER: 0%, total WER: 26.2088%, total TER: 12.7987%, progress (thread 0): 93.6076%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_806.36_806.63, WER: 0%, TER: 0%, total WER: 26.2085%, total TER: 12.7986%, progress (thread 0): 93.6155%]
|T|: o k a y
|P|:
[sample: EN2002a_H00_MEE073_927.86_928.13, WER: 100%, TER: 100%, total WER: 26.2093%, total TER: 12.7994%, progress (thread 0): 93.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1223.91_1224.18, WER: 0%, TER: 0%, total WER: 26.209%, total TER: 12.7993%, progress (thread 0): 93.6313%]
|T|: y e a h
|P|: y o u n w
[sample: EN2002a_H00_MEE073_1319.85_1320.12, WER: 100%, TER: 100%, total WER: 26.2099%, total TER: 12.8001%, progress (thread 0): 93.6392%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_1452_1452.27, WER: 100%, TER: 50%, total WER: 26.2107%, total TER: 12.8005%, progress (thread 0): 93.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1516.15_1516.42, WER: 0%, TER: 0%, total WER: 26.2104%, total TER: 12.8004%, progress (thread 0): 93.6551%]
|T|: y e a h
|P|: y e s
[sample: EN2002a_H03_MEE071_1533.19_1533.46, WER: 100%, TER: 50%, total WER: 26.2112%, total TER: 12.8007%, progress (thread 0): 93.663%]
|T|: y e a h
|P|: y o n
[sample: EN2002a_H00_MEE073_1556.33_1556.6, WER: 100%, TER: 75%, total WER: 26.2121%, total TER: 12.8013%, progress (thread 0): 93.6709%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1945.11_1945.38, WER: 0%, TER: 0%, total WER: 26.2118%, total TER: 12.8012%, progress (thread 0): 93.6788%]
|T|: o h
|P|: o h
[sample: EN2002a_H00_MEE073_1989_1989.27, WER: 0%, TER: 0%, total WER: 26.2115%, total TER: 12.8011%, progress (thread 0): 93.6867%]
|T|: o h
|P|: u m
[sample: EN2002a_H00_MEE073_2003.83_2004.1, WER: 100%, TER: 100%, total WER: 26.2123%, total TER: 12.8015%, progress (thread 0): 93.6946%]
|T|: n o
|P|: y o u | k n o w
[sample: EN2002a_H01_FEO070_2105.6_2105.87, WER: 200%, TER: 300%, total WER: 26.2142%, total TER: 12.8029%, progress (thread 0): 93.7025%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_355.59_355.86, WER: 0%, TER: 0%, total WER: 26.214%, total TER: 12.8028%, progress (thread 0): 93.7104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_485.61_485.88, WER: 0%, TER: 0%, total WER: 26.2137%, total TER: 12.8027%, progress (thread 0): 93.7184%]
|T|: r i g h t
|P|: g r i a t
[sample: EN2002b_H03_MEE073_1027.72_1027.99, WER: 100%, TER: 60%, total WER: 26.2145%, total TER: 12.8032%, progress (thread 0): 93.7263%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_1296.06_1296.33, WER: 0%, TER: 0%, total WER: 26.2142%, total TER: 12.8032%, progress (thread 0): 93.7342%]
|T|: s o
|P|: s
[sample: EN2002b_H01_MEE071_1333.58_1333.85, WER: 100%, TER: 50%, total WER: 26.215%, total TER: 12.8034%, progress (thread 0): 93.7421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1339.47_1339.74, WER: 0%, TER: 0%, total WER: 26.2147%, total TER: 12.8032%, progress (thread 0): 93.75%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1448.9_1449.17, WER: 0%, TER: 0%, total WER: 26.2144%, total TER: 12.8031%, progress (thread 0): 93.7579%]
|T|: a l r i g h t
|P|: a l r i g h t
[sample: EN2002c_H03_MEE073_558.45_558.72, WER: 0%, TER: 0%, total WER: 26.2141%, total TER: 12.8029%, progress (thread 0): 93.7658%]
|T|: h m m
|P|: h m
[sample: EN2002c_H01_FEO072_655.42_655.69, WER: 100%, TER: 33.3333%, total WER: 26.215%, total TER: 12.803%, progress (thread 0): 93.7737%]
|T|: c o o l
|P|: c o o l
[sample: EN2002c_H01_FEO072_778.13_778.4, WER: 0%, TER: 0%, total WER: 26.2147%, total TER: 12.8029%, progress (thread 0): 93.7816%]
|T|: o r
|P|: o l
[sample: EN2002c_H01_FEO072_798.93_799.2, WER: 100%, TER: 50%, total WER: 26.2155%, total TER: 12.8031%, progress (thread 0): 93.7896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_938.98_939.25, WER: 0%, TER: 0%, total WER: 26.2152%, total TER: 12.803%, progress (thread 0): 93.7975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1122.75_1123.02, WER: 0%, TER: 0%, total WER: 26.2149%, total TER: 12.8029%, progress (thread 0): 93.8054%]
|T|: o k a y
|P|:
[sample: EN2002c_H02_MEE071_1353.05_1353.32, WER: 100%, TER: 100%, total WER: 26.2157%, total TER: 12.8037%, progress (thread 0): 93.8133%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1574.37_1574.64, WER: 0%, TER: 0%, total WER: 26.2154%, total TER: 12.8035%, progress (thread 0): 93.8212%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_1672.35_1672.62, WER: 100%, TER: 25%, total WER: 26.2163%, total TER: 12.8036%, progress (thread 0): 93.8291%]
|T|: y e a h
|P|: n o
[sample: EN2002c_H03_MEE073_1740.26_1740.53, WER: 100%, TER: 100%, total WER: 26.2171%, total TER: 12.8045%, progress (thread 0): 93.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1980.58_1980.85, WER: 0%, TER: 0%, total WER: 26.2168%, total TER: 12.8043%, progress (thread 0): 93.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2155.14_2155.41, WER: 0%, TER: 0%, total WER: 26.2165%, total TER: 12.8042%, progress (thread 0): 93.8528%]
|T|: a l r i g h t
|P|: a h
[sample: EN2002c_H03_MEE073_2202.51_2202.78, WER: 100%, TER: 71.4286%, total WER: 26.2174%, total TER: 12.8052%, progress (thread 0): 93.8608%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2272.51_2272.78, WER: 200%, TER: 175%, total WER: 26.2193%, total TER: 12.8067%, progress (thread 0): 93.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2294.4_2294.67, WER: 0%, TER: 0%, total WER: 26.219%, total TER: 12.8066%, progress (thread 0): 93.8766%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2396.65_2396.92, WER: 0%, TER: 0%, total WER: 26.2187%, total TER: 12.8065%, progress (thread 0): 93.8845%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2568.46_2568.73, WER: 0%, TER: 0%, total WER: 26.2184%, total TER: 12.8063%, progress (thread 0): 93.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2705.6_2705.87, WER: 0%, TER: 0%, total WER: 26.2181%, total TER: 12.8062%, progress (thread 0): 93.9003%]
|T|: m m
|P|: h m
[sample: EN2002c_H01_FEO072_2790.29_2790.56, WER: 100%, TER: 50%, total WER: 26.219%, total TER: 12.8064%, progress (thread 0): 93.9082%]
|T|: i | k n o w
|P|: n
[sample: EN2002d_H03_MEE073_903.75_904.02, WER: 100%, TER: 83.3333%, total WER: 26.2206%, total TER: 12.8074%, progress (thread 0): 93.9161%]
|T|: b u t
|P|: k h a t
[sample: EN2002d_H01_FEO072_946.69_946.96, WER: 100%, TER: 100%, total WER: 26.2214%, total TER: 12.808%, progress (thread 0): 93.924%]
|T|: s o r r y
|P|: s o
[sample: EN2002d_H00_FEO070_966.35_966.62, WER: 100%, TER: 60%, total WER: 26.2223%, total TER: 12.8085%, progress (thread 0): 93.932%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1127.55_1127.82, WER: 0%, TER: 0%, total WER: 26.222%, total TER: 12.8084%, progress (thread 0): 93.9399%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1281.17_1281.44, WER: 0%, TER: 0%, total WER: 26.2217%, total TER: 12.8083%, progress (thread 0): 93.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1305.04_1305.31, WER: 0%, TER: 0%, total WER: 26.2214%, total TER: 12.8082%, progress (thread 0): 93.9557%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002d_H00_FEO070_1507.8_1508.07, WER: 100%, TER: 100%, total WER: 26.2231%, total TER: 12.8096%, progress (thread 0): 93.9636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_1832.82_1833.09, WER: 0%, TER: 0%, total WER: 26.2228%, total TER: 12.8095%, progress (thread 0): 93.9715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_2000.66_2000.93, WER: 0%, TER: 0%, total WER: 26.2225%, total TER: 12.8094%, progress (thread 0): 93.9794%]
|T|: w h a t
|P|: w e l l
[sample: EN2002d_H02_MEE071_2072.88_2073.15, WER: 100%, TER: 75%, total WER: 26.2233%, total TER: 12.8099%, progress (thread 0): 93.9873%]
|T|: y e p
|P|: y e p
[sample: EN2002d_H03_MEE073_2169.42_2169.69, WER: 0%, TER: 0%, total WER: 26.223%, total TER: 12.8099%, progress (thread 0): 93.9953%]
|T|: y e a h
|P|: y o a | h
[sample: ES2004a_H00_MEO015_17.88_18.14, WER: 200%, TER: 50%, total WER: 26.225%, total TER: 12.8102%, progress (thread 0): 94.0032%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_966.22_966.48, WER: 0%, TER: 0%, total WER: 26.2247%, total TER: 12.8101%, progress (thread 0): 94.0111%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_89.12_89.38, WER: 100%, TER: 40%, total WER: 26.2255%, total TER: 12.8104%, progress (thread 0): 94.019%]
|T|: t h e r e | w e | g o
|P|:
[sample: ES2004b_H02_MEE014_830.72_830.98, WER: 100%, TER: 100%, total WER: 26.228%, total TER: 12.8126%, progress (thread 0): 94.0269%]
|T|: h u h
|P|: a
[sample: ES2004b_H02_MEE014_1039.01_1039.27, WER: 100%, TER: 100%, total WER: 26.2288%, total TER: 12.8132%, progress (thread 0): 94.0348%]
|T|: p e n s
|P|: b e e n
[sample: ES2004b_H00_MEO015_1162.44_1162.7, WER: 100%, TER: 75%, total WER: 26.2296%, total TER: 12.8138%, progress (thread 0): 94.0427%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1848.94_1849.2, WER: 0%, TER: 0%, total WER: 26.2293%, total TER: 12.8137%, progress (thread 0): 94.0506%]
|T|: ' k a y
|P|: t o
[sample: ES2004c_H00_MEO015_188.1_188.36, WER: 100%, TER: 100%, total WER: 26.2302%, total TER: 12.8145%, progress (thread 0): 94.0585%]
|T|: y e p
|P|: y u p
[sample: ES2004c_H00_MEO015_540.57_540.83, WER: 100%, TER: 33.3333%, total WER: 26.231%, total TER: 12.8146%, progress (thread 0): 94.0665%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1192.94_1193.2, WER: 0%, TER: 0%, total WER: 26.2307%, total TER: 12.8145%, progress (thread 0): 94.0744%]
|T|: m m h m m
|P|: u h | h u m
[sample: ES2004d_H00_MEO015_39.85_40.11, WER: 200%, TER: 80%, total WER: 26.2327%, total TER: 12.8153%, progress (thread 0): 94.0823%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_129.84_130.1, WER: 0%, TER: 0%, total WER: 26.2324%, total TER: 12.8152%, progress (thread 0): 94.0902%]
|T|: o h
|P|: o m
[sample: ES2004d_H03_FEE016_1400.02_1400.28, WER: 100%, TER: 50%, total WER: 26.2332%, total TER: 12.8154%, progress (thread 0): 94.0981%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1553.35_1553.61, WER: 0%, TER: 0%, total WER: 26.2329%, total TER: 12.8152%, progress (thread 0): 94.106%]
|T|: n o
|P|: n o
[sample: ES2004d_H01_FEE013_1608.41_1608.67, WER: 0%, TER: 0%, total WER: 26.2326%, total TER: 12.8152%, progress (thread 0): 94.1139%]
|T|: m m h m m
|P|: m h m
[sample: ES2004d_H00_MEO015_1680.41_1680.67, WER: 100%, TER: 40%, total WER: 26.2334%, total TER: 12.8155%, progress (thread 0): 94.1218%]
|T|: m m h m m
|P|: h
[sample: IS1009a_H03_FIO089_195.56_195.82, WER: 100%, TER: 80%, total WER: 26.2343%, total TER: 12.8163%, progress (thread 0): 94.1297%]
|T|: o k a y
|P|: a k
[sample: IS1009b_H01_FIO087_250.63_250.89, WER: 100%, TER: 75%, total WER: 26.2351%, total TER: 12.8169%, progress (thread 0): 94.1377%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_289.01_289.27, WER: 0%, TER: 0%, total WER: 26.2348%, total TER: 12.8168%, progress (thread 0): 94.1456%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_892.83_893.09, WER: 100%, TER: 100%, total WER: 26.2356%, total TER: 12.8178%, progress (thread 0): 94.1535%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_2020.18_2020.44, WER: 0%, TER: 0%, total WER: 26.2353%, total TER: 12.8177%, progress (thread 0): 94.1614%]
|T|: m m h m m
|P|: y e | h
[sample: IS1009c_H00_FIE088_626.71_626.97, WER: 200%, TER: 100%, total WER: 26.2373%, total TER: 12.8187%, progress (thread 0): 94.1693%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_766.43_766.69, WER: 0%, TER: 0%, total WER: 26.237%, total TER: 12.8186%, progress (thread 0): 94.1772%]
|T|: m m
|P|:
[sample: IS1009d_H01_FIO087_656.85_657.11, WER: 100%, TER: 100%, total WER: 26.2378%, total TER: 12.819%, progress (thread 0): 94.1851%]
|T|: ' c a u s e
|P|:
[sample: TS3003a_H03_MTD012ME_1376.63_1376.89, WER: 100%, TER: 100%, total WER: 26.2387%, total TER: 12.8202%, progress (thread 0): 94.193%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H02_MTD0010ID_1474.04_1474.3, WER: 0%, TER: 0%, total WER: 26.2384%, total TER: 12.8201%, progress (thread 0): 94.201%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_206.59_206.85, WER: 100%, TER: 25%, total WER: 26.2392%, total TER: 12.8202%, progress (thread 0): 94.2089%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H01_MTD011UID_581.65_581.91, WER: 0%, TER: 0%, total WER: 26.2389%, total TER: 12.8201%, progress (thread 0): 94.2168%]
|T|: h m m
|P|: h m
[sample: TS3003b_H03_MTD012ME_883.3_883.56, WER: 100%, TER: 33.3333%, total WER: 26.2397%, total TER: 12.8202%, progress (thread 0): 94.2247%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1533.07_1533.33, WER: 100%, TER: 40%, total WER: 26.2406%, total TER: 12.8205%, progress (thread 0): 94.2326%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_2048.3_2048.56, WER: 0%, TER: 0%, total WER: 26.2403%, total TER: 12.8205%, progress (thread 0): 94.2405%]
|T|: m m
|P|: t e
[sample: TS3003c_H01_MTD011UID_1933.15_1933.41, WER: 100%, TER: 100%, total WER: 26.2411%, total TER: 12.8209%, progress (thread 0): 94.2484%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1980.3_1980.56, WER: 0%, TER: 0%, total WER: 26.2408%, total TER: 12.8207%, progress (thread 0): 94.2563%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_694.34_694.6, WER: 0%, TER: 0%, total WER: 26.2405%, total TER: 12.8206%, progress (thread 0): 94.2642%]
|T|: t w o
|P|: t o
[sample: TS3003d_H03_MTD012ME_1897.81_1898.07, WER: 100%, TER: 33.3333%, total WER: 26.2413%, total TER: 12.8208%, progress (thread 0): 94.2722%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2141.52_2141.78, WER: 0%, TER: 0%, total WER: 26.241%, total TER: 12.8206%, progress (thread 0): 94.2801%]
|T|: m m h m m
|P|: m h | h
[sample: EN2002a_H02_FEO072_36.34_36.6, WER: 200%, TER: 60%, total WER: 26.243%, total TER: 12.8212%, progress (thread 0): 94.288%]
|T|: y e a h
|P|: y h
[sample: EN2002a_H00_MEE073_319.23_319.49, WER: 100%, TER: 50%, total WER: 26.2438%, total TER: 12.8215%, progress (thread 0): 94.2959%]
|T|: y e a h
|P|: e h
[sample: EN2002a_H03_MEE071_671.05_671.31, WER: 100%, TER: 50%, total WER: 26.2446%, total TER: 12.8219%, progress (thread 0): 94.3038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_721.89_722.15, WER: 0%, TER: 0%, total WER: 26.2443%, total TER: 12.8218%, progress (thread 0): 94.3117%]
|T|: t r u e
|P|: t r u e
[sample: EN2002a_H00_MEE073_751.8_752.06, WER: 0%, TER: 0%, total WER: 26.2441%, total TER: 12.8217%, progress (thread 0): 94.3196%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1052.1_1052.36, WER: 0%, TER: 0%, total WER: 26.2438%, total TER: 12.8215%, progress (thread 0): 94.3275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1446.67_1446.93, WER: 0%, TER: 0%, total WER: 26.2435%, total TER: 12.8214%, progress (thread 0): 94.3354%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1777.39_1777.65, WER: 0%, TER: 0%, total WER: 26.2432%, total TER: 12.8213%, progress (thread 0): 94.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1868.15_1868.41, WER: 0%, TER: 0%, total WER: 26.2429%, total TER: 12.8212%, progress (thread 0): 94.3513%]
|T|: o k a y
|P|: g
[sample: EN2002b_H02_FEO072_93.22_93.48, WER: 100%, TER: 100%, total WER: 26.2437%, total TER: 12.822%, progress (thread 0): 94.3592%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_252.07_252.33, WER: 200%, TER: 175%, total WER: 26.2457%, total TER: 12.8235%, progress (thread 0): 94.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_460.58_460.84, WER: 0%, TER: 0%, total WER: 26.2454%, total TER: 12.8234%, progress (thread 0): 94.375%]
|T|: o h
|P|: o h
[sample: EN2002b_H03_MEE073_674.06_674.32, WER: 0%, TER: 0%, total WER: 26.2451%, total TER: 12.8233%, progress (thread 0): 94.3829%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_1026.26_1026.52, WER: 200%, TER: 175%, total WER: 26.247%, total TER: 12.8248%, progress (thread 0): 94.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1104.98_1105.24, WER: 0%, TER: 0%, total WER: 26.2467%, total TER: 12.8247%, progress (thread 0): 94.3987%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_1549.41_1549.67, WER: 100%, TER: 33.3333%, total WER: 26.2476%, total TER: 12.8249%, progress (thread 0): 94.4066%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1643.55_1643.81, WER: 0%, TER: 0%, total WER: 26.2473%, total TER: 12.8247%, progress (thread 0): 94.4146%]
|T|: r i g h t
|P|: r i
[sample: EN2002c_H02_MEE071_46.45_46.71, WER: 100%, TER: 60%, total WER: 26.2481%, total TER: 12.8253%, progress (thread 0): 94.4225%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_577.1_577.36, WER: 0%, TER: 0%, total WER: 26.2478%, total TER: 12.8252%, progress (thread 0): 94.4304%]
|T|: y e p
|P|: n o
[sample: EN2002c_H01_FEO072_600.57_600.83, WER: 100%, TER: 100%, total WER: 26.2486%, total TER: 12.8258%, progress (thread 0): 94.4383%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_775.24_775.5, WER: 0%, TER: 0%, total WER: 26.2483%, total TER: 12.8257%, progress (thread 0): 94.4462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1079.16_1079.42, WER: 0%, TER: 0%, total WER: 26.248%, total TER: 12.8256%, progress (thread 0): 94.4541%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_1105.57_1105.83, WER: 100%, TER: 40%, total WER: 26.2489%, total TER: 12.8259%, progress (thread 0): 94.462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1119.48_1119.74, WER: 0%, TER: 0%, total WER: 26.2486%, total TER: 12.8258%, progress (thread 0): 94.4699%]
|T|: m m h m m
|P|: s o
[sample: EN2002c_H03_MEE073_1983.15_1983.41, WER: 100%, TER: 100%, total WER: 26.2494%, total TER: 12.8268%, progress (thread 0): 94.4779%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1999.1_1999.36, WER: 0%, TER: 0%, total WER: 26.2491%, total TER: 12.8266%, progress (thread 0): 94.4858%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2082.11_2082.37, WER: 0%, TER: 0%, total WER: 26.2488%, total TER: 12.8265%, progress (thread 0): 94.4937%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2586.11_2586.37, WER: 0%, TER: 0%, total WER: 26.2485%, total TER: 12.8264%, progress (thread 0): 94.5016%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_347.04_347.3, WER: 0%, TER: 0%, total WER: 26.2482%, total TER: 12.8263%, progress (thread 0): 94.5095%]
|T|: w h o
|P|: h m
[sample: EN2002d_H00_FEO070_501.31_501.57, WER: 100%, TER: 66.6667%, total WER: 26.249%, total TER: 12.8267%, progress (thread 0): 94.5174%]
|T|: y e a h
|P|: y e p
[sample: EN2002d_H02_MEE071_728.46_728.72, WER: 100%, TER: 50%, total WER: 26.2499%, total TER: 12.827%, progress (thread 0): 94.5253%]
|T|: w e l l | i t ' s
|P|: j u s t
[sample: EN2002d_H03_MEE073_882.54_882.8, WER: 100%, TER: 88.8889%, total WER: 26.2515%, total TER: 12.8286%, progress (thread 0): 94.5332%]
|T|: s u p e r
|P|: s u p
[sample: EN2002d_H03_MEE073_953.8_954.06, WER: 100%, TER: 40%, total WER: 26.2524%, total TER: 12.8289%, progress (thread 0): 94.5411%]
|T|: o h | y e a h
|P|:
[sample: EN2002d_H00_FEO070_1517.63_1517.89, WER: 100%, TER: 100%, total WER: 26.254%, total TER: 12.8303%, progress (thread 0): 94.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1704.28_1704.54, WER: 0%, TER: 0%, total WER: 26.2537%, total TER: 12.8302%, progress (thread 0): 94.557%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_2135.36_2135.62, WER: 100%, TER: 33.3333%, total WER: 26.2546%, total TER: 12.8304%, progress (thread 0): 94.5649%]
|T|: m m
|P|: h m
[sample: ES2004a_H03_FEE016_666.69_666.94, WER: 100%, TER: 50%, total WER: 26.2554%, total TER: 12.8305%, progress (thread 0): 94.5728%]
|T|: y e p
|P|: y e p
[sample: ES2004b_H00_MEO015_817.97_818.22, WER: 0%, TER: 0%, total WER: 26.2551%, total TER: 12.8305%, progress (thread 0): 94.5807%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_2175.08_2175.33, WER: 100%, TER: 40%, total WER: 26.2559%, total TER: 12.8308%, progress (thread 0): 94.5886%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_620.36_620.61, WER: 100%, TER: 40%, total WER: 26.2567%, total TER: 12.8311%, progress (thread 0): 94.5965%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1866.87_1867.12, WER: 100%, TER: 100%, total WER: 26.2576%, total TER: 12.8317%, progress (thread 0): 94.6044%]
|T|: s o r r y
|P|: s o r r y
[sample: ES2004d_H01_FEE013_455.38_455.63, WER: 0%, TER: 0%, total WER: 26.2573%, total TER: 12.8315%, progress (thread 0): 94.6123%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_607.67_607.92, WER: 0%, TER: 0%, total WER: 26.257%, total TER: 12.8314%, progress (thread 0): 94.6203%]
|T|: y e s
|P|: y e a h
[sample: ES2004d_H03_FEE016_1135.45_1135.7, WER: 100%, TER: 66.6667%, total WER: 26.2578%, total TER: 12.8318%, progress (thread 0): 94.6282%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1553.38_1553.63, WER: 0%, TER: 0%, total WER: 26.2575%, total TER: 12.8317%, progress (thread 0): 94.6361%]
|T|: m m h m m
|P|: m h m
[sample: IS1009b_H03_FIO089_1086.32_1086.57, WER: 100%, TER: 40%, total WER: 26.2583%, total TER: 12.832%, progress (thread 0): 94.644%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1900.42_1900.67, WER: 0%, TER: 0%, total WER: 26.2581%, total TER: 12.8319%, progress (thread 0): 94.6519%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1909.04_1909.29, WER: 0%, TER: 0%, total WER: 26.2578%, total TER: 12.8318%, progress (thread 0): 94.6598%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H00_FIE088_324.58_324.83, WER: 0%, TER: 0%, total WER: 26.2575%, total TER: 12.8316%, progress (thread 0): 94.6677%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1287.33_1287.58, WER: 0%, TER: 0%, total WER: 26.2572%, total TER: 12.8315%, progress (thread 0): 94.6756%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1781.33_1781.58, WER: 0%, TER: 0%, total WER: 26.2569%, total TER: 12.8314%, progress (thread 0): 94.6835%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_351.32_351.57, WER: 0%, TER: 0%, total WER: 26.2566%, total TER: 12.8313%, progress (thread 0): 94.6915%]
|T|: m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_427.43_427.68, WER: 100%, TER: 200%, total WER: 26.2574%, total TER: 12.8322%, progress (thread 0): 94.6994%]
|T|: m m
|P|: a
[sample: IS1009d_H02_FIO084_826.51_826.76, WER: 100%, TER: 100%, total WER: 26.2582%, total TER: 12.8326%, progress (thread 0): 94.7073%]
|T|: n o
|P|: n o
[sample: TS3003a_H03_MTD012ME_1222.74_1222.99, WER: 0%, TER: 0%, total WER: 26.2579%, total TER: 12.8325%, progress (thread 0): 94.7152%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_281.83_282.08, WER: 100%, TER: 50%, total WER: 26.2588%, total TER: 12.8327%, progress (thread 0): 94.7231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1559.52_1559.77, WER: 0%, TER: 0%, total WER: 26.2585%, total TER: 12.8326%, progress (thread 0): 94.731%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_1839.11_1839.36, WER: 0%, TER: 0%, total WER: 26.2582%, total TER: 12.8324%, progress (thread 0): 94.7389%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H02_MTD0010ID_2094.57_2094.82, WER: 100%, TER: 50%, total WER: 26.259%, total TER: 12.8328%, progress (thread 0): 94.7468%]
|T|: m m h m m
|P|: m h m
[sample: TS3003c_H03_MTD012ME_1972.52_1972.77, WER: 100%, TER: 40%, total WER: 26.2598%, total TER: 12.8331%, progress (thread 0): 94.7548%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_303.27_303.52, WER: 100%, TER: 75%, total WER: 26.2607%, total TER: 12.8337%, progress (thread 0): 94.7627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_583.29_583.54, WER: 0%, TER: 0%, total WER: 26.2604%, total TER: 12.8336%, progress (thread 0): 94.7706%]
|T|: y e a h
|P|: y e h
[sample: TS3003d_H02_MTD0010ID_847.16_847.41, WER: 100%, TER: 25%, total WER: 26.2612%, total TER: 12.8337%, progress (thread 0): 94.7785%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1370.99_1371.24, WER: 100%, TER: 75%, total WER: 26.262%, total TER: 12.8343%, progress (thread 0): 94.7864%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1371.48_1371.73, WER: 0%, TER: 0%, total WER: 26.2617%, total TER: 12.8341%, progress (thread 0): 94.7943%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_1404.32_1404.57, WER: 100%, TER: 75%, total WER: 26.2626%, total TER: 12.8347%, progress (thread 0): 94.8022%]
|T|: g o o d
|P|: c a u s e
[sample: TS3003d_H03_MTD012ME_1696.35_1696.6, WER: 100%, TER: 125%, total WER: 26.2634%, total TER: 12.8358%, progress (thread 0): 94.8101%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2064.36_2064.61, WER: 0%, TER: 0%, total WER: 26.2631%, total TER: 12.8356%, progress (thread 0): 94.818%]
|T|: y e a h
|P|:
[sample: TS3003d_H02_MTD0010ID_2111_2111.25, WER: 100%, TER: 100%, total WER: 26.2639%, total TER: 12.8365%, progress (thread 0): 94.826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2138.4_2138.65, WER: 0%, TER: 0%, total WER: 26.2636%, total TER: 12.8363%, progress (thread 0): 94.8339%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2216.27_2216.52, WER: 0%, TER: 0%, total WER: 26.2633%, total TER: 12.8362%, progress (thread 0): 94.8418%]
|T|: a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2381.92_2382.17, WER: 100%, TER: 100%, total WER: 26.2642%, total TER: 12.8366%, progress (thread 0): 94.8497%]
|T|: s o
|P|: s o
[sample: EN2002a_H00_MEE073_202.42_202.67, WER: 0%, TER: 0%, total WER: 26.2639%, total TER: 12.8366%, progress (thread 0): 94.8576%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002a_H00_MEE073_749.45_749.7, WER: 200%, TER: 175%, total WER: 26.2658%, total TER: 12.8381%, progress (thread 0): 94.8655%]
|T|: y e a h
|P|: t o
[sample: EN2002a_H00_MEE073_1642_1642.25, WER: 100%, TER: 100%, total WER: 26.2666%, total TER: 12.8389%, progress (thread 0): 94.8734%]
|T|: s o
|P|: s o
[sample: EN2002a_H02_FEO072_1664.11_1664.36, WER: 0%, TER: 0%, total WER: 26.2663%, total TER: 12.8388%, progress (thread 0): 94.8813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1964.06_1964.31, WER: 0%, TER: 0%, total WER: 26.2661%, total TER: 12.8387%, progress (thread 0): 94.8892%]
|T|: y o u ' d | b e | s
|P|: i n p
[sample: EN2002a_H00_MEE073_2096.92_2097.17, WER: 100%, TER: 100%, total WER: 26.2685%, total TER: 12.8408%, progress (thread 0): 94.8971%]
|T|: y e a h
|P|: y e p
[sample: EN2002b_H01_MEE071_93.68_93.93, WER: 100%, TER: 50%, total WER: 26.2694%, total TER: 12.8411%, progress (thread 0): 94.9051%]
|T|: r i g h t
|P|: i
[sample: EN2002b_H03_MEE073_311.61_311.86, WER: 100%, TER: 80%, total WER: 26.2702%, total TER: 12.8419%, progress (thread 0): 94.913%]
|T|: a l r i g h t
|P|: r g h t
[sample: EN2002b_H03_MEE073_1129.86_1130.11, WER: 100%, TER: 42.8571%, total WER: 26.271%, total TER: 12.8424%, progress (thread 0): 94.9209%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1222.15_1222.4, WER: 0%, TER: 0%, total WER: 26.2707%, total TER: 12.8423%, progress (thread 0): 94.9288%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1237.83_1238.08, WER: 0%, TER: 0%, total WER: 26.2704%, total TER: 12.8422%, progress (thread 0): 94.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1245.09_1245.34, WER: 0%, TER: 0%, total WER: 26.2701%, total TER: 12.842%, progress (thread 0): 94.9446%]
|T|: a n d
|P|: t h e n
[sample: EN2002b_H01_MEE071_1309.75_1310, WER: 100%, TER: 133.333%, total WER: 26.271%, total TER: 12.8429%, progress (thread 0): 94.9525%]
|T|: y e p
|P|: n o
[sample: EN2002b_H02_FEO072_1594.84_1595.09, WER: 100%, TER: 100%, total WER: 26.2718%, total TER: 12.8435%, progress (thread 0): 94.9604%]
|T|: y e a h
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_1643.59_1643.84, WER: 200%, TER: 175%, total WER: 26.2737%, total TER: 12.845%, progress (thread 0): 94.9684%]
|T|: s u r e
|P|: s u e
[sample: EN2002c_H02_MEE071_67.06_67.31, WER: 100%, TER: 25%, total WER: 26.2746%, total TER: 12.8451%, progress (thread 0): 94.9763%]
|T|: o k a y
|P|: n
[sample: EN2002c_H03_MEE073_76.13_76.38, WER: 100%, TER: 100%, total WER: 26.2754%, total TER: 12.8459%, progress (thread 0): 94.9842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_253.63_253.88, WER: 0%, TER: 0%, total WER: 26.2751%, total TER: 12.8458%, progress (thread 0): 94.9921%]
|T|: m m h m m
|P|: m h m
[sample: EN2002c_H03_MEE073_470.85_471.1, WER: 100%, TER: 40%, total WER: 26.2759%, total TER: 12.8461%, progress (thread 0): 95%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_759.06_759.31, WER: 0%, TER: 0%, total WER: 26.2756%, total TER: 12.846%, progress (thread 0): 95.0079%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1000.82_1001.07, WER: 0%, TER: 0%, total WER: 26.2753%, total TER: 12.8459%, progress (thread 0): 95.0158%]
|T|: y e a h
|P|: y e a | h | w
[sample: EN2002c_H03_MEE073_1023.06_1023.31, WER: 300%, TER: 75%, total WER: 26.2784%, total TER: 12.8464%, progress (thread 0): 95.0237%]
|T|: m m h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1102.48_1102.73, WER: 100%, TER: 60%, total WER: 26.2793%, total TER: 12.847%, progress (thread 0): 95.0316%]
|T|: y e a h
|P|: y e s
[sample: EN2002c_H02_MEE071_1122.76_1123.01, WER: 100%, TER: 50%, total WER: 26.2801%, total TER: 12.8473%, progress (thread 0): 95.0396%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_1359.23_1359.48, WER: 100%, TER: 66.6667%, total WER: 26.2809%, total TER: 12.8477%, progress (thread 0): 95.0475%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_1508.23_1508.48, WER: 0%, TER: 0%, total WER: 26.2806%, total TER: 12.8476%, progress (thread 0): 95.0554%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2100.94_2101.19, WER: 0%, TER: 0%, total WER: 26.2803%, total TER: 12.8475%, progress (thread 0): 95.0633%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2354.17_2354.42, WER: 0%, TER: 0%, total WER: 26.28%, total TER: 12.8474%, progress (thread 0): 95.0712%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_146.37_146.62, WER: 100%, TER: 28.5714%, total WER: 26.2808%, total TER: 12.8476%, progress (thread 0): 95.0791%]
|T|: o h
|P|: o
[sample: EN2002d_H00_FEO070_286.77_287.02, WER: 100%, TER: 50%, total WER: 26.2817%, total TER: 12.8478%, progress (thread 0): 95.087%]
|T|: y e a h
|P|: y h m
[sample: EN2002d_H03_MEE073_589.91_590.16, WER: 100%, TER: 75%, total WER: 26.2825%, total TER: 12.8484%, progress (thread 0): 95.0949%]
|T|: y e a h
|P|: c a n c e
[sample: EN2002d_H03_MEE073_850.49_850.74, WER: 100%, TER: 125%, total WER: 26.2833%, total TER: 12.8494%, progress (thread 0): 95.1028%]
|T|: s o
|P|: s o
[sample: EN2002d_H02_MEE071_1251.8_1252.05, WER: 0%, TER: 0%, total WER: 26.283%, total TER: 12.8494%, progress (thread 0): 95.1108%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_1830.85_1831.1, WER: 100%, TER: 33.3333%, total WER: 26.2839%, total TER: 12.8495%, progress (thread 0): 95.1187%]
|T|: o k a y
|P|: c o n
[sample: EN2002d_H03_MEE073_1854.42_1854.67, WER: 100%, TER: 100%, total WER: 26.2847%, total TER: 12.8503%, progress (thread 0): 95.1266%]
|T|: m m
|P|: o
[sample: EN2002d_H01_FEO072_2083.17_2083.42, WER: 100%, TER: 100%, total WER: 26.2855%, total TER: 12.8508%, progress (thread 0): 95.1345%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2192.17_2192.42, WER: 0%, TER: 0%, total WER: 26.2852%, total TER: 12.8506%, progress (thread 0): 95.1424%]
|T|: s
|P|: s
[sample: ES2004a_H02_MEE014_484.48_484.72, WER: 0%, TER: 0%, total WER: 26.2849%, total TER: 12.8506%, progress (thread 0): 95.1503%]
|T|: m m
|P|:
[sample: ES2004a_H03_FEE016_775.6_775.84, WER: 100%, TER: 100%, total WER: 26.2858%, total TER: 12.851%, progress (thread 0): 95.1582%]
|T|: s o r r y
|P|: t
[sample: ES2004b_H00_MEO015_100.7_100.94, WER: 100%, TER: 100%, total WER: 26.2866%, total TER: 12.852%, progress (thread 0): 95.1661%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1311.92_1312.16, WER: 0%, TER: 0%, total WER: 26.2863%, total TER: 12.8519%, progress (thread 0): 95.174%]
|T|: m m h m m
|P|: m h u m
[sample: ES2004b_H00_MEO015_1636.65_1636.89, WER: 100%, TER: 40%, total WER: 26.2871%, total TER: 12.8522%, progress (thread 0): 95.182%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1305.92_1306.16, WER: 100%, TER: 25%, total WER: 26.2879%, total TER: 12.8523%, progress (thread 0): 95.1899%]
|T|: m m
|P|: h m
[sample: ES2004c_H03_FEE016_1345.16_1345.4, WER: 100%, TER: 50%, total WER: 26.2888%, total TER: 12.8525%, progress (thread 0): 95.1978%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_1364.93_1365.17, WER: 100%, TER: 100%, total WER: 26.2896%, total TER: 12.8529%, progress (thread 0): 95.2057%]
|T|: f i n e
|P|: p o i n t
[sample: ES2004c_H00_MEO015_2156.57_2156.81, WER: 100%, TER: 75%, total WER: 26.2904%, total TER: 12.8535%, progress (thread 0): 95.2136%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_973.73_973.97, WER: 0%, TER: 0%, total WER: 26.2901%, total TER: 12.8534%, progress (thread 0): 95.2215%]
|T|: m m
|P|: h
[sample: ES2004d_H03_FEE016_1286.7_1286.94, WER: 100%, TER: 100%, total WER: 26.291%, total TER: 12.8538%, progress (thread 0): 95.2294%]
|T|: o h
|P|: o h
[sample: ES2004d_H01_FEE013_2176.67_2176.91, WER: 0%, TER: 0%, total WER: 26.2907%, total TER: 12.8537%, progress (thread 0): 95.2373%]
|T|: n o
|P|: h
[sample: IS1009a_H03_FIO089_145.55_145.79, WER: 100%, TER: 100%, total WER: 26.2915%, total TER: 12.8541%, progress (thread 0): 95.2453%]
|T|: m m h m m
|P|:
[sample: IS1009a_H00_FIE088_466.21_466.45, WER: 100%, TER: 100%, total WER: 26.2923%, total TER: 12.8551%, progress (thread 0): 95.2532%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_556.58_556.82, WER: 0%, TER: 0%, total WER: 26.292%, total TER: 12.855%, progress (thread 0): 95.2611%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1033.16_1033.4, WER: 0%, TER: 0%, total WER: 26.2917%, total TER: 12.8549%, progress (thread 0): 95.269%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_1099.19_1099.43, WER: 0%, TER: 0%, total WER: 26.2914%, total TER: 12.8548%, progress (thread 0): 95.2769%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1247.84_1248.08, WER: 100%, TER: 60%, total WER: 26.2923%, total TER: 12.8553%, progress (thread 0): 95.2848%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H03_FIO089_752.07_752.31, WER: 100%, TER: 40%, total WER: 26.2931%, total TER: 12.8557%, progress (thread 0): 95.2927%]
|T|: m m | r i g h t
|P|: r i g t
[sample: IS1009c_H00_FIE088_764.18_764.42, WER: 100%, TER: 50%, total WER: 26.2947%, total TER: 12.8563%, progress (thread 0): 95.3006%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1110.14_1110.38, WER: 100%, TER: 40%, total WER: 26.2956%, total TER: 12.8567%, progress (thread 0): 95.3085%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1123.59_1123.83, WER: 100%, TER: 40%, total WER: 26.2964%, total TER: 12.857%, progress (thread 0): 95.3165%]
|T|: h e l l o
|P|: n o
[sample: IS1009d_H02_FIO084_41.31_41.55, WER: 100%, TER: 80%, total WER: 26.2972%, total TER: 12.8578%, progress (thread 0): 95.3244%]
|T|: m m
|P|: m m
[sample: IS1009d_H03_FIO089_516.52_516.76, WER: 0%, TER: 0%, total WER: 26.2969%, total TER: 12.8577%, progress (thread 0): 95.3323%]
|T|: m m h m m
|P|: h m
[sample: IS1009d_H02_FIO084_656.9_657.14, WER: 100%, TER: 60%, total WER: 26.2978%, total TER: 12.8583%, progress (thread 0): 95.3402%]
|T|: y e a h
|P|: y o n o
[sample: IS1009d_H02_FIO084_1011.72_1011.96, WER: 100%, TER: 75%, total WER: 26.2986%, total TER: 12.8588%, progress (thread 0): 95.3481%]
|T|: o k a y
|P|: o k a
[sample: IS1009d_H03_FIO089_1883.01_1883.25, WER: 100%, TER: 25%, total WER: 26.2994%, total TER: 12.8589%, progress (thread 0): 95.356%]
|T|: w e
|P|: w
[sample: TS3003a_H00_MTD009PM_1109.9_1110.14, WER: 100%, TER: 50%, total WER: 26.3002%, total TER: 12.8591%, progress (thread 0): 95.3639%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1420.32_1420.56, WER: 100%, TER: 50%, total WER: 26.3011%, total TER: 12.8593%, progress (thread 0): 95.3718%]
|T|: n o
|P|: n o
[sample: TS3003b_H02_MTD0010ID_1474.4_1474.64, WER: 0%, TER: 0%, total WER: 26.3008%, total TER: 12.8592%, progress (thread 0): 95.3797%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_1677.79_1678.03, WER: 0%, TER: 0%, total WER: 26.3005%, total TER: 12.8591%, progress (thread 0): 95.3877%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1975.67_1975.91, WER: 100%, TER: 100%, total WER: 26.3013%, total TER: 12.8595%, progress (thread 0): 95.3956%]
|T|: y e a h
|P|: h u m
[sample: TS3003b_H01_MTD011UID_2083.9_2084.14, WER: 100%, TER: 100%, total WER: 26.3021%, total TER: 12.8603%, progress (thread 0): 95.4035%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_1240.02_1240.26, WER: 100%, TER: 75%, total WER: 26.303%, total TER: 12.8609%, progress (thread 0): 95.4114%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1254.68_1254.92, WER: 0%, TER: 0%, total WER: 26.3027%, total TER: 12.8608%, progress (thread 0): 95.4193%]
|T|: s o
|P|: s o
[sample: TS3003c_H01_MTD011UID_1417.83_1418.07, WER: 0%, TER: 0%, total WER: 26.3024%, total TER: 12.8607%, progress (thread 0): 95.4272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1477.96_1478.2, WER: 0%, TER: 0%, total WER: 26.3021%, total TER: 12.8606%, progress (thread 0): 95.4351%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1566.64_1566.88, WER: 100%, TER: 25%, total WER: 26.3029%, total TER: 12.8607%, progress (thread 0): 95.443%]
|T|: y e a h
|P|: a h
[sample: TS3003c_H01_MTD011UID_1716.46_1716.7, WER: 100%, TER: 50%, total WER: 26.3037%, total TER: 12.8611%, progress (thread 0): 95.451%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2217.59_2217.83, WER: 0%, TER: 0%, total WER: 26.3034%, total TER: 12.861%, progress (thread 0): 95.4589%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2243.54_2243.78, WER: 0%, TER: 0%, total WER: 26.3031%, total TER: 12.8608%, progress (thread 0): 95.4668%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1049.33_1049.57, WER: 0%, TER: 0%, total WER: 26.3028%, total TER: 12.8607%, progress (thread 0): 95.4747%]
|T|: y e a h
|P|: o h
[sample: TS3003d_H01_MTD011UID_1496.68_1496.92, WER: 100%, TER: 75%, total WER: 26.3037%, total TER: 12.8613%, progress (thread 0): 95.4826%]
|T|: d | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_106.38_106.62, WER: 50%, TER: 33.3333%, total WER: 26.3042%, total TER: 12.8616%, progress (thread 0): 95.4905%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_140.88_141.12, WER: 0%, TER: 0%, total WER: 26.3039%, total TER: 12.8615%, progress (thread 0): 95.4984%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_333.3_333.54, WER: 0%, TER: 0%, total WER: 26.3036%, total TER: 12.8613%, progress (thread 0): 95.5063%]
|T|: y e a h
|P|: h m
[sample: EN2002a_H01_FEO070_398.18_398.42, WER: 100%, TER: 100%, total WER: 26.3044%, total TER: 12.8621%, progress (thread 0): 95.5142%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H01_FEO070_481.62_481.86, WER: 100%, TER: 50%, total WER: 26.3053%, total TER: 12.8625%, progress (thread 0): 95.5222%]
|T|: y e a h
|P|: e m
[sample: EN2002a_H00_MEE073_682.97_683.21, WER: 100%, TER: 75%, total WER: 26.3061%, total TER: 12.863%, progress (thread 0): 95.5301%]
|T|: m m h m m
|P|: m h m
[sample: EN2002a_H02_FEO072_1009.94_1010.18, WER: 100%, TER: 40%, total WER: 26.3069%, total TER: 12.8634%, progress (thread 0): 95.538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1041.03_1041.27, WER: 0%, TER: 0%, total WER: 26.3066%, total TER: 12.8632%, progress (thread 0): 95.5459%]
|T|: i | s e e
|P|: a s
[sample: EN2002a_H00_MEE073_1171.72_1171.96, WER: 100%, TER: 80%, total WER: 26.3083%, total TER: 12.864%, progress (thread 0): 95.5538%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002a_H00_MEE073_1209.32_1209.56, WER: 100%, TER: 85.7143%, total WER: 26.3099%, total TER: 12.8652%, progress (thread 0): 95.5617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1310.69_1310.93, WER: 0%, TER: 0%, total WER: 26.3096%, total TER: 12.8651%, progress (thread 0): 95.5696%]
|T|: o p e n
|P|:
[sample: EN2002a_H02_FEO072_1350.04_1350.28, WER: 100%, TER: 100%, total WER: 26.3105%, total TER: 12.8659%, progress (thread 0): 95.5775%]
|T|: m m
|P|: h m
[sample: EN2002a_H03_MEE071_1405.73_1405.97, WER: 100%, TER: 50%, total WER: 26.3113%, total TER: 12.8661%, progress (thread 0): 95.5854%]
|T|: r i g h t
|P|: r i g t
[sample: EN2002a_H00_MEE073_1791.17_1791.41, WER: 100%, TER: 20%, total WER: 26.3121%, total TER: 12.8662%, progress (thread 0): 95.5934%]
|T|: y e a h
|P|: t e h
[sample: EN2002a_H00_MEE073_1834.42_1834.66, WER: 100%, TER: 50%, total WER: 26.313%, total TER: 12.8665%, progress (thread 0): 95.6013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2028.12_2028.36, WER: 0%, TER: 0%, total WER: 26.3127%, total TER: 12.8664%, progress (thread 0): 95.6092%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_2040.58_2040.82, WER: 0%, TER: 0%, total WER: 26.3124%, total TER: 12.8663%, progress (thread 0): 95.6171%]
|T|: o h
|P|: o h
[sample: EN2002a_H01_FEO070_2098.91_2099.15, WER: 0%, TER: 0%, total WER: 26.3121%, total TER: 12.8662%, progress (thread 0): 95.625%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_522.48_522.72, WER: 0%, TER: 0%, total WER: 26.3118%, total TER: 12.8661%, progress (thread 0): 95.6329%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_612.23_612.47, WER: 0%, TER: 0%, total WER: 26.3115%, total TER: 12.866%, progress (thread 0): 95.6408%]
|T|: m m
|P|:
[sample: EN2002b_H02_FEO072_1475.84_1476.08, WER: 100%, TER: 100%, total WER: 26.3123%, total TER: 12.8664%, progress (thread 0): 95.6487%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_235.84_236.08, WER: 100%, TER: 33.3333%, total WER: 26.3131%, total TER: 12.8665%, progress (thread 0): 95.6566%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_571.47_571.71, WER: 0%, TER: 0%, total WER: 26.3128%, total TER: 12.8664%, progress (thread 0): 95.6646%]
|T|: h m m
|P|: y o u h
[sample: EN2002c_H03_MEE073_574.34_574.58, WER: 100%, TER: 133.333%, total WER: 26.3137%, total TER: 12.8672%, progress (thread 0): 95.6725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_598.8_599.04, WER: 0%, TER: 0%, total WER: 26.3134%, total TER: 12.8671%, progress (thread 0): 95.6804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1009.87_1010.11, WER: 0%, TER: 0%, total WER: 26.3131%, total TER: 12.867%, progress (thread 0): 95.6883%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1018.66_1018.9, WER: 0%, TER: 0%, total WER: 26.3128%, total TER: 12.8669%, progress (thread 0): 95.6962%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1281.36_1281.6, WER: 100%, TER: 33.3333%, total WER: 26.3136%, total TER: 12.867%, progress (thread 0): 95.7041%]
|T|: y e a h
|P|: y e a | k n | w
[sample: EN2002c_H03_MEE073_1396.34_1396.58, WER: 300%, TER: 125%, total WER: 26.3167%, total TER: 12.868%, progress (thread 0): 95.712%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1906.84_1907.08, WER: 0%, TER: 0%, total WER: 26.3164%, total TER: 12.8679%, progress (thread 0): 95.7199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1913.84_1914.08, WER: 0%, TER: 0%, total WER: 26.3161%, total TER: 12.8678%, progress (thread 0): 95.7279%]
|T|: ' c a u s e
|P|: a s
[sample: EN2002c_H03_MEE073_2264.19_2264.43, WER: 100%, TER: 66.6667%, total WER: 26.3169%, total TER: 12.8686%, progress (thread 0): 95.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2309.3_2309.54, WER: 0%, TER: 0%, total WER: 26.3166%, total TER: 12.8684%, progress (thread 0): 95.7437%]
|T|: r i g h t
|P|: i
[sample: EN2002c_H03_MEE073_2341.41_2341.65, WER: 100%, TER: 80%, total WER: 26.3174%, total TER: 12.8692%, progress (thread 0): 95.7516%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2370.76_2371, WER: 0%, TER: 0%, total WER: 26.3171%, total TER: 12.8691%, progress (thread 0): 95.7595%]
|T|: g o o d
|P|: g o o d
[sample: EN2002d_H01_FEO072_337.97_338.21, WER: 0%, TER: 0%, total WER: 26.3169%, total TER: 12.869%, progress (thread 0): 95.7674%]
|T|: y e a h
|P|: t o
[sample: EN2002d_H03_MEE073_390.81_391.05, WER: 100%, TER: 100%, total WER: 26.3177%, total TER: 12.8698%, progress (thread 0): 95.7753%]
|T|: r i g h t
|P|: r i g h
[sample: EN2002d_H01_FEO072_496.78_497.02, WER: 100%, TER: 20%, total WER: 26.3185%, total TER: 12.8698%, progress (thread 0): 95.7832%]
|T|: o k a y
|P|: u h
[sample: EN2002d_H00_FEO070_513.66_513.9, WER: 100%, TER: 100%, total WER: 26.3193%, total TER: 12.8707%, progress (thread 0): 95.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1190.34_1190.58, WER: 0%, TER: 0%, total WER: 26.319%, total TER: 12.8705%, progress (thread 0): 95.799%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1371.83_1372.07, WER: 0%, TER: 0%, total WER: 26.3187%, total TER: 12.8704%, progress (thread 0): 95.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1477.13_1477.37, WER: 0%, TER: 0%, total WER: 26.3184%, total TER: 12.8703%, progress (thread 0): 95.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1706.39_1706.63, WER: 0%, TER: 0%, total WER: 26.3182%, total TER: 12.8702%, progress (thread 0): 95.8228%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H03_FEE016_1717.78_1718.01, WER: 100%, TER: 200%, total WER: 26.319%, total TER: 12.8711%, progress (thread 0): 95.8307%]
|T|: m m h m m
|P|: h
[sample: ES2004b_H00_MEO015_1860.91_1861.14, WER: 100%, TER: 80%, total WER: 26.3198%, total TER: 12.8718%, progress (thread 0): 95.8386%]
|T|: t r u e
|P|: j u
[sample: ES2004b_H00_MEO015_2294.79_2295.02, WER: 100%, TER: 75%, total WER: 26.3206%, total TER: 12.8724%, progress (thread 0): 95.8465%]
|T|: y e a h
|P|: a n d
[sample: ES2004c_H00_MEO015_85.13_85.36, WER: 100%, TER: 100%, total WER: 26.3215%, total TER: 12.8732%, progress (thread 0): 95.8544%]
|T|: ' k a y
|P|: k a
[sample: ES2004c_H00_MEO015_356.94_357.17, WER: 100%, TER: 50%, total WER: 26.3223%, total TER: 12.8736%, progress (thread 0): 95.8623%]
|T|: s o
|P|: a
[sample: ES2004c_H02_MEE014_748.27_748.5, WER: 100%, TER: 100%, total WER: 26.3231%, total TER: 12.874%, progress (thread 0): 95.8702%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_1121.33_1121.56, WER: 100%, TER: 100%, total WER: 26.3239%, total TER: 12.8744%, progress (thread 0): 95.8782%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1169.98_1170.21, WER: 0%, TER: 0%, total WER: 26.3236%, total TER: 12.8743%, progress (thread 0): 95.8861%]
|T|: o k a y
|P|: o
[sample: ES2004d_H03_FEE016_711.82_712.05, WER: 100%, TER: 75%, total WER: 26.3245%, total TER: 12.8748%, progress (thread 0): 95.894%]
|T|: t w o
|P|: d o
[sample: ES2004d_H00_MEO015_732.46_732.69, WER: 100%, TER: 66.6667%, total WER: 26.3253%, total TER: 12.8752%, progress (thread 0): 95.9019%]
|T|: m m h m m
|P|: h
[sample: ES2004d_H00_MEO015_822.98_823.21, WER: 100%, TER: 80%, total WER: 26.3261%, total TER: 12.876%, progress (thread 0): 95.9098%]
|T|: ' k a y
|P|: s c h o o
[sample: ES2004d_H00_MEO015_1813.16_1813.39, WER: 100%, TER: 125%, total WER: 26.3269%, total TER: 12.877%, progress (thread 0): 95.9177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_195.04_195.27, WER: 0%, TER: 0%, total WER: 26.3267%, total TER: 12.8769%, progress (thread 0): 95.9256%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_532.46_532.69, WER: 0%, TER: 0%, total WER: 26.3264%, total TER: 12.8768%, progress (thread 0): 95.9335%]
|T|: m m
|P|: h m
[sample: IS1009b_H02_FIO084_55.21_55.44, WER: 100%, TER: 50%, total WER: 26.3272%, total TER: 12.877%, progress (thread 0): 95.9415%]
|T|: y e a h
|P|: y e
[sample: IS1009b_H02_FIO084_1919.19_1919.42, WER: 100%, TER: 50%, total WER: 26.328%, total TER: 12.8774%, progress (thread 0): 95.9494%]
|T|: m m h m m
|P|: h m
[sample: IS1009c_H00_FIE088_554.54_554.77, WER: 100%, TER: 60%, total WER: 26.3288%, total TER: 12.8779%, progress (thread 0): 95.9573%]
|T|: m m | y e s
|P|: y e a h
[sample: IS1009c_H01_FIO087_728.73_728.96, WER: 100%, TER: 83.3333%, total WER: 26.3305%, total TER: 12.8789%, progress (thread 0): 95.9652%]
|T|: y e p
|P|: y e a h
[sample: IS1009c_H03_FIO089_1639.78_1640.01, WER: 100%, TER: 66.6667%, total WER: 26.3313%, total TER: 12.8793%, progress (thread 0): 95.9731%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_747.97_748.2, WER: 100%, TER: 66.6667%, total WER: 26.3321%, total TER: 12.8796%, progress (thread 0): 95.981%]
|T|: h m m
|P|:
[sample: IS1009d_H02_FIO084_748.42_748.65, WER: 100%, TER: 100%, total WER: 26.333%, total TER: 12.8803%, progress (thread 0): 95.9889%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_758.1_758.33, WER: 100%, TER: 100%, total WER: 26.3338%, total TER: 12.8813%, progress (thread 0): 95.9968%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_1695.75_1695.98, WER: 0%, TER: 0%, total WER: 26.3335%, total TER: 12.8812%, progress (thread 0): 96.0047%]
|T|: o k a y
|P|: y e
[sample: IS1009d_H03_FIO089_1889.3_1889.53, WER: 100%, TER: 100%, total WER: 26.3343%, total TER: 12.882%, progress (thread 0): 96.0127%]
|T|: ' k a y
|P|: o k a
[sample: TS3003a_H03_MTD012ME_843.57_843.8, WER: 100%, TER: 50%, total WER: 26.3352%, total TER: 12.8823%, progress (thread 0): 96.0206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1334.2_1334.43, WER: 0%, TER: 0%, total WER: 26.3349%, total TER: 12.8822%, progress (thread 0): 96.0285%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1346.08_1346.31, WER: 0%, TER: 0%, total WER: 26.3346%, total TER: 12.8821%, progress (thread 0): 96.0364%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1078.3_1078.53, WER: 100%, TER: 50%, total WER: 26.3354%, total TER: 12.8823%, progress (thread 0): 96.0443%]
|T|: n o
|P|: n o
[sample: TS3003b_H01_MTD011UID_1716.14_1716.37, WER: 0%, TER: 0%, total WER: 26.3351%, total TER: 12.8822%, progress (thread 0): 96.0522%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_1250.59_1250.82, WER: 0%, TER: 0%, total WER: 26.3348%, total TER: 12.8822%, progress (thread 0): 96.0601%]
|T|: y e s
|P|: j u s t
[sample: TS3003c_H02_MTD0010ID_1518.39_1518.62, WER: 100%, TER: 100%, total WER: 26.3356%, total TER: 12.8828%, progress (thread 0): 96.068%]
|T|: m m
|P|: a m
[sample: TS3003c_H01_MTD011UID_1654.26_1654.49, WER: 100%, TER: 50%, total WER: 26.3365%, total TER: 12.8829%, progress (thread 0): 96.076%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_2049.46_2049.69, WER: 100%, TER: 75%, total WER: 26.3373%, total TER: 12.8835%, progress (thread 0): 96.0839%]
|T|: s o
|P|: s o
[sample: TS3003d_H01_MTD011UID_261.45_261.68, WER: 0%, TER: 0%, total WER: 26.337%, total TER: 12.8835%, progress (thread 0): 96.0918%]
|T|: y e p
|P|: y e p
[sample: TS3003d_H02_MTD0010ID_1021.27_1021.5, WER: 0%, TER: 0%, total WER: 26.3367%, total TER: 12.8834%, progress (thread 0): 96.0997%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_1945.96_1946.19, WER: 100%, TER: 100%, total WER: 26.3375%, total TER: 12.8842%, progress (thread 0): 96.1076%]
|T|: y e a h
|P|: e a h
[sample: TS3003d_H01_MTD011UID_2064.77_2065, WER: 100%, TER: 25%, total WER: 26.3383%, total TER: 12.8843%, progress (thread 0): 96.1155%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_424.58_424.81, WER: 0%, TER: 0%, total WER: 26.338%, total TER: 12.8841%, progress (thread 0): 96.1234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_781.91_782.14, WER: 0%, TER: 0%, total WER: 26.3377%, total TER: 12.884%, progress (thread 0): 96.1313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_855.85_856.08, WER: 0%, TER: 0%, total WER: 26.3375%, total TER: 12.8839%, progress (thread 0): 96.1392%]
|T|: h m m
|P|:
[sample: EN2002a_H02_FEO072_877.24_877.47, WER: 100%, TER: 100%, total WER: 26.3383%, total TER: 12.8845%, progress (thread 0): 96.1471%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1019.4_1019.63, WER: 100%, TER: 33.3333%, total WER: 26.3391%, total TER: 12.8847%, progress (thread 0): 96.1551%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_1188.61_1188.84, WER: 100%, TER: 66.6667%, total WER: 26.3399%, total TER: 12.885%, progress (thread 0): 96.163%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1295.18_1295.41, WER: 0%, TER: 0%, total WER: 26.3396%, total TER: 12.8849%, progress (thread 0): 96.1709%]
|T|: y e a h
|P|: t o
[sample: EN2002a_H01_FEO070_1408.29_1408.52, WER: 100%, TER: 100%, total WER: 26.3405%, total TER: 12.8857%, progress (thread 0): 96.1788%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1571.85_1572.08, WER: 0%, TER: 0%, total WER: 26.3402%, total TER: 12.8857%, progress (thread 0): 96.1867%]
|T|: o h
|P|: o
[sample: EN2002b_H03_MEE073_29.06_29.29, WER: 100%, TER: 50%, total WER: 26.341%, total TER: 12.8858%, progress (thread 0): 96.1946%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_194.27_194.5, WER: 0%, TER: 0%, total WER: 26.3407%, total TER: 12.8857%, progress (thread 0): 96.2025%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_211.63_211.86, WER: 0%, TER: 0%, total WER: 26.3404%, total TER: 12.8856%, progress (thread 0): 96.2104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_493.43_493.66, WER: 0%, TER: 0%, total WER: 26.3401%, total TER: 12.8855%, progress (thread 0): 96.2184%]
|T|: h m m
|P|: h m
[sample: EN2002b_H00_FEO070_534.29_534.52, WER: 100%, TER: 33.3333%, total WER: 26.3409%, total TER: 12.8856%, progress (thread 0): 96.2263%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_672.54_672.77, WER: 100%, TER: 33.3333%, total WER: 26.3418%, total TER: 12.8858%, progress (thread 0): 96.2342%]
|T|: m m h m m
|P|: y e
[sample: EN2002b_H03_MEE073_1361.79_1362.02, WER: 100%, TER: 100%, total WER: 26.3426%, total TER: 12.8868%, progress (thread 0): 96.2421%]
|T|: y e a h
|P|: e a h
[sample: EN2002c_H03_MEE073_555.02_555.25, WER: 100%, TER: 25%, total WER: 26.3434%, total TER: 12.8869%, progress (thread 0): 96.25%]
|T|: h m m
|P|: b u h
[sample: EN2002c_H01_FEO072_699.88_700.11, WER: 100%, TER: 100%, total WER: 26.3442%, total TER: 12.8875%, progress (thread 0): 96.2579%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1900.45_1900.68, WER: 0%, TER: 0%, total WER: 26.3439%, total TER: 12.8874%, progress (thread 0): 96.2658%]
|T|: d o h
|P|: d o
[sample: EN2002c_H02_MEE071_2395.6_2395.83, WER: 100%, TER: 33.3333%, total WER: 26.3448%, total TER: 12.8875%, progress (thread 0): 96.2737%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2817.5_2817.73, WER: 0%, TER: 0%, total WER: 26.3445%, total TER: 12.8874%, progress (thread 0): 96.2816%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2846.79_2847.02, WER: 0%, TER: 0%, total WER: 26.3442%, total TER: 12.8873%, progress (thread 0): 96.2896%]
|T|: o k a y
|P|: k a
[sample: EN2002d_H03_MEE073_107.69_107.92, WER: 100%, TER: 50%, total WER: 26.345%, total TER: 12.8876%, progress (thread 0): 96.2975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_503.45_503.68, WER: 0%, TER: 0%, total WER: 26.3447%, total TER: 12.8875%, progress (thread 0): 96.3054%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_548.74_548.97, WER: 100%, TER: 40%, total WER: 26.3455%, total TER: 12.8878%, progress (thread 0): 96.3133%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_767.8_768.03, WER: 0%, TER: 0%, total WER: 26.3452%, total TER: 12.8877%, progress (thread 0): 96.3212%]
|T|: n o
|P|: n o
[sample: EN2002d_H00_FEO070_1427.88_1428.11, WER: 0%, TER: 0%, total WER: 26.3449%, total TER: 12.8876%, progress (thread 0): 96.3291%]
|T|: h m m
|P|: h m
[sample: EN2002d_H01_FEO072_1760.63_1760.86, WER: 100%, TER: 33.3333%, total WER: 26.3458%, total TER: 12.8878%, progress (thread 0): 96.337%]
|T|: o k a y
|P|:
[sample: EN2002d_H00_FEO070_2207.62_2207.85, WER: 100%, TER: 100%, total WER: 26.3466%, total TER: 12.8886%, progress (thread 0): 96.3449%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_1022.74_1022.96, WER: 0%, TER: 0%, total WER: 26.3463%, total TER: 12.8885%, progress (thread 0): 96.3528%]
|T|: m m
|P|: h m
[sample: ES2004b_H02_MEE014_1537.12_1537.34, WER: 100%, TER: 50%, total WER: 26.3471%, total TER: 12.8886%, progress (thread 0): 96.3608%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004c_H00_MEO015_575.7_575.92, WER: 0%, TER: 0%, total WER: 26.3468%, total TER: 12.8885%, progress (thread 0): 96.3687%]
|T|: m m h m m
|P|: y m | h
[sample: ES2004c_H03_FEE016_1311.86_1312.08, WER: 200%, TER: 80%, total WER: 26.3488%, total TER: 12.8893%, progress (thread 0): 96.3766%]
|T|: r i g h t
|P|: i
[sample: ES2004c_H00_MEO015_2116.51_2116.73, WER: 100%, TER: 80%, total WER: 26.3496%, total TER: 12.8901%, progress (thread 0): 96.3845%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_2159.26_2159.48, WER: 0%, TER: 0%, total WER: 26.3493%, total TER: 12.8899%, progress (thread 0): 96.3924%]
|T|: y e a h
|P|: e h
[sample: ES2004d_H02_MEE014_968.07_968.29, WER: 100%, TER: 50%, total WER: 26.3501%, total TER: 12.8903%, progress (thread 0): 96.4003%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1220.58_1220.8, WER: 0%, TER: 0%, total WER: 26.3498%, total TER: 12.8902%, progress (thread 0): 96.4082%]
|T|: o k a y
|P|:
[sample: ES2004d_H03_FEE016_1281.25_1281.47, WER: 100%, TER: 100%, total WER: 26.3507%, total TER: 12.891%, progress (thread 0): 96.4161%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1684.26_1684.48, WER: 0%, TER: 0%, total WER: 26.3504%, total TER: 12.8909%, progress (thread 0): 96.424%]
|T|: o k a y
|P|:
[sample: IS1009b_H03_FIO089_45.13_45.35, WER: 100%, TER: 100%, total WER: 26.3512%, total TER: 12.8917%, progress (thread 0): 96.432%]
|T|: ' k a y
|P|: c o
[sample: IS1009b_H02_FIO084_155.16_155.38, WER: 100%, TER: 100%, total WER: 26.352%, total TER: 12.8925%, progress (thread 0): 96.4399%]
|T|: m m h m m
|P|: h m
[sample: IS1009b_H02_FIO084_404.12_404.34, WER: 100%, TER: 60%, total WER: 26.3528%, total TER: 12.893%, progress (thread 0): 96.4478%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_961.79_962.01, WER: 0%, TER: 0%, total WER: 26.3525%, total TER: 12.8929%, progress (thread 0): 96.4557%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1007.66_1007.88, WER: 0%, TER: 0%, total WER: 26.3523%, total TER: 12.8928%, progress (thread 0): 96.4636%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H00_FIE088_337.53_337.75, WER: 0%, TER: 0%, total WER: 26.352%, total TER: 12.8927%, progress (thread 0): 96.4715%]
|T|: o k a y
|P|: o
[sample: IS1009c_H01_FIO087_1263.61_1263.83, WER: 100%, TER: 75%, total WER: 26.3528%, total TER: 12.8933%, progress (thread 0): 96.4794%]
|T|: m m
|P|: e
[sample: IS1009d_H03_FIO089_590.61_590.83, WER: 100%, TER: 100%, total WER: 26.3536%, total TER: 12.8937%, progress (thread 0): 96.4873%]
|T|: m m
|P|:
[sample: IS1009d_H02_FIO084_801.03_801.25, WER: 100%, TER: 100%, total WER: 26.3544%, total TER: 12.8941%, progress (thread 0): 96.4953%]
|T|: m m h m m
|P|: h
[sample: IS1009d_H02_FIO084_1687.52_1687.74, WER: 100%, TER: 80%, total WER: 26.3553%, total TER: 12.8948%, progress (thread 0): 96.5032%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1775.76_1775.98, WER: 0%, TER: 0%, total WER: 26.355%, total TER: 12.8947%, progress (thread 0): 96.5111%]
|T|: o k a y
|P|:
[sample: IS1009d_H03_FIO089_1846.51_1846.73, WER: 100%, TER: 100%, total WER: 26.3558%, total TER: 12.8955%, progress (thread 0): 96.519%]
|T|: m o r n i n g
|P|: m o n e y
[sample: TS3003a_H01_MTD011UID_15.34_15.56, WER: 100%, TER: 57.1429%, total WER: 26.3566%, total TER: 12.8963%, progress (thread 0): 96.5269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1316.76_1316.98, WER: 0%, TER: 0%, total WER: 26.3563%, total TER: 12.8961%, progress (thread 0): 96.5348%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1357.36_1357.58, WER: 100%, TER: 50%, total WER: 26.3571%, total TER: 12.8963%, progress (thread 0): 96.5427%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_888.56_888.78, WER: 0%, TER: 0%, total WER: 26.3568%, total TER: 12.8962%, progress (thread 0): 96.5506%]
|T|: h m m
|P|: h m
[sample: TS3003b_H03_MTD012ME_1371.93_1372.15, WER: 100%, TER: 33.3333%, total WER: 26.3577%, total TER: 12.8963%, progress (thread 0): 96.5585%]
|T|: y e a h
|P|: a n d
[sample: TS3003b_H01_MTD011UID_1600.07_1600.29, WER: 100%, TER: 100%, total WER: 26.3585%, total TER: 12.8971%, progress (thread 0): 96.5665%]
|T|: n o
|P|: n
[sample: TS3003c_H01_MTD011UID_109.75_109.97, WER: 100%, TER: 50%, total WER: 26.3593%, total TER: 12.8973%, progress (thread 0): 96.5744%]
|T|: m a y b
|P|: i
[sample: TS3003c_H00_MTD009PM_437.36_437.58, WER: 100%, TER: 100%, total WER: 26.3602%, total TER: 12.8981%, progress (thread 0): 96.5823%]
|T|: m m h m m
|P|:
[sample: TS3003c_H01_MTD011UID_1100.48_1100.7, WER: 100%, TER: 100%, total WER: 26.361%, total TER: 12.8991%, progress (thread 0): 96.5902%]
|T|: y e a h
|P|: h m
[sample: TS3003c_H01_MTD011UID_1304.78_1305, WER: 100%, TER: 100%, total WER: 26.3618%, total TER: 12.9%, progress (thread 0): 96.5981%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_2263.6_2263.82, WER: 0%, TER: 0%, total WER: 26.3615%, total TER: 12.8998%, progress (thread 0): 96.606%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_563.58_563.8, WER: 100%, TER: 75%, total WER: 26.3623%, total TER: 12.9004%, progress (thread 0): 96.6139%]
|T|: y e p
|P|: y u p
[sample: TS3003d_H02_MTD0010ID_577.11_577.33, WER: 100%, TER: 33.3333%, total WER: 26.3632%, total TER: 12.9006%, progress (thread 0): 96.6218%]
|T|: n o
|P|: u h
[sample: TS3003d_H01_MTD011UID_1458.81_1459.03, WER: 100%, TER: 100%, total WER: 26.364%, total TER: 12.901%, progress (thread 0): 96.6297%]
|T|: m m
|P|: h m
[sample: TS3003d_H01_MTD011UID_1674.77_1674.99, WER: 100%, TER: 50%, total WER: 26.3648%, total TER: 12.9011%, progress (thread 0): 96.6377%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1736.91_1737.13, WER: 0%, TER: 0%, total WER: 26.3645%, total TER: 12.901%, progress (thread 0): 96.6456%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_1751.36_1751.58, WER: 0%, TER: 0%, total WER: 26.3642%, total TER: 12.901%, progress (thread 0): 96.6535%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1991.39_1991.61, WER: 0%, TER: 0%, total WER: 26.3639%, total TER: 12.9008%, progress (thread 0): 96.6614%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H03_MTD012ME_1998.36_1998.58, WER: 100%, TER: 25%, total WER: 26.3647%, total TER: 12.9009%, progress (thread 0): 96.6693%]
|T|: y e a h
|P|: y o u | k n o
[sample: EN2002a_H00_MEE073_482.53_482.75, WER: 200%, TER: 150%, total WER: 26.3667%, total TER: 12.9022%, progress (thread 0): 96.6772%]
|T|: h m m
|P|: y m
[sample: EN2002a_H01_FEO070_706.29_706.51, WER: 100%, TER: 66.6667%, total WER: 26.3675%, total TER: 12.9026%, progress (thread 0): 96.6851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_805.91_806.13, WER: 0%, TER: 0%, total WER: 26.3672%, total TER: 12.9025%, progress (thread 0): 96.693%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1103.91_1104.13, WER: 0%, TER: 0%, total WER: 26.3669%, total TER: 12.9023%, progress (thread 0): 96.701%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_1216.04_1216.26, WER: 100%, TER: 66.6667%, total WER: 26.3678%, total TER: 12.9027%, progress (thread 0): 96.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1537.4_1537.62, WER: 0%, TER: 0%, total WER: 26.3675%, total TER: 12.9026%, progress (thread 0): 96.7168%]
|T|: j u s t
|P|: j u s t
[sample: EN2002a_H01_FEO070_1871.92_1872.14, WER: 0%, TER: 0%, total WER: 26.3672%, total TER: 12.9025%, progress (thread 0): 96.7247%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1963.02_1963.24, WER: 0%, TER: 0%, total WER: 26.3669%, total TER: 12.9023%, progress (thread 0): 96.7326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_639.41_639.63, WER: 0%, TER: 0%, total WER: 26.3666%, total TER: 12.9022%, progress (thread 0): 96.7405%]
|T|: r i g h t
|P|: w u t
[sample: EN2002b_H03_MEE073_1397.63_1397.85, WER: 100%, TER: 80%, total WER: 26.3674%, total TER: 12.903%, progress (thread 0): 96.7484%]
|T|: h m m
|P|: h m
[sample: EN2002b_H03_MEE073_1547.56_1547.78, WER: 100%, TER: 33.3333%, total WER: 26.3682%, total TER: 12.9032%, progress (thread 0): 96.7563%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_575.92_576.14, WER: 100%, TER: 28.5714%, total WER: 26.369%, total TER: 12.9034%, progress (thread 0): 96.7642%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H02_MEE071_589.02_589.24, WER: 100%, TER: 80%, total WER: 26.3699%, total TER: 12.9042%, progress (thread 0): 96.7722%]
|T|: ' k a y
|P|: y e h
[sample: EN2002c_H03_MEE073_684.52_684.74, WER: 100%, TER: 100%, total WER: 26.3707%, total TER: 12.905%, progress (thread 0): 96.7801%]
|T|: o h | y e a h
|P|: e
[sample: EN2002c_H03_MEE073_1506.68_1506.9, WER: 100%, TER: 85.7143%, total WER: 26.3723%, total TER: 12.9062%, progress (thread 0): 96.788%]
|T|: y e a h
|P|: t o
[sample: EN2002c_H03_MEE073_1602.82_1603.04, WER: 100%, TER: 100%, total WER: 26.3732%, total TER: 12.907%, progress (thread 0): 96.7959%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_1707.51_1707.73, WER: 100%, TER: 33.3333%, total WER: 26.374%, total TER: 12.9071%, progress (thread 0): 96.8038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2260.94_2261.16, WER: 0%, TER: 0%, total WER: 26.3737%, total TER: 12.907%, progress (thread 0): 96.8117%]
|T|: y e a h
|P|: e m
[sample: EN2002c_H03_MEE073_2697.66_2697.88, WER: 100%, TER: 75%, total WER: 26.3745%, total TER: 12.9076%, progress (thread 0): 96.8196%]
|T|: o k a y
|P|:
[sample: EN2002d_H03_MEE073_402.37_402.59, WER: 100%, TER: 100%, total WER: 26.3754%, total TER: 12.9084%, progress (thread 0): 96.8275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_736.63_736.85, WER: 0%, TER: 0%, total WER: 26.3751%, total TER: 12.9083%, progress (thread 0): 96.8354%]
|T|: o h
|P|: h o m
[sample: EN2002d_H00_FEO070_909.94_910.16, WER: 100%, TER: 100%, total WER: 26.3759%, total TER: 12.9087%, progress (thread 0): 96.8434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1061.24_1061.46, WER: 0%, TER: 0%, total WER: 26.3756%, total TER: 12.9086%, progress (thread 0): 96.8513%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1400.88_1401.1, WER: 0%, TER: 0%, total WER: 26.3753%, total TER: 12.9085%, progress (thread 0): 96.8592%]
|T|: b u t
|P|: b u t
[sample: EN2002d_H00_FEO070_1658_1658.22, WER: 0%, TER: 0%, total WER: 26.375%, total TER: 12.9084%, progress (thread 0): 96.8671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1732.66_1732.88, WER: 0%, TER: 0%, total WER: 26.3747%, total TER: 12.9082%, progress (thread 0): 96.875%]
|T|: m m
|P|: h m
[sample: ES2004a_H03_FEE016_622.02_622.23, WER: 100%, TER: 50%, total WER: 26.3755%, total TER: 12.9084%, progress (thread 0): 96.8829%]
|T|: y e a h
|P|: y u p
[sample: ES2004b_H00_MEO015_1323.05_1323.26, WER: 100%, TER: 75%, total WER: 26.3763%, total TER: 12.909%, progress (thread 0): 96.8908%]
|T|: m m h m m
|P|: u | h m
[sample: ES2004b_H00_MEO015_1464.74_1464.95, WER: 200%, TER: 60%, total WER: 26.3783%, total TER: 12.9095%, progress (thread 0): 96.8987%]
|T|: m m h m m
|P|: u h u
[sample: ES2004c_H00_MEO015_96.42_96.63, WER: 100%, TER: 80%, total WER: 26.3791%, total TER: 12.9103%, progress (thread 0): 96.9066%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1385.07_1385.28, WER: 0%, TER: 0%, total WER: 26.3788%, total TER: 12.9102%, progress (thread 0): 96.9146%]
|T|: h m m
|P|: h m
[sample: ES2004d_H00_MEO015_26.63_26.84, WER: 100%, TER: 33.3333%, total WER: 26.3796%, total TER: 12.9103%, progress (thread 0): 96.9225%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_627.79_628, WER: 100%, TER: 66.6667%, total WER: 26.3805%, total TER: 12.9107%, progress (thread 0): 96.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_39.06_39.27, WER: 0%, TER: 0%, total WER: 26.3802%, total TER: 12.9106%, progress (thread 0): 96.9383%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_169.39_169.6, WER: 100%, TER: 100%, total WER: 26.381%, total TER: 12.9116%, progress (thread 0): 96.9462%]
|T|: m m h m m
|P|:
[sample: IS1009b_H00_FIE088_1286.43_1286.64, WER: 100%, TER: 100%, total WER: 26.3818%, total TER: 12.9126%, progress (thread 0): 96.9541%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H00_FIE088_1289.44_1289.65, WER: 0%, TER: 0%, total WER: 26.3815%, total TER: 12.9125%, progress (thread 0): 96.962%]
|T|: m m
|P|: a n
[sample: IS1009b_H02_FIO084_1632.09_1632.3, WER: 100%, TER: 100%, total WER: 26.3824%, total TER: 12.9129%, progress (thread 0): 96.9699%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_284.08_284.29, WER: 0%, TER: 0%, total WER: 26.3821%, total TER: 12.9128%, progress (thread 0): 96.9778%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1563.53_1563.74, WER: 0%, TER: 0%, total WER: 26.3818%, total TER: 12.9126%, progress (thread 0): 96.9858%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1707.23_1707.44, WER: 0%, TER: 0%, total WER: 26.3815%, total TER: 12.9125%, progress (thread 0): 96.9937%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_741.38_741.59, WER: 0%, TER: 0%, total WER: 26.3812%, total TER: 12.9124%, progress (thread 0): 97.0016%]
|T|: y e a h
|P|:
[sample: IS1009d_H02_FIO084_1371.67_1371.88, WER: 100%, TER: 100%, total WER: 26.382%, total TER: 12.9132%, progress (thread 0): 97.0095%]
|T|: m m h m m
|P|:
[sample: IS1009d_H03_FIO089_1880.74_1880.95, WER: 100%, TER: 100%, total WER: 26.3828%, total TER: 12.9142%, progress (thread 0): 97.0174%]
|T|: y e a h
|P|: y e s
[sample: TS3003a_H01_MTD011UID_1295.8_1296.01, WER: 100%, TER: 50%, total WER: 26.3836%, total TER: 12.9146%, progress (thread 0): 97.0253%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_901.66_901.87, WER: 0%, TER: 0%, total WER: 26.3834%, total TER: 12.9145%, progress (thread 0): 97.0332%]
|T|: y e a h
|P|: h u h
[sample: TS3003b_H01_MTD011UID_1547.15_1547.36, WER: 100%, TER: 75%, total WER: 26.3842%, total TER: 12.915%, progress (thread 0): 97.0411%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1091.82_1092.03, WER: 0%, TER: 0%, total WER: 26.3839%, total TER: 12.9149%, progress (thread 0): 97.049%]
|T|: y e a h
|P|: h u h
[sample: TS3003c_H01_MTD011UID_1210.7_1210.91, WER: 100%, TER: 75%, total WER: 26.3847%, total TER: 12.9155%, progress (thread 0): 97.057%]
|T|: y e a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_530.79_531, WER: 100%, TER: 75%, total WER: 26.3855%, total TER: 12.9161%, progress (thread 0): 97.0649%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1465.64_1465.85, WER: 0%, TER: 0%, total WER: 26.3852%, total TER: 12.916%, progress (thread 0): 97.0728%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1695.15_1695.36, WER: 0%, TER: 0%, total WER: 26.3849%, total TER: 12.9158%, progress (thread 0): 97.0807%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1711.66_1711.87, WER: 0%, TER: 0%, total WER: 26.3846%, total TER: 12.9157%, progress (thread 0): 97.0886%]
|T|: y e a h
|P|: y e p
[sample: TS3003d_H00_MTD009PM_1789.64_1789.85, WER: 100%, TER: 50%, total WER: 26.3855%, total TER: 12.9161%, progress (thread 0): 97.0965%]
|T|: n o
|P|: n o
[sample: TS3003d_H03_MTD012ME_2014.88_2015.09, WER: 0%, TER: 0%, total WER: 26.3852%, total TER: 12.916%, progress (thread 0): 97.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2064.37_2064.58, WER: 0%, TER: 0%, total WER: 26.3849%, total TER: 12.9159%, progress (thread 0): 97.1123%]
|T|: y e a h
|P|: e h
[sample: TS3003d_H01_MTD011UID_2107.64_2107.85, WER: 100%, TER: 50%, total WER: 26.3857%, total TER: 12.9162%, progress (thread 0): 97.1203%]
|T|: y e a h
|P|: h m
[sample: TS3003d_H01_MTD011UID_2462.55_2462.76, WER: 100%, TER: 100%, total WER: 26.3865%, total TER: 12.917%, progress (thread 0): 97.1282%]
|T|: s o r r y
|P|: s o r r
[sample: EN2002a_H02_FEO072_377.93_378.14, WER: 100%, TER: 20%, total WER: 26.3873%, total TER: 12.9171%, progress (thread 0): 97.1361%]
|T|: o h
|P|:
[sample: EN2002a_H03_MEE071_483.42_483.63, WER: 100%, TER: 100%, total WER: 26.3882%, total TER: 12.9175%, progress (thread 0): 97.144%]
|T|: y e a h
|P|: y a h
[sample: EN2002a_H03_MEE071_648.41_648.62, WER: 100%, TER: 25%, total WER: 26.389%, total TER: 12.9176%, progress (thread 0): 97.1519%]
|T|: y e a h
|P|: c o m
[sample: EN2002a_H01_FEO070_1319.66_1319.87, WER: 100%, TER: 100%, total WER: 26.3898%, total TER: 12.9184%, progress (thread 0): 97.1598%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1429.36_1429.57, WER: 0%, TER: 0%, total WER: 26.3895%, total TER: 12.9183%, progress (thread 0): 97.1677%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1896.08_1896.29, WER: 0%, TER: 0%, total WER: 26.3892%, total TER: 12.9182%, progress (thread 0): 97.1756%]
|T|: y e a h
|P|: y e
[sample: EN2002a_H03_MEE071_2122.73_2122.94, WER: 100%, TER: 50%, total WER: 26.3901%, total TER: 12.9185%, progress (thread 0): 97.1835%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_266.76_266.97, WER: 0%, TER: 0%, total WER: 26.3898%, total TER: 12.9184%, progress (thread 0): 97.1915%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_291.79_292, WER: 0%, TER: 0%, total WER: 26.3895%, total TER: 12.9183%, progress (thread 0): 97.1994%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1335.23_1335.44, WER: 0%, TER: 0%, total WER: 26.3892%, total TER: 12.9182%, progress (thread 0): 97.2073%]
|T|: ' k a y
|P|: k u
[sample: EN2002c_H03_MEE073_21.49_21.7, WER: 100%, TER: 75%, total WER: 26.39%, total TER: 12.9188%, progress (thread 0): 97.2152%]
|T|: o k a y
|P|:
[sample: EN2002c_H03_MEE073_183.87_184.08, WER: 100%, TER: 100%, total WER: 26.3908%, total TER: 12.9196%, progress (thread 0): 97.2231%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1748.27_1748.48, WER: 0%, TER: 0%, total WER: 26.3905%, total TER: 12.9194%, progress (thread 0): 97.231%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002c_H03_MEE073_1810.36_1810.57, WER: 100%, TER: 85.7143%, total WER: 26.3922%, total TER: 12.9206%, progress (thread 0): 97.2389%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1861.15_1861.36, WER: 0%, TER: 0%, total WER: 26.3919%, total TER: 12.9205%, progress (thread 0): 97.2468%]
|T|: y e a h
|P|: y o u | k n
[sample: EN2002c_H03_MEE073_2180.5_2180.71, WER: 200%, TER: 125%, total WER: 26.3938%, total TER: 12.9215%, progress (thread 0): 97.2547%]
|T|: t r u e
|P|: t r u e
[sample: EN2002c_H03_MEE073_2453.86_2454.07, WER: 0%, TER: 0%, total WER: 26.3935%, total TER: 12.9214%, progress (thread 0): 97.2627%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2819.2_2819.41, WER: 0%, TER: 0%, total WER: 26.3932%, total TER: 12.9213%, progress (thread 0): 97.2706%]
|T|: m m h m m
|P|: u h m
[sample: EN2002d_H01_FEO072_164.51_164.72, WER: 100%, TER: 60%, total WER: 26.3941%, total TER: 12.9218%, progress (thread 0): 97.2785%]
|T|: r i g h t
|P|: t o
[sample: EN2002d_H03_MEE073_338.56_338.77, WER: 100%, TER: 100%, total WER: 26.3949%, total TER: 12.9229%, progress (thread 0): 97.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_647.49_647.7, WER: 0%, TER: 0%, total WER: 26.3946%, total TER: 12.9227%, progress (thread 0): 97.2943%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_782.98_783.19, WER: 100%, TER: 100%, total WER: 26.3954%, total TER: 12.9231%, progress (thread 0): 97.3022%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_998.01_998.22, WER: 0%, TER: 0%, total WER: 26.3951%, total TER: 12.923%, progress (thread 0): 97.3101%]
|T|: o h | y e a h
|P|: c
[sample: EN2002d_H00_FEO070_1377.89_1378.1, WER: 100%, TER: 100%, total WER: 26.3968%, total TER: 12.9244%, progress (thread 0): 97.318%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1391.57_1391.78, WER: 0%, TER: 0%, total WER: 26.3965%, total TER: 12.9243%, progress (thread 0): 97.326%]
|T|: o h | y e a h
|P|: y e
[sample: EN2002d_H03_MEE073_1912.21_1912.42, WER: 100%, TER: 71.4286%, total WER: 26.3981%, total TER: 12.9252%, progress (thread 0): 97.3339%]
|T|: h m m
|P|: h m
[sample: EN2002d_H03_MEE073_2147.17_2147.38, WER: 100%, TER: 33.3333%, total WER: 26.3989%, total TER: 12.9254%, progress (thread 0): 97.3418%]
|T|: o k a y
|P|: j
[sample: ES2004b_H00_MEO015_338.55_338.75, WER: 100%, TER: 100%, total WER: 26.3998%, total TER: 12.9262%, progress (thread 0): 97.3497%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1514.44_1514.64, WER: 0%, TER: 0%, total WER: 26.3995%, total TER: 12.9261%, progress (thread 0): 97.3576%]
|T|: o o p s
|P|:
[sample: ES2004c_H00_MEO015_54.23_54.43, WER: 100%, TER: 100%, total WER: 26.4003%, total TER: 12.9269%, progress (thread 0): 97.3655%]
|T|: m m h m m
|P|: h
[sample: ES2004c_H00_MEO015_1395.98_1396.18, WER: 100%, TER: 80%, total WER: 26.4011%, total TER: 12.9277%, progress (thread 0): 97.3734%]
|T|: ' k a y
|P|: t
[sample: ES2004d_H00_MEO015_562.82_563.02, WER: 100%, TER: 100%, total WER: 26.4019%, total TER: 12.9285%, progress (thread 0): 97.3813%]
|T|: y e p
|P|: y e a h
[sample: ES2004d_H02_MEE014_922.35_922.55, WER: 100%, TER: 66.6667%, total WER: 26.4028%, total TER: 12.9289%, progress (thread 0): 97.3892%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1214.04_1214.24, WER: 0%, TER: 0%, total WER: 26.4025%, total TER: 12.9287%, progress (thread 0): 97.3972%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1952.42_1952.62, WER: 0%, TER: 0%, total WER: 26.4022%, total TER: 12.9286%, progress (thread 0): 97.4051%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_784.26_784.46, WER: 0%, TER: 0%, total WER: 26.4019%, total TER: 12.9285%, progress (thread 0): 97.413%]
|T|: ' k a y
|P|: t o
[sample: IS1009a_H03_FIO089_794.48_794.68, WER: 100%, TER: 100%, total WER: 26.4027%, total TER: 12.9293%, progress (thread 0): 97.4209%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H02_FIO084_179.12_179.32, WER: 100%, TER: 200%, total WER: 26.4035%, total TER: 12.9302%, progress (thread 0): 97.4288%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1111.83_1112.03, WER: 0%, TER: 0%, total WER: 26.4032%, total TER: 12.9301%, progress (thread 0): 97.4367%]
|T|: m m h m m
|P|:
[sample: IS1009b_H02_FIO084_2011.99_2012.19, WER: 100%, TER: 100%, total WER: 26.404%, total TER: 12.9311%, progress (thread 0): 97.4446%]
|T|: y e a h
|P|: y e a
[sample: IS1009c_H03_FIO089_285.99_286.19, WER: 100%, TER: 25%, total WER: 26.4049%, total TER: 12.9312%, progress (thread 0): 97.4525%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1689.26_1689.46, WER: 0%, TER: 0%, total WER: 26.4046%, total TER: 12.9311%, progress (thread 0): 97.4604%]
|T|: m m
|P|:
[sample: IS1009d_H01_FIO087_964.59_964.79, WER: 100%, TER: 100%, total WER: 26.4054%, total TER: 12.9315%, progress (thread 0): 97.4684%]
|T|: y e a h
|P|: a n d
[sample: TS3003a_H01_MTD011UID_1286.22_1286.42, WER: 100%, TER: 100%, total WER: 26.4062%, total TER: 12.9323%, progress (thread 0): 97.4763%]
|T|: m m
|P|: y
[sample: TS3003a_H01_MTD011UID_1431.86_1432.06, WER: 100%, TER: 100%, total WER: 26.407%, total TER: 12.9327%, progress (thread 0): 97.4842%]
|T|: ' k a y
|P|: i n g
[sample: TS3003b_H03_MTD012ME_550.33_550.53, WER: 100%, TER: 100%, total WER: 26.4079%, total TER: 12.9335%, progress (thread 0): 97.4921%]
|T|: m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_576.96_577.16, WER: 100%, TER: 50%, total WER: 26.4087%, total TER: 12.9337%, progress (thread 0): 97.5%]
|T|: y e a h
|P|: u h
[sample: TS3003b_H01_MTD011UID_1340.71_1340.91, WER: 100%, TER: 75%, total WER: 26.4095%, total TER: 12.9342%, progress (thread 0): 97.5079%]
|T|: m m
|P|: y e
[sample: TS3003b_H01_MTD011UID_1635.98_1636.18, WER: 100%, TER: 100%, total WER: 26.4103%, total TER: 12.9346%, progress (thread 0): 97.5158%]
|T|: m m
|P|: h m
[sample: TS3003c_H03_MTD012ME_1936.99_1937.19, WER: 100%, TER: 50%, total WER: 26.4112%, total TER: 12.9348%, progress (thread 0): 97.5237%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_214.36_214.56, WER: 0%, TER: 0%, total WER: 26.4109%, total TER: 12.9347%, progress (thread 0): 97.5316%]
|T|: y e p
|P|: y e
[sample: TS3003d_H00_MTD009PM_418.75_418.95, WER: 100%, TER: 33.3333%, total WER: 26.4117%, total TER: 12.9348%, progress (thread 0): 97.5396%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1236.15_1236.35, WER: 0%, TER: 0%, total WER: 26.4114%, total TER: 12.9347%, progress (thread 0): 97.5475%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1334.1_1334.3, WER: 0%, TER: 0%, total WER: 26.4111%, total TER: 12.9346%, progress (thread 0): 97.5554%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1736.65_1736.85, WER: 0%, TER: 0%, total WER: 26.4108%, total TER: 12.9345%, progress (thread 0): 97.5633%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1862.16_1862.36, WER: 0%, TER: 0%, total WER: 26.4105%, total TER: 12.9344%, progress (thread 0): 97.5712%]
|T|: h m m
|P|: w h e n
[sample: TS3003d_H01_MTD011UID_2423.71_2423.91, WER: 100%, TER: 100%, total WER: 26.4113%, total TER: 12.935%, progress (thread 0): 97.5791%]
|T|: y e a h
|P|: h
[sample: EN2002a_H00_MEE073_6.65_6.85, WER: 100%, TER: 75%, total WER: 26.4122%, total TER: 12.9355%, progress (thread 0): 97.587%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_207.6_207.8, WER: 0%, TER: 0%, total WER: 26.4119%, total TER: 12.9354%, progress (thread 0): 97.5949%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_325.97_326.17, WER: 100%, TER: 33.3333%, total WER: 26.4127%, total TER: 12.9356%, progress (thread 0): 97.6029%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_517.59_517.79, WER: 0%, TER: 0%, total WER: 26.4124%, total TER: 12.9354%, progress (thread 0): 97.6108%]
|T|: m m h m m
|P|: e h
[sample: EN2002a_H00_MEE073_625.1_625.3, WER: 100%, TER: 80%, total WER: 26.4132%, total TER: 12.9362%, progress (thread 0): 97.6187%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1082.64_1082.84, WER: 0%, TER: 0%, total WER: 26.4129%, total TER: 12.9361%, progress (thread 0): 97.6266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1296.98_1297.18, WER: 0%, TER: 0%, total WER: 26.4126%, total TER: 12.936%, progress (thread 0): 97.6345%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1544.23_1544.43, WER: 100%, TER: 33.3333%, total WER: 26.4134%, total TER: 12.9361%, progress (thread 0): 97.6424%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_1988.65_1988.85, WER: 0%, TER: 0%, total WER: 26.4132%, total TER: 12.936%, progress (thread 0): 97.6503%]
|T|: y e p
|P|: y e p
[sample: EN2002b_H00_FEO070_58.73_58.93, WER: 0%, TER: 0%, total WER: 26.4129%, total TER: 12.9359%, progress (thread 0): 97.6582%]
|T|: d o | w e | d
|P|: d w e
[sample: EN2002b_H01_MEE071_223.55_223.75, WER: 100%, TER: 57.1429%, total WER: 26.4153%, total TER: 12.9366%, progress (thread 0): 97.6661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_233.29_233.49, WER: 0%, TER: 0%, total WER: 26.415%, total TER: 12.9365%, progress (thread 0): 97.674%]
|T|: h m m
|P|: y e h
[sample: EN2002b_H03_MEE073_259.41_259.61, WER: 100%, TER: 100%, total WER: 26.4159%, total TER: 12.9371%, progress (thread 0): 97.682%]
|T|: y e a h
|P|: t o
[sample: EN2002b_H03_MEE073_498.07_498.27, WER: 100%, TER: 100%, total WER: 26.4167%, total TER: 12.9379%, progress (thread 0): 97.6899%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1591.2_1591.4, WER: 0%, TER: 0%, total WER: 26.4164%, total TER: 12.9378%, progress (thread 0): 97.6978%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H00_FEO070_1723.38_1723.58, WER: 0%, TER: 0%, total WER: 26.4161%, total TER: 12.9377%, progress (thread 0): 97.7057%]
|T|: o k a y
|P|: g o
[sample: EN2002c_H03_MEE073_241.42_241.62, WER: 100%, TER: 100%, total WER: 26.4169%, total TER: 12.9385%, progress (thread 0): 97.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_404.9_405.1, WER: 0%, TER: 0%, total WER: 26.4166%, total TER: 12.9384%, progress (thread 0): 97.7215%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_817.73_817.93, WER: 100%, TER: 33.3333%, total WER: 26.4174%, total TER: 12.9385%, progress (thread 0): 97.7294%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1319.11_1319.31, WER: 0%, TER: 0%, total WER: 26.4171%, total TER: 12.9384%, progress (thread 0): 97.7373%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1392.95_1393.15, WER: 0%, TER: 0%, total WER: 26.4168%, total TER: 12.9383%, progress (thread 0): 97.7453%]
|T|: r i g h t
|P|: f r i r t
[sample: EN2002c_H03_MEE073_1395.54_1395.74, WER: 100%, TER: 60%, total WER: 26.4177%, total TER: 12.9388%, progress (thread 0): 97.7532%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1637.64_1637.84, WER: 0%, TER: 0%, total WER: 26.4174%, total TER: 12.9387%, progress (thread 0): 97.7611%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1911.7_1911.9, WER: 0%, TER: 0%, total WER: 26.4171%, total TER: 12.9385%, progress (thread 0): 97.769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1939.05_1939.25, WER: 0%, TER: 0%, total WER: 26.4168%, total TER: 12.9384%, progress (thread 0): 97.7769%]
|T|: y e a h
|P|: t o
[sample: EN2002d_H03_MEE073_327.41_327.61, WER: 100%, TER: 100%, total WER: 26.4176%, total TER: 12.9392%, progress (thread 0): 97.7848%]
|T|: y e a h
|P|: s h n g
[sample: EN2002d_H03_MEE073_967.12_967.32, WER: 100%, TER: 100%, total WER: 26.4184%, total TER: 12.94%, progress (thread 0): 97.7927%]
|T|: y e p
|P|: y u p
[sample: ES2004a_H03_FEE016_1048.29_1048.48, WER: 100%, TER: 33.3333%, total WER: 26.4193%, total TER: 12.9401%, progress (thread 0): 97.8006%]
|T|: o o p s
|P|:
[sample: ES2004b_H00_MEO015_572.79_572.98, WER: 100%, TER: 100%, total WER: 26.4201%, total TER: 12.941%, progress (thread 0): 97.8085%]
|T|: a l r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1308.81_1309, WER: 100%, TER: 28.5714%, total WER: 26.4209%, total TER: 12.9412%, progress (thread 0): 97.8165%]
|T|: h u h
|P|: h
[sample: ES2004b_H02_MEE014_1454.52_1454.71, WER: 100%, TER: 66.6667%, total WER: 26.4217%, total TER: 12.9416%, progress (thread 0): 97.8244%]
|T|: m m
|P|: y o u | k
[sample: ES2004b_H03_FEE016_1635.41_1635.6, WER: 200%, TER: 250%, total WER: 26.4237%, total TER: 12.9427%, progress (thread 0): 97.8323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1436.81_1437, WER: 0%, TER: 0%, total WER: 26.4234%, total TER: 12.9426%, progress (thread 0): 97.8402%]
|T|: t h i n g
|P|: t h i n g
[sample: ES2004d_H02_MEE014_2209.98_2210.17, WER: 0%, TER: 0%, total WER: 26.4231%, total TER: 12.9424%, progress (thread 0): 97.8481%]
|T|: y e a h
|P|: t h e r
[sample: IS1009a_H02_FIO084_805.49_805.68, WER: 100%, TER: 100%, total WER: 26.4239%, total TER: 12.9432%, progress (thread 0): 97.856%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_690.57_690.76, WER: 0%, TER: 0%, total WER: 26.4236%, total TER: 12.9431%, progress (thread 0): 97.8639%]
|T|: m m h m m
|P|:
[sample: IS1009c_H00_FIE088_808.35_808.54, WER: 100%, TER: 100%, total WER: 26.4244%, total TER: 12.9441%, progress (thread 0): 97.8718%]
|T|: b a t t e r y
|P|: t h a t
[sample: IS1009c_H01_FIO087_1631.84_1632.03, WER: 100%, TER: 85.7143%, total WER: 26.4252%, total TER: 12.9453%, progress (thread 0): 97.8798%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1314.07_1314.26, WER: 0%, TER: 0%, total WER: 26.425%, total TER: 12.9452%, progress (thread 0): 97.8877%]
|T|: ' k a y
|P|: k a n
[sample: TS3003a_H03_MTD012ME_506.74_506.93, WER: 100%, TER: 50%, total WER: 26.4258%, total TER: 12.9455%, progress (thread 0): 97.8956%]
|T|: m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_1214.55_1214.74, WER: 100%, TER: 50%, total WER: 26.4266%, total TER: 12.9457%, progress (thread 0): 97.9035%]
|T|: ' k a y
|P|: g a y
[sample: TS3003a_H01_MTD011UID_1401.33_1401.52, WER: 100%, TER: 50%, total WER: 26.4274%, total TER: 12.946%, progress (thread 0): 97.9114%]
|T|: y e a h
|P|: n o
[sample: TS3003b_H01_MTD011UID_2147.67_2147.86, WER: 100%, TER: 100%, total WER: 26.4282%, total TER: 12.9468%, progress (thread 0): 97.9193%]
|T|: y e a h
|P|: i
[sample: TS3003c_H01_MTD011UID_1090.92_1091.11, WER: 100%, TER: 100%, total WER: 26.4291%, total TER: 12.9476%, progress (thread 0): 97.9272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1632.95_1633.14, WER: 0%, TER: 0%, total WER: 26.4288%, total TER: 12.9475%, progress (thread 0): 97.9351%]
|T|: y e a h
|P|: a n
[sample: TS3003d_H01_MTD011UID_375.66_375.85, WER: 100%, TER: 75%, total WER: 26.4296%, total TER: 12.9481%, progress (thread 0): 97.943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_507.6_507.79, WER: 0%, TER: 0%, total WER: 26.4293%, total TER: 12.948%, progress (thread 0): 97.951%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_816.71_816.9, WER: 0%, TER: 0%, total WER: 26.429%, total TER: 12.9479%, progress (thread 0): 97.9589%]
|T|: m m
|P|:
[sample: TS3003d_H01_MTD011UID_966.74_966.93, WER: 100%, TER: 100%, total WER: 26.4298%, total TER: 12.9483%, progress (thread 0): 97.9668%]
|T|: m m
|P|: h m
[sample: TS3003d_H00_MTD009PM_1884.46_1884.65, WER: 100%, TER: 50%, total WER: 26.4306%, total TER: 12.9484%, progress (thread 0): 97.9747%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1975.04_1975.23, WER: 100%, TER: 66.6667%, total WER: 26.4315%, total TER: 12.9488%, progress (thread 0): 97.9826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1998.6_1998.79, WER: 0%, TER: 0%, total WER: 26.4312%, total TER: 12.9487%, progress (thread 0): 97.9905%]
|T|: y e p
|P|: y u p
[sample: EN2002a_H00_MEE073_399.03_399.22, WER: 100%, TER: 33.3333%, total WER: 26.432%, total TER: 12.9488%, progress (thread 0): 97.9984%]
|T|: y e a h
|P|: r i g h t
[sample: EN2002a_H03_MEE071_741.39_741.58, WER: 100%, TER: 100%, total WER: 26.4328%, total TER: 12.9496%, progress (thread 0): 98.0063%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1037.67_1037.86, WER: 0%, TER: 0%, total WER: 26.4325%, total TER: 12.9495%, progress (thread 0): 98.0142%]
|T|: w h
|P|:
[sample: EN2002a_H03_MEE071_1305.67_1305.86, WER: 100%, TER: 100%, total WER: 26.4333%, total TER: 12.9499%, progress (thread 0): 98.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1781.11_1781.3, WER: 0%, TER: 0%, total WER: 26.4331%, total TER: 12.9498%, progress (thread 0): 98.0301%]
|T|: y e a h
|P|: y e p
[sample: EN2002a_H02_FEO072_1823.51_1823.7, WER: 100%, TER: 50%, total WER: 26.4339%, total TER: 12.9501%, progress (thread 0): 98.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1836.03_1836.22, WER: 0%, TER: 0%, total WER: 26.4336%, total TER: 12.95%, progress (thread 0): 98.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1886.9_1887.09, WER: 0%, TER: 0%, total WER: 26.4333%, total TER: 12.9499%, progress (thread 0): 98.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1917.91_1918.1, WER: 0%, TER: 0%, total WER: 26.433%, total TER: 12.9498%, progress (thread 0): 98.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_142.44_142.63, WER: 0%, TER: 0%, total WER: 26.4327%, total TER: 12.9497%, progress (thread 0): 98.0696%]
|T|: u h h u h
|P|: w
[sample: EN2002c_H03_MEE073_1527.18_1527.37, WER: 100%, TER: 100%, total WER: 26.4335%, total TER: 12.9507%, progress (thread 0): 98.0775%]
|T|: h m m
|P|: y e a
[sample: EN2002c_H03_MEE073_1630_1630.19, WER: 100%, TER: 100%, total WER: 26.4343%, total TER: 12.9513%, progress (thread 0): 98.0854%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2672.39_2672.58, WER: 100%, TER: 33.3333%, total WER: 26.4352%, total TER: 12.9514%, progress (thread 0): 98.0934%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H03_MEE073_2679.33_2679.52, WER: 100%, TER: 80%, total WER: 26.436%, total TER: 12.9522%, progress (thread 0): 98.1013%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_647.1_647.29, WER: 0%, TER: 0%, total WER: 26.4357%, total TER: 12.9521%, progress (thread 0): 98.1092%]
|T|: y e a h
|P|: t h
[sample: EN2002d_H03_MEE073_649.08_649.27, WER: 100%, TER: 75%, total WER: 26.4365%, total TER: 12.9526%, progress (thread 0): 98.1171%]
|T|: o k
|P|: i ' m
[sample: EN2002d_H03_MEE073_909.71_909.9, WER: 100%, TER: 150%, total WER: 26.4373%, total TER: 12.9533%, progress (thread 0): 98.125%]
|T|: s u r e
|P|: t r e
[sample: EN2002d_H03_MEE073_1568.39_1568.58, WER: 100%, TER: 50%, total WER: 26.4382%, total TER: 12.9536%, progress (thread 0): 98.1329%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_1708.21_1708.4, WER: 100%, TER: 66.6667%, total WER: 26.439%, total TER: 12.954%, progress (thread 0): 98.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2037.78_2037.97, WER: 0%, TER: 0%, total WER: 26.4387%, total TER: 12.9539%, progress (thread 0): 98.1487%]
|T|: s o
|P|: s h
[sample: ES2004a_H03_FEE016_605.84_606.02, WER: 100%, TER: 50%, total WER: 26.4395%, total TER: 12.954%, progress (thread 0): 98.1566%]
|T|: ' k a y
|P|:
[sample: ES2004b_H00_MEO015_1027.78_1027.96, WER: 100%, TER: 100%, total WER: 26.4403%, total TER: 12.9549%, progress (thread 0): 98.1646%]
|T|: m m h m m
|P|:
[sample: ES2004c_H03_FEE016_1360.96_1361.14, WER: 100%, TER: 100%, total WER: 26.4412%, total TER: 12.9559%, progress (thread 0): 98.1725%]
|T|: m m
|P|: h m
[sample: ES2004d_H02_MEE014_2221.15_2221.33, WER: 100%, TER: 50%, total WER: 26.442%, total TER: 12.956%, progress (thread 0): 98.1804%]
|T|: m m h m m
|P|: a
[sample: IS1009b_H02_FIO084_361.5_361.68, WER: 100%, TER: 100%, total WER: 26.4428%, total TER: 12.957%, progress (thread 0): 98.1883%]
|T|: u m
|P|: i | m a
[sample: IS1009c_H03_FIO089_1368.34_1368.52, WER: 200%, TER: 150%, total WER: 26.4447%, total TER: 12.9577%, progress (thread 0): 98.1962%]
|T|: a n d
|P|: a n d
[sample: IS1009c_H01_FIO087_1687.91_1688.09, WER: 0%, TER: 0%, total WER: 26.4444%, total TER: 12.9576%, progress (thread 0): 98.2041%]
|T|: m m
|P|: h m
[sample: IS1009d_H00_FIE088_1039.16_1039.34, WER: 100%, TER: 50%, total WER: 26.4453%, total TER: 12.9578%, progress (thread 0): 98.212%]
|T|: y e s
|P|: i t ' s
[sample: IS1009d_H01_FIO087_1532.02_1532.2, WER: 100%, TER: 100%, total WER: 26.4461%, total TER: 12.9584%, progress (thread 0): 98.2199%]
|T|: n o
|P|: y o u | k
[sample: TS3003a_H00_MTD009PM_1017.51_1017.69, WER: 200%, TER: 200%, total WER: 26.448%, total TER: 12.9592%, progress (thread 0): 98.2278%]
|T|: y e a h
|P|: y e a
[sample: TS3003b_H00_MTD009PM_583.19_583.37, WER: 100%, TER: 25%, total WER: 26.4488%, total TER: 12.9594%, progress (thread 0): 98.2358%]
|T|: m m
|P|: h h
[sample: TS3003b_H01_MTD011UID_1188.77_1188.95, WER: 100%, TER: 100%, total WER: 26.4497%, total TER: 12.9598%, progress (thread 0): 98.2437%]
|T|: y e a h
|P|: y o
[sample: TS3003b_H02_MTD0010ID_1801.24_1801.42, WER: 100%, TER: 75%, total WER: 26.4505%, total TER: 12.9603%, progress (thread 0): 98.2516%]
|T|: m m
|P|:
[sample: TS3003b_H01_MTD011UID_1801.82_1802, WER: 100%, TER: 100%, total WER: 26.4513%, total TER: 12.9607%, progress (thread 0): 98.2595%]
|T|: h m m
|P|: r i g h t
[sample: TS3003d_H01_MTD011UID_1131_1131.18, WER: 100%, TER: 166.667%, total WER: 26.4521%, total TER: 12.9618%, progress (thread 0): 98.2674%]
|T|: a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_2394.1_2394.28, WER: 100%, TER: 50%, total WER: 26.453%, total TER: 12.962%, progress (thread 0): 98.2753%]
|T|: n o
|P|: h m
[sample: EN2002a_H00_MEE073_23.11_23.29, WER: 100%, TER: 100%, total WER: 26.4538%, total TER: 12.9624%, progress (thread 0): 98.2832%]
|T|: y e a h
|P|: y o
[sample: EN2002a_H01_FEO070_32.53_32.71, WER: 100%, TER: 75%, total WER: 26.4546%, total TER: 12.963%, progress (thread 0): 98.2911%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_106.69_106.87, WER: 100%, TER: 33.3333%, total WER: 26.4554%, total TER: 12.9631%, progress (thread 0): 98.299%]
|T|: y e a h
|P|: a n
[sample: EN2002a_H00_MEE073_1441.15_1441.33, WER: 100%, TER: 75%, total WER: 26.4563%, total TER: 12.9637%, progress (thread 0): 98.307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1717.2_1717.38, WER: 0%, TER: 0%, total WER: 26.456%, total TER: 12.9636%, progress (thread 0): 98.3149%]
|T|: o k a y
|P|:
[sample: EN2002b_H03_MEE073_530.52_530.7, WER: 100%, TER: 100%, total WER: 26.4568%, total TER: 12.9644%, progress (thread 0): 98.3228%]
|T|: i s | i t
|P|: i s
[sample: EN2002b_H01_MEE071_1142.07_1142.25, WER: 50%, TER: 60%, total WER: 26.4573%, total TER: 12.9649%, progress (thread 0): 98.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1339.32_1339.5, WER: 0%, TER: 0%, total WER: 26.457%, total TER: 12.9648%, progress (thread 0): 98.3386%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_186.7_186.88, WER: 0%, TER: 0%, total WER: 26.4567%, total TER: 12.9647%, progress (thread 0): 98.3465%]
|T|: a l r i g h t
|P|: r
[sample: EN2002c_H03_MEE073_536.86_537.04, WER: 100%, TER: 85.7143%, total WER: 26.4575%, total TER: 12.9658%, progress (thread 0): 98.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1667.09_1667.27, WER: 0%, TER: 0%, total WER: 26.4572%, total TER: 12.9657%, progress (thread 0): 98.3623%]
|T|: h m m
|P|:
[sample: EN2002d_H01_FEO072_363.49_363.67, WER: 100%, TER: 100%, total WER: 26.4581%, total TER: 12.9663%, progress (thread 0): 98.3703%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_500.44_500.62, WER: 0%, TER: 0%, total WER: 26.4578%, total TER: 12.9662%, progress (thread 0): 98.3782%]
|T|: w h a t
|P|: w h i t
[sample: EN2002d_H00_FEO070_508.48_508.66, WER: 100%, TER: 25%, total WER: 26.4586%, total TER: 12.9663%, progress (thread 0): 98.3861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_512.59_512.77, WER: 0%, TER: 0%, total WER: 26.4583%, total TER: 12.9662%, progress (thread 0): 98.394%]
|T|: y e a h
|P|: i t s
[sample: EN2002d_H02_MEE071_518.66_518.84, WER: 100%, TER: 100%, total WER: 26.4591%, total TER: 12.967%, progress (thread 0): 98.4019%]
|T|: y e a h
|P|: h u h
[sample: EN2002d_H00_FEO070_882.18_882.36, WER: 100%, TER: 75%, total WER: 26.4599%, total TER: 12.9676%, progress (thread 0): 98.4098%]
|T|: m m
|P|:
[sample: EN2002d_H02_MEE071_1451.31_1451.49, WER: 100%, TER: 100%, total WER: 26.4608%, total TER: 12.968%, progress (thread 0): 98.4177%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1581.55_1581.73, WER: 0%, TER: 0%, total WER: 26.4605%, total TER: 12.9678%, progress (thread 0): 98.4256%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_2062.62_2062.8, WER: 0%, TER: 0%, total WER: 26.4602%, total TER: 12.9677%, progress (thread 0): 98.4335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2166.41_2166.59, WER: 0%, TER: 0%, total WER: 26.4599%, total TER: 12.9676%, progress (thread 0): 98.4415%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_2172.96_2173.14, WER: 0%, TER: 0%, total WER: 26.4596%, total TER: 12.9674%, progress (thread 0): 98.4494%]
|T|: a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_843.75_843.92, WER: 100%, TER: 100%, total WER: 26.4604%, total TER: 12.9678%, progress (thread 0): 98.4573%]
|T|: y e p
|P|: y e a h
[sample: ES2004c_H00_MEO015_2220.58_2220.75, WER: 100%, TER: 66.6667%, total WER: 26.4612%, total TER: 12.9682%, progress (thread 0): 98.4652%]
|T|: y e a h
|P|:
[sample: IS1009b_H02_FIO084_381.27_381.44, WER: 100%, TER: 100%, total WER: 26.462%, total TER: 12.969%, progress (thread 0): 98.4731%]
|T|: h i
|P|: i n
[sample: IS1009c_H02_FIO084_43.25_43.42, WER: 100%, TER: 100%, total WER: 26.4629%, total TER: 12.9694%, progress (thread 0): 98.481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_347.27_347.44, WER: 0%, TER: 0%, total WER: 26.4626%, total TER: 12.9693%, progress (thread 0): 98.4889%]
|T|: m m
|P|: h
[sample: IS1009d_H02_FIO084_784.48_784.65, WER: 100%, TER: 100%, total WER: 26.4634%, total TER: 12.9697%, progress (thread 0): 98.4968%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_182.69_182.86, WER: 100%, TER: 66.6667%, total WER: 26.4642%, total TER: 12.9701%, progress (thread 0): 98.5047%]
|T|: h m m
|P|: h m
[sample: TS3003a_H01_MTD011UID_748.2_748.37, WER: 100%, TER: 33.3333%, total WER: 26.465%, total TER: 12.9702%, progress (thread 0): 98.5127%]
|T|: y e a h
|P|: a n d
[sample: TS3003a_H01_MTD011UID_1432.6_1432.77, WER: 100%, TER: 100%, total WER: 26.4659%, total TER: 12.971%, progress (thread 0): 98.5206%]
|T|: n o
|P|: t h e t
[sample: TS3003a_H00_MTD009PM_1442.45_1442.62, WER: 100%, TER: 200%, total WER: 26.4667%, total TER: 12.9719%, progress (thread 0): 98.5285%]
|T|: y e a h
|P|:
[sample: TS3003b_H01_MTD011UID_1584.22_1584.39, WER: 100%, TER: 100%, total WER: 26.4675%, total TER: 12.9727%, progress (thread 0): 98.5364%]
|T|: n o
|P|: u h
[sample: TS3003b_H01_MTD011UID_1779.4_1779.57, WER: 100%, TER: 100%, total WER: 26.4683%, total TER: 12.9731%, progress (thread 0): 98.5443%]
|T|: y e a h
|P|: o p
[sample: TS3003b_H03_MTD012ME_2009.37_2009.54, WER: 100%, TER: 100%, total WER: 26.4691%, total TER: 12.9739%, progress (thread 0): 98.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1183.13_1183.3, WER: 0%, TER: 0%, total WER: 26.4688%, total TER: 12.9738%, progress (thread 0): 98.5601%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2272.21_2272.38, WER: 0%, TER: 0%, total WER: 26.4686%, total TER: 12.9737%, progress (thread 0): 98.568%]
|T|: y e a h
|P|: e a h
[sample: TS3003d_H01_MTD011UID_306.52_306.69, WER: 100%, TER: 25%, total WER: 26.4694%, total TER: 12.9738%, progress (thread 0): 98.576%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_1283.14_1283.31, WER: 100%, TER: 100%, total WER: 26.4702%, total TER: 12.9746%, progress (thread 0): 98.5839%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_549.83_550, WER: 100%, TER: 100%, total WER: 26.471%, total TER: 12.9752%, progress (thread 0): 98.5918%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_1153.54_1153.71, WER: 100%, TER: 100%, total WER: 26.4718%, total TER: 12.9758%, progress (thread 0): 98.5997%]
|T|: y e a h
|P|:
[sample: EN2002a_H00_MEE073_1180.31_1180.48, WER: 100%, TER: 100%, total WER: 26.4727%, total TER: 12.9766%, progress (thread 0): 98.6076%]
|T|: h m m
|P|: h m
[sample: EN2002a_H02_FEO072_2053.69_2053.86, WER: 100%, TER: 33.3333%, total WER: 26.4735%, total TER: 12.9768%, progress (thread 0): 98.6155%]
|T|: y e a h
|P|: j u s
[sample: EN2002a_H01_FEO070_2086.54_2086.71, WER: 100%, TER: 100%, total WER: 26.4743%, total TER: 12.9776%, progress (thread 0): 98.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_423.51_423.68, WER: 0%, TER: 0%, total WER: 26.474%, total TER: 12.9774%, progress (thread 0): 98.6313%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1344.27_1344.44, WER: 100%, TER: 100%, total WER: 26.4748%, total TER: 12.9783%, progress (thread 0): 98.6392%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1400.92_1401.09, WER: 0%, TER: 0%, total WER: 26.4745%, total TER: 12.9781%, progress (thread 0): 98.6472%]
|T|: y e a h
|P|: e s t
[sample: EN2002b_H01_MEE071_1518.9_1519.07, WER: 100%, TER: 75%, total WER: 26.4754%, total TER: 12.9787%, progress (thread 0): 98.6551%]
|T|: y e a h
|P|: i n
[sample: EN2002c_H03_MEE073_55.6_55.77, WER: 100%, TER: 100%, total WER: 26.4762%, total TER: 12.9795%, progress (thread 0): 98.663%]
|T|: y e a h
|P|:
[sample: EN2002c_H03_MEE073_711.52_711.69, WER: 100%, TER: 100%, total WER: 26.477%, total TER: 12.9803%, progress (thread 0): 98.6709%]
|T|: b u t
|P|: t a e
[sample: EN2002d_H02_MEE071_431.16_431.33, WER: 100%, TER: 100%, total WER: 26.4778%, total TER: 12.9809%, progress (thread 0): 98.6788%]
|T|: y e a h
|P|: i s
[sample: EN2002d_H02_MEE071_649.62_649.79, WER: 100%, TER: 100%, total WER: 26.4786%, total TER: 12.9817%, progress (thread 0): 98.6867%]
|T|: y e a h
|P|: h
[sample: EN2002d_H03_MEE073_831.51_831.68, WER: 100%, TER: 75%, total WER: 26.4795%, total TER: 12.9823%, progress (thread 0): 98.6946%]
|T|: s o
|P|:
[sample: EN2002d_H03_MEE073_1058.82_1058.99, WER: 100%, TER: 100%, total WER: 26.4803%, total TER: 12.9827%, progress (thread 0): 98.7025%]
|T|: o h
|P|: i
[sample: EN2002d_H00_FEO070_1459.48_1459.65, WER: 100%, TER: 100%, total WER: 26.4811%, total TER: 12.9831%, progress (thread 0): 98.7104%]
|T|: r i g h t
|P|: r i h t
[sample: EN2002d_H03_MEE073_1858.1_1858.27, WER: 100%, TER: 20%, total WER: 26.4819%, total TER: 12.9832%, progress (thread 0): 98.7184%]
|T|: i | m e a n
|P|: i | m n
[sample: ES2004c_H03_FEE016_691.32_691.48, WER: 50%, TER: 33.3333%, total WER: 26.4825%, total TER: 12.9835%, progress (thread 0): 98.7263%]
|T|: o k a y
|P|:
[sample: ES2004c_H03_FEE016_1560.36_1560.52, WER: 100%, TER: 100%, total WER: 26.4833%, total TER: 12.9843%, progress (thread 0): 98.7342%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H00_FIE088_757.84_758, WER: 0%, TER: 0%, total WER: 26.483%, total TER: 12.9842%, progress (thread 0): 98.7421%]
|T|: n o
|P|: n o
[sample: IS1009d_H01_FIO087_584.12_584.28, WER: 0%, TER: 0%, total WER: 26.4827%, total TER: 12.9841%, progress (thread 0): 98.75%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_740.48_740.64, WER: 0%, TER: 0%, total WER: 26.4824%, total TER: 12.984%, progress (thread 0): 98.7579%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_826.94_827.1, WER: 0%, TER: 0%, total WER: 26.4821%, total TER: 12.9839%, progress (thread 0): 98.7658%]
|T|: n o
|P|: n o
[sample: IS1009d_H03_FIO089_862.59_862.75, WER: 0%, TER: 0%, total WER: 26.4818%, total TER: 12.9838%, progress (thread 0): 98.7737%]
|T|: y e a h
|P|: y e a
[sample: IS1009d_H02_FIO084_1025.48_1025.64, WER: 100%, TER: 25%, total WER: 26.4826%, total TER: 12.9839%, progress (thread 0): 98.7816%]
|T|: y e a h
|P|: a n
[sample: TS3003a_H01_MTD011UID_580.47_580.63, WER: 100%, TER: 75%, total WER: 26.4834%, total TER: 12.9845%, progress (thread 0): 98.7896%]
|T|: u h
|P|: r i h
[sample: TS3003a_H00_MTD009PM_617.55_617.71, WER: 100%, TER: 100%, total WER: 26.4843%, total TER: 12.9849%, progress (thread 0): 98.7975%]
|T|: u h
|P|: e a h
[sample: TS3003a_H00_MTD009PM_1421.57_1421.73, WER: 100%, TER: 100%, total WER: 26.4851%, total TER: 12.9853%, progress (thread 0): 98.8054%]
|T|: h m m
|P|: h m
[sample: TS3003b_H01_MTD011UID_1789.09_1789.25, WER: 100%, TER: 33.3333%, total WER: 26.4859%, total TER: 12.9855%, progress (thread 0): 98.8133%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2009.54_2009.7, WER: 0%, TER: 0%, total WER: 26.4856%, total TER: 12.9853%, progress (thread 0): 98.8212%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1865.26_1865.42, WER: 100%, TER: 66.6667%, total WER: 26.4864%, total TER: 12.9857%, progress (thread 0): 98.8291%]
|T|: o h
|P|: t o
[sample: TS3003d_H00_MTD009PM_2587.23_2587.39, WER: 100%, TER: 100%, total WER: 26.4872%, total TER: 12.9861%, progress (thread 0): 98.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_376.27_376.43, WER: 0%, TER: 0%, total WER: 26.487%, total TER: 12.986%, progress (thread 0): 98.8449%]
|T|: y e a h
|P|: s i n e
[sample: EN2002a_H00_MEE073_720.58_720.74, WER: 100%, TER: 100%, total WER: 26.4878%, total TER: 12.9868%, progress (thread 0): 98.8529%]
|T|: y e a h
|P|: b u t
[sample: EN2002a_H00_MEE073_737.85_738.01, WER: 100%, TER: 100%, total WER: 26.4886%, total TER: 12.9876%, progress (thread 0): 98.8608%]
|T|: y e a h
|P|: y o
[sample: EN2002a_H00_MEE073_844.91_845.07, WER: 100%, TER: 75%, total WER: 26.4894%, total TER: 12.9882%, progress (thread 0): 98.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1041.33_1041.49, WER: 0%, TER: 0%, total WER: 26.4891%, total TER: 12.9881%, progress (thread 0): 98.8766%]
|T|: y e a h
|P|: b u t
[sample: EN2002a_H00_MEE073_1050.03_1050.19, WER: 100%, TER: 100%, total WER: 26.4899%, total TER: 12.9889%, progress (thread 0): 98.8845%]
|T|: w h y
|P|: w
[sample: EN2002a_H00_MEE073_1237.91_1238.07, WER: 100%, TER: 66.6667%, total WER: 26.4908%, total TER: 12.9893%, progress (thread 0): 98.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1596.11_1596.27, WER: 0%, TER: 0%, total WER: 26.4905%, total TER: 12.9891%, progress (thread 0): 98.9003%]
|T|: s o r r y
|P|: t u e
[sample: EN2002a_H00_MEE073_1813.61_1813.77, WER: 100%, TER: 100%, total WER: 26.4913%, total TER: 12.9901%, progress (thread 0): 98.9082%]
|T|: s u r e
|P|:
[sample: EN2002a_H00_MEE073_1881.96_1882.12, WER: 100%, TER: 100%, total WER: 26.4921%, total TER: 12.991%, progress (thread 0): 98.9161%]
|T|: y e a h
|P|: y o u
[sample: EN2002b_H02_FEO072_39.76_39.92, WER: 100%, TER: 75%, total WER: 26.4929%, total TER: 12.9915%, progress (thread 0): 98.924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_317.94_318.1, WER: 0%, TER: 0%, total WER: 26.4926%, total TER: 12.9914%, progress (thread 0): 98.932%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1075.72_1075.88, WER: 0%, TER: 0%, total WER: 26.4923%, total TER: 12.9913%, progress (thread 0): 98.9399%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1155.04_1155.2, WER: 100%, TER: 100%, total WER: 26.4932%, total TER: 12.9921%, progress (thread 0): 98.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_713.06_713.22, WER: 0%, TER: 0%, total WER: 26.4929%, total TER: 12.9919%, progress (thread 0): 98.9557%]
|T|: o h
|P|:
[sample: EN2002c_H03_MEE073_1210.59_1210.75, WER: 100%, TER: 100%, total WER: 26.4937%, total TER: 12.9924%, progress (thread 0): 98.9636%]
|T|: r i g h t
|P|: o r
[sample: EN2002c_H03_MEE073_1484.69_1484.85, WER: 100%, TER: 100%, total WER: 26.4945%, total TER: 12.9934%, progress (thread 0): 98.9715%]
|T|: y e a h
|P|: e
[sample: EN2002c_H03_MEE073_2554.98_2555.14, WER: 100%, TER: 75%, total WER: 26.4953%, total TER: 12.9939%, progress (thread 0): 98.9794%]
|T|: h m m
|P|: h m
[sample: EN2002c_H03_MEE073_2776.86_2777.02, WER: 100%, TER: 33.3333%, total WER: 26.4961%, total TER: 12.9941%, progress (thread 0): 98.9873%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_645.29_645.45, WER: 100%, TER: 100%, total WER: 26.497%, total TER: 12.9945%, progress (thread 0): 98.9952%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1397.11_1397.27, WER: 0%, TER: 0%, total WER: 26.4967%, total TER: 12.9944%, progress (thread 0): 99.0032%]
|T|: o o p s
|P|:
[sample: ES2004a_H00_MEO015_188.28_188.43, WER: 100%, TER: 100%, total WER: 26.4975%, total TER: 12.9952%, progress (thread 0): 99.0111%]
|T|: o o h
|P|: i t
[sample: ES2004b_H02_MEE014_534.45_534.6, WER: 100%, TER: 100%, total WER: 26.4983%, total TER: 12.9958%, progress (thread 0): 99.019%]
|T|: ' k a y
|P|:
[sample: ES2004c_H01_FEE013_472.18_472.33, WER: 100%, TER: 100%, total WER: 26.4991%, total TER: 12.9966%, progress (thread 0): 99.0269%]
|T|: y e s
|P|:
[sample: IS1009a_H01_FIO087_693.82_693.97, WER: 100%, TER: 100%, total WER: 26.5%, total TER: 12.9972%, progress (thread 0): 99.0348%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1647.59_1647.74, WER: 0%, TER: 0%, total WER: 26.4997%, total TER: 12.9971%, progress (thread 0): 99.0427%]
|T|: y e a h
|P|: s a m e
[sample: IS1009d_H02_FIO084_1392_1392.15, WER: 100%, TER: 100%, total WER: 26.5005%, total TER: 12.9979%, progress (thread 0): 99.0506%]
|T|: t w o
|P|: d o
[sample: IS1009d_H01_FIO087_1586.28_1586.43, WER: 100%, TER: 66.6667%, total WER: 26.5013%, total TER: 12.9983%, progress (thread 0): 99.0585%]
|T|: n o
|P|:
[sample: IS1009d_H01_FIO087_1824.98_1825.13, WER: 100%, TER: 100%, total WER: 26.5021%, total TER: 12.9987%, progress (thread 0): 99.0665%]
|T|: y e a h
|P|: n
[sample: IS1009d_H02_FIO084_1883.61_1883.76, WER: 100%, TER: 100%, total WER: 26.5029%, total TER: 12.9995%, progress (thread 0): 99.0744%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_567.51_567.66, WER: 0%, TER: 0%, total WER: 26.5026%, total TER: 12.9993%, progress (thread 0): 99.0823%]
|T|: y e a h
|P|:
[sample: TS3003c_H02_MTD0010ID_2049.54_2049.69, WER: 100%, TER: 100%, total WER: 26.5035%, total TER: 13.0002%, progress (thread 0): 99.0902%]
|T|: m m
|P|: m
[sample: TS3003d_H01_MTD011UID_2041.85_2042, WER: 100%, TER: 50%, total WER: 26.5043%, total TER: 13.0003%, progress (thread 0): 99.0981%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_639.89_640.04, WER: 100%, TER: 66.6667%, total WER: 26.5051%, total TER: 13.0007%, progress (thread 0): 99.106%]
|T|: y e a h
|P|: a n
[sample: EN2002a_H00_MEE073_967.58_967.73, WER: 100%, TER: 75%, total WER: 26.5059%, total TER: 13.0013%, progress (thread 0): 99.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1030.96_1031.11, WER: 0%, TER: 0%, total WER: 26.5056%, total TER: 13.0012%, progress (thread 0): 99.1218%]
|T|: y e a h
|P|: h o
[sample: EN2002a_H01_FEO070_1104.83_1104.98, WER: 100%, TER: 100%, total WER: 26.5065%, total TER: 13.002%, progress (thread 0): 99.1297%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1527.69_1527.84, WER: 100%, TER: 33.3333%, total WER: 26.5073%, total TER: 13.0021%, progress (thread 0): 99.1377%]
|T|: y e a h
|P|:
[sample: EN2002a_H00_MEE073_1539.77_1539.92, WER: 100%, TER: 100%, total WER: 26.5081%, total TER: 13.0029%, progress (thread 0): 99.1456%]
|T|: y e a h
|P|: r e
[sample: EN2002a_H00_MEE073_1744.01_1744.16, WER: 100%, TER: 75%, total WER: 26.5089%, total TER: 13.0035%, progress (thread 0): 99.1535%]
|T|: y e a h
|P|: y o a
[sample: EN2002a_H01_FEO070_1785.21_1785.36, WER: 100%, TER: 50%, total WER: 26.5097%, total TER: 13.0038%, progress (thread 0): 99.1614%]
|T|: y e p
|P|: y e p
[sample: EN2002b_H03_MEE073_382.32_382.47, WER: 0%, TER: 0%, total WER: 26.5094%, total TER: 13.0037%, progress (thread 0): 99.1693%]
|T|: u h
|P|: y e a h
[sample: EN2002b_H02_FEO072_424.35_424.5, WER: 100%, TER: 150%, total WER: 26.5103%, total TER: 13.0044%, progress (thread 0): 99.1772%]
|T|: y e a h
|P|:
[sample: EN2002b_H00_FEO070_496.03_496.18, WER: 100%, TER: 100%, total WER: 26.5111%, total TER: 13.0052%, progress (thread 0): 99.1851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_897.7_897.85, WER: 0%, TER: 0%, total WER: 26.5108%, total TER: 13.0051%, progress (thread 0): 99.193%]
|T|: y e a h
|P|: t h
[sample: EN2002c_H03_MEE073_2198.65_2198.8, WER: 100%, TER: 75%, total WER: 26.5116%, total TER: 13.0056%, progress (thread 0): 99.201%]
|T|: y e a h
|P|: i t
[sample: EN2002d_H00_FEO070_1290.13_1290.28, WER: 100%, TER: 100%, total WER: 26.5124%, total TER: 13.0065%, progress (thread 0): 99.2089%]
|T|: n o
|P|:
[sample: EN2002d_H03_MEE073_1424.03_1424.18, WER: 100%, TER: 100%, total WER: 26.5132%, total TER: 13.0069%, progress (thread 0): 99.2168%]
|T|: r i g h t
|P|:
[sample: EN2002d_H03_MEE073_1909.61_1909.76, WER: 100%, TER: 100%, total WER: 26.5141%, total TER: 13.0079%, progress (thread 0): 99.2247%]
|T|: o k a y
|P|:
[sample: EN2002d_H03_MEE073_1985.35_1985.5, WER: 100%, TER: 100%, total WER: 26.5149%, total TER: 13.0087%, progress (thread 0): 99.2326%]
|T|: i | t h
|P|: i
[sample: ES2004b_H01_FEE013_2305.5_2305.64, WER: 50%, TER: 75%, total WER: 26.5154%, total TER: 13.0093%, progress (thread 0): 99.2405%]
|T|: m m
|P|:
[sample: ES2004c_H03_FEE016_777.13_777.27, WER: 100%, TER: 100%, total WER: 26.5162%, total TER: 13.0097%, progress (thread 0): 99.2484%]
|T|: y e a h
|P|:
[sample: IS1009a_H02_FIO084_56.9_57.04, WER: 100%, TER: 100%, total WER: 26.5171%, total TER: 13.0105%, progress (thread 0): 99.2563%]
|T|: y e s
|P|: y e a
[sample: IS1009d_H01_FIO087_645.27_645.41, WER: 100%, TER: 33.3333%, total WER: 26.5179%, total TER: 13.0106%, progress (thread 0): 99.2642%]
|T|: m m h m m
|P|:
[sample: IS1009d_H00_FIE088_770.97_771.11, WER: 100%, TER: 100%, total WER: 26.5187%, total TER: 13.0116%, progress (thread 0): 99.2721%]
|T|: h m m
|P|:
[sample: TS3003a_H01_MTD011UID_189.69_189.83, WER: 100%, TER: 100%, total WER: 26.5195%, total TER: 13.0122%, progress (thread 0): 99.2801%]
|T|: m m
|P|:
[sample: TS3003a_H01_MTD011UID_903.08_903.22, WER: 100%, TER: 100%, total WER: 26.5203%, total TER: 13.0126%, progress (thread 0): 99.288%]
|T|: m m
|P|:
[sample: TS3003b_H01_MTD011UID_2047.35_2047.49, WER: 100%, TER: 100%, total WER: 26.5212%, total TER: 13.013%, progress (thread 0): 99.2959%]
|T|: ' k a y
|P|:
[sample: TS3003b_H03_MTD012ME_2140.48_2140.62, WER: 100%, TER: 100%, total WER: 26.522%, total TER: 13.0138%, progress (thread 0): 99.3038%]
|T|: y e a h
|P|:
[sample: TS3003c_H03_MTD012ME_1869.23_1869.37, WER: 100%, TER: 100%, total WER: 26.5228%, total TER: 13.0147%, progress (thread 0): 99.3117%]
|T|: y e a h
|P|:
[sample: TS3003d_H00_MTD009PM_1390.61_1390.75, WER: 100%, TER: 100%, total WER: 26.5236%, total TER: 13.0155%, progress (thread 0): 99.3196%]
|T|: y e a h
|P|:
[sample: TS3003d_H00_MTD009PM_2021.71_2021.85, WER: 100%, TER: 100%, total WER: 26.5244%, total TER: 13.0163%, progress (thread 0): 99.3275%]
|T|: r i g h t
|P|: i k
[sample: EN2002a_H00_MEE073_1184.15_1184.29, WER: 100%, TER: 80%, total WER: 26.5253%, total TER: 13.017%, progress (thread 0): 99.3354%]
|T|: h m m
|P|: i h m
[sample: EN2002a_H00_MEE073_1193.94_1194.08, WER: 100%, TER: 66.6667%, total WER: 26.5261%, total TER: 13.0174%, progress (thread 0): 99.3434%]
|T|: y e p
|P|: y e h
[sample: EN2002a_H00_MEE073_2048.07_2048.21, WER: 100%, TER: 33.3333%, total WER: 26.5269%, total TER: 13.0176%, progress (thread 0): 99.3513%]
|T|: ' k a y
|P|:
[sample: EN2002c_H03_MEE073_103.84_103.98, WER: 100%, TER: 100%, total WER: 26.5277%, total TER: 13.0184%, progress (thread 0): 99.3592%]
|T|: y e a h
|P|: g e
[sample: EN2002c_H02_MEE071_531.32_531.46, WER: 100%, TER: 75%, total WER: 26.5285%, total TER: 13.0189%, progress (thread 0): 99.3671%]
|T|: y e a h
|P|: s o
[sample: EN2002c_H03_MEE073_667.22_667.36, WER: 100%, TER: 100%, total WER: 26.5294%, total TER: 13.0198%, progress (thread 0): 99.375%]
|T|: y e a h
|P|: y e
[sample: EN2002c_H02_MEE071_1476.08_1476.22, WER: 100%, TER: 50%, total WER: 26.5302%, total TER: 13.0201%, progress (thread 0): 99.3829%]
|T|: m m
|P|:
[sample: EN2002c_H03_MEE073_2096.91_2097.05, WER: 100%, TER: 100%, total WER: 26.531%, total TER: 13.0205%, progress (thread 0): 99.3908%]
|T|: o k a y
|P|: e
[sample: EN2002d_H00_FEO070_1251.9_1252.04, WER: 100%, TER: 100%, total WER: 26.5318%, total TER: 13.0213%, progress (thread 0): 99.3987%]
|T|: m m
|P|: y e a
[sample: ES2004c_H03_FEE016_98.28_98.41, WER: 100%, TER: 150%, total WER: 26.5326%, total TER: 13.0219%, progress (thread 0): 99.4066%]
|T|: s
|P|:
[sample: ES2004d_H03_FEE016_1370.74_1370.87, WER: 100%, TER: 100%, total WER: 26.5335%, total TER: 13.0221%, progress (thread 0): 99.4146%]
|T|: o r | a | b
|P|:
[sample: IS1009a_H01_FIO087_573.12_573.25, WER: 100%, TER: 100%, total WER: 26.5359%, total TER: 13.0234%, progress (thread 0): 99.4225%]
|T|: h m m
|P|: y e
[sample: IS1009c_H02_FIO084_144.08_144.21, WER: 100%, TER: 100%, total WER: 26.5367%, total TER: 13.024%, progress (thread 0): 99.4304%]
|T|: o k a y
|P|: j u s
[sample: IS1009d_H00_FIE088_604.16_604.29, WER: 100%, TER: 100%, total WER: 26.5376%, total TER: 13.0248%, progress (thread 0): 99.4383%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_942.55_942.68, WER: 100%, TER: 100%, total WER: 26.5384%, total TER: 13.0254%, progress (thread 0): 99.4462%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_392.63_392.76, WER: 100%, TER: 66.6667%, total WER: 26.5392%, total TER: 13.0258%, progress (thread 0): 99.4541%]
|T|: h u h
|P|:
[sample: TS3003a_H01_MTD011UID_1266.08_1266.21, WER: 100%, TER: 100%, total WER: 26.54%, total TER: 13.0264%, progress (thread 0): 99.462%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1335.11_1335.24, WER: 100%, TER: 100%, total WER: 26.5408%, total TER: 13.0268%, progress (thread 0): 99.4699%]
|T|: y e p
|P|: y h a
[sample: TS3003a_H01_MTD011UID_1475.42_1475.55, WER: 100%, TER: 66.6667%, total WER: 26.5417%, total TER: 13.0271%, progress (thread 0): 99.4778%]
|T|: y e a h
|P|:
[sample: TS3003b_H01_MTD011UID_1528.52_1528.65, WER: 100%, TER: 100%, total WER: 26.5425%, total TER: 13.0279%, progress (thread 0): 99.4858%]
|T|: m m
|P|:
[sample: TS3003c_H01_MTD011UID_1173.23_1173.36, WER: 100%, TER: 100%, total WER: 26.5433%, total TER: 13.0284%, progress (thread 0): 99.4937%]
|T|: y e a h
|P|: t h
[sample: TS3003d_H00_MTD009PM_1693.45_1693.58, WER: 100%, TER: 75%, total WER: 26.5441%, total TER: 13.0289%, progress (thread 0): 99.5016%]
|T|: ' k a y
|P|:
[sample: EN2002a_H00_MEE073_32.58_32.71, WER: 100%, TER: 100%, total WER: 26.5449%, total TER: 13.0297%, progress (thread 0): 99.5095%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_984.87_985, WER: 100%, TER: 100%, total WER: 26.5458%, total TER: 13.0303%, progress (thread 0): 99.5174%]
|T|: w e l l | f
|P|: b u h
[sample: EN2002a_H00_MEE073_1458.73_1458.86, WER: 100%, TER: 100%, total WER: 26.5474%, total TER: 13.0316%, progress (thread 0): 99.5253%]
|T|: y e p
|P|: y e a
[sample: EN2002b_H01_MEE071_493.19_493.32, WER: 100%, TER: 33.3333%, total WER: 26.5482%, total TER: 13.0317%, progress (thread 0): 99.5332%]
|T|: y e a h
|P|:
[sample: EN2002c_H03_MEE073_665.01_665.14, WER: 100%, TER: 100%, total WER: 26.549%, total TER: 13.0325%, progress (thread 0): 99.5411%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_135.16_135.29, WER: 100%, TER: 100%, total WER: 26.5499%, total TER: 13.0329%, progress (thread 0): 99.549%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H01_FEO072_726.53_726.66, WER: 100%, TER: 25%, total WER: 26.5507%, total TER: 13.033%, progress (thread 0): 99.557%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H01_FEO072_794.79_794.92, WER: 100%, TER: 25%, total WER: 26.5515%, total TER: 13.0331%, progress (thread 0): 99.5649%]
|T|: y e a h
|P|: g o
[sample: EN2002d_H00_FEO070_1595.77_1595.9, WER: 100%, TER: 100%, total WER: 26.5523%, total TER: 13.0339%, progress (thread 0): 99.5728%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_1641.64_1641.77, WER: 100%, TER: 100%, total WER: 26.5531%, total TER: 13.0343%, progress (thread 0): 99.5807%]
|T|: y e a h
|P|:
[sample: ES2004b_H00_MEO015_922.62_922.74, WER: 100%, TER: 100%, total WER: 26.554%, total TER: 13.0351%, progress (thread 0): 99.5886%]
|T|: o k a y
|P|: j u
[sample: ES2004b_H00_MEO015_1977.87_1977.99, WER: 100%, TER: 100%, total WER: 26.5548%, total TER: 13.036%, progress (thread 0): 99.5965%]
|T|: y e a h
|P|: i
[sample: IS1009b_H02_FIO084_173.86_173.98, WER: 100%, TER: 100%, total WER: 26.5556%, total TER: 13.0368%, progress (thread 0): 99.6044%]
|T|: h m m
|P|:
[sample: IS1009c_H02_FIO084_1703.14_1703.26, WER: 100%, TER: 100%, total WER: 26.5564%, total TER: 13.0374%, progress (thread 0): 99.6123%]
|T|: m m | m m
|P|:
[sample: IS1009d_H03_FIO089_870.76_870.88, WER: 100%, TER: 100%, total WER: 26.5581%, total TER: 13.0384%, progress (thread 0): 99.6203%]
|T|: y e p
|P|: i f
[sample: TS3003a_H01_MTD011UID_257.2_257.32, WER: 100%, TER: 100%, total WER: 26.5589%, total TER: 13.039%, progress (thread 0): 99.6282%]
|T|: y e a h
|P|: j u s
[sample: EN2002a_H03_MEE071_170.81_170.93, WER: 100%, TER: 100%, total WER: 26.5597%, total TER: 13.0398%, progress (thread 0): 99.6361%]
|T|: h m m
|P|: h m
[sample: EN2002a_H00_MEE073_1468.26_1468.38, WER: 100%, TER: 33.3333%, total WER: 26.5605%, total TER: 13.0399%, progress (thread 0): 99.644%]
|T|: n o
|P|: n h
[sample: EN2002b_H03_MEE073_4.83_4.95, WER: 100%, TER: 50%, total WER: 26.5613%, total TER: 13.0401%, progress (thread 0): 99.6519%]
|T|: a l t e r
|P|: u
[sample: EN2002b_H03_MEE073_170.46_170.58, WER: 100%, TER: 100%, total WER: 26.5622%, total TER: 13.0411%, progress (thread 0): 99.6598%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_1641.92_1642.04, WER: 100%, TER: 100%, total WER: 26.563%, total TER: 13.0419%, progress (thread 0): 99.6677%]
|T|: y e a h
|P|: y e
[sample: EN2002d_H03_MEE073_806.78_806.9, WER: 100%, TER: 50%, total WER: 26.5638%, total TER: 13.0423%, progress (thread 0): 99.6756%]
|T|: o k a y
|P|:
[sample: ES2004a_H00_MEO015_71.95_72.06, WER: 100%, TER: 100%, total WER: 26.5646%, total TER: 13.0431%, progress (thread 0): 99.6835%]
|T|: o
|P|:
[sample: ES2004a_H03_FEE016_403.24_403.35, WER: 100%, TER: 100%, total WER: 26.5654%, total TER: 13.0433%, progress (thread 0): 99.6915%]
|T|: ' k a y
|P|:
[sample: ES2004a_H03_FEE016_1042.19_1042.3, WER: 100%, TER: 100%, total WER: 26.5662%, total TER: 13.0441%, progress (thread 0): 99.6994%]
|T|: m m
|P|:
[sample: IS1009c_H02_FIO084_620.84_620.95, WER: 100%, TER: 100%, total WER: 26.5671%, total TER: 13.0445%, progress (thread 0): 99.7073%]
|T|: y e a h
|P|:
[sample: TS3003a_H01_MTD011UID_436.27_436.38, WER: 100%, TER: 100%, total WER: 26.5679%, total TER: 13.0453%, progress (thread 0): 99.7152%]
|T|: y e a h
|P|: o
[sample: EN2002a_H00_MEE073_669.47_669.58, WER: 100%, TER: 100%, total WER: 26.5687%, total TER: 13.0461%, progress (thread 0): 99.7231%]
|T|: y e a h
|P|:
[sample: EN2002a_H01_FEO070_1169.61_1169.72, WER: 100%, TER: 100%, total WER: 26.5695%, total TER: 13.0469%, progress (thread 0): 99.731%]
|T|: n o
|P|:
[sample: EN2002b_H02_FEO072_4.48_4.59, WER: 100%, TER: 100%, total WER: 26.5703%, total TER: 13.0473%, progress (thread 0): 99.7389%]
|T|: y e p
|P|:
[sample: EN2002b_H03_MEE073_307.97_308.08, WER: 100%, TER: 100%, total WER: 26.5712%, total TER: 13.0479%, progress (thread 0): 99.7468%]
|T|: h m m
|P|:
[sample: EN2002b_H03_MEE073_1578.82_1578.93, WER: 100%, TER: 100%, total WER: 26.572%, total TER: 13.0485%, progress (thread 0): 99.7547%]
|T|: h m m
|P|:
[sample: EN2002c_H03_MEE073_1446.36_1446.47, WER: 100%, TER: 100%, total WER: 26.5728%, total TER: 13.0491%, progress (thread 0): 99.7627%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_118.11_118.22, WER: 100%, TER: 100%, total WER: 26.5736%, total TER: 13.0495%, progress (thread 0): 99.7706%]
|T|: s o
|P|:
[sample: EN2002d_H02_MEE071_2107.05_2107.16, WER: 100%, TER: 100%, total WER: 26.5744%, total TER: 13.0499%, progress (thread 0): 99.7785%]
|T|: t
|P|:
[sample: ES2004c_H02_MEE014_1184.81_1184.91, WER: 100%, TER: 100%, total WER: 26.5753%, total TER: 13.0501%, progress (thread 0): 99.7864%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_1744.56_1744.66, WER: 100%, TER: 100%, total WER: 26.5761%, total TER: 13.0507%, progress (thread 0): 99.7943%]
|T|: o h
|P|:
[sample: TS3003c_H02_MTD0010ID_1023.66_1023.76, WER: 100%, TER: 100%, total WER: 26.5769%, total TER: 13.0512%, progress (thread 0): 99.8022%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_2013_2013.1, WER: 100%, TER: 100%, total WER: 26.5777%, total TER: 13.0518%, progress (thread 0): 99.8101%]
|T|: y e a h
|P|:
[sample: EN2002b_H00_FEO070_43.63_43.73, WER: 100%, TER: 100%, total WER: 26.5785%, total TER: 13.0526%, progress (thread 0): 99.818%]
|T|: y e a h
|P|:
[sample: EN2002b_H03_MEE073_293.52_293.62, WER: 100%, TER: 100%, total WER: 26.5794%, total TER: 13.0534%, progress (thread 0): 99.826%]
|T|: y e p
|P|: e
[sample: EN2002c_H03_MEE073_763_763.1, WER: 100%, TER: 66.6667%, total WER: 26.5802%, total TER: 13.0537%, progress (thread 0): 99.8339%]
|T|: m m
|P|:
[sample: EN2002d_H03_MEE073_2099.51_2099.61, WER: 100%, TER: 100%, total WER: 26.581%, total TER: 13.0541%, progress (thread 0): 99.8418%]
|T|: ' k a y
|P|:
[sample: ES2004a_H00_MEO015_168.79_168.88, WER: 100%, TER: 100%, total WER: 26.5818%, total TER: 13.055%, progress (thread 0): 99.8497%]
|T|: y e p
|P|:
[sample: ES2004c_H00_MEO015_360.73_360.82, WER: 100%, TER: 100%, total WER: 26.5826%, total TER: 13.0556%, progress (thread 0): 99.8576%]
|T|: m m h m m
|P|:
[sample: ES2004c_H03_FEE016_1132.2_1132.29, WER: 100%, TER: 100%, total WER: 26.5835%, total TER: 13.0566%, progress (thread 0): 99.8655%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1651.84_1651.93, WER: 100%, TER: 100%, total WER: 26.5843%, total TER: 13.0572%, progress (thread 0): 99.8734%]
|T|: m m h m m
|P|:
[sample: IS1009c_H02_FIO084_1753.49_1753.58, WER: 100%, TER: 100%, total WER: 26.5851%, total TER: 13.0582%, progress (thread 0): 99.8813%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_749.88_749.97, WER: 100%, TER: 100%, total WER: 26.5859%, total TER: 13.0588%, progress (thread 0): 99.8892%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1718.32_1718.41, WER: 100%, TER: 100%, total WER: 26.5867%, total TER: 13.0598%, progress (thread 0): 99.8972%]
|T|: w h a t
|P|:
[sample: TS3003d_H00_MTD009PM_1597.33_1597.42, WER: 100%, TER: 100%, total WER: 26.5875%, total TER: 13.0606%, progress (thread 0): 99.9051%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_887.36_887.45, WER: 100%, TER: 100%, total WER: 26.5884%, total TER: 13.0612%, progress (thread 0): 99.913%]
|T|: d a m n
|P|:
[sample: EN2002a_H00_MEE073_1500.91_1501, WER: 100%, TER: 100%, total WER: 26.5892%, total TER: 13.062%, progress (thread 0): 99.9209%]
|T|: h m m
|P|:
[sample: EN2002c_H03_MEE073_429.63_429.72, WER: 100%, TER: 100%, total WER: 26.59%, total TER: 13.0626%, progress (thread 0): 99.9288%]
|T|: y e a h
|P|: a
[sample: EN2002c_H02_MEE071_2578.94_2579.03, WER: 100%, TER: 75%, total WER: 26.5908%, total TER: 13.0632%, progress (thread 0): 99.9367%]
|T|: d a m n
|P|:
[sample: EN2002d_H03_MEE073_999.94_1000.03, WER: 100%, TER: 100%, total WER: 26.5916%, total TER: 13.064%, progress (thread 0): 99.9446%]
|T|: m m
|P|:
[sample: ES2004a_H00_MEO015_252.48_252.56, WER: 100%, TER: 100%, total WER: 26.5925%, total TER: 13.0644%, progress (thread 0): 99.9525%]
|T|: m m
|P|:
[sample: ES2004c_H00_MEO015_1885.03_1885.11, WER: 100%, TER: 100%, total WER: 26.5933%, total TER: 13.0648%, progress (thread 0): 99.9604%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1313.86_1313.94, WER: 100%, TER: 100%, total WER: 26.5941%, total TER: 13.0652%, progress (thread 0): 99.9684%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_185.9_185.98, WER: 100%, TER: 100%, total WER: 26.5949%, total TER: 13.0658%, progress (thread 0): 99.9763%]
|T|: g
|P|:
[sample: EN2002d_H03_MEE073_241.62_241.69, WER: 100%, TER: 100%, total WER: 26.5957%, total TER: 13.066%, progress (thread 0): 99.9842%]
|T|: h m m
|P|:
[sample: IS1009a_H02_FIO084_490.4_490.46, WER: 100%, TER: 100%, total WER: 26.5966%, total TER: 13.0666%, progress (thread 0): 99.9921%]
|T|: ' k a y
|P|:
[sample: EN2002b_H03_MEE073_613.94_614, WER: 100%, TER: 100%, total WER: 26.5974%, total TER: 13.0674%, progress (thread 0): 100%]
I1224 05:05:23.029392 9187 Test.cpp:418] ------
I1224 05:05:23.029417 9187 Test.cpp:419] [Test ami_limited_supervision/test.lst (12640 samples) in 363.179s (actual decoding time 0.0287s/sample) -- WER: 26.5974%, TER: 13.0674%]
###Markdown
We can see that the viterbi WER is 26.6% before finetuning. Step 3: Run FinetuningNow, let's run finetuning with the AMI Corpus to see if we can improve the WER. Important parameters for `fl_asr_finetune_ctc`:`--train`, `--valid` - list files for training and validation sets respectively. Use comma to separate multiple files`--datadir` - [optional] base path to be used for `--train`, `--valid` flags`--lr` - learning rate for SGD`--momentum` - SGD momentum `--lr_decay` - epoch at which learning decay starts `--lr_decay_step` - learning rate halves after this epoch interval starting from epoch given by `lr_decay` `--arch` - architecture file. Tune droupout if necessary. `--tokens` - tokens file `--batchsize` - batchsize per process`--lexicon` - lexicon file `--rundir` - path to store checkpoint logs`--reportiters` - Number of updates after which we will run evaluation on validation data and save model, if 0 we only do this at end of each epoch>Amount of train data | Config to use >---|---|> 10 min| --train train_10min_0.lst> 1 hr| --train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst> 10 hr| --train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lstLet's run finetuning with 10hr AMI data (**~7min** for 1000 updates with evaluation on dev set)
###Code
! ./flashlight/build/bin/asr/fl_asr_tutorial_finetune_ctc model.bin \
--datadir ami_limited_supervision \
--train train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lst \
--valid dev:dev.lst \
--arch arch.txt \
--tokens tokens.txt \
--lexicon lexicon.txt \
--rundir checkpoint \
--lr 0.025 \
--netoptim sgd \
--momentum 0.8 \
--reportiters 1000 \
--lr_decay 100 \
--lr_decay_step 50 \
--iter 25000 \
--batchsize 4 \
--warmup 0
###Output
I1224 06:39:48.599629 11517 FinetuneCTC.cpp:76] Parsing command line flags
Initialized NCCL 2.7.8 successfully!
I1224 06:39:49.002488 11517 FinetuneCTC.cpp:106] Gflags after parsing
--flagfile=; --fromenv=; --tryfromenv=; --undefok=; --tab_completion_columns=80; --tab_completion_word=; --help=false; --helpfull=false; --helpmatch=; --helpon=; --helppackage=false; --helpshort=false; --helpxml=false; --version=false; --adambeta1=0.94999999999999996; --adambeta2=0.98999999999999999; --am=; --am_decoder_tr_dropout=0.20000000000000001; --am_decoder_tr_layerdrop=0.20000000000000001; --am_decoder_tr_layers=6; --arch=arch.txt; --attention=keyvalue; --attentionthreshold=2147483647; --attnWindow=softPretrain; --attnconvchannel=0; --attnconvkernel=0; --attndim=0; --batching_max_duration=0; --batching_strategy=none; --batchsize=4; --beamsize=2500; --beamsizetoken=250000; --beamthreshold=25; --channels=1; --criterion=ctc; --critoptim=adagrad; --datadir=ami_limited_supervision; --decoderattnround=1; --decoderdropout=0; --decoderrnnlayer=1; --decodertype=wrd; --devwin=0; --emission_dir=; --emission_queue_size=3000; --enable_distributed=true; --encoderdim=256; --eosscore=0; --eostoken=false; --everstoredb=false; --fftcachesize=1; --filterbanks=80; --fl_amp_max_scale_factor=32000; --fl_amp_scale_factor=4096; --fl_amp_scale_factor_update_interval=2000; --fl_amp_use_mixed_precision=false; --fl_benchmark_mode=true; --fl_log_level=; --fl_optim_mode=; --fl_vlog_level=0; --flagsfile=; --framesizems=25; --framestridems=10; --gamma=1; --gumbeltemperature=1; --highfreqfilterbank=-1; --inputfeeding=false; --isbeamdump=false; --iter=25000; --itersave=false; --labelsmooth=0.050000000000000003; --leftWindowSize=50; --lexicon=lexicon.txt; --linlr=-1; --linlrcrit=-1; --linseg=0; --lm=; --lm_memory=5000; --lm_vocab=; --lmtype=kenlm; --lmweight=0; --lmweight_high=4; --lmweight_low=0; --lmweight_step=0.20000000000000001; --localnrmlleftctx=0; --localnrmlrightctx=0; --logadd=false; --lowfreqfilterbank=0; --lr=0.025000000000000001; --lr_decay=100; --lr_decay_step=50; --lrcosine=false; --lrcrit=0.02; --max_devices_per_node=8; --maxdecoderoutputlen=400; --maxgradnorm=0.10000000000000001; --maxload=-1; --maxrate=10; --maxsil=50; --maxword=-1; --melfloor=1; --mfcc=false; --mfcccoeffs=13; --mfsc=true; --minrate=3; --minsil=0; --momentum=0.80000000000000004; --netoptim=sgd; --nthread=6; --nthread_decoder=1; --nthread_decoder_am_forward=1; --numattnhead=8; --onorm=target; --optimepsilon=1e-08; --optimrho=0.90000000000000002; --pctteacherforcing=99; --pcttraineval=1; --pow=false; --pretrainWindow=0; --replabel=0; --reportiters=1000; --rightWindowSize=50; --rndv_filepath=; --rundir=checkpoint; --samplerate=16000; --sampletarget=0.01; --samplingstrategy=rand; --saug_fmaskf=30; --saug_fmaskn=2; --saug_start_update=24000; --saug_tmaskn=10; --saug_tmaskp=0.050000000000000003; --saug_tmaskt=30; --sclite=; --seed=0; --sfx_config=; --show=false; --showletters=false; --silscore=0; --smearing=none; --smoothingtemperature=1; --softwoffset=10; --softwrate=5; --softwstd=4; --sqnorm=true; --stepsize=9223372036854775807; --surround=; --test=; --tokens=tokens.txt; --train=train_10min_0.lst,train_10min_1.lst,train_10min_2.lst,train_10min_3.lst,train_10min_4.lst,train_10min_5.lst,train_9hr.lst; --trainWithWindow=true; --transdiag=0; --unkscore=-inf; --use_memcache=false; --uselexicon=true; --usewordpiece=false; --valid=dev:dev.lst; --validbatchsize=-1; --warmup=0; --weightdecay=0; --wordscore=0; --wordseparator=|; --world_rank=0; --world_size=1; --alsologtoemail=; --alsologtostderr=false; --colorlogtostderr=false; --drop_log_memory=true; --log_backtrace_at=; --log_dir=; --log_link=; --log_prefix=true; --logbuflevel=0; --logbufsecs=30; --logemaillevel=999; --logfile_mode=436; --logmailer=/bin/mail; --logtostderr=true; --max_log_size=1800; --minloglevel=0; --stderrthreshold=2; --stop_logging_if_full_disk=false; --symbolize_stacktrace=true; --v=0; --vmodule=;
I1224 06:39:49.002910 11517 FinetuneCTC.cpp:107] Experiment path: checkpoint
I1224 06:39:49.002919 11517 FinetuneCTC.cpp:108] Experiment runidx: 1
I1224 06:39:49.003252 11517 FinetuneCTC.cpp:153] Number of classes (network): 29
I1224 06:39:49.248888 11517 FinetuneCTC.cpp:160] Number of words: 200001
I1224 06:39:50.344347 11517 FinetuneCTC.cpp:248] Loading architecture file from arch.txt
I1224 06:39:50.868463 11517 FinetuneCTC.cpp:277] [Network] Sequential [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> (19) -> (20) -> (21) -> (22) -> (23) -> (24) -> (25) -> (26) -> (27) -> (28) -> (29) -> (30) -> (31) -> (32) -> (33) -> (34) -> (35) -> (36) -> (37) -> (38) -> (39) -> (40) -> (41) -> (42) -> output]
(0): View (-1 1 80 0)
(1): LayerNorm ( axis : { 0 1 2 } , size : -1)
(2): Conv2D (80->768, 7x1, 3,1, SAME,0, 1, 1) (with bias)
(3): GatedLinearUnit (2)
(4): Dropout (0.050000)
(5): Reorder (2,0,3,1)
(6): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(7): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(8): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(9): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(10): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(11): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(12): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(13): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(14): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(15): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(16): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(17): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(18): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(19): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(20): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(21): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(22): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(23): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(24): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(25): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(26): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(27): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(28): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(29): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(30): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(31): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(32): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(33): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(34): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(35): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(36): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(37): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(38): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(39): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(40): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(41): Transformer (nHeads: 4), (pDropout: 0.05), (pLayerdrop: 0.05), (bptt: 920), (useMask: 0), (preLayerNorm: 0)
(42): Linear (384->29) (with bias)
I1224 06:39:50.868662 11517 FinetuneCTC.cpp:278] [Network Params: 70498735]
I1224 06:39:50.868705 11517 FinetuneCTC.cpp:283] [Criterion] ConnectionistTemporalClassificationCriterion
I1224 06:39:51.004284 11517 FinetuneCTC.cpp:287] [Network Optimizer] SGD (momentum=0.8)
I1224 06:39:51.005266 11517 FinetuneCTC.cpp:547] Shuffling trainset
I1224 06:39:51.005805 11517 FinetuneCTC.cpp:554] Epoch 1 started!
I1224 06:46:52.988443 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 1000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:32 | bch(ms): 212.42 | smp(ms): 3.21 | fwd(ms): 82.29 | crit-fwd(ms): 2.90 | bwd(ms): 94.35 | optim(ms): 31.26 | loss: 2.78453 | train-TER: 9.68 | train-WER: 21.00 | dev-loss: 2.59365 | dev-TER: 10.09 | dev-WER: 19.50 | avg-isz: 287 | avg-tsz: 040 | max-tsz: 339 | avr-batchsz: 4.00 | hrs: 3.20 | thrpt(sec/sec): 54.18 | timestamp: 2020-12-24 06:46:52
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 06:53:31.970283 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 2000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:06 | bch(ms): 186.76 | smp(ms): 0.04 | fwd(ms): 69.67 | crit-fwd(ms): 2.02 | bwd(ms): 86.59 | optim(ms): 30.22 | loss: 2.63802 | train-TER: 9.86 | train-WER: 22.43 | dev-loss: 2.54714 | dev-TER: 9.84 | dev-WER: 18.86 | avg-isz: 259 | avg-tsz: 036 | max-tsz: 345 | avr-batchsz: 4.00 | hrs: 2.88 | thrpt(sec/sec): 55.57 | timestamp: 2020-12-24 06:53:31
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 07:00:07.246326 11517 FinetuneCTC.cpp:331] epoch: 1 | nupdates: 3000 | lr: 0.025000 | lrcriterion: 0.000000 | runtime: 00:03:02 | bch(ms): 182.40 | smp(ms): 0.03 | fwd(ms): 67.86 | crit-fwd(ms): 1.92 | bwd(ms): 84.27 | optim(ms): 30.00 | loss: 2.57714 | train-TER: 12.22 | train-WER: 24.38 | dev-loss: 2.45296 | dev-TER: 9.55 | dev-WER: 18.37 | avg-isz: 248 | avg-tsz: 035 | max-tsz: 257 | avr-batchsz: 4.00 | hrs: 2.76 | thrpt(sec/sec): 54.53 | timestamp: 2020-12-24 07:00:07
Memory Manager Stats
Type: CachingMemoryManager
Device: 0, Capacity: 14.72 GiB, Allocated: 12.90 GiB, Cached: 12.36 GiB
Total native calls: 1059(mallocs), 541(frees)
I1224 07:01:30.886020 11517 FinetuneCTC.cpp:547] Shuffling trainset
I1224 07:01:30.886448 11517 FinetuneCTC.cpp:554] Epoch 2 started!
[5e8e495af856:11519] *** Process received signal ***
[5e8e495af856:11519] Signal: Segmentation fault (11)
[5e8e495af856:11519] Signal code: Address not mapped (1)
[5e8e495af856:11519] Failing at address: 0x7f848b62120d
[5e8e495af856:11519] [ 0] /lib/x86_64-linux-gnu/libpthread.so.0(+0x12980)[0x7f848e2cd980]
[5e8e495af856:11519] [ 1] /lib/x86_64-linux-gnu/libc.so.6(getenv+0xa5)[0x7f848df0c8a5]
[5e8e495af856:11519] [ 2] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(_ZN13TCMallocGuardD1Ev+0x34)[0x7f848e777e44]
[5e8e495af856:11519] [ 3] /lib/x86_64-linux-gnu/libc.so.6(__cxa_finalize+0xf5)[0x7f848df0d735]
[5e8e495af856:11519] [ 4] /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4(+0x13cb3)[0x7f848e775cb3]
[5e8e495af856:11519] *** End of error message ***
^C
###Markdown
Step 4: Run Decoding Viterbi decoding
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am checkpoint/001_model_dev.bin --datadir '' --emission_dir '' --uselexicon false \
--test ami_limited_supervision/test.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1046.72_1047.07, WER: 0%, TER: 0%, total WER: 18.9985%, total TER: 8.47729%, progress (thread 0): 86.8275%]
|T|: m m
|P|: m m
[sample: ES2004c_H01_FEE013_1294.34_1294.69, WER: 0%, TER: 0%, total WER: 18.9983%, total TER: 8.47725%, progress (thread 0): 86.8354%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1302.15_1302.5, WER: 100%, TER: 0%, total WER: 18.9992%, total TER: 8.47715%, progress (thread 0): 86.8434%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1515.72_1516.07, WER: 0%, TER: 0%, total WER: 18.999%, total TER: 8.47707%, progress (thread 0): 86.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1690.13_1690.48, WER: 0%, TER: 0%, total WER: 18.9988%, total TER: 8.47699%, progress (thread 0): 86.8592%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2078.48_2078.83, WER: 100%, TER: 0%, total WER: 18.9997%, total TER: 8.47689%, progress (thread 0): 86.8671%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H02_MEE014_2291.54_2291.89, WER: 0%, TER: 0%, total WER: 18.9995%, total TER: 8.47681%, progress (thread 0): 86.875%]
|T|: o h
|P|: u m
[sample: ES2004d_H00_MEO015_127.17_127.52, WER: 100%, TER: 100%, total WER: 19.0004%, total TER: 8.47725%, progress (thread 0): 86.8829%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_561.86_562.21, WER: 0%, TER: 0%, total WER: 19.0002%, total TER: 8.47717%, progress (thread 0): 86.8908%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004d_H00_MEO015_640.16_640.51, WER: 100%, TER: 25%, total WER: 19.0011%, total TER: 8.47732%, progress (thread 0): 86.8987%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_821.44_821.79, WER: 0%, TER: 0%, total WER: 19.0009%, total TER: 8.47722%, progress (thread 0): 86.9066%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H01_FEE013_822.51_822.86, WER: 100%, TER: 0%, total WER: 19.0019%, total TER: 8.47712%, progress (thread 0): 86.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1778.02_1778.37, WER: 0%, TER: 0%, total WER: 19.0016%, total TER: 8.47704%, progress (thread 0): 86.9225%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_76.08_76.43, WER: 100%, TER: 0%, total WER: 19.0026%, total TER: 8.47694%, progress (thread 0): 86.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_431.19_431.54, WER: 0%, TER: 0%, total WER: 19.0023%, total TER: 8.47686%, progress (thread 0): 86.9383%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_492.57_492.92, WER: 100%, TER: 66.6667%, total WER: 19.0033%, total TER: 8.47727%, progress (thread 0): 86.9462%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_232.67_233.02, WER: 0%, TER: 0%, total WER: 19.003%, total TER: 8.47719%, progress (thread 0): 86.9541%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_361.11_361.46, WER: 0%, TER: 0%, total WER: 19.0028%, total TER: 8.47711%, progress (thread 0): 86.962%]
|T|: y e p
|P|: y e a h
[sample: IS1009b_H00_FIE088_723.57_723.92, WER: 100%, TER: 66.6667%, total WER: 19.0038%, total TER: 8.47752%, progress (thread 0): 86.9699%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_993.71_994.06, WER: 100%, TER: 60%, total WER: 19.0047%, total TER: 8.47813%, progress (thread 0): 86.9778%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1007.39_1007.74, WER: 0%, TER: 0%, total WER: 19.0045%, total TER: 8.47807%, progress (thread 0): 86.9858%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1079.18_1079.53, WER: 100%, TER: 0%, total WER: 19.0054%, total TER: 8.47797%, progress (thread 0): 86.9937%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1082.71_1083.06, WER: 100%, TER: 0%, total WER: 19.0063%, total TER: 8.47787%, progress (thread 0): 87.0016%]
|T|: t h i s | o n e
|P|: t h i s | o n e
[sample: IS1009b_H00_FIE088_1179.44_1179.79, WER: 0%, TER: 0%, total WER: 19.0059%, total TER: 8.47771%, progress (thread 0): 87.0095%]
|T|: y o u ' r e | t h r e e
|P|: y o u | t h r e e
[sample: IS1009b_H00_FIE088_1203.06_1203.41, WER: 50%, TER: 25%, total WER: 19.0066%, total TER: 8.47818%, progress (thread 0): 87.0174%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1699.07_1699.42, WER: 0%, TER: 0%, total WER: 19.0064%, total TER: 8.4781%, progress (thread 0): 87.0253%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1821.76_1822.11, WER: 0%, TER: 0%, total WER: 19.0061%, total TER: 8.47802%, progress (thread 0): 87.0332%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_527.63_527.98, WER: 100%, TER: 0%, total WER: 19.0071%, total TER: 8.47792%, progress (thread 0): 87.0411%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_546.82_547.17, WER: 100%, TER: 0%, total WER: 19.008%, total TER: 8.47782%, progress (thread 0): 87.049%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_609.9_610.25, WER: 100%, TER: 0%, total WER: 19.0089%, total TER: 8.47772%, progress (thread 0): 87.057%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_688.49_688.84, WER: 100%, TER: 20%, total WER: 19.0098%, total TER: 8.47786%, progress (thread 0): 87.0649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1303.58_1303.93, WER: 0%, TER: 0%, total WER: 19.0096%, total TER: 8.47778%, progress (thread 0): 87.0728%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H01_FIO087_1514.93_1515.28, WER: 100%, TER: 0%, total WER: 19.0105%, total TER: 8.47768%, progress (thread 0): 87.0807%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1737.76_1738.11, WER: 100%, TER: 0%, total WER: 19.0115%, total TER: 8.47758%, progress (thread 0): 87.0886%]
|T|: a h
|P|: m m
[sample: IS1009d_H02_FIO084_734.11_734.46, WER: 100%, TER: 100%, total WER: 19.0124%, total TER: 8.47801%, progress (thread 0): 87.0965%]
|T|: y e s
|P|: y e s
[sample: IS1009d_H01_FIO087_742.93_743.28, WER: 0%, TER: 0%, total WER: 19.0122%, total TER: 8.47795%, progress (thread 0): 87.1044%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_743.52_743.87, WER: 0%, TER: 0%, total WER: 19.0119%, total TER: 8.47787%, progress (thread 0): 87.1123%]
|T|: y e a h
|P|: w h a t
[sample: IS1009d_H02_FIO084_953.71_954.06, WER: 100%, TER: 75%, total WER: 19.0129%, total TER: 8.4785%, progress (thread 0): 87.1203%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1243.4_1243.75, WER: 0%, TER: 0%, total WER: 19.0126%, total TER: 8.47842%, progress (thread 0): 87.1282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1323.32_1323.67, WER: 0%, TER: 0%, total WER: 19.0124%, total TER: 8.47834%, progress (thread 0): 87.1361%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_1417.8_1418.15, WER: 100%, TER: 0%, total WER: 19.0133%, total TER: 8.47824%, progress (thread 0): 87.144%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1674.44_1674.79, WER: 100%, TER: 0%, total WER: 19.0143%, total TER: 8.47814%, progress (thread 0): 87.1519%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_1745.56_1745.91, WER: 100%, TER: 40%, total WER: 19.0152%, total TER: 8.47851%, progress (thread 0): 87.1598%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_506.21_506.56, WER: 0%, TER: 0%, total WER: 19.015%, total TER: 8.47843%, progress (thread 0): 87.1677%]
|T|: t h e | p e n
|P|: t e
[sample: TS3003a_H02_MTD0010ID_1074.9_1075.25, WER: 100%, TER: 71.4286%, total WER: 19.0168%, total TER: 8.47947%, progress (thread 0): 87.1756%]
|T|: r i g h t
|P|: r i g h t
[sample: TS3003a_H03_MTD012ME_1219.55_1219.9, WER: 0%, TER: 0%, total WER: 19.0166%, total TER: 8.47937%, progress (thread 0): 87.1835%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003a_H01_MTD011UID_1371.45_1371.8, WER: 100%, TER: 20%, total WER: 19.0175%, total TER: 8.4795%, progress (thread 0): 87.1915%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_1453.73_1454.08, WER: 0%, TER: 0%, total WER: 19.0173%, total TER: 8.47942%, progress (thread 0): 87.1994%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1295.49_1295.84, WER: 0%, TER: 0%, total WER: 19.0171%, total TER: 8.47938%, progress (thread 0): 87.2073%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1351.56_1351.91, WER: 0%, TER: 0%, total WER: 19.0169%, total TER: 8.47934%, progress (thread 0): 87.2152%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_180.89_181.24, WER: 0%, TER: 0%, total WER: 19.0167%, total TER: 8.47926%, progress (thread 0): 87.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1380.5_1380.85, WER: 0%, TER: 0%, total WER: 19.0164%, total TER: 8.47918%, progress (thread 0): 87.231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1531.23_1531.58, WER: 0%, TER: 0%, total WER: 19.0162%, total TER: 8.4791%, progress (thread 0): 87.2389%]
|T|: t h a t ' s | g o o d
|P|: t h a t ' s g o
[sample: TS3003c_H03_MTD012ME_1567.88_1568.23, WER: 100%, TER: 27.2727%, total WER: 19.0181%, total TER: 8.47959%, progress (thread 0): 87.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1713.34_1713.69, WER: 0%, TER: 0%, total WER: 19.0178%, total TER: 8.47951%, progress (thread 0): 87.2547%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_2280.01_2280.36, WER: 0%, TER: 0%, total WER: 19.0176%, total TER: 8.47943%, progress (thread 0): 87.2627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_306.6_306.95, WER: 0%, TER: 0%, total WER: 19.0174%, total TER: 8.47935%, progress (thread 0): 87.2706%]
|T|: n o
|P|: n o
[sample: TS3003d_H00_MTD009PM_819.47_819.82, WER: 0%, TER: 0%, total WER: 19.0172%, total TER: 8.47931%, progress (thread 0): 87.2785%]
|T|: a l r i g h t
|P|: i
[sample: TS3003d_H00_MTD009PM_965.64_965.99, WER: 100%, TER: 85.7143%, total WER: 19.0181%, total TER: 8.48058%, progress (thread 0): 87.2864%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1373.9_1374.25, WER: 100%, TER: 66.6667%, total WER: 19.019%, total TER: 8.48099%, progress (thread 0): 87.2943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1847.37_1847.72, WER: 0%, TER: 0%, total WER: 19.0188%, total TER: 8.48091%, progress (thread 0): 87.3022%]
|T|: n i n e
|P|: n i n e
[sample: TS3003d_H03_MTD012ME_1899.7_1900.05, WER: 0%, TER: 0%, total WER: 19.0186%, total TER: 8.48083%, progress (thread 0): 87.3101%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H01_MTD011UID_2218.91_2219.26, WER: 0%, TER: 0%, total WER: 19.0184%, total TER: 8.48075%, progress (thread 0): 87.318%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_142.16_142.51, WER: 0%, TER: 0%, total WER: 19.0182%, total TER: 8.48065%, progress (thread 0): 87.326%]
|T|: h m m
|P|: m m m m
[sample: EN2002a_H00_MEE073_203.92_204.27, WER: 100%, TER: 66.6667%, total WER: 19.0191%, total TER: 8.48107%, progress (thread 0): 87.3339%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_220.21_220.56, WER: 0%, TER: 0%, total WER: 19.0189%, total TER: 8.48099%, progress (thread 0): 87.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1393.11_1393.46, WER: 0%, TER: 0%, total WER: 19.0187%, total TER: 8.48091%, progress (thread 0): 87.3497%]
|T|: n o
|P|: n o
[sample: EN2002a_H02_FEO072_1494.01_1494.36, WER: 0%, TER: 0%, total WER: 19.0184%, total TER: 8.48087%, progress (thread 0): 87.3576%]
|T|: n o
|P|: y e a h
[sample: EN2002a_H01_FEO070_1616.41_1616.76, WER: 100%, TER: 200%, total WER: 19.0194%, total TER: 8.48177%, progress (thread 0): 87.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2062.28_2062.63, WER: 0%, TER: 0%, total WER: 19.0192%, total TER: 8.48169%, progress (thread 0): 87.3734%]
|T|: t h e n | u m
|P|: y o u | k n o w
[sample: EN2002b_H03_MEE073_306.19_306.54, WER: 100%, TER: 114.286%, total WER: 19.021%, total TER: 8.48343%, progress (thread 0): 87.3813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_679.83_680.18, WER: 0%, TER: 0%, total WER: 19.0208%, total TER: 8.48335%, progress (thread 0): 87.3892%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1132.98_1133.33, WER: 0%, TER: 0%, total WER: 19.0206%, total TER: 8.48327%, progress (thread 0): 87.3972%]
|T|: s h o u l d n ' t | n o
|P|: s h o u d n ' n
[sample: EN2002b_H01_MEE071_1400.16_1400.51, WER: 100%, TER: 33.3333%, total WER: 19.0224%, total TER: 8.48398%, progress (thread 0): 87.4051%]
|T|: n o
|P|: y e a h
[sample: EN2002b_H02_FEO072_1650.79_1651.14, WER: 100%, TER: 200%, total WER: 19.0233%, total TER: 8.48488%, progress (thread 0): 87.413%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_550.49_550.84, WER: 0%, TER: 0%, total WER: 19.0231%, total TER: 8.4848%, progress (thread 0): 87.4209%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_586.84_587.19, WER: 0%, TER: 0%, total WER: 19.0229%, total TER: 8.48472%, progress (thread 0): 87.4288%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_725.66_726.01, WER: 0%, TER: 0%, total WER: 19.0227%, total TER: 8.48468%, progress (thread 0): 87.4367%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_743.92_744.27, WER: 0%, TER: 0%, total WER: 19.0225%, total TER: 8.48464%, progress (thread 0): 87.4446%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_975_975.35, WER: 0%, TER: 0%, total WER: 19.0222%, total TER: 8.48456%, progress (thread 0): 87.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1520.32_1520.67, WER: 0%, TER: 0%, total WER: 19.022%, total TER: 8.48448%, progress (thread 0): 87.4604%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_1667.21_1667.56, WER: 0%, TER: 0%, total WER: 19.0218%, total TER: 8.48444%, progress (thread 0): 87.4684%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_2075.98_2076.33, WER: 0%, TER: 0%, total WER: 19.0216%, total TER: 8.4844%, progress (thread 0): 87.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2182.63_2182.98, WER: 0%, TER: 0%, total WER: 19.0214%, total TER: 8.48432%, progress (thread 0): 87.4842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_352.2_352.55, WER: 0%, TER: 0%, total WER: 19.0212%, total TER: 8.48424%, progress (thread 0): 87.4921%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002d_H03_MEE073_387.18_387.53, WER: 0%, TER: 0%, total WER: 19.0207%, total TER: 8.4841%, progress (thread 0): 87.5%]
|T|: m m
|P|: m m
[sample: EN2002d_H01_FEO072_831.78_832.13, WER: 0%, TER: 0%, total WER: 19.0205%, total TER: 8.48406%, progress (thread 0): 87.5079%]
|T|: n o
|P|: n o
[sample: EN2002d_H03_MEE073_909.36_909.71, WER: 0%, TER: 0%, total WER: 19.0203%, total TER: 8.48402%, progress (thread 0): 87.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1113.08_1113.43, WER: 0%, TER: 0%, total WER: 19.0201%, total TER: 8.48394%, progress (thread 0): 87.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1233.84_1234.19, WER: 0%, TER: 0%, total WER: 19.0199%, total TER: 8.48386%, progress (thread 0): 87.5316%]
|T|: i | d o n ' t | k n o w
|P|: i d
[sample: EN2002d_H01_FEO072_1245.04_1245.39, WER: 100%, TER: 83.3333%, total WER: 19.0226%, total TER: 8.48597%, progress (thread 0): 87.5396%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1798.12_1798.47, WER: 0%, TER: 0%, total WER: 19.0224%, total TER: 8.48589%, progress (thread 0): 87.5475%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1926.51_1926.86, WER: 0%, TER: 0%, total WER: 19.0222%, total TER: 8.48581%, progress (thread 0): 87.5554%]
|T|: u h h u h
|P|: u m
[sample: EN2002d_H00_FEO070_2027.85_2028.2, WER: 100%, TER: 80%, total WER: 19.0231%, total TER: 8.48666%, progress (thread 0): 87.5633%]
|T|: o h | s o r r y
|P|: o k a y
[sample: ES2004a_H00_MEO015_255.91_256.25, WER: 100%, TER: 75%, total WER: 19.025%, total TER: 8.48791%, progress (thread 0): 87.5712%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H01_FEE013_545.3_545.64, WER: 100%, TER: 0%, total WER: 19.0259%, total TER: 8.48781%, progress (thread 0): 87.5791%]
|T|: r e a l l y
|P|: r e a l l y
[sample: ES2004a_H01_FEE013_603.88_604.22, WER: 0%, TER: 0%, total WER: 19.0257%, total TER: 8.48769%, progress (thread 0): 87.587%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_635.54_635.88, WER: 100%, TER: 0%, total WER: 19.0266%, total TER: 8.48759%, progress (thread 0): 87.5949%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H03_FEE016_811.15_811.49, WER: 0%, TER: 0%, total WER: 19.0264%, total TER: 8.48751%, progress (thread 0): 87.6028%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_954.93_955.27, WER: 100%, TER: 0%, total WER: 19.0273%, total TER: 8.48741%, progress (thread 0): 87.6108%]
|T|: m m h m m
|P|: m m
[sample: ES2004b_H03_FEE016_1445.71_1446.05, WER: 100%, TER: 60%, total WER: 19.0282%, total TER: 8.48802%, progress (thread 0): 87.6187%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1995.5_1995.84, WER: 100%, TER: 0%, total WER: 19.0291%, total TER: 8.48792%, progress (thread 0): 87.6266%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H03_FEE016_22.85_23.19, WER: 100%, TER: 25%, total WER: 19.03%, total TER: 8.48807%, progress (thread 0): 87.6345%]
|T|: t h a n k | y o u
|P|: o k a y
[sample: ES2004c_H03_FEE016_201.1_201.44, WER: 100%, TER: 77.7778%, total WER: 19.0319%, total TER: 8.48954%, progress (thread 0): 87.6424%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_451.06_451.4, WER: 100%, TER: 25%, total WER: 19.0328%, total TER: 8.4897%, progress (thread 0): 87.6503%]
|T|: n o
|P|: n o t
[sample: ES2004c_H02_MEE014_535.19_535.53, WER: 100%, TER: 50%, total WER: 19.0337%, total TER: 8.48989%, progress (thread 0): 87.6582%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1178.32_1178.66, WER: 0%, TER: 0%, total WER: 19.0335%, total TER: 8.48981%, progress (thread 0): 87.6661%]
|T|: h m m
|P|: m m
[sample: ES2004c_H02_MEE014_1181.26_1181.6, WER: 100%, TER: 33.3333%, total WER: 19.0344%, total TER: 8.48999%, progress (thread 0): 87.674%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1478.47_1478.81, WER: 0%, TER: 0%, total WER: 19.0342%, total TER: 8.48995%, progress (thread 0): 87.682%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1640.61_1640.95, WER: 100%, TER: 0%, total WER: 19.0351%, total TER: 8.48985%, progress (thread 0): 87.6899%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2025.11_2025.45, WER: 100%, TER: 0%, total WER: 19.036%, total TER: 8.48975%, progress (thread 0): 87.6978%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_2072.31_2072.65, WER: 100%, TER: 0%, total WER: 19.037%, total TER: 8.48965%, progress (thread 0): 87.7057%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_100.76_101.1, WER: 0%, TER: 0%, total WER: 19.0367%, total TER: 8.48957%, progress (thread 0): 87.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H01_FEE013_139.4_139.74, WER: 0%, TER: 0%, total WER: 19.0365%, total TER: 8.48947%, progress (thread 0): 87.7215%]
|T|: y e p
|P|: y e p
[sample: ES2004d_H00_MEO015_155.57_155.91, WER: 0%, TER: 0%, total WER: 19.0363%, total TER: 8.48941%, progress (thread 0): 87.7294%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_178.12_178.46, WER: 0%, TER: 0%, total WER: 19.0361%, total TER: 8.48933%, progress (thread 0): 87.7373%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_405.44_405.78, WER: 0%, TER: 0%, total WER: 19.0359%, total TER: 8.48925%, progress (thread 0): 87.7453%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H00_MEO015_548.5_548.84, WER: 100%, TER: 0%, total WER: 19.0368%, total TER: 8.48915%, progress (thread 0): 87.7532%]
|T|: t h r e e
|P|: t h r e e
[sample: ES2004d_H02_MEE014_646.42_646.76, WER: 0%, TER: 0%, total WER: 19.0366%, total TER: 8.48905%, progress (thread 0): 87.7611%]
|T|: f o u r
|P|: f o u r
[sample: ES2004d_H01_FEE013_911.53_911.87, WER: 0%, TER: 0%, total WER: 19.0364%, total TER: 8.48897%, progress (thread 0): 87.769%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1135.09_1135.43, WER: 0%, TER: 0%, total WER: 19.0362%, total TER: 8.48889%, progress (thread 0): 87.7769%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_1953.89_1954.23, WER: 0%, TER: 0%, total WER: 19.0359%, total TER: 8.48885%, progress (thread 0): 87.7848%]
|T|: y e s
|P|: y e s
[sample: IS1009a_H01_FIO087_792.79_793.13, WER: 0%, TER: 0%, total WER: 19.0357%, total TER: 8.48879%, progress (thread 0): 87.7927%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_43.16_43.5, WER: 100%, TER: 0%, total WER: 19.0366%, total TER: 8.48869%, progress (thread 0): 87.8006%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_260.98_261.32, WER: 0%, TER: 0%, total WER: 19.0364%, total TER: 8.48861%, progress (thread 0): 87.8085%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_425.4_425.74, WER: 0%, TER: 0%, total WER: 19.0362%, total TER: 8.48853%, progress (thread 0): 87.8165%]
|T|: y e p
|P|: y o h
[sample: IS1009b_H00_FIE088_598.28_598.62, WER: 100%, TER: 66.6667%, total WER: 19.0371%, total TER: 8.48894%, progress (thread 0): 87.8244%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_613.29_613.63, WER: 0%, TER: 0%, total WER: 19.0369%, total TER: 8.48886%, progress (thread 0): 87.8323%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1002.09_1002.43, WER: 0%, TER: 0%, total WER: 19.0367%, total TER: 8.4888%, progress (thread 0): 87.8402%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H00_FIE088_1223.66_1224, WER: 100%, TER: 20%, total WER: 19.0376%, total TER: 8.48894%, progress (thread 0): 87.8481%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1225.44_1225.78, WER: 100%, TER: 0%, total WER: 19.0385%, total TER: 8.48884%, progress (thread 0): 87.856%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H03_FIO089_1734.63_1734.97, WER: 100%, TER: 200%, total WER: 19.0395%, total TER: 8.48974%, progress (thread 0): 87.8639%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1834.47_1834.81, WER: 0%, TER: 0%, total WER: 19.0392%, total TER: 8.48966%, progress (thread 0): 87.8718%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_1882.63_1882.97, WER: 0%, TER: 0%, total WER: 19.039%, total TER: 8.48958%, progress (thread 0): 87.8797%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H01_FIO087_1960.82_1961.16, WER: 100%, TER: 0%, total WER: 19.0399%, total TER: 8.48948%, progress (thread 0): 87.8877%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_535.66_536, WER: 100%, TER: 0%, total WER: 19.0409%, total TER: 8.48938%, progress (thread 0): 87.8956%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_575.77_576.11, WER: 100%, TER: 0%, total WER: 19.0418%, total TER: 8.48928%, progress (thread 0): 87.9035%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_950.04_950.38, WER: 100%, TER: 0%, total WER: 19.0427%, total TER: 8.48918%, progress (thread 0): 87.9114%]
|T|: i t | w o r k s
|P|: b u t
[sample: IS1009c_H01_FIO087_1083.82_1084.16, WER: 100%, TER: 100%, total WER: 19.0445%, total TER: 8.4909%, progress (thread 0): 87.9193%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_1109.75_1110.09, WER: 100%, TER: 0%, total WER: 19.0455%, total TER: 8.4908%, progress (thread 0): 87.9272%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1603.38_1603.72, WER: 100%, TER: 0%, total WER: 19.0464%, total TER: 8.4907%, progress (thread 0): 87.9351%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1613.06_1613.4, WER: 100%, TER: 0%, total WER: 19.0473%, total TER: 8.4906%, progress (thread 0): 87.943%]
|T|: y e s
|P|: y e a s
[sample: IS1009c_H01_FIO087_1633.76_1634.1, WER: 100%, TER: 33.3333%, total WER: 19.0482%, total TER: 8.49078%, progress (thread 0): 87.951%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_434.93_435.27, WER: 100%, TER: 0%, total WER: 19.0491%, total TER: 8.49068%, progress (thread 0): 87.9589%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_637.66_638, WER: 100%, TER: 40%, total WER: 19.0501%, total TER: 8.49105%, progress (thread 0): 87.9668%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_649.7_650.04, WER: 0%, TER: 0%, total WER: 19.0498%, total TER: 8.49095%, progress (thread 0): 87.9747%]
|T|: h m m
|P|: m h t
[sample: IS1009d_H02_FIO084_674.88_675.22, WER: 100%, TER: 100%, total WER: 19.0508%, total TER: 8.49159%, progress (thread 0): 87.9826%]
|T|: o o p s
|P|: u p s
[sample: IS1009d_H00_FIE088_1068.99_1069.33, WER: 100%, TER: 50%, total WER: 19.0517%, total TER: 8.49199%, progress (thread 0): 87.9905%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H01_FIO087_1509.75_1510.09, WER: 0%, TER: 0%, total WER: 19.0515%, total TER: 8.49193%, progress (thread 0): 87.9984%]
|T|: t h a n k | y o u
|P|: t h a n k | y o u
[sample: TS3003a_H03_MTD012ME_48.1_48.44, WER: 0%, TER: 0%, total WER: 19.051%, total TER: 8.49175%, progress (thread 0): 88.0063%]
|T|: g u e s s
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_983.96_984.3, WER: 100%, TER: 80%, total WER: 19.0519%, total TER: 8.49259%, progress (thread 0): 88.0142%]
|T|: t h e | p e n
|P|: m m h
[sample: TS3003a_H02_MTD0010ID_1090.49_1090.83, WER: 100%, TER: 100%, total WER: 19.0538%, total TER: 8.49409%, progress (thread 0): 88.0222%]
|T|: t h i n g
|P|: t h i n g
[sample: TS3003a_H00_MTD009PM_1320.19_1320.53, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49399%, progress (thread 0): 88.0301%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_132.84_133.18, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49395%, progress (thread 0): 88.038%]
|T|: r i g h t
|P|: b u h
[sample: TS3003b_H01_MTD011UID_2086.12_2086.46, WER: 100%, TER: 80%, total WER: 19.0543%, total TER: 8.4948%, progress (thread 0): 88.0459%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2089.85_2090.19, WER: 0%, TER: 0%, total WER: 19.0541%, total TER: 8.49472%, progress (thread 0): 88.0538%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_982.32_982.66, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49464%, progress (thread 0): 88.0617%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_1007.54_1007.88, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49456%, progress (thread 0): 88.0696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1240.35_1240.69, WER: 0%, TER: 0%, total WER: 19.0534%, total TER: 8.49448%, progress (thread 0): 88.0775%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1413.87_1414.21, WER: 0%, TER: 0%, total WER: 19.0532%, total TER: 8.4944%, progress (thread 0): 88.0854%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1849.58_1849.92, WER: 0%, TER: 0%, total WER: 19.053%, total TER: 8.49432%, progress (thread 0): 88.0934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_595.75_596.09, WER: 0%, TER: 0%, total WER: 19.0528%, total TER: 8.49424%, progress (thread 0): 88.1013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_921.62_921.96, WER: 0%, TER: 0%, total WER: 19.0525%, total TER: 8.49416%, progress (thread 0): 88.1092%]
|T|: u h | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1308.11_1308.45, WER: 50%, TER: 42.8571%, total WER: 19.0532%, total TER: 8.49472%, progress (thread 0): 88.1171%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1388.52_1388.86, WER: 0%, TER: 0%, total WER: 19.053%, total TER: 8.49464%, progress (thread 0): 88.125%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1802.71_1803.05, WER: 0%, TER: 0%, total WER: 19.0528%, total TER: 8.49456%, progress (thread 0): 88.1329%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1835.63_1835.97, WER: 100%, TER: 25%, total WER: 19.0537%, total TER: 8.49472%, progress (thread 0): 88.1408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2091.81_2092.15, WER: 0%, TER: 0%, total WER: 19.0535%, total TER: 8.49464%, progress (thread 0): 88.1487%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_2217.62_2217.96, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49456%, progress (thread 0): 88.1566%]
|T|: m m | y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2323.41_2323.75, WER: 50%, TER: 42.8571%, total WER: 19.054%, total TER: 8.49512%, progress (thread 0): 88.1646%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2420.39_2420.73, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49504%, progress (thread 0): 88.1725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_406.15_406.49, WER: 0%, TER: 0%, total WER: 19.0536%, total TER: 8.49496%, progress (thread 0): 88.1804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_601.54_601.88, WER: 0%, TER: 0%, total WER: 19.0533%, total TER: 8.49488%, progress (thread 0): 88.1883%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H02_FEO072_713.14_713.48, WER: 0%, TER: 0%, total WER: 19.0531%, total TER: 8.49482%, progress (thread 0): 88.1962%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_859.83_860.17, WER: 100%, TER: 66.6667%, total WER: 19.0541%, total TER: 8.49524%, progress (thread 0): 88.2041%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002a_H01_FEO070_920.16_920.5, WER: 100%, TER: 28.5714%, total WER: 19.055%, total TER: 8.49557%, progress (thread 0): 88.212%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1586.73_1587.07, WER: 0%, TER: 0%, total WER: 19.0548%, total TER: 8.49549%, progress (thread 0): 88.2199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1927.75_1928.09, WER: 0%, TER: 0%, total WER: 19.0545%, total TER: 8.49541%, progress (thread 0): 88.2279%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1929.86_1930.2, WER: 0%, TER: 0%, total WER: 19.0543%, total TER: 8.49533%, progress (thread 0): 88.2358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_2048.6_2048.94, WER: 0%, TER: 0%, total WER: 19.0541%, total TER: 8.49525%, progress (thread 0): 88.2437%]
|T|: s o | i t ' s
|P|: t m
[sample: EN2002a_H03_MEE071_2098.66_2099, WER: 100%, TER: 85.7143%, total WER: 19.0559%, total TER: 8.49652%, progress (thread 0): 88.2516%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_2129.89_2130.23, WER: 0%, TER: 0%, total WER: 19.0557%, total TER: 8.49644%, progress (thread 0): 88.2595%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_144.51_144.85, WER: 0%, TER: 0%, total WER: 19.0555%, total TER: 8.49636%, progress (thread 0): 88.2674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_190.18_190.52, WER: 0%, TER: 0%, total WER: 19.0553%, total TER: 8.49628%, progress (thread 0): 88.2753%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_302.27_302.61, WER: 100%, TER: 66.6667%, total WER: 19.0562%, total TER: 8.49669%, progress (thread 0): 88.2832%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_309.52_309.86, WER: 0%, TER: 0%, total WER: 19.056%, total TER: 8.49661%, progress (thread 0): 88.2911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_363.35_363.69, WER: 0%, TER: 0%, total WER: 19.0558%, total TER: 8.49653%, progress (thread 0): 88.299%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_409.14_409.48, WER: 0%, TER: 0%, total WER: 19.0556%, total TER: 8.49645%, progress (thread 0): 88.307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_736.4_736.74, WER: 0%, TER: 0%, total WER: 19.0553%, total TER: 8.49637%, progress (thread 0): 88.3149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_935.86_936.2, WER: 0%, TER: 0%, total WER: 19.0551%, total TER: 8.49629%, progress (thread 0): 88.3228%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002b_H03_MEE073_1252.52_1252.86, WER: 0%, TER: 0%, total WER: 19.0547%, total TER: 8.49615%, progress (thread 0): 88.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1563.14_1563.48, WER: 0%, TER: 0%, total WER: 19.0545%, total TER: 8.49607%, progress (thread 0): 88.3386%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1626.33_1626.67, WER: 0%, TER: 0%, total WER: 19.0543%, total TER: 8.49599%, progress (thread 0): 88.3465%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H01_FEO072_67.59_67.93, WER: 0%, TER: 0%, total WER: 19.054%, total TER: 8.49591%, progress (thread 0): 88.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_273.14_273.48, WER: 0%, TER: 0%, total WER: 19.0538%, total TER: 8.49583%, progress (thread 0): 88.3623%]
|T|: m m h m m
|P|: m m m m
[sample: EN2002c_H03_MEE073_444.82_445.16, WER: 100%, TER: 20%, total WER: 19.0548%, total TER: 8.49596%, progress (thread 0): 88.3703%]
|T|: o r
|P|: m h
[sample: EN2002c_H01_FEO072_486.4_486.74, WER: 100%, TER: 100%, total WER: 19.0557%, total TER: 8.4964%, progress (thread 0): 88.3782%]
|T|: o h | w e l l
|P|: o h
[sample: EN2002c_H03_MEE073_497.23_497.57, WER: 50%, TER: 71.4286%, total WER: 19.0564%, total TER: 8.49743%, progress (thread 0): 88.3861%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002c_H01_FEO072_721.91_722.25, WER: 200%, TER: 14.2857%, total WER: 19.0584%, total TER: 8.49753%, progress (thread 0): 88.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1305.34_1305.68, WER: 0%, TER: 0%, total WER: 19.0582%, total TER: 8.49745%, progress (thread 0): 88.4019%]
|T|: o h
|P|: o h
[sample: EN2002c_H01_FEO072_1925.9_1926.24, WER: 0%, TER: 0%, total WER: 19.058%, total TER: 8.49741%, progress (thread 0): 88.4098%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2040.49_2040.83, WER: 0%, TER: 0%, total WER: 19.0578%, total TER: 8.49733%, progress (thread 0): 88.4177%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002c_H03_MEE073_2245.81_2246.15, WER: 100%, TER: 20%, total WER: 19.0587%, total TER: 8.49746%, progress (thread 0): 88.4256%]
|T|: y e a h
|P|: r e a h
[sample: EN2002d_H03_MEE073_344.02_344.36, WER: 100%, TER: 25%, total WER: 19.0596%, total TER: 8.49762%, progress (thread 0): 88.4335%]
|T|: w h a t | w a s | i t
|P|: w h a | w a s | i t
[sample: EN2002d_H03_MEE073_508.22_508.56, WER: 33.3333%, TER: 9.09091%, total WER: 19.0601%, total TER: 8.49763%, progress (thread 0): 88.4415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_688.4_688.74, WER: 0%, TER: 0%, total WER: 19.0599%, total TER: 8.49755%, progress (thread 0): 88.4494%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_715.16_715.5, WER: 0%, TER: 0%, total WER: 19.0597%, total TER: 8.49747%, progress (thread 0): 88.4573%]
|T|: u h
|P|: m m
[sample: EN2002d_H03_MEE073_1125.77_1126.11, WER: 100%, TER: 100%, total WER: 19.0606%, total TER: 8.4979%, progress (thread 0): 88.4652%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1325.04_1325.38, WER: 100%, TER: 33.3333%, total WER: 19.0615%, total TER: 8.49808%, progress (thread 0): 88.4731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1423.14_1423.48, WER: 0%, TER: 0%, total WER: 19.0613%, total TER: 8.498%, progress (thread 0): 88.481%]
|T|: n o | w e | p
|P|: n o | w e
[sample: EN2002d_H00_FEO070_1437.94_1438.28, WER: 33.3333%, TER: 28.5714%, total WER: 19.0618%, total TER: 8.49833%, progress (thread 0): 88.4889%]
|T|: w i t h
|P|: w i t h
[sample: EN2002d_H02_MEE071_2073.15_2073.49, WER: 0%, TER: 0%, total WER: 19.0616%, total TER: 8.49825%, progress (thread 0): 88.4968%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_258.31_258.64, WER: 100%, TER: 0%, total WER: 19.0625%, total TER: 8.49815%, progress (thread 0): 88.5048%]
|T|: m m | y e a h
|P|: m y e a h
[sample: ES2004a_H00_MEO015_1048.05_1048.38, WER: 100%, TER: 28.5714%, total WER: 19.0643%, total TER: 8.49848%, progress (thread 0): 88.5127%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_736.03_736.36, WER: 0%, TER: 0%, total WER: 19.0641%, total TER: 8.4984%, progress (thread 0): 88.5206%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1313.86_1314.19, WER: 0%, TER: 0%, total WER: 19.0639%, total TER: 8.49832%, progress (thread 0): 88.5285%]
|T|: m m
|P|: m m
[sample: ES2004b_H01_FEE013_1711.46_1711.79, WER: 0%, TER: 0%, total WER: 19.0637%, total TER: 8.49828%, progress (thread 0): 88.5364%]
|T|: u h h u h
|P|: m h | h u h
[sample: ES2004b_H03_FEE016_2208.79_2209.12, WER: 200%, TER: 40%, total WER: 19.0657%, total TER: 8.49865%, progress (thread 0): 88.5443%]
|T|: o k a y
|P|: o k a y
[sample: ES2004b_H03_FEE016_2308.52_2308.85, WER: 0%, TER: 0%, total WER: 19.0655%, total TER: 8.49857%, progress (thread 0): 88.5522%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_172.32_172.65, WER: 0%, TER: 0%, total WER: 19.0653%, total TER: 8.49849%, progress (thread 0): 88.5601%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_560.34_560.67, WER: 0%, TER: 0%, total WER: 19.0651%, total TER: 8.49845%, progress (thread 0): 88.568%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H01_FEE013_873.37_873.7, WER: 100%, TER: 0%, total WER: 19.066%, total TER: 8.49835%, progress (thread 0): 88.576%]
|T|: w e l l
|P|: w e l l
[sample: ES2004c_H02_MEE014_925.27_925.6, WER: 0%, TER: 0%, total WER: 19.0658%, total TER: 8.49827%, progress (thread 0): 88.5839%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1268.11_1268.44, WER: 0%, TER: 0%, total WER: 19.0656%, total TER: 8.49819%, progress (thread 0): 88.5918%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H03_FEE016_1965.56_1965.89, WER: 100%, TER: 0%, total WER: 19.0665%, total TER: 8.49809%, progress (thread 0): 88.5997%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H01_FEE013_607.94_608.27, WER: 100%, TER: 0%, total WER: 19.0674%, total TER: 8.49799%, progress (thread 0): 88.6076%]
|T|: t w o
|P|: t w o
[sample: ES2004d_H02_MEE014_753.08_753.41, WER: 0%, TER: 0%, total WER: 19.0672%, total TER: 8.49793%, progress (thread 0): 88.6155%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H03_FEE016_876.07_876.4, WER: 100%, TER: 0%, total WER: 19.0681%, total TER: 8.49783%, progress (thread 0): 88.6234%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H00_MEO015_1304.99_1305.32, WER: 0%, TER: 0%, total WER: 19.0679%, total TER: 8.49777%, progress (thread 0): 88.6313%]
|T|: s u r e
|P|: s u r e
[sample: ES2004d_H03_FEE016_1499.95_1500.28, WER: 0%, TER: 0%, total WER: 19.0677%, total TER: 8.49769%, progress (thread 0): 88.6392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1845.84_1846.17, WER: 0%, TER: 0%, total WER: 19.0674%, total TER: 8.49761%, progress (thread 0): 88.6471%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1856.75_1857.08, WER: 0%, TER: 0%, total WER: 19.0672%, total TER: 8.49753%, progress (thread 0): 88.6551%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1984.68_1985.01, WER: 0%, TER: 0%, total WER: 19.067%, total TER: 8.49745%, progress (thread 0): 88.663%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_2126.04_2126.37, WER: 0%, TER: 0%, total WER: 19.0668%, total TER: 8.49741%, progress (thread 0): 88.6709%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_55.7_56.03, WER: 0%, TER: 0%, total WER: 19.0666%, total TER: 8.49733%, progress (thread 0): 88.6788%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_226.94_227.27, WER: 100%, TER: 0%, total WER: 19.0675%, total TER: 8.49723%, progress (thread 0): 88.6867%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_318.12_318.45, WER: 0%, TER: 0%, total WER: 19.0673%, total TER: 8.49715%, progress (thread 0): 88.6946%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_652.91_653.24, WER: 100%, TER: 0%, total WER: 19.0682%, total TER: 8.49705%, progress (thread 0): 88.7025%]
|T|: t h e n
|P|: a n d | t h e n
[sample: IS1009a_H00_FIE088_714.21_714.54, WER: 100%, TER: 100%, total WER: 19.0691%, total TER: 8.49791%, progress (thread 0): 88.7104%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_741.37_741.7, WER: 100%, TER: 0%, total WER: 19.07%, total TER: 8.49781%, progress (thread 0): 88.7184%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_790.95_791.28, WER: 100%, TER: 0%, total WER: 19.0709%, total TER: 8.49771%, progress (thread 0): 88.7263%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_787.47_787.8, WER: 100%, TER: 0%, total WER: 19.0719%, total TER: 8.49761%, progress (thread 0): 88.7342%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1063.18_1063.51, WER: 100%, TER: 0%, total WER: 19.0728%, total TER: 8.49751%, progress (thread 0): 88.7421%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1809.47_1809.8, WER: 0%, TER: 0%, total WER: 19.0726%, total TER: 8.49743%, progress (thread 0): 88.75%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1882.22_1882.55, WER: 100%, TER: 0%, total WER: 19.0735%, total TER: 8.49733%, progress (thread 0): 88.7579%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_408.02_408.35, WER: 100%, TER: 0%, total WER: 19.0744%, total TER: 8.49723%, progress (thread 0): 88.7658%]
|T|: m m h m m
|P|: m m
[sample: IS1009c_H00_FIE088_430.74_431.07, WER: 100%, TER: 60%, total WER: 19.0753%, total TER: 8.49784%, progress (thread 0): 88.7737%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1552.21_1552.54, WER: 0%, TER: 0%, total WER: 19.0751%, total TER: 8.49776%, progress (thread 0): 88.7816%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_383.89_384.22, WER: 100%, TER: 0%, total WER: 19.076%, total TER: 8.49766%, progress (thread 0): 88.7896%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_388.95_389.28, WER: 100%, TER: 0%, total WER: 19.0769%, total TER: 8.49756%, progress (thread 0): 88.7975%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_483.18_483.51, WER: 100%, TER: 0%, total WER: 19.0779%, total TER: 8.49746%, progress (thread 0): 88.8054%]
|T|: t h a t ' s | r i g h t
|P|: t h a t ' s | r g h t
[sample: IS1009d_H00_FIE088_757.72_758.05, WER: 50%, TER: 8.33333%, total WER: 19.0786%, total TER: 8.49745%, progress (thread 0): 88.8133%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H01_FIO087_784.18_784.51, WER: 100%, TER: 0%, total WER: 19.0795%, total TER: 8.49735%, progress (thread 0): 88.8212%]
|T|: y e a h
|P|: m m
[sample: IS1009d_H02_FIO084_975.71_976.04, WER: 100%, TER: 100%, total WER: 19.0804%, total TER: 8.49822%, progress (thread 0): 88.8291%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_1183.1_1183.43, WER: 0%, TER: 0%, total WER: 19.0802%, total TER: 8.49818%, progress (thread 0): 88.837%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H02_FIO084_1185.85_1186.18, WER: 100%, TER: 0%, total WER: 19.0811%, total TER: 8.49808%, progress (thread 0): 88.8449%]
|T|: m m h m m
|P|: m m m
[sample: IS1009d_H02_FIO084_1310.32_1310.65, WER: 100%, TER: 40%, total WER: 19.082%, total TER: 8.49845%, progress (thread 0): 88.8528%]
|T|: u h
|P|: u m
[sample: TS3003a_H01_MTD011UID_915.22_915.55, WER: 100%, TER: 50%, total WER: 19.0829%, total TER: 8.49864%, progress (thread 0): 88.8608%]
|T|: o r | w h o
|P|: o h | w h o
[sample: TS3003a_H01_MTD011UID_975.03_975.36, WER: 50%, TER: 16.6667%, total WER: 19.0836%, total TER: 8.49876%, progress (thread 0): 88.8687%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1261.99_1262.32, WER: 0%, TER: 0%, total WER: 19.0834%, total TER: 8.49872%, progress (thread 0): 88.8766%]
|T|: s o
|P|: s o
[sample: TS3003b_H03_MTD012ME_280.47_280.8, WER: 0%, TER: 0%, total WER: 19.0832%, total TER: 8.49868%, progress (thread 0): 88.8845%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_842.62_842.95, WER: 0%, TER: 0%, total WER: 19.083%, total TER: 8.4986%, progress (thread 0): 88.8924%]
|T|: o k a y
|P|: h m h
[sample: TS3003b_H00_MTD009PM_923.51_923.84, WER: 100%, TER: 100%, total WER: 19.0839%, total TER: 8.49946%, progress (thread 0): 88.9003%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1173.66_1173.99, WER: 0%, TER: 0%, total WER: 19.0837%, total TER: 8.49938%, progress (thread 0): 88.9082%]
|T|: n a h
|P|: n e a h
[sample: TS3003c_H01_MTD011UID_1009.07_1009.4, WER: 100%, TER: 33.3333%, total WER: 19.0846%, total TER: 8.49955%, progress (thread 0): 88.9161%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1321.92_1322.25, WER: 100%, TER: 25%, total WER: 19.0855%, total TER: 8.49971%, progress (thread 0): 88.924%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1395.11_1395.44, WER: 0%, TER: 0%, total WER: 19.0853%, total TER: 8.49967%, progress (thread 0): 88.932%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1507.79_1508.12, WER: 0%, TER: 0%, total WER: 19.0851%, total TER: 8.49959%, progress (thread 0): 88.9399%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_1540.01_1540.34, WER: 100%, TER: 0%, total WER: 19.086%, total TER: 8.49949%, progress (thread 0): 88.9478%]
|T|: y e p
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1653.43_1653.76, WER: 100%, TER: 66.6667%, total WER: 19.0869%, total TER: 8.4999%, progress (thread 0): 88.9557%]
|T|: u h
|P|: m h m m
[sample: TS3003c_H01_MTD011UID_1692.63_1692.96, WER: 100%, TER: 150%, total WER: 19.0878%, total TER: 8.50056%, progress (thread 0): 88.9636%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2074.03_2074.36, WER: 0%, TER: 0%, total WER: 19.0876%, total TER: 8.50048%, progress (thread 0): 88.9715%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2241.48_2241.81, WER: 0%, TER: 0%, total WER: 19.0874%, total TER: 8.5004%, progress (thread 0): 88.9794%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_693.96_694.29, WER: 0%, TER: 0%, total WER: 19.0872%, total TER: 8.50032%, progress (thread 0): 88.9873%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_872.34_872.67, WER: 0%, TER: 0%, total WER: 19.087%, total TER: 8.50024%, progress (thread 0): 88.9953%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_889.13_889.46, WER: 0%, TER: 0%, total WER: 19.0868%, total TER: 8.50016%, progress (thread 0): 89.0032%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H00_MTD009PM_1661.82_1662.15, WER: 0%, TER: 0%, total WER: 19.0865%, total TER: 8.50008%, progress (thread 0): 89.0111%]
|T|: y e a h
|P|: n a h
[sample: TS3003d_H01_MTD011UID_2072.21_2072.54, WER: 100%, TER: 50%, total WER: 19.0875%, total TER: 8.50047%, progress (thread 0): 89.019%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_2085.82_2086.15, WER: 100%, TER: 0%, total WER: 19.0884%, total TER: 8.50037%, progress (thread 0): 89.0269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2164.01_2164.34, WER: 0%, TER: 0%, total WER: 19.0882%, total TER: 8.50029%, progress (thread 0): 89.0348%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2179.8_2180.13, WER: 0%, TER: 0%, total WER: 19.0879%, total TER: 8.50021%, progress (thread 0): 89.0427%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_2461.76_2462.09, WER: 100%, TER: 0%, total WER: 19.0889%, total TER: 8.50011%, progress (thread 0): 89.0506%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_299.48_299.81, WER: 0%, TER: 0%, total WER: 19.0886%, total TER: 8.50003%, progress (thread 0): 89.0585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_359.5_359.83, WER: 0%, TER: 0%, total WER: 19.0884%, total TER: 8.49995%, progress (thread 0): 89.0665%]
|T|: w a i t
|P|: w a i t
[sample: EN2002a_H02_FEO072_387.53_387.86, WER: 0%, TER: 0%, total WER: 19.0882%, total TER: 8.49987%, progress (thread 0): 89.0744%]
|T|: y e a h | y e a h
|P|: e h | y e a h
[sample: EN2002a_H03_MEE071_593.65_593.98, WER: 50%, TER: 22.2222%, total WER: 19.0889%, total TER: 8.50016%, progress (thread 0): 89.0823%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_594.32_594.65, WER: 0%, TER: 0%, total WER: 19.0887%, total TER: 8.50008%, progress (thread 0): 89.0902%]
|T|: y e a h
|P|: n e a h
[sample: EN2002a_H01_FEO070_690.97_691.3, WER: 100%, TER: 25%, total WER: 19.0896%, total TER: 8.50024%, progress (thread 0): 89.0981%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_1018.98_1019.31, WER: 0%, TER: 0%, total WER: 19.0894%, total TER: 8.50016%, progress (thread 0): 89.106%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1028.68_1029.01, WER: 0%, TER: 0%, total WER: 19.0892%, total TER: 8.50008%, progress (thread 0): 89.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1142.23_1142.56, WER: 0%, TER: 0%, total WER: 19.089%, total TER: 8.5%, progress (thread 0): 89.1218%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1361.22_1361.55, WER: 0%, TER: 0%, total WER: 19.0887%, total TER: 8.49992%, progress (thread 0): 89.1297%]
|T|: r i g h t
|P|: i h
[sample: EN2002a_H03_MEE071_1383.46_1383.79, WER: 100%, TER: 60%, total WER: 19.0897%, total TER: 8.50053%, progress (thread 0): 89.1377%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1864.48_1864.81, WER: 0%, TER: 0%, total WER: 19.0894%, total TER: 8.50045%, progress (thread 0): 89.1456%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1964.31_1964.64, WER: 0%, TER: 0%, total WER: 19.0892%, total TER: 8.50037%, progress (thread 0): 89.1535%]
|T|: u m
|P|: m m
[sample: EN2002b_H03_MEE073_139.44_139.77, WER: 100%, TER: 50%, total WER: 19.0901%, total TER: 8.50056%, progress (thread 0): 89.1614%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_417.94_418.27, WER: 0%, TER: 0%, total WER: 19.0899%, total TER: 8.50048%, progress (thread 0): 89.1693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_420.46_420.79, WER: 0%, TER: 0%, total WER: 19.0897%, total TER: 8.5004%, progress (thread 0): 89.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_705.52_705.85, WER: 0%, TER: 0%, total WER: 19.0895%, total TER: 8.50032%, progress (thread 0): 89.1851%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_731.78_732.11, WER: 0%, TER: 0%, total WER: 19.0893%, total TER: 8.50024%, progress (thread 0): 89.193%]
|T|: y e a h | t
|P|: y e a h
[sample: EN2002b_H02_FEO072_1235.77_1236.1, WER: 50%, TER: 33.3333%, total WER: 19.09%, total TER: 8.50059%, progress (thread 0): 89.201%]
|T|: h m m
|P|: m m m
[sample: EN2002b_H03_MEE073_1236.72_1237.05, WER: 100%, TER: 33.3333%, total WER: 19.0909%, total TER: 8.50077%, progress (thread 0): 89.2089%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_1256.5_1256.83, WER: 0%, TER: 0%, total WER: 19.0907%, total TER: 8.50069%, progress (thread 0): 89.2168%]
|T|: h m m
|P|: m m m
[sample: EN2002b_H02_FEO072_1475.51_1475.84, WER: 100%, TER: 33.3333%, total WER: 19.0916%, total TER: 8.50086%, progress (thread 0): 89.2247%]
|T|: o h | r i g h t
|P|: a l | r i g h t
[sample: EN2002b_H02_FEO072_1611.8_1612.13, WER: 50%, TER: 25%, total WER: 19.0923%, total TER: 8.50117%, progress (thread 0): 89.2326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_93.86_94.19, WER: 0%, TER: 0%, total WER: 19.0921%, total TER: 8.50109%, progress (thread 0): 89.2405%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_245.2_245.53, WER: 0%, TER: 0%, total WER: 19.0919%, total TER: 8.50101%, progress (thread 0): 89.2484%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H01_FEO072_447.06_447.39, WER: 100%, TER: 0%, total WER: 19.0928%, total TER: 8.50091%, progress (thread 0): 89.2563%]
|T|: o h | y e a h
|P|: o h | y e a h
[sample: EN2002c_H03_MEE073_823.26_823.59, WER: 0%, TER: 0%, total WER: 19.0924%, total TER: 8.50077%, progress (thread 0): 89.2642%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_850.62_850.95, WER: 0%, TER: 0%, total WER: 19.0921%, total TER: 8.50069%, progress (thread 0): 89.2722%]
|T|: t h a t ' s | a
|P|: o s a y
[sample: EN2002c_H03_MEE073_903.73_904.06, WER: 100%, TER: 87.5%, total WER: 19.094%, total TER: 8.50218%, progress (thread 0): 89.2801%]
|T|: o h
|P|: o h
[sample: EN2002c_H03_MEE073_962_962.33, WER: 0%, TER: 0%, total WER: 19.0938%, total TER: 8.50214%, progress (thread 0): 89.288%]
|T|: y e a h | r i g h t
|P|: y e a h | r g h t
[sample: EN2002c_H03_MEE073_1287.41_1287.74, WER: 50%, TER: 10%, total WER: 19.0945%, total TER: 8.50217%, progress (thread 0): 89.2959%]
|T|: t h a t ' s | t r u e
|P|: t h a t ' s | t r e
[sample: EN2002c_H03_MEE073_1299.73_1300.06, WER: 50%, TER: 9.09091%, total WER: 19.0952%, total TER: 8.50219%, progress (thread 0): 89.3038%]
|T|: y o u | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_2044.96_2045.29, WER: 0%, TER: 0%, total WER: 19.0947%, total TER: 8.50203%, progress (thread 0): 89.3117%]
|T|: m m
|P|: m m
[sample: EN2002c_H02_MEE071_2072_2072.33, WER: 0%, TER: 0%, total WER: 19.0945%, total TER: 8.50199%, progress (thread 0): 89.3196%]
|T|: o o h
|P|: o o h
[sample: EN2002c_H01_FEO072_2817.88_2818.21, WER: 0%, TER: 0%, total WER: 19.0943%, total TER: 8.50193%, progress (thread 0): 89.3275%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002d_H01_FEO072_419.86_420.19, WER: 200%, TER: 14.2857%, total WER: 19.0963%, total TER: 8.50202%, progress (thread 0): 89.3354%]
|T|: m m
|P|: u m
[sample: EN2002d_H03_MEE073_488.3_488.63, WER: 100%, TER: 50%, total WER: 19.0973%, total TER: 8.50222%, progress (thread 0): 89.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1089.65_1089.98, WER: 0%, TER: 0%, total WER: 19.097%, total TER: 8.50214%, progress (thread 0): 89.3513%]
|T|: w e l l
|P|: n o
[sample: EN2002d_H03_MEE073_1132.11_1132.44, WER: 100%, TER: 100%, total WER: 19.098%, total TER: 8.503%, progress (thread 0): 89.3592%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1131.27_1131.6, WER: 100%, TER: 33.3333%, total WER: 19.0989%, total TER: 8.50317%, progress (thread 0): 89.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1257.89_1258.22, WER: 0%, TER: 0%, total WER: 19.0987%, total TER: 8.50309%, progress (thread 0): 89.375%]
|T|: r e a l l y
|P|: r e a l l y
[sample: EN2002d_H01_FEO072_1345.97_1346.3, WER: 0%, TER: 0%, total WER: 19.0984%, total TER: 8.50297%, progress (thread 0): 89.3829%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1429.74_1430.07, WER: 100%, TER: 33.3333%, total WER: 19.0994%, total TER: 8.50315%, progress (thread 0): 89.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1957.8_1958.13, WER: 0%, TER: 0%, total WER: 19.0991%, total TER: 8.50307%, progress (thread 0): 89.3987%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_863.21_863.53, WER: 100%, TER: 0%, total WER: 19.1001%, total TER: 8.50297%, progress (thread 0): 89.4066%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_895.43_895.75, WER: 100%, TER: 0%, total WER: 19.101%, total TER: 8.50287%, progress (thread 0): 89.4146%]
|T|: m m
|P|: m m
[sample: ES2004b_H01_FEE013_1140.58_1140.9, WER: 0%, TER: 0%, total WER: 19.1008%, total TER: 8.50283%, progress (thread 0): 89.4225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1182.75_1183.07, WER: 0%, TER: 0%, total WER: 19.1005%, total TER: 8.50275%, progress (thread 0): 89.4304%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1471.32_1471.64, WER: 100%, TER: 0%, total WER: 19.1015%, total TER: 8.50265%, progress (thread 0): 89.4383%]
|T|: s
|P|: m m
[sample: ES2004b_H01_FEE013_1922.51_1922.83, WER: 100%, TER: 200%, total WER: 19.1024%, total TER: 8.5031%, progress (thread 0): 89.4462%]
|T|: m m h m m
|P|: m m
[sample: ES2004b_H03_FEE016_1953.34_1953.66, WER: 100%, TER: 60%, total WER: 19.1033%, total TER: 8.5037%, progress (thread 0): 89.4541%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1966.24_1966.56, WER: 0%, TER: 0%, total WER: 19.1031%, total TER: 8.50362%, progress (thread 0): 89.462%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_806.95_807.27, WER: 0%, TER: 0%, total WER: 19.1029%, total TER: 8.50354%, progress (thread 0): 89.4699%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H03_FEE016_1066.11_1066.43, WER: 0%, TER: 0%, total WER: 19.1026%, total TER: 8.50346%, progress (thread 0): 89.4779%]
|T|: m m
|P|: m m
[sample: ES2004c_H00_MEO015_1890.24_1890.56, WER: 0%, TER: 0%, total WER: 19.1024%, total TER: 8.50342%, progress (thread 0): 89.4858%]
|T|: y e a h
|P|: y e a p
[sample: ES2004d_H00_MEO015_341.64_341.96, WER: 100%, TER: 25%, total WER: 19.1033%, total TER: 8.50358%, progress (thread 0): 89.4937%]
|T|: n o
|P|: n o
[sample: ES2004d_H00_MEO015_400.96_401.28, WER: 0%, TER: 0%, total WER: 19.1031%, total TER: 8.50354%, progress (thread 0): 89.5016%]
|T|: o o p s
|P|: i p s
[sample: ES2004d_H03_FEE016_430.55_430.87, WER: 100%, TER: 50%, total WER: 19.104%, total TER: 8.50393%, progress (thread 0): 89.5095%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_774.15_774.47, WER: 0%, TER: 0%, total WER: 19.1038%, total TER: 8.50385%, progress (thread 0): 89.5174%]
|T|: o n e
|P|: w e l l
[sample: ES2004d_H02_MEE014_785.53_785.85, WER: 100%, TER: 133.333%, total WER: 19.1047%, total TER: 8.50473%, progress (thread 0): 89.5253%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_840.47_840.79, WER: 0%, TER: 0%, total WER: 19.1045%, total TER: 8.50467%, progress (thread 0): 89.5332%]
|T|: m m h m m
|P|: m m m m
[sample: ES2004d_H03_FEE016_1930.95_1931.27, WER: 100%, TER: 20%, total WER: 19.1054%, total TER: 8.5048%, progress (thread 0): 89.5411%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2146.44_2146.76, WER: 0%, TER: 0%, total WER: 19.1052%, total TER: 8.50472%, progress (thread 0): 89.549%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_290.82_291.14, WER: 100%, TER: 0%, total WER: 19.1061%, total TER: 8.50462%, progress (thread 0): 89.557%]
|T|: o o p s
|P|: s o
[sample: IS1009a_H03_FIO089_420.14_420.46, WER: 100%, TER: 75%, total WER: 19.1071%, total TER: 8.50525%, progress (thread 0): 89.5649%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H01_FIO087_494.72_495.04, WER: 0%, TER: 0%, total WER: 19.1068%, total TER: 8.50517%, progress (thread 0): 89.5728%]
|T|: y e a h
|P|: h
[sample: IS1009a_H02_FIO084_577.05_577.37, WER: 100%, TER: 75%, total WER: 19.1078%, total TER: 8.50579%, progress (thread 0): 89.5807%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H02_FIO084_641.8_642.12, WER: 100%, TER: 20%, total WER: 19.1087%, total TER: 8.50593%, progress (thread 0): 89.5886%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_197.33_197.65, WER: 0%, TER: 0%, total WER: 19.1085%, total TER: 8.50585%, progress (thread 0): 89.5965%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_595.82_596.14, WER: 100%, TER: 60%, total WER: 19.1094%, total TER: 8.50645%, progress (thread 0): 89.6044%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1073.83_1074.15, WER: 100%, TER: 0%, total WER: 19.1103%, total TER: 8.50635%, progress (thread 0): 89.6123%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1139.48_1139.8, WER: 100%, TER: 0%, total WER: 19.1112%, total TER: 8.50625%, progress (thread 0): 89.6202%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1268.25_1268.57, WER: 100%, TER: 0%, total WER: 19.1121%, total TER: 8.50615%, progress (thread 0): 89.6282%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1877.89_1878.21, WER: 0%, TER: 0%, total WER: 19.1119%, total TER: 8.50607%, progress (thread 0): 89.6361%]
|T|: m m h m m
|P|: m e n
[sample: IS1009b_H03_FIO089_1894.73_1895.05, WER: 100%, TER: 80%, total WER: 19.1128%, total TER: 8.50691%, progress (thread 0): 89.644%]
|T|: h i
|P|: k a y
[sample: IS1009c_H01_FIO087_43.77_44.09, WER: 100%, TER: 150%, total WER: 19.1137%, total TER: 8.50758%, progress (thread 0): 89.6519%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_405.29_405.61, WER: 100%, TER: 0%, total WER: 19.1146%, total TER: 8.50748%, progress (thread 0): 89.6598%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_524.19_524.51, WER: 100%, TER: 0%, total WER: 19.1156%, total TER: 8.50738%, progress (thread 0): 89.6677%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_1640_1640.32, WER: 0%, TER: 0%, total WER: 19.1153%, total TER: 8.50732%, progress (thread 0): 89.6756%]
|T|: m e n u
|P|: m e n u
[sample: IS1009d_H00_FIE088_523.67_523.99, WER: 0%, TER: 0%, total WER: 19.1151%, total TER: 8.50724%, progress (thread 0): 89.6835%]
|T|: p a r d o n | m e
|P|: b u d d o n
[sample: IS1009d_H01_FIO087_699.16_699.48, WER: 100%, TER: 66.6667%, total WER: 19.117%, total TER: 8.50847%, progress (thread 0): 89.6915%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_799.6_799.92, WER: 100%, TER: 60%, total WER: 19.1179%, total TER: 8.50907%, progress (thread 0): 89.6994%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1056.69_1057.01, WER: 0%, TER: 0%, total WER: 19.1177%, total TER: 8.50899%, progress (thread 0): 89.7073%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1722.84_1723.16, WER: 100%, TER: 0%, total WER: 19.1186%, total TER: 8.50889%, progress (thread 0): 89.7152%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1741.45_1741.77, WER: 100%, TER: 0%, total WER: 19.1195%, total TER: 8.50879%, progress (thread 0): 89.7231%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1791.05_1791.37, WER: 0%, TER: 0%, total WER: 19.1193%, total TER: 8.50871%, progress (thread 0): 89.731%]
|T|: y e a h
|P|: m m
[sample: TS3003b_H01_MTD011UID_1651.31_1651.63, WER: 100%, TER: 100%, total WER: 19.1202%, total TER: 8.50957%, progress (thread 0): 89.7389%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_1722.53_1722.85, WER: 100%, TER: 25%, total WER: 19.1211%, total TER: 8.50973%, progress (thread 0): 89.7468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2090.25_2090.57, WER: 0%, TER: 0%, total WER: 19.1209%, total TER: 8.50965%, progress (thread 0): 89.7547%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_2074.3_2074.62, WER: 100%, TER: 0%, total WER: 19.1218%, total TER: 8.50955%, progress (thread 0): 89.7627%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_2134.48_2134.8, WER: 100%, TER: 0%, total WER: 19.1227%, total TER: 8.50945%, progress (thread 0): 89.7706%]
|T|: n a y
|P|: n o
[sample: TS3003d_H01_MTD011UID_417.84_418.16, WER: 100%, TER: 66.6667%, total WER: 19.1236%, total TER: 8.50986%, progress (thread 0): 89.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_694.59_694.91, WER: 0%, TER: 0%, total WER: 19.1234%, total TER: 8.50978%, progress (thread 0): 89.7864%]
|T|: o r | b
|P|: o r
[sample: TS3003d_H03_MTD012ME_896.59_896.91, WER: 50%, TER: 50%, total WER: 19.1241%, total TER: 8.51017%, progress (thread 0): 89.7943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1403.4_1403.72, WER: 0%, TER: 0%, total WER: 19.1239%, total TER: 8.51009%, progress (thread 0): 89.8022%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1492.24_1492.56, WER: 0%, TER: 0%, total WER: 19.1237%, total TER: 8.51001%, progress (thread 0): 89.8101%]
|T|: y e a h
|P|: n o
[sample: TS3003d_H01_MTD011UID_2024.39_2024.71, WER: 100%, TER: 100%, total WER: 19.1246%, total TER: 8.51087%, progress (thread 0): 89.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2381.84_2382.16, WER: 0%, TER: 0%, total WER: 19.1244%, total TER: 8.51079%, progress (thread 0): 89.826%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H03_MEE071_68.23_68.55, WER: 0%, TER: 0%, total WER: 19.1242%, total TER: 8.51069%, progress (thread 0): 89.8339%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_359.54_359.86, WER: 50%, TER: 42.8571%, total WER: 19.1249%, total TER: 8.51125%, progress (thread 0): 89.8418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_496.88_497.2, WER: 0%, TER: 0%, total WER: 19.1247%, total TER: 8.51117%, progress (thread 0): 89.8497%]
|T|: h m m
|P|: n m m
[sample: EN2002a_H02_FEO072_504.18_504.5, WER: 100%, TER: 33.3333%, total WER: 19.1256%, total TER: 8.51135%, progress (thread 0): 89.8576%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_567.01_567.33, WER: 0%, TER: 0%, total WER: 19.1254%, total TER: 8.51131%, progress (thread 0): 89.8655%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_604.34_604.66, WER: 100%, TER: 0%, total WER: 19.1263%, total TER: 8.51121%, progress (thread 0): 89.8734%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_729.25_729.57, WER: 0%, TER: 0%, total WER: 19.1261%, total TER: 8.51113%, progress (thread 0): 89.8813%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_800.36_800.68, WER: 0%, TER: 0%, total WER: 19.1258%, total TER: 8.51103%, progress (thread 0): 89.8892%]
|T|: o h
|P|: n o
[sample: EN2002a_H02_FEO072_856.5_856.82, WER: 100%, TER: 100%, total WER: 19.1268%, total TER: 8.51146%, progress (thread 0): 89.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1050.6_1050.92, WER: 0%, TER: 0%, total WER: 19.1265%, total TER: 8.51138%, progress (thread 0): 89.9051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1101.18_1101.5, WER: 0%, TER: 0%, total WER: 19.1263%, total TER: 8.5113%, progress (thread 0): 89.913%]
|T|: o h | n o
|P|: o o
[sample: EN2002a_H01_FEO070_1448.67_1448.99, WER: 100%, TER: 60%, total WER: 19.1282%, total TER: 8.5119%, progress (thread 0): 89.9209%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1448.83_1449.15, WER: 100%, TER: 33.3333%, total WER: 19.1291%, total TER: 8.51208%, progress (thread 0): 89.9288%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1537.39_1537.71, WER: 0%, TER: 0%, total WER: 19.1289%, total TER: 8.512%, progress (thread 0): 89.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1862.6_1862.92, WER: 0%, TER: 0%, total WER: 19.1286%, total TER: 8.51192%, progress (thread 0): 89.9446%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_283.05_283.37, WER: 100%, TER: 100%, total WER: 19.1295%, total TER: 8.51278%, progress (thread 0): 89.9525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_363.29_363.61, WER: 0%, TER: 0%, total WER: 19.1293%, total TER: 8.5127%, progress (thread 0): 89.9604%]
|T|: o h | y e a h
|P|: m e a h
[sample: EN2002b_H03_MEE073_679.41_679.73, WER: 100%, TER: 57.1429%, total WER: 19.1312%, total TER: 8.5135%, progress (thread 0): 89.9684%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_922.82_923.14, WER: 0%, TER: 0%, total WER: 19.1309%, total TER: 8.51342%, progress (thread 0): 89.9763%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H02_FEO072_1100.2_1100.52, WER: 100%, TER: 0%, total WER: 19.1319%, total TER: 8.51332%, progress (thread 0): 89.9842%]
|T|: g o o d | p o i n t
|P|: t o e | p o i n t
[sample: EN2002b_H03_MEE073_1217.83_1218.15, WER: 50%, TER: 30%, total WER: 19.1326%, total TER: 8.51382%, progress (thread 0): 89.9921%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1261.76_1262.08, WER: 0%, TER: 0%, total WER: 19.1323%, total TER: 8.51374%, progress (thread 0): 90%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1598.38_1598.7, WER: 100%, TER: 33.3333%, total WER: 19.1333%, total TER: 8.51392%, progress (thread 0): 90.0079%]
|T|: y e a h | y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1611.81_1612.13, WER: 50%, TER: 55.5556%, total WER: 19.134%, total TER: 8.51491%, progress (thread 0): 90.0158%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1705.25_1705.57, WER: 0%, TER: 0%, total WER: 19.1337%, total TER: 8.51483%, progress (thread 0): 90.0237%]
|T|: g o o d
|P|: o k a y
[sample: EN2002c_H01_FEO072_489.73_490.05, WER: 100%, TER: 100%, total WER: 19.1347%, total TER: 8.51569%, progress (thread 0): 90.0316%]
|T|: s o
|P|: s o
[sample: EN2002c_H02_MEE071_653.98_654.3, WER: 0%, TER: 0%, total WER: 19.1344%, total TER: 8.51565%, progress (thread 0): 90.0396%]
|T|: y e a h
|P|: e h
[sample: EN2002c_H01_FEO072_1352.96_1353.28, WER: 100%, TER: 50%, total WER: 19.1354%, total TER: 8.51604%, progress (thread 0): 90.0475%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_2296.14_2296.46, WER: 0%, TER: 0%, total WER: 19.1351%, total TER: 8.516%, progress (thread 0): 90.0554%]
|T|: i | t h i n k
|P|: i | t h i n k
[sample: EN2002c_H01_FEO072_2336.35_2336.67, WER: 0%, TER: 0%, total WER: 19.1347%, total TER: 8.51586%, progress (thread 0): 90.0633%]
|T|: o h
|P|: e h
[sample: EN2002c_H01_FEO072_2368.69_2369.01, WER: 100%, TER: 50%, total WER: 19.1356%, total TER: 8.51605%, progress (thread 0): 90.0712%]
|T|: w h a t
|P|: w e l l
[sample: EN2002c_H01_FEO072_2484.6_2484.92, WER: 100%, TER: 75%, total WER: 19.1365%, total TER: 8.51668%, progress (thread 0): 90.0791%]
|T|: h m m
|P|: m m m
[sample: EN2002c_H03_MEE073_2800.26_2800.58, WER: 100%, TER: 33.3333%, total WER: 19.1375%, total TER: 8.51685%, progress (thread 0): 90.087%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_2829.54_2829.86, WER: 0%, TER: 0%, total WER: 19.1372%, total TER: 8.51677%, progress (thread 0): 90.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_347.79_348.11, WER: 0%, TER: 0%, total WER: 19.137%, total TER: 8.51669%, progress (thread 0): 90.1028%]
|T|: i t ' s | g o o d
|P|: t h a ' s | g o
[sample: EN2002d_H00_FEO070_420.4_420.72, WER: 100%, TER: 55.5556%, total WER: 19.1388%, total TER: 8.51769%, progress (thread 0): 90.1108%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002d_H01_FEO072_574.1_574.42, WER: 100%, TER: 20%, total WER: 19.1398%, total TER: 8.51782%, progress (thread 0): 90.1187%]
|T|: b u t
|P|: b u t
[sample: EN2002d_H01_FEO072_590.21_590.53, WER: 0%, TER: 0%, total WER: 19.1395%, total TER: 8.51776%, progress (thread 0): 90.1266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_769.35_769.67, WER: 0%, TER: 0%, total WER: 19.1393%, total TER: 8.51768%, progress (thread 0): 90.1345%]
|T|: o h
|P|: o h
[sample: EN2002d_H03_MEE073_939.78_940.1, WER: 0%, TER: 0%, total WER: 19.1391%, total TER: 8.51764%, progress (thread 0): 90.1424%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_954.69_955.01, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.51756%, progress (thread 0): 90.1503%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1189.57_1189.89, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51748%, progress (thread 0): 90.1582%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1203.2_1203.52, WER: 0%, TER: 0%, total WER: 19.1385%, total TER: 8.5174%, progress (thread 0): 90.1661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1255.64_1255.96, WER: 0%, TER: 0%, total WER: 19.1382%, total TER: 8.51732%, progress (thread 0): 90.174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1754.3_1754.62, WER: 0%, TER: 0%, total WER: 19.138%, total TER: 8.51724%, progress (thread 0): 90.182%]
|T|: s o
|P|: s o
[sample: EN2002d_H01_FEO072_1974.1_1974.42, WER: 0%, TER: 0%, total WER: 19.1378%, total TER: 8.5172%, progress (thread 0): 90.1899%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_2190.35_2190.67, WER: 0%, TER: 0%, total WER: 19.1376%, total TER: 8.51716%, progress (thread 0): 90.1978%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_582.6_582.91, WER: 0%, TER: 0%, total WER: 19.1374%, total TER: 8.51708%, progress (thread 0): 90.2057%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H00_MEO015_687.25_687.56, WER: 100%, TER: 0%, total WER: 19.1383%, total TER: 8.51698%, progress (thread 0): 90.2136%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_688.13_688.44, WER: 0%, TER: 0%, total WER: 19.1381%, total TER: 8.5169%, progress (thread 0): 90.2215%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004a_H03_FEE016_777.34_777.65, WER: 100%, TER: 0%, total WER: 19.139%, total TER: 8.5168%, progress (thread 0): 90.2294%]
|T|: y
|P|: y
[sample: ES2004a_H02_MEE014_949.9_950.21, WER: 0%, TER: 0%, total WER: 19.1388%, total TER: 8.51678%, progress (thread 0): 90.2373%]
|T|: n o
|P|: n o
[sample: ES2004b_H03_FEE016_607.28_607.59, WER: 0%, TER: 0%, total WER: 19.1386%, total TER: 8.51674%, progress (thread 0): 90.2453%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1612.5_1612.81, WER: 100%, TER: 0%, total WER: 19.1395%, total TER: 8.51664%, progress (thread 0): 90.2532%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H01_FEE013_2237.11_2237.42, WER: 0%, TER: 0%, total WER: 19.1393%, total TER: 8.51656%, progress (thread 0): 90.2611%]
|T|: b u t
|P|: b u t
[sample: ES2004c_H02_MEE014_571.73_572.04, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.5165%, progress (thread 0): 90.269%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1321.04_1321.35, WER: 0%, TER: 0%, total WER: 19.1388%, total TER: 8.51642%, progress (thread 0): 90.2769%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H01_FEE013_1582.98_1583.29, WER: 0%, TER: 0%, total WER: 19.1386%, total TER: 8.51634%, progress (thread 0): 90.2848%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2288.73_2289.04, WER: 0%, TER: 0%, total WER: 19.1384%, total TER: 8.51626%, progress (thread 0): 90.2927%]
|T|: m m
|P|: h m m
[sample: ES2004d_H00_MEO015_846.15_846.46, WER: 100%, TER: 50%, total WER: 19.1393%, total TER: 8.51646%, progress (thread 0): 90.3006%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1669.25_1669.56, WER: 0%, TER: 0%, total WER: 19.1391%, total TER: 8.51638%, progress (thread 0): 90.3085%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1669.66_1669.97, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.5163%, progress (thread 0): 90.3165%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1825.79_1826.1, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51622%, progress (thread 0): 90.3244%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1901.99_1902.3, WER: 0%, TER: 0%, total WER: 19.1384%, total TER: 8.51614%, progress (thread 0): 90.3323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1967.13_1967.44, WER: 0%, TER: 0%, total WER: 19.1382%, total TER: 8.51606%, progress (thread 0): 90.3402%]
|T|: w e l l
|P|: o h
[sample: ES2004d_H02_MEE014_2085.81_2086.12, WER: 100%, TER: 100%, total WER: 19.1391%, total TER: 8.51692%, progress (thread 0): 90.3481%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_2220.03_2220.34, WER: 0%, TER: 0%, total WER: 19.1389%, total TER: 8.51684%, progress (thread 0): 90.356%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_90.21_90.52, WER: 0%, TER: 0%, total WER: 19.1387%, total TER: 8.51676%, progress (thread 0): 90.3639%]
|T|: u h
|P|: m m
[sample: IS1009a_H02_FIO084_451.06_451.37, WER: 100%, TER: 100%, total WER: 19.1396%, total TER: 8.51719%, progress (thread 0): 90.3718%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_702.97_703.28, WER: 0%, TER: 0%, total WER: 19.1394%, total TER: 8.51711%, progress (thread 0): 90.3797%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_795.57_795.88, WER: 0%, TER: 0%, total WER: 19.1392%, total TER: 8.51703%, progress (thread 0): 90.3877%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_218.1_218.41, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.51699%, progress (thread 0): 90.3956%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_616.32_616.63, WER: 100%, TER: 0%, total WER: 19.1399%, total TER: 8.51689%, progress (thread 0): 90.4035%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1692.92_1693.23, WER: 0%, TER: 0%, total WER: 19.1397%, total TER: 8.51681%, progress (thread 0): 90.4114%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1832.99_1833.3, WER: 0%, TER: 0%, total WER: 19.1395%, total TER: 8.51673%, progress (thread 0): 90.4193%]
|T|: y e s
|P|: y e s
[sample: IS1009b_H01_FIO087_1844.23_1844.54, WER: 0%, TER: 0%, total WER: 19.1392%, total TER: 8.51667%, progress (thread 0): 90.4272%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H03_FIO089_1904.39_1904.7, WER: 0%, TER: 0%, total WER: 19.139%, total TER: 8.51657%, progress (thread 0): 90.4351%]
|T|: m m h m m
|P|: m e m
[sample: IS1009c_H00_FIE088_1140.15_1140.46, WER: 100%, TER: 60%, total WER: 19.1399%, total TER: 8.51717%, progress (thread 0): 90.443%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_1163.03_1163.34, WER: 100%, TER: 0%, total WER: 19.1409%, total TER: 8.51707%, progress (thread 0): 90.451%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_1237.53_1237.84, WER: 100%, TER: 0%, total WER: 19.1418%, total TER: 8.51697%, progress (thread 0): 90.4589%]
|T|: ' k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1429.75_1430.06, WER: 100%, TER: 25%, total WER: 19.1427%, total TER: 8.51712%, progress (thread 0): 90.4668%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H02_FIO084_1554.5_1554.81, WER: 100%, TER: 0%, total WER: 19.1436%, total TER: 8.51702%, progress (thread 0): 90.4747%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H01_FIO087_1580.06_1580.37, WER: 100%, TER: 0%, total WER: 19.1445%, total TER: 8.51692%, progress (thread 0): 90.4826%]
|T|: o h
|P|: o h
[sample: IS1009c_H00_FIE088_1694.98_1695.29, WER: 0%, TER: 0%, total WER: 19.1443%, total TER: 8.51688%, progress (thread 0): 90.4905%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_363.39_363.7, WER: 100%, TER: 0%, total WER: 19.1452%, total TER: 8.51678%, progress (thread 0): 90.4984%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_894.86_895.17, WER: 0%, TER: 0%, total WER: 19.145%, total TER: 8.51674%, progress (thread 0): 90.5063%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_903.74_904.05, WER: 0%, TER: 0%, total WER: 19.1448%, total TER: 8.51666%, progress (thread 0): 90.5142%]
|T|: n o
|P|: n o
[sample: IS1009d_H00_FIE088_1011.62_1011.93, WER: 0%, TER: 0%, total WER: 19.1446%, total TER: 8.51662%, progress (thread 0): 90.5222%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1012.22_1012.53, WER: 100%, TER: 0%, total WER: 19.1455%, total TER: 8.51652%, progress (thread 0): 90.5301%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1105.97_1106.28, WER: 100%, TER: 0%, total WER: 19.1464%, total TER: 8.51642%, progress (thread 0): 90.538%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1171.27_1171.58, WER: 0%, TER: 0%, total WER: 19.1462%, total TER: 8.51634%, progress (thread 0): 90.5459%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1243.74_1244.05, WER: 100%, TER: 66.6667%, total WER: 19.1471%, total TER: 8.51675%, progress (thread 0): 90.5538%]
|T|: w h y
|P|: w o w
[sample: IS1009d_H00_FIE088_1442.55_1442.86, WER: 100%, TER: 66.6667%, total WER: 19.148%, total TER: 8.51716%, progress (thread 0): 90.5617%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1681.21_1681.52, WER: 0%, TER: 0%, total WER: 19.1478%, total TER: 8.51708%, progress (thread 0): 90.5696%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_216.48_216.79, WER: 0%, TER: 0%, total WER: 19.1476%, total TER: 8.517%, progress (thread 0): 90.5775%]
|T|: m e a t
|P|: n e a t
[sample: TS3003a_H03_MTD012ME_595.66_595.97, WER: 100%, TER: 25%, total WER: 19.1485%, total TER: 8.51716%, progress (thread 0): 90.5854%]
|T|: s o
|P|: s o
[sample: TS3003a_H03_MTD012ME_884.45_884.76, WER: 0%, TER: 0%, total WER: 19.1483%, total TER: 8.51712%, progress (thread 0): 90.5934%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1418.64_1418.95, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51704%, progress (thread 0): 90.6013%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_71.8_72.11, WER: 0%, TER: 0%, total WER: 19.1478%, total TER: 8.51696%, progress (thread 0): 90.6092%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_538.03_538.34, WER: 0%, TER: 0%, total WER: 19.1476%, total TER: 8.51688%, progress (thread 0): 90.6171%]
|T|: m m
|P|: m h
[sample: TS3003b_H01_MTD011UID_1469.03_1469.34, WER: 100%, TER: 50%, total WER: 19.1485%, total TER: 8.51707%, progress (thread 0): 90.625%]
|T|: n o
|P|: n o
[sample: TS3003b_H00_MTD009PM_1715.8_1716.11, WER: 0%, TER: 0%, total WER: 19.1483%, total TER: 8.51703%, progress (thread 0): 90.6329%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1822.91_1823.22, WER: 0%, TER: 0%, total WER: 19.1481%, total TER: 8.51695%, progress (thread 0): 90.6408%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_794.24_794.55, WER: 0%, TER: 0%, total WER: 19.1479%, total TER: 8.51687%, progress (thread 0): 90.6487%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H00_MTD009PM_934.63_934.94, WER: 100%, TER: 25%, total WER: 19.1488%, total TER: 8.51703%, progress (thread 0): 90.6566%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1136.65_1136.96, WER: 0%, TER: 0%, total WER: 19.1486%, total TER: 8.51699%, progress (thread 0): 90.6646%]
|T|: y e a h
|P|: y e m
[sample: TS3003c_H01_MTD011UID_1444.43_1444.74, WER: 100%, TER: 50%, total WER: 19.1495%, total TER: 8.51738%, progress (thread 0): 90.6725%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1813.2_1813.51, WER: 0%, TER: 0%, total WER: 19.1493%, total TER: 8.5173%, progress (thread 0): 90.6804%]
|T|: o k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_255.36_255.67, WER: 0%, TER: 0%, total WER: 19.1491%, total TER: 8.51722%, progress (thread 0): 90.6883%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_453.54_453.85, WER: 0%, TER: 0%, total WER: 19.1488%, total TER: 8.51718%, progress (thread 0): 90.6962%]
|T|: y e p
|P|: o h
[sample: TS3003d_H02_MTD0010ID_562.3_562.61, WER: 100%, TER: 100%, total WER: 19.1498%, total TER: 8.51782%, progress (thread 0): 90.7041%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1317.08_1317.39, WER: 0%, TER: 0%, total WER: 19.1495%, total TER: 8.51774%, progress (thread 0): 90.712%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003d_H03_MTD012ME_1546.63_1546.94, WER: 100%, TER: 0%, total WER: 19.1505%, total TER: 8.51764%, progress (thread 0): 90.7199%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2324.36_2324.67, WER: 0%, TER: 0%, total WER: 19.1502%, total TER: 8.51756%, progress (thread 0): 90.7278%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2373.13_2373.44, WER: 0%, TER: 0%, total WER: 19.15%, total TER: 8.51748%, progress (thread 0): 90.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_595.97_596.28, WER: 0%, TER: 0%, total WER: 19.1498%, total TER: 8.5174%, progress (thread 0): 90.7437%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_694.14_694.45, WER: 0%, TER: 0%, total WER: 19.1496%, total TER: 8.51732%, progress (thread 0): 90.7516%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_704.35_704.66, WER: 0%, TER: 0%, total WER: 19.1494%, total TER: 8.51724%, progress (thread 0): 90.7595%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_910.51_910.82, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51716%, progress (thread 0): 90.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1104.75_1105.06, WER: 0%, TER: 0%, total WER: 19.1489%, total TER: 8.51708%, progress (thread 0): 90.7753%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1619.48_1619.79, WER: 100%, TER: 33.3333%, total WER: 19.1499%, total TER: 8.51726%, progress (thread 0): 90.7832%]
|T|: o h
|P|: m m
[sample: EN2002a_H02_FEO072_1674.71_1675.02, WER: 100%, TER: 100%, total WER: 19.1508%, total TER: 8.51769%, progress (thread 0): 90.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1973.2_1973.51, WER: 0%, TER: 0%, total WER: 19.1505%, total TER: 8.51761%, progress (thread 0): 90.799%]
|T|: o h | r i g h t
|P|: o h | r i g h t
[sample: EN2002a_H03_MEE071_1971.01_1971.32, WER: 0%, TER: 0%, total WER: 19.1501%, total TER: 8.51745%, progress (thread 0): 90.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_346.95_347.26, WER: 0%, TER: 0%, total WER: 19.1499%, total TER: 8.51737%, progress (thread 0): 90.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_372.58_372.89, WER: 0%, TER: 0%, total WER: 19.1497%, total TER: 8.51729%, progress (thread 0): 90.8228%]
|T|: m m
|P|: m m
[sample: EN2002b_H03_MEE073_565.42_565.73, WER: 0%, TER: 0%, total WER: 19.1495%, total TER: 8.51725%, progress (thread 0): 90.8307%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_729.79_730.1, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51717%, progress (thread 0): 90.8386%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1214.71_1215.02, WER: 0%, TER: 0%, total WER: 19.149%, total TER: 8.51709%, progress (thread 0): 90.8465%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1323.81_1324.12, WER: 0%, TER: 0%, total WER: 19.1488%, total TER: 8.51701%, progress (thread 0): 90.8544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1461.61_1461.92, WER: 0%, TER: 0%, total WER: 19.1486%, total TER: 8.51693%, progress (thread 0): 90.8623%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1750.66_1750.97, WER: 0%, TER: 0%, total WER: 19.1484%, total TER: 8.51685%, progress (thread 0): 90.8703%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_441.29_441.6, WER: 0%, TER: 0%, total WER: 19.1482%, total TER: 8.51681%, progress (thread 0): 90.8782%]
|T|: n o
|P|: n o
[sample: EN2002c_H03_MEE073_543.24_543.55, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51677%, progress (thread 0): 90.8861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_543.24_543.55, WER: 0%, TER: 0%, total WER: 19.1477%, total TER: 8.51669%, progress (thread 0): 90.894%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_982.53_982.84, WER: 100%, TER: 33.3333%, total WER: 19.1486%, total TER: 8.51686%, progress (thread 0): 90.9019%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1524.82_1525.13, WER: 0%, TER: 0%, total WER: 19.1484%, total TER: 8.51678%, progress (thread 0): 90.9098%]
|T|: u m
|P|: u m
[sample: EN2002c_H03_MEE073_1904.01_1904.32, WER: 0%, TER: 0%, total WER: 19.1482%, total TER: 8.51674%, progress (thread 0): 90.9177%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1958.34_1958.65, WER: 0%, TER: 0%, total WER: 19.148%, total TER: 8.51666%, progress (thread 0): 90.9256%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_2240.67_2240.98, WER: 100%, TER: 0%, total WER: 19.1489%, total TER: 8.51656%, progress (thread 0): 90.9335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2358.92_2359.23, WER: 0%, TER: 0%, total WER: 19.1487%, total TER: 8.51648%, progress (thread 0): 90.9415%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2477.51_2477.82, WER: 0%, TER: 0%, total WER: 19.1485%, total TER: 8.5164%, progress (thread 0): 90.9494%]
|T|: n o
|P|: n i c
[sample: EN2002c_H02_MEE071_2608.15_2608.46, WER: 100%, TER: 100%, total WER: 19.1494%, total TER: 8.51683%, progress (thread 0): 90.9573%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2724.28_2724.59, WER: 0%, TER: 0%, total WER: 19.1492%, total TER: 8.51675%, progress (thread 0): 90.9652%]
|T|: t i c k
|P|: t i c k
[sample: EN2002c_H01_FEO072_2878.5_2878.81, WER: 0%, TER: 0%, total WER: 19.149%, total TER: 8.51667%, progress (thread 0): 90.9731%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_391.81_392.12, WER: 0%, TER: 0%, total WER: 19.1487%, total TER: 8.51659%, progress (thread 0): 90.981%]
|T|: y e a h | o k a y
|P|: o k a y
[sample: EN2002d_H00_FEO070_1379.88_1380.19, WER: 50%, TER: 55.5556%, total WER: 19.1494%, total TER: 8.51758%, progress (thread 0): 90.9889%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H01_FEO072_1512.76_1513.07, WER: 100%, TER: 0%, total WER: 19.1504%, total TER: 8.51748%, progress (thread 0): 90.9968%]
|T|: s o
|P|: y e a h
[sample: EN2002d_H00_FEO070_1542.52_1542.83, WER: 100%, TER: 200%, total WER: 19.1513%, total TER: 8.51838%, progress (thread 0): 91.0047%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1546.76_1547.07, WER: 0%, TER: 0%, total WER: 19.1511%, total TER: 8.5183%, progress (thread 0): 91.0127%]
|T|: s o
|P|: s o
[sample: EN2002d_H03_MEE073_1822.41_1822.72, WER: 0%, TER: 0%, total WER: 19.1508%, total TER: 8.51826%, progress (thread 0): 91.0206%]
|T|: m m
|P|: m m
[sample: EN2002d_H00_FEO070_1872.39_1872.7, WER: 0%, TER: 0%, total WER: 19.1506%, total TER: 8.51822%, progress (thread 0): 91.0285%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2114.3_2114.61, WER: 0%, TER: 0%, total WER: 19.1504%, total TER: 8.51814%, progress (thread 0): 91.0364%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_819.96_820.26, WER: 0%, TER: 0%, total WER: 19.1502%, total TER: 8.51806%, progress (thread 0): 91.0443%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1232.15_1232.45, WER: 0%, TER: 0%, total WER: 19.15%, total TER: 8.51798%, progress (thread 0): 91.0522%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1572.74_1573.04, WER: 100%, TER: 0%, total WER: 19.1509%, total TER: 8.51788%, progress (thread 0): 91.0601%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H03_FEE016_1784.08_1784.38, WER: 100%, TER: 0%, total WER: 19.1518%, total TER: 8.51778%, progress (thread 0): 91.068%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_2252.76_2253.06, WER: 0%, TER: 0%, total WER: 19.1516%, total TER: 8.5177%, progress (thread 0): 91.076%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_849.84_850.14, WER: 0%, TER: 0%, total WER: 19.1514%, total TER: 8.51762%, progress (thread 0): 91.0839%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1555.92_1556.22, WER: 0%, TER: 0%, total WER: 19.1511%, total TER: 8.51754%, progress (thread 0): 91.0918%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H00_MEO015_1640.82_1641.12, WER: 100%, TER: 0%, total WER: 19.1521%, total TER: 8.51744%, progress (thread 0): 91.0997%]
|T|: ' k a y
|P|: k e y
[sample: ES2004d_H00_MEO015_271.77_272.07, WER: 100%, TER: 50%, total WER: 19.153%, total TER: 8.51783%, progress (thread 0): 91.1076%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H01_FEE013_639.49_639.79, WER: 0%, TER: 0%, total WER: 19.1528%, total TER: 8.51775%, progress (thread 0): 91.1155%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1390.02_1390.32, WER: 0%, TER: 0%, total WER: 19.1525%, total TER: 8.51767%, progress (thread 0): 91.1234%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1711.3_1711.6, WER: 0%, TER: 0%, total WER: 19.1523%, total TER: 8.51759%, progress (thread 0): 91.1313%]
|T|: m m
|P|: m m
[sample: ES2004d_H01_FEE013_2002_2002.3, WER: 0%, TER: 0%, total WER: 19.1521%, total TER: 8.51755%, progress (thread 0): 91.1392%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2096.66_2096.96, WER: 0%, TER: 0%, total WER: 19.1519%, total TER: 8.51747%, progress (thread 0): 91.1472%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_617.96_618.26, WER: 100%, TER: 0%, total WER: 19.1528%, total TER: 8.51737%, progress (thread 0): 91.1551%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H01_FIO087_782.9_783.2, WER: 0%, TER: 0%, total WER: 19.1526%, total TER: 8.51729%, progress (thread 0): 91.163%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_182.06_182.36, WER: 0%, TER: 0%, total WER: 19.1524%, total TER: 8.51721%, progress (thread 0): 91.1709%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_574.49_574.79, WER: 100%, TER: 0%, total WER: 19.1533%, total TER: 8.51711%, progress (thread 0): 91.1788%]
|T|: m m h m m
|P|: m m m
[sample: IS1009b_H03_FIO089_707.08_707.38, WER: 100%, TER: 40%, total WER: 19.1542%, total TER: 8.51748%, progress (thread 0): 91.1867%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1025.61_1025.91, WER: 0%, TER: 0%, total WER: 19.154%, total TER: 8.5174%, progress (thread 0): 91.1946%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_1366.95_1367.25, WER: 0%, TER: 0%, total WER: 19.1538%, total TER: 8.51736%, progress (thread 0): 91.2025%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1371.17_1371.47, WER: 100%, TER: 0%, total WER: 19.1547%, total TER: 8.51726%, progress (thread 0): 91.2104%]
|T|: h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1604.76_1605.06, WER: 100%, TER: 33.3333%, total WER: 19.1556%, total TER: 8.51744%, progress (thread 0): 91.2184%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1617.66_1617.96, WER: 0%, TER: 0%, total WER: 19.1554%, total TER: 8.51736%, progress (thread 0): 91.2263%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_335.7_336, WER: 0%, TER: 0%, total WER: 19.1552%, total TER: 8.51728%, progress (thread 0): 91.2342%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H03_FIO089_1106.15_1106.45, WER: 100%, TER: 0%, total WER: 19.1561%, total TER: 8.51718%, progress (thread 0): 91.2421%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1237.83_1238.13, WER: 0%, TER: 0%, total WER: 19.1559%, total TER: 8.51712%, progress (thread 0): 91.25%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1259.97_1260.27, WER: 0%, TER: 0%, total WER: 19.1556%, total TER: 8.51704%, progress (thread 0): 91.2579%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1297.17_1297.47, WER: 0%, TER: 0%, total WER: 19.1554%, total TER: 8.51696%, progress (thread 0): 91.2658%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H00_FIE088_607.11_607.41, WER: 100%, TER: 0%, total WER: 19.1563%, total TER: 8.51686%, progress (thread 0): 91.2737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_769.42_769.72, WER: 0%, TER: 0%, total WER: 19.1561%, total TER: 8.51678%, progress (thread 0): 91.2816%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1062.93_1063.23, WER: 0%, TER: 0%, total WER: 19.1559%, total TER: 8.5167%, progress (thread 0): 91.2896%]
|T|: o n e
|P|: b u t
[sample: IS1009d_H01_FIO087_1482_1482.3, WER: 100%, TER: 100%, total WER: 19.1568%, total TER: 8.51734%, progress (thread 0): 91.2975%]
|T|: o n e
|P|: o n e
[sample: IS1009d_H00_FIE088_1528.1_1528.4, WER: 0%, TER: 0%, total WER: 19.1566%, total TER: 8.51728%, progress (thread 0): 91.3054%]
|T|: y e a h
|P|: i
[sample: IS1009d_H02_FIO084_1824.41_1824.71, WER: 100%, TER: 100%, total WER: 19.1575%, total TER: 8.51814%, progress (thread 0): 91.3133%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1853.09_1853.39, WER: 0%, TER: 0%, total WER: 19.1573%, total TER: 8.51806%, progress (thread 0): 91.3212%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_238.31_238.61, WER: 0%, TER: 0%, total WER: 19.1571%, total TER: 8.51798%, progress (thread 0): 91.3291%]
|T|: s o
|P|: s o
[sample: TS3003a_H02_MTD0010ID_1080.77_1081.07, WER: 0%, TER: 0%, total WER: 19.1569%, total TER: 8.51794%, progress (thread 0): 91.337%]
|T|: h m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1308.88_1309.18, WER: 100%, TER: 33.3333%, total WER: 19.1578%, total TER: 8.51812%, progress (thread 0): 91.3449%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_1423.96_1424.26, WER: 0%, TER: 0%, total WER: 19.1576%, total TER: 8.51806%, progress (thread 0): 91.3529%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1463.25_1463.55, WER: 0%, TER: 0%, total WER: 19.1573%, total TER: 8.51798%, progress (thread 0): 91.3608%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_1535.95_1536.25, WER: 0%, TER: 0%, total WER: 19.1571%, total TER: 8.51792%, progress (thread 0): 91.3687%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003b_H03_MTD012ME_1635.81_1636.11, WER: 100%, TER: 0%, total WER: 19.158%, total TER: 8.51782%, progress (thread 0): 91.3766%]
|T|: h m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1834.41_1834.71, WER: 100%, TER: 50%, total WER: 19.159%, total TER: 8.51801%, progress (thread 0): 91.3845%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1871.5_1871.8, WER: 0%, TER: 0%, total WER: 19.1587%, total TER: 8.51797%, progress (thread 0): 91.3924%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2279.98_2280.28, WER: 0%, TER: 0%, total WER: 19.1585%, total TER: 8.51789%, progress (thread 0): 91.4003%]
|T|: s
|P|: y m
[sample: TS3003d_H01_MTD011UID_856.51_856.81, WER: 100%, TER: 200%, total WER: 19.1594%, total TER: 8.51834%, progress (thread 0): 91.4082%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1057.72_1058.02, WER: 0%, TER: 0%, total WER: 19.1592%, total TER: 8.51826%, progress (thread 0): 91.4161%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1709.82_1710.12, WER: 0%, TER: 0%, total WER: 19.159%, total TER: 8.51818%, progress (thread 0): 91.424%]
|T|: y e s
|P|: y e s
[sample: TS3003d_H02_MTD0010ID_1732.89_1733.19, WER: 0%, TER: 0%, total WER: 19.1588%, total TER: 8.51812%, progress (thread 0): 91.432%]
|T|: y e a h
|P|: y m a m
[sample: TS3003d_H00_MTD009PM_1821.73_1822.03, WER: 100%, TER: 50%, total WER: 19.1597%, total TER: 8.51851%, progress (thread 0): 91.4399%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2250.87_2251.17, WER: 0%, TER: 0%, total WER: 19.1595%, total TER: 8.51843%, progress (thread 0): 91.4478%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2482.46_2482.76, WER: 0%, TER: 0%, total WER: 19.1593%, total TER: 8.51835%, progress (thread 0): 91.4557%]
|T|: r i g h t
|P|: y o u r e r i g h t
[sample: EN2002a_H03_MEE071_11.83_12.13, WER: 100%, TER: 100%, total WER: 19.1602%, total TER: 8.51942%, progress (thread 0): 91.4636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H02_FEO072_48.72_49.02, WER: 0%, TER: 0%, total WER: 19.16%, total TER: 8.51934%, progress (thread 0): 91.4715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_203.25_203.55, WER: 0%, TER: 0%, total WER: 19.1597%, total TER: 8.51926%, progress (thread 0): 91.4794%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_261.14_261.44, WER: 100%, TER: 33.3333%, total WER: 19.1607%, total TER: 8.51944%, progress (thread 0): 91.4873%]
|T|: w h y | n o t
|P|: w h y | d o n ' t
[sample: EN2002a_H03_MEE071_375.98_376.28, WER: 50%, TER: 42.8571%, total WER: 19.1614%, total TER: 8.52%, progress (thread 0): 91.4953%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_469.41_469.71, WER: 0%, TER: 0%, total WER: 19.1611%, total TER: 8.51992%, progress (thread 0): 91.5032%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_486.08_486.38, WER: 0%, TER: 0%, total WER: 19.1609%, total TER: 8.51984%, progress (thread 0): 91.5111%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_508.71_509.01, WER: 0%, TER: 0%, total WER: 19.1607%, total TER: 8.51976%, progress (thread 0): 91.519%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_521.78_522.08, WER: 0%, TER: 0%, total WER: 19.1605%, total TER: 8.51968%, progress (thread 0): 91.5269%]
|T|: s o
|P|: s o
[sample: EN2002a_H03_MEE071_738.15_738.45, WER: 0%, TER: 0%, total WER: 19.1603%, total TER: 8.51964%, progress (thread 0): 91.5348%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_747.85_748.15, WER: 100%, TER: 0%, total WER: 19.1612%, total TER: 8.51954%, progress (thread 0): 91.5427%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_795.73_796.03, WER: 100%, TER: 33.3333%, total WER: 19.1621%, total TER: 8.51971%, progress (thread 0): 91.5506%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002a_H00_MEE073_877.67_877.97, WER: 100%, TER: 0%, total WER: 19.163%, total TER: 8.51961%, progress (thread 0): 91.5585%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_918.33_918.63, WER: 0%, TER: 0%, total WER: 19.1628%, total TER: 8.51954%, progress (thread 0): 91.5665%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1889.11_1889.41, WER: 0%, TER: 0%, total WER: 19.1626%, total TER: 8.51946%, progress (thread 0): 91.5744%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H02_FEO072_2116.83_2117.13, WER: 0%, TER: 0%, total WER: 19.1624%, total TER: 8.51936%, progress (thread 0): 91.5823%]
|T|: o h | y e a h
|P|: h m h
[sample: EN2002b_H03_MEE073_211.33_211.63, WER: 100%, TER: 71.4286%, total WER: 19.1642%, total TER: 8.52039%, progress (thread 0): 91.5902%]
|T|: w e ' l l | s e e
|P|: w e l l | s e
[sample: EN2002b_H00_FEO070_255.08_255.38, WER: 100%, TER: 22.2222%, total WER: 19.166%, total TER: 8.52068%, progress (thread 0): 91.5981%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_368.42_368.72, WER: 0%, TER: 0%, total WER: 19.1658%, total TER: 8.5206%, progress (thread 0): 91.606%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H03_MEE073_391.92_392.22, WER: 100%, TER: 0%, total WER: 19.1667%, total TER: 8.5205%, progress (thread 0): 91.6139%]
|T|: m m
|P|: m m
[sample: EN2002b_H03_MEE073_526.11_526.41, WER: 0%, TER: 0%, total WER: 19.1665%, total TER: 8.52046%, progress (thread 0): 91.6218%]
|T|: a l r i g h t
|P|: a l | r i g h t
[sample: EN2002b_H02_FEO072_627.98_628.28, WER: 200%, TER: 14.2857%, total WER: 19.1685%, total TER: 8.52055%, progress (thread 0): 91.6298%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1551.52_1551.82, WER: 0%, TER: 0%, total WER: 19.1683%, total TER: 8.52047%, progress (thread 0): 91.6377%]
|T|: b u t
|P|: b h u t
[sample: EN2002b_H01_MEE071_1611.31_1611.61, WER: 100%, TER: 33.3333%, total WER: 19.1692%, total TER: 8.52065%, progress (thread 0): 91.6456%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1729.42_1729.72, WER: 0%, TER: 0%, total WER: 19.169%, total TER: 8.52057%, progress (thread 0): 91.6535%]
|T|: i | t h i n k
|P|: i
[sample: EN2002c_H01_FEO072_232.37_232.67, WER: 50%, TER: 85.7143%, total WER: 19.1697%, total TER: 8.52183%, progress (thread 0): 91.6614%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_355.18_355.48, WER: 0%, TER: 0%, total WER: 19.1695%, total TER: 8.52175%, progress (thread 0): 91.6693%]
|T|: n o | n o
|P|: m m
[sample: EN2002c_H03_MEE073_543.55_543.85, WER: 100%, TER: 100%, total WER: 19.1713%, total TER: 8.52282%, progress (thread 0): 91.6772%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_700.09_700.39, WER: 100%, TER: 0%, total WER: 19.1722%, total TER: 8.52272%, progress (thread 0): 91.6851%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H02_MEE071_1131.17_1131.47, WER: 100%, TER: 66.6667%, total WER: 19.1731%, total TER: 8.52313%, progress (thread 0): 91.693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1143.34_1143.64, WER: 0%, TER: 0%, total WER: 19.1729%, total TER: 8.52305%, progress (thread 0): 91.701%]
|T|: s o
|P|: s o
[sample: EN2002c_H03_MEE073_1321.8_1322.1, WER: 0%, TER: 0%, total WER: 19.1727%, total TER: 8.52301%, progress (thread 0): 91.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1600.62_1600.92, WER: 0%, TER: 0%, total WER: 19.1725%, total TER: 8.52293%, progress (thread 0): 91.7168%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2301.16_2301.46, WER: 0%, TER: 0%, total WER: 19.1723%, total TER: 8.52285%, progress (thread 0): 91.7247%]
|T|: w e l l
|P|: w e l l
[sample: EN2002c_H01_FEO072_2443.4_2443.7, WER: 0%, TER: 0%, total WER: 19.1721%, total TER: 8.52277%, progress (thread 0): 91.7326%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H02_MEE071_2847.83_2848.13, WER: 100%, TER: 0%, total WER: 19.173%, total TER: 8.52267%, progress (thread 0): 91.7405%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_673.96_674.26, WER: 0%, TER: 0%, total WER: 19.1728%, total TER: 8.52259%, progress (thread 0): 91.7484%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_834.35_834.65, WER: 0%, TER: 0%, total WER: 19.1725%, total TER: 8.52251%, progress (thread 0): 91.7563%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_925.24_925.54, WER: 0%, TER: 0%, total WER: 19.1723%, total TER: 8.52243%, progress (thread 0): 91.7642%]
|T|: u m
|P|: u m
[sample: EN2002d_H01_FEO072_1191.17_1191.47, WER: 0%, TER: 0%, total WER: 19.1721%, total TER: 8.52239%, progress (thread 0): 91.7721%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1205.4_1205.7, WER: 0%, TER: 0%, total WER: 19.1719%, total TER: 8.52229%, progress (thread 0): 91.7801%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H03_MEE073_1221.17_1221.47, WER: 100%, TER: 0%, total WER: 19.1728%, total TER: 8.52219%, progress (thread 0): 91.788%]
|T|: y e a h
|P|: m m
[sample: EN2002d_H00_FEO070_1249.7_1250, WER: 100%, TER: 100%, total WER: 19.1737%, total TER: 8.52305%, progress (thread 0): 91.7959%]
|T|: s u r e
|P|: s u r e
[sample: EN2002d_H03_MEE073_1672.53_1672.83, WER: 0%, TER: 0%, total WER: 19.1735%, total TER: 8.52297%, progress (thread 0): 91.8038%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_2192.66_2192.96, WER: 0%, TER: 0%, total WER: 19.1733%, total TER: 8.52293%, progress (thread 0): 91.8117%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_747.66_747.95, WER: 0%, TER: 0%, total WER: 19.1731%, total TER: 8.52285%, progress (thread 0): 91.8196%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004a_H00_MEO015_784.99_785.28, WER: 100%, TER: 20%, total WER: 19.174%, total TER: 8.52299%, progress (thread 0): 91.8275%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_826.06_826.35, WER: 0%, TER: 0%, total WER: 19.1738%, total TER: 8.52289%, progress (thread 0): 91.8354%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2019.84_2020.13, WER: 0%, TER: 0%, total WER: 19.1736%, total TER: 8.52281%, progress (thread 0): 91.8434%]
|T|: o k a y
|P|: m m
[sample: ES2004b_H02_MEE014_2295.26_2295.55, WER: 100%, TER: 100%, total WER: 19.1745%, total TER: 8.52366%, progress (thread 0): 91.8513%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_316.02_316.31, WER: 0%, TER: 0%, total WER: 19.1742%, total TER: 8.52358%, progress (thread 0): 91.8592%]
|T|: u h h u h
|P|: u h | h u h
[sample: ES2004c_H00_MEO015_1261.19_1261.48, WER: 200%, TER: 20%, total WER: 19.1763%, total TER: 8.52372%, progress (thread 0): 91.8671%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H01_FEE013_1987.8_1988.09, WER: 100%, TER: 0%, total WER: 19.1772%, total TER: 8.52362%, progress (thread 0): 91.875%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_2244.26_2244.55, WER: 0%, TER: 0%, total WER: 19.177%, total TER: 8.52354%, progress (thread 0): 91.8829%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_211.03_211.32, WER: 0%, TER: 0%, total WER: 19.1768%, total TER: 8.52344%, progress (thread 0): 91.8908%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H03_FEE016_810.67_810.96, WER: 0%, TER: 0%, total WER: 19.1766%, total TER: 8.52334%, progress (thread 0): 91.8987%]
|T|: m m
|P|: m m
[sample: ES2004d_H03_FEE016_1204.42_1204.71, WER: 0%, TER: 0%, total WER: 19.1763%, total TER: 8.5233%, progress (thread 0): 91.9066%]
|T|: u m
|P|: u m
[sample: ES2004d_H01_FEE013_1227.88_1228.17, WER: 0%, TER: 0%, total WER: 19.1761%, total TER: 8.52326%, progress (thread 0): 91.9146%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1634.52_1634.81, WER: 0%, TER: 0%, total WER: 19.1759%, total TER: 8.52318%, progress (thread 0): 91.9225%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1787.34_1787.63, WER: 0%, TER: 0%, total WER: 19.1757%, total TER: 8.5231%, progress (thread 0): 91.9304%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1964.63_1964.92, WER: 0%, TER: 0%, total WER: 19.1755%, total TER: 8.52302%, progress (thread 0): 91.9383%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_118.76_119.05, WER: 0%, TER: 0%, total WER: 19.1753%, total TER: 8.52294%, progress (thread 0): 91.9462%]
|T|: h m m
|P|: m m
[sample: IS1009a_H02_FIO084_803.39_803.68, WER: 100%, TER: 33.3333%, total WER: 19.1762%, total TER: 8.52311%, progress (thread 0): 91.9541%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_708.64_708.93, WER: 100%, TER: 0%, total WER: 19.1771%, total TER: 8.52301%, progress (thread 0): 91.962%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_998.44_998.73, WER: 0%, TER: 0%, total WER: 19.1769%, total TER: 8.52293%, progress (thread 0): 91.9699%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H03_FIO089_1311.53_1311.82, WER: 100%, TER: 0%, total WER: 19.1778%, total TER: 8.52284%, progress (thread 0): 91.9778%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1356.24_1356.53, WER: 100%, TER: 60%, total WER: 19.1787%, total TER: 8.52344%, progress (thread 0): 91.9858%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1658.74_1659.03, WER: 100%, TER: 60%, total WER: 19.1796%, total TER: 8.52404%, progress (thread 0): 91.9937%]
|T|: h m m
|P|: m m
[sample: IS1009b_H02_FIO084_1899.66_1899.95, WER: 100%, TER: 33.3333%, total WER: 19.1805%, total TER: 8.52422%, progress (thread 0): 92.0016%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H03_FIO089_1999.16_1999.45, WER: 100%, TER: 20%, total WER: 19.1814%, total TER: 8.52435%, progress (thread 0): 92.0095%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H01_FIO087_284.27_284.56, WER: 0%, TER: 0%, total WER: 19.1812%, total TER: 8.52429%, progress (thread 0): 92.0174%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_381_381.29, WER: 100%, TER: 20%, total WER: 19.1821%, total TER: 8.52442%, progress (thread 0): 92.0253%]
|T|: w e | c a n
|P|: w e g e t
[sample: IS1009c_H01_FIO087_755.64_755.93, WER: 100%, TER: 66.6667%, total WER: 19.1839%, total TER: 8.52524%, progress (thread 0): 92.0332%]
|T|: y e a h | b u t | w
|P|: y e a h
[sample: IS1009c_H02_FIO084_1559.51_1559.8, WER: 66.6667%, TER: 60%, total WER: 19.1855%, total TER: 8.52645%, progress (thread 0): 92.0411%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H02_FIO084_1608.9_1609.19, WER: 100%, TER: 20%, total WER: 19.1865%, total TER: 8.52658%, progress (thread 0): 92.049%]
|T|: h e l l o
|P|: o
[sample: IS1009d_H01_FIO087_41.27_41.56, WER: 100%, TER: 80%, total WER: 19.1874%, total TER: 8.52742%, progress (thread 0): 92.057%]
|T|: n o
|P|: n
[sample: IS1009d_H02_FIO084_952.51_952.8, WER: 100%, TER: 50%, total WER: 19.1883%, total TER: 8.52761%, progress (thread 0): 92.0649%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1056.71_1057, WER: 100%, TER: 0%, total WER: 19.1892%, total TER: 8.52751%, progress (thread 0): 92.0728%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_1344.32_1344.61, WER: 100%, TER: 0%, total WER: 19.1901%, total TER: 8.52741%, progress (thread 0): 92.0807%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_1389.87_1390.16, WER: 0%, TER: 0%, total WER: 19.1899%, total TER: 8.52733%, progress (thread 0): 92.0886%]
|T|: m m h m m
|P|: y e a m
[sample: IS1009d_H02_FIO084_1790.06_1790.35, WER: 100%, TER: 80%, total WER: 19.1908%, total TER: 8.52817%, progress (thread 0): 92.0965%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_704.99_705.28, WER: 0%, TER: 0%, total WER: 19.1906%, total TER: 8.52809%, progress (thread 0): 92.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_977.31_977.6, WER: 0%, TER: 0%, total WER: 19.1904%, total TER: 8.52801%, progress (thread 0): 92.1123%]
|T|: y o u
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1267.17_1267.46, WER: 100%, TER: 100%, total WER: 19.1913%, total TER: 8.52865%, progress (thread 0): 92.1203%]
|T|: y e s
|P|: y e s
[sample: TS3003b_H03_MTD012ME_178.78_179.07, WER: 0%, TER: 0%, total WER: 19.1911%, total TER: 8.52859%, progress (thread 0): 92.1282%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1577.63_1577.92, WER: 0%, TER: 0%, total WER: 19.1908%, total TER: 8.52851%, progress (thread 0): 92.1361%]
|T|: w e l l
|P|: w e l l
[sample: TS3003b_H03_MTD012ME_1752.79_1753.08, WER: 0%, TER: 0%, total WER: 19.1906%, total TER: 8.52843%, progress (thread 0): 92.144%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_2033.23_2033.52, WER: 0%, TER: 0%, total WER: 19.1904%, total TER: 8.52835%, progress (thread 0): 92.1519%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2083.17_2083.46, WER: 0%, TER: 0%, total WER: 19.1902%, total TER: 8.52827%, progress (thread 0): 92.1598%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_2083.37_2083.66, WER: 0%, TER: 0%, total WER: 19.19%, total TER: 8.52819%, progress (thread 0): 92.1677%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H01_MTD011UID_1289.74_1290.03, WER: 100%, TER: 0%, total WER: 19.1909%, total TER: 8.52809%, progress (thread 0): 92.1756%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1452.67_1452.96, WER: 0%, TER: 0%, total WER: 19.1907%, total TER: 8.52801%, progress (thread 0): 92.1835%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003d_H03_MTD012ME_695.88_696.17, WER: 100%, TER: 20%, total WER: 19.1916%, total TER: 8.52815%, progress (thread 0): 92.1915%]
|T|: n a y
|P|: n m h
[sample: TS3003d_H01_MTD011UID_835.49_835.78, WER: 100%, TER: 66.6667%, total WER: 19.1925%, total TER: 8.52856%, progress (thread 0): 92.1994%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1010.19_1010.48, WER: 100%, TER: 33.3333%, total WER: 19.1934%, total TER: 8.52873%, progress (thread 0): 92.2073%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H02_MTD0010ID_1075.11_1075.4, WER: 100%, TER: 75%, total WER: 19.1943%, total TER: 8.52935%, progress (thread 0): 92.2152%]
|T|: t r u e
|P|: t r u e
[sample: TS3003d_H03_MTD012ME_1129.3_1129.59, WER: 0%, TER: 0%, total WER: 19.1941%, total TER: 8.52927%, progress (thread 0): 92.2231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1862.62_1862.91, WER: 0%, TER: 0%, total WER: 19.1939%, total TER: 8.52919%, progress (thread 0): 92.231%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2370.37_2370.66, WER: 0%, TER: 0%, total WER: 19.1937%, total TER: 8.52915%, progress (thread 0): 92.2389%]
|T|: h m m
|P|: m m
[sample: TS3003d_H00_MTD009PM_2517.23_2517.52, WER: 100%, TER: 33.3333%, total WER: 19.1946%, total TER: 8.52933%, progress (thread 0): 92.2468%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2571.69_2571.98, WER: 0%, TER: 0%, total WER: 19.1944%, total TER: 8.52925%, progress (thread 0): 92.2547%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_218.41_218.7, WER: 100%, TER: 33.3333%, total WER: 19.1953%, total TER: 8.52942%, progress (thread 0): 92.2627%]
|T|: i | d o n ' t | k n o w
|P|: i | d o n t
[sample: EN2002a_H00_MEE073_234.63_234.92, WER: 66.6667%, TER: 50%, total WER: 19.1969%, total TER: 8.53059%, progress (thread 0): 92.2706%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_526.91_527.2, WER: 100%, TER: 66.6667%, total WER: 19.1978%, total TER: 8.531%, progress (thread 0): 92.2785%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_646.99_647.28, WER: 0%, TER: 0%, total WER: 19.1976%, total TER: 8.53092%, progress (thread 0): 92.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_723.91_724.2, WER: 0%, TER: 0%, total WER: 19.1974%, total TER: 8.53084%, progress (thread 0): 92.2943%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_749.38_749.67, WER: 0%, TER: 0%, total WER: 19.1971%, total TER: 8.53076%, progress (thread 0): 92.3022%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_856.03_856.32, WER: 0%, TER: 0%, total WER: 19.1969%, total TER: 8.53068%, progress (thread 0): 92.3101%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_872.78_873.07, WER: 0%, TER: 0%, total WER: 19.1967%, total TER: 8.5306%, progress (thread 0): 92.318%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1076.05_1076.34, WER: 100%, TER: 33.3333%, total WER: 19.1976%, total TER: 8.53077%, progress (thread 0): 92.326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1255.44_1255.73, WER: 0%, TER: 0%, total WER: 19.1974%, total TER: 8.53069%, progress (thread 0): 92.3339%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1364.93_1365.22, WER: 0%, TER: 0%, total WER: 19.1972%, total TER: 8.53061%, progress (thread 0): 92.3418%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1593.85_1594.14, WER: 0%, TER: 0%, total WER: 19.197%, total TER: 8.53053%, progress (thread 0): 92.3497%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1595.68_1595.97, WER: 0%, TER: 0%, total WER: 19.1968%, total TER: 8.53045%, progress (thread 0): 92.3576%]
|T|: m m h m m
|P|: m m
[sample: EN2002a_H02_FEO072_1620.66_1620.95, WER: 100%, TER: 60%, total WER: 19.1977%, total TER: 8.53105%, progress (thread 0): 92.3655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1960.6_1960.89, WER: 0%, TER: 0%, total WER: 19.1975%, total TER: 8.53097%, progress (thread 0): 92.3734%]
|T|: y e a h
|P|: o m
[sample: EN2002a_H00_MEE073_2127.38_2127.67, WER: 100%, TER: 100%, total WER: 19.1984%, total TER: 8.53183%, progress (thread 0): 92.3813%]
|T|: v e r y | g o o d
|P|: m o n
[sample: EN2002b_H02_FEO072_142.65_142.94, WER: 100%, TER: 88.8889%, total WER: 19.2002%, total TER: 8.53352%, progress (thread 0): 92.3892%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_382.41_382.7, WER: 0%, TER: 0%, total WER: 19.2%, total TER: 8.53344%, progress (thread 0): 92.3972%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_419.68_419.97, WER: 0%, TER: 0%, total WER: 19.1998%, total TER: 8.53336%, progress (thread 0): 92.4051%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H00_FEO070_734.04_734.33, WER: 0%, TER: 0%, total WER: 19.1995%, total TER: 8.53328%, progress (thread 0): 92.413%]
|T|: h m m
|P|: n o
[sample: EN2002b_H03_MEE073_1150.51_1150.8, WER: 100%, TER: 100%, total WER: 19.2005%, total TER: 8.53393%, progress (thread 0): 92.4209%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002b_H03_MEE073_1286.29_1286.58, WER: 100%, TER: 0%, total WER: 19.2014%, total TER: 8.53383%, progress (thread 0): 92.4288%]
|T|: s o
|P|: s o
[sample: EN2002b_H03_MEE073_1517.43_1517.72, WER: 0%, TER: 0%, total WER: 19.2011%, total TER: 8.53379%, progress (thread 0): 92.4367%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_61.76_62.05, WER: 0%, TER: 0%, total WER: 19.2009%, total TER: 8.53371%, progress (thread 0): 92.4446%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_697.18_697.47, WER: 0%, TER: 0%, total WER: 19.2007%, total TER: 8.53367%, progress (thread 0): 92.4525%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1026.29_1026.58, WER: 0%, TER: 0%, total WER: 19.2005%, total TER: 8.53359%, progress (thread 0): 92.4604%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_1088_1088.29, WER: 100%, TER: 0%, total WER: 19.2014%, total TER: 8.53349%, progress (thread 0): 92.4684%]
|T|: t h a t
|P|: o k y
[sample: EN2002c_H03_MEE073_1302.84_1303.13, WER: 100%, TER: 100%, total WER: 19.2023%, total TER: 8.53434%, progress (thread 0): 92.4763%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1313.99_1314.28, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53426%, progress (thread 0): 92.4842%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_1543.66_1543.95, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53422%, progress (thread 0): 92.4921%]
|T|: ' c a u s e
|P|: t h e s
[sample: EN2002c_H03_MEE073_2609.79_2610.08, WER: 100%, TER: 83.3333%, total WER: 19.2028%, total TER: 8.53527%, progress (thread 0): 92.5%]
|T|: a c t u a l l y
|P|: a c t u a l l y
[sample: EN2002d_H03_MEE073_783.19_783.48, WER: 0%, TER: 0%, total WER: 19.2026%, total TER: 8.53512%, progress (thread 0): 92.5079%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H02_MEE071_833.43_833.72, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53502%, progress (thread 0): 92.5158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1031.38_1031.67, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53494%, progress (thread 0): 92.5237%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1490.73_1491.02, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53486%, progress (thread 0): 92.5316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2069.6_2069.89, WER: 0%, TER: 0%, total WER: 19.2017%, total TER: 8.53478%, progress (thread 0): 92.5396%]
|T|: n o
|P|: n m m
[sample: ES2004b_H03_FEE016_602.63_602.91, WER: 100%, TER: 100%, total WER: 19.2026%, total TER: 8.5352%, progress (thread 0): 92.5475%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1146.25_1146.53, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53512%, progress (thread 0): 92.5554%]
|T|: m m
|P|: m m
[sample: ES2004b_H03_FEE016_1316.47_1316.75, WER: 0%, TER: 0%, total WER: 19.2022%, total TER: 8.53508%, progress (thread 0): 92.5633%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1939.86_1940.14, WER: 0%, TER: 0%, total WER: 19.202%, total TER: 8.53498%, progress (thread 0): 92.5712%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_2216.73_2217.01, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.5349%, progress (thread 0): 92.5791%]
|T|: t h a n k s
|P|: m
[sample: ES2004c_H01_FEE013_54.47_54.75, WER: 100%, TER: 100%, total WER: 19.2027%, total TER: 8.53619%, progress (thread 0): 92.587%]
|T|: u h
|P|: u h
[sample: ES2004c_H02_MEE014_1126.8_1127.08, WER: 0%, TER: 0%, total WER: 19.2025%, total TER: 8.53615%, progress (thread 0): 92.5949%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1180.98_1181.26, WER: 0%, TER: 0%, total WER: 19.2022%, total TER: 8.53611%, progress (thread 0): 92.6029%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1398.69_1398.97, WER: 0%, TER: 0%, total WER: 19.202%, total TER: 8.53603%, progress (thread 0): 92.6108%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_2123.64_2123.92, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.53595%, progress (thread 0): 92.6187%]
|T|: n o
|P|: n o
[sample: ES2004d_H02_MEE014_1050.44_1050.72, WER: 0%, TER: 0%, total WER: 19.2016%, total TER: 8.53591%, progress (thread 0): 92.6266%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004d_H00_MEO015_1500.31_1500.59, WER: 100%, TER: 0%, total WER: 19.2025%, total TER: 8.53581%, progress (thread 0): 92.6345%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1843.24_1843.52, WER: 0%, TER: 0%, total WER: 19.2023%, total TER: 8.53573%, progress (thread 0): 92.6424%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004d_H00_MEO015_2015.69_2015.97, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53563%, progress (thread 0): 92.6503%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H01_FEE013_2120.6_2120.88, WER: 0%, TER: 0%, total WER: 19.2018%, total TER: 8.53555%, progress (thread 0): 92.6582%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H03_FIO089_188.06_188.34, WER: 0%, TER: 0%, total WER: 19.2016%, total TER: 8.53547%, progress (thread 0): 92.6661%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H00_FIE088_793.23_793.51, WER: 100%, TER: 0%, total WER: 19.2025%, total TER: 8.53537%, progress (thread 0): 92.674%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_804.79_805.07, WER: 0%, TER: 0%, total WER: 19.2023%, total TER: 8.53529%, progress (thread 0): 92.682%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_894.05_894.33, WER: 0%, TER: 0%, total WER: 19.2021%, total TER: 8.53521%, progress (thread 0): 92.6899%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1764.17_1764.45, WER: 0%, TER: 0%, total WER: 19.2019%, total TER: 8.53513%, progress (thread 0): 92.6978%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009c_H02_FIO084_724.39_724.67, WER: 100%, TER: 100%, total WER: 19.2028%, total TER: 8.5362%, progress (thread 0): 92.7057%]
|T|: y e s
|P|: y e s
[sample: IS1009c_H03_FIO089_1140.8_1141.08, WER: 0%, TER: 0%, total WER: 19.2026%, total TER: 8.53614%, progress (thread 0): 92.7136%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1619.41_1619.69, WER: 0%, TER: 0%, total WER: 19.2024%, total TER: 8.53606%, progress (thread 0): 92.7215%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_617.29_617.57, WER: 100%, TER: 0%, total WER: 19.2033%, total TER: 8.53596%, progress (thread 0): 92.7294%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009d_H00_FIE088_773.28_773.56, WER: 0%, TER: 0%, total WER: 19.2031%, total TER: 8.53586%, progress (thread 0): 92.7373%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_980.1_980.38, WER: 0%, TER: 0%, total WER: 19.2028%, total TER: 8.53578%, progress (thread 0): 92.7452%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1792.74_1793.02, WER: 100%, TER: 20%, total WER: 19.2038%, total TER: 8.53591%, progress (thread 0): 92.7532%]
|T|: h m m
|P|: m m
[sample: IS1009d_H02_FIO084_1830.44_1830.72, WER: 100%, TER: 33.3333%, total WER: 19.2047%, total TER: 8.53609%, progress (thread 0): 92.7611%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1876.62_1876.9, WER: 100%, TER: 20%, total WER: 19.2056%, total TER: 8.53622%, progress (thread 0): 92.769%]
|T|: m o r n i n g
|P|: o
[sample: TS3003a_H02_MTD0010ID_16.42_16.7, WER: 100%, TER: 85.7143%, total WER: 19.2065%, total TER: 8.53749%, progress (thread 0): 92.7769%]
|T|: s u r e
|P|: s u r e
[sample: TS3003a_H03_MTD012ME_163.72_164, WER: 0%, TER: 0%, total WER: 19.2063%, total TER: 8.53741%, progress (thread 0): 92.7848%]
|T|: h m m
|P|: h m m
[sample: TS3003a_H03_MTD012ME_661.22_661.5, WER: 0%, TER: 0%, total WER: 19.2061%, total TER: 8.53735%, progress (thread 0): 92.7927%]
|T|: h m m
|P|: m m
[sample: TS3003a_H00_MTD009PM_1235.68_1235.96, WER: 100%, TER: 33.3333%, total WER: 19.207%, total TER: 8.53752%, progress (thread 0): 92.8006%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_823.58_823.86, WER: 0%, TER: 0%, total WER: 19.2068%, total TER: 8.53744%, progress (thread 0): 92.8085%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1892.2_1892.48, WER: 0%, TER: 0%, total WER: 19.2065%, total TER: 8.53736%, progress (thread 0): 92.8165%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1860.06_1860.34, WER: 0%, TER: 0%, total WER: 19.2063%, total TER: 8.53728%, progress (thread 0): 92.8244%]
|T|: m m h m m
|P|: m m h m m
[sample: TS3003c_H03_MTD012ME_1891.82_1892.1, WER: 100%, TER: 0%, total WER: 19.2072%, total TER: 8.53718%, progress (thread 0): 92.8323%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_267.26_267.54, WER: 0%, TER: 0%, total WER: 19.207%, total TER: 8.5371%, progress (thread 0): 92.8402%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_365.09_365.37, WER: 0%, TER: 0%, total WER: 19.2068%, total TER: 8.53702%, progress (thread 0): 92.8481%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_816.6_816.88, WER: 100%, TER: 66.6667%, total WER: 19.2077%, total TER: 8.53743%, progress (thread 0): 92.856%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_860.45_860.73, WER: 0%, TER: 0%, total WER: 19.2075%, total TER: 8.53735%, progress (thread 0): 92.8639%]
|T|: a n d | i t
|P|: a n d | i t
[sample: TS3003d_H02_MTD0010ID_1310.28_1310.56, WER: 0%, TER: 0%, total WER: 19.2071%, total TER: 8.53723%, progress (thread 0): 92.8718%]
|T|: s h
|P|: s
[sample: TS3003d_H02_MTD0010ID_1311.62_1311.9, WER: 100%, TER: 50%, total WER: 19.208%, total TER: 8.53742%, progress (thread 0): 92.8797%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1403.4_1403.68, WER: 100%, TER: 66.6667%, total WER: 19.2089%, total TER: 8.53783%, progress (thread 0): 92.8877%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1945.66_1945.94, WER: 0%, TER: 0%, total WER: 19.2087%, total TER: 8.53775%, progress (thread 0): 92.8956%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2142.04_2142.32, WER: 0%, TER: 0%, total WER: 19.2084%, total TER: 8.53767%, progress (thread 0): 92.9035%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_49.78_50.06, WER: 0%, TER: 0%, total WER: 19.2082%, total TER: 8.53759%, progress (thread 0): 92.9114%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_478.66_478.94, WER: 100%, TER: 66.6667%, total WER: 19.2091%, total TER: 8.538%, progress (thread 0): 92.9193%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_758.4_758.68, WER: 0%, TER: 0%, total WER: 19.2089%, total TER: 8.53792%, progress (thread 0): 92.9272%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H01_FEO070_762.35_762.63, WER: 100%, TER: 100%, total WER: 19.2098%, total TER: 8.53878%, progress (thread 0): 92.9351%]
|T|: b u t
|P|: b u t
[sample: EN2002a_H01_FEO070_1024.58_1024.86, WER: 0%, TER: 0%, total WER: 19.2096%, total TER: 8.53872%, progress (thread 0): 92.943%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H01_FEO070_1306.05_1306.33, WER: 0%, TER: 0%, total WER: 19.2094%, total TER: 8.53864%, progress (thread 0): 92.951%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1392.09_1392.37, WER: 0%, TER: 0%, total WER: 19.2092%, total TER: 8.53856%, progress (thread 0): 92.9589%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1638.28_1638.56, WER: 0%, TER: 0%, total WER: 19.209%, total TER: 8.53848%, progress (thread 0): 92.9668%]
|T|: s e e
|P|: s e e
[sample: EN2002a_H01_FEO070_1702.66_1702.94, WER: 0%, TER: 0%, total WER: 19.2088%, total TER: 8.53842%, progress (thread 0): 92.9747%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1796.46_1796.74, WER: 0%, TER: 0%, total WER: 19.2085%, total TER: 8.53834%, progress (thread 0): 92.9826%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1831.52_1831.8, WER: 0%, TER: 0%, total WER: 19.2083%, total TER: 8.53826%, progress (thread 0): 92.9905%]
|T|: o h
|P|: a h
[sample: EN2002a_H02_FEO072_1981.37_1981.65, WER: 100%, TER: 50%, total WER: 19.2092%, total TER: 8.53845%, progress (thread 0): 92.9984%]
|T|: i | t h
|P|: i
[sample: EN2002a_H00_MEE073_2026.22_2026.5, WER: 50%, TER: 75%, total WER: 19.2099%, total TER: 8.53907%, progress (thread 0): 93.0063%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H01_MEE071_16.64_16.92, WER: 0%, TER: 0%, total WER: 19.2097%, total TER: 8.53899%, progress (thread 0): 93.0142%]
|T|: m m h m m
|P|: m m
[sample: EN2002b_H03_MEE073_335.63_335.91, WER: 100%, TER: 60%, total WER: 19.2106%, total TER: 8.5396%, progress (thread 0): 93.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_356.11_356.39, WER: 0%, TER: 0%, total WER: 19.2104%, total TER: 8.53952%, progress (thread 0): 93.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_987.48_987.76, WER: 0%, TER: 0%, total WER: 19.2102%, total TER: 8.53944%, progress (thread 0): 93.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1285.46_1285.74, WER: 0%, TER: 0%, total WER: 19.21%, total TER: 8.53936%, progress (thread 0): 93.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1450.71_1450.99, WER: 0%, TER: 0%, total WER: 19.2098%, total TER: 8.53928%, progress (thread 0): 93.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1588.08_1588.36, WER: 0%, TER: 0%, total WER: 19.2095%, total TER: 8.5392%, progress (thread 0): 93.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_264.61_264.89, WER: 0%, TER: 0%, total WER: 19.2093%, total TER: 8.53912%, progress (thread 0): 93.0696%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_638.33_638.61, WER: 100%, TER: 0%, total WER: 19.2102%, total TER: 8.53902%, progress (thread 0): 93.0775%]
|T|: a h
|P|: o h
[sample: EN2002c_H01_FEO072_1466.83_1467.11, WER: 100%, TER: 50%, total WER: 19.2111%, total TER: 8.53921%, progress (thread 0): 93.0854%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1508.55_1508.83, WER: 100%, TER: 28.5714%, total WER: 19.2121%, total TER: 8.53954%, progress (thread 0): 93.0934%]
|T|: i | k n o w
|P|: y o u | k n o w
[sample: EN2002c_H03_MEE073_1627.18_1627.46, WER: 50%, TER: 50%, total WER: 19.2127%, total TER: 8.54012%, progress (thread 0): 93.1013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2613.98_2614.26, WER: 0%, TER: 0%, total WER: 19.2125%, total TER: 8.54004%, progress (thread 0): 93.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2738.83_2739.11, WER: 0%, TER: 0%, total WER: 19.2123%, total TER: 8.53996%, progress (thread 0): 93.1171%]
|T|: w e l l
|P|: m m
[sample: EN2002c_H03_MEE073_2837.32_2837.6, WER: 100%, TER: 100%, total WER: 19.2132%, total TER: 8.54082%, progress (thread 0): 93.125%]
|T|: i | t h
|P|: i
[sample: EN2002d_H03_MEE073_209.34_209.62, WER: 50%, TER: 75%, total WER: 19.2139%, total TER: 8.54144%, progress (thread 0): 93.1329%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002d_H03_MEE073_379.93_380.21, WER: 100%, TER: 0%, total WER: 19.2148%, total TER: 8.54134%, progress (thread 0): 93.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_446.25_446.53, WER: 0%, TER: 0%, total WER: 19.2146%, total TER: 8.54126%, progress (thread 0): 93.1487%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_622.33_622.61, WER: 0%, TER: 0%, total WER: 19.2144%, total TER: 8.54118%, progress (thread 0): 93.1566%]
|T|: y e a h
|P|: y m a m
[sample: EN2002d_H03_MEE073_989.71_989.99, WER: 100%, TER: 50%, total WER: 19.2153%, total TER: 8.54157%, progress (thread 0): 93.1646%]
|T|: h m m
|P|: m m
[sample: EN2002d_H02_MEE071_1294.26_1294.54, WER: 100%, TER: 33.3333%, total WER: 19.2162%, total TER: 8.54174%, progress (thread 0): 93.1725%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H01_FEO072_1744.58_1744.86, WER: 0%, TER: 0%, total WER: 19.216%, total TER: 8.54166%, progress (thread 0): 93.1804%]
|T|: t h e
|P|: y m a h
[sample: EN2002d_H03_MEE073_1888.86_1889.14, WER: 100%, TER: 133.333%, total WER: 19.2169%, total TER: 8.54254%, progress (thread 0): 93.1883%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_2067.26_2067.54, WER: 0%, TER: 0%, total WER: 19.2167%, total TER: 8.54246%, progress (thread 0): 93.1962%]
|T|: s u r e
|P|: s r u e
[sample: ES2004b_H00_MEO015_1306.29_1306.56, WER: 100%, TER: 50%, total WER: 19.2176%, total TER: 8.54284%, progress (thread 0): 93.2041%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1834.89_1835.16, WER: 0%, TER: 0%, total WER: 19.2174%, total TER: 8.54276%, progress (thread 0): 93.212%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004b_H00_MEO015_1954.73_1955, WER: 100%, TER: 0%, total WER: 19.2183%, total TER: 8.54266%, progress (thread 0): 93.2199%]
|T|: m m h m m
|P|: m m m
[sample: ES2004c_H03_FEE016_652.96_653.23, WER: 100%, TER: 40%, total WER: 19.2192%, total TER: 8.54303%, progress (thread 0): 93.2278%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004c_H00_MEO015_1141.18_1141.45, WER: 100%, TER: 20%, total WER: 19.2201%, total TER: 8.54317%, progress (thread 0): 93.2358%]
|T|: m m
|P|: s o
[sample: ES2004c_H03_FEE016_1281.44_1281.71, WER: 100%, TER: 100%, total WER: 19.221%, total TER: 8.54359%, progress (thread 0): 93.2437%]
|T|: o k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1403.89_1404.16, WER: 0%, TER: 0%, total WER: 19.2208%, total TER: 8.54351%, progress (thread 0): 93.2516%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1730.76_1731.03, WER: 0%, TER: 0%, total WER: 19.2206%, total TER: 8.54343%, progress (thread 0): 93.2595%]
|T|: m m h m m
|P|: m m h m m
[sample: ES2004c_H00_MEO015_1835.22_1835.49, WER: 100%, TER: 0%, total WER: 19.2215%, total TER: 8.54333%, progress (thread 0): 93.2674%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1921.81_1922.08, WER: 0%, TER: 0%, total WER: 19.2213%, total TER: 8.54325%, progress (thread 0): 93.2753%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H01_FEE013_2149.62_2149.89, WER: 0%, TER: 0%, total WER: 19.2211%, total TER: 8.54317%, progress (thread 0): 93.2832%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_654.95_655.22, WER: 100%, TER: 20%, total WER: 19.222%, total TER: 8.54331%, progress (thread 0): 93.2911%]
|T|: t w o
|P|: t o
[sample: ES2004d_H02_MEE014_738.4_738.67, WER: 100%, TER: 33.3333%, total WER: 19.2229%, total TER: 8.54348%, progress (thread 0): 93.299%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_793.57_793.84, WER: 0%, TER: 0%, total WER: 19.2227%, total TER: 8.5434%, progress (thread 0): 93.307%]
|T|: n o
|P|: n o
[sample: ES2004d_H03_FEE016_1418.58_1418.85, WER: 0%, TER: 0%, total WER: 19.2225%, total TER: 8.54336%, progress (thread 0): 93.3149%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1497.73_1498, WER: 0%, TER: 0%, total WER: 19.2222%, total TER: 8.54328%, progress (thread 0): 93.3228%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1559.56_1559.83, WER: 0%, TER: 0%, total WER: 19.222%, total TER: 8.5432%, progress (thread 0): 93.3307%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_2099.96_2100.23, WER: 0%, TER: 0%, total WER: 19.2218%, total TER: 8.54312%, progress (thread 0): 93.3386%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009a_H03_FIO089_159.51_159.78, WER: 100%, TER: 0%, total WER: 19.2227%, total TER: 8.54302%, progress (thread 0): 93.3465%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_495.24_495.51, WER: 0%, TER: 0%, total WER: 19.2225%, total TER: 8.54294%, progress (thread 0): 93.3544%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H02_FIO084_130.02_130.29, WER: 100%, TER: 20%, total WER: 19.2234%, total TER: 8.54308%, progress (thread 0): 93.3623%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_195.35_195.62, WER: 100%, TER: 60%, total WER: 19.2243%, total TER: 8.54368%, progress (thread 0): 93.3703%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H00_FIE088_1093_1093.27, WER: 100%, TER: 20%, total WER: 19.2252%, total TER: 8.54381%, progress (thread 0): 93.3782%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H01_FIO087_323.83_324.1, WER: 0%, TER: 0%, total WER: 19.225%, total TER: 8.54371%, progress (thread 0): 93.3861%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009c_H00_FIE088_770.63_770.9, WER: 100%, TER: 0%, total WER: 19.2259%, total TER: 8.54361%, progress (thread 0): 93.394%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_922.16_922.43, WER: 100%, TER: 20%, total WER: 19.2268%, total TER: 8.54375%, progress (thread 0): 93.4019%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_1095.56_1095.83, WER: 100%, TER: 20%, total WER: 19.2277%, total TER: 8.54388%, progress (thread 0): 93.4098%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H03_FIO089_1374.42_1374.69, WER: 0%, TER: 0%, total WER: 19.2275%, total TER: 8.5438%, progress (thread 0): 93.4177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_215.46_215.73, WER: 0%, TER: 0%, total WER: 19.2273%, total TER: 8.54372%, progress (thread 0): 93.4256%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_377.8_378.07, WER: 100%, TER: 0%, total WER: 19.2282%, total TER: 8.54362%, progress (thread 0): 93.4335%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009d_H03_FIO089_381.16_381.43, WER: 100%, TER: 0%, total WER: 19.2291%, total TER: 8.54352%, progress (thread 0): 93.4415%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H02_FIO084_938.34_938.61, WER: 100%, TER: 20%, total WER: 19.23%, total TER: 8.54365%, progress (thread 0): 93.4494%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009d_H03_FIO089_1114.59_1114.86, WER: 100%, TER: 20%, total WER: 19.2309%, total TER: 8.54379%, progress (thread 0): 93.4573%]
|T|: o h
|P|: o h
[sample: IS1009d_H00_FIE088_1436.69_1436.96, WER: 0%, TER: 0%, total WER: 19.2307%, total TER: 8.54375%, progress (thread 0): 93.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1775.25_1775.52, WER: 0%, TER: 0%, total WER: 19.2305%, total TER: 8.54367%, progress (thread 0): 93.4731%]
|T|: o k a y
|P|: o h
[sample: IS1009d_H01_FIO087_1908.84_1909.11, WER: 100%, TER: 75%, total WER: 19.2314%, total TER: 8.54429%, progress (thread 0): 93.481%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_684.57_684.84, WER: 100%, TER: 150%, total WER: 19.2323%, total TER: 8.54495%, progress (thread 0): 93.4889%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H00_MTD009PM_790.17_790.44, WER: 100%, TER: 25%, total WER: 19.2332%, total TER: 8.54511%, progress (thread 0): 93.4968%]
|T|: n o
|P|: n o
[sample: TS3003b_H03_MTD012ME_1326.95_1327.22, WER: 0%, TER: 0%, total WER: 19.233%, total TER: 8.54507%, progress (thread 0): 93.5047%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1909.01_1909.28, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54499%, progress (thread 0): 93.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1979.53_1979.8, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54491%, progress (thread 0): 93.5206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2145.84_2146.11, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54483%, progress (thread 0): 93.5285%]
|T|: o h
|P|: m m
[sample: TS3003d_H01_MTD011UID_364.71_364.98, WER: 100%, TER: 100%, total WER: 19.2333%, total TER: 8.54525%, progress (thread 0): 93.5364%]
|T|: t e n
|P|: t e n
[sample: TS3003d_H02_MTD0010ID_864.45_864.72, WER: 0%, TER: 0%, total WER: 19.2331%, total TER: 8.54519%, progress (thread 0): 93.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_958.91_959.18, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54511%, progress (thread 0): 93.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1378.52_1378.79, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54503%, progress (thread 0): 93.5601%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_2176.17_2176.44, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54499%, progress (thread 0): 93.568%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2345.04_2345.31, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54495%, progress (thread 0): 93.576%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2364.31_2364.58, WER: 0%, TER: 0%, total WER: 19.232%, total TER: 8.54487%, progress (thread 0): 93.5839%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_2435.3_2435.57, WER: 100%, TER: 25%, total WER: 19.2329%, total TER: 8.54503%, progress (thread 0): 93.5918%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_501.72_501.99, WER: 100%, TER: 33.3333%, total WER: 19.2338%, total TER: 8.5452%, progress (thread 0): 93.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_727.19_727.46, WER: 0%, TER: 0%, total WER: 19.2336%, total TER: 8.54512%, progress (thread 0): 93.6076%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_806.36_806.63, WER: 0%, TER: 0%, total WER: 19.2334%, total TER: 8.54504%, progress (thread 0): 93.6155%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_927.86_928.13, WER: 0%, TER: 0%, total WER: 19.2332%, total TER: 8.54496%, progress (thread 0): 93.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1223.91_1224.18, WER: 0%, TER: 0%, total WER: 19.2329%, total TER: 8.54488%, progress (thread 0): 93.6313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1319.85_1320.12, WER: 0%, TER: 0%, total WER: 19.2327%, total TER: 8.5448%, progress (thread 0): 93.6392%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1452_1452.27, WER: 0%, TER: 0%, total WER: 19.2325%, total TER: 8.54472%, progress (thread 0): 93.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1516.15_1516.42, WER: 0%, TER: 0%, total WER: 19.2323%, total TER: 8.54464%, progress (thread 0): 93.6551%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1533.19_1533.46, WER: 0%, TER: 0%, total WER: 19.2321%, total TER: 8.54456%, progress (thread 0): 93.663%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1556.33_1556.6, WER: 0%, TER: 0%, total WER: 19.2319%, total TER: 8.54448%, progress (thread 0): 93.6709%]
|T|: o k a y
|P|: o k a y
[sample: EN2002a_H00_MEE073_1945.11_1945.38, WER: 0%, TER: 0%, total WER: 19.2316%, total TER: 8.5444%, progress (thread 0): 93.6788%]
|T|: o h
|P|: o h
[sample: EN2002a_H00_MEE073_1989_1989.27, WER: 0%, TER: 0%, total WER: 19.2314%, total TER: 8.54436%, progress (thread 0): 93.6867%]
|T|: o h
|P|: u m
[sample: EN2002a_H00_MEE073_2003.83_2004.1, WER: 100%, TER: 100%, total WER: 19.2323%, total TER: 8.54479%, progress (thread 0): 93.6946%]
|T|: n o
|P|: n
[sample: EN2002a_H01_FEO070_2105.6_2105.87, WER: 100%, TER: 50%, total WER: 19.2332%, total TER: 8.54498%, progress (thread 0): 93.7025%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_355.59_355.86, WER: 0%, TER: 0%, total WER: 19.233%, total TER: 8.54494%, progress (thread 0): 93.7104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_485.61_485.88, WER: 0%, TER: 0%, total WER: 19.2328%, total TER: 8.54486%, progress (thread 0): 93.7184%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1027.72_1027.99, WER: 0%, TER: 0%, total WER: 19.2326%, total TER: 8.54476%, progress (thread 0): 93.7263%]
|T|: n o
|P|: n o
[sample: EN2002b_H00_FEO070_1296.06_1296.33, WER: 0%, TER: 0%, total WER: 19.2324%, total TER: 8.54472%, progress (thread 0): 93.7342%]
|T|: s o
|P|: s o
[sample: EN2002b_H01_MEE071_1333.58_1333.85, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54468%, progress (thread 0): 93.7421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_1339.47_1339.74, WER: 0%, TER: 0%, total WER: 19.2319%, total TER: 8.5446%, progress (thread 0): 93.75%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1448.9_1449.17, WER: 0%, TER: 0%, total WER: 19.2317%, total TER: 8.54452%, progress (thread 0): 93.7579%]
|T|: a l r i g h t
|P|: a l r i g h t
[sample: EN2002c_H03_MEE073_558.45_558.72, WER: 0%, TER: 0%, total WER: 19.2315%, total TER: 8.54439%, progress (thread 0): 93.7658%]
|T|: h m m
|P|: m m
[sample: EN2002c_H01_FEO072_655.42_655.69, WER: 100%, TER: 33.3333%, total WER: 19.2324%, total TER: 8.54456%, progress (thread 0): 93.7737%]
|T|: c o o l
|P|: c o o l
[sample: EN2002c_H01_FEO072_778.13_778.4, WER: 0%, TER: 0%, total WER: 19.2322%, total TER: 8.54448%, progress (thread 0): 93.7816%]
|T|: o r
|P|: o h
[sample: EN2002c_H01_FEO072_798.93_799.2, WER: 100%, TER: 50%, total WER: 19.2331%, total TER: 8.54467%, progress (thread 0): 93.7896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_938.98_939.25, WER: 0%, TER: 0%, total WER: 19.2329%, total TER: 8.54459%, progress (thread 0): 93.7975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1122.75_1123.02, WER: 0%, TER: 0%, total WER: 19.2327%, total TER: 8.54451%, progress (thread 0): 93.8054%]
|T|: o k a y
|P|: m h m m
[sample: EN2002c_H02_MEE071_1353.05_1353.32, WER: 100%, TER: 100%, total WER: 19.2336%, total TER: 8.54537%, progress (thread 0): 93.8133%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1574.37_1574.64, WER: 0%, TER: 0%, total WER: 19.2334%, total TER: 8.54527%, progress (thread 0): 93.8212%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_1672.35_1672.62, WER: 100%, TER: 25%, total WER: 19.2343%, total TER: 8.54542%, progress (thread 0): 93.8291%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1740.26_1740.53, WER: 0%, TER: 0%, total WER: 19.2341%, total TER: 8.54534%, progress (thread 0): 93.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1980.58_1980.85, WER: 0%, TER: 0%, total WER: 19.2338%, total TER: 8.54526%, progress (thread 0): 93.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2155.14_2155.41, WER: 0%, TER: 0%, total WER: 19.2336%, total TER: 8.54518%, progress (thread 0): 93.8528%]
|T|: a l r i g h t
|P|: o h r i
[sample: EN2002c_H03_MEE073_2202.51_2202.78, WER: 100%, TER: 71.4286%, total WER: 19.2345%, total TER: 8.54621%, progress (thread 0): 93.8608%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2272.51_2272.78, WER: 0%, TER: 0%, total WER: 19.2343%, total TER: 8.54613%, progress (thread 0): 93.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2294.4_2294.67, WER: 0%, TER: 0%, total WER: 19.2341%, total TER: 8.54605%, progress (thread 0): 93.8766%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2396.65_2396.92, WER: 0%, TER: 0%, total WER: 19.2339%, total TER: 8.54597%, progress (thread 0): 93.8845%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2568.46_2568.73, WER: 0%, TER: 0%, total WER: 19.2337%, total TER: 8.54589%, progress (thread 0): 93.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2705.6_2705.87, WER: 0%, TER: 0%, total WER: 19.2335%, total TER: 8.54581%, progress (thread 0): 93.9003%]
|T|: m m
|P|: m m
[sample: EN2002c_H01_FEO072_2790.29_2790.56, WER: 0%, TER: 0%, total WER: 19.2332%, total TER: 8.54577%, progress (thread 0): 93.9082%]
|T|: i | k n o w
|P|: y o u | k n o w
[sample: EN2002d_H03_MEE073_903.75_904.02, WER: 50%, TER: 50%, total WER: 19.2339%, total TER: 8.54635%, progress (thread 0): 93.9161%]
|T|: b u t
|P|: w h l
[sample: EN2002d_H01_FEO072_946.69_946.96, WER: 100%, TER: 100%, total WER: 19.2348%, total TER: 8.54699%, progress (thread 0): 93.924%]
|T|: s o r r y
|P|: s o r r y
[sample: EN2002d_H00_FEO070_966.35_966.62, WER: 0%, TER: 0%, total WER: 19.2346%, total TER: 8.54689%, progress (thread 0): 93.932%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1127.55_1127.82, WER: 0%, TER: 0%, total WER: 19.2344%, total TER: 8.54681%, progress (thread 0): 93.9399%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1281.17_1281.44, WER: 0%, TER: 0%, total WER: 19.2342%, total TER: 8.54673%, progress (thread 0): 93.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1305.04_1305.31, WER: 0%, TER: 0%, total WER: 19.234%, total TER: 8.54665%, progress (thread 0): 93.9557%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002d_H00_FEO070_1507.8_1508.07, WER: 100%, TER: 100%, total WER: 19.2358%, total TER: 8.54815%, progress (thread 0): 93.9636%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_1832.82_1833.09, WER: 0%, TER: 0%, total WER: 19.2356%, total TER: 8.54807%, progress (thread 0): 93.9715%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_2000.66_2000.93, WER: 0%, TER: 0%, total WER: 19.2354%, total TER: 8.54799%, progress (thread 0): 93.9794%]
|T|: w h a t
|P|: w e a l
[sample: EN2002d_H02_MEE071_2072.88_2073.15, WER: 100%, TER: 50%, total WER: 19.2363%, total TER: 8.54838%, progress (thread 0): 93.9873%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_2169.42_2169.69, WER: 100%, TER: 66.6667%, total WER: 19.2372%, total TER: 8.54879%, progress (thread 0): 93.9953%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H00_MEO015_17.88_18.14, WER: 0%, TER: 0%, total WER: 19.237%, total TER: 8.54871%, progress (thread 0): 94.0032%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004a_H00_MEO015_966.22_966.48, WER: 0%, TER: 0%, total WER: 19.2367%, total TER: 8.54861%, progress (thread 0): 94.0111%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_89.12_89.38, WER: 100%, TER: 20%, total WER: 19.2376%, total TER: 8.54874%, progress (thread 0): 94.019%]
|T|: t h e r e | w e | g o
|P|: o k a y
[sample: ES2004b_H02_MEE014_830.72_830.98, WER: 100%, TER: 100%, total WER: 19.2404%, total TER: 8.55109%, progress (thread 0): 94.0269%]
|T|: h u h
|P|: m m
[sample: ES2004b_H02_MEE014_1039.01_1039.27, WER: 100%, TER: 100%, total WER: 19.2413%, total TER: 8.55173%, progress (thread 0): 94.0348%]
|T|: p e n s
|P|: i n
[sample: ES2004b_H00_MEO015_1162.44_1162.7, WER: 100%, TER: 75%, total WER: 19.2422%, total TER: 8.55235%, progress (thread 0): 94.0427%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H02_MEE014_1848.94_1849.2, WER: 0%, TER: 0%, total WER: 19.242%, total TER: 8.55227%, progress (thread 0): 94.0506%]
|T|: ' k a y
|P|: k a y
[sample: ES2004c_H00_MEO015_188.1_188.36, WER: 100%, TER: 25%, total WER: 19.2429%, total TER: 8.55243%, progress (thread 0): 94.0585%]
|T|: y e p
|P|: y e p
[sample: ES2004c_H00_MEO015_540.57_540.83, WER: 0%, TER: 0%, total WER: 19.2427%, total TER: 8.55237%, progress (thread 0): 94.0665%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H03_FEE016_1192.94_1193.2, WER: 0%, TER: 0%, total WER: 19.2425%, total TER: 8.55229%, progress (thread 0): 94.0744%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_39.85_40.11, WER: 100%, TER: 20%, total WER: 19.2434%, total TER: 8.55242%, progress (thread 0): 94.0823%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_129.84_130.1, WER: 0%, TER: 0%, total WER: 19.2431%, total TER: 8.55234%, progress (thread 0): 94.0902%]
|T|: o h
|P|: m h
[sample: ES2004d_H03_FEE016_1400.02_1400.28, WER: 100%, TER: 50%, total WER: 19.2441%, total TER: 8.55253%, progress (thread 0): 94.0981%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1553.35_1553.61, WER: 0%, TER: 0%, total WER: 19.2438%, total TER: 8.55245%, progress (thread 0): 94.106%]
|T|: n o
|P|: y e a h
[sample: ES2004d_H01_FEE013_1608.41_1608.67, WER: 100%, TER: 200%, total WER: 19.2447%, total TER: 8.55335%, progress (thread 0): 94.1139%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004d_H00_MEO015_1680.41_1680.67, WER: 100%, TER: 20%, total WER: 19.2457%, total TER: 8.55348%, progress (thread 0): 94.1218%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H03_FIO089_195.56_195.82, WER: 100%, TER: 20%, total WER: 19.2466%, total TER: 8.55362%, progress (thread 0): 94.1297%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_250.63_250.89, WER: 0%, TER: 0%, total WER: 19.2463%, total TER: 8.55354%, progress (thread 0): 94.1377%]
|T|: y e s
|P|: y e a h
[sample: IS1009b_H01_FIO087_289.01_289.27, WER: 100%, TER: 66.6667%, total WER: 19.2473%, total TER: 8.55394%, progress (thread 0): 94.1456%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_892.83_893.09, WER: 100%, TER: 60%, total WER: 19.2482%, total TER: 8.55455%, progress (thread 0): 94.1535%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H01_FIO087_2020.18_2020.44, WER: 0%, TER: 0%, total WER: 19.2479%, total TER: 8.55447%, progress (thread 0): 94.1614%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_626.71_626.97, WER: 100%, TER: 40%, total WER: 19.2489%, total TER: 8.55483%, progress (thread 0): 94.1693%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_766.43_766.69, WER: 0%, TER: 0%, total WER: 19.2486%, total TER: 8.55475%, progress (thread 0): 94.1772%]
|T|: m m
|P|: y m m
[sample: IS1009d_H01_FIO087_656.85_657.11, WER: 100%, TER: 50%, total WER: 19.2495%, total TER: 8.55495%, progress (thread 0): 94.1851%]
|T|: ' c a u s e
|P|: s
[sample: TS3003a_H03_MTD012ME_1376.63_1376.89, WER: 100%, TER: 83.3333%, total WER: 19.2505%, total TER: 8.55599%, progress (thread 0): 94.193%]
|T|: o k a y
|P|: o k a y
[sample: TS3003a_H02_MTD0010ID_1474.04_1474.3, WER: 0%, TER: 0%, total WER: 19.2502%, total TER: 8.55591%, progress (thread 0): 94.201%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_206.59_206.85, WER: 100%, TER: 25%, total WER: 19.2511%, total TER: 8.55607%, progress (thread 0): 94.2089%]
|T|: o k a y
|P|: o k a y
[sample: TS3003b_H01_MTD011UID_581.65_581.91, WER: 0%, TER: 0%, total WER: 19.2509%, total TER: 8.55599%, progress (thread 0): 94.2168%]
|T|: h m m
|P|: m m
[sample: TS3003b_H03_MTD012ME_883.3_883.56, WER: 100%, TER: 33.3333%, total WER: 19.2518%, total TER: 8.55616%, progress (thread 0): 94.2247%]
|T|: m m h m m
|P|: m h m
[sample: TS3003b_H03_MTD012ME_1533.07_1533.33, WER: 100%, TER: 40%, total WER: 19.2527%, total TER: 8.55653%, progress (thread 0): 94.2326%]
|T|: b u t
|P|: b u t
[sample: TS3003b_H03_MTD012ME_2048.3_2048.56, WER: 0%, TER: 0%, total WER: 19.2525%, total TER: 8.55647%, progress (thread 0): 94.2405%]
|T|: m m
|P|: h m m
[sample: TS3003c_H01_MTD011UID_1933.15_1933.41, WER: 100%, TER: 50%, total WER: 19.2534%, total TER: 8.55666%, progress (thread 0): 94.2484%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1980.3_1980.56, WER: 0%, TER: 0%, total WER: 19.2532%, total TER: 8.55658%, progress (thread 0): 94.2563%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_694.34_694.6, WER: 0%, TER: 0%, total WER: 19.253%, total TER: 8.5565%, progress (thread 0): 94.2642%]
|T|: t w o
|P|: t w o
[sample: TS3003d_H03_MTD012ME_1897.81_1898.07, WER: 0%, TER: 0%, total WER: 19.2528%, total TER: 8.55644%, progress (thread 0): 94.2722%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2141.52_2141.78, WER: 0%, TER: 0%, total WER: 19.2526%, total TER: 8.55636%, progress (thread 0): 94.2801%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002a_H02_FEO072_36.34_36.6, WER: 100%, TER: 20%, total WER: 19.2535%, total TER: 8.5565%, progress (thread 0): 94.288%]
|T|: y e a h
|P|: y m a m
[sample: EN2002a_H00_MEE073_319.23_319.49, WER: 100%, TER: 50%, total WER: 19.2544%, total TER: 8.55688%, progress (thread 0): 94.2959%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_671.05_671.31, WER: 0%, TER: 0%, total WER: 19.2542%, total TER: 8.5568%, progress (thread 0): 94.3038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_721.89_722.15, WER: 0%, TER: 0%, total WER: 19.254%, total TER: 8.55672%, progress (thread 0): 94.3117%]
|T|: t r u e
|P|: t r u e
[sample: EN2002a_H00_MEE073_751.8_752.06, WER: 0%, TER: 0%, total WER: 19.2537%, total TER: 8.55664%, progress (thread 0): 94.3196%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1052.1_1052.36, WER: 0%, TER: 0%, total WER: 19.2535%, total TER: 8.55656%, progress (thread 0): 94.3275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1446.67_1446.93, WER: 0%, TER: 0%, total WER: 19.2533%, total TER: 8.55648%, progress (thread 0): 94.3354%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1777.39_1777.65, WER: 0%, TER: 0%, total WER: 19.2531%, total TER: 8.5564%, progress (thread 0): 94.3434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1868.15_1868.41, WER: 0%, TER: 0%, total WER: 19.2529%, total TER: 8.55632%, progress (thread 0): 94.3513%]
|T|: o k a y
|P|: o g a y
[sample: EN2002b_H02_FEO072_93.22_93.48, WER: 100%, TER: 25%, total WER: 19.2538%, total TER: 8.55648%, progress (thread 0): 94.3592%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_252.07_252.33, WER: 0%, TER: 0%, total WER: 19.2536%, total TER: 8.5564%, progress (thread 0): 94.3671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_460.58_460.84, WER: 0%, TER: 0%, total WER: 19.2533%, total TER: 8.55632%, progress (thread 0): 94.375%]
|T|: o h
|P|: o h
[sample: EN2002b_H03_MEE073_674.06_674.32, WER: 0%, TER: 0%, total WER: 19.2531%, total TER: 8.55628%, progress (thread 0): 94.3829%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1026.26_1026.52, WER: 0%, TER: 0%, total WER: 19.2529%, total TER: 8.5562%, progress (thread 0): 94.3908%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_1104.98_1105.24, WER: 0%, TER: 0%, total WER: 19.2527%, total TER: 8.55612%, progress (thread 0): 94.3987%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1549.41_1549.67, WER: 100%, TER: 33.3333%, total WER: 19.2536%, total TER: 8.55629%, progress (thread 0): 94.4066%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1643.55_1643.81, WER: 0%, TER: 0%, total WER: 19.2534%, total TER: 8.55621%, progress (thread 0): 94.4146%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H02_MEE071_46.45_46.71, WER: 0%, TER: 0%, total WER: 19.2532%, total TER: 8.55611%, progress (thread 0): 94.4225%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_577.1_577.36, WER: 0%, TER: 0%, total WER: 19.253%, total TER: 8.55605%, progress (thread 0): 94.4304%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_600.57_600.83, WER: 100%, TER: 66.6667%, total WER: 19.2539%, total TER: 8.55646%, progress (thread 0): 94.4383%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_775.24_775.5, WER: 0%, TER: 0%, total WER: 19.2536%, total TER: 8.55638%, progress (thread 0): 94.4462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1079.16_1079.42, WER: 0%, TER: 0%, total WER: 19.2534%, total TER: 8.5563%, progress (thread 0): 94.4541%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002c_H03_MEE073_1105.57_1105.83, WER: 100%, TER: 20%, total WER: 19.2543%, total TER: 8.55643%, progress (thread 0): 94.462%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1119.48_1119.74, WER: 0%, TER: 0%, total WER: 19.2541%, total TER: 8.55635%, progress (thread 0): 94.4699%]
|T|: m m h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1983.15_1983.41, WER: 100%, TER: 60%, total WER: 19.255%, total TER: 8.55695%, progress (thread 0): 94.4779%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1999.1_1999.36, WER: 0%, TER: 0%, total WER: 19.2548%, total TER: 8.55685%, progress (thread 0): 94.4858%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2082.11_2082.37, WER: 0%, TER: 0%, total WER: 19.2546%, total TER: 8.55677%, progress (thread 0): 94.4937%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2586.11_2586.37, WER: 0%, TER: 0%, total WER: 19.2544%, total TER: 8.55669%, progress (thread 0): 94.5016%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_347.04_347.3, WER: 0%, TER: 0%, total WER: 19.2542%, total TER: 8.55661%, progress (thread 0): 94.5095%]
|T|: w h o
|P|: h m m
[sample: EN2002d_H00_FEO070_501.31_501.57, WER: 100%, TER: 100%, total WER: 19.2551%, total TER: 8.55726%, progress (thread 0): 94.5174%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_728.46_728.72, WER: 0%, TER: 0%, total WER: 19.2549%, total TER: 8.55718%, progress (thread 0): 94.5253%]
|T|: w e l l | i t ' s
|P|: j u s t
[sample: EN2002d_H03_MEE073_882.54_882.8, WER: 100%, TER: 88.8889%, total WER: 19.2567%, total TER: 8.55886%, progress (thread 0): 94.5332%]
|T|: s u p e r
|P|: s u p e
[sample: EN2002d_H03_MEE073_953.8_954.06, WER: 100%, TER: 20%, total WER: 19.2576%, total TER: 8.559%, progress (thread 0): 94.5411%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1517.63_1517.89, WER: 50%, TER: 42.8571%, total WER: 19.2583%, total TER: 8.55956%, progress (thread 0): 94.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1704.28_1704.54, WER: 0%, TER: 0%, total WER: 19.2581%, total TER: 8.55948%, progress (thread 0): 94.557%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_2135.36_2135.62, WER: 100%, TER: 33.3333%, total WER: 19.259%, total TER: 8.55965%, progress (thread 0): 94.5649%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_666.69_666.94, WER: 0%, TER: 0%, total WER: 19.2587%, total TER: 8.55961%, progress (thread 0): 94.5728%]
|T|: y e p
|P|: y e a h
[sample: ES2004b_H00_MEO015_817.97_818.22, WER: 100%, TER: 66.6667%, total WER: 19.2597%, total TER: 8.56002%, progress (thread 0): 94.5807%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_2175.08_2175.33, WER: 100%, TER: 20%, total WER: 19.2606%, total TER: 8.56015%, progress (thread 0): 94.5886%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004c_H00_MEO015_620.36_620.61, WER: 100%, TER: 20%, total WER: 19.2615%, total TER: 8.56029%, progress (thread 0): 94.5965%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1866.87_1867.12, WER: 100%, TER: 100%, total WER: 19.2624%, total TER: 8.56093%, progress (thread 0): 94.6044%]
|T|: s o r r y
|P|: s o r r y
[sample: ES2004d_H01_FEE013_455.38_455.63, WER: 0%, TER: 0%, total WER: 19.2622%, total TER: 8.56083%, progress (thread 0): 94.6123%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_607.67_607.92, WER: 0%, TER: 0%, total WER: 19.2619%, total TER: 8.56075%, progress (thread 0): 94.6203%]
|T|: y e s
|P|: y e a h
[sample: ES2004d_H03_FEE016_1135.45_1135.7, WER: 100%, TER: 66.6667%, total WER: 19.2628%, total TER: 8.56115%, progress (thread 0): 94.6282%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1553.38_1553.63, WER: 0%, TER: 0%, total WER: 19.2626%, total TER: 8.56107%, progress (thread 0): 94.6361%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009b_H03_FIO089_1086.32_1086.57, WER: 100%, TER: 20%, total WER: 19.2635%, total TER: 8.56121%, progress (thread 0): 94.644%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1900.42_1900.67, WER: 0%, TER: 0%, total WER: 19.2633%, total TER: 8.56113%, progress (thread 0): 94.6519%]
|T|: y e s
|P|: y e a h
[sample: IS1009b_H01_FIO087_1909.04_1909.29, WER: 100%, TER: 66.6667%, total WER: 19.2642%, total TER: 8.56153%, progress (thread 0): 94.6598%]
|T|: t h r e e
|P|: t h r e e
[sample: IS1009c_H00_FIE088_324.58_324.83, WER: 0%, TER: 0%, total WER: 19.264%, total TER: 8.56143%, progress (thread 0): 94.6677%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1287.33_1287.58, WER: 0%, TER: 0%, total WER: 19.2638%, total TER: 8.56135%, progress (thread 0): 94.6756%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1781.33_1781.58, WER: 0%, TER: 0%, total WER: 19.2636%, total TER: 8.56127%, progress (thread 0): 94.6835%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_351.32_351.57, WER: 0%, TER: 0%, total WER: 19.2634%, total TER: 8.56119%, progress (thread 0): 94.6915%]
|T|: m m
|P|: m m m
[sample: IS1009d_H03_FIO089_427.43_427.68, WER: 100%, TER: 50%, total WER: 19.2643%, total TER: 8.56139%, progress (thread 0): 94.6994%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_826.51_826.76, WER: 0%, TER: 0%, total WER: 19.2641%, total TER: 8.56135%, progress (thread 0): 94.7073%]
|T|: n o
|P|: n o
[sample: TS3003a_H03_MTD012ME_1222.74_1222.99, WER: 0%, TER: 0%, total WER: 19.2638%, total TER: 8.56131%, progress (thread 0): 94.7152%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_281.83_282.08, WER: 0%, TER: 0%, total WER: 19.2636%, total TER: 8.56127%, progress (thread 0): 94.7231%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_1559.52_1559.77, WER: 0%, TER: 0%, total WER: 19.2634%, total TER: 8.56119%, progress (thread 0): 94.731%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H02_MTD0010ID_1839.11_1839.36, WER: 0%, TER: 0%, total WER: 19.2632%, total TER: 8.56111%, progress (thread 0): 94.7389%]
|T|: y e a h
|P|: y e p
[sample: TS3003b_H02_MTD0010ID_2094.57_2094.82, WER: 100%, TER: 50%, total WER: 19.2641%, total TER: 8.5615%, progress (thread 0): 94.7468%]
|T|: m m h m m
|P|: m h m m
[sample: TS3003c_H03_MTD012ME_1972.52_1972.77, WER: 100%, TER: 20%, total WER: 19.265%, total TER: 8.56163%, progress (thread 0): 94.7548%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_303.27_303.52, WER: 100%, TER: 75%, total WER: 19.2659%, total TER: 8.56225%, progress (thread 0): 94.7627%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_583.29_583.54, WER: 0%, TER: 0%, total WER: 19.2657%, total TER: 8.56217%, progress (thread 0): 94.7706%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_847.16_847.41, WER: 0%, TER: 0%, total WER: 19.2655%, total TER: 8.56209%, progress (thread 0): 94.7785%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1370.99_1371.24, WER: 0%, TER: 0%, total WER: 19.2653%, total TER: 8.56201%, progress (thread 0): 94.7864%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1371.48_1371.73, WER: 0%, TER: 0%, total WER: 19.265%, total TER: 8.56193%, progress (thread 0): 94.7943%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_1404.32_1404.57, WER: 100%, TER: 75%, total WER: 19.266%, total TER: 8.56255%, progress (thread 0): 94.8022%]
|T|: g o o d
|P|: o k a y
[sample: TS3003d_H03_MTD012ME_1696.35_1696.6, WER: 100%, TER: 100%, total WER: 19.2669%, total TER: 8.5634%, progress (thread 0): 94.8101%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_2064.36_2064.61, WER: 0%, TER: 0%, total WER: 19.2666%, total TER: 8.56332%, progress (thread 0): 94.818%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_2111_2111.25, WER: 0%, TER: 0%, total WER: 19.2664%, total TER: 8.56324%, progress (thread 0): 94.826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2138.4_2138.65, WER: 0%, TER: 0%, total WER: 19.2662%, total TER: 8.56316%, progress (thread 0): 94.8339%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2216.27_2216.52, WER: 0%, TER: 0%, total WER: 19.266%, total TER: 8.56308%, progress (thread 0): 94.8418%]
|T|: a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2381.92_2382.17, WER: 100%, TER: 100%, total WER: 19.2669%, total TER: 8.56351%, progress (thread 0): 94.8497%]
|T|: s o
|P|: s o
[sample: EN2002a_H00_MEE073_202.42_202.67, WER: 0%, TER: 0%, total WER: 19.2667%, total TER: 8.56347%, progress (thread 0): 94.8576%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_749.45_749.7, WER: 0%, TER: 0%, total WER: 19.2665%, total TER: 8.56339%, progress (thread 0): 94.8655%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1642_1642.25, WER: 0%, TER: 0%, total WER: 19.2663%, total TER: 8.56331%, progress (thread 0): 94.8734%]
|T|: s o
|P|: s o
[sample: EN2002a_H02_FEO072_1664.11_1664.36, WER: 0%, TER: 0%, total WER: 19.266%, total TER: 8.56327%, progress (thread 0): 94.8813%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1964.06_1964.31, WER: 0%, TER: 0%, total WER: 19.2658%, total TER: 8.56319%, progress (thread 0): 94.8892%]
|T|: y o u ' d | b e | s
|P|: c i m p e
[sample: EN2002a_H00_MEE073_2096.92_2097.17, WER: 100%, TER: 90%, total WER: 19.2685%, total TER: 8.56509%, progress (thread 0): 94.8971%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_93.68_93.93, WER: 0%, TER: 0%, total WER: 19.2683%, total TER: 8.56501%, progress (thread 0): 94.9051%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_311.61_311.86, WER: 0%, TER: 0%, total WER: 19.2681%, total TER: 8.56491%, progress (thread 0): 94.913%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1129.86_1130.11, WER: 100%, TER: 28.5714%, total WER: 19.269%, total TER: 8.56524%, progress (thread 0): 94.9209%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H01_MEE071_1222.15_1222.4, WER: 0%, TER: 0%, total WER: 19.2688%, total TER: 8.56518%, progress (thread 0): 94.9288%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1237.83_1238.08, WER: 0%, TER: 0%, total WER: 19.2686%, total TER: 8.5651%, progress (thread 0): 94.9367%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1245.09_1245.34, WER: 0%, TER: 0%, total WER: 19.2684%, total TER: 8.56502%, progress (thread 0): 94.9446%]
|T|: a n d
|P|: t h e n
[sample: EN2002b_H01_MEE071_1309.75_1310, WER: 100%, TER: 133.333%, total WER: 19.2693%, total TER: 8.56589%, progress (thread 0): 94.9525%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H02_FEO072_1594.84_1595.09, WER: 100%, TER: 66.6667%, total WER: 19.2702%, total TER: 8.5663%, progress (thread 0): 94.9604%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1643.59_1643.84, WER: 0%, TER: 0%, total WER: 19.27%, total TER: 8.56622%, progress (thread 0): 94.9684%]
|T|: s u r e
|P|: s u r e
[sample: EN2002c_H02_MEE071_67.06_67.31, WER: 0%, TER: 0%, total WER: 19.2697%, total TER: 8.56614%, progress (thread 0): 94.9763%]
|T|: o k a y
|P|: m m
[sample: EN2002c_H03_MEE073_76.13_76.38, WER: 100%, TER: 100%, total WER: 19.2707%, total TER: 8.56699%, progress (thread 0): 94.9842%]
|T|: o k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_253.63_253.88, WER: 0%, TER: 0%, total WER: 19.2704%, total TER: 8.56691%, progress (thread 0): 94.9921%]
|T|: m m h m m
|P|: m m h m m
[sample: EN2002c_H03_MEE073_470.85_471.1, WER: 100%, TER: 0%, total WER: 19.2713%, total TER: 8.56681%, progress (thread 0): 95%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_759.06_759.31, WER: 0%, TER: 0%, total WER: 19.2711%, total TER: 8.56671%, progress (thread 0): 95.0079%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1000.82_1001.07, WER: 0%, TER: 0%, total WER: 19.2709%, total TER: 8.56663%, progress (thread 0): 95.0158%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1023.06_1023.31, WER: 0%, TER: 0%, total WER: 19.2707%, total TER: 8.56655%, progress (thread 0): 95.0237%]
|T|: m m h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1102.48_1102.73, WER: 100%, TER: 60%, total WER: 19.2716%, total TER: 8.56715%, progress (thread 0): 95.0316%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1122.76_1123.01, WER: 0%, TER: 0%, total WER: 19.2714%, total TER: 8.56707%, progress (thread 0): 95.0396%]
|T|: y e p
|P|: y e a h
[sample: EN2002c_H01_FEO072_1359.23_1359.48, WER: 100%, TER: 66.6667%, total WER: 19.2723%, total TER: 8.56748%, progress (thread 0): 95.0475%]
|T|: b u t
|P|: b u t
[sample: EN2002c_H02_MEE071_1508.23_1508.48, WER: 0%, TER: 0%, total WER: 19.2721%, total TER: 8.56742%, progress (thread 0): 95.0554%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_2100.94_2101.19, WER: 0%, TER: 0%, total WER: 19.2719%, total TER: 8.56734%, progress (thread 0): 95.0633%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2354.17_2354.42, WER: 0%, TER: 0%, total WER: 19.2716%, total TER: 8.56726%, progress (thread 0): 95.0712%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_146.37_146.62, WER: 100%, TER: 28.5714%, total WER: 19.2725%, total TER: 8.56759%, progress (thread 0): 95.0791%]
|T|: o h
|P|: m m
[sample: EN2002d_H00_FEO070_286.77_287.02, WER: 100%, TER: 100%, total WER: 19.2735%, total TER: 8.56801%, progress (thread 0): 95.087%]
|T|: y e a h
|P|: m m
[sample: EN2002d_H03_MEE073_589.91_590.16, WER: 100%, TER: 100%, total WER: 19.2744%, total TER: 8.56887%, progress (thread 0): 95.0949%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_850.49_850.74, WER: 0%, TER: 0%, total WER: 19.2741%, total TER: 8.56879%, progress (thread 0): 95.1028%]
|T|: s o
|P|: h m h
[sample: EN2002d_H02_MEE071_1251.8_1252.05, WER: 100%, TER: 150%, total WER: 19.2751%, total TER: 8.56945%, progress (thread 0): 95.1108%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_1830.85_1831.1, WER: 100%, TER: 33.3333%, total WER: 19.276%, total TER: 8.56962%, progress (thread 0): 95.1187%]
|T|: o k a y
|P|: c o y
[sample: EN2002d_H03_MEE073_1854.42_1854.67, WER: 100%, TER: 75%, total WER: 19.2769%, total TER: 8.57024%, progress (thread 0): 95.1266%]
|T|: m m
|P|: h m m
[sample: EN2002d_H01_FEO072_2083.17_2083.42, WER: 100%, TER: 50%, total WER: 19.2778%, total TER: 8.57044%, progress (thread 0): 95.1345%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2192.17_2192.42, WER: 0%, TER: 0%, total WER: 19.2776%, total TER: 8.57036%, progress (thread 0): 95.1424%]
|T|: s
|P|: s
[sample: ES2004a_H02_MEE014_484.48_484.72, WER: 0%, TER: 0%, total WER: 19.2773%, total TER: 8.57033%, progress (thread 0): 95.1503%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_775.6_775.84, WER: 0%, TER: 0%, total WER: 19.2771%, total TER: 8.57029%, progress (thread 0): 95.1582%]
|T|: s o r r y
|P|: m m
[sample: ES2004b_H00_MEO015_100.7_100.94, WER: 100%, TER: 100%, total WER: 19.278%, total TER: 8.57136%, progress (thread 0): 95.1661%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1311.92_1312.16, WER: 0%, TER: 0%, total WER: 19.2778%, total TER: 8.57126%, progress (thread 0): 95.174%]
|T|: m m h m m
|P|: m h m m
[sample: ES2004b_H00_MEO015_1636.65_1636.89, WER: 100%, TER: 20%, total WER: 19.2787%, total TER: 8.5714%, progress (thread 0): 95.182%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_1305.92_1306.16, WER: 100%, TER: 25%, total WER: 19.2796%, total TER: 8.57155%, progress (thread 0): 95.1899%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1345.16_1345.4, WER: 0%, TER: 0%, total WER: 19.2794%, total TER: 8.57151%, progress (thread 0): 95.1978%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1364.93_1365.17, WER: 0%, TER: 0%, total WER: 19.2792%, total TER: 8.57147%, progress (thread 0): 95.2057%]
|T|: f i n e
|P|: i n y
[sample: ES2004c_H00_MEO015_2156.57_2156.81, WER: 100%, TER: 50%, total WER: 19.2801%, total TER: 8.57186%, progress (thread 0): 95.2136%]
|T|: o n e
|P|: o n e
[sample: ES2004d_H01_FEE013_973.73_973.97, WER: 0%, TER: 0%, total WER: 19.2799%, total TER: 8.5718%, progress (thread 0): 95.2215%]
|T|: m m
|P|: m m
[sample: ES2004d_H03_FEE016_1286.7_1286.94, WER: 0%, TER: 0%, total WER: 19.2797%, total TER: 8.57176%, progress (thread 0): 95.2294%]
|T|: o h
|P|: o h
[sample: ES2004d_H01_FEE013_2176.67_2176.91, WER: 0%, TER: 0%, total WER: 19.2794%, total TER: 8.57172%, progress (thread 0): 95.2373%]
|T|: n o
|P|: m m
[sample: IS1009a_H03_FIO089_145.55_145.79, WER: 100%, TER: 100%, total WER: 19.2804%, total TER: 8.57214%, progress (thread 0): 95.2453%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009a_H00_FIE088_466.21_466.45, WER: 100%, TER: 20%, total WER: 19.2813%, total TER: 8.57228%, progress (thread 0): 95.2532%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_556.58_556.82, WER: 0%, TER: 0%, total WER: 19.281%, total TER: 8.5722%, progress (thread 0): 95.2611%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1033.16_1033.4, WER: 0%, TER: 0%, total WER: 19.2808%, total TER: 8.57212%, progress (thread 0): 95.269%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H00_FIE088_1099.19_1099.43, WER: 0%, TER: 0%, total WER: 19.2806%, total TER: 8.57204%, progress (thread 0): 95.2769%]
|T|: m m h m m
|P|: m m h m m
[sample: IS1009b_H00_FIE088_1247.84_1248.08, WER: 100%, TER: 0%, total WER: 19.2815%, total TER: 8.57194%, progress (thread 0): 95.2848%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H03_FIO089_752.07_752.31, WER: 100%, TER: 20%, total WER: 19.2824%, total TER: 8.57207%, progress (thread 0): 95.2927%]
|T|: m m | r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_764.18_764.42, WER: 50%, TER: 37.5%, total WER: 19.2831%, total TER: 8.57261%, progress (thread 0): 95.3006%]
|T|: m m h m m
|P|: m h m
[sample: IS1009c_H00_FIE088_1110.14_1110.38, WER: 100%, TER: 40%, total WER: 19.284%, total TER: 8.57298%, progress (thread 0): 95.3085%]
|T|: m m h m m
|P|: m h m m
[sample: IS1009c_H00_FIE088_1123.59_1123.83, WER: 100%, TER: 20%, total WER: 19.2849%, total TER: 8.57311%, progress (thread 0): 95.3165%]
|T|: h e l l o
|P|: n o
[sample: IS1009d_H02_FIO084_41.31_41.55, WER: 100%, TER: 80%, total WER: 19.2858%, total TER: 8.57394%, progress (thread 0): 95.3244%]
|T|: m m
|P|: m m
[sample: IS1009d_H03_FIO089_516.52_516.76, WER: 0%, TER: 0%, total WER: 19.2856%, total TER: 8.5739%, progress (thread 0): 95.3323%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_656.9_657.14, WER: 100%, TER: 60%, total WER: 19.2865%, total TER: 8.5745%, progress (thread 0): 95.3402%]
|T|: y e a h
|P|: n o
[sample: IS1009d_H02_FIO084_1011.72_1011.96, WER: 100%, TER: 100%, total WER: 19.2874%, total TER: 8.57536%, progress (thread 0): 95.3481%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_1883.01_1883.25, WER: 0%, TER: 0%, total WER: 19.2872%, total TER: 8.57528%, progress (thread 0): 95.356%]
|T|: w e
|P|: w e
[sample: TS3003a_H00_MTD009PM_1109.9_1110.14, WER: 0%, TER: 0%, total WER: 19.287%, total TER: 8.57524%, progress (thread 0): 95.3639%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1420.32_1420.56, WER: 0%, TER: 0%, total WER: 19.2868%, total TER: 8.5752%, progress (thread 0): 95.3718%]
|T|: n o
|P|: n o
[sample: TS3003b_H02_MTD0010ID_1474.4_1474.64, WER: 0%, TER: 0%, total WER: 19.2866%, total TER: 8.57516%, progress (thread 0): 95.3797%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_1677.79_1678.03, WER: 0%, TER: 0%, total WER: 19.2863%, total TER: 8.57508%, progress (thread 0): 95.3877%]
|T|: m m
|P|: u h
[sample: TS3003b_H01_MTD011UID_1975.67_1975.91, WER: 100%, TER: 100%, total WER: 19.2873%, total TER: 8.5755%, progress (thread 0): 95.3956%]
|T|: y e a h
|P|: h m m
[sample: TS3003b_H01_MTD011UID_2083.9_2084.14, WER: 100%, TER: 100%, total WER: 19.2882%, total TER: 8.57635%, progress (thread 0): 95.4035%]
|T|: y e a h
|P|: a h
[sample: TS3003c_H01_MTD011UID_1240.02_1240.26, WER: 100%, TER: 50%, total WER: 19.2891%, total TER: 8.57674%, progress (thread 0): 95.4114%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1254.68_1254.92, WER: 0%, TER: 0%, total WER: 19.2888%, total TER: 8.57666%, progress (thread 0): 95.4193%]
|T|: s o
|P|: s o
[sample: TS3003c_H01_MTD011UID_1417.83_1418.07, WER: 0%, TER: 0%, total WER: 19.2886%, total TER: 8.57662%, progress (thread 0): 95.4272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1477.96_1478.2, WER: 0%, TER: 0%, total WER: 19.2884%, total TER: 8.57654%, progress (thread 0): 95.4351%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003c_H03_MTD012ME_1566.64_1566.88, WER: 100%, TER: 25%, total WER: 19.2893%, total TER: 8.57669%, progress (thread 0): 95.443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1716.46_1716.7, WER: 0%, TER: 0%, total WER: 19.2891%, total TER: 8.57661%, progress (thread 0): 95.451%]
|T|: o k a y
|P|: o k a y
[sample: TS3003c_H02_MTD0010ID_2217.59_2217.83, WER: 0%, TER: 0%, total WER: 19.2889%, total TER: 8.57653%, progress (thread 0): 95.4589%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2243.54_2243.78, WER: 0%, TER: 0%, total WER: 19.2887%, total TER: 8.57645%, progress (thread 0): 95.4668%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1049.33_1049.57, WER: 0%, TER: 0%, total WER: 19.2885%, total TER: 8.57637%, progress (thread 0): 95.4747%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1496.68_1496.92, WER: 100%, TER: 75%, total WER: 19.2894%, total TER: 8.57699%, progress (thread 0): 95.4826%]
|T|: d | y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_106.38_106.62, WER: 50%, TER: 33.3333%, total WER: 19.2901%, total TER: 8.57734%, progress (thread 0): 95.4905%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_140.88_141.12, WER: 0%, TER: 0%, total WER: 19.2898%, total TER: 8.57726%, progress (thread 0): 95.4984%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_333.3_333.54, WER: 0%, TER: 0%, total WER: 19.2896%, total TER: 8.57716%, progress (thread 0): 95.5063%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H01_FEO070_398.18_398.42, WER: 100%, TER: 100%, total WER: 19.2905%, total TER: 8.57801%, progress (thread 0): 95.5142%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_481.62_481.86, WER: 0%, TER: 0%, total WER: 19.2903%, total TER: 8.57793%, progress (thread 0): 95.5222%]
|T|: y e a h
|P|: y e a m
[sample: EN2002a_H00_MEE073_682.97_683.21, WER: 100%, TER: 25%, total WER: 19.2912%, total TER: 8.57809%, progress (thread 0): 95.5301%]
|T|: m m h m m
|P|: m h m m
[sample: EN2002a_H02_FEO072_1009.94_1010.18, WER: 100%, TER: 20%, total WER: 19.2921%, total TER: 8.57822%, progress (thread 0): 95.538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1041.03_1041.27, WER: 0%, TER: 0%, total WER: 19.2919%, total TER: 8.57814%, progress (thread 0): 95.5459%]
|T|: i | s e e
|P|: a t ' s
[sample: EN2002a_H00_MEE073_1171.72_1171.96, WER: 100%, TER: 100%, total WER: 19.2937%, total TER: 8.57921%, progress (thread 0): 95.5538%]
|T|: o h | y e a h
|P|: w e
[sample: EN2002a_H00_MEE073_1209.32_1209.56, WER: 100%, TER: 85.7143%, total WER: 19.2955%, total TER: 8.58047%, progress (thread 0): 95.5617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1310.69_1310.93, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.58039%, progress (thread 0): 95.5696%]
|T|: o p e n
|P|: o p a y
[sample: EN2002a_H02_FEO072_1350.04_1350.28, WER: 100%, TER: 50%, total WER: 19.2962%, total TER: 8.58077%, progress (thread 0): 95.5775%]
|T|: m m
|P|: m m
[sample: EN2002a_H03_MEE071_1405.73_1405.97, WER: 0%, TER: 0%, total WER: 19.296%, total TER: 8.58073%, progress (thread 0): 95.5854%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1791.17_1791.41, WER: 0%, TER: 0%, total WER: 19.2958%, total TER: 8.58063%, progress (thread 0): 95.5934%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1834.42_1834.66, WER: 0%, TER: 0%, total WER: 19.2956%, total TER: 8.58055%, progress (thread 0): 95.6013%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_2028.12_2028.36, WER: 0%, TER: 0%, total WER: 19.2954%, total TER: 8.58047%, progress (thread 0): 95.6092%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_2040.58_2040.82, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.58039%, progress (thread 0): 95.6171%]
|T|: o h
|P|: o h
[sample: EN2002a_H01_FEO070_2098.91_2099.15, WER: 0%, TER: 0%, total WER: 19.2949%, total TER: 8.58035%, progress (thread 0): 95.625%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_522.48_522.72, WER: 0%, TER: 0%, total WER: 19.2947%, total TER: 8.58027%, progress (thread 0): 95.6329%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_612.23_612.47, WER: 0%, TER: 0%, total WER: 19.2945%, total TER: 8.58019%, progress (thread 0): 95.6408%]
|T|: m m
|P|: m m
[sample: EN2002b_H02_FEO072_1475.84_1476.08, WER: 0%, TER: 0%, total WER: 19.2943%, total TER: 8.58015%, progress (thread 0): 95.6487%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_235.84_236.08, WER: 100%, TER: 33.3333%, total WER: 19.2952%, total TER: 8.58033%, progress (thread 0): 95.6566%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_571.47_571.71, WER: 0%, TER: 0%, total WER: 19.295%, total TER: 8.58025%, progress (thread 0): 95.6646%]
|T|: h m m
|P|: y e a h
[sample: EN2002c_H03_MEE073_574.34_574.58, WER: 100%, TER: 133.333%, total WER: 19.2959%, total TER: 8.58112%, progress (thread 0): 95.6725%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_598.8_599.04, WER: 0%, TER: 0%, total WER: 19.2956%, total TER: 8.58104%, progress (thread 0): 95.6804%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1009.87_1010.11, WER: 0%, TER: 0%, total WER: 19.2954%, total TER: 8.58096%, progress (thread 0): 95.6883%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1018.66_1018.9, WER: 0%, TER: 0%, total WER: 19.2952%, total TER: 8.58086%, progress (thread 0): 95.6962%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1281.36_1281.6, WER: 100%, TER: 33.3333%, total WER: 19.2961%, total TER: 8.58103%, progress (thread 0): 95.7041%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1396.34_1396.58, WER: 0%, TER: 0%, total WER: 19.2959%, total TER: 8.58095%, progress (thread 0): 95.712%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1906.84_1907.08, WER: 0%, TER: 0%, total WER: 19.2957%, total TER: 8.58087%, progress (thread 0): 95.7199%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1913.84_1914.08, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58079%, progress (thread 0): 95.7279%]
|T|: ' c a u s e
|P|: t h e ' s
[sample: EN2002c_H03_MEE073_2264.19_2264.43, WER: 100%, TER: 83.3333%, total WER: 19.2964%, total TER: 8.58184%, progress (thread 0): 95.7358%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2309.3_2309.54, WER: 0%, TER: 0%, total WER: 19.2962%, total TER: 8.58176%, progress (thread 0): 95.7437%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2341.41_2341.65, WER: 0%, TER: 0%, total WER: 19.2959%, total TER: 8.58166%, progress (thread 0): 95.7516%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2370.76_2371, WER: 0%, TER: 0%, total WER: 19.2957%, total TER: 8.58156%, progress (thread 0): 95.7595%]
|T|: g o o d
|P|: g o o d
[sample: EN2002d_H01_FEO072_337.97_338.21, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58148%, progress (thread 0): 95.7674%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_390.81_391.05, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.5814%, progress (thread 0): 95.7753%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H01_FEO072_496.78_497.02, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.5813%, progress (thread 0): 95.7832%]
|T|: o k a y
|P|: h u h
[sample: EN2002d_H00_FEO070_513.66_513.9, WER: 100%, TER: 100%, total WER: 19.296%, total TER: 8.58215%, progress (thread 0): 95.7911%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1190.34_1190.58, WER: 0%, TER: 0%, total WER: 19.2958%, total TER: 8.58207%, progress (thread 0): 95.799%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_1371.83_1372.07, WER: 0%, TER: 0%, total WER: 19.2955%, total TER: 8.58199%, progress (thread 0): 95.807%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1477.13_1477.37, WER: 0%, TER: 0%, total WER: 19.2953%, total TER: 8.58191%, progress (thread 0): 95.8149%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1706.39_1706.63, WER: 0%, TER: 0%, total WER: 19.2951%, total TER: 8.58183%, progress (thread 0): 95.8228%]
|T|: m m
|P|: m m
[sample: ES2004b_H03_FEE016_1717.78_1718.01, WER: 0%, TER: 0%, total WER: 19.2949%, total TER: 8.58179%, progress (thread 0): 95.8307%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1860.91_1861.14, WER: 100%, TER: 40%, total WER: 19.2958%, total TER: 8.58216%, progress (thread 0): 95.8386%]
|T|: t r u e
|P|: s u e
[sample: ES2004b_H00_MEO015_2294.79_2295.02, WER: 100%, TER: 50%, total WER: 19.2967%, total TER: 8.58254%, progress (thread 0): 95.8465%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_85.13_85.36, WER: 0%, TER: 0%, total WER: 19.2965%, total TER: 8.58246%, progress (thread 0): 95.8544%]
|T|: ' k a y
|P|: o k a y
[sample: ES2004c_H00_MEO015_356.94_357.17, WER: 100%, TER: 25%, total WER: 19.2974%, total TER: 8.58262%, progress (thread 0): 95.8623%]
|T|: s o
|P|: s a e
[sample: ES2004c_H02_MEE014_748.27_748.5, WER: 100%, TER: 100%, total WER: 19.2983%, total TER: 8.58304%, progress (thread 0): 95.8702%]
|T|: m m
|P|: m m
[sample: ES2004c_H03_FEE016_1121.33_1121.56, WER: 0%, TER: 0%, total WER: 19.2981%, total TER: 8.583%, progress (thread 0): 95.8782%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_1169.98_1170.21, WER: 0%, TER: 0%, total WER: 19.2979%, total TER: 8.58292%, progress (thread 0): 95.8861%]
|T|: o k a y
|P|: o k a y
[sample: ES2004d_H03_FEE016_711.82_712.05, WER: 0%, TER: 0%, total WER: 19.2977%, total TER: 8.58284%, progress (thread 0): 95.894%]
|T|: t w o
|P|: t o
[sample: ES2004d_H00_MEO015_732.46_732.69, WER: 100%, TER: 33.3333%, total WER: 19.2986%, total TER: 8.58301%, progress (thread 0): 95.9019%]
|T|: m m h m m
|P|: m e h m
[sample: ES2004d_H00_MEO015_822.98_823.21, WER: 100%, TER: 40%, total WER: 19.2995%, total TER: 8.58338%, progress (thread 0): 95.9098%]
|T|: ' k a y
|P|: s o
[sample: ES2004d_H00_MEO015_1813.16_1813.39, WER: 100%, TER: 100%, total WER: 19.3004%, total TER: 8.58423%, progress (thread 0): 95.9177%]
|T|: o k a y
|P|: o k a y
[sample: IS1009a_H00_FIE088_195.04_195.27, WER: 0%, TER: 0%, total WER: 19.3002%, total TER: 8.58415%, progress (thread 0): 95.9256%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_532.46_532.69, WER: 100%, TER: 66.6667%, total WER: 19.3011%, total TER: 8.58456%, progress (thread 0): 95.9335%]
|T|: m m
|P|: m m
[sample: IS1009b_H02_FIO084_55.21_55.44, WER: 0%, TER: 0%, total WER: 19.3008%, total TER: 8.58452%, progress (thread 0): 95.9415%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1919.19_1919.42, WER: 0%, TER: 0%, total WER: 19.3006%, total TER: 8.58444%, progress (thread 0): 95.9494%]
|T|: m m h m m
|P|: m m
[sample: IS1009c_H00_FIE088_554.54_554.77, WER: 100%, TER: 60%, total WER: 19.3015%, total TER: 8.58504%, progress (thread 0): 95.9573%]
|T|: m m | y e s
|P|: y e a h
[sample: IS1009c_H01_FIO087_728.73_728.96, WER: 100%, TER: 83.3333%, total WER: 19.3033%, total TER: 8.58608%, progress (thread 0): 95.9652%]
|T|: y e p
|P|: y e a h
[sample: IS1009c_H03_FIO089_1639.78_1640.01, WER: 100%, TER: 66.6667%, total WER: 19.3042%, total TER: 8.58649%, progress (thread 0): 95.9731%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_747.97_748.2, WER: 100%, TER: 66.6667%, total WER: 19.3052%, total TER: 8.5869%, progress (thread 0): 95.981%]
|T|: h m m
|P|: m m
[sample: IS1009d_H02_FIO084_748.42_748.65, WER: 100%, TER: 33.3333%, total WER: 19.3061%, total TER: 8.58707%, progress (thread 0): 95.9889%]
|T|: m m h m m
|P|: y e a h
[sample: IS1009d_H03_FIO089_758.1_758.33, WER: 100%, TER: 100%, total WER: 19.307%, total TER: 8.58814%, progress (thread 0): 95.9968%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_1695.75_1695.98, WER: 100%, TER: 66.6667%, total WER: 19.3079%, total TER: 8.58854%, progress (thread 0): 96.0047%]
|T|: o k a y
|P|: y e a h
[sample: IS1009d_H03_FIO089_1889.3_1889.53, WER: 100%, TER: 75%, total WER: 19.3088%, total TER: 8.58916%, progress (thread 0): 96.0127%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_843.57_843.8, WER: 100%, TER: 25%, total WER: 19.3097%, total TER: 8.58931%, progress (thread 0): 96.0206%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1334.2_1334.43, WER: 0%, TER: 0%, total WER: 19.3095%, total TER: 8.58923%, progress (thread 0): 96.0285%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1346.08_1346.31, WER: 0%, TER: 0%, total WER: 19.3092%, total TER: 8.58915%, progress (thread 0): 96.0364%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1078.3_1078.53, WER: 0%, TER: 0%, total WER: 19.309%, total TER: 8.58911%, progress (thread 0): 96.0443%]
|T|: n o
|P|: n o
[sample: TS3003b_H01_MTD011UID_1716.14_1716.37, WER: 0%, TER: 0%, total WER: 19.3088%, total TER: 8.58907%, progress (thread 0): 96.0522%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_1250.59_1250.82, WER: 0%, TER: 0%, total WER: 19.3086%, total TER: 8.58903%, progress (thread 0): 96.0601%]
|T|: y e s
|P|: j u s t
[sample: TS3003c_H02_MTD0010ID_1518.39_1518.62, WER: 100%, TER: 100%, total WER: 19.3095%, total TER: 8.58967%, progress (thread 0): 96.068%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1654.26_1654.49, WER: 0%, TER: 0%, total WER: 19.3093%, total TER: 8.58963%, progress (thread 0): 96.076%]
|T|: y e a h
|P|: u h
[sample: TS3003c_H01_MTD011UID_2049.46_2049.69, WER: 100%, TER: 75%, total WER: 19.3102%, total TER: 8.59025%, progress (thread 0): 96.0839%]
|T|: s o
|P|: s o
[sample: TS3003d_H01_MTD011UID_261.45_261.68, WER: 0%, TER: 0%, total WER: 19.31%, total TER: 8.59021%, progress (thread 0): 96.0918%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_1021.27_1021.5, WER: 100%, TER: 66.6667%, total WER: 19.3109%, total TER: 8.59062%, progress (thread 0): 96.0997%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1945.96_1946.19, WER: 100%, TER: 75%, total WER: 19.3118%, total TER: 8.59124%, progress (thread 0): 96.1076%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2064.77_2065, WER: 0%, TER: 0%, total WER: 19.3116%, total TER: 8.59116%, progress (thread 0): 96.1155%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_424.58_424.81, WER: 0%, TER: 0%, total WER: 19.3114%, total TER: 8.59106%, progress (thread 0): 96.1234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_781.91_782.14, WER: 0%, TER: 0%, total WER: 19.3111%, total TER: 8.59098%, progress (thread 0): 96.1313%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_855.85_856.08, WER: 0%, TER: 0%, total WER: 19.3109%, total TER: 8.5909%, progress (thread 0): 96.1392%]
|T|: h m m
|P|: m m
[sample: EN2002a_H02_FEO072_877.24_877.47, WER: 100%, TER: 33.3333%, total WER: 19.3118%, total TER: 8.59107%, progress (thread 0): 96.1471%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H00_MEE073_1019.4_1019.63, WER: 0%, TER: 0%, total WER: 19.3116%, total TER: 8.59101%, progress (thread 0): 96.1551%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H01_FEO070_1188.61_1188.84, WER: 100%, TER: 66.6667%, total WER: 19.3125%, total TER: 8.59142%, progress (thread 0): 96.163%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1295.18_1295.41, WER: 0%, TER: 0%, total WER: 19.3123%, total TER: 8.59134%, progress (thread 0): 96.1709%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1408.29_1408.52, WER: 0%, TER: 0%, total WER: 19.3121%, total TER: 8.59126%, progress (thread 0): 96.1788%]
|T|: n o
|P|: n o
[sample: EN2002a_H01_FEO070_1571.85_1572.08, WER: 0%, TER: 0%, total WER: 19.3119%, total TER: 8.59122%, progress (thread 0): 96.1867%]
|T|: o h
|P|: o
[sample: EN2002b_H03_MEE073_29.06_29.29, WER: 100%, TER: 50%, total WER: 19.3128%, total TER: 8.59141%, progress (thread 0): 96.1946%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_194.27_194.5, WER: 0%, TER: 0%, total WER: 19.3125%, total TER: 8.59133%, progress (thread 0): 96.2025%]
|T|: c o o l
|P|: c o o l
[sample: EN2002b_H03_MEE073_211.63_211.86, WER: 0%, TER: 0%, total WER: 19.3123%, total TER: 8.59125%, progress (thread 0): 96.2104%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_493.43_493.66, WER: 0%, TER: 0%, total WER: 19.3121%, total TER: 8.59117%, progress (thread 0): 96.2184%]
|T|: h m m
|P|: m m
[sample: EN2002b_H00_FEO070_534.29_534.52, WER: 100%, TER: 33.3333%, total WER: 19.313%, total TER: 8.59134%, progress (thread 0): 96.2263%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_672.54_672.77, WER: 100%, TER: 33.3333%, total WER: 19.3139%, total TER: 8.59152%, progress (thread 0): 96.2342%]
|T|: m m h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1361.79_1362.02, WER: 100%, TER: 60%, total WER: 19.3148%, total TER: 8.59211%, progress (thread 0): 96.2421%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_555.02_555.25, WER: 0%, TER: 0%, total WER: 19.3146%, total TER: 8.59203%, progress (thread 0): 96.25%]
|T|: h m m
|P|: h m m
[sample: EN2002c_H01_FEO072_699.88_700.11, WER: 0%, TER: 0%, total WER: 19.3144%, total TER: 8.59197%, progress (thread 0): 96.2579%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_1900.45_1900.68, WER: 0%, TER: 0%, total WER: 19.3142%, total TER: 8.59189%, progress (thread 0): 96.2658%]
|T|: d o h
|P|: s o
[sample: EN2002c_H02_MEE071_2395.6_2395.83, WER: 100%, TER: 66.6667%, total WER: 19.3151%, total TER: 8.5923%, progress (thread 0): 96.2737%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2817.5_2817.73, WER: 0%, TER: 0%, total WER: 19.3149%, total TER: 8.59222%, progress (thread 0): 96.2816%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2846.79_2847.02, WER: 0%, TER: 0%, total WER: 19.3147%, total TER: 8.59214%, progress (thread 0): 96.2896%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_107.69_107.92, WER: 0%, TER: 0%, total WER: 19.3144%, total TER: 8.59206%, progress (thread 0): 96.2975%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_503.45_503.68, WER: 0%, TER: 0%, total WER: 19.3142%, total TER: 8.59198%, progress (thread 0): 96.3054%]
|T|: m m h m m
|P|: m m
[sample: EN2002d_H01_FEO072_548.74_548.97, WER: 100%, TER: 60%, total WER: 19.3151%, total TER: 8.59258%, progress (thread 0): 96.3133%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_767.8_768.03, WER: 0%, TER: 0%, total WER: 19.3149%, total TER: 8.5925%, progress (thread 0): 96.3212%]
|T|: n o
|P|: n o
[sample: EN2002d_H00_FEO070_1427.88_1428.11, WER: 0%, TER: 0%, total WER: 19.3147%, total TER: 8.59246%, progress (thread 0): 96.3291%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_1760.63_1760.86, WER: 100%, TER: 33.3333%, total WER: 19.3156%, total TER: 8.59263%, progress (thread 0): 96.337%]
|T|: o k a y
|P|: y e h
[sample: EN2002d_H00_FEO070_2207.62_2207.85, WER: 100%, TER: 100%, total WER: 19.3165%, total TER: 8.59348%, progress (thread 0): 96.3449%]
|T|: y e a h
|P|: y e a h
[sample: ES2004a_H02_MEE014_1022.74_1022.96, WER: 0%, TER: 0%, total WER: 19.3163%, total TER: 8.5934%, progress (thread 0): 96.3528%]
|T|: m m
|P|: m m
[sample: ES2004b_H02_MEE014_1537.12_1537.34, WER: 0%, TER: 0%, total WER: 19.3161%, total TER: 8.59336%, progress (thread 0): 96.3608%]
|T|: r i g h t
|P|: r i g h t
[sample: ES2004c_H00_MEO015_575.7_575.92, WER: 0%, TER: 0%, total WER: 19.3159%, total TER: 8.59326%, progress (thread 0): 96.3687%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H03_FEE016_1311.86_1312.08, WER: 100%, TER: 40%, total WER: 19.3168%, total TER: 8.59363%, progress (thread 0): 96.3766%]
|T|: r i g h t
|P|: i k
[sample: ES2004c_H00_MEO015_2116.51_2116.73, WER: 100%, TER: 80%, total WER: 19.3177%, total TER: 8.59446%, progress (thread 0): 96.3845%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H00_MEO015_2159.26_2159.48, WER: 0%, TER: 0%, total WER: 19.3174%, total TER: 8.59438%, progress (thread 0): 96.3924%]
|T|: y e a h
|P|: y e m
[sample: ES2004d_H02_MEE014_968.07_968.29, WER: 100%, TER: 50%, total WER: 19.3183%, total TER: 8.59477%, progress (thread 0): 96.4003%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H02_MEE014_1220.58_1220.8, WER: 0%, TER: 0%, total WER: 19.3181%, total TER: 8.59469%, progress (thread 0): 96.4082%]
|T|: o k a y
|P|: y o u t
[sample: ES2004d_H03_FEE016_1281.25_1281.47, WER: 100%, TER: 100%, total WER: 19.319%, total TER: 8.59554%, progress (thread 0): 96.4161%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1684.26_1684.48, WER: 0%, TER: 0%, total WER: 19.3188%, total TER: 8.59546%, progress (thread 0): 96.424%]
|T|: o k a y
|P|: o k a y
[sample: IS1009b_H03_FIO089_45.13_45.35, WER: 0%, TER: 0%, total WER: 19.3186%, total TER: 8.59538%, progress (thread 0): 96.432%]
|T|: ' k a y
|P|: m m
[sample: IS1009b_H02_FIO084_155.16_155.38, WER: 100%, TER: 100%, total WER: 19.3195%, total TER: 8.59623%, progress (thread 0): 96.4399%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_404.12_404.34, WER: 100%, TER: 60%, total WER: 19.3204%, total TER: 8.59683%, progress (thread 0): 96.4478%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_961.79_962.01, WER: 0%, TER: 0%, total WER: 19.3202%, total TER: 8.59675%, progress (thread 0): 96.4557%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H03_FIO089_1007.66_1007.88, WER: 0%, TER: 0%, total WER: 19.32%, total TER: 8.59667%, progress (thread 0): 96.4636%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H00_FIE088_337.53_337.75, WER: 0%, TER: 0%, total WER: 19.3198%, total TER: 8.59659%, progress (thread 0): 96.4715%]
|T|: o k a y
|P|: o k a y
[sample: IS1009c_H01_FIO087_1263.61_1263.83, WER: 0%, TER: 0%, total WER: 19.3195%, total TER: 8.59651%, progress (thread 0): 96.4794%]
|T|: m m
|P|: m m m
[sample: IS1009d_H03_FIO089_590.61_590.83, WER: 100%, TER: 50%, total WER: 19.3204%, total TER: 8.5967%, progress (thread 0): 96.4873%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_801.03_801.25, WER: 0%, TER: 0%, total WER: 19.3202%, total TER: 8.59666%, progress (thread 0): 96.4953%]
|T|: m m h m m
|P|: m m
[sample: IS1009d_H02_FIO084_1687.52_1687.74, WER: 100%, TER: 60%, total WER: 19.3211%, total TER: 8.59726%, progress (thread 0): 96.5032%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_1775.76_1775.98, WER: 0%, TER: 0%, total WER: 19.3209%, total TER: 8.59718%, progress (thread 0): 96.5111%]
|T|: o k a y
|P|: o k a y
[sample: IS1009d_H03_FIO089_1846.51_1846.73, WER: 0%, TER: 0%, total WER: 19.3207%, total TER: 8.5971%, progress (thread 0): 96.519%]
|T|: m o r n i n g
|P|: m o n e y
[sample: TS3003a_H01_MTD011UID_15.34_15.56, WER: 100%, TER: 57.1429%, total WER: 19.3216%, total TER: 8.59789%, progress (thread 0): 96.5269%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H03_MTD012ME_1316.76_1316.98, WER: 0%, TER: 0%, total WER: 19.3214%, total TER: 8.59781%, progress (thread 0): 96.5348%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1357.36_1357.58, WER: 0%, TER: 0%, total WER: 19.3212%, total TER: 8.59777%, progress (thread 0): 96.5427%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_888.56_888.78, WER: 0%, TER: 0%, total WER: 19.321%, total TER: 8.59769%, progress (thread 0): 96.5506%]
|T|: h m m
|P|: m m
[sample: TS3003b_H03_MTD012ME_1371.93_1372.15, WER: 100%, TER: 33.3333%, total WER: 19.3219%, total TER: 8.59787%, progress (thread 0): 96.5585%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_1600.07_1600.29, WER: 0%, TER: 0%, total WER: 19.3216%, total TER: 8.59779%, progress (thread 0): 96.5665%]
|T|: n o
|P|: n o
[sample: TS3003c_H01_MTD011UID_109.75_109.97, WER: 0%, TER: 0%, total WER: 19.3214%, total TER: 8.59775%, progress (thread 0): 96.5744%]
|T|: m a y b
|P|: i
[sample: TS3003c_H00_MTD009PM_437.36_437.58, WER: 100%, TER: 100%, total WER: 19.3223%, total TER: 8.5986%, progress (thread 0): 96.5823%]
|T|: m m h m m
|P|: o k a y
[sample: TS3003c_H01_MTD011UID_1100.48_1100.7, WER: 100%, TER: 100%, total WER: 19.3232%, total TER: 8.59966%, progress (thread 0): 96.5902%]
|T|: y e a h
|P|: m m
[sample: TS3003c_H01_MTD011UID_1304.78_1305, WER: 100%, TER: 100%, total WER: 19.3241%, total TER: 8.60051%, progress (thread 0): 96.5981%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_2263.6_2263.82, WER: 0%, TER: 0%, total WER: 19.3239%, total TER: 8.60043%, progress (thread 0): 96.606%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_563.58_563.8, WER: 0%, TER: 0%, total WER: 19.3237%, total TER: 8.60035%, progress (thread 0): 96.6139%]
|T|: y e p
|P|: y e a p
[sample: TS3003d_H02_MTD0010ID_577.11_577.33, WER: 100%, TER: 33.3333%, total WER: 19.3246%, total TER: 8.60053%, progress (thread 0): 96.6218%]
|T|: n o
|P|: o h
[sample: TS3003d_H01_MTD011UID_1458.81_1459.03, WER: 100%, TER: 100%, total WER: 19.3255%, total TER: 8.60095%, progress (thread 0): 96.6297%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1674.77_1674.99, WER: 0%, TER: 0%, total WER: 19.3253%, total TER: 8.60091%, progress (thread 0): 96.6377%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1736.91_1737.13, WER: 0%, TER: 0%, total WER: 19.3251%, total TER: 8.60083%, progress (thread 0): 96.6456%]
|T|: n o
|P|: n o
[sample: TS3003d_H01_MTD011UID_1751.36_1751.58, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60079%, progress (thread 0): 96.6535%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1991.39_1991.61, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60071%, progress (thread 0): 96.6614%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_1998.36_1998.58, WER: 0%, TER: 0%, total WER: 19.3244%, total TER: 8.60063%, progress (thread 0): 96.6693%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_482.53_482.75, WER: 0%, TER: 0%, total WER: 19.3242%, total TER: 8.60055%, progress (thread 0): 96.6772%]
|T|: h m m
|P|: m m
[sample: EN2002a_H01_FEO070_706.29_706.51, WER: 100%, TER: 33.3333%, total WER: 19.3251%, total TER: 8.60073%, progress (thread 0): 96.6851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_805.91_806.13, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60064%, progress (thread 0): 96.693%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002a_H00_MEE073_1103.91_1104.13, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60054%, progress (thread 0): 96.701%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H03_MEE071_1216.04_1216.26, WER: 100%, TER: 66.6667%, total WER: 19.3256%, total TER: 8.60095%, progress (thread 0): 96.7089%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1537.4_1537.62, WER: 0%, TER: 0%, total WER: 19.3254%, total TER: 8.60087%, progress (thread 0): 96.7168%]
|T|: j u s t
|P|: j u s t
[sample: EN2002a_H01_FEO070_1871.92_1872.14, WER: 0%, TER: 0%, total WER: 19.3252%, total TER: 8.60079%, progress (thread 0): 96.7247%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1963.02_1963.24, WER: 0%, TER: 0%, total WER: 19.3249%, total TER: 8.60071%, progress (thread 0): 96.7326%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_639.41_639.63, WER: 0%, TER: 0%, total WER: 19.3247%, total TER: 8.60063%, progress (thread 0): 96.7405%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1397.63_1397.85, WER: 0%, TER: 0%, total WER: 19.3245%, total TER: 8.60053%, progress (thread 0): 96.7484%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_1547.56_1547.78, WER: 100%, TER: 33.3333%, total WER: 19.3254%, total TER: 8.6007%, progress (thread 0): 96.7563%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_575.92_576.14, WER: 100%, TER: 28.5714%, total WER: 19.3263%, total TER: 8.60103%, progress (thread 0): 96.7642%]
|T|: r i g h t
|P|: b u t
[sample: EN2002c_H02_MEE071_589.02_589.24, WER: 100%, TER: 80%, total WER: 19.3272%, total TER: 8.60186%, progress (thread 0): 96.7722%]
|T|: ' k a y
|P|: y e a h
[sample: EN2002c_H03_MEE073_684.52_684.74, WER: 100%, TER: 75%, total WER: 19.3281%, total TER: 8.60248%, progress (thread 0): 96.7801%]
|T|: o h | y e a h
|P|: m h
[sample: EN2002c_H03_MEE073_1506.68_1506.9, WER: 100%, TER: 85.7143%, total WER: 19.3299%, total TER: 8.60374%, progress (thread 0): 96.788%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1602.82_1603.04, WER: 0%, TER: 0%, total WER: 19.3297%, total TER: 8.60366%, progress (thread 0): 96.7959%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_1707.51_1707.73, WER: 100%, TER: 33.3333%, total WER: 19.3306%, total TER: 8.60383%, progress (thread 0): 96.8038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2260.94_2261.16, WER: 0%, TER: 0%, total WER: 19.3304%, total TER: 8.60375%, progress (thread 0): 96.8117%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2697.66_2697.88, WER: 0%, TER: 0%, total WER: 19.3302%, total TER: 8.60367%, progress (thread 0): 96.8196%]
|T|: o k a y
|P|: o k a y
[sample: EN2002d_H03_MEE073_402.37_402.59, WER: 0%, TER: 0%, total WER: 19.33%, total TER: 8.60359%, progress (thread 0): 96.8275%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_736.63_736.85, WER: 0%, TER: 0%, total WER: 19.3298%, total TER: 8.60351%, progress (thread 0): 96.8354%]
|T|: o h
|P|: h u h
[sample: EN2002d_H00_FEO070_909.94_910.16, WER: 100%, TER: 100%, total WER: 19.3307%, total TER: 8.60393%, progress (thread 0): 96.8434%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1061.24_1061.46, WER: 0%, TER: 0%, total WER: 19.3304%, total TER: 8.60385%, progress (thread 0): 96.8513%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_1400.88_1401.1, WER: 0%, TER: 0%, total WER: 19.3302%, total TER: 8.60377%, progress (thread 0): 96.8592%]
|T|: b u t
|P|: b u h
[sample: EN2002d_H00_FEO070_1658_1658.22, WER: 100%, TER: 33.3333%, total WER: 19.3311%, total TER: 8.60395%, progress (thread 0): 96.8671%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1732.66_1732.88, WER: 0%, TER: 0%, total WER: 19.3309%, total TER: 8.60387%, progress (thread 0): 96.875%]
|T|: m m
|P|: m m
[sample: ES2004a_H03_FEE016_622.02_622.23, WER: 0%, TER: 0%, total WER: 19.3307%, total TER: 8.60383%, progress (thread 0): 96.8829%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H00_MEO015_1323.05_1323.26, WER: 0%, TER: 0%, total WER: 19.3305%, total TER: 8.60375%, progress (thread 0): 96.8908%]
|T|: m m h m m
|P|: m h m
[sample: ES2004b_H00_MEO015_1464.74_1464.95, WER: 100%, TER: 40%, total WER: 19.3314%, total TER: 8.60411%, progress (thread 0): 96.8987%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_96.42_96.63, WER: 100%, TER: 40%, total WER: 19.3323%, total TER: 8.60448%, progress (thread 0): 96.9066%]
|T|: y e a h
|P|: y e a h
[sample: ES2004c_H02_MEE014_1385.07_1385.28, WER: 0%, TER: 0%, total WER: 19.3321%, total TER: 8.6044%, progress (thread 0): 96.9146%]
|T|: h m m
|P|: m m
[sample: ES2004d_H00_MEO015_26.63_26.84, WER: 100%, TER: 33.3333%, total WER: 19.333%, total TER: 8.60457%, progress (thread 0): 96.9225%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_627.79_628, WER: 100%, TER: 66.6667%, total WER: 19.3339%, total TER: 8.60498%, progress (thread 0): 96.9304%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_39.06_39.27, WER: 0%, TER: 0%, total WER: 19.3337%, total TER: 8.6049%, progress (thread 0): 96.9383%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_169.39_169.6, WER: 100%, TER: 60%, total WER: 19.3346%, total TER: 8.60549%, progress (thread 0): 96.9462%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H00_FIE088_1286.43_1286.64, WER: 100%, TER: 60%, total WER: 19.3355%, total TER: 8.60609%, progress (thread 0): 96.9541%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009b_H00_FIE088_1289.44_1289.65, WER: 0%, TER: 0%, total WER: 19.3353%, total TER: 8.60599%, progress (thread 0): 96.962%]
|T|: m m
|P|: o n
[sample: IS1009b_H02_FIO084_1632.09_1632.3, WER: 100%, TER: 100%, total WER: 19.3362%, total TER: 8.60642%, progress (thread 0): 96.9699%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_284.08_284.29, WER: 0%, TER: 0%, total WER: 19.3359%, total TER: 8.60634%, progress (thread 0): 96.9778%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1563.53_1563.74, WER: 0%, TER: 0%, total WER: 19.3357%, total TER: 8.60626%, progress (thread 0): 96.9858%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1707.23_1707.44, WER: 0%, TER: 0%, total WER: 19.3355%, total TER: 8.60618%, progress (thread 0): 96.9937%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_741.38_741.59, WER: 0%, TER: 0%, total WER: 19.3353%, total TER: 8.6061%, progress (thread 0): 97.0016%]
|T|: y e a h
|P|:
[sample: IS1009d_H02_FIO084_1371.67_1371.88, WER: 100%, TER: 100%, total WER: 19.3362%, total TER: 8.60695%, progress (thread 0): 97.0095%]
|T|: m m h m m
|P|: m h m
[sample: IS1009d_H03_FIO089_1880.74_1880.95, WER: 100%, TER: 40%, total WER: 19.3371%, total TER: 8.60731%, progress (thread 0): 97.0174%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1295.8_1296.01, WER: 0%, TER: 0%, total WER: 19.3369%, total TER: 8.60723%, progress (thread 0): 97.0253%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_901.66_901.87, WER: 0%, TER: 0%, total WER: 19.3367%, total TER: 8.60715%, progress (thread 0): 97.0332%]
|T|: y e a h
|P|: h u h
[sample: TS3003b_H01_MTD011UID_1547.15_1547.36, WER: 100%, TER: 75%, total WER: 19.3376%, total TER: 8.60777%, progress (thread 0): 97.0411%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1091.82_1092.03, WER: 0%, TER: 0%, total WER: 19.3374%, total TER: 8.60769%, progress (thread 0): 97.049%]
|T|: y e a h
|P|: h u h
[sample: TS3003c_H01_MTD011UID_1210.7_1210.91, WER: 100%, TER: 75%, total WER: 19.3383%, total TER: 8.60831%, progress (thread 0): 97.057%]
|T|: y e a h
|P|: m h
[sample: TS3003d_H01_MTD011UID_530.79_531, WER: 100%, TER: 75%, total WER: 19.3392%, total TER: 8.60893%, progress (thread 0): 97.0649%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1465.64_1465.85, WER: 0%, TER: 0%, total WER: 19.3389%, total TER: 8.60885%, progress (thread 0): 97.0728%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1695.15_1695.36, WER: 0%, TER: 0%, total WER: 19.3387%, total TER: 8.60877%, progress (thread 0): 97.0807%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1711.66_1711.87, WER: 0%, TER: 0%, total WER: 19.3385%, total TER: 8.60869%, progress (thread 0): 97.0886%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1789.64_1789.85, WER: 0%, TER: 0%, total WER: 19.3383%, total TER: 8.60861%, progress (thread 0): 97.0965%]
|T|: n o
|P|: n o
[sample: TS3003d_H03_MTD012ME_2014.88_2015.09, WER: 0%, TER: 0%, total WER: 19.3381%, total TER: 8.60857%, progress (thread 0): 97.1044%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H03_MTD012ME_2064.37_2064.58, WER: 0%, TER: 0%, total WER: 19.3379%, total TER: 8.60849%, progress (thread 0): 97.1123%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_2107.64_2107.85, WER: 0%, TER: 0%, total WER: 19.3376%, total TER: 8.60841%, progress (thread 0): 97.1203%]
|T|: y e a h
|P|: y m
[sample: TS3003d_H01_MTD011UID_2462.55_2462.76, WER: 100%, TER: 75%, total WER: 19.3385%, total TER: 8.60903%, progress (thread 0): 97.1282%]
|T|: s o r r y
|P|: s o r r y
[sample: EN2002a_H02_FEO072_377.93_378.14, WER: 0%, TER: 0%, total WER: 19.3383%, total TER: 8.60893%, progress (thread 0): 97.1361%]
|T|: o h
|P|: u h
[sample: EN2002a_H03_MEE071_483.42_483.63, WER: 100%, TER: 50%, total WER: 19.3392%, total TER: 8.60912%, progress (thread 0): 97.144%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_648.41_648.62, WER: 0%, TER: 0%, total WER: 19.339%, total TER: 8.60904%, progress (thread 0): 97.1519%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1319.66_1319.87, WER: 0%, TER: 0%, total WER: 19.3388%, total TER: 8.60896%, progress (thread 0): 97.1598%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H03_MEE071_1429.36_1429.57, WER: 0%, TER: 0%, total WER: 19.3386%, total TER: 8.60888%, progress (thread 0): 97.1677%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1896.08_1896.29, WER: 0%, TER: 0%, total WER: 19.3384%, total TER: 8.6088%, progress (thread 0): 97.1756%]
|T|: y e a h
|P|: h e h
[sample: EN2002a_H03_MEE071_2122.73_2122.94, WER: 100%, TER: 50%, total WER: 19.3393%, total TER: 8.60918%, progress (thread 0): 97.1835%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_266.76_266.97, WER: 0%, TER: 0%, total WER: 19.3391%, total TER: 8.6091%, progress (thread 0): 97.1915%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_291.79_292, WER: 0%, TER: 0%, total WER: 19.3388%, total TER: 8.60902%, progress (thread 0): 97.1994%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_1335.23_1335.44, WER: 0%, TER: 0%, total WER: 19.3386%, total TER: 8.60894%, progress (thread 0): 97.2073%]
|T|: ' k a y
|P|: o k a y
[sample: EN2002c_H03_MEE073_21.49_21.7, WER: 100%, TER: 25%, total WER: 19.3395%, total TER: 8.6091%, progress (thread 0): 97.2152%]
|T|: o k a y
|P|: o m a y
[sample: EN2002c_H03_MEE073_183.87_184.08, WER: 100%, TER: 25%, total WER: 19.3404%, total TER: 8.60925%, progress (thread 0): 97.2231%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1748.27_1748.48, WER: 0%, TER: 0%, total WER: 19.3402%, total TER: 8.60915%, progress (thread 0): 97.231%]
|T|: o h | y e a h
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1810.36_1810.57, WER: 100%, TER: 100%, total WER: 19.342%, total TER: 8.61064%, progress (thread 0): 97.2389%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1861.15_1861.36, WER: 0%, TER: 0%, total WER: 19.3418%, total TER: 8.61056%, progress (thread 0): 97.2468%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2180.5_2180.71, WER: 0%, TER: 0%, total WER: 19.3416%, total TER: 8.61048%, progress (thread 0): 97.2547%]
|T|: t r u e
|P|: t r u e
[sample: EN2002c_H03_MEE073_2453.86_2454.07, WER: 0%, TER: 0%, total WER: 19.3414%, total TER: 8.6104%, progress (thread 0): 97.2627%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2819.2_2819.41, WER: 0%, TER: 0%, total WER: 19.3411%, total TER: 8.61032%, progress (thread 0): 97.2706%]
|T|: m m h m m
|P|: m h m
[sample: EN2002d_H01_FEO072_164.51_164.72, WER: 100%, TER: 40%, total WER: 19.3421%, total TER: 8.61068%, progress (thread 0): 97.2785%]
|T|: r i g h t
|P|: y e a h
[sample: EN2002d_H03_MEE073_338.56_338.77, WER: 100%, TER: 80%, total WER: 19.343%, total TER: 8.61151%, progress (thread 0): 97.2864%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_647.49_647.7, WER: 0%, TER: 0%, total WER: 19.3427%, total TER: 8.61143%, progress (thread 0): 97.2943%]
|T|: m m
|P|: y e a h
[sample: EN2002d_H03_MEE073_782.98_783.19, WER: 100%, TER: 200%, total WER: 19.3436%, total TER: 8.61232%, progress (thread 0): 97.3022%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_998.01_998.22, WER: 0%, TER: 0%, total WER: 19.3434%, total TER: 8.61222%, progress (thread 0): 97.3101%]
|T|: o h | y e a h
|P|: o
[sample: EN2002d_H00_FEO070_1377.89_1378.1, WER: 100%, TER: 85.7143%, total WER: 19.3452%, total TER: 8.61348%, progress (thread 0): 97.318%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1391.57_1391.78, WER: 0%, TER: 0%, total WER: 19.345%, total TER: 8.6134%, progress (thread 0): 97.326%]
|T|: o h | y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_1912.21_1912.42, WER: 50%, TER: 42.8571%, total WER: 19.3457%, total TER: 8.61396%, progress (thread 0): 97.3339%]
|T|: h m m
|P|: m m
[sample: EN2002d_H03_MEE073_2147.17_2147.38, WER: 100%, TER: 33.3333%, total WER: 19.3466%, total TER: 8.61413%, progress (thread 0): 97.3418%]
|T|: o k a y
|P|: h m m
[sample: ES2004b_H00_MEO015_338.55_338.75, WER: 100%, TER: 100%, total WER: 19.3475%, total TER: 8.61498%, progress (thread 0): 97.3497%]
|T|: y e a h
|P|: y e a h
[sample: ES2004b_H03_FEE016_1514.44_1514.64, WER: 0%, TER: 0%, total WER: 19.3473%, total TER: 8.6149%, progress (thread 0): 97.3576%]
|T|: o o p s
|P|: o p
[sample: ES2004c_H00_MEO015_54.23_54.43, WER: 100%, TER: 50%, total WER: 19.3482%, total TER: 8.61529%, progress (thread 0): 97.3655%]
|T|: m m h m m
|P|: m h m
[sample: ES2004c_H00_MEO015_1395.98_1396.18, WER: 100%, TER: 40%, total WER: 19.3491%, total TER: 8.61565%, progress (thread 0): 97.3734%]
|T|: ' k a y
|P|: t
[sample: ES2004d_H00_MEO015_562.82_563.02, WER: 100%, TER: 100%, total WER: 19.35%, total TER: 8.6165%, progress (thread 0): 97.3813%]
|T|: y e p
|P|: y e a h
[sample: ES2004d_H02_MEE014_922.35_922.55, WER: 100%, TER: 66.6667%, total WER: 19.3509%, total TER: 8.61691%, progress (thread 0): 97.3892%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1214.04_1214.24, WER: 0%, TER: 0%, total WER: 19.3507%, total TER: 8.61683%, progress (thread 0): 97.3972%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H03_FEE016_1952.42_1952.62, WER: 0%, TER: 0%, total WER: 19.3505%, total TER: 8.61675%, progress (thread 0): 97.4051%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_784.26_784.46, WER: 0%, TER: 0%, total WER: 19.3503%, total TER: 8.61667%, progress (thread 0): 97.413%]
|T|: ' k a y
|P|: k e a y
[sample: IS1009a_H03_FIO089_794.48_794.68, WER: 100%, TER: 50%, total WER: 19.3512%, total TER: 8.61705%, progress (thread 0): 97.4209%]
|T|: m m
|P|: y e a h
[sample: IS1009b_H02_FIO084_179.12_179.32, WER: 100%, TER: 200%, total WER: 19.3521%, total TER: 8.61794%, progress (thread 0): 97.4288%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1111.83_1112.03, WER: 0%, TER: 0%, total WER: 19.3518%, total TER: 8.61786%, progress (thread 0): 97.4367%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_2011.99_2012.19, WER: 100%, TER: 60%, total WER: 19.3527%, total TER: 8.61846%, progress (thread 0): 97.4446%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H03_FIO089_285.99_286.19, WER: 0%, TER: 0%, total WER: 19.3525%, total TER: 8.61838%, progress (thread 0): 97.4525%]
|T|: y e a h
|P|: y e a h
[sample: IS1009c_H02_FIO084_1689.26_1689.46, WER: 0%, TER: 0%, total WER: 19.3523%, total TER: 8.6183%, progress (thread 0): 97.4604%]
|T|: m m
|P|: m m
[sample: IS1009d_H01_FIO087_964.59_964.79, WER: 0%, TER: 0%, total WER: 19.3521%, total TER: 8.61826%, progress (thread 0): 97.4684%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1286.22_1286.42, WER: 0%, TER: 0%, total WER: 19.3519%, total TER: 8.61818%, progress (thread 0): 97.4763%]
|T|: m m
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1431.86_1432.06, WER: 100%, TER: 200%, total WER: 19.3528%, total TER: 8.61907%, progress (thread 0): 97.4842%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003b_H03_MTD012ME_550.33_550.53, WER: 100%, TER: 25%, total WER: 19.3537%, total TER: 8.61922%, progress (thread 0): 97.4921%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_576.96_577.16, WER: 0%, TER: 0%, total WER: 19.3535%, total TER: 8.61918%, progress (thread 0): 97.5%]
|T|: y e a h
|P|: e h
[sample: TS3003b_H01_MTD011UID_1340.71_1340.91, WER: 100%, TER: 50%, total WER: 19.3544%, total TER: 8.61957%, progress (thread 0): 97.5079%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1635.98_1636.18, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61953%, progress (thread 0): 97.5158%]
|T|: m m
|P|: m m
[sample: TS3003c_H03_MTD012ME_1936.99_1937.19, WER: 0%, TER: 0%, total WER: 19.3539%, total TER: 8.61949%, progress (thread 0): 97.5237%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_214.36_214.56, WER: 0%, TER: 0%, total WER: 19.3537%, total TER: 8.61941%, progress (thread 0): 97.5316%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_418.75_418.95, WER: 100%, TER: 66.6667%, total WER: 19.3546%, total TER: 8.61981%, progress (thread 0): 97.5396%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1236.15_1236.35, WER: 0%, TER: 0%, total WER: 19.3544%, total TER: 8.61973%, progress (thread 0): 97.5475%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1334.1_1334.3, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61965%, progress (thread 0): 97.5554%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1736.65_1736.85, WER: 0%, TER: 0%, total WER: 19.354%, total TER: 8.61957%, progress (thread 0): 97.5633%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1862.16_1862.36, WER: 0%, TER: 0%, total WER: 19.3538%, total TER: 8.61949%, progress (thread 0): 97.5712%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2423.71_2423.91, WER: 100%, TER: 33.3333%, total WER: 19.3547%, total TER: 8.61967%, progress (thread 0): 97.5791%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_6.65_6.85, WER: 0%, TER: 0%, total WER: 19.3544%, total TER: 8.61959%, progress (thread 0): 97.587%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_207.6_207.8, WER: 0%, TER: 0%, total WER: 19.3542%, total TER: 8.61951%, progress (thread 0): 97.5949%]
|T|: h m m
|P|: h m m
[sample: EN2002a_H00_MEE073_325.97_326.17, WER: 0%, TER: 0%, total WER: 19.354%, total TER: 8.61945%, progress (thread 0): 97.6029%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_517.59_517.79, WER: 0%, TER: 0%, total WER: 19.3538%, total TER: 8.61937%, progress (thread 0): 97.6108%]
|T|: m m h m m
|P|: m m
[sample: EN2002a_H00_MEE073_625.1_625.3, WER: 100%, TER: 60%, total WER: 19.3547%, total TER: 8.61996%, progress (thread 0): 97.6187%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1082.64_1082.84, WER: 0%, TER: 0%, total WER: 19.3545%, total TER: 8.61988%, progress (thread 0): 97.6266%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1296.98_1297.18, WER: 0%, TER: 0%, total WER: 19.3543%, total TER: 8.6198%, progress (thread 0): 97.6345%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1544.23_1544.43, WER: 100%, TER: 33.3333%, total WER: 19.3552%, total TER: 8.61998%, progress (thread 0): 97.6424%]
|T|: w h a t
|P|: w h a t
[sample: EN2002a_H01_FEO070_1988.65_1988.85, WER: 0%, TER: 0%, total WER: 19.3549%, total TER: 8.6199%, progress (thread 0): 97.6503%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H00_FEO070_58.73_58.93, WER: 100%, TER: 66.6667%, total WER: 19.3558%, total TER: 8.6203%, progress (thread 0): 97.6582%]
|T|: d o | w e | d
|P|: d o m e
[sample: EN2002b_H01_MEE071_223.55_223.75, WER: 100%, TER: 57.1429%, total WER: 19.3586%, total TER: 8.62109%, progress (thread 0): 97.6661%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_233.29_233.49, WER: 0%, TER: 0%, total WER: 19.3583%, total TER: 8.62101%, progress (thread 0): 97.674%]
|T|: h m m
|P|: m m
[sample: EN2002b_H03_MEE073_259.41_259.61, WER: 100%, TER: 33.3333%, total WER: 19.3592%, total TER: 8.62118%, progress (thread 0): 97.682%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_498.07_498.27, WER: 0%, TER: 0%, total WER: 19.359%, total TER: 8.6211%, progress (thread 0): 97.6899%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1591.2_1591.4, WER: 0%, TER: 0%, total WER: 19.3588%, total TER: 8.621%, progress (thread 0): 97.6978%]
|T|: b u t
|P|: b u t
[sample: EN2002b_H00_FEO070_1723.38_1723.58, WER: 0%, TER: 0%, total WER: 19.3586%, total TER: 8.62094%, progress (thread 0): 97.7057%]
|T|: o k a y
|P|: y e a y
[sample: EN2002c_H03_MEE073_241.42_241.62, WER: 100%, TER: 50%, total WER: 19.3595%, total TER: 8.62133%, progress (thread 0): 97.7136%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_404.9_405.1, WER: 0%, TER: 0%, total WER: 19.3593%, total TER: 8.62123%, progress (thread 0): 97.7215%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_817.73_817.93, WER: 100%, TER: 33.3333%, total WER: 19.3602%, total TER: 8.6214%, progress (thread 0): 97.7294%]
|T|: y e a h
|P|: m e m
[sample: EN2002c_H02_MEE071_1319.11_1319.31, WER: 100%, TER: 75%, total WER: 19.3611%, total TER: 8.62202%, progress (thread 0): 97.7373%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1392.95_1393.15, WER: 0%, TER: 0%, total WER: 19.3609%, total TER: 8.62194%, progress (thread 0): 97.7453%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1395.54_1395.74, WER: 0%, TER: 0%, total WER: 19.3606%, total TER: 8.62184%, progress (thread 0): 97.7532%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1637.64_1637.84, WER: 0%, TER: 0%, total WER: 19.3604%, total TER: 8.62174%, progress (thread 0): 97.7611%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_1911.7_1911.9, WER: 0%, TER: 0%, total WER: 19.3602%, total TER: 8.62164%, progress (thread 0): 97.769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1939.05_1939.25, WER: 0%, TER: 0%, total WER: 19.36%, total TER: 8.62156%, progress (thread 0): 97.7769%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_327.41_327.61, WER: 0%, TER: 0%, total WER: 19.3598%, total TER: 8.62148%, progress (thread 0): 97.7848%]
|T|: y e a h
|P|: y e m m
[sample: EN2002d_H03_MEE073_967.12_967.32, WER: 100%, TER: 50%, total WER: 19.3607%, total TER: 8.62186%, progress (thread 0): 97.7927%]
|T|: y e p
|P|: y e a p
[sample: ES2004a_H03_FEE016_1048.29_1048.48, WER: 100%, TER: 33.3333%, total WER: 19.3616%, total TER: 8.62203%, progress (thread 0): 97.8006%]
|T|: o o p s
|P|: m
[sample: ES2004b_H00_MEO015_572.79_572.98, WER: 100%, TER: 100%, total WER: 19.3625%, total TER: 8.62288%, progress (thread 0): 97.8085%]
|T|: a l r i g h t
|P|: r i g h t
[sample: ES2004b_H00_MEO015_1308.81_1309, WER: 100%, TER: 28.5714%, total WER: 19.3634%, total TER: 8.62321%, progress (thread 0): 97.8165%]
|T|: h u h
|P|: m m
[sample: ES2004b_H02_MEE014_1454.52_1454.71, WER: 100%, TER: 100%, total WER: 19.3643%, total TER: 8.62385%, progress (thread 0): 97.8244%]
|T|: m m
|P|: y e a h
[sample: ES2004b_H03_FEE016_1635.41_1635.6, WER: 100%, TER: 200%, total WER: 19.3652%, total TER: 8.62474%, progress (thread 0): 97.8323%]
|T|: y e a h
|P|: y e a h
[sample: ES2004d_H00_MEO015_1436.81_1437, WER: 0%, TER: 0%, total WER: 19.365%, total TER: 8.62466%, progress (thread 0): 97.8402%]
|T|: t h i n g
|P|: t h i n g
[sample: ES2004d_H02_MEE014_2209.98_2210.17, WER: 0%, TER: 0%, total WER: 19.3648%, total TER: 8.62456%, progress (thread 0): 97.8481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_805.49_805.68, WER: 0%, TER: 0%, total WER: 19.3645%, total TER: 8.62448%, progress (thread 0): 97.856%]
|T|: r i g h t
|P|: r i g h t
[sample: IS1009c_H00_FIE088_690.57_690.76, WER: 0%, TER: 0%, total WER: 19.3643%, total TER: 8.62438%, progress (thread 0): 97.8639%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H00_FIE088_808.35_808.54, WER: 100%, TER: 80%, total WER: 19.3652%, total TER: 8.62521%, progress (thread 0): 97.8718%]
|T|: b a t t e r y
|P|: t h a t
[sample: IS1009c_H01_FIO087_1631.84_1632.03, WER: 100%, TER: 85.7143%, total WER: 19.3661%, total TER: 8.62646%, progress (thread 0): 97.8798%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1314.07_1314.26, WER: 0%, TER: 0%, total WER: 19.3659%, total TER: 8.62638%, progress (thread 0): 97.8877%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H03_MTD012ME_506.74_506.93, WER: 100%, TER: 25%, total WER: 19.3668%, total TER: 8.62653%, progress (thread 0): 97.8956%]
|T|: m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_1214.55_1214.74, WER: 0%, TER: 0%, total WER: 19.3666%, total TER: 8.62649%, progress (thread 0): 97.9035%]
|T|: ' k a y
|P|: o k a y
[sample: TS3003a_H01_MTD011UID_1401.33_1401.52, WER: 100%, TER: 25%, total WER: 19.3675%, total TER: 8.62665%, progress (thread 0): 97.9114%]
|T|: y e a h
|P|: n o h
[sample: TS3003b_H01_MTD011UID_2147.67_2147.86, WER: 100%, TER: 75%, total WER: 19.3684%, total TER: 8.62726%, progress (thread 0): 97.9193%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1090.92_1091.11, WER: 0%, TER: 0%, total WER: 19.3682%, total TER: 8.62718%, progress (thread 0): 97.9272%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_1632.95_1633.14, WER: 0%, TER: 0%, total WER: 19.368%, total TER: 8.6271%, progress (thread 0): 97.9351%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_375.66_375.85, WER: 100%, TER: 75%, total WER: 19.3689%, total TER: 8.62772%, progress (thread 0): 97.943%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_507.6_507.79, WER: 0%, TER: 0%, total WER: 19.3687%, total TER: 8.62764%, progress (thread 0): 97.951%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H02_MTD0010ID_816.71_816.9, WER: 0%, TER: 0%, total WER: 19.3684%, total TER: 8.62756%, progress (thread 0): 97.9589%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_966.74_966.93, WER: 0%, TER: 0%, total WER: 19.3682%, total TER: 8.62752%, progress (thread 0): 97.9668%]
|T|: m m
|P|: m m
[sample: TS3003d_H00_MTD009PM_1884.46_1884.65, WER: 0%, TER: 0%, total WER: 19.368%, total TER: 8.62748%, progress (thread 0): 97.9747%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1975.04_1975.23, WER: 100%, TER: 66.6667%, total WER: 19.3689%, total TER: 8.62789%, progress (thread 0): 97.9826%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_1998.6_1998.79, WER: 0%, TER: 0%, total WER: 19.3687%, total TER: 8.6278%, progress (thread 0): 97.9905%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H00_MEE073_399.03_399.22, WER: 100%, TER: 66.6667%, total WER: 19.3696%, total TER: 8.62821%, progress (thread 0): 97.9984%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H03_MEE071_741.39_741.58, WER: 100%, TER: 100%, total WER: 19.3705%, total TER: 8.62906%, progress (thread 0): 98.0063%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1037.67_1037.86, WER: 0%, TER: 0%, total WER: 19.3703%, total TER: 8.62898%, progress (thread 0): 98.0142%]
|T|: w h
|P|: m m
[sample: EN2002a_H03_MEE071_1305.67_1305.86, WER: 100%, TER: 100%, total WER: 19.3712%, total TER: 8.62941%, progress (thread 0): 98.0221%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1781.11_1781.3, WER: 0%, TER: 0%, total WER: 19.371%, total TER: 8.62932%, progress (thread 0): 98.0301%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1823.51_1823.7, WER: 0%, TER: 0%, total WER: 19.3707%, total TER: 8.62924%, progress (thread 0): 98.038%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H02_FEO072_1836.03_1836.22, WER: 0%, TER: 0%, total WER: 19.3705%, total TER: 8.62916%, progress (thread 0): 98.0459%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1886.9_1887.09, WER: 0%, TER: 0%, total WER: 19.3703%, total TER: 8.62908%, progress (thread 0): 98.0538%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1917.91_1918.1, WER: 0%, TER: 0%, total WER: 19.3701%, total TER: 8.629%, progress (thread 0): 98.0617%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_142.44_142.63, WER: 0%, TER: 0%, total WER: 19.3699%, total TER: 8.62892%, progress (thread 0): 98.0696%]
|T|: u h h u h
|P|: m m
[sample: EN2002c_H03_MEE073_1527.18_1527.37, WER: 100%, TER: 100%, total WER: 19.3708%, total TER: 8.62999%, progress (thread 0): 98.0775%]
|T|: h m m
|P|: m e h
[sample: EN2002c_H03_MEE073_1630_1630.19, WER: 100%, TER: 100%, total WER: 19.3717%, total TER: 8.63062%, progress (thread 0): 98.0854%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_2672.39_2672.58, WER: 100%, TER: 33.3333%, total WER: 19.3726%, total TER: 8.6308%, progress (thread 0): 98.0934%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_2679.33_2679.52, WER: 0%, TER: 0%, total WER: 19.3724%, total TER: 8.6307%, progress (thread 0): 98.1013%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_647.1_647.29, WER: 0%, TER: 0%, total WER: 19.3722%, total TER: 8.6306%, progress (thread 0): 98.1092%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_649.08_649.27, WER: 0%, TER: 0%, total WER: 19.3719%, total TER: 8.63051%, progress (thread 0): 98.1171%]
|T|: o k
|P|: o k a y
[sample: EN2002d_H03_MEE073_909.71_909.9, WER: 100%, TER: 100%, total WER: 19.3728%, total TER: 8.63094%, progress (thread 0): 98.125%]
|T|: s u r e
|P|: t r u e
[sample: EN2002d_H03_MEE073_1568.39_1568.58, WER: 100%, TER: 75%, total WER: 19.3737%, total TER: 8.63156%, progress (thread 0): 98.1329%]
|T|: y e p
|P|: y e a h
[sample: EN2002d_H03_MEE073_1708.21_1708.4, WER: 100%, TER: 66.6667%, total WER: 19.3746%, total TER: 8.63196%, progress (thread 0): 98.1408%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2037.78_2037.97, WER: 0%, TER: 0%, total WER: 19.3744%, total TER: 8.63188%, progress (thread 0): 98.1487%]
|T|: s o
|P|: s o
[sample: ES2004a_H03_FEE016_605.84_606.02, WER: 0%, TER: 0%, total WER: 19.3742%, total TER: 8.63184%, progress (thread 0): 98.1566%]
|T|: ' k a y
|P|:
[sample: ES2004b_H00_MEO015_1027.78_1027.96, WER: 100%, TER: 100%, total WER: 19.3751%, total TER: 8.63269%, progress (thread 0): 98.1646%]
|T|: m m h m m
|P|: m m
[sample: ES2004c_H03_FEE016_1360.96_1361.14, WER: 100%, TER: 60%, total WER: 19.376%, total TER: 8.63329%, progress (thread 0): 98.1725%]
|T|: m m
|P|: h m m
[sample: ES2004d_H02_MEE014_2221.15_2221.33, WER: 100%, TER: 50%, total WER: 19.3769%, total TER: 8.63348%, progress (thread 0): 98.1804%]
|T|: m m h m m
|P|: m m
[sample: IS1009b_H02_FIO084_361.5_361.68, WER: 100%, TER: 60%, total WER: 19.3778%, total TER: 8.63408%, progress (thread 0): 98.1883%]
|T|: u m
|P|: i m a n
[sample: IS1009c_H03_FIO089_1368.34_1368.52, WER: 100%, TER: 150%, total WER: 19.3787%, total TER: 8.63474%, progress (thread 0): 98.1962%]
|T|: a n d
|P|: a n d
[sample: IS1009c_H01_FIO087_1687.91_1688.09, WER: 0%, TER: 0%, total WER: 19.3785%, total TER: 8.63468%, progress (thread 0): 98.2041%]
|T|: m m
|P|: m m
[sample: IS1009d_H00_FIE088_1039.16_1039.34, WER: 0%, TER: 0%, total WER: 19.3783%, total TER: 8.63464%, progress (thread 0): 98.212%]
|T|: y e s
|P|: y e a
[sample: IS1009d_H01_FIO087_1532.02_1532.2, WER: 100%, TER: 33.3333%, total WER: 19.3792%, total TER: 8.63481%, progress (thread 0): 98.2199%]
|T|: n o
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1017.51_1017.69, WER: 100%, TER: 200%, total WER: 19.3801%, total TER: 8.6357%, progress (thread 0): 98.2278%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_583.19_583.37, WER: 0%, TER: 0%, total WER: 19.3799%, total TER: 8.63562%, progress (thread 0): 98.2358%]
|T|: m m
|P|: h m h
[sample: TS3003b_H01_MTD011UID_1188.77_1188.95, WER: 100%, TER: 100%, total WER: 19.3808%, total TER: 8.63604%, progress (thread 0): 98.2437%]
|T|: y e a h
|P|: y e m
[sample: TS3003b_H02_MTD0010ID_1801.24_1801.42, WER: 100%, TER: 50%, total WER: 19.3817%, total TER: 8.63643%, progress (thread 0): 98.2516%]
|T|: m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1801.82_1802, WER: 0%, TER: 0%, total WER: 19.3815%, total TER: 8.63639%, progress (thread 0): 98.2595%]
|T|: h m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_1131_1131.18, WER: 100%, TER: 33.3333%, total WER: 19.3824%, total TER: 8.63656%, progress (thread 0): 98.2674%]
|T|: a h
|P|: u h
[sample: TS3003d_H01_MTD011UID_2394.1_2394.28, WER: 100%, TER: 50%, total WER: 19.3833%, total TER: 8.63675%, progress (thread 0): 98.2753%]
|T|: n o
|P|: m h
[sample: EN2002a_H00_MEE073_23.11_23.29, WER: 100%, TER: 100%, total WER: 19.3842%, total TER: 8.63718%, progress (thread 0): 98.2832%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_32.53_32.71, WER: 0%, TER: 0%, total WER: 19.3839%, total TER: 8.6371%, progress (thread 0): 98.2911%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_106.69_106.87, WER: 100%, TER: 33.3333%, total WER: 19.3848%, total TER: 8.63727%, progress (thread 0): 98.299%]
|T|: y e a h
|P|: m h
[sample: EN2002a_H00_MEE073_1441.15_1441.33, WER: 100%, TER: 75%, total WER: 19.3857%, total TER: 8.63789%, progress (thread 0): 98.307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1717.2_1717.38, WER: 0%, TER: 0%, total WER: 19.3855%, total TER: 8.63781%, progress (thread 0): 98.3149%]
|T|: o k a y
|P|: o k a y
[sample: EN2002b_H03_MEE073_530.52_530.7, WER: 0%, TER: 0%, total WER: 19.3853%, total TER: 8.63773%, progress (thread 0): 98.3228%]
|T|: i s | i t
|P|: i s | i t
[sample: EN2002b_H01_MEE071_1142.07_1142.25, WER: 0%, TER: 0%, total WER: 19.3849%, total TER: 8.63763%, progress (thread 0): 98.3307%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_1339.32_1339.5, WER: 0%, TER: 0%, total WER: 19.3847%, total TER: 8.63755%, progress (thread 0): 98.3386%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_186.7_186.88, WER: 0%, TER: 0%, total WER: 19.3844%, total TER: 8.63744%, progress (thread 0): 98.3465%]
|T|: a l r i g h t
|P|: r i g h t
[sample: EN2002c_H03_MEE073_536.86_537.04, WER: 100%, TER: 28.5714%, total WER: 19.3853%, total TER: 8.63777%, progress (thread 0): 98.3544%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_1667.09_1667.27, WER: 0%, TER: 0%, total WER: 19.3851%, total TER: 8.63769%, progress (thread 0): 98.3623%]
|T|: h m m
|P|: m m
[sample: EN2002d_H01_FEO072_363.49_363.67, WER: 100%, TER: 33.3333%, total WER: 19.386%, total TER: 8.63786%, progress (thread 0): 98.3703%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_500.44_500.62, WER: 0%, TER: 0%, total WER: 19.3858%, total TER: 8.63776%, progress (thread 0): 98.3782%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_508.48_508.66, WER: 0%, TER: 0%, total WER: 19.3856%, total TER: 8.63768%, progress (thread 0): 98.3861%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_512.59_512.77, WER: 0%, TER: 0%, total WER: 19.3854%, total TER: 8.6376%, progress (thread 0): 98.394%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_518.66_518.84, WER: 0%, TER: 0%, total WER: 19.3852%, total TER: 8.63752%, progress (thread 0): 98.4019%]
|T|: y e a h
|P|: h u h
[sample: EN2002d_H00_FEO070_882.18_882.36, WER: 100%, TER: 75%, total WER: 19.3861%, total TER: 8.63814%, progress (thread 0): 98.4098%]
|T|: m m
|P|: m m
[sample: EN2002d_H02_MEE071_1451.31_1451.49, WER: 0%, TER: 0%, total WER: 19.3859%, total TER: 8.6381%, progress (thread 0): 98.4177%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_1581.55_1581.73, WER: 0%, TER: 0%, total WER: 19.3856%, total TER: 8.638%, progress (thread 0): 98.4256%]
|T|: w h a t
|P|: w h a t
[sample: EN2002d_H00_FEO070_2062.62_2062.8, WER: 0%, TER: 0%, total WER: 19.3854%, total TER: 8.63792%, progress (thread 0): 98.4335%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_2166.41_2166.59, WER: 0%, TER: 0%, total WER: 19.3852%, total TER: 8.63784%, progress (thread 0): 98.4415%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002d_H03_MEE073_2172.96_2173.14, WER: 0%, TER: 0%, total WER: 19.385%, total TER: 8.63774%, progress (thread 0): 98.4494%]
|T|: a h
|P|: e h
[sample: ES2004b_H01_FEE013_843.75_843.92, WER: 100%, TER: 50%, total WER: 19.3859%, total TER: 8.63793%, progress (thread 0): 98.4573%]
|T|: y e p
|P|: y e a h
[sample: ES2004c_H00_MEO015_2220.58_2220.75, WER: 100%, TER: 66.6667%, total WER: 19.3868%, total TER: 8.63833%, progress (thread 0): 98.4652%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_381.27_381.44, WER: 0%, TER: 0%, total WER: 19.3866%, total TER: 8.63825%, progress (thread 0): 98.4731%]
|T|: h i
|P|: h i
[sample: IS1009c_H02_FIO084_43.25_43.42, WER: 0%, TER: 0%, total WER: 19.3864%, total TER: 8.63821%, progress (thread 0): 98.481%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_347.27_347.44, WER: 0%, TER: 0%, total WER: 19.3861%, total TER: 8.63813%, progress (thread 0): 98.4889%]
|T|: m m
|P|: m m
[sample: IS1009d_H02_FIO084_784.48_784.65, WER: 0%, TER: 0%, total WER: 19.3859%, total TER: 8.63809%, progress (thread 0): 98.4968%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_182.69_182.86, WER: 100%, TER: 66.6667%, total WER: 19.3868%, total TER: 8.6385%, progress (thread 0): 98.5047%]
|T|: h m m
|P|: m m
[sample: TS3003a_H01_MTD011UID_748.2_748.37, WER: 100%, TER: 33.3333%, total WER: 19.3877%, total TER: 8.63867%, progress (thread 0): 98.5127%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1432.6_1432.77, WER: 0%, TER: 0%, total WER: 19.3875%, total TER: 8.63859%, progress (thread 0): 98.5206%]
|T|: n o
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1442.45_1442.62, WER: 100%, TER: 200%, total WER: 19.3884%, total TER: 8.63948%, progress (thread 0): 98.5285%]
|T|: y e a h
|P|: a h
[sample: TS3003b_H01_MTD011UID_1584.22_1584.39, WER: 100%, TER: 50%, total WER: 19.3893%, total TER: 8.63986%, progress (thread 0): 98.5364%]
|T|: n o
|P|: u h
[sample: TS3003b_H01_MTD011UID_1779.4_1779.57, WER: 100%, TER: 100%, total WER: 19.3902%, total TER: 8.64029%, progress (thread 0): 98.5443%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H03_MTD012ME_2009.37_2009.54, WER: 0%, TER: 0%, total WER: 19.39%, total TER: 8.64021%, progress (thread 0): 98.5522%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H00_MTD009PM_1183.13_1183.3, WER: 0%, TER: 0%, total WER: 19.3898%, total TER: 8.64013%, progress (thread 0): 98.5601%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H01_MTD011UID_2272.21_2272.38, WER: 0%, TER: 0%, total WER: 19.3896%, total TER: 8.64005%, progress (thread 0): 98.568%]
|T|: y e a h
|P|: y e a h
[sample: TS3003d_H01_MTD011UID_306.52_306.69, WER: 0%, TER: 0%, total WER: 19.3893%, total TER: 8.63997%, progress (thread 0): 98.576%]
|T|: y e a h
|P|: n o h
[sample: TS3003d_H01_MTD011UID_1283.14_1283.31, WER: 100%, TER: 75%, total WER: 19.3902%, total TER: 8.64058%, progress (thread 0): 98.5839%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_549.83_550, WER: 100%, TER: 33.3333%, total WER: 19.3911%, total TER: 8.64076%, progress (thread 0): 98.5918%]
|T|: h m m
|P|: m h
[sample: EN2002a_H00_MEE073_1153.54_1153.71, WER: 100%, TER: 66.6667%, total WER: 19.392%, total TER: 8.64116%, progress (thread 0): 98.5997%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1180.31_1180.48, WER: 0%, TER: 0%, total WER: 19.3918%, total TER: 8.64108%, progress (thread 0): 98.6076%]
|T|: h m m
|P|: m m
[sample: EN2002a_H02_FEO072_2053.69_2053.86, WER: 100%, TER: 33.3333%, total WER: 19.3927%, total TER: 8.64125%, progress (thread 0): 98.6155%]
|T|: y e a h
|P|: y e a m
[sample: EN2002a_H01_FEO070_2086.54_2086.71, WER: 100%, TER: 25%, total WER: 19.3936%, total TER: 8.6414%, progress (thread 0): 98.6234%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H03_MEE073_423.51_423.68, WER: 0%, TER: 0%, total WER: 19.3934%, total TER: 8.64132%, progress (thread 0): 98.6313%]
|T|: y e a h
|P|: m h
[sample: EN2002b_H03_MEE073_1344.27_1344.44, WER: 100%, TER: 75%, total WER: 19.3943%, total TER: 8.64194%, progress (thread 0): 98.6392%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_1400.92_1401.09, WER: 100%, TER: 100%, total WER: 19.3952%, total TER: 8.64279%, progress (thread 0): 98.6472%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_1518.9_1519.07, WER: 0%, TER: 0%, total WER: 19.395%, total TER: 8.64271%, progress (thread 0): 98.6551%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_55.6_55.77, WER: 0%, TER: 0%, total WER: 19.3948%, total TER: 8.64263%, progress (thread 0): 98.663%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_711.52_711.69, WER: 0%, TER: 0%, total WER: 19.3946%, total TER: 8.64255%, progress (thread 0): 98.6709%]
|T|: b u t
|P|: m
[sample: EN2002d_H02_MEE071_431.16_431.33, WER: 100%, TER: 100%, total WER: 19.3955%, total TER: 8.64319%, progress (thread 0): 98.6788%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H02_MEE071_649.62_649.79, WER: 0%, TER: 0%, total WER: 19.3952%, total TER: 8.64311%, progress (thread 0): 98.6867%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H03_MEE073_831.51_831.68, WER: 0%, TER: 0%, total WER: 19.395%, total TER: 8.64303%, progress (thread 0): 98.6946%]
|T|: s o
|P|: m
[sample: EN2002d_H03_MEE073_1058.82_1058.99, WER: 100%, TER: 100%, total WER: 19.3959%, total TER: 8.64345%, progress (thread 0): 98.7025%]
|T|: o h
|P|: o h
[sample: EN2002d_H00_FEO070_1459.48_1459.65, WER: 0%, TER: 0%, total WER: 19.3957%, total TER: 8.64341%, progress (thread 0): 98.7104%]
|T|: r i g h t
|P|: r i g t
[sample: EN2002d_H03_MEE073_1858.1_1858.27, WER: 100%, TER: 20%, total WER: 19.3966%, total TER: 8.64354%, progress (thread 0): 98.7184%]
|T|: i | m e a n
|P|: a n y
[sample: ES2004c_H03_FEE016_691.32_691.48, WER: 100%, TER: 83.3333%, total WER: 19.3984%, total TER: 8.64458%, progress (thread 0): 98.7263%]
|T|: o k a y
|P|: m
[sample: ES2004c_H03_FEE016_1560.36_1560.52, WER: 100%, TER: 100%, total WER: 19.3993%, total TER: 8.64543%, progress (thread 0): 98.7342%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H00_FIE088_757.84_758, WER: 0%, TER: 0%, total WER: 19.3991%, total TER: 8.64535%, progress (thread 0): 98.7421%]
|T|: n o
|P|: n o
[sample: IS1009d_H01_FIO087_584.12_584.28, WER: 0%, TER: 0%, total WER: 19.3989%, total TER: 8.64531%, progress (thread 0): 98.75%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H03_FIO089_740.48_740.64, WER: 0%, TER: 0%, total WER: 19.3987%, total TER: 8.64523%, progress (thread 0): 98.7579%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H00_FIE088_826.94_827.1, WER: 0%, TER: 0%, total WER: 19.3984%, total TER: 8.64515%, progress (thread 0): 98.7658%]
|T|: n o
|P|: n o
[sample: IS1009d_H03_FIO089_862.59_862.75, WER: 0%, TER: 0%, total WER: 19.3982%, total TER: 8.64511%, progress (thread 0): 98.7737%]
|T|: y e a h
|P|: y e a h
[sample: IS1009d_H02_FIO084_1025.48_1025.64, WER: 0%, TER: 0%, total WER: 19.398%, total TER: 8.64503%, progress (thread 0): 98.7816%]
|T|: y e a h
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_580.47_580.63, WER: 0%, TER: 0%, total WER: 19.3978%, total TER: 8.64495%, progress (thread 0): 98.7896%]
|T|: u h
|P|: i h
[sample: TS3003a_H00_MTD009PM_617.55_617.71, WER: 100%, TER: 50%, total WER: 19.3987%, total TER: 8.64514%, progress (thread 0): 98.7975%]
|T|: u h
|P|: y e a h
[sample: TS3003a_H00_MTD009PM_1421.57_1421.73, WER: 100%, TER: 150%, total WER: 19.3996%, total TER: 8.6458%, progress (thread 0): 98.8054%]
|T|: h m m
|P|: m m
[sample: TS3003b_H01_MTD011UID_1789.09_1789.25, WER: 100%, TER: 33.3333%, total WER: 19.4005%, total TER: 8.64597%, progress (thread 0): 98.8133%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H01_MTD011UID_2009.54_2009.7, WER: 0%, TER: 0%, total WER: 19.4003%, total TER: 8.64589%, progress (thread 0): 98.8212%]
|T|: y e p
|P|: y e a h
[sample: TS3003d_H00_MTD009PM_1865.26_1865.42, WER: 100%, TER: 66.6667%, total WER: 19.4012%, total TER: 8.6463%, progress (thread 0): 98.8291%]
|T|: o h
|P|: t o
[sample: TS3003d_H00_MTD009PM_2587.23_2587.39, WER: 100%, TER: 100%, total WER: 19.4021%, total TER: 8.64672%, progress (thread 0): 98.837%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_376.27_376.43, WER: 0%, TER: 0%, total WER: 19.4019%, total TER: 8.64664%, progress (thread 0): 98.8449%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_720.58_720.74, WER: 0%, TER: 0%, total WER: 19.4017%, total TER: 8.64656%, progress (thread 0): 98.8529%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H00_MEE073_737.85_738.01, WER: 100%, TER: 100%, total WER: 19.4026%, total TER: 8.64741%, progress (thread 0): 98.8608%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_844.91_845.07, WER: 0%, TER: 0%, total WER: 19.4023%, total TER: 8.64733%, progress (thread 0): 98.8687%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1041.33_1041.49, WER: 0%, TER: 0%, total WER: 19.4021%, total TER: 8.64725%, progress (thread 0): 98.8766%]
|T|: y e a h
|P|: m e h
[sample: EN2002a_H00_MEE073_1050.03_1050.19, WER: 100%, TER: 50%, total WER: 19.403%, total TER: 8.64763%, progress (thread 0): 98.8845%]
|T|: w h y
|P|: m
[sample: EN2002a_H00_MEE073_1237.91_1238.07, WER: 100%, TER: 100%, total WER: 19.4039%, total TER: 8.64827%, progress (thread 0): 98.8924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_1596.11_1596.27, WER: 0%, TER: 0%, total WER: 19.4037%, total TER: 8.64819%, progress (thread 0): 98.9003%]
|T|: s o r r y
|P|: s u r e
[sample: EN2002a_H00_MEE073_1813.61_1813.77, WER: 100%, TER: 60%, total WER: 19.4046%, total TER: 8.64879%, progress (thread 0): 98.9082%]
|T|: s u r e
|P|: s u e
[sample: EN2002a_H00_MEE073_1881.96_1882.12, WER: 100%, TER: 25%, total WER: 19.4055%, total TER: 8.64894%, progress (thread 0): 98.9161%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H02_FEO072_39.76_39.92, WER: 0%, TER: 0%, total WER: 19.4053%, total TER: 8.64886%, progress (thread 0): 98.924%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_317.94_318.1, WER: 0%, TER: 0%, total WER: 19.4051%, total TER: 8.64878%, progress (thread 0): 98.932%]
|T|: r i g h t
|P|: r i g h t
[sample: EN2002b_H03_MEE073_1075.72_1075.88, WER: 0%, TER: 0%, total WER: 19.4049%, total TER: 8.64868%, progress (thread 0): 98.9399%]
|T|: y e a h
|P|: m m
[sample: EN2002b_H03_MEE073_1155.04_1155.2, WER: 100%, TER: 100%, total WER: 19.4058%, total TER: 8.64953%, progress (thread 0): 98.9478%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H01_FEO072_713.06_713.22, WER: 0%, TER: 0%, total WER: 19.4055%, total TER: 8.64945%, progress (thread 0): 98.9557%]
|T|: o h
|P|: m m
[sample: EN2002c_H03_MEE073_1210.59_1210.75, WER: 100%, TER: 100%, total WER: 19.4064%, total TER: 8.64987%, progress (thread 0): 98.9636%]
|T|: r i g h t
|P|: o r h
[sample: EN2002c_H03_MEE073_1484.69_1484.85, WER: 100%, TER: 80%, total WER: 19.4073%, total TER: 8.6507%, progress (thread 0): 98.9715%]
|T|: y e a h
|P|: m m
[sample: EN2002c_H03_MEE073_2554.98_2555.14, WER: 100%, TER: 100%, total WER: 19.4082%, total TER: 8.65155%, progress (thread 0): 98.9794%]
|T|: h m m
|P|: m m
[sample: EN2002c_H03_MEE073_2776.86_2777.02, WER: 100%, TER: 33.3333%, total WER: 19.4091%, total TER: 8.65172%, progress (thread 0): 98.9873%]
|T|: m m
|P|: m m
[sample: EN2002d_H03_MEE073_645.29_645.45, WER: 0%, TER: 0%, total WER: 19.4089%, total TER: 8.65168%, progress (thread 0): 98.9952%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1397.11_1397.27, WER: 0%, TER: 0%, total WER: 19.4087%, total TER: 8.6516%, progress (thread 0): 99.0032%]
|T|: o o p s
|P|: m
[sample: ES2004a_H00_MEO015_188.28_188.43, WER: 100%, TER: 100%, total WER: 19.4096%, total TER: 8.65245%, progress (thread 0): 99.0111%]
|T|: o o h
|P|: m m
[sample: ES2004b_H02_MEE014_534.45_534.6, WER: 100%, TER: 100%, total WER: 19.4105%, total TER: 8.65309%, progress (thread 0): 99.019%]
|T|: ' k a y
|P|: y e a h
[sample: ES2004c_H01_FEE013_472.18_472.33, WER: 100%, TER: 75%, total WER: 19.4114%, total TER: 8.6537%, progress (thread 0): 99.0269%]
|T|: y e s
|P|: y e a h
[sample: IS1009a_H01_FIO087_693.82_693.97, WER: 100%, TER: 66.6667%, total WER: 19.4123%, total TER: 8.65411%, progress (thread 0): 99.0348%]
|T|: y e a h
|P|: y e a h
[sample: IS1009b_H02_FIO084_1647.59_1647.74, WER: 0%, TER: 0%, total WER: 19.4121%, total TER: 8.65403%, progress (thread 0): 99.0427%]
|T|: y e a h
|P|: s o m
[sample: IS1009d_H02_FIO084_1392_1392.15, WER: 100%, TER: 100%, total WER: 19.413%, total TER: 8.65488%, progress (thread 0): 99.0506%]
|T|: t w o
|P|: d o
[sample: IS1009d_H01_FIO087_1586.28_1586.43, WER: 100%, TER: 66.6667%, total WER: 19.4139%, total TER: 8.65528%, progress (thread 0): 99.0585%]
|T|: n o
|P|: y e h
[sample: IS1009d_H01_FIO087_1824.98_1825.13, WER: 100%, TER: 150%, total WER: 19.4148%, total TER: 8.65594%, progress (thread 0): 99.0665%]
|T|: y e a h
|P|: m e a h
[sample: IS1009d_H02_FIO084_1883.61_1883.76, WER: 100%, TER: 25%, total WER: 19.4157%, total TER: 8.65609%, progress (thread 0): 99.0744%]
|T|: y e a h
|P|: y e a h
[sample: TS3003b_H00_MTD009PM_567.51_567.66, WER: 0%, TER: 0%, total WER: 19.4155%, total TER: 8.65601%, progress (thread 0): 99.0823%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H02_MTD0010ID_2049.54_2049.69, WER: 0%, TER: 0%, total WER: 19.4153%, total TER: 8.65593%, progress (thread 0): 99.0902%]
|T|: m m
|P|: m m
[sample: TS3003d_H01_MTD011UID_2041.85_2042, WER: 0%, TER: 0%, total WER: 19.415%, total TER: 8.65589%, progress (thread 0): 99.0981%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_639.89_640.04, WER: 100%, TER: 66.6667%, total WER: 19.4159%, total TER: 8.65629%, progress (thread 0): 99.106%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H00_MEE073_967.58_967.73, WER: 0%, TER: 0%, total WER: 19.4157%, total TER: 8.65621%, progress (thread 0): 99.1139%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1030.96_1031.11, WER: 0%, TER: 0%, total WER: 19.4155%, total TER: 8.65613%, progress (thread 0): 99.1218%]
|T|: y e a h
|P|: o h
[sample: EN2002a_H01_FEO070_1104.83_1104.98, WER: 100%, TER: 75%, total WER: 19.4164%, total TER: 8.65675%, progress (thread 0): 99.1297%]
|T|: h m m
|P|: m m
[sample: EN2002a_H00_MEE073_1527.69_1527.84, WER: 100%, TER: 33.3333%, total WER: 19.4173%, total TER: 8.65692%, progress (thread 0): 99.1377%]
|T|: y e a h
|P|: m m
[sample: EN2002a_H00_MEE073_1539.77_1539.92, WER: 100%, TER: 100%, total WER: 19.4182%, total TER: 8.65777%, progress (thread 0): 99.1456%]
|T|: y e a h
|P|: m h
[sample: EN2002a_H00_MEE073_1744.01_1744.16, WER: 100%, TER: 75%, total WER: 19.4191%, total TER: 8.65839%, progress (thread 0): 99.1535%]
|T|: y e a h
|P|: y e a h
[sample: EN2002a_H01_FEO070_1785.21_1785.36, WER: 0%, TER: 0%, total WER: 19.4189%, total TER: 8.65831%, progress (thread 0): 99.1614%]
|T|: y e p
|P|: y e a h
[sample: EN2002b_H03_MEE073_382.32_382.47, WER: 100%, TER: 66.6667%, total WER: 19.4198%, total TER: 8.65871%, progress (thread 0): 99.1693%]
|T|: u h
|P|: y e a h
[sample: EN2002b_H02_FEO072_424.35_424.5, WER: 100%, TER: 150%, total WER: 19.4207%, total TER: 8.65937%, progress (thread 0): 99.1772%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H00_FEO070_496.03_496.18, WER: 0%, TER: 0%, total WER: 19.4205%, total TER: 8.65929%, progress (thread 0): 99.1851%]
|T|: y e a h
|P|: y e a h
[sample: EN2002b_H01_MEE071_897.7_897.85, WER: 0%, TER: 0%, total WER: 19.4203%, total TER: 8.65921%, progress (thread 0): 99.193%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H03_MEE073_2198.65_2198.8, WER: 0%, TER: 0%, total WER: 19.42%, total TER: 8.65913%, progress (thread 0): 99.201%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H00_FEO070_1290.13_1290.28, WER: 0%, TER: 0%, total WER: 19.4198%, total TER: 8.65904%, progress (thread 0): 99.2089%]
|T|: n o
|P|: o h
[sample: EN2002d_H03_MEE073_1424.03_1424.18, WER: 100%, TER: 100%, total WER: 19.4207%, total TER: 8.65947%, progress (thread 0): 99.2168%]
|T|: r i g h t
|P|: h
[sample: EN2002d_H03_MEE073_1909.61_1909.76, WER: 100%, TER: 80%, total WER: 19.4216%, total TER: 8.6603%, progress (thread 0): 99.2247%]
|T|: o k a y
|P|: m
[sample: EN2002d_H03_MEE073_1985.35_1985.5, WER: 100%, TER: 100%, total WER: 19.4225%, total TER: 8.66115%, progress (thread 0): 99.2326%]
|T|: i | t h
|P|: i
[sample: ES2004b_H01_FEE013_2305.5_2305.64, WER: 50%, TER: 75%, total WER: 19.4232%, total TER: 8.66176%, progress (thread 0): 99.2405%]
|T|: m m
|P|: m
[sample: ES2004c_H03_FEE016_777.13_777.27, WER: 100%, TER: 50%, total WER: 19.4241%, total TER: 8.66196%, progress (thread 0): 99.2484%]
|T|: y e a h
|P|: y e a h
[sample: IS1009a_H02_FIO084_56.9_57.04, WER: 0%, TER: 0%, total WER: 19.4239%, total TER: 8.66187%, progress (thread 0): 99.2563%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_645.27_645.41, WER: 100%, TER: 66.6667%, total WER: 19.4248%, total TER: 8.66228%, progress (thread 0): 99.2642%]
|T|: m m h m m
|P|: s o
[sample: IS1009d_H00_FIE088_770.97_771.11, WER: 100%, TER: 100%, total WER: 19.4257%, total TER: 8.66334%, progress (thread 0): 99.2721%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_189.69_189.83, WER: 100%, TER: 66.6667%, total WER: 19.4266%, total TER: 8.66374%, progress (thread 0): 99.2801%]
|T|: m m
|P|: m
[sample: TS3003a_H01_MTD011UID_903.08_903.22, WER: 100%, TER: 50%, total WER: 19.4275%, total TER: 8.66394%, progress (thread 0): 99.288%]
|T|: m m
|P|: m
[sample: TS3003b_H01_MTD011UID_2047.35_2047.49, WER: 100%, TER: 50%, total WER: 19.4284%, total TER: 8.66413%, progress (thread 0): 99.2959%]
|T|: ' k a y
|P|: y h
[sample: TS3003b_H03_MTD012ME_2140.48_2140.62, WER: 100%, TER: 100%, total WER: 19.4293%, total TER: 8.66498%, progress (thread 0): 99.3038%]
|T|: y e a h
|P|: y e a h
[sample: TS3003c_H03_MTD012ME_1869.23_1869.37, WER: 0%, TER: 0%, total WER: 19.4291%, total TER: 8.6649%, progress (thread 0): 99.3117%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_1390.61_1390.75, WER: 100%, TER: 25%, total WER: 19.43%, total TER: 8.66505%, progress (thread 0): 99.3196%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_2021.71_2021.85, WER: 100%, TER: 25%, total WER: 19.4309%, total TER: 8.6652%, progress (thread 0): 99.3275%]
|T|: r i g h t
|P|: i h
[sample: EN2002a_H00_MEE073_1184.15_1184.29, WER: 100%, TER: 60%, total WER: 19.4318%, total TER: 8.6658%, progress (thread 0): 99.3354%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_1193.94_1194.08, WER: 100%, TER: 66.6667%, total WER: 19.4327%, total TER: 8.6662%, progress (thread 0): 99.3434%]
|T|: y e p
|P|: y e a h
[sample: EN2002a_H00_MEE073_2048.07_2048.21, WER: 100%, TER: 66.6667%, total WER: 19.4336%, total TER: 8.6666%, progress (thread 0): 99.3513%]
|T|: ' k a y
|P|:
[sample: EN2002c_H03_MEE073_103.84_103.98, WER: 100%, TER: 100%, total WER: 19.4345%, total TER: 8.66745%, progress (thread 0): 99.3592%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_531.32_531.46, WER: 0%, TER: 0%, total WER: 19.4342%, total TER: 8.66737%, progress (thread 0): 99.3671%]
|T|: y e a h
|P|: y e h
[sample: EN2002c_H03_MEE073_667.22_667.36, WER: 100%, TER: 25%, total WER: 19.4351%, total TER: 8.66752%, progress (thread 0): 99.375%]
|T|: y e a h
|P|: y e a h
[sample: EN2002c_H02_MEE071_1476.08_1476.22, WER: 0%, TER: 0%, total WER: 19.4349%, total TER: 8.66744%, progress (thread 0): 99.3829%]
|T|: m m
|P|: m
[sample: EN2002c_H03_MEE073_2096.91_2097.05, WER: 100%, TER: 50%, total WER: 19.4358%, total TER: 8.66764%, progress (thread 0): 99.3908%]
|T|: o k a y
|P|: m e m
[sample: EN2002d_H00_FEO070_1251.9_1252.04, WER: 100%, TER: 100%, total WER: 19.4367%, total TER: 8.66848%, progress (thread 0): 99.3987%]
|T|: m m
|P|: m e a
[sample: ES2004c_H03_FEE016_98.28_98.41, WER: 100%, TER: 100%, total WER: 19.4376%, total TER: 8.66891%, progress (thread 0): 99.4066%]
|T|: s
|P|: m
[sample: ES2004d_H03_FEE016_1370.74_1370.87, WER: 100%, TER: 100%, total WER: 19.4385%, total TER: 8.66912%, progress (thread 0): 99.4146%]
|T|: o r | a | b
|P|: m a
[sample: IS1009a_H01_FIO087_573.12_573.25, WER: 100%, TER: 83.3333%, total WER: 19.4412%, total TER: 8.67016%, progress (thread 0): 99.4225%]
|T|: h m m
|P|: m m
[sample: IS1009c_H02_FIO084_144.08_144.21, WER: 100%, TER: 33.3333%, total WER: 19.4421%, total TER: 8.67033%, progress (thread 0): 99.4304%]
|T|: o k a y
|P|: m
[sample: IS1009d_H00_FIE088_604.16_604.29, WER: 100%, TER: 100%, total WER: 19.443%, total TER: 8.67118%, progress (thread 0): 99.4383%]
|T|: y e s
|P|: y e a h
[sample: IS1009d_H01_FIO087_942.55_942.68, WER: 100%, TER: 66.6667%, total WER: 19.4439%, total TER: 8.67159%, progress (thread 0): 99.4462%]
|T|: h m m
|P|: m
[sample: TS3003a_H01_MTD011UID_392.63_392.76, WER: 100%, TER: 66.6667%, total WER: 19.4448%, total TER: 8.67199%, progress (thread 0): 99.4541%]
|T|: h u h
|P|: m
[sample: TS3003a_H01_MTD011UID_1266.08_1266.21, WER: 100%, TER: 100%, total WER: 19.4457%, total TER: 8.67263%, progress (thread 0): 99.462%]
|T|: o h
|P|: m h
[sample: TS3003a_H01_MTD011UID_1335.11_1335.24, WER: 100%, TER: 50%, total WER: 19.4466%, total TER: 8.67282%, progress (thread 0): 99.4699%]
|T|: y e p
|P|: y e a h
[sample: TS3003a_H01_MTD011UID_1475.42_1475.55, WER: 100%, TER: 66.6667%, total WER: 19.4475%, total TER: 8.67322%, progress (thread 0): 99.4778%]
|T|: y e a h
|P|: m m
[sample: TS3003b_H01_MTD011UID_1528.52_1528.65, WER: 100%, TER: 100%, total WER: 19.4484%, total TER: 8.67407%, progress (thread 0): 99.4858%]
|T|: m m
|P|: m m
[sample: TS3003c_H01_MTD011UID_1173.23_1173.36, WER: 0%, TER: 0%, total WER: 19.4482%, total TER: 8.67403%, progress (thread 0): 99.4937%]
|T|: y e a h
|P|: y e a
[sample: TS3003d_H00_MTD009PM_1693.45_1693.58, WER: 100%, TER: 25%, total WER: 19.4491%, total TER: 8.67418%, progress (thread 0): 99.5016%]
|T|: ' k a y
|P|: m e m
[sample: EN2002a_H00_MEE073_32.58_32.71, WER: 100%, TER: 100%, total WER: 19.45%, total TER: 8.67503%, progress (thread 0): 99.5095%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_984.87_985, WER: 100%, TER: 66.6667%, total WER: 19.4509%, total TER: 8.67544%, progress (thread 0): 99.5174%]
|T|: w e l l | f
|P|: h
[sample: EN2002a_H00_MEE073_1458.73_1458.86, WER: 100%, TER: 100%, total WER: 19.4527%, total TER: 8.67671%, progress (thread 0): 99.5253%]
|T|: y e p
|P|: y e a
[sample: EN2002b_H01_MEE071_493.19_493.32, WER: 100%, TER: 33.3333%, total WER: 19.4536%, total TER: 8.67688%, progress (thread 0): 99.5332%]
|T|: y e a h
|P|: y e a
[sample: EN2002c_H03_MEE073_665.01_665.14, WER: 100%, TER: 25%, total WER: 19.4545%, total TER: 8.67703%, progress (thread 0): 99.5411%]
|T|: o h
|P|: m
[sample: EN2002d_H01_FEO072_135.16_135.29, WER: 100%, TER: 100%, total WER: 19.4554%, total TER: 8.67746%, progress (thread 0): 99.549%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_726.53_726.66, WER: 0%, TER: 0%, total WER: 19.4552%, total TER: 8.67738%, progress (thread 0): 99.557%]
|T|: y e a h
|P|: y e a h
[sample: EN2002d_H01_FEO072_794.79_794.92, WER: 0%, TER: 0%, total WER: 19.455%, total TER: 8.67729%, progress (thread 0): 99.5649%]
|T|: y e a h
|P|: m e a
[sample: EN2002d_H00_FEO070_1595.77_1595.9, WER: 100%, TER: 50%, total WER: 19.4559%, total TER: 8.67768%, progress (thread 0): 99.5728%]
|T|: o h
|P|:
[sample: EN2002d_H01_FEO072_1641.64_1641.77, WER: 100%, TER: 100%, total WER: 19.4568%, total TER: 8.6781%, progress (thread 0): 99.5807%]
|T|: y e a h
|P|: m m
[sample: ES2004b_H00_MEO015_922.62_922.74, WER: 100%, TER: 100%, total WER: 19.4576%, total TER: 8.67895%, progress (thread 0): 99.5886%]
|T|: o k a y
|P|: m h
[sample: ES2004b_H00_MEO015_1977.87_1977.99, WER: 100%, TER: 100%, total WER: 19.4585%, total TER: 8.6798%, progress (thread 0): 99.5965%]
|T|: y e a h
|P|: m e m
[sample: IS1009b_H02_FIO084_173.86_173.98, WER: 100%, TER: 75%, total WER: 19.4594%, total TER: 8.68042%, progress (thread 0): 99.6044%]
|T|: h m m
|P|: m
[sample: IS1009c_H02_FIO084_1703.14_1703.26, WER: 100%, TER: 66.6667%, total WER: 19.4603%, total TER: 8.68082%, progress (thread 0): 99.6123%]
|T|: m m | m m
|P|:
[sample: IS1009d_H03_FIO089_870.76_870.88, WER: 100%, TER: 100%, total WER: 19.4621%, total TER: 8.68188%, progress (thread 0): 99.6203%]
|T|: y e p
|P|: y e a
[sample: TS3003a_H01_MTD011UID_257.2_257.32, WER: 100%, TER: 33.3333%, total WER: 19.463%, total TER: 8.68205%, progress (thread 0): 99.6282%]
|T|: y e a h
|P|: m
[sample: EN2002a_H03_MEE071_170.81_170.93, WER: 100%, TER: 100%, total WER: 19.4639%, total TER: 8.6829%, progress (thread 0): 99.6361%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_1468.26_1468.38, WER: 100%, TER: 66.6667%, total WER: 19.4648%, total TER: 8.6833%, progress (thread 0): 99.644%]
|T|: n o
|P|: m h
[sample: EN2002b_H03_MEE073_4.83_4.95, WER: 100%, TER: 100%, total WER: 19.4657%, total TER: 8.68373%, progress (thread 0): 99.6519%]
|T|: a l t e r
|P|: y e a
[sample: EN2002b_H03_MEE073_170.46_170.58, WER: 100%, TER: 80%, total WER: 19.4666%, total TER: 8.68456%, progress (thread 0): 99.6598%]
|T|: y e a h
|P|: m
[sample: EN2002b_H03_MEE073_1641.92_1642.04, WER: 100%, TER: 100%, total WER: 19.4675%, total TER: 8.6854%, progress (thread 0): 99.6677%]
|T|: y e a h
|P|: y e a
[sample: EN2002d_H03_MEE073_806.78_806.9, WER: 100%, TER: 25%, total WER: 19.4684%, total TER: 8.68556%, progress (thread 0): 99.6756%]
|T|: o k a y
|P|: y e a
[sample: ES2004a_H00_MEO015_71.95_72.06, WER: 100%, TER: 75%, total WER: 19.4693%, total TER: 8.68617%, progress (thread 0): 99.6835%]
|T|: o
|P|: y a
[sample: ES2004a_H03_FEE016_403.24_403.35, WER: 100%, TER: 200%, total WER: 19.4702%, total TER: 8.68662%, progress (thread 0): 99.6915%]
|T|: ' k a y
|P|:
[sample: ES2004a_H03_FEE016_1042.19_1042.3, WER: 100%, TER: 100%, total WER: 19.4711%, total TER: 8.68746%, progress (thread 0): 99.6994%]
|T|: m m
|P|: m m
[sample: IS1009c_H02_FIO084_620.84_620.95, WER: 0%, TER: 0%, total WER: 19.4709%, total TER: 8.68742%, progress (thread 0): 99.7073%]
|T|: y e a h
|P|: m
[sample: TS3003a_H01_MTD011UID_436.27_436.38, WER: 100%, TER: 100%, total WER: 19.4718%, total TER: 8.68827%, progress (thread 0): 99.7152%]
|T|: y e a h
|P|: m
[sample: EN2002a_H00_MEE073_669.47_669.58, WER: 100%, TER: 100%, total WER: 19.4727%, total TER: 8.68912%, progress (thread 0): 99.7231%]
|T|: y e a h
|P|: y a
[sample: EN2002a_H01_FEO070_1169.61_1169.72, WER: 100%, TER: 50%, total WER: 19.4736%, total TER: 8.6895%, progress (thread 0): 99.731%]
|T|: n o
|P|: y e
[sample: EN2002b_H02_FEO072_4.48_4.59, WER: 100%, TER: 100%, total WER: 19.4745%, total TER: 8.68993%, progress (thread 0): 99.7389%]
|T|: y e p
|P|: m
[sample: EN2002b_H03_MEE073_307.97_308.08, WER: 100%, TER: 100%, total WER: 19.4754%, total TER: 8.69056%, progress (thread 0): 99.7468%]
|T|: h m m
|P|: m
[sample: EN2002b_H03_MEE073_1578.82_1578.93, WER: 100%, TER: 66.6667%, total WER: 19.4763%, total TER: 8.69097%, progress (thread 0): 99.7547%]
|T|: h m m
|P|: m e
[sample: EN2002c_H03_MEE073_1446.36_1446.47, WER: 100%, TER: 66.6667%, total WER: 19.4772%, total TER: 8.69137%, progress (thread 0): 99.7627%]
|T|: o h
|P|: h
[sample: EN2002d_H01_FEO072_118.11_118.22, WER: 100%, TER: 50%, total WER: 19.4781%, total TER: 8.69156%, progress (thread 0): 99.7706%]
|T|: s o
|P|: m
[sample: EN2002d_H02_MEE071_2107.05_2107.16, WER: 100%, TER: 100%, total WER: 19.479%, total TER: 8.69199%, progress (thread 0): 99.7785%]
|T|: t
|P|: m
[sample: ES2004c_H02_MEE014_1184.81_1184.91, WER: 100%, TER: 100%, total WER: 19.4799%, total TER: 8.6922%, progress (thread 0): 99.7864%]
|T|: y e s
|P|: m a
[sample: IS1009d_H01_FIO087_1744.56_1744.66, WER: 100%, TER: 100%, total WER: 19.4808%, total TER: 8.69284%, progress (thread 0): 99.7943%]
|T|: o h
|P|: h
[sample: TS3003c_H02_MTD0010ID_1023.66_1023.76, WER: 100%, TER: 50%, total WER: 19.4817%, total TER: 8.69303%, progress (thread 0): 99.8022%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_2013_2013.1, WER: 100%, TER: 66.6667%, total WER: 19.4826%, total TER: 8.69343%, progress (thread 0): 99.8101%]
|T|: y e a h
|P|: m
[sample: EN2002b_H00_FEO070_43.63_43.73, WER: 100%, TER: 100%, total WER: 19.4835%, total TER: 8.69428%, progress (thread 0): 99.818%]
|T|: y e a h
|P|: y e a
[sample: EN2002b_H03_MEE073_293.52_293.62, WER: 100%, TER: 25%, total WER: 19.4844%, total TER: 8.69443%, progress (thread 0): 99.826%]
|T|: y e p
|P|: y e
[sample: EN2002c_H03_MEE073_763_763.1, WER: 100%, TER: 33.3333%, total WER: 19.4853%, total TER: 8.6946%, progress (thread 0): 99.8339%]
|T|: m m
|P|: m
[sample: EN2002d_H03_MEE073_2099.51_2099.61, WER: 100%, TER: 50%, total WER: 19.4862%, total TER: 8.69479%, progress (thread 0): 99.8418%]
|T|: ' k a y
|P|: m
[sample: ES2004a_H00_MEO015_168.79_168.88, WER: 100%, TER: 100%, total WER: 19.4871%, total TER: 8.69564%, progress (thread 0): 99.8497%]
|T|: y e p
|P|: y e
[sample: ES2004c_H00_MEO015_360.73_360.82, WER: 100%, TER: 33.3333%, total WER: 19.488%, total TER: 8.69581%, progress (thread 0): 99.8576%]
|T|: m m h m m
|P|: m
[sample: ES2004c_H03_FEE016_1132.2_1132.29, WER: 100%, TER: 80%, total WER: 19.4889%, total TER: 8.69664%, progress (thread 0): 99.8655%]
|T|: h m m
|P|:
[sample: ES2004c_H03_FEE016_1651.84_1651.93, WER: 100%, TER: 100%, total WER: 19.4898%, total TER: 8.69728%, progress (thread 0): 99.8734%]
|T|: m m h m m
|P|: m
[sample: IS1009c_H02_FIO084_1753.49_1753.58, WER: 100%, TER: 80%, total WER: 19.4907%, total TER: 8.69811%, progress (thread 0): 99.8813%]
|T|: y e s
|P|:
[sample: IS1009d_H01_FIO087_749.88_749.97, WER: 100%, TER: 100%, total WER: 19.4916%, total TER: 8.69874%, progress (thread 0): 99.8892%]
|T|: m m h m m
|P|:
[sample: IS1009d_H02_FIO084_1718.32_1718.41, WER: 100%, TER: 100%, total WER: 19.4925%, total TER: 8.6998%, progress (thread 0): 99.8972%]
|T|: w h a t
|P|: m
[sample: TS3003d_H00_MTD009PM_1597.33_1597.42, WER: 100%, TER: 100%, total WER: 19.4934%, total TER: 8.70065%, progress (thread 0): 99.9051%]
|T|: h m m
|P|: m
[sample: EN2002a_H00_MEE073_887.36_887.45, WER: 100%, TER: 66.6667%, total WER: 19.4943%, total TER: 8.70105%, progress (thread 0): 99.913%]
|T|: d a m n
|P|: m
[sample: EN2002a_H00_MEE073_1500.91_1501, WER: 100%, TER: 75%, total WER: 19.4952%, total TER: 8.70167%, progress (thread 0): 99.9209%]
|T|: h m m
|P|: m
[sample: EN2002c_H03_MEE073_429.63_429.72, WER: 100%, TER: 66.6667%, total WER: 19.4961%, total TER: 8.70207%, progress (thread 0): 99.9288%]
|T|: y e a h
|P|: y a
[sample: EN2002c_H02_MEE071_2578.94_2579.03, WER: 100%, TER: 50%, total WER: 19.497%, total TER: 8.70246%, progress (thread 0): 99.9367%]
|T|: d a m n
|P|: y e a
[sample: EN2002d_H03_MEE073_999.94_1000.03, WER: 100%, TER: 100%, total WER: 19.4979%, total TER: 8.7033%, progress (thread 0): 99.9446%]
|T|: m m
|P|:
[sample: ES2004a_H00_MEO015_252.48_252.56, WER: 100%, TER: 100%, total WER: 19.4988%, total TER: 8.70373%, progress (thread 0): 99.9525%]
|T|: m m
|P|:
[sample: ES2004c_H00_MEO015_1885.03_1885.11, WER: 100%, TER: 100%, total WER: 19.4997%, total TER: 8.70415%, progress (thread 0): 99.9604%]
|T|: o h
|P|:
[sample: TS3003a_H01_MTD011UID_1313.86_1313.94, WER: 100%, TER: 100%, total WER: 19.5006%, total TER: 8.70458%, progress (thread 0): 99.9684%]
|T|: h m m
|P|:
[sample: EN2002a_H00_MEE073_185.9_185.98, WER: 100%, TER: 100%, total WER: 19.5014%, total TER: 8.70521%, progress (thread 0): 99.9763%]
|T|: g
|P|: m
[sample: EN2002d_H03_MEE073_241.62_241.69, WER: 100%, TER: 100%, total WER: 19.5023%, total TER: 8.70542%, progress (thread 0): 99.9842%]
|T|: h m m
|P|:
[sample: IS1009a_H02_FIO084_490.4_490.46, WER: 100%, TER: 100%, total WER: 19.5032%, total TER: 8.70606%, progress (thread 0): 99.9921%]
|T|: ' k a y
|P|:
[sample: EN2002b_H03_MEE073_613.94_614, WER: 100%, TER: 100%, total WER: 19.5041%, total TER: 8.70691%, progress (thread 0): 100%]
I1224 07:09:02.842401 11830 Test.cpp:418] ------
I1224 07:09:02.842427 11830 Test.cpp:419] [Test ami_limited_supervision/test.lst (12640 samples) in 361.478s (actual decoding time 0.0286s/sample) -- WER: 19.5041%, TER: 8.70691%]
###Markdown
Viterbi WER improved from 26.6% to 19.5% after 1 epoch with finetuning... Beam Search decoding with a language model To do this, download the finetuned model and use the [Inference CTC tutorial](https://colab.research.google.com/github/facebookresearch/flashlight/blob/master/flashlight/app/asr/tutorial/notebooks/InferenceAndAlignmentCTC.ipynb) Step 5: Running with your own data To finetune on your own data, create `train`, `dev` and `test` list files and run the finetuning step. Each list file consists of multiple lines with each line describing one sample in the following format : ``` ```For example, let's take a look at the `dev.lst` file from AMI corpus.
###Code
! head ami_limited_supervision/dev.lst
###Output
ES2011a_H00_FEE041_34.27_37.14 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_34.27_37.14.flac 2870.0 here we go
ES2011a_H00_FEE041_37.14_39.15 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_37.14_39.15.flac 2010.0 welcome everybody
ES2011a_H00_FEE041_43.32_44.39 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_43.32_44.39.flac 1070.0 you can call me abbie
ES2011a_H00_FEE041_39.15_43.32 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_39.15_43.32.flac 4170.0 um i'm abigail claflin
ES2011a_H00_FEE041_46.43_47.63 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_46.43_47.63.flac 1200.0 's see
ES2011a_H00_FEE041_51.33_55.53 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_51.33_55.53.flac 4200.0 so this is our kick off meeting
ES2011a_H00_FEE041_55.53_56.85 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_55.53_56.85.flac 1320.0 um
ES2011a_H00_FEE041_47.63_50.2 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_47.63_50.2.flac 2570.0 powerpoint that's not it
ES2011a_H00_FEE041_50.2_51.33 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_50.2_51.33.flac 1130.0 there we go
ES2011a_H00_FEE041_62.17_64.28 ami_limited_supervision/audio/ES2011a/ES2011a_H00_FEE041_62.17_64.28.flac 2110.0 let's shall we all introduce ourselves
###Markdown
Recording your own audioFor example, you can record your own audio and finetune the model...Installing a few packages first...
###Code
!apt-get install sox
!pip install ffmpeg-python sox
from flashlight.scripts.colab.record import record_audio
###Output
_____no_output_____
###Markdown
**Let's record now the following sentences:****1:** A flashlight or torch is a small, portable spotlight.
###Code
record_audio("recorded_audio_1")
###Output
_____no_output_____
###Markdown
**2:** Its function is a beam of light which helps to see and it usually requires batteries.
###Code
record_audio("recorded_audio_2")
###Output
_____no_output_____
###Markdown
**3:** In 1896, the first dry cell battery was invented.
###Code
record_audio("recorded_audio_3")
###Output
_____no_output_____
###Markdown
**4:** Unlike previous batteries, it used a paste electrolyte instead of a liquid.
###Code
record_audio("recorded_audio_4")
###Output
_____no_output_____
###Markdown
**5** This was the first battery suitable for portable electrical devices, as it did not spill or break easily and worked in any orientation.
###Code
record_audio("recorded_audio_5")
###Output
_____no_output_____
###Markdown
Create now new training/dev lists:(yes, you need to edit transcriptions below to your recordings)
###Code
import sox
transcriptions = [
"a flashlight or torch is a small portable spotlight",
"its function is a beam of light which helps to see and it usually requires batteries",
"in eighteen ninthy six the first dry cell battery was invented",
"unlike previous batteries it used a paste electrolyte instead of a liquid",
"this was the first battery suitable for portable electrical devices, as it did not spill or break easily and worked in any orientation"
]
with open("own_train.lst", "w") as f_train, open("own_dev.lst", "w") as f_dev:
for index, transcription in enumerate(transcriptions):
fname = "recorded_audio_" + str(index + 1) + ".wav"
duration_ms = sox.file_info.duration(fname) * 1000
if index % 2 == 0:
f_train.write("{}\t{}\t{}\t{}\n".format(
index + 1, fname, duration_ms, transcription))
else:
f_dev.write("{}\t{}\t{}\t{}\n".format(
index + 1, fname, duration_ms, transcription))
###Output
_____no_output_____
###Markdown
Check at first model quality on dev before finetuning
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am model.bin --datadir '' --emission_dir '' --uselexicon false \
--test own_dev.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
_____no_output_____
###Markdown
Finetune on recorded audio samplesPlay with parameters if needed.
###Code
! ./flashlight/build/bin/asr/fl_asr_tutorial_finetune_ctc model.bin \
--datadir= \
--train own_train.lst \
--valid dev:own_dev.lst \
--arch arch.txt \
--tokens tokens.txt \
--lexicon lexicon.txt \
--rundir own_checkpoint \
--lr 0.025 \
--netoptim sgd \
--momentum 0.8 \
--reportiters 1000 \
--lr_decay 100 \
--lr_decay_step 50 \
--iter 25000 \
--batchsize 4 \
--warmup 0
###Output
_____no_output_____
###Markdown
Test finetuned model(unlikely you get significant improvement with just five phrases, but let's check!)
###Code
! ./flashlight/build/bin/asr/fl_asr_test --am own_checkpoint/001_model_dev.bin --datadir '' --emission_dir '' --uselexicon false \
--test own_dev.lst --tokens tokens.txt --lexicon lexicon.txt --show
###Output
_____no_output_____
|
CIFAR-10/Feed Forward Files/[4] Students on CIFAR10 Using FF.ipynb
|
###Markdown
TCN
###Code
# Testing with LR
s1=define_model("s1")
s2=define_model("s2")
s3=define_model("s3")
s4=define_model("s4")
opt=Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, amsgrad=False)
s1.compile(loss='mse', optimizer=opt)
s2.compile(loss='mse', optimizer=opt)
s1.fit(X_train,s1Train,
batch_size=256,
epochs=80,
verbose=1,
validation_data=(X_val,s1Val))
s2.fit(X_train,s2Train,
batch_size=256,
epochs=60,
verbose=1,
validation_data=(X_val,s2Val))
s3.compile(loss='mse', optimizer=opt)
s4.compile(loss='mse', optimizer=opt)
s3.fit(X_train,s3Train,
batch_size=256,
epochs=70,
verbose=1,
validation_data=(X_val,s3Val))
s4.fit(X_train,s4Train,
batch_size=256,
epochs=60,
verbose=1,
validation_data=(X_val,s4Val))
o1=s1.get_layer("reqs1").output
o2=s2.get_layer("reqs2").output
o3=s3.get_layer("reqs3").output
o4=s4.get_layer("reqs4").output
output=tensorflow.keras.layers.concatenate([o1,o2,o3,o4])
output=Activation('relu')(output)
output2=Dropout(0.5)(output) # For reguralization
output3=Dense(10,activation="softmax", name="d1")(output2)
mm4=Model([s1.get_layer("s1").input,s2.get_layer("s2").input,
s3.get_layer("s3").input,s4.get_layer("s4").input], output3)
my_weights=teacher.get_layer('dense_2').get_weights()
mm4.get_layer('d1').set_weights(my_weights)
i=0
for l in mm4.layers[:len(mm4.layers)-2]:
l.trainable=False
# print(l)
mm4.compile(loss='categorical_crossentropy',
optimizer=Adam(learning_rate=0.0002),
metrics=['accuracy'])
# Without finetune
batch_size = 256
mm4_history=mm4.fit([X_train,X_train,X_train,X_train], Y_train,
batch_size=batch_size,
epochs=50,
verbose=1,
validation_data=([X_val,X_val,X_val,X_val], Y_val))
###Output
Epoch 1/50
157/157 [==============================] - 4s 23ms/step - loss: 0.9913 - accuracy: 0.7471 - val_loss: 0.7647 - val_accuracy: 0.8104
Epoch 2/50
157/157 [==============================] - 3s 19ms/step - loss: 0.7192 - accuracy: 0.8060 - val_loss: 0.6967 - val_accuracy: 0.8192
Epoch 3/50
157/157 [==============================] - 3s 19ms/step - loss: 0.6643 - accuracy: 0.8211 - val_loss: 0.6739 - val_accuracy: 0.8208
Epoch 4/50
157/157 [==============================] - 3s 18ms/step - loss: 0.6479 - accuracy: 0.8212 - val_loss: 0.6600 - val_accuracy: 0.8220
Epoch 5/50
157/157 [==============================] - 3s 19ms/step - loss: 0.6364 - accuracy: 0.8214 - val_loss: 0.6491 - val_accuracy: 0.8224
Epoch 6/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5985 - accuracy: 0.8325 - val_loss: 0.6433 - val_accuracy: 0.8213
Epoch 7/50
157/157 [==============================] - 3s 18ms/step - loss: 0.6152 - accuracy: 0.8231 - val_loss: 0.6360 - val_accuracy: 0.8218
Epoch 8/50
157/157 [==============================] - 3s 19ms/step - loss: 0.5931 - accuracy: 0.8305 - val_loss: 0.6286 - val_accuracy: 0.8214
Epoch 9/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5943 - accuracy: 0.8275 - val_loss: 0.6229 - val_accuracy: 0.8216
Epoch 10/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5735 - accuracy: 0.8330 - val_loss: 0.6169 - val_accuracy: 0.8224
Epoch 11/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5605 - accuracy: 0.8363 - val_loss: 0.6121 - val_accuracy: 0.8221
Epoch 12/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5672 - accuracy: 0.8323 - val_loss: 0.6075 - val_accuracy: 0.8220
Epoch 13/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5537 - accuracy: 0.8371 - val_loss: 0.6054 - val_accuracy: 0.8223
Epoch 14/50
157/157 [==============================] - 3s 19ms/step - loss: 0.5463 - accuracy: 0.8387 - val_loss: 0.6021 - val_accuracy: 0.8216
Epoch 15/50
157/157 [==============================] - 3s 20ms/step - loss: 0.5546 - accuracy: 0.8361 - val_loss: 0.5990 - val_accuracy: 0.8219
Epoch 16/50
157/157 [==============================] - 3s 19ms/step - loss: 0.5541 - accuracy: 0.8372 - val_loss: 0.5969 - val_accuracy: 0.8219
Epoch 17/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5451 - accuracy: 0.8405 - val_loss: 0.5940 - val_accuracy: 0.8217
Epoch 18/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5476 - accuracy: 0.8391 - val_loss: 0.5921 - val_accuracy: 0.8221
Epoch 19/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5297 - accuracy: 0.8397 - val_loss: 0.5892 - val_accuracy: 0.8221
Epoch 20/50
157/157 [==============================] - 3s 19ms/step - loss: 0.5221 - accuracy: 0.8433 - val_loss: 0.5867 - val_accuracy: 0.8224
Epoch 21/50
157/157 [==============================] - 3s 19ms/step - loss: 0.5297 - accuracy: 0.8438 - val_loss: 0.5866 - val_accuracy: 0.8216
Epoch 22/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5326 - accuracy: 0.8435 - val_loss: 0.5849 - val_accuracy: 0.8219
Epoch 23/50
157/157 [==============================] - 3s 17ms/step - loss: 0.5302 - accuracy: 0.8436 - val_loss: 0.5818 - val_accuracy: 0.8217
Epoch 24/50
157/157 [==============================] - 3s 17ms/step - loss: 0.5322 - accuracy: 0.8410 - val_loss: 0.5797 - val_accuracy: 0.8226
Epoch 25/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5164 - accuracy: 0.8480 - val_loss: 0.5796 - val_accuracy: 0.8215
Epoch 26/50
157/157 [==============================] - 3s 21ms/step - loss: 0.5151 - accuracy: 0.8454 - val_loss: 0.5776 - val_accuracy: 0.8224
Epoch 27/50
157/157 [==============================] - 3s 19ms/step - loss: 0.5117 - accuracy: 0.8474 - val_loss: 0.5768 - val_accuracy: 0.8215
Epoch 28/50
157/157 [==============================] - 3s 20ms/step - loss: 0.5119 - accuracy: 0.8475 - val_loss: 0.5753 - val_accuracy: 0.8219
Epoch 29/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5184 - accuracy: 0.8433 - val_loss: 0.5742 - val_accuracy: 0.8225
Epoch 30/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5087 - accuracy: 0.8481 - val_loss: 0.5743 - val_accuracy: 0.8216
Epoch 31/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5002 - accuracy: 0.8488 - val_loss: 0.5741 - val_accuracy: 0.8219
Epoch 32/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5053 - accuracy: 0.8479 - val_loss: 0.5730 - val_accuracy: 0.8219
Epoch 33/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5025 - accuracy: 0.8500 - val_loss: 0.5715 - val_accuracy: 0.8228
Epoch 34/50
157/157 [==============================] - 3s 17ms/step - loss: 0.5129 - accuracy: 0.8479 - val_loss: 0.5717 - val_accuracy: 0.8214
Epoch 35/50
157/157 [==============================] - 3s 18ms/step - loss: 0.4986 - accuracy: 0.8511 - val_loss: 0.5711 - val_accuracy: 0.8214
Epoch 36/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5098 - accuracy: 0.8450 - val_loss: 0.5709 - val_accuracy: 0.8217
Epoch 37/50
157/157 [==============================] - 3s 18ms/step - loss: 0.4995 - accuracy: 0.8476 - val_loss: 0.5685 - val_accuracy: 0.8228
Epoch 38/50
157/157 [==============================] - 3s 18ms/step - loss: 0.4909 - accuracy: 0.8515 - val_loss: 0.5698 - val_accuracy: 0.8224
Epoch 39/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5016 - accuracy: 0.8494 - val_loss: 0.5690 - val_accuracy: 0.8217
Epoch 40/50
157/157 [==============================] - 3s 17ms/step - loss: 0.5128 - accuracy: 0.8475 - val_loss: 0.5666 - val_accuracy: 0.8224
Epoch 41/50
157/157 [==============================] - 3s 18ms/step - loss: 0.4964 - accuracy: 0.8513 - val_loss: 0.5690 - val_accuracy: 0.8223
Epoch 42/50
157/157 [==============================] - 3s 18ms/step - loss: 0.4935 - accuracy: 0.8494 - val_loss: 0.5680 - val_accuracy: 0.8221
Epoch 43/50
157/157 [==============================] - 3s 18ms/step - loss: 0.4980 - accuracy: 0.8494 - val_loss: 0.5688 - val_accuracy: 0.8220
Epoch 44/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5105 - accuracy: 0.8473 - val_loss: 0.5673 - val_accuracy: 0.8223
Epoch 45/50
157/157 [==============================] - 3s 17ms/step - loss: 0.4980 - accuracy: 0.8527 - val_loss: 0.5675 - val_accuracy: 0.8226
Epoch 46/50
157/157 [==============================] - 3s 19ms/step - loss: 0.5010 - accuracy: 0.8470 - val_loss: 0.5660 - val_accuracy: 0.8224
Epoch 47/50
157/157 [==============================] - 3s 18ms/step - loss: 0.5018 - accuracy: 0.8500 - val_loss: 0.5659 - val_accuracy: 0.8224
Epoch 48/50
157/157 [==============================] - 3s 17ms/step - loss: 0.4855 - accuracy: 0.8549 - val_loss: 0.5669 - val_accuracy: 0.8216
Epoch 49/50
157/157 [==============================] - 3s 18ms/step - loss: 0.4912 - accuracy: 0.8526 - val_loss: 0.5667 - val_accuracy: 0.8222
Epoch 50/50
157/157 [==============================] - 3s 19ms/step - loss: 0.4958 - accuracy: 0.8523 - val_loss: 0.5661 - val_accuracy: 0.8224
|
courses/machine_learning/deepdive2/production_ml/labs/samples/core/ai_platform/local/Chicago Crime Research.ipynb
|
###Markdown
Chicago Crime PredictionThe model forecasts how many crimes are expected to be reported the next day, based on how many were reported over the previous `n` days. Imports
###Code
%%capture
%pip install --upgrade pip
%pip install --upgrade seaborn
%pip install --upgrade numpy
%pip install --upgrade pandas
%pip install --upgrade "tensorflow<2"
%pip install --upgrade scikit-learn
%pip install --upgrade google-cloud-bigquery
from math import sqrt
import numpy as np
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import matplotlib.pyplot as plt
register_matplotlib_converters()
import seaborn as sns
from sklearn.preprocessing import RobustScaler
from sklearn.metrics import mean_squared_error
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import LSTM, Dense, Dropout
import warnings
###Output
_____no_output_____
###Markdown
Load data
###Code
from google.cloud import bigquery
sql = """
SELECT count(*) as count, TIMESTAMP_TRUNC(date, DAY) as day
FROM `bigquery-public-data.chicago_crime.crime`
GROUP BY day
ORDER BY day
"""
client = bigquery.Client()
df = client.query(sql).result().to_dataframe()
df.index = df.day
df = df[['count']]
df.head()
###Output
_____no_output_____
###Markdown
Visualize data
###Code
plt.figure(figsize=(20, 6))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sns.lineplot(data=df).set_title('Daily Crime Reports')
plt.show()
###Output
_____no_output_____
###Markdown
Preprocess data
###Code
# Split dataset into sequences of previous values and current values
# For example, given a dataset: [1, 2, 3, 4, 5] and a window size of 2:
# data_X = [[1, 2], [2, 3], [3, 4]]
# data_y = [3, 4, 5]
def create_dataset(dataset, window_size = 1):
data_X, data_y = [], []
df = pd.DataFrame(dataset)
columns = [df.shift(i) for i in reversed(range(1, window_size+1))]
data_X = pd.concat(columns, axis=1).dropna().values
data_y = df.shift(-window_size).dropna().values
return data_X, data_y
# The % of data we should use for training
TRAINING_SPLIT = 0.8
# The # of observations to use to predict the next observation
WINDOW_SIZE = 7
def preprocess_data(df, window_size):
# Normalize inputs to improve learning process
scaler = RobustScaler()
# Time series: split latest data into test set
train = df.values[:int(TRAINING_SPLIT * len(df)), :]
train = scaler.fit_transform(train)
test = df.values[int(TRAINING_SPLIT * len(df)):, :]
test = scaler.transform(test)
# Create test and training sets
train_X, train_y = create_dataset(train, window_size)
test_X, test_y = create_dataset(test, window_size)
# Reshape input data
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))
return train_X, train_y, test_X, test_y, scaler
train_X, train_y, test_X, test_y, scaler = preprocess_data(df, WINDOW_SIZE)
###Output
_____no_output_____
###Markdown
Train model
###Code
def input_fn(features, labels, shuffle, num_epochs, batch_size):
"""Generates an input function to be used for model training.
Args:
features: numpy array of features used for training or inference
labels: numpy array of labels for each example
shuffle: boolean for whether to shuffle the data or not (set True for
training, False for evaluation)
num_epochs: number of epochs to provide the data for
batch_size: batch size for training
Returns:
A tf.data.Dataset that can provide data to the Keras model for training or
evaluation
"""
if labels is None:
inputs = features
else:
inputs = (features, labels)
dataset = tf.data.Dataset.from_tensor_slices(inputs)
if shuffle:
dataset = dataset.shuffle(buffer_size=len(features))
# We call repeat after shuffling, rather than before, to prevent separate
# epochs from blending together.
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size)
return dataset
def create_keras_model(input_dim, learning_rate, window_size):
"""Creates Keras model for regression.
Args:
input_dim: How many features the input has
learning_rate: Learning rate for training
Returns:
The compiled Keras model (still needs to be trained)
"""
model = keras.Sequential([
LSTM(4, dropout = 0.2, input_shape = (input_dim, window_size)),
Dense(1)
])
model.compile(loss='mean_squared_error', optimizer=tf.train.AdamOptimizer(
learning_rate=learning_rate))
return(model)
def train_and_evaluate(batch_size, learning_rate, num_epochs, window_size):
# Dimensions
num_train_examples, input_dim, _ = train_X.shape
num_eval_examples = test_X.shape[0]
# Create the Keras Model
keras_model = create_keras_model(
input_dim=input_dim, learning_rate=learning_rate, window_size=window_size)
# Pass a numpy array by passing DataFrame.values
training_dataset = input_fn(
features=train_X,
labels=train_y,
shuffle=False,
num_epochs=num_epochs,
batch_size=batch_size)
# Pass a numpy array by passing DataFrame.values
validation_dataset = input_fn(
features=test_X,
labels=test_y,
shuffle=False,
num_epochs=num_epochs,
batch_size=num_eval_examples)
# Train model
keras_model.fit(
training_dataset,
steps_per_epoch=int(num_train_examples / batch_size),
epochs=num_epochs,
validation_data=validation_dataset,
validation_steps=1,
verbose=1,
shuffle=False,
)
return keras_model
BATCH_SIZE = 256
LEARNING_RATE = 0.01
NUM_EPOCHS = 25
model = train_and_evaluate(BATCH_SIZE, LEARNING_RATE, NUM_EPOCHS, WINDOW_SIZE)
###Output
_____no_output_____
###Markdown
Evaluate model
###Code
def predict(model, X, y, scaler):
y_true = scaler.inverse_transform(y)
y_pred = scaler.inverse_transform(model.predict(X))
rmse = sqrt(mean_squared_error(y_true, y_pred))
return y_pred, rmse
train_predict, _ = predict(model, train_X, train_y, scaler)
test_predict, rmse = predict(model, test_X, test_y, scaler)
model.evaluate(train_X, train_y)
print(rmse)
###Output
_____no_output_____
###Markdown
Plot predictions
###Code
# Create new dataframe with similar indexes and columns to store prediction array
df_test_predict = pd.DataFrame().reindex_like(df)
# Assign test predictions to end of dataframe
df_test_predict['count'][len(train_predict) + (WINDOW_SIZE * 2):len(df)] = np.squeeze(test_predict)
# Append the test predictions to the end of the existing dataframe, while renaming the column to avoid collision
df_combined = df.join(df_test_predict.rename(index=str, columns={'count':'predicted'}))
# Plot the predicted vs actual counts
plt.figure(figsize=(20, 6))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sns.lineplot(data=df_combined).set_title('Daily Crime Reports')
plt.show()
###Output
_____no_output_____
###Markdown
Chicago Crime PredictionThe model forecasts how many crimes are expected to be reported the next day, based on how many were reported over the previous `n` days. Imports
###Code
%%capture
%pip install --upgrade pip
%pip install --upgrade seaborn
%pip install --upgrade numpy
%pip install --upgrade pandas
%pip install --upgrade "tensorflow<2"
%pip install --upgrade scikit-learn
%pip install --upgrade google-cloud-bigquery
from math import sqrt
import numpy as np
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import matplotlib.pyplot as plt
register_matplotlib_converters()
import seaborn as sns
from sklearn.preprocessing import RobustScaler
from sklearn.metrics import mean_squared_error
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import LSTM, Dense, Dropout
import warnings
###Output
_____no_output_____
###Markdown
Load data
###Code
from google.cloud import bigquery
sql = """
SELECT count(*) as count, TIMESTAMP_TRUNC(date, DAY) as day
FROM `bigquery-public-data.chicago_crime.crime`
GROUP BY day
ORDER BY day
"""
client = bigquery.Client()
df = client.query(sql).result().to_dataframe()
df.index = df.day
df = df[['count']]
df.head()
###Output
_____no_output_____
###Markdown
Visualize data
###Code
plt.figure(figsize=(20, 6))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sns.lineplot(data=df).set_title('Daily Crime Reports')
plt.show()
###Output
_____no_output_____
###Markdown
Preprocess data
###Code
# Split dataset into sequences of previous values and current values
# For example, given a dataset: [1, 2, 3, 4, 5] and a window size of 2:
# data_X = [[1, 2], [2, 3], [3, 4]]
# data_y = [3, 4, 5]
def create_dataset(dataset, window_size = 1):
data_X, data_y = [], []
df = pd.DataFrame(dataset)
columns = [df.shift(i) for i in reversed(range(1, window_size+1))]
data_X = pd.concat(columns, axis=1).dropna().values
data_y = df.shift(-window_size).dropna().values
return data_X, data_y
# The % of data we should use for training
TRAINING_SPLIT = 0.8
# The # of observations to use to predict the next observation
WINDOW_SIZE = 7
def preprocess_data(df, window_size):
# Normalize inputs to improve learning process
scaler = RobustScaler()
# Time series: split latest data into test set
train = df.values[:int(TRAINING_SPLIT * len(df)), :]
train = scaler.fit_transform(train)
test = df.values[int(TRAINING_SPLIT * len(df)):, :]
test = scaler.transform(test)
# Create test and training sets
train_X, train_y = create_dataset(train, window_size)
test_X, test_y = create_dataset(test, window_size)
# Reshape input data
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))
return train_X, train_y, test_X, test_y, scaler
train_X, train_y, test_X, test_y, scaler = preprocess_data(df, WINDOW_SIZE)
###Output
_____no_output_____
###Markdown
Train model
###Code
def input_fn(features, labels, shuffle, num_epochs, batch_size):
"""Generates an input function to be used for model training.
Args:
features: numpy array of features used for training or inference
labels: numpy array of labels for each example
shuffle: boolean for whether to shuffle the data or not (set True for
training, False for evaluation)
num_epochs: number of epochs to provide the data for
batch_size: batch size for training
Returns:
A tf.data.Dataset that can provide data to the Keras model for training or
evaluation
"""
if labels is None:
inputs = features
else:
inputs = (features, labels)
dataset = tf.data.Dataset.from_tensor_slices(inputs)
if shuffle:
dataset = dataset.shuffle(buffer_size=len(features))
# We call repeat after shuffling, rather than before, to prevent separate
# epochs from blending together.
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size)
return dataset
def create_keras_model(input_dim, learning_rate, window_size):
"""Creates Keras model for regression.
Args:
input_dim: How many features the input has
learning_rate: Learning rate for training
Returns:
The compiled Keras model (still needs to be trained)
"""
model = keras.Sequential([
LSTM(4, dropout = 0.2, input_shape = (input_dim, window_size)),
Dense(1)
])
model.compile(loss='mean_squared_error', optimizer=tf.train.AdamOptimizer(
learning_rate=learning_rate))
return(model)
def train_and_evaluate(batch_size, learning_rate, num_epochs, window_size):
# Dimensions
num_train_examples, input_dim, _ = train_X.shape
num_eval_examples = test_X.shape[0]
# Create the Keras Model
keras_model = create_keras_model(
input_dim=input_dim, learning_rate=learning_rate, window_size=window_size)
# Pass a numpy array by passing DataFrame.values
training_dataset = input_fn(
features=train_X,
labels=train_y,
shuffle=False,
num_epochs=num_epochs,
batch_size=batch_size)
# Pass a numpy array by passing DataFrame.values
validation_dataset = input_fn(
features=test_X,
labels=test_y,
shuffle=False,
num_epochs=num_epochs,
batch_size=num_eval_examples)
# Train model
keras_model.fit(
training_dataset,
steps_per_epoch=int(num_train_examples / batch_size),
epochs=num_epochs,
validation_data=validation_dataset,
validation_steps=1,
verbose=1,
shuffle=False,
)
return keras_model
BATCH_SIZE = 256
LEARNING_RATE = 0.01
NUM_EPOCHS = 25
model = train_and_evaluate(BATCH_SIZE, LEARNING_RATE, NUM_EPOCHS, WINDOW_SIZE)
###Output
_____no_output_____
###Markdown
Evaluate model
###Code
def predict(model, X, y, scaler):
y_true = scaler.inverse_transform(y)
y_pred = scaler.inverse_transform(model.predict(X))
rmse = sqrt(mean_squared_error(y_true, y_pred))
return y_pred, rmse
train_predict, _ = predict(model, train_X, train_y, scaler)
test_predict, rmse = predict(model, test_X, test_y, scaler)
model.evaluate(train_X, train_y)
print(rmse)
###Output
_____no_output_____
###Markdown
Plot predictions
###Code
# Create new dataframe with similar indexes and columns to store prediction array
df_test_predict = pd.DataFrame().reindex_like(df)
# Assign test predictions to end of dataframe
df_test_predict['count'][len(train_predict) + (WINDOW_SIZE * 2):len(df)] = np.squeeze(test_predict)
# Append the test predictions to the end of the existing dataframe, while renaming the column to avoid collision
df_combined = df.join(df_test_predict.rename(index=str, columns={'count':'predicted'}))
# Plot the predicted vs actual counts
plt.figure(figsize=(20, 6))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sns.lineplot(data=df_combined).set_title('Daily Crime Reports')
plt.show()
###Output
_____no_output_____
|
data_extract_code/wcde.ipynb
|
###Markdown
###Code
from google.colab import drive
drive.mount('drive')
import pandas as pd
import numpy as np
import csv
data = pd.read_csv("/content/drive/MyDrive/wcde_data.csv")
df = pd.DataFrame(data)
age = list(df.Age.unique())
for i in range(0,len(age),2):
sr = df.loc[((df['Age'] == age[i]) | (df['Age']==age[i+1])) & ((df['Year']<=2015) & (df['Year']>=1960))]
sr.to_csv('/content/drive/My Drive/wcde/wcde'+age[i][:4]+age[i+1][4:]+'.csv', encoding='utf-8', index=False)
countries = list(df.Area.unique())
def init_timeline():
timeline = [i for i in range(1960,2016)]
timeline_dic={c:{t : 0 if not t%5 else np.NaN for t in timeline } for c in countries}
return timeline_dic
def convert_to_df():
dft = pd.DataFrame.from_dict(timeline_dic,orient='index')
dft.insert(0, "Country", countries, True)
return dft
for i in range(0,len(age),2):
timeline_dic = init_timeline()
data = pd.read_csv("/content/drive/MyDrive/wcde/wcde"+age[i][:4]+age[i+1][4:]+".csv")
dfs = pd.DataFrame(data)
for j in range(len(data)):
timeline_dic[dfs.iloc[j]['Area']][dfs.iloc[j]['Year']] += dfs.iloc[j]['Years']
round(timeline_dic[dfs.iloc[j]['Area']][dfs.iloc[j]['Year']], 2)
dft = convert_to_df()
dft.to_csv('/content/drive/My Drive/wcde/modified/wcde-'+age[i][:4]+age[i+1][4:]+'.csv', encoding='utf-8', index=False)
###Output
_____no_output_____
|
samples/jupyter-notebooks/DML Tips and Tricks (aka Fun With DML).ipynb
|
###Markdown
1. [Cross Validation](CrossValidation)* [Value-based join of two Matrices](JoinMatrices)* [Filter Matrix to include only Frequent Column Values](FilterMatrix)* [Construct (sparse) Matrix from (rowIndex, colIndex, values) triplets](Construct_sparse_Matrix)* [Find and remove duplicates in columns or rows](Find_and_remove_duplicates)* [Set based Indexing](Set_based_Indexing)* [Group by Aggregate using Linear Algebra](Multi_column_Sorting)* [Cumulative Summation with Decay Multiplier](CumSum_Product)* [Invert Lower Triangular Matrix](Invert_Lower_Triangular_Matrix)
###Code
from systemml import MLContext, dml, jvm_stdout
ml = MLContext(sc)
print (ml.buildTime())
###Output
2017-08-18 21:33:18 UTC
###Markdown
Cross Validation Perform kFold cross validation by running in parallel fold creation, training algorithm, test algorithm, and evaluation.
###Code
prog = """
holdOut = 1/3
kFolds = 1/holdOut
nRows = 6; nCols = 3;
X = matrix(seq(1, nRows * nCols), rows = nRows, cols = nCols) # X data
y = matrix(seq(1, nRows), rows = nRows, cols = 1) # y label data
Xy = cbind (X,y) # Xy Data for CV
sv = rand (rows = nRows, cols = 1, min = 0.0, max = 1.0, pdf = "uniform") # sv selection vector for fold creation
sv = (order(target=sv, by=1, index.return=TRUE)) %% kFolds + 1 # with numbers between 1 .. kFolds
stats = matrix(0, rows=kFolds, cols=1) # stats per kFolds model on test data
parfor (i in 1:kFolds)
{
# Skip empty training data or test data.
if ( sum (sv == i) > 0 & sum (sv == i) < nrow(X) )
{
Xyi = removeEmpty(target = Xy, margin = "rows", select = (sv == i)) # Xyi fold, i.e. 1/k of rows (test data)
Xyni = removeEmpty(target = Xy, margin = "rows", select = (sv != i)) # Xyni data, i.e. (k-1)/k of rows (train data)
# Skip extreme label inbalance
distinctLabels = aggregate( target = Xyni[,1], groups = Xyni[,1], fn = "count")
if ( nrow(distinctLabels) > 1)
{
wi = trainAlg (Xyni[ ,1:ncol(Xy)-1], Xyni[ ,ncol(Xy)]) # wi Model for i-th training data
pi = testAlg (Xyi [ ,1:ncol(Xy)-1], wi) # pi Prediction for i-th test data
ei = evalPrediction (pi, Xyi[ ,ncol(Xy)]) # stats[i,] evaluation of prediction of i-th fold
stats[i,] = ei
print ( "Test data Xyi" + i + "\n" + toString(Xyi)
+ "\nTrain data Xyni" + i + "\n" + toString(Xyni)
+ "\nw" + i + "\n" + toString(wi)
+ "\nstats" + i + "\n" + toString(stats[i,])
+ "\n")
}
else
{
print ("Training data for fold " + i + " has only " + nrow(distinctLabels) + " distinct labels. Needs to be > 1.")
}
}
else
{
print ("Training data or test data for fold " + i + " is empty. Fold not validated.")
}
}
print ("SV selection vector:\n" + toString(sv))
trainAlg = function (matrix[double] X, matrix[double] y)
return (matrix[double] w)
{
w = t(X) %*% y
}
testAlg = function (matrix[double] X, matrix[double] w)
return (matrix[double] p)
{
p = X %*% w
}
evalPrediction = function (matrix[double] p, matrix[double] y)
return (matrix[double] e)
{
e = as.matrix(sum (p - y))
}
"""
with jvm_stdout(True):
ml.execute(dml(prog))
###Output
Test data Xyi2
10.000 11.000 12.000 4.000
16.000 17.000 18.000 6.000
Train data Xyni2
1.000 2.000 3.000 1.000
4.000 5.000 6.000 2.000
7.000 8.000 9.000 3.000
13.000 14.000 15.000 5.000
w2
95.000
106.000
117.000
stats2
8938.000
Test data Xyi3
1.000 2.000 3.000 1.000
7.000 8.000 9.000 3.000
Train data Xyni3
4.000 5.000 6.000 2.000
10.000 11.000 12.000 4.000
13.000 14.000 15.000 5.000
16.000 17.000 18.000 6.000
w3
209.000
226.000
243.000
stats3
6844.000
Test data Xyi1
4.000 5.000 6.000 2.000
13.000 14.000 15.000 5.000
Train data Xyni1
1.000 2.000 3.000 1.000
7.000 8.000 9.000 3.000
10.000 11.000 12.000 4.000
16.000 17.000 18.000 6.000
w1
158.000
172.000
186.000
stats1
9853.000
SV selection vector:
3.000
1.000
3.000
2.000
1.000
2.000
SystemML Statistics:
Total execution time: 0.024 sec.
Number of executed Spark inst: 0.
###Markdown
Value-based join of two Matrices Given matrix M1 and M2, join M1 on column 2 with M2 on column 2, and return matching rows of M1.
###Code
prog = """
M1 = matrix ('1 1 2 3 3 3 4 4 5 3 6 4 7 1 8 2 9 1', rows = 9, cols = 2)
M2 = matrix ('1 1 2 8 3 3 4 3 5 1', rows = 5, cols = 2)
I = rowSums (outer (M1[,2], t(M2[,2]), "==")) # I : indicator matrix for M1
M12 = removeEmpty (target = M1, margin = "rows", select = I) # apply filter to retrieve join result
print ("M1 \n" + toString(M1))
print ("M2 \n" + toString(M2))
print ("M1[,2] joined with M2[,2], and return matching M1 rows\n" + toString(M12))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
M1
1.000 1.000
2.000 3.000
3.000 3.000
4.000 4.000
5.000 3.000
6.000 4.000
7.000 1.000
8.000 2.000
9.000 1.000
M2
1.000 1.000
2.000 8.000
3.000 3.000
4.000 3.000
5.000 1.000
M1[,2] joined with M2[,2], and return matching M1 rows
1.000 1.000
2.000 3.000
3.000 3.000
5.000 3.000
7.000 1.000
9.000 1.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Filter Matrix to include only Frequent Column Values Given a matrix, filter the matrix to only include rows with column values that appear more often than MinFreq.
###Code
prog = """
MinFreq = 3 # minimum frequency of tokens
M = matrix ('1 1 2 3 3 3 4 4 5 3 6 4 7 1 8 2 9 1', rows = 9, cols = 2)
gM = aggregate (target = M[,2], groups = M[,2], fn = "count") # gM: group by and count (grouped matrix)
gv = cbind (seq(1,nrow(gM)), gM) # gv: add group values to counts (group values)
fg = removeEmpty (target = gv * (gv[,2] >= MinFreq), margin = "rows") # fg: filtered groups
I = rowSums (outer (M[,2] ,t(fg[,1]), "==")) # I : indicator of size M with filtered groups
fM = removeEmpty (target = M, margin = "rows", select = I) # FM: filter matrix
print (toString(M))
print (toString(fM))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
1.000 1.000
2.000 3.000
3.000 3.000
4.000 4.000
5.000 3.000
6.000 4.000
7.000 1.000
8.000 2.000
9.000 1.000
1.000 1.000
2.000 3.000
3.000 3.000
5.000 3.000
7.000 1.000
9.000 1.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Construct (sparse) Matrix from (rowIndex, colIndex, values) triplets Given rowIndex, colIndex, and values as column vectors, construct (sparse) matrix.
###Code
prog = """
I = matrix ("1 3 3 4 5", rows = 5, cols = 1)
J = matrix ("2 3 4 1 6", rows = 5, cols = 1)
V = matrix ("10 20 30 40 50", rows = 5, cols = 1)
M = table (I, J, V)
print (toString (M))
"""
ml.execute(dml(prog).output('M')).get('M').toNumPy()
###Output
_____no_output_____
###Markdown
Find and remove duplicates in columns or rows Assuming values are sorted.
###Code
prog = """
X = matrix ("1 2 3 3 3 4 5 10", rows = 8, cols = 1)
I = rbind (matrix (1,1,1), (X[1:nrow (X)-1,] != X[2:nrow (X),])); # compare current with next value
res = removeEmpty (target = X, margin = "rows", select = I); # select where different
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
No assumptions on values.
###Code
prog = """
X = matrix ("3 2 1 3 3 4 5 10", rows = 8, cols = 1)
I = aggregate (target = X, groups = X[,1], fn = "count") # group and count duplicates
res = removeEmpty (target = seq (1, max (X[,1])), margin = "rows", select = (I != 0)); # select groups
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Order the values and then remove duplicates.
###Code
prog = """
X = matrix ("3 2 1 3 3 4 5 10", rows = 8, cols = 1)
X = order (target = X, by = 1) # order values
I = rbind (matrix (1,1,1), (X[1:nrow (X)-1,] != X[2:nrow (X),]));
res = removeEmpty (target = X, margin = "rows", select = I);
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Set based Indexing Given a matrix X, and a indicator matrix J with indices into X. Use J to perform operation on X, e.g. add value 10 to cells in X indicated by J.
###Code
prog = """
X = matrix (1, rows = 1, cols = 100)
J = matrix ("10 20 25 26 28 31 50 67 79", rows = 1, cols = 9)
res = X + table (matrix (1, rows = 1, cols = ncol (J)), J, 10)
print (toString (res))
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Group by Aggregate using Linear Algebra Given a matrix PCV as (Position, Category, Value), sort PCV by category, and within each category by value in descending order. Create indicator vector for category changes, create distinct categories, and perform linear algebra operations.
###Code
prog = """
C = matrix ('50 40 20 10 30 20 40 20 30', rows = 9, cols = 1) # category data
V = matrix ('20 11 49 33 94 29 48 74 57', rows = 9, cols = 1) # value data
PCV = cbind (cbind (seq (1, nrow (C), 1), C), V); # PCV representation
PCV = order (target = PCV, by = 3, decreasing = TRUE, index.return = FALSE);
PCV = order (target = PCV, by = 2, decreasing = FALSE, index.return = FALSE);
# Find all rows of PCV where the category has a new value, in comparison to the previous row
is_new_C = matrix (1, rows = 1, cols = 1);
if (nrow (C) > 1) {
is_new_C = rbind (is_new_C, (PCV [1:nrow(C) - 1, 2] < PCV [2:nrow(C), 2]));
}
# Associate each category with its index
index_C = cumsum (is_new_C); # cumsum
# For each category, compute:
# - the list of distinct categories
# - the maximum value for each category
# - 0-1 aggregation matrix that adds records of the same category
distinct_C = removeEmpty (target = PCV [, 2], margin = "rows", select = is_new_C);
max_V_per_C = removeEmpty (target = PCV [, 3], margin = "rows", select = is_new_C);
C_indicator = table (index_C, PCV [, 1], max (index_C), nrow (C)); # table
sum_V_per_C = C_indicator %*% V
"""
res = ml.execute(dml(prog).output('PCV','distinct_C', 'max_V_per_C', 'C_indicator', 'sum_V_per_C'))
print (res.get('PCV').toNumPy())
print (res.get('distinct_C').toNumPy())
print (res.get('max_V_per_C').toNumPy())
print (res.get('C_indicator').toNumPy())
print (res.get('sum_V_per_C').toNumPy())
###Output
_____no_output_____
###Markdown
Cumulative Summation with Decay Multiplier Given matrix X, compute: Y[i] = X[i] + X[i-1] * C[i] + X[i-2] * C[i] * C[i-1] + X[i-3] * C[i] * C[i-1] * C[i-2] + ...
###Code
cumsum_prod_def = """
cumsum_prod = function (Matrix[double] X, Matrix[double] C, double start)
return (Matrix[double] Y)
# Computes the following recurrence in log-number of steps:
# Y [1, ] = X [1, ] + C [1, ] * start;
# Y [i+1, ] = X [i+1, ] + C [i+1, ] * Y [i, ]
{
Y = X; P = C; m = nrow(X); k = 1;
Y [1,] = Y [1,] + C [1,] * start;
while (k < m) {
Y [k + 1:m,] = Y [k + 1:m,] + Y [1:m - k,] * P [k + 1:m,];
P [k + 1:m,] = P [1:m - k,] * P [k + 1:m,];
k = 2 * k;
}
}
"""
###Output
_____no_output_____
###Markdown
In this example we use cumsum_prod for cumulative summation with "breaks", that is, multiple cumulative summations in one.
###Code
prog = cumsum_prod_def + """
X = matrix ("1 2 3 4 5 6 7 8 9", rows = 9, cols = 1);
#Zeros in C cause "breaks" that restart the cumulative summation from 0
C = matrix ("0 1 1 0 1 1 1 0 1", rows = 9, cols = 1);
Y = cumsum_prod (X, C, 0);
print (toString(Y))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
In this example, we copy selected rows downward to all consecutive non-selected rows.
###Code
prog = cumsum_prod_def + """
X = matrix ("1 2 3 4 5 6 7 8 9", rows = 9, cols = 1);
# Ones in S represent selected rows to be copied, zeros represent non-selected rows
S = matrix ("1 0 0 1 0 0 0 1 0", rows = 9, cols = 1);
Y = cumsum_prod (X * S, 1 - S, 0);
print (toString(Y))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
This is a naive implementation of cumulative summation with decay multiplier.
###Code
cumsum_prod_naive_def = """
cumsum_prod_naive = function (Matrix[double] X, Matrix[double] C, double start)
return (Matrix[double] Y)
{
Y = matrix (0, rows = nrow(X), cols = ncol(X));
Y [1,] = X [1,] + C [1,] * start;
for (i in 2:nrow(X))
{
Y [i,] = X [i,] + C [i,] * Y [i - 1,]
}
}
"""
###Output
_____no_output_____
###Markdown
There is a significant performance difference between the naive implementation and the tricky implementation.
###Code
prog = cumsum_prod_def + cumsum_prod_naive_def + """
X = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
C = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
Y1 = cumsum_prod_naive (X, C, 0.123);
"""
with jvm_stdout():
ml.execute(dml(prog))
prog = cumsum_prod_def + cumsum_prod_naive_def + """
X = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
C = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
Y2 = cumsum_prod (X, C, 0.123);
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
Invert Lower Triangular Matrix In this example, we invert a lower triangular matrix using a the following divide-and-conquer approach. Given lower triangular matrix L, we compute its inverse X which is also lower triangular by splitting both matrices in the middle into 4 blocks (in a 2x2 fashion), and multiplying them together to get the identity matrix:\begin{equation}L \text{ %*% } X = \left(\begin{matrix} L_1 & 0 \\ L_2 & L_3 \end{matrix}\right)\text{ %*% } \left(\begin{matrix} X_1 & 0 \\ X_2 & X_3 \end{matrix}\right)= \left(\begin{matrix} L_1 X_1 & 0 \\ L_2 X_1 + L_3 X_2 & L_3 X_3 \end{matrix}\right)= \left(\begin{matrix} I & 0 \\ 0 & I \end{matrix}\right)\nonumber\end{equation}If we multiply blockwise, we get three equations: $\begin{equation}L1 \text{ %*% } X1 = 1\\ L3 \text{ %*% } X3 = 1\\L2 \text{ %*% } X1 + L3 \text{ %*% } X2 = 0\\\end{equation}$Solving these equation gives the following formulas for X:$\begin{equation}X1 = inv(L1) \\X3 = inv(L3) \\X2 = - X3 \text{ %*% } L2 \text{ %*% } X1 \\\end{equation}$If we already recursively inverted L1 and L3, we can invert L2. This suggests an algorithm that starts at the diagonal and iterates away from the diagonal, involving bigger and bigger blocks (of size 1, 2, 4, 8, etc.) There is a logarithmic number of steps, and inside each step, the inversions can be performed in parallel using a parfor-loop.Function "invert_lower_triangular" occurs within more general inverse operations and matrix decompositions. The divide-and-conquer idea allows to derive more efficient algorithms for other matrix decompositions.
###Code
invert_lower_triangular_def = """
invert_lower_triangular = function (Matrix[double] LI)
return (Matrix[double] LO)
{
n = nrow (LI);
LO = matrix (0, rows = n, cols = n);
LO = LO + diag (1 / diag (LI));
k = 1;
while (k < n)
{
LPF = matrix (0, rows = n, cols = n);
parfor (p in 0:((n - 1) / (2 * k)), check = 0)
{
i = 2 * k * p;
j = i + k;
q = min (n, j + k);
if (j + 1 <= q) {
L1 = LO [i + 1:j, i + 1:j];
L2 = LI [j + 1:q, i + 1:j];
L3 = LO [j + 1:q, j + 1:q];
LPF [j + 1:q, i + 1:j] = -L3 %*% L2 %*% L1;
}
}
LO = LO + LPF;
k = 2 * k;
}
}
"""
prog = invert_lower_triangular_def + """
n = 1000;
A = rand (rows = n, cols = n, min = -1, max = 1, pdf = "uniform", sparsity = 1.0);
Mask = cumsum (diag (matrix (1, rows = n, cols = 1)));
L = (A %*% t(A)) * Mask; # Generate L for stability of the inverse
X = invert_lower_triangular (L);
print ("Maximum difference between X %*% L and Identity = " + max (abs (X %*% L - diag (matrix (1, rows = n, cols = 1)))));
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
This is a naive implementation of inverting a lower triangular matrix.
###Code
invert_lower_triangular_naive_def = """
invert_lower_triangular_naive = function (Matrix[double] LI)
return (Matrix[double] LO)
{
n = nrow (LI);
LO = diag (matrix (1, rows = n, cols = 1));
for (i in 1:n - 1)
{
LO [i,] = LO [i,] / LI [i, i];
LO [i + 1:n,] = LO [i + 1:n,] - LI [i + 1:n, i] %*% LO [i,];
}
LO [n,] = LO [n,] / LI [n, n];
}
"""
###Output
_____no_output_____
###Markdown
The naive implementation is significantly slower than the divide-and-conquer implementation.
###Code
prog = invert_lower_triangular_naive_def + """
n = 1000;
A = rand (rows = n, cols = n, min = -1, max = 1, pdf = "uniform", sparsity = 1.0);
Mask = cumsum (diag (matrix (1, rows = n, cols = 1)));
L = (A %*% t(A)) * Mask; # Generate L for stability of the inverse
X = invert_lower_triangular_naive (L);
print ("Maximum difference between X %*% L and Identity = " + max (abs (X %*% L - diag (matrix (1, rows = n, cols = 1)))));
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
1. [Value-based join of two Matrices](JoinMatrices)* [Filter Matrix to include only Frequent Column Values](FilterMatrix)* [Construct (sparse) Matrix from (rowIndex, colIndex, values) triplets](Construct_sparse_Matrix)* [Find and remove duplicates in columns or rows](Find_and_remove_duplicates)* [Set based Indexing](Set_based_Indexing)* [Group by Aggregate using Linear Algebra](Multi_column_Sorting)* [Cumulative Summation with Decay Multiplier](CumSum_Product)* [Invert Lower Triangular Matrix](Invert_Lower_Triangular_Matrix)
###Code
from systemml import MLContext, dml, jvm_stdout
ml = MLContext(sc)
print (ml.buildTime())
###Output
_____no_output_____
###Markdown
Value-based join of two Matrices Given matrix M1 and M2, join M1 on column 2 with M2 on column 2, and return matching rows of M1.
###Code
prog = """
M1 = matrix ('1 1 2 3 3 3 4 4 5 3 6 4 7 1 8 2 9 1', rows = 9, cols = 2)
M2 = matrix ('1 1 2 8 3 3 4 3 5 1', rows = 5, cols = 2)
I = rowSums (outer (M1[,2], t(M2[,2]), "==")) # I : indicator matrix for M1
M12 = removeEmpty (target = M1, margin = "rows", select = I) # apply filter to retrieve join result
print ("M1 \n" + toString(M1))
print ("M2 \n" + toString(M2))
print ("M1[,2] joined with M2[,2], and return matching M1 rows\n" + toString(M12))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
M1
1.000 1.000
2.000 3.000
3.000 3.000
4.000 4.000
5.000 3.000
6.000 4.000
7.000 1.000
8.000 2.000
9.000 1.000
M2
1.000 1.000
2.000 8.000
3.000 3.000
4.000 3.000
5.000 1.000
M1[,2] joined with M2[,2], and return matching M1 rows
1.000 1.000
2.000 3.000
3.000 3.000
5.000 3.000
7.000 1.000
9.000 1.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Filter Matrix to include only Frequent Column Values Given a matrix, filter the matrix to only include rows with column values that appear more often than MinFreq.
###Code
prog = """
MinFreq = 3 # minimum frequency of tokens
M = matrix ('1 1 2 3 3 3 4 4 5 3 6 4 7 1 8 2 9 1', rows = 9, cols = 2)
gM = aggregate (target = M[,2], groups = M[,2], fn = "count") # gM: group by and count (grouped matrix)
gv = cbind (seq(1,nrow(gM)), gM) # gv: add group values to counts (group values)
fg = removeEmpty (target = gv * (gv[,2] >= MinFreq), margin = "rows") # fg: filtered groups
I = rowSums (outer (M[,2] ,t(fg[,1]), "==")) # I : indicator of size M with filtered groups
fM = removeEmpty (target = M, margin = "rows", select = I) # FM: filter matrix
print (toString(M))
print (toString(fM))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
1.000 1.000
2.000 3.000
3.000 3.000
4.000 4.000
5.000 3.000
6.000 4.000
7.000 1.000
8.000 2.000
9.000 1.000
1.000 1.000
2.000 3.000
3.000 3.000
5.000 3.000
7.000 1.000
9.000 1.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Construct (sparse) Matrix from (rowIndex, colIndex, values) triplets Given rowIndex, colIndex, and values as column vectors, construct (sparse) matrix.
###Code
prog = """
I = matrix ("1 3 3 4 5", rows = 5, cols = 1)
J = matrix ("2 3 4 1 6", rows = 5, cols = 1)
V = matrix ("10 20 30 40 50", rows = 5, cols = 1)
M = table (I, J, V)
print (toString (M))
"""
ml.execute(dml(prog).output('M')).get('M').toNumPy()
###Output
_____no_output_____
###Markdown
Find and remove duplicates in columns or rows Assuming values are sorted.
###Code
prog = """
X = matrix ("1 2 3 3 3 4 5 10", rows = 8, cols = 1)
I = rbind (matrix (1,1,1), (X[1:nrow (X)-1,] != X[2:nrow (X),])); # compare current with next value
res = removeEmpty (target = X, margin = "rows", select = I); # select where different
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
No assumptions on values.
###Code
prog = """
X = matrix ("3 2 1 3 3 4 5 10", rows = 8, cols = 1)
I = aggregate (target = X, groups = X[,1], fn = "count") # group and count duplicates
res = removeEmpty (target = seq (1, max (X[,1])), margin = "rows", select = (I != 0)); # select groups
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Order the values and then remove duplicates.
###Code
prog = """
X = matrix ("3 2 1 3 3 4 5 10", rows = 8, cols = 1)
X = order (target = X, by = 1) # order values
I = rbind (matrix (1,1,1), (X[1:nrow (X)-1,] != X[2:nrow (X),]));
res = removeEmpty (target = X, margin = "rows", select = I);
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Set based Indexing Given a matrix X, and a indicator matrix J with indices into X. Use J to perform operation on X, e.g. add value 10 to cells in X indicated by J.
###Code
prog = """
X = matrix (1, rows = 1, cols = 100)
J = matrix ("10 20 25 26 28 31 50 67 79", rows = 1, cols = 9)
res = X + table (matrix (1, rows = 1, cols = ncol (J)), J, 10)
print (toString (res))
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Group by Aggregate using Linear Algebra Given a matrix PCV as (Position, Category, Value), sort PCV by category, and within each category by value in descending order. Create indicator vector for category changes, create distinct categories, and perform linear algebra operations.
###Code
prog = """
C = matrix ('50 40 20 10 30 20 40 20 30', rows = 9, cols = 1) # category data
V = matrix ('20 11 49 33 94 29 48 74 57', rows = 9, cols = 1) # value data
PCV = cbind (cbind (seq (1, nrow (C), 1), C), V); # PCV representation
PCV = order (target = PCV, by = 3, decreasing = TRUE, index.return = FALSE);
PCV = order (target = PCV, by = 2, decreasing = FALSE, index.return = FALSE);
# Find all rows of PCV where the category has a new value, in comparison to the previous row
is_new_C = matrix (1, rows = 1, cols = 1);
if (nrow (C) > 1) {
is_new_C = rbind (is_new_C, (PCV [1:nrow(C) - 1, 2] < PCV [2:nrow(C), 2]));
}
# Associate each category with its index
index_C = cumsum (is_new_C); # cumsum
# For each category, compute:
# - the list of distinct categories
# - the maximum value for each category
# - 0-1 aggregation matrix that adds records of the same category
distinct_C = removeEmpty (target = PCV [, 2], margin = "rows", select = is_new_C);
max_V_per_C = removeEmpty (target = PCV [, 3], margin = "rows", select = is_new_C);
C_indicator = table (index_C, PCV [, 1], max (index_C), nrow (C)); # table
sum_V_per_C = C_indicator %*% V
"""
res = ml.execute(dml(prog).output('PCV','distinct_C', 'max_V_per_C', 'C_indicator', 'sum_V_per_C'))
print (res.get('PCV').toNumPy())
print (res.get('distinct_C').toNumPy())
print (res.get('max_V_per_C').toNumPy())
print (res.get('C_indicator').toNumPy())
print (res.get('sum_V_per_C').toNumPy())
###Output
_____no_output_____
###Markdown
Cumulative Summation with Decay Multiplier Given matrix X, compute: Y[i] = X[i] + X[i-1] * C[i] + X[i-2] * C[i] * C[i-1] + X[i-3] * C[i] * C[i-1] * C[i-2] + ...
###Code
cumsum_prod_def = """
cumsum_prod = function (Matrix[double] X, Matrix[double] C, double start)
return (Matrix[double] Y)
# Computes the following recurrence in log-number of steps:
# Y [1, ] = X [1, ] + C [1, ] * start;
# Y [i+1, ] = X [i+1, ] + C [i+1, ] * Y [i, ]
{
Y = X; P = C; m = nrow(X); k = 1;
Y [1,] = Y [1,] + C [1,] * start;
while (k < m) {
Y [k + 1:m,] = Y [k + 1:m,] + Y [1:m - k,] * P [k + 1:m,];
P [k + 1:m,] = P [1:m - k,] * P [k + 1:m,];
k = 2 * k;
}
}
"""
###Output
_____no_output_____
###Markdown
In this example we use cumsum_prod for cumulative summation with "breaks", that is, multiple cumulative summations in one.
###Code
prog = cumsum_prod_def + """
X = matrix ("1 2 3 4 5 6 7 8 9", rows = 9, cols = 1);
#Zeros in C cause "breaks" that restart the cumulative summation from 0
C = matrix ("0 1 1 0 1 1 1 0 1", rows = 9, cols = 1);
Y = cumsum_prod (X, C, 0);
print (toString(Y))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
In this example, we copy selected rows downward to all consecutive non-selected rows.
###Code
prog = cumsum_prod_def + """
X = matrix ("1 2 3 4 5 6 7 8 9", rows = 9, cols = 1);
# Ones in S represent selected rows to be copied, zeros represent non-selected rows
S = matrix ("1 0 0 1 0 0 0 1 0", rows = 9, cols = 1);
Y = cumsum_prod (X * S, 1 - S, 0);
print (toString(Y))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
This is a naive implementation of cumulative summation with decay multiplier.
###Code
cumsum_prod_naive_def = """
cumsum_prod_naive = function (Matrix[double] X, Matrix[double] C, double start)
return (Matrix[double] Y)
{
Y = matrix (0, rows = nrow(X), cols = ncol(X));
Y [1,] = X [1,] + C [1,] * start;
for (i in 2:nrow(X))
{
Y [i,] = X [i,] + C [i,] * Y [i - 1,]
}
}
"""
###Output
_____no_output_____
###Markdown
There is a significant performance difference between the naive implementation and the tricky implementation.
###Code
prog = cumsum_prod_def + cumsum_prod_naive_def + """
X = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
C = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
Y1 = cumsum_prod_naive (X, C, 0.123);
"""
with jvm_stdout():
ml.execute(dml(prog))
prog = cumsum_prod_def + cumsum_prod_naive_def + """
X = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
C = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
Y2 = cumsum_prod (X, C, 0.123);
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
Invert Lower Triangular Matrix In this example, we invert a lower triangular matrix using a the following divide-and-conquer approach. Given lower triangular matrix L, we compute its inverse X which is also lower triangular by splitting both matrices in the middle into 4 blocks (in a 2x2 fashion), and multiplying them together to get the identity matrix:\begin{equation}L \text{ %*% } X = \left(\begin{matrix} L_1 & 0 \\ L_2 & L_3 \end{matrix}\right)\text{ %*% } \left(\begin{matrix} X_1 & 0 \\ X_2 & X_3 \end{matrix}\right)= \left(\begin{matrix} L_1 X_1 & 0 \\ L_2 X_1 + L_3 X_2 & L_3 X_3 \end{matrix}\right)= \left(\begin{matrix} I & 0 \\ 0 & I \end{matrix}\right)\nonumber\end{equation}If we multiply blockwise, we get three equations: $\begin{equation}L1 \text{ %*% } X1 = 1\\ L3 \text{ %*% } X3 = 1\\L2 \text{ %*% } X1 + L3 \text{ %*% } X2 = 0\\\end{equation}$Solving these equation gives the following formulas for X:$\begin{equation}X1 = inv(L1) \\X3 = inv(L3) \\X2 = - X3 \text{ %*% } L2 \text{ %*% } X1 \\\end{equation}$If we already recursively inverted L1 and L3, we can invert L2. This suggests an algorithm that starts at the diagonal and iterates away from the diagonal, involving bigger and bigger blocks (of size 1, 2, 4, 8, etc.) There is a logarithmic number of steps, and inside each step, the inversions can be performed in parallel using a parfor-loop.Function "invert_lower_triangular" occurs within more general inverse operations and matrix decompositions. The divide-and-conquer idea allows to derive more efficient algorithms for other matrix decompositions.
###Code
invert_lower_triangular_def = """
invert_lower_triangular = function (Matrix[double] LI)
return (Matrix[double] LO)
{
n = nrow (LI);
LO = matrix (0, rows = n, cols = n);
LO = LO + diag (1 / diag (LI));
k = 1;
while (k < n)
{
LPF = matrix (0, rows = n, cols = n);
parfor (p in 0:((n - 1) / (2 * k)), check = 0)
{
i = 2 * k * p;
j = i + k;
q = min (n, j + k);
if (j + 1 <= q) {
L1 = LO [i + 1:j, i + 1:j];
L2 = LI [j + 1:q, i + 1:j];
L3 = LO [j + 1:q, j + 1:q];
LPF [j + 1:q, i + 1:j] = -L3 %*% L2 %*% L1;
}
}
LO = LO + LPF;
k = 2 * k;
}
}
"""
prog = invert_lower_triangular_def + """
n = 1000;
A = rand (rows = n, cols = n, min = -1, max = 1, pdf = "uniform", sparsity = 1.0);
Mask = cumsum (diag (matrix (1, rows = n, cols = 1)));
L = (A %*% t(A)) * Mask; # Generate L for stability of the inverse
X = invert_lower_triangular (L);
print ("Maximum difference between X %*% L and Identity = " + max (abs (X %*% L - diag (matrix (1, rows = n, cols = 1)))));
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
This is a naive implementation of inverting a lower triangular matrix.
###Code
invert_lower_triangular_naive_def = """
invert_lower_triangular_naive = function (Matrix[double] LI)
return (Matrix[double] LO)
{
n = nrow (LI);
LO = diag (matrix (1, rows = n, cols = 1));
for (i in 1:n - 1)
{
LO [i,] = LO [i,] / LI [i, i];
LO [i + 1:n,] = LO [i + 1:n,] - LI [i + 1:n, i] %*% LO [i,];
}
LO [n,] = LO [n,] / LI [n, n];
}
"""
###Output
_____no_output_____
###Markdown
The naive implementation is significantly slower than the divide-and-conquer implementation.
###Code
prog = invert_lower_triangular_naive_def + """
n = 1000;
A = rand (rows = n, cols = n, min = -1, max = 1, pdf = "uniform", sparsity = 1.0);
Mask = cumsum (diag (matrix (1, rows = n, cols = 1)));
L = (A %*% t(A)) * Mask; # Generate L for stability of the inverse
X = invert_lower_triangular_naive (L);
print ("Maximum difference between X %*% L and Identity = " + max (abs (X %*% L - diag (matrix (1, rows = n, cols = 1)))));
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
1. [Replace NaN with mode](NaN2Mode)* [Use sample builtin function to create sample from matrix](sample)* [Count of Matching Values in two Matrices/Vectors](MatchinRows)* [Cross Validation](CrossValidation)* [Value-based join of two Matrices](JoinMatrices)* [Filter Matrix to include only Frequent Column Values](FilterMatrix)* [Construct (sparse) Matrix from (rowIndex, colIndex, values) triplets](Construct_sparse_Matrix)* [Find and remove duplicates in columns or rows](Find_and_remove_duplicates)* [Set based Indexing](Set_based_Indexing)* [Group by Aggregate using Linear Algebra](Multi_column_Sorting)* [Cumulative Summation with Decay Multiplier](CumSum_Product)* [Invert Lower Triangular Matrix](Invert_Lower_Triangular_Matrix)
###Code
from systemml import MLContext, dml, jvm_stdout
ml = MLContext(sc)
print (ml.buildTime())
###Output
2017-09-22 07:57:57 UTC
###Markdown
Replace NaN with mode This functions replaces NaN in column with mode of column
###Code
prog="""
# Function for NaN-aware replacement with mode
replaceNaNwithMode = function (matrix[double] X, integer colId)
return (matrix[double] X)
{
Xi = replace (target=X[,colId], pattern=0/0, replacement=max(X[,colId])+1) # replace NaN with largest value + 1
agg = aggregate (target=Xi, groups=Xi, fn="count") # count each distinct value
mode = as.scalar (rowIndexMax(t(agg[1:nrow(agg)-1, ]))) # mode is max frequent value except last value
X[,colId] = replace (target=Xi, pattern=max(Xi), replacement=mode) # fill in mode
}
X = matrix('1 NaN 1 NaN 1 2 2 1 1 2', rows = 5, cols = 2)
Y = replaceNaNwithMode (X, 2)
print ("Before: \n" + toString(X))
print ("After: \n" + toString(Y))
"""
with jvm_stdout(True):
ml.execute(dml(prog))
###Output
Before:
1.000 NaN
1.000 NaN
1.000 2.000
2.000 1.000
1.000 2.000
After:
1.000 2.000
1.000 2.000
1.000 2.000
2.000 1.000
1.000 2.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Use sample builtin function to create sample from matrix Use sample() function, create permutation matrix using table(), and pull sample from X.
###Code
prog="""
X = matrix ('2 1 8 3 5 6 7 9 4 4', rows = 5, cols = 2 )
nbrSamples = 2
sv = order (target = sample (nrow (X), nbrSamples, FALSE)) # samples w/o replacement, and order
P = table (seq (1, nbrSamples), sv, nbrSamples, nrow(X)) # permutation matrix
samples = P %*% X; # apply P to perform selection
print ("X: \n" + toString(X))
print ("sv: \n" + toString(sv))
print ("samples: \n" + toString(samples))
"""
with jvm_stdout(True):
ml.execute(dml(prog))
###Output
X:
2.000 1.000
8.000 3.000
5.000 6.000
7.000 9.000
4.000 4.000
sv:
1.000
4.000
samples:
2.000 1.000
7.000 9.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Count of Matching Values in two Matrices/Vectors Given two matrices/vectors X and Y, get a count of the rows where X and Y have the same value.
###Code
prog="""
X = matrix('8 4 5 4 9 10', rows = 6, cols = 1)
Y = matrix('4 9 5 1 9 7 ', rows = 6, cols = 1)
matches = sum (X == Y)
print ("t(X): " + toString(t(X)))
print ("t(Y): " + toString(t(Y)))
print ("Number of Matches: " + matches + "\n")
"""
with jvm_stdout(True):
ml.execute(dml(prog))
###Output
t(X): 8.000 4.000 5.000 4.000 9.000 10.000
t(Y): 4.000 9.000 5.000 1.000 9.000 7.000
Number of Matches: 2.0
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Cross Validation Perform kFold cross validation by running in parallel fold creation, training algorithm, test algorithm, and evaluation.
###Code
prog = """
holdOut = 1/3
kFolds = 1/holdOut
nRows = 6; nCols = 3;
X = matrix(seq(1, nRows * nCols), rows = nRows, cols = nCols) # X data
y = matrix(seq(1, nRows), rows = nRows, cols = 1) # y label data
Xy = cbind (X,y) # Xy Data for CV
sv = rand (rows = nRows, cols = 1, min = 0.0, max = 1.0, pdf = "uniform") # sv selection vector for fold creation
sv = (order(target=sv, by=1, index.return=TRUE)) %% kFolds + 1 # with numbers between 1 .. kFolds
stats = matrix(0, rows=kFolds, cols=1) # stats per kFolds model on test data
parfor (i in 1:kFolds)
{
# Skip empty training data or test data.
if ( sum (sv == i) > 0 & sum (sv == i) < nrow(X) )
{
Xyi = removeEmpty(target = Xy, margin = "rows", select = (sv == i)) # Xyi fold, i.e. 1/k of rows (test data)
Xyni = removeEmpty(target = Xy, margin = "rows", select = (sv != i)) # Xyni data, i.e. (k-1)/k of rows (train data)
# Skip extreme label inbalance
distinctLabels = aggregate( target = Xyni[,1], groups = Xyni[,1], fn = "count")
if ( nrow(distinctLabels) > 1)
{
wi = trainAlg (Xyni[ ,1:ncol(Xy)-1], Xyni[ ,ncol(Xy)]) # wi Model for i-th training data
pi = testAlg (Xyi [ ,1:ncol(Xy)-1], wi) # pi Prediction for i-th test data
ei = evalPrediction (pi, Xyi[ ,ncol(Xy)]) # stats[i,] evaluation of prediction of i-th fold
stats[i,] = ei
print ( "Test data Xyi" + i + "\n" + toString(Xyi)
+ "\nTrain data Xyni" + i + "\n" + toString(Xyni)
+ "\nw" + i + "\n" + toString(wi)
+ "\nstats" + i + "\n" + toString(stats[i,])
+ "\n")
}
else
{
print ("Training data for fold " + i + " has only " + nrow(distinctLabels) + " distinct labels. Needs to be > 1.")
}
}
else
{
print ("Training data or test data for fold " + i + " is empty. Fold not validated.")
}
}
print ("SV selection vector:\n" + toString(sv))
trainAlg = function (matrix[double] X, matrix[double] y)
return (matrix[double] w)
{
w = t(X) %*% y
}
testAlg = function (matrix[double] X, matrix[double] w)
return (matrix[double] p)
{
p = X %*% w
}
evalPrediction = function (matrix[double] p, matrix[double] y)
return (matrix[double] e)
{
e = as.matrix(sum (p - y))
}
"""
with jvm_stdout(True):
ml.execute(dml(prog))
###Output
Test data Xyi2
10.000 11.000 12.000 4.000
16.000 17.000 18.000 6.000
Train data Xyni2
1.000 2.000 3.000 1.000
4.000 5.000 6.000 2.000
7.000 8.000 9.000 3.000
13.000 14.000 15.000 5.000
w2
95.000
106.000
117.000
stats2
8938.000
Test data Xyi3
1.000 2.000 3.000 1.000
7.000 8.000 9.000 3.000
Train data Xyni3
4.000 5.000 6.000 2.000
10.000 11.000 12.000 4.000
13.000 14.000 15.000 5.000
16.000 17.000 18.000 6.000
w3
209.000
226.000
243.000
stats3
6844.000
Test data Xyi1
4.000 5.000 6.000 2.000
13.000 14.000 15.000 5.000
Train data Xyni1
1.000 2.000 3.000 1.000
7.000 8.000 9.000 3.000
10.000 11.000 12.000 4.000
16.000 17.000 18.000 6.000
w1
158.000
172.000
186.000
stats1
9853.000
SV selection vector:
3.000
1.000
3.000
2.000
1.000
2.000
SystemML Statistics:
Total execution time: 0.024 sec.
Number of executed Spark inst: 0.
###Markdown
Value-based join of two Matrices Given matrix M1 and M2, join M1 on column 2 with M2 on column 2, and return matching rows of M1.
###Code
prog = """
M1 = matrix ('1 1 2 3 3 3 4 4 5 3 6 4 7 1 8 2 9 1', rows = 9, cols = 2)
M2 = matrix ('1 1 2 8 3 3 4 3 5 1', rows = 5, cols = 2)
I = rowSums (outer (M1[,2], t(M2[,2]), "==")) # I : indicator matrix for M1
M12 = removeEmpty (target = M1, margin = "rows", select = I) # apply filter to retrieve join result
print ("M1 \n" + toString(M1))
print ("M2 \n" + toString(M2))
print ("M1[,2] joined with M2[,2], and return matching M1 rows\n" + toString(M12))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
M1
1.000 1.000
2.000 3.000
3.000 3.000
4.000 4.000
5.000 3.000
6.000 4.000
7.000 1.000
8.000 2.000
9.000 1.000
M2
1.000 1.000
2.000 8.000
3.000 3.000
4.000 3.000
5.000 1.000
M1[,2] joined with M2[,2], and return matching M1 rows
1.000 1.000
2.000 3.000
3.000 3.000
5.000 3.000
7.000 1.000
9.000 1.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Filter Matrix to include only Frequent Column Values Given a matrix, filter the matrix to only include rows with column values that appear more often than MinFreq.
###Code
prog = """
MinFreq = 3 # minimum frequency of tokens
M = matrix ('1 1 2 3 3 3 4 4 5 3 6 4 7 1 8 2 9 1', rows = 9, cols = 2)
gM = aggregate (target = M[,2], groups = M[,2], fn = "count") # gM: group by and count (grouped matrix)
gv = cbind (seq(1,nrow(gM)), gM) # gv: add group values to counts (group values)
fg = removeEmpty (target = gv * (gv[,2] >= MinFreq), margin = "rows") # fg: filtered groups
I = rowSums (outer (M[,2] ,t(fg[,1]), "==")) # I : indicator of size M with filtered groups
fM = removeEmpty (target = M, margin = "rows", select = I) # FM: filter matrix
print (toString(M))
print (toString(fM))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
1.000 1.000
2.000 3.000
3.000 3.000
4.000 4.000
5.000 3.000
6.000 4.000
7.000 1.000
8.000 2.000
9.000 1.000
1.000 1.000
2.000 3.000
3.000 3.000
5.000 3.000
7.000 1.000
9.000 1.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Construct (sparse) Matrix from (rowIndex, colIndex, values) triplets Given rowIndex, colIndex, and values as column vectors, construct (sparse) matrix.
###Code
prog = """
I = matrix ("1 3 3 4 5", rows = 5, cols = 1)
J = matrix ("2 3 4 1 6", rows = 5, cols = 1)
V = matrix ("10 20 30 40 50", rows = 5, cols = 1)
M = table (I, J, V)
print (toString (M))
"""
ml.execute(dml(prog).output('M')).get('M').toNumPy()
###Output
_____no_output_____
###Markdown
Find and remove duplicates in columns or rows Assuming values are sorted.
###Code
prog = """
X = matrix ("1 2 3 3 3 4 5 10", rows = 8, cols = 1)
I = rbind (matrix (1,1,1), (X[1:nrow (X)-1,] != X[2:nrow (X),])); # compare current with next value
res = removeEmpty (target = X, margin = "rows", select = I); # select where different
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
No assumptions on values.
###Code
prog = """
X = matrix ("3 2 1 3 3 4 5 10", rows = 8, cols = 1)
I = aggregate (target = X, groups = X[,1], fn = "count") # group and count duplicates
res = removeEmpty (target = seq (1, max (X[,1])), margin = "rows", select = (I != 0)); # select groups
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Order the values and then remove duplicates.
###Code
prog = """
X = matrix ("3 2 1 3 3 4 5 10", rows = 8, cols = 1)
X = order (target = X, by = 1) # order values
I = rbind (matrix (1,1,1), (X[1:nrow (X)-1,] != X[2:nrow (X),]));
res = removeEmpty (target = X, margin = "rows", select = I);
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Set based Indexing Given a matrix X, and a indicator matrix J with indices into X. Use J to perform operation on X, e.g. add value 10 to cells in X indicated by J.
###Code
prog = """
X = matrix (1, rows = 1, cols = 100)
J = matrix ("10 20 25 26 28 31 50 67 79", rows = 1, cols = 9)
res = X + table (matrix (1, rows = 1, cols = ncol (J)), J, 10)
print (toString (res))
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
_____no_output_____
###Markdown
Group by Aggregate using Linear Algebra Given a matrix PCV as (Position, Category, Value), sort PCV by category, and within each category by value in descending order. Create indicator vector for category changes, create distinct categories, and perform linear algebra operations.
###Code
prog = """
C = matrix ('50 40 20 10 30 20 40 20 30', rows = 9, cols = 1) # category data
V = matrix ('20 11 49 33 94 29 48 74 57', rows = 9, cols = 1) # value data
PCV = cbind (cbind (seq (1, nrow (C), 1), C), V); # PCV representation
PCV = order (target = PCV, by = 3, decreasing = TRUE, index.return = FALSE);
PCV = order (target = PCV, by = 2, decreasing = FALSE, index.return = FALSE);
# Find all rows of PCV where the category has a new value, in comparison to the previous row
is_new_C = matrix (1, rows = 1, cols = 1);
if (nrow (C) > 1) {
is_new_C = rbind (is_new_C, (PCV [1:nrow(C) - 1, 2] < PCV [2:nrow(C), 2]));
}
# Associate each category with its index
index_C = cumsum (is_new_C); # cumsum
# For each category, compute:
# - the list of distinct categories
# - the maximum value for each category
# - 0-1 aggregation matrix that adds records of the same category
distinct_C = removeEmpty (target = PCV [, 2], margin = "rows", select = is_new_C);
max_V_per_C = removeEmpty (target = PCV [, 3], margin = "rows", select = is_new_C);
C_indicator = table (index_C, PCV [, 1], max (index_C), nrow (C)); # table
sum_V_per_C = C_indicator %*% V
"""
res = ml.execute(dml(prog).output('PCV','distinct_C', 'max_V_per_C', 'C_indicator', 'sum_V_per_C'))
print (res.get('PCV').toNumPy())
print (res.get('distinct_C').toNumPy())
print (res.get('max_V_per_C').toNumPy())
print (res.get('C_indicator').toNumPy())
print (res.get('sum_V_per_C').toNumPy())
###Output
_____no_output_____
###Markdown
Cumulative Summation with Decay Multiplier Given matrix X, compute: Y[i] = X[i] + X[i-1] * C[i] + X[i-2] * C[i] * C[i-1] + X[i-3] * C[i] * C[i-1] * C[i-2] + ...
###Code
cumsum_prod_def = """
cumsum_prod = function (Matrix[double] X, Matrix[double] C, double start)
return (Matrix[double] Y)
# Computes the following recurrence in log-number of steps:
# Y [1, ] = X [1, ] + C [1, ] * start;
# Y [i+1, ] = X [i+1, ] + C [i+1, ] * Y [i, ]
{
Y = X; P = C; m = nrow(X); k = 1;
Y [1,] = Y [1,] + C [1,] * start;
while (k < m) {
Y [k + 1:m,] = Y [k + 1:m,] + Y [1:m - k,] * P [k + 1:m,];
P [k + 1:m,] = P [1:m - k,] * P [k + 1:m,];
k = 2 * k;
}
}
"""
###Output
_____no_output_____
###Markdown
In this example we use cumsum_prod for cumulative summation with "breaks", that is, multiple cumulative summations in one.
###Code
prog = cumsum_prod_def + """
X = matrix ("1 2 3 4 5 6 7 8 9", rows = 9, cols = 1);
#Zeros in C cause "breaks" that restart the cumulative summation from 0
C = matrix ("0 1 1 0 1 1 1 0 1", rows = 9, cols = 1);
Y = cumsum_prod (X, C, 0);
print (toString(Y))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
In this example, we copy selected rows downward to all consecutive non-selected rows.
###Code
prog = cumsum_prod_def + """
X = matrix ("1 2 3 4 5 6 7 8 9", rows = 9, cols = 1);
# Ones in S represent selected rows to be copied, zeros represent non-selected rows
S = matrix ("1 0 0 1 0 0 0 1 0", rows = 9, cols = 1);
Y = cumsum_prod (X * S, 1 - S, 0);
print (toString(Y))
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
This is a naive implementation of cumulative summation with decay multiplier.
###Code
cumsum_prod_naive_def = """
cumsum_prod_naive = function (Matrix[double] X, Matrix[double] C, double start)
return (Matrix[double] Y)
{
Y = matrix (0, rows = nrow(X), cols = ncol(X));
Y [1,] = X [1,] + C [1,] * start;
for (i in 2:nrow(X))
{
Y [i,] = X [i,] + C [i,] * Y [i - 1,]
}
}
"""
###Output
_____no_output_____
###Markdown
There is a significant performance difference between the naive implementation and the tricky implementation.
###Code
prog = cumsum_prod_def + cumsum_prod_naive_def + """
X = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
C = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
Y1 = cumsum_prod_naive (X, C, 0.123);
"""
with jvm_stdout():
ml.execute(dml(prog))
prog = cumsum_prod_def + cumsum_prod_naive_def + """
X = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
C = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
Y2 = cumsum_prod (X, C, 0.123);
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
Invert Lower Triangular Matrix In this example, we invert a lower triangular matrix using a the following divide-and-conquer approach. Given lower triangular matrix L, we compute its inverse X which is also lower triangular by splitting both matrices in the middle into 4 blocks (in a 2x2 fashion), and multiplying them together to get the identity matrix:\begin{equation}L \text{ %*% } X = \left(\begin{matrix} L_1 & 0 \\ L_2 & L_3 \end{matrix}\right)\text{ %*% } \left(\begin{matrix} X_1 & 0 \\ X_2 & X_3 \end{matrix}\right)= \left(\begin{matrix} L_1 X_1 & 0 \\ L_2 X_1 + L_3 X_2 & L_3 X_3 \end{matrix}\right)= \left(\begin{matrix} I & 0 \\ 0 & I \end{matrix}\right)\nonumber\end{equation}If we multiply blockwise, we get three equations: $\begin{equation}L1 \text{ %*% } X1 = 1\\ L3 \text{ %*% } X3 = 1\\L2 \text{ %*% } X1 + L3 \text{ %*% } X2 = 0\\\end{equation}$Solving these equation gives the following formulas for X:$\begin{equation}X1 = inv(L1) \\X3 = inv(L3) \\X2 = - X3 \text{ %*% } L2 \text{ %*% } X1 \\\end{equation}$If we already recursively inverted L1 and L3, we can invert L2. This suggests an algorithm that starts at the diagonal and iterates away from the diagonal, involving bigger and bigger blocks (of size 1, 2, 4, 8, etc.) There is a logarithmic number of steps, and inside each step, the inversions can be performed in parallel using a parfor-loop.Function "invert_lower_triangular" occurs within more general inverse operations and matrix decompositions. The divide-and-conquer idea allows to derive more efficient algorithms for other matrix decompositions.
###Code
invert_lower_triangular_def = """
invert_lower_triangular = function (Matrix[double] LI)
return (Matrix[double] LO)
{
n = nrow (LI);
LO = matrix (0, rows = n, cols = n);
LO = LO + diag (1 / diag (LI));
k = 1;
while (k < n)
{
LPF = matrix (0, rows = n, cols = n);
parfor (p in 0:((n - 1) / (2 * k)), check = 0)
{
i = 2 * k * p;
j = i + k;
q = min (n, j + k);
if (j + 1 <= q) {
L1 = LO [i + 1:j, i + 1:j];
L2 = LI [j + 1:q, i + 1:j];
L3 = LO [j + 1:q, j + 1:q];
LPF [j + 1:q, i + 1:j] = -L3 %*% L2 %*% L1;
}
}
LO = LO + LPF;
k = 2 * k;
}
}
"""
prog = invert_lower_triangular_def + """
n = 1000;
A = rand (rows = n, cols = n, min = -1, max = 1, pdf = "uniform", sparsity = 1.0);
Mask = cumsum (diag (matrix (1, rows = n, cols = 1)));
L = (A %*% t(A)) * Mask; # Generate L for stability of the inverse
X = invert_lower_triangular (L);
print ("Maximum difference between X %*% L and Identity = " + max (abs (X %*% L - diag (matrix (1, rows = n, cols = 1)))));
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
This is a naive implementation of inverting a lower triangular matrix.
###Code
invert_lower_triangular_naive_def = """
invert_lower_triangular_naive = function (Matrix[double] LI)
return (Matrix[double] LO)
{
n = nrow (LI);
LO = diag (matrix (1, rows = n, cols = 1));
for (i in 1:n - 1)
{
LO [i,] = LO [i,] / LI [i, i];
LO [i + 1:n,] = LO [i + 1:n,] - LI [i + 1:n, i] %*% LO [i,];
}
LO [n,] = LO [n,] / LI [n, n];
}
"""
###Output
_____no_output_____
###Markdown
The naive implementation is significantly slower than the divide-and-conquer implementation.
###Code
prog = invert_lower_triangular_naive_def + """
n = 1000;
A = rand (rows = n, cols = n, min = -1, max = 1, pdf = "uniform", sparsity = 1.0);
Mask = cumsum (diag (matrix (1, rows = n, cols = 1)));
L = (A %*% t(A)) * Mask; # Generate L for stability of the inverse
X = invert_lower_triangular_naive (L);
print ("Maximum difference between X %*% L and Identity = " + max (abs (X %*% L - diag (matrix (1, rows = n, cols = 1)))));
"""
with jvm_stdout():
ml.execute(dml(prog))
###Output
_____no_output_____
###Markdown
1. [Create all value pairs for v1 and v2](AllValuePairs)* [Replace NaN with mode](NaN2Mode)* [Use sample builtin function to create sample from matrix](sample)* [Count of Matching Values in two Matrices/Vectors](MatchinRows)* [Cross Validation](CrossValidation)* [Value-based join of two Matrices](JoinMatrices)* [Filter Matrix to include only Frequent Column Values](FilterMatrix)* [(Sparse) Matrix to/from (rowIndex, colIndex, values) conversions (i,j,v)](Construct_sparse_Matrix)* [Find and remove duplicates in columns or rows](Find_and_remove_duplicates)* [Set based Indexing](Set_based_Indexing)* [Group by Aggregate using Linear Algebra](Multi_column_Sorting)* [Cumulative Summation with Decay Multiplier](CumSum_Product)* [Invert Lower Triangular Matrix](Invert_Lower_Triangular_Matrix)
###Code
from systemml import MLContext, dml
ml = MLContext(sc)
print (ml.buildTime())
###Output
_____no_output_____
###Markdown
Create all value pairs for v1 and v2
###Code
prog="""
v1 = matrix ('2 1 8 3 5 6 7', rows = 7, cols = 1 )
v2 = matrix ('80 20 50', rows = 3, cols = 1 )
nv1 = nrow (v1);
nv2 = nrow (v2);
R = cbind (
matrix (v1 %*% matrix(1, 1, nv2), nv1*nv2, 1),
matrix (matrix(1, nv1, 1) %*% t(v2), nv1*nv2, 1))
print(toString(v1));
print(toString(v2));
print(toString(R));
"""
res = ml.execute(dml(prog))
###Output
2.000
1.000
8.000
3.000
5.000
6.000
7.000
80.000
20.000
50.000
2.000 80.000
2.000 20.000
2.000 50.000
1.000 80.000
1.000 20.000
1.000 50.000
8.000 80.000
8.000 20.000
8.000 50.000
3.000 80.000
3.000 20.000
3.000 50.000
5.000 80.000
5.000 20.000
5.000 50.000
6.000 80.000
6.000 20.000
6.000 50.000
7.000 80.000
7.000 20.000
7.000 50.000
SystemML Statistics:
Total execution time: 0.000 sec.
Number of executed Spark inst: 0.
###Markdown
Replace NaN with mode This functions replaces NaN in column i with mode of column i.
###Code
prog="""
# Function for NaN-aware replacement with mode
replaceNaNwithMode = function (matrix[double] X, integer colId)
return (matrix[double] X)
{
Xi = replace (target=X[,colId], pattern=NaN, replacement=-Inf) # replace NaN with -Inf
Xi = replace (target=Xi, pattern=-Inf, replacement=max(Xi)+1) # replace -Inf with largest value + 1
agg = aggregate (target=Xi, groups=Xi, fn="count") # count each distinct value
mode = as.scalar (rowIndexMax(t(agg[1:nrow(agg)-1, ]))) # mode is max frequent value except last value
X[,colId] = replace (target=Xi, pattern=max(Xi), replacement=mode) # fill in mode
}
X = matrix('1 NaN 1 NaN 1 2 2 1 1 2', rows = 5, cols = 2)
Y = replaceNaNwithMode (X, 2)
print ("Before: \n" + toString(X))
print ("After: \n" + toString(Y))
"""
res = ml.execute(dml(prog))
###Output
Before:
1.000 NaN
1.000 NaN
1.000 2.000
2.000 1.000
1.000 2.000
After:
1.000 2.000
1.000 2.000
1.000 2.000
2.000 1.000
1.000 2.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Use sample builtin function to create sample from matrix Use sample() function, create permutation matrix using table(), and pull sample from X.
###Code
prog="""
X = matrix ('2 1 8 3 5 6 7 9 4 4', rows = 5, cols = 2 )
nbrSamples = 2
sv = order (target = sample (nrow (X), nbrSamples, FALSE)) # samples w/o replacement, and order
P = table (seq (1, nbrSamples), sv, nbrSamples, nrow(X)) # permutation matrix
samples = P %*% X; # apply P to perform selection
print ("X: \n" + toString(X))
print ("sv: \n" + toString(sv))
print ("samples: \n" + toString(samples))
"""
res = ml.execute(dml(prog))
###Output
X:
2.000 1.000
8.000 3.000
5.000 6.000
7.000 9.000
4.000 4.000
sv:
1.000
5.000
samples:
2.000 1.000
4.000 4.000
SystemML Statistics:
Total execution time: 0.000 sec.
Number of executed Spark inst: 0.
###Markdown
Count of Matching Values in two Matrices/Vectors Given two matrices/vectors X and Y, get a count of the rows where X and Y have the same value.
###Code
prog="""
X = matrix('8 4 5 4 9 10', rows = 6, cols = 1)
Y = matrix('4 9 5 1 9 7 ', rows = 6, cols = 1)
matches = sum (X == Y)
print ("t(X): " + toString(t(X)))
print ("t(Y): " + toString(t(Y)))
print ("Number of Matches: " + matches + "\n")
"""
res = ml.execute(dml(prog))
###Output
t(X): 8.000 4.000 5.000 4.000 9.000 10.000
t(Y): 4.000 9.000 5.000 1.000 9.000 7.000
Number of Matches: 2.0
SystemML Statistics:
Total execution time: 0.000 sec.
Number of executed Spark inst: 0.
###Markdown
Cross Validation Perform kFold cross validation by running in parallel fold creation, training algorithm, test algorithm, and evaluation.
###Code
prog = """
holdOut = 1/3
kFolds = 1/holdOut
nRows = 6; nCols = 3;
X = matrix(seq(1, nRows * nCols), rows = nRows, cols = nCols) # X data
y = matrix(seq(1, nRows), rows = nRows, cols = 1) # y label data
Xy = cbind (X,y) # Xy Data for CV
sv = rand (rows = nRows, cols = 1, min = 0.0, max = 1.0, pdf = "uniform") # sv selection vector for fold creation
sv = (order(target=sv, by=1, index.return=TRUE)) %% kFolds + 1 # with numbers between 1 .. kFolds
stats = matrix(0, rows=kFolds, cols=1) # stats per kFolds model on test data
parfor (i in 1:kFolds)
{
# Skip empty training data or test data.
if ( sum (sv == i) > 0 & sum (sv == i) < nrow(X) )
{
Xyi = removeEmpty(target = Xy, margin = "rows", select = (sv == i)) # Xyi fold, i.e. 1/k of rows (test data)
Xyni = removeEmpty(target = Xy, margin = "rows", select = (sv != i)) # Xyni data, i.e. (k-1)/k of rows (train data)
# Skip extreme label inbalance
distinctLabels = aggregate( target = Xyni[,1], groups = Xyni[,1], fn = "count")
if ( nrow(distinctLabels) > 1)
{
w_i = trainAlg (Xyni[ ,1:ncol(Xy)-1], Xyni[ ,ncol(Xy)]) # w_i Model for i-th training data
p_i = testAlg (Xyi [ ,1:ncol(Xy)-1], w_i) # p_i Prediction for i-th test data
e_i = evalPrediction (p_i, Xyi[ ,ncol(Xy)]) # stats[i,] evaluation of prediction of i-th fold
stats[i,] = e_i
print ( "Test data Xyi" + i + "\n" + toString(Xyi)
+ "\nTrain data Xyni" + i + "\n" + toString(Xyni)
+ "\nw_" + i + "\n" + toString(w_i)
+ "\nstats" + i + "\n" + toString(stats[i,])
+ "\n")
}
else
{
print ("Training data for fold " + i + " has only " + nrow(distinctLabels) + " distinct labels. Needs to be > 1.")
}
}
else
{
print ("Training data or test data for fold " + i + " is empty. Fold not validated.")
}
}
print ("SV selection vector:\n" + toString(sv))
trainAlg = function (matrix[double] X, matrix[double] y)
return (matrix[double] w)
{
w = t(X) %*% y
}
testAlg = function (matrix[double] X, matrix[double] w)
return (matrix[double] p)
{
p = X %*% w
}
evalPrediction = function (matrix[double] p, matrix[double] y)
return (matrix[double] e)
{
e = as.matrix(sum (p - y))
}
"""
res = ml.execute(dml(prog))
###Output
Test data Xyi1
7.000 8.000 9.000 3.000
10.000 11.000 12.000 4.000
Train data Xyni1
1.000 2.000 3.000 1.000
4.000 5.000 6.000 2.000
13.000 14.000 15.000 5.000
16.000 17.000 18.000 6.000
w_1
170.000
184.000
198.000
stats1
10537.000
Test data Xyi2
13.000 14.000 15.000 5.000
16.000 17.000 18.000 6.000
Train data Xyni2
1.000 2.000 3.000 1.000
4.000 5.000 6.000 2.000
7.000 8.000 9.000 3.000
10.000 11.000 12.000 4.000
w_2
70.000
80.000
90.000
stats2
7469.000
Test data Xyi3
1.000 2.000 3.000 1.000
4.000 5.000 6.000 2.000
Train data Xyni3
7.000 8.000 9.000 3.000
10.000 11.000 12.000 4.000
13.000 14.000 15.000 5.000
16.000 17.000 18.000 6.000
w_3
222.000
240.000
258.000
stats3
5109.000
SV selection vector:
3.000
3.000
1.000
1.000
2.000
2.000
SystemML Statistics:
Total execution time: 0.014 sec.
Number of executed Spark inst: 0.
###Markdown
Value-based join of two Matrices Given matrix M1 and M2, join M1 on column 2 with M2 on column 2, and return matching rows of M1.
###Code
prog = """
M1 = matrix ('1 1 2 3 3 3 4 4 5 3 6 4 7 1 8 2 9 1', rows = 9, cols = 2)
M2 = matrix ('1 1 2 8 3 3 4 3 5 1', rows = 5, cols = 2)
I = rowSums (outer (M1[,2], t(M2[,2]), "==")) # I : indicator matrix for M1
M12 = removeEmpty (target = M1, margin = "rows", select = I) # apply filter to retrieve join result
print ("M1 \n" + toString(M1))
print ("M2 \n" + toString(M2))
print ("M1[,2] joined with M2[,2], and return matching M1 rows\n" + toString(M12))
"""
res = ml.execute(dml(prog))
###Output
M1
1.000 1.000
2.000 3.000
3.000 3.000
4.000 4.000
5.000 3.000
6.000 4.000
7.000 1.000
8.000 2.000
9.000 1.000
M2
1.000 1.000
2.000 8.000
3.000 3.000
4.000 3.000
5.000 1.000
M1[,2] joined with M2[,2], and return matching M1 rows
1.000 1.000
2.000 3.000
3.000 3.000
5.000 3.000
7.000 1.000
9.000 1.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Filter Matrix to include only Frequent Column Values Given a matrix, filter the matrix to only include rows with column values that appear more often than MinFreq.
###Code
prog = """
MinFreq = 3 # minimum frequency of tokens
M = matrix ('1 1 2 3 3 3 4 4 5 3 6 4 7 1 8 2 9 1', rows = 9, cols = 2)
gM = aggregate (target = M[,2], groups = M[,2], fn = "count") # gM: group by and count (grouped matrix)
gv = cbind (seq(1,nrow(gM)), gM) # gv: add group values to counts (group values)
fg = removeEmpty (target = gv * (gv[,2] >= MinFreq), margin = "rows") # fg: filtered groups
I = rowSums (outer (M[,2] ,t(fg[,1]), "==")) # I : indicator of size M with filtered groups
fM = removeEmpty (target = M, margin = "rows", select = I) # FM: filter matrix
print (toString(M))
print (toString(fM))
"""
res = ml.execute(dml(prog))
###Output
1.000 1.000
2.000 3.000
3.000 3.000
4.000 4.000
5.000 3.000
6.000 4.000
7.000 1.000
8.000 2.000
9.000 1.000
1.000 1.000
2.000 3.000
3.000 3.000
5.000 3.000
7.000 1.000
9.000 1.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
(Sparse) Matrix to/from (rowIndex, colIndex, values) conversions (i,j,v) Given rowIndex, colIndex, and values as column vectors, construct (sparse) matrix.
###Code
prog = """
I = matrix ("1 3 3 4 5", rows = 5, cols = 1)
J = matrix ("2 3 4 1 6", rows = 5, cols = 1)
V = matrix ("10 20 30 40 50", rows = 5, cols = 1)
IJVs = cbind(I, J, V)
M = table (I, J, V)
print (toString (IJVs))
print (toString (M))
"""
res = ml.execute(dml(prog).output('M')).get('M').toNumPy()
###Output
1.000 2.000 10.000
3.000 3.000 20.000
3.000 4.000 30.000
4.000 1.000 40.000
5.000 6.000 50.000
0.000 10.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 20.000 30.000 0.000 0.000
40.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 50.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Given a sparse matrix, construct ```` matrix with 3 columns rowIndex, colIndex, and values.
###Code
prog = """
M = matrix ("0 23 10 0 18 0 0 20", rows = 4, cols = 2)
m = nrow(M);
n = ncol(M);
I = matrix((M!=0)*seq(1,m), m*n, 1)
J = matrix((M!=0)*t(seq(1,n)), m*n, 1)
V = matrix(M, m*n, 1)
IJVd = cbind(I, J, V);
IJVs = removeEmpty(target=IJVd, margin="rows");
print ("M:\n" + toString(M))
print ("IJVs:\n" + toString (IJVs))
"""
res = ml.execute(dml(prog).output('M')).get('M').toNumPy()
###Output
M:
0.000 23.000
10.000 0.000
18.000 0.000
0.000 20.000
IJVs:
1.000 2.000 23.000
2.000 1.000 10.000
3.000 1.000 18.000
4.000 2.000 20.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Find and remove duplicates in columns or rows Assuming values are sorted.
###Code
prog = """
X = matrix ("1 2 3 3 3 4 5 10", rows = 8, cols = 1)
I = rbind (matrix (1,1,1), (X[1:nrow (X)-1,] != X[2:nrow (X),])); # compare current with next value
res = removeEmpty (target = X, margin = "rows", select = I); # select where different
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
SystemML Statistics:
Total execution time: 0.000 sec.
Number of executed Spark inst: 0.
###Markdown
No assumptions on values.
###Code
prog = """
X = matrix ("3 2 1 3 3 4 5 10", rows = 8, cols = 1)
I = aggregate (target = X, groups = X[,1], fn = "count") # group and count duplicates
res = removeEmpty (target = seq (1, max (X[,1])), margin = "rows", select = (I != 0)); # select groups
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
SystemML Statistics:
Total execution time: 0.076 sec.
Number of executed Spark inst: 6.
###Markdown
Order the values and then remove duplicates.
###Code
prog = """
X = matrix ("3 2 1 3 3 4 5 10", rows = 8, cols = 1)
X = order (target = X, by = 1) # order values
I = rbind (matrix (1,1,1), (X[1:nrow (X)-1,] != X[2:nrow (X),]));
res = removeEmpty (target = X, margin = "rows", select = I);
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
SystemML Statistics:
Total execution time: 0.000 sec.
Number of executed Spark inst: 0.
###Markdown
Set based Indexing Given a matrix X, and a indicator matrix J with indices into X. Use J to perform operation on X, e.g. add value 10 to cells in X indicated by J.
###Code
prog = """
X = matrix (1, rows = 1, cols = 100)
J = matrix ("10 20 25 26 28 31 50 67 79", rows = 1, cols = 9)
res = X + table (matrix (1, rows = 1, cols = ncol (J)), J, 10)
print (toString (res))
"""
ml.execute(dml(prog).output('res')).get('res').toNumPy()
###Output
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 11.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 11.000 1.000 1.000 1.000 1.000 11.000 11.000 1.000 11.000 1.000 1.000 11.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 11.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 11.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 11.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
Group by Aggregate using Linear Algebra Given a matrix PCV as (Position, Category, Value), sort PCV by category, and within each category by value in descending order. Create indicator vector for category changes, create distinct categories, and perform linear algebra operations.
###Code
prog = """
C = matrix ('50 40 20 10 30 20 40 20 30', rows = 9, cols = 1) # category data
V = matrix ('20 11 49 33 94 29 48 74 57', rows = 9, cols = 1) # value data
PCV = cbind (cbind (seq (1, nrow (C), 1), C), V); # PCV representation
PCV = order (target = PCV, by = 3, decreasing = TRUE, index.return = FALSE);
PCV = order (target = PCV, by = 2, decreasing = FALSE, index.return = FALSE);
# Find all rows of PCV where the category has a new value, in comparison to the previous row
is_new_C = matrix (1, rows = 1, cols = 1);
if (nrow (C) > 1) {
is_new_C = rbind (is_new_C, (PCV [1:nrow(C) - 1, 2] < PCV [2:nrow(C), 2]));
}
# Associate each category with its index
index_C = cumsum (is_new_C); # cumsum
# For each category, compute:
# - the list of distinct categories
# - the maximum value for each category
# - 0-1 aggregation matrix that adds records of the same category
distinct_C = removeEmpty (target = PCV [, 2], margin = "rows", select = is_new_C);
max_V_per_C = removeEmpty (target = PCV [, 3], margin = "rows", select = is_new_C);
C_indicator = table (index_C, PCV [, 1], max (index_C), nrow (C)); # table
sum_V_per_C = C_indicator %*% V
"""
res = ml.execute(dml(prog).output('PCV','distinct_C', 'max_V_per_C', 'C_indicator', 'sum_V_per_C'))
print (res.get('PCV').toNumPy())
print (res.get('distinct_C').toNumPy())
print (res.get('max_V_per_C').toNumPy())
print (res.get('C_indicator').toNumPy())
print (res.get('sum_V_per_C').toNumPy())
###Output
SystemML Statistics:
Total execution time: 0.002 sec.
Number of executed Spark inst: 0.
[[ 4. 10. 33.]
[ 8. 20. 74.]
[ 3. 20. 49.]
[ 6. 20. 29.]
[ 5. 30. 94.]
[ 9. 30. 57.]
[ 7. 40. 48.]
[ 2. 40. 11.]
[ 1. 50. 20.]]
[[ 10.]
[ 20.]
[ 30.]
[ 40.]
[ 50.]]
[[ 33.]
[ 74.]
[ 94.]
[ 48.]
[ 20.]]
[[ 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[ 0. 0. 1. 0. 0. 1. 0. 1. 0.]
[ 0. 0. 0. 0. 1. 0. 0. 0. 1.]
[ 0. 1. 0. 0. 0. 0. 1. 0. 0.]
[ 1. 0. 0. 0. 0. 0. 0. 0. 0.]]
[[ 33.]
[ 152.]
[ 151.]
[ 59.]
[ 20.]]
###Markdown
Cumulative Summation with Decay Multiplier Given matrix X, compute: Y[i] = X[i] + X[i-1] * C[i] + X[i-2] * C[i] * C[i-1] + X[i-3] * C[i] * C[i-1] * C[i-2] + ...
###Code
cumsum_prod_def = """
cumsum_prod = function (Matrix[double] X, Matrix[double] C, double start)
return (Matrix[double] Y)
# Computes the following recurrence in log-number of steps:
# Y [1, ] = X [1, ] + C [1, ] * start;
# Y [i+1, ] = X [i+1, ] + C [i+1, ] * Y [i, ]
{
Y = X; P = C; m = nrow(X); k = 1;
Y [1,] = Y [1,] + C [1,] * start;
while (k < m) {
Y [k + 1:m,] = Y [k + 1:m,] + Y [1:m - k,] * P [k + 1:m,];
P [k + 1:m,] = P [1:m - k,] * P [k + 1:m,];
k = 2 * k;
}
}
"""
###Output
_____no_output_____
###Markdown
In this example we use cumsum_prod for cumulative summation with "breaks", that is, multiple cumulative summations in one.
###Code
prog = cumsum_prod_def + """
X = matrix ("1 2 3 4 5 6 7 8 9", rows = 9, cols = 1);
#Zeros in C cause "breaks" that restart the cumulative summation from 0
C = matrix ("0 1 1 0 1 1 1 0 1", rows = 9, cols = 1);
Y = cumsum_prod (X, C, 0);
print (toString(Y))
"""
ml.execute(dml(prog))
###Output
1.000
3.000
6.000
4.000
9.000
15.000
22.000
8.000
17.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
In this example, we copy selected rows downward to all consecutive non-selected rows.
###Code
prog = cumsum_prod_def + """
X = matrix ("1 2 3 4 5 6 7 8 9", rows = 9, cols = 1);
# Ones in S represent selected rows to be copied, zeros represent non-selected rows
S = matrix ("1 0 0 1 0 0 0 1 0", rows = 9, cols = 1);
Y = cumsum_prod (X * S, 1 - S, 0);
print (toString(Y))
"""
ml.execute(dml(prog))
###Output
1.000
1.000
1.000
4.000
4.000
4.000
4.000
8.000
8.000
SystemML Statistics:
Total execution time: 0.001 sec.
Number of executed Spark inst: 0.
###Markdown
This is a naive implementation of cumulative summation with decay multiplier.
###Code
cumsum_prod_naive_def = """
cumsum_prod_naive = function (Matrix[double] X, Matrix[double] C, double start)
return (Matrix[double] Y)
{
Y = matrix (0, rows = nrow(X), cols = ncol(X));
Y [1,] = X [1,] + C [1,] * start;
for (i in 2:nrow(X))
{
Y [i,] = X [i,] + C [i,] * Y [i - 1,]
}
}
"""
###Output
_____no_output_____
###Markdown
There is a significant performance difference between the naive implementation and the tricky implementation.
###Code
prog = cumsum_prod_def + cumsum_prod_naive_def + """
X = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
C = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
Y1 = cumsum_prod_naive (X, C, 0.123);
"""
ml.execute(dml(prog))
prog = cumsum_prod_def + cumsum_prod_naive_def + """
X = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
C = rand (rows = 20000, cols = 10, min = 0, max = 1, pdf = "uniform", sparsity = 1.0);
Y2 = cumsum_prod (X, C, 0.123);
"""
ml.execute(dml(prog))
###Output
SystemML Statistics:
Total execution time: 0.074 sec.
Number of executed Spark inst: 0.
###Markdown
Invert Lower Triangular Matrix In this example, we invert a lower triangular matrix using a the following divide-and-conquer approach. Given lower triangular matrix L, we compute its inverse X which is also lower triangular by splitting both matrices in the middle into 4 blocks (in a 2x2 fashion), and multiplying them together to get the identity matrix:\begin{equation}L \text{ %*% } X = \left(\begin{matrix} L_1 & 0 \\ L_2 & L_3 \end{matrix}\right)\text{ %*% } \left(\begin{matrix} X_1 & 0 \\ X_2 & X_3 \end{matrix}\right)= \left(\begin{matrix} L_1 X_1 & 0 \\ L_2 X_1 + L_3 X_2 & L_3 X_3 \end{matrix}\right)= \left(\begin{matrix} I & 0 \\ 0 & I \end{matrix}\right)\nonumber\end{equation}If we multiply blockwise, we get three equations: $\begin{equation}L1 \text{ %*% } X1 = 1\\ L3 \text{ %*% } X3 = 1\\L2 \text{ %*% } X1 + L3 \text{ %*% } X2 = 0\\\end{equation}$Solving these equation gives the following formulas for X:$\begin{equation}X1 = inv(L1) \\X3 = inv(L3) \\X2 = - X3 \text{ %*% } L2 \text{ %*% } X1 \\\end{equation}$If we already recursively inverted L1 and L3, we can invert L2. This suggests an algorithm that starts at the diagonal and iterates away from the diagonal, involving bigger and bigger blocks (of size 1, 2, 4, 8, etc.) There is a logarithmic number of steps, and inside each step, the inversions can be performed in parallel using a parfor-loop.Function "invert_lower_triangular" occurs within more general inverse operations and matrix decompositions. The divide-and-conquer idea allows to derive more efficient algorithms for other matrix decompositions.
###Code
invert_lower_triangular_def = """
invert_lower_triangular = function (Matrix[double] LI)
return (Matrix[double] LO)
{
n = nrow (LI);
LO = matrix (0, rows = n, cols = n);
LO = LO + diag (1 / diag (LI));
k = 1;
while (k < n)
{
LPF = matrix (0, rows = n, cols = n);
parfor (p in 0:((n - 1) / (2 * k)), check = 0)
{
i = 2 * k * p;
j = i + k;
q = min (n, j + k);
if (j + 1 <= q) {
L1 = LO [i + 1:j, i + 1:j];
L2 = LI [j + 1:q, i + 1:j];
L3 = LO [j + 1:q, j + 1:q];
LPF [j + 1:q, i + 1:j] = -L3 %*% L2 %*% L1;
}
}
LO = LO + LPF;
k = 2 * k;
}
}
"""
prog = invert_lower_triangular_def + """
n = 1000;
A = rand (rows = n, cols = n, min = -1, max = 1, pdf = "uniform", sparsity = 1.0);
Mask = cumsum (diag (matrix (1, rows = n, cols = 1)));
L = (A %*% t(A)) * Mask; # Generate L for stability of the inverse
X = invert_lower_triangular (L);
print ("Maximum difference between X %*% L and Identity = " + max (abs (X %*% L - diag (matrix (1, rows = n, cols = 1)))));
"""
ml.execute(dml(prog))
###Output
Maximum difference between X %*% L and Identity = 2.220446049250313E-16
SystemML Statistics:
Total execution time: 0.309 sec.
Number of executed Spark inst: 0.
###Markdown
This is a naive implementation of inverting a lower triangular matrix.
###Code
invert_lower_triangular_naive_def = """
invert_lower_triangular_naive = function (Matrix[double] LI)
return (Matrix[double] LO)
{
n = nrow (LI);
LO = diag (matrix (1, rows = n, cols = 1));
for (i in 1:n - 1)
{
LO [i,] = LO [i,] / LI [i, i];
LO [i + 1:n,] = LO [i + 1:n,] - LI [i + 1:n, i] %*% LO [i,];
}
LO [n,] = LO [n,] / LI [n, n];
}
"""
###Output
_____no_output_____
###Markdown
The naive implementation is significantly slower than the divide-and-conquer implementation.
###Code
prog = invert_lower_triangular_naive_def + """
n = 1000;
A = rand (rows = n, cols = n, min = -1, max = 1, pdf = "uniform", sparsity = 1.0);
Mask = cumsum (diag (matrix (1, rows = n, cols = 1)));
L = (A %*% t(A)) * Mask; # Generate L for stability of the inverse
X = invert_lower_triangular_naive (L);
print ("Maximum difference between X %*% L and Identity = " + max (abs (X %*% L - diag (matrix (1, rows = n, cols = 1)))));
"""
ml.execute(dml(prog))
###Output
Maximum difference between X %*% L and Identity = 4.718447854656915E-16
SystemML Statistics:
Total execution time: 6.890 sec.
Number of executed Spark inst: 0.
|
notebooks/example_preprocessing.ipynb
|
###Markdown
Example 1: Preprocessing WorkflowThis is meant as a very simple example for a preprocessing workflow. In this workflow we will conduct the following steps:1. Motion correction of functional images with FSL's MCFLIRT2. Coregistration of functional images to anatomical images (according to FSL's FEAT pipeline)3. Smoothing of coregistrated functional images with FWHM set to 4mm and 8mm4. Artifact Detection in functional images (to detect outlier volumes) PreparationBefore we can start with anything we first need to download the data (the other 9 subjects in the dataset). This can be done very quickly with the following `datalad` command.**Note:** This might take a while, as datalad needs to download ~700MB of data
###Code
%%bash
datalad get -J4 /data/ds000114/derivatives/fmriprep/sub-*/anat/*preproc.nii.gz \
/data/ds000114/sub-*/ses-test/func/*fingerfootlips*
###Output
_____no_output_____
###Markdown
Inspect the dataFor every subject we have one anatomical T1w and 5 functional images. As a short recap, the image properties of the anatomy and the **fingerfootlips** functional image are:
###Code
%%bash
cd /data/ds000114/
nib-ls derivatives/fmriprep/sub-01/*/*t1w_preproc.nii.gz sub-01/ses-test/f*/*fingerfootlips*.nii.gz
###Output
_____no_output_____
###Markdown
**So, let's start!** ImportsFirst, let's import all modules we later will be needing.
###Code
%matplotlib inline
from os.path import join as opj
import os
import json
from nipype.interfaces.fsl import (BET, ExtractROI, FAST, FLIRT, ImageMaths,
MCFLIRT, SliceTimer, Threshold)
from nipype.interfaces.spm import Smooth
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.algorithms.rapidart import ArtifactDetect
from nipype import Workflow, Node
###Output
_____no_output_____
###Markdown
Experiment parametersIt's always a good idea to specify all parameters that might change between experiments at the beginning of your script. We will use one functional image for fingerfootlips task for ten subjects.
###Code
experiment_dir = '/output'
output_dir = 'datasink'
working_dir = 'workingdir'
# list of subject identifiers
subject_list = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10']
# list of session identifiers
task_list = ['fingerfootlips']
# Smoothing widths to apply
fwhm = [4, 8]
# TR of functional images
with open('/data/ds000114/task-fingerfootlips_bold.json', 'rt') as fp:
task_info = json.load(fp)
TR = task_info['RepetitionTime']
# Isometric resample of functional images to voxel size (in mm)
iso_size = 4
###Output
_____no_output_____
###Markdown
Specify Nodes for the main workflowInitiate all the different interfaces (represented as nodes) that you want to use in your workflow.
###Code
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=4, t_size=-1, output_type='NIFTI'),
name="extract")
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True,
save_plots=True,
output_type='NIFTI'),
name="mcflirt")
# SliceTimer - correct for slice wise acquisition
slicetimer = Node(SliceTimer(index_dir=False,
interleaved=True,
output_type='NIFTI',
time_repetition=TR),
name="slicetimer")
# Smooth - image smoothing
smooth = Node(Smooth(), name="smooth")
smooth.iterables = ("fwhm", fwhm)
# Artifact Detection - determines outliers in functional images
art = Node(ArtifactDetect(norm_threshold=2,
zintensity_threshold=3,
mask_type='spm_global',
parameter_source='FSL',
use_differences=[True, False],
plot_type='svg'),
name="art")
###Output
_____no_output_____
###Markdown
Coregistration WorkflowInitiate a workflow that coregistrates the functional images to the anatomical image (according to FSL's FEAT pipeline).
###Code
# BET - Skullstrip anatomical Image
bet_anat = Node(BET(frac=0.5,
robust=True,
output_type='NIFTI_GZ'),
name="bet_anat")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI_GZ'),
name="segmentation")
# Select WM segmentation file from segmentation output
def get_wm(files):
return files[-1]
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5,
args='-bin',
output_type='NIFTI_GZ'),
name="threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'),
name="coreg_pre")
# FLIRT - coregistration of functional images to anatomical images with BBR
coreg_bbr = Node(FLIRT(dof=6,
cost='bbr',
schedule=opj(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch'),
output_type='NIFTI_GZ'),
name="coreg_bbr")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="applywarp")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="applywarp_mean")
# Create a coregistration workflow
coregwf = Workflow(name='coregwf')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([(bet_anat, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_wm),
'in_file')]),
(bet_anat, coreg_pre, [('out_file', 'reference')]),
(threshold, coreg_bbr, [('out_file', 'wm_seg')]),
(coreg_pre, coreg_bbr, [('out_matrix_file', 'in_matrix_file')]),
(coreg_bbr, applywarp, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp_mean, [('out_file', 'reference')]),
])
###Output
_____no_output_____
###Markdown
Specify input & output streamSpecify where the input data can be found & where and how to save the output data.
###Code
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'task_name']),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('task_name', task_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj('derivatives', 'fmriprep', 'sub-{subject_id}', 'anat', 'sub-{subject_id}_t1w_preproc.nii.gz')
func_file = opj('sub-{subject_id}', 'ses-test', 'func',
'sub-{subject_id}_ses-test_task-{task_name}_bold.nii.gz')
templates = {'anat': anat_file,
'func': func_file}
selectfiles = Node(SelectFiles(templates,
base_directory='/data/ds000114'),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
## Use the following DataSink output substitutions
substitutions = [('_subject_id_', 'sub-'),
('_task_name_', '/task-'),
('_fwhm_', 'fwhm-'),
('_roi', ''),
('_mcf', ''),
('_st', ''),
('_flirt', ''),
('.nii_mean_reg', '_mean'),
('.nii.par', '.par'),
]
subjFolders = [('fwhm-%s/' % f, 'fwhm-%s_' % f) for f in fwhm]
substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
###Output
_____no_output_____
###Markdown
Specify WorkflowCreate a workflow and connect the interface nodes and the I/O stream to each other.
###Code
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the preprocessing workflow
preproc.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('task_name', 'task_name')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, mcflirt, [('roi_file', 'in_file')]),
(mcflirt, slicetimer, [('out_file', 'in_file')]),
(selectfiles, coregwf, [('anat', 'bet_anat.in_file'),
('anat', 'coreg_bbr.reference')]),
(mcflirt, coregwf, [('mean_img', 'coreg_pre.in_file'),
('mean_img', 'coreg_bbr.in_file'),
('mean_img', 'applywarp_mean.in_file')]),
(slicetimer, coregwf, [('slice_time_corrected_file', 'applywarp.in_file')]),
(coregwf, smooth, [('applywarp.out_file', 'in_files')]),
(mcflirt, datasink, [('par_file', 'preproc.@par')]),
(smooth, datasink, [('smoothed_files', 'preproc.@smooth')]),
(coregwf, datasink, [('applywarp_mean.out_file', 'preproc.@mean')]),
(coregwf, art, [('applywarp.out_file', 'realigned_files')]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(coregwf, datasink, [('coreg_bbr.out_matrix_file', 'preproc.@mat_file'),
('bet_anat.out_file', 'preproc.@brain')]),
(art, datasink, [('outlier_files', 'preproc.@outlier_files'),
('plot_files', 'preproc.@plot_files')]),
])
###Output
_____no_output_____
###Markdown
Visualize the workflowIt always helps to visualize your workflow.
###Code
# Create preproc output graph
preproc.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
from IPython.display import Image
Image(filename=opj(preproc.base_dir, 'preproc', 'graph.png'))
# Visualize the detailed graph
preproc.write_graph(graph2use='flat', format='png', simple_form=True)
Image(filename=opj(preproc.base_dir, 'preproc', 'graph_detailed.png'))
###Output
_____no_output_____
###Markdown
Run the WorkflowNow that everything is ready, we can run the preprocessing workflow. Change ``n_procs`` to the number of jobs/cores you want to use. **Note** that if you're using a Docker container and FLIRT fails to run without any good reason, you might need to change memory settings in the Docker preferences (6 GB should be enough for this workflow).
###Code
preproc.run('MultiProc', plugin_args={'n_procs': 8})
###Output
_____no_output_____
###Markdown
Inspect outputLet's check the structure of the output folder, to see if we have everything we wanted to save.
###Code
!tree /output/datasink/preproc
###Output
_____no_output_____
###Markdown
Visualize resultsLet's check the effect of the different smoothing kernels.
###Code
from nilearn import image, plotting
out_path = '/output/datasink/preproc/sub-01/task-fingerfootlips'
plotting.plot_epi(
'/data/ds000114/derivatives/fmriprep/sub-01/anat/sub-01_t1w_preproc.nii.gz',
title="T1", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(opj(out_path, 'sub-01_ses-test_task-fingerfootlips_bold_mean.nii.gz'),
title="fwhm = 0mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-4_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 4mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-8_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 8mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
###Output
_____no_output_____
###Markdown
Now, let's investigate the motion parameters. How much did the subject move and turn in the scanner?
###Code
import numpy as np
import matplotlib.pyplot as plt
par = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/sub-01_ses-test_task-fingerfootlips_bold.par')
fig, axes = plt.subplots(2, 1, figsize=(15, 5))
axes[0].set_ylabel('rotation (radians)')
axes[0].plot(par[0:, :3])
axes[1].plot(par[0:, 3:])
axes[1].set_xlabel('time (TR)')
axes[1].set_ylabel('translation (mm)');
###Output
_____no_output_____
###Markdown
There seems to be a rather drastic motion around volume 102. Let's check if the outliers detection algorithm was able to pick this up.
###Code
import numpy as np
outlier_ids = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/art.sub-01_ses-test_task-fingerfootlips_bold_outliers.txt')
print('Outliers were detected at volumes: %s' % outlier_ids)
from IPython.display import SVG
SVG(filename='/output/datasink/preproc/sub-01/task-fingerfootlips/plot.sub-01_ses-test_task-fingerfootlips_bold.svg')
###Output
_____no_output_____
###Markdown
Example 1: Preprocessing WorkflowThis is meant as a very simple example for a preprocessing workflow. In this workflow we will conduct the following steps:1. Motion correction of functional images with FSL's MCFLIRT2. Coregistration of functional images to anatomical images (according to FSL's FEAT pipeline)3. Smoothing of coregistrated functional images with FWHM set to 4mm and 8mm4. Artifact Detection in functional images (to detect outlier volumes) PreparationBefore we can start with anything we first need to download the data (the other 9 subjects in the dataset). This can be done very quickly with the following `datalad` command.**Note:** This might take a while, as datalad needs to download ~700MB of data
###Code
%%bash
datalad get -J4 /data/ds000114/derivatives/fmriprep/sub-*/anat/*preproc.nii.gz \
/data/ds000114/sub-*/ses-test/func/*fingerfootlips*
###Output
_____no_output_____
###Markdown
Inspect the dataFor every subject we have one anatomical T1w and 5 functional images. As a short recap, the image properties of the anatomy and the **fingerfootlips** functional image are:
###Code
%%bash
cd /data/ds000114/
nib-ls derivatives/fmriprep/sub-01/*/*t1w_preproc.nii.gz sub-01/ses-test/f*/*fingerfootlips*.nii.gz
###Output
_____no_output_____
###Markdown
**So, let's start!** ImportsFirst, let's import all modules we later will be needing.
###Code
%matplotlib inline
from os.path import join as opj
import os
import json
from nipype.interfaces.fsl import (BET, ExtractROI, FAST, FLIRT, ImageMaths,
MCFLIRT, SliceTimer, Threshold)
from nipype.interfaces.spm import Smooth
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.pipeline.engine import Workflow, Node
###Output
_____no_output_____
###Markdown
Experiment parametersIt's always a good idea to specify all parameters that might change between experiments at the beginning of your script. We will use one functional image for fingerfootlips task for ten subjects.
###Code
experiment_dir = '/output'
output_dir = 'datasink'
working_dir = 'workingdir'
# list of subject identifiers
subject_list = ['sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05',
'sub-06', 'sub-07', 'sub-08', 'sub-09', 'sub-10']
# list of session identifiers
task_list = ['fingerfootlips']
# Smoothing widths to apply
fwhm = [4, 8]
# TR of functional images
with open('/data/ds000114/task-fingerfootlips_bold.json', 'rt') as fp:
task_info = json.load(fp)
TR = task_info['RepetitionTime']
# Isometric resample of functional images to voxel size (in mm)
iso_size = 4
###Output
_____no_output_____
###Markdown
Specify Nodes for the main workflowInitiate all the different interfaces (represented as nodes) that you want to use in your workflow.
###Code
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=4, t_size=-1),
output_type='NIFTI',
name="extract")
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True,
save_plots=True,
output_type='NIFTI'),
name="mcflirt")
# SliceTimer - correct for slice wise acquisition
slicetimer = Node(SliceTimer(index_dir=False,
interleaved=True,
output_type='NIFTI',
time_repetition=TR),
name="slicetimer")
# Smooth - image smoothing
smooth = Node(Smooth(), name="smooth")
smooth.iterables = ("fwhm", fwhm)
# Artifact Detection - determines outliers in functional images
art = Node(ArtifactDetect(norm_threshold=2,
zintensity_threshold=3,
mask_type='spm_global',
parameter_source='FSL',
use_differences=[True, False],
plot_type='svg'),
name="art")
###Output
_____no_output_____
###Markdown
Coregistration WorkflowInitiate a workflow that coregistrates the functional images to the anatomical image (according to FSL's FEAT pipeline).
###Code
# BET - Skullstrip anatomical Image
bet_anat = Node(BET(frac=0.5,
robust=True,
output_type='NIFTI_GZ'),
name="bet_anat")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI_GZ'),
name="segmentation")
# Select WM segmentation file from segmentation output
def get_wm(files):
return files[-1]
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5,
args='-bin',
output_type='NIFTI_GZ'),
name="threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'),
name="coreg_pre")
# FLIRT - coregistration of functional images to anatomical images with BBR
coreg_bbr = Node(FLIRT(dof=6,
cost='bbr',
schedule=opj(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch'),
output_type='NIFTI_GZ'),
name="coreg_bbr")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="applywarp")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="applywarp_mean")
# Create a coregistration workflow
coregwf = Workflow(name='coregwf')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([(bet_anat, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_wm),
'in_file')]),
(bet_anat, coreg_pre, [('out_file', 'reference')]),
(threshold, coreg_bbr, [('out_file', 'wm_seg')]),
(coreg_pre, coreg_bbr, [('out_matrix_file', 'in_matrix_file')]),
(coreg_bbr, applywarp, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp_mean, [('out_file', 'reference')]),
])
###Output
_____no_output_____
###Markdown
Specify input & output streamSpecify where the input data can be found & where and how to save the output data.
###Code
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'task_name']),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('task_name', task_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj('derivatives', 'fmriprep', '{subject_id}', 'anat', '{subject_id}_t1w_preproc.nii.gz')
func_file = opj('{subject_id}', 'ses-test', 'func',
'{subject_id}_ses-test_task-{task_name}_bold.nii.gz')
templates = {'anat': anat_file,
'func': func_file}
selectfiles = Node(SelectFiles(templates,
base_directory='/data/ds000114'),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
## Use the following DataSink output substitutions
substitutions = [('_subject_id_', ''),
('_task_name_', '/task-'),
('_fwhm_', 'fwhm-'),
('_roi', ''),
('_mcf', ''),
('_st', ''),
('_flirt', ''),
('.nii_mean_reg', '_mean'),
('.nii.par', '.par'),
]
subjFolders = [('fwhm-%s/' % f, 'fwhm-%s_' % f) for f in fwhm]
substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
###Output
_____no_output_____
###Markdown
Specify WorkflowCreate a workflow and connect the interface nodes and the I/O stream to each other.
###Code
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the preprocessing workflow
preproc.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('task_name', 'task_name')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, mcflirt, [('roi_file', 'in_file')]),
(mcflirt, slicetimer, [('out_file', 'in_file')]),
(selectfiles, coregwf, [('anat', 'bet_anat.in_file'),
('anat', 'coreg_bbr.reference')]),
(mcflirt, coregwf, [('mean_img', 'coreg_pre.in_file'),
('mean_img', 'coreg_bbr.in_file'),
('mean_img', 'applywarp_mean.in_file')]),
(slicetimer, coregwf, [('slice_time_corrected_file', 'applywarp.in_file')]),
(coregwf, smooth, [('applywarp.out_file', 'in_files')]),
(mcflirt, datasink, [('par_file', 'preproc.@par')]),
(smooth, datasink, [('smoothed_files', 'preproc.@smooth')]),
(coregwf, datasink, [('applywarp_mean.out_file', 'preproc.@mean')]),
(coregwf, art, [('applywarp.out_file', 'realigned_files')]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(coregwf, datasink, [('coreg_bbr.out_matrix_file', 'preproc.@mat_file'),
('bet_anat.out_file', 'preproc.@brain')]),
(art, datasink, [('outlier_files', 'preproc.@outlier_files'),
('plot_files', 'preproc.@plot_files')]),
])
###Output
_____no_output_____
###Markdown
Visualize the workflowIt always helps to visualize your workflow.
###Code
# Create preproc output graph
preproc.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
from IPython.display import Image
Image(filename=opj(preproc.base_dir, 'preproc', 'graph.png'))
# Visualize the detailed graph
preproc.write_graph(graph2use='flat', format='png', simple_form=True)
Image(filename=opj(preproc.base_dir, 'preproc', 'graph_detailed.png'))
###Output
_____no_output_____
###Markdown
Run the WorkflowNow that everything is ready, we can run the preprocessing workflow. Change ``n_procs`` to the number of jobs/cores you want to use. **Note** that if you're using a Docker container and FLIRT fails to run without any good reason, you might need to change memory settings in the Docker preferences (6 GB should be enough for this workflow).
###Code
preproc.run('MultiProc', plugin_args={'n_procs': 4})
###Output
_____no_output_____
###Markdown
Inspect outputLet's check the structure of the output folder, to see if we have everything we wanted to save.
###Code
!tree /output/datasink/preproc
###Output
_____no_output_____
###Markdown
Visualize resultsLet's check the effect of the different smoothing kernels.
###Code
from nilearn import image, plotting
out_path = '/output/datasink/preproc/sub-01/task-fingerfootlips'
plotting.plot_epi(
'/data/ds000114/derivatives/fmriprep/sub-01/anat/sub-01_t1w_preproc.nii.gz',
title="T1", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(opj(out_path, 'sub-01_ses-test_task-fingerfootlips_bold_mean.nii.gz'),
title="fwhm = 0mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-4_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 4mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-8_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 8mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
###Output
_____no_output_____
###Markdown
Now, let's investigate the motion parameters. How much did the subject move and turn in the scanner?
###Code
import numpy as np
import pylab as plt
par = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/sub-01_ses-test_task-fingerfootlips_bold.par')
fig, axes = plt.subplots(2, 1, figsize=(15, 5))
axes[0].set_ylabel('rotation (radians)')
axes[0].plot(par[0:, :3])
axes[1].plot(par[0:, 3:])
axes[1].set_xlabel('time (TR)')
axes[1].set_ylabel('translation (mm)');
###Output
_____no_output_____
###Markdown
There seems to be a rather drastic motion around volume 102. Let's check if the outliers detection algorithm was able to pick this up.
###Code
import numpy as np
outlier_ids = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/art.sub-01_ses-test_task-fingerfootlips_bold_outliers.txt')
print('Outliers were detected at volumes: %s' % outlier_ids)
from IPython.display import SVG
SVG(filename='/output/datasink/preproc/sub-01/task-fingerfootlips/plot.sub-01_ses-test_task-fingerfootlips_bold.svg')
###Output
_____no_output_____
###Markdown
Example 1: Preprocessing WorkflowThis is meant as a very simple example for a preprocessing workflow. In this workflow we will conduct the following steps:1. Motion correction of functional images with FSL's MCFLIRT2. Coregistration of functional images to anatomical images (according to FSL's FEAT pipeline)3. Smoothing of coregistrated functional images with FWHM set to 4mm and 8mm4. Artifact Detection in functional images (to detect outlier volumes) PreparationBefore we can start with anything we first need to download the data (the other 9 subjects in the dataset). This can be done very quickly with the following `datalad` command.**Note:** This might take a while, as datalad needs to download ~700MB of data
###Code
%%bash
datalad get -J 4 /data/ds000114/derivatives/fmriprep/sub-*/anat/*preproc.nii.gz \
/data/ds000114/sub-*/ses-test/func/*fingerfootlips*
###Output
_____no_output_____
###Markdown
Inspect the dataFor every subject we have one anatomical T1w and 5 functional images. As a short recap, the image properties of the anatomy and the **fingerfootlips** functional image are:
###Code
%%bash
cd /data/ds000114/
nib-ls derivatives/fmriprep/sub-01/*/*t1w_preproc.nii.gz sub-01/ses-test/f*/*fingerfootlips*.nii.gz
###Output
_____no_output_____
###Markdown
**So, let's start!** ImportsFirst, let's import all modules we later will be needing.
###Code
from nilearn import plotting
%matplotlib inline
from os.path import join as opj
import os
import json
from nipype.interfaces.fsl import (BET, ExtractROI, FAST, FLIRT, ImageMaths,
MCFLIRT, SliceTimer, Threshold)
from nipype.interfaces.spm import Smooth
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.algorithms.rapidart import ArtifactDetect
from nipype import Workflow, Node
###Output
_____no_output_____
###Markdown
Experiment parametersIt's always a good idea to specify all parameters that might change between experiments at the beginning of your script. We will use one functional image for fingerfootlips task for ten subjects.
###Code
experiment_dir = '/output'
output_dir = 'datasink'
working_dir = 'workingdir'
# list of subject identifiers
subject_list = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10']
# list of session identifiers
task_list = ['fingerfootlips']
# Smoothing widths to apply
fwhm = [4, 8]
# TR of functional images
with open('/data/ds000114/task-fingerfootlips_bold.json', 'rt') as fp:
task_info = json.load(fp)
TR = task_info['RepetitionTime']
# Isometric resample of functional images to voxel size (in mm)
iso_size = 4
###Output
_____no_output_____
###Markdown
Specify Nodes for the main workflowInitiate all the different interfaces (represented as nodes) that you want to use in your workflow.
###Code
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=4, t_size=-1, output_type='NIFTI'),
name="extract")
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True,
save_plots=True,
output_type='NIFTI'),
name="mcflirt")
# SliceTimer - correct for slice wise acquisition
slicetimer = Node(SliceTimer(index_dir=False,
interleaved=True,
output_type='NIFTI',
time_repetition=TR),
name="slicetimer")
# Smooth - image smoothing
smooth = Node(Smooth(), name="smooth")
smooth.iterables = ("fwhm", fwhm)
# Artifact Detection - determines outliers in functional images
art = Node(ArtifactDetect(norm_threshold=2,
zintensity_threshold=3,
mask_type='spm_global',
parameter_source='FSL',
use_differences=[True, False],
plot_type='svg'),
name="art")
###Output
_____no_output_____
###Markdown
Coregistration WorkflowInitiate a workflow that coregistrates the functional images to the anatomical image (according to FSL's FEAT pipeline).
###Code
# BET - Skullstrip anatomical Image
bet_anat = Node(BET(frac=0.5,
robust=True,
output_type='NIFTI_GZ'),
name="bet_anat")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI_GZ'),
name="segmentation", mem_gb=4)
# Select WM segmentation file from segmentation output
def get_wm(files):
return files[-1]
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5,
args='-bin',
output_type='NIFTI_GZ'),
name="threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'),
name="coreg_pre")
# FLIRT - coregistration of functional images to anatomical images with BBR
coreg_bbr = Node(FLIRT(dof=6,
cost='bbr',
schedule=opj(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch'),
output_type='NIFTI_GZ'),
name="coreg_bbr")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="applywarp")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="applywarp_mean")
# Create a coregistration workflow
coregwf = Workflow(name='coregwf')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([(bet_anat, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_wm),
'in_file')]),
(bet_anat, coreg_pre, [('out_file', 'reference')]),
(threshold, coreg_bbr, [('out_file', 'wm_seg')]),
(coreg_pre, coreg_bbr, [('out_matrix_file', 'in_matrix_file')]),
(coreg_bbr, applywarp, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp_mean, [('out_file', 'reference')]),
])
###Output
_____no_output_____
###Markdown
Specify input & output streamSpecify where the input data can be found & where and how to save the output data.
###Code
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'task_name']),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('task_name', task_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj('derivatives', 'fmriprep', 'sub-{subject_id}', 'anat', 'sub-{subject_id}_t1w_preproc.nii.gz')
func_file = opj('sub-{subject_id}', 'ses-test', 'func',
'sub-{subject_id}_ses-test_task-{task_name}_bold.nii.gz')
templates = {'anat': anat_file,
'func': func_file}
selectfiles = Node(SelectFiles(templates,
base_directory='/data/ds000114'),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
## Use the following DataSink output substitutions
substitutions = [('_subject_id_', 'sub-'),
('_task_name_', '/task-'),
('_fwhm_', 'fwhm-'),
('_roi', ''),
('_mcf', ''),
('_st', ''),
('_flirt', ''),
('.nii_mean_reg', '_mean'),
('.nii.par', '.par'),
]
subjFolders = [('fwhm-%s/' % f, 'fwhm-%s_' % f) for f in fwhm]
substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
###Output
_____no_output_____
###Markdown
Specify WorkflowCreate a workflow and connect the interface nodes and the I/O stream to each other.
###Code
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the preprocessing workflow
preproc.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('task_name', 'task_name')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, mcflirt, [('roi_file', 'in_file')]),
(mcflirt, slicetimer, [('out_file', 'in_file')]),
(selectfiles, coregwf, [('anat', 'bet_anat.in_file'),
('anat', 'coreg_bbr.reference')]),
(mcflirt, coregwf, [('mean_img', 'coreg_pre.in_file'),
('mean_img', 'coreg_bbr.in_file'),
('mean_img', 'applywarp_mean.in_file')]),
(slicetimer, coregwf, [('slice_time_corrected_file', 'applywarp.in_file')]),
(coregwf, smooth, [('applywarp.out_file', 'in_files')]),
(mcflirt, datasink, [('par_file', 'preproc.@par')]),
(smooth, datasink, [('smoothed_files', 'preproc.@smooth')]),
(coregwf, datasink, [('applywarp_mean.out_file', 'preproc.@mean')]),
(coregwf, art, [('applywarp.out_file', 'realigned_files')]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(coregwf, datasink, [('coreg_bbr.out_matrix_file', 'preproc.@mat_file'),
('bet_anat.out_file', 'preproc.@brain')]),
(art, datasink, [('outlier_files', 'preproc.@outlier_files'),
('plot_files', 'preproc.@plot_files')]),
])
###Output
_____no_output_____
###Markdown
Visualize the workflowIt always helps to visualize your workflow.
###Code
# Create preproc output graph
preproc.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
from IPython.display import Image
Image(filename=opj(preproc.base_dir, 'preproc', 'graph.png'))
# Visualize the detailed graph
preproc.write_graph(graph2use='flat', format='png', simple_form=True)
Image(filename=opj(preproc.base_dir, 'preproc', 'graph_detailed.png'))
###Output
_____no_output_____
###Markdown
Run the WorkflowNow that everything is ready, we can run the preprocessing workflow. Change ``n_procs`` to the number of jobs/cores you want to use. **Note** that if you're using a Docker container and FLIRT fails to run without any good reason, you might need to change memory settings in the Docker preferences (6 GB should be enough for this workflow).
###Code
preproc.run('MultiProc', plugin_args={'n_procs': 4})
###Output
_____no_output_____
###Markdown
Inspect outputLet's check the structure of the output folder, to see if we have everything we wanted to save.
###Code
!tree /output/datasink/preproc
###Output
_____no_output_____
###Markdown
Visualize resultsLet's check the effect of the different smoothing kernels.
###Code
from nilearn import image, plotting
out_path = '/output/datasink/preproc/sub-01/task-fingerfootlips'
plotting.plot_epi(
'/data/ds000114/derivatives/fmriprep/sub-01/anat/sub-01_t1w_preproc.nii.gz',
title="T1", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(opj(out_path, 'sub-01_ses-test_task-fingerfootlips_bold_mean.nii.gz'),
title="fwhm = 0mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-4_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 4mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-8_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 8mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
###Output
_____no_output_____
###Markdown
Now, let's investigate the motion parameters. How much did the subject move and turn in the scanner?
###Code
import numpy as np
import matplotlib.pyplot as plt
par = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/sub-01_ses-test_task-fingerfootlips_bold.par')
fig, axes = plt.subplots(2, 1, figsize=(15, 5))
axes[0].set_ylabel('rotation (radians)')
axes[0].plot(par[0:, :3])
axes[1].plot(par[0:, 3:])
axes[1].set_xlabel('time (TR)')
axes[1].set_ylabel('translation (mm)');
###Output
_____no_output_____
###Markdown
There seems to be a rather drastic motion around volume 102. Let's check if the outliers detection algorithm was able to pick this up.
###Code
import numpy as np
outlier_ids = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/art.sub-01_ses-test_task-fingerfootlips_bold_outliers.txt')
print('Outliers were detected at volumes: %s' % outlier_ids)
from IPython.display import SVG
SVG(filename='/output/datasink/preproc/sub-01/task-fingerfootlips/plot.sub-01_ses-test_task-fingerfootlips_bold.svg')
###Output
_____no_output_____
###Markdown
Example 1: Preprocessing WorkflowThis is meant as a very simple example for a preprocessing workflow. In this workflow we will conduct the following steps:1. Motion correction of functional images with FSL's MCFLIRT2. Coregistration of functional images to anatomical images (according to FSL's FEAT pipeline)3. Smoothing of coregistered functional images with FWHM set to 4mm and 8mm4. Artifact Detection in functional images (to detect outlier volumes) PreparationBefore we can start with anything we first need to download the data (the other 9 subjects in the dataset). This can be done very quickly with the following `datalad` command.**Note:** This might take a while, as datalad needs to download ~700MB of data
###Code
%%bash
datalad get -J 4 -d /data/ds000114 \
/data/ds000114/derivatives/fmriprep/sub-*/anat/*preproc.nii.gz \
/data/ds000114/sub-*/ses-test/func/*fingerfootlips*
###Output
_____no_output_____
###Markdown
Inspect the dataFor every subject we have one anatomical T1w and 5 functional images. As a short recap, the image properties of the anatomy and the **fingerfootlips** functional image are:
###Code
%%bash
cd /data/ds000114/
nib-ls derivatives/fmriprep/sub-01/*/*t1w_preproc.nii.gz sub-01/ses-test/f*/*fingerfootlips*.nii.gz
###Output
_____no_output_____
###Markdown
**So, let's start!** ImportsFirst, let's import all the modules we later will be needing.
###Code
from nilearn import plotting
%matplotlib inline
from os.path import join as opj
import os
import json
from nipype.interfaces.fsl import (BET, ExtractROI, FAST, FLIRT, ImageMaths,
MCFLIRT, SliceTimer, Threshold)
from nipype.interfaces.spm import Smooth
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.algorithms.rapidart import ArtifactDetect
from nipype import Workflow, Node
###Output
_____no_output_____
###Markdown
Experiment parametersIt's always a good idea to specify all parameters that might change between experiments at the beginning of your script. We will use one functional image for fingerfootlips task for ten subjects.
###Code
experiment_dir = '/output'
output_dir = 'datasink'
working_dir = 'workingdir'
# list of subject identifiers
subject_list = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10']
# list of session identifiers
task_list = ['fingerfootlips']
# Smoothing widths to apply
fwhm = [4, 8]
# TR of functional images
with open('/data/ds000114/task-fingerfootlips_bold.json', 'rt') as fp:
task_info = json.load(fp)
TR = task_info['RepetitionTime']
# Isometric resample of functional images to voxel size (in mm)
iso_size = 4
###Output
_____no_output_____
###Markdown
Specify Nodes for the main workflowInitiate all the different interfaces (represented as nodes) that you want to use in your workflow.
###Code
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=4, t_size=-1, output_type='NIFTI'),
name="extract")
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True,
save_plots=True,
output_type='NIFTI'),
name="mcflirt")
# SliceTimer - correct for slice wise acquisition
slicetimer = Node(SliceTimer(index_dir=False,
interleaved=True,
output_type='NIFTI',
time_repetition=TR),
name="slicetimer")
# Smooth - image smoothing
smooth = Node(Smooth(), name="smooth")
smooth.iterables = ("fwhm", fwhm)
# Artifact Detection - determines outliers in functional images
art = Node(ArtifactDetect(norm_threshold=2,
zintensity_threshold=3,
mask_type='spm_global',
parameter_source='FSL',
use_differences=[True, False],
plot_type='svg'),
name="art")
###Output
_____no_output_____
###Markdown
Coregistration WorkflowInitiate a workflow that coregistrates the functional images to the anatomical image (according to FSL's FEAT pipeline).
###Code
# BET - Skullstrip anatomical Image
bet_anat = Node(BET(frac=0.5,
robust=True,
output_type='NIFTI_GZ'),
name="bet_anat")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI_GZ'),
name="segmentation", mem_gb=4)
# Select WM segmentation file from segmentation output
def get_wm(files):
return files[-1]
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5,
args='-bin',
output_type='NIFTI_GZ'),
name="threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'),
name="coreg_pre")
# FLIRT - coregistration of functional images to anatomical images with BBR
coreg_bbr = Node(FLIRT(dof=6,
cost='bbr',
schedule=opj(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch'),
output_type='NIFTI_GZ'),
name="coreg_bbr")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="applywarp")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="applywarp_mean")
# Create a coregistration workflow
coregwf = Workflow(name='coregwf')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([(bet_anat, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_wm),
'in_file')]),
(bet_anat, coreg_pre, [('out_file', 'reference')]),
(threshold, coreg_bbr, [('out_file', 'wm_seg')]),
(coreg_pre, coreg_bbr, [('out_matrix_file', 'in_matrix_file')]),
(coreg_bbr, applywarp, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp_mean, [('out_file', 'reference')]),
])
###Output
_____no_output_____
###Markdown
Specify input & output streamSpecify where the input data can be found & where and how to save the output data.
###Code
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'task_name']),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('task_name', task_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj('derivatives', 'fmriprep', 'sub-{subject_id}', 'anat', 'sub-{subject_id}_t1w_preproc.nii.gz')
func_file = opj('sub-{subject_id}', 'ses-test', 'func',
'sub-{subject_id}_ses-test_task-{task_name}_bold.nii.gz')
templates = {'anat': anat_file,
'func': func_file}
selectfiles = Node(SelectFiles(templates,
base_directory='/data/ds000114'),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
## Use the following DataSink output substitutions
substitutions = [('_subject_id_', 'sub-'),
('_task_name_', '/task-'),
('_fwhm_', 'fwhm-'),
('_roi', ''),
('_mcf', ''),
('_st', ''),
('_flirt', ''),
('.nii_mean_reg', '_mean'),
('.nii.par', '.par'),
]
subjFolders = [('fwhm-%s/' % f, 'fwhm-%s_' % f) for f in fwhm]
substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
###Output
_____no_output_____
###Markdown
Specify WorkflowCreate a workflow and connect the interface nodes and the I/O stream to each other.
###Code
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the preprocessing workflow
preproc.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('task_name', 'task_name')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, mcflirt, [('roi_file', 'in_file')]),
(mcflirt, slicetimer, [('out_file', 'in_file')]),
(selectfiles, coregwf, [('anat', 'bet_anat.in_file'),
('anat', 'coreg_bbr.reference')]),
(mcflirt, coregwf, [('mean_img', 'coreg_pre.in_file'),
('mean_img', 'coreg_bbr.in_file'),
('mean_img', 'applywarp_mean.in_file')]),
(slicetimer, coregwf, [('slice_time_corrected_file', 'applywarp.in_file')]),
(coregwf, smooth, [('applywarp.out_file', 'in_files')]),
(mcflirt, datasink, [('par_file', 'preproc.@par')]),
(smooth, datasink, [('smoothed_files', 'preproc.@smooth')]),
(coregwf, datasink, [('applywarp_mean.out_file', 'preproc.@mean')]),
(coregwf, art, [('applywarp.out_file', 'realigned_files')]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(coregwf, datasink, [('coreg_bbr.out_matrix_file', 'preproc.@mat_file'),
('bet_anat.out_file', 'preproc.@brain')]),
(art, datasink, [('outlier_files', 'preproc.@outlier_files'),
('plot_files', 'preproc.@plot_files')]),
])
###Output
_____no_output_____
###Markdown
Visualize the workflowIt always helps to visualize your workflow.
###Code
# Create preproc output graph
preproc.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
from IPython.display import Image
Image(filename=opj(preproc.base_dir, 'preproc', 'graph.png'))
# Visualize the detailed graph
preproc.write_graph(graph2use='flat', format='png', simple_form=True)
Image(filename=opj(preproc.base_dir, 'preproc', 'graph_detailed.png'))
###Output
_____no_output_____
###Markdown
Run the WorkflowNow that everything is ready, we can run the preprocessing workflow. Change ``n_procs`` to the number of jobs/cores you want to use. **Note** that if you're using a Docker container and FLIRT fails to run without any good reason, you might need to change memory settings in the Docker preferences (6 GB should be enough for this workflow).
###Code
preproc.run('MultiProc', plugin_args={'n_procs': 4})
###Output
_____no_output_____
###Markdown
Inspect outputLet's check the structure of the output folder, to see if we have everything we wanted to save.
###Code
!tree /output/datasink/preproc
###Output
_____no_output_____
###Markdown
Visualize resultsLet's check the effect of the different smoothing kernels.
###Code
from nilearn import image, plotting
out_path = '/output/datasink/preproc/sub-01/task-fingerfootlips'
plotting.plot_epi(
'/data/ds000114/derivatives/fmriprep/sub-01/anat/sub-01_t1w_preproc.nii.gz',
title="T1", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(opj(out_path, 'sub-01_ses-test_task-fingerfootlips_bold_mean.nii.gz'),
title="fwhm = 0mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-4_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 4mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-8_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 8mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
###Output
_____no_output_____
###Markdown
Now, let's investigate the motion parameters. How much did the subject move and turn in the scanner?
###Code
import numpy as np
import matplotlib.pyplot as plt
par = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/sub-01_ses-test_task-fingerfootlips_bold.par')
fig, axes = plt.subplots(2, 1, figsize=(15, 5))
axes[0].set_ylabel('rotation (radians)')
axes[0].plot(par[0:, :3])
axes[1].plot(par[0:, 3:])
axes[1].set_xlabel('time (TR)')
axes[1].set_ylabel('translation (mm)');
###Output
_____no_output_____
###Markdown
There seems to be a rather drastic motion around volume 102. Let's check if the outliers detection algorithm was able to pick this up.
###Code
import numpy as np
outlier_ids = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/art.sub-01_ses-test_task-fingerfootlips_bold_outliers.txt')
print('Outliers were detected at volumes: %s' % outlier_ids)
from IPython.display import SVG
SVG(filename='/output/datasink/preproc/sub-01/task-fingerfootlips/plot.sub-01_ses-test_task-fingerfootlips_bold.svg')
###Output
_____no_output_____
###Markdown
Example 1: Preprocessing WorkflowThis is meant as a very simple example for a preprocessing workflow. In this workflow we will conduct the following steps:1. Motion correction of functional images with FSL's MCFLIRT2. Coregistration of functional images to anatomical images (according to FSL's FEAT pipeline)3. Smoothing of coregistered functional images with FWHM set to 4mm and 8mm4. Artifact Detection in functional images (to detect outlier volumes) PreparationBefore we can start with anything we first need to download the data (the other 9 subjects in the dataset). This can be done very quickly with the following `datalad` command.**Note:** This might take a while, as datalad needs to download ~700MB of data
###Code
%%bash
datalad get -J 4 /data/ds000114/derivatives/fmriprep/sub-*/anat/*preproc.nii.gz \
/data/ds000114/sub-*/ses-test/func/*fingerfootlips*
###Output
_____no_output_____
###Markdown
Inspect the dataFor every subject we have one anatomical T1w and 5 functional images. As a short recap, the image properties of the anatomy and the **fingerfootlips** functional image are:
###Code
%%bash
cd /data/ds000114/
nib-ls derivatives/fmriprep/sub-01/*/*t1w_preproc.nii.gz sub-01/ses-test/f*/*fingerfootlips*.nii.gz
###Output
_____no_output_____
###Markdown
**So, let's start!** ImportsFirst, let's import all the modules we later will be needing.
###Code
from nilearn import plotting
%matplotlib inline
from os.path import join as opj
import os
import json
from nipype.interfaces.fsl import (BET, ExtractROI, FAST, FLIRT, ImageMaths,
MCFLIRT, SliceTimer, Threshold)
from nipype.interfaces.spm import Smooth
from nipype.interfaces.utility import IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.algorithms.rapidart import ArtifactDetect
from nipype import Workflow, Node
###Output
_____no_output_____
###Markdown
Experiment parametersIt's always a good idea to specify all parameters that might change between experiments at the beginning of your script. We will use one functional image for fingerfootlips task for ten subjects.
###Code
experiment_dir = '/output'
output_dir = 'datasink'
working_dir = 'workingdir'
# list of subject identifiers
subject_list = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10']
# list of session identifiers
task_list = ['fingerfootlips']
# Smoothing widths to apply
fwhm = [4, 8]
# TR of functional images
with open('/data/ds000114/task-fingerfootlips_bold.json', 'rt') as fp:
task_info = json.load(fp)
TR = task_info['RepetitionTime']
# Isometric resample of functional images to voxel size (in mm)
iso_size = 4
###Output
_____no_output_____
###Markdown
Specify Nodes for the main workflowInitiate all the different interfaces (represented as nodes) that you want to use in your workflow.
###Code
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=4, t_size=-1, output_type='NIFTI'),
name="extract")
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True,
save_plots=True,
output_type='NIFTI'),
name="mcflirt")
# SliceTimer - correct for slice wise acquisition
slicetimer = Node(SliceTimer(index_dir=False,
interleaved=True,
output_type='NIFTI',
time_repetition=TR),
name="slicetimer")
# Smooth - image smoothing
smooth = Node(Smooth(), name="smooth")
smooth.iterables = ("fwhm", fwhm)
# Artifact Detection - determines outliers in functional images
art = Node(ArtifactDetect(norm_threshold=2,
zintensity_threshold=3,
mask_type='spm_global',
parameter_source='FSL',
use_differences=[True, False],
plot_type='svg'),
name="art")
###Output
_____no_output_____
###Markdown
Coregistration WorkflowInitiate a workflow that coregistrates the functional images to the anatomical image (according to FSL's FEAT pipeline).
###Code
# BET - Skullstrip anatomical Image
bet_anat = Node(BET(frac=0.5,
robust=True,
output_type='NIFTI_GZ'),
name="bet_anat")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI_GZ'),
name="segmentation", mem_gb=4)
# Select WM segmentation file from segmentation output
def get_wm(files):
return files[-1]
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5,
args='-bin',
output_type='NIFTI_GZ'),
name="threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'),
name="coreg_pre")
# FLIRT - coregistration of functional images to anatomical images with BBR
coreg_bbr = Node(FLIRT(dof=6,
cost='bbr',
schedule=opj(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch'),
output_type='NIFTI_GZ'),
name="coreg_bbr")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="applywarp")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="applywarp_mean")
# Create a coregistration workflow
coregwf = Workflow(name='coregwf')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([(bet_anat, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_wm),
'in_file')]),
(bet_anat, coreg_pre, [('out_file', 'reference')]),
(threshold, coreg_bbr, [('out_file', 'wm_seg')]),
(coreg_pre, coreg_bbr, [('out_matrix_file', 'in_matrix_file')]),
(coreg_bbr, applywarp, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp_mean, [('out_file', 'reference')]),
])
###Output
_____no_output_____
###Markdown
Specify input & output streamSpecify where the input data can be found & where and how to save the output data.
###Code
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'task_name']),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('task_name', task_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj('derivatives', 'fmriprep', 'sub-{subject_id}', 'anat', 'sub-{subject_id}_t1w_preproc.nii.gz')
func_file = opj('sub-{subject_id}', 'ses-test', 'func',
'sub-{subject_id}_ses-test_task-{task_name}_bold.nii.gz')
templates = {'anat': anat_file,
'func': func_file}
selectfiles = Node(SelectFiles(templates,
base_directory='/data/ds000114'),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
## Use the following DataSink output substitutions
substitutions = [('_subject_id_', 'sub-'),
('_task_name_', '/task-'),
('_fwhm_', 'fwhm-'),
('_roi', ''),
('_mcf', ''),
('_st', ''),
('_flirt', ''),
('.nii_mean_reg', '_mean'),
('.nii.par', '.par'),
]
subjFolders = [('fwhm-%s/' % f, 'fwhm-%s_' % f) for f in fwhm]
substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
###Output
_____no_output_____
###Markdown
Specify WorkflowCreate a workflow and connect the interface nodes and the I/O stream to each other.
###Code
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the preprocessing workflow
preproc.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('task_name', 'task_name')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, mcflirt, [('roi_file', 'in_file')]),
(mcflirt, slicetimer, [('out_file', 'in_file')]),
(selectfiles, coregwf, [('anat', 'bet_anat.in_file'),
('anat', 'coreg_bbr.reference')]),
(mcflirt, coregwf, [('mean_img', 'coreg_pre.in_file'),
('mean_img', 'coreg_bbr.in_file'),
('mean_img', 'applywarp_mean.in_file')]),
(slicetimer, coregwf, [('slice_time_corrected_file', 'applywarp.in_file')]),
(coregwf, smooth, [('applywarp.out_file', 'in_files')]),
(mcflirt, datasink, [('par_file', 'preproc.@par')]),
(smooth, datasink, [('smoothed_files', 'preproc.@smooth')]),
(coregwf, datasink, [('applywarp_mean.out_file', 'preproc.@mean')]),
(coregwf, art, [('applywarp.out_file', 'realigned_files')]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(coregwf, datasink, [('coreg_bbr.out_matrix_file', 'preproc.@mat_file'),
('bet_anat.out_file', 'preproc.@brain')]),
(art, datasink, [('outlier_files', 'preproc.@outlier_files'),
('plot_files', 'preproc.@plot_files')]),
])
###Output
_____no_output_____
###Markdown
Visualize the workflowIt always helps to visualize your workflow.
###Code
# Create preproc output graph
preproc.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
from IPython.display import Image
Image(filename=opj(preproc.base_dir, 'preproc', 'graph.png'))
# Visualize the detailed graph
preproc.write_graph(graph2use='flat', format='png', simple_form=True)
Image(filename=opj(preproc.base_dir, 'preproc', 'graph_detailed.png'))
###Output
_____no_output_____
###Markdown
Run the WorkflowNow that everything is ready, we can run the preprocessing workflow. Change ``n_procs`` to the number of jobs/cores you want to use. **Note** that if you're using a Docker container and FLIRT fails to run without any good reason, you might need to change memory settings in the Docker preferences (6 GB should be enough for this workflow).
###Code
preproc.run('MultiProc', plugin_args={'n_procs': 4})
###Output
_____no_output_____
###Markdown
Inspect outputLet's check the structure of the output folder, to see if we have everything we wanted to save.
###Code
!tree /output/datasink/preproc
###Output
_____no_output_____
###Markdown
Visualize resultsLet's check the effect of the different smoothing kernels.
###Code
from nilearn import image, plotting
out_path = '/output/datasink/preproc/sub-01/task-fingerfootlips'
plotting.plot_epi(
'/data/ds000114/derivatives/fmriprep/sub-01/anat/sub-01_t1w_preproc.nii.gz',
title="T1", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(opj(out_path, 'sub-01_ses-test_task-fingerfootlips_bold_mean.nii.gz'),
title="fwhm = 0mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-4_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 4mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
plotting.plot_epi(image.mean_img(opj(out_path, 'fwhm-8_ssub-01_ses-test_task-fingerfootlips_bold.nii')),
title="fwhm = 8mm", display_mode='ortho', annotate=False, draw_cross=False, cmap='gray');
###Output
_____no_output_____
###Markdown
Now, let's investigate the motion parameters. How much did the subject move and turn in the scanner?
###Code
import numpy as np
import matplotlib.pyplot as plt
par = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/sub-01_ses-test_task-fingerfootlips_bold.par')
fig, axes = plt.subplots(2, 1, figsize=(15, 5))
axes[0].set_ylabel('rotation (radians)')
axes[0].plot(par[0:, :3])
axes[1].plot(par[0:, 3:])
axes[1].set_xlabel('time (TR)')
axes[1].set_ylabel('translation (mm)');
###Output
_____no_output_____
###Markdown
There seems to be a rather drastic motion around volume 102. Let's check if the outliers detection algorithm was able to pick this up.
###Code
import numpy as np
outlier_ids = np.loadtxt('/output/datasink/preproc/sub-01/task-fingerfootlips/art.sub-01_ses-test_task-fingerfootlips_bold_outliers.txt')
print('Outliers were detected at volumes: %s' % outlier_ids)
from IPython.display import SVG
SVG(filename='/output/datasink/preproc/sub-01/task-fingerfootlips/plot.sub-01_ses-test_task-fingerfootlips_bold.svg')
###Output
_____no_output_____
|
jupyter-notebooks/2 - reproject_sentinel2.ipynb
|
###Markdown
Reproject Sentinel-2 data to WGS84
###Code
import rasterio
from rasterio.merge import merge
from rasterio.plot import show
from rasterio.warp import calculate_default_transform, reproject, Resampling
import glob
import os
def reproject_raster(in_path, out_path, dst_crs = 'EPSG:4326'):
# reproject raster to project crs
with rasterio.open(in_path) as src:
src_crs = src.crs
transform, width, height = calculate_default_transform(src_crs, dst_crs, src.width, src.height, *src.bounds)
kwargs = src.meta.copy()
kwargs.update({
'crs': dst_crs,
'transform': transform,
'width': width,
'height': height})
with rasterio.open(out_path, 'w', **kwargs) as dst:
for i in range(1, src.count + 1):
reproject(
source=rasterio.band(src, i),
destination=rasterio.band(dst, i),
src_transform=src.transform,
src_crs=src.crs,
dst_transform=transform,
dst_crs=dst_crs,
resampling=Resampling.nearest)
return(out_path)
# File and folder paths
dirpath = r"data\sentinel2"
search_criteria = "*\R10m\TCI.jp2"
q = os.path.join(dirpath, search_criteria)
print(q)
paths = glob.glob(q)
paths
files_to_mosaic = []
for path_in in paths:
folders_split = path_in.split('\\')
filename_split = folders_split[-1].split('.')
filename_split[0] = 'transformed_tci'
filename_out = '.'.join(filename_split)
folders_split[-1] = filename_out
path_out = '\\'.join(folders_split)
reproject_raster(path_in, path_out)
###Output
_____no_output_____
|
tutorials/getting-started-train/3.pytorch-model-cloud-data.ipynb
|
###Markdown
Tutorial: Bring your own data (Part 3 of 3) IntroductionIn the previous [Tutorial: Train a model in the cloud](2.train-model.ipynb) article, the CIFAR10 data was downloaded using the builtin `torchvision.datasets.CIFAR10` method in the PyTorch API. However, in many cases you are going to want to use your own data in a remote training run. This article focuses on the workflow you can leverage such that you can work with your own data in Azure Machine Learning. By the end of this tutorial you would have a better understanding of:- How to upload your data to Azure- Best practices for working with cloud data in Azure Machine Learning- Working with command-line arguments--- Your machine learning codeBy now you have your training script running in Azure Machine Learning, and can monitor the model performance. Let's _parametrize_ the training script by introducingarguments. Using arguments will allow you to easily compare different hyperparmeters.Presently our training script is set to download the CIFAR10 dataset on each run. The python code in [train-with-cloud-data-and-logging.py](../../code/models/pytorch/cifar10-cnn/train-with-cloud-data-and-logging.py) now uses **`argparse` to parametize the script.** Understanding your machine learning code changesThe script `train-with-cloud-data-and-logging.py` has leveraged the `argparse` library to set up the `--data-path`, `--learning-rate`, `--momentum`, and `--epochs` arguments:```pythonimport argparse...parser = argparse.ArgumentParser()parser.add_argument("--data-path", type=str, help="Path to the training data")parser.add_argument("--learning-rate", type=float, default=0.001, help="Learning rate for SGD")parser.add_argument("--momentum", type=float, default=0.9, help="Momentum for SGD")parser.add_argument("--epochs", type=int, default=2, help="Number of epochs to train")args = parser.parse_args()```The script was adapted to update the optimizer to use the user-defined parameters:```pythonoptimizer = optim.SGD( net.parameters(), lr=args.learning_rate, get learning rate from command-line argument momentum=args.momentum, get momentum from command-line argument)```Similarly the training loop was adapted to update the number of epochs to train to use the user-defined parameters:```pythonfor epoch in range(args.epochs):``` Upload your data to AzureIn order to run this script in Azure Machine Learning, you need to make your training data available in Azure. Your Azure Machine Learning workspace comes equipped with a _default_ **Datastore** - an Azure Blob storage account - that you can use to store your training data.> ! NOTE > Azure Machine Learning allows you to connect other cloud-based datastores that store your data. For more details, see [datastores documentation](./concept-data.md).
###Code
!pip install --upgrade torchvision
from azureml.core import Workspace, Dataset
from torchvision import datasets
ws = Workspace.from_config()
datasets.CIFAR10(".", download=True)
ds = ws.get_default_datastore()
ds.upload(
src_dir="cifar-10-batches-py",
target_path="datasets/cifar10",
overwrite=False,
)
import os
import shutil
os.remove("cifar-10-python.tar.gz")
shutil.rmtree("cifar-10-batches-py")
###Output
_____no_output_____
###Markdown
The `target_path` specifies the path on the datastore where the CIFAR10 data will be uploaded. Submit your machine learning code to Azure Machine LearningAs you have done previously, create a new Python control script:
###Code
import git
from pathlib import Path
prefix = Path(git.Repo(".", search_parent_directories=True).working_tree_dir)
prefix
from azureml.core import (
Workspace,
Experiment,
Environment,
ScriptRunConfig,
Dataset,
)
from azureml.widgets import RunDetails
ws = Workspace.from_config()
ds = Dataset.File.from_files(
path=(ws.get_default_datastore(), "datasets/cifar10")
)
env = Environment.from_conda_specification(
name="pytorch-env-tutorial",
file_path=prefix.joinpath("environments", "pytorch-example.yml"),
)
exp = Experiment(
workspace=ws, name="getting-started-train-model-cloud-data-tutorial"
)
src = ScriptRunConfig(
source_directory=prefix.joinpath(
"code", "models", "pytorch", "cifar10-cnn"
),
script="train-with-cloud-data-and-logging.py",
compute_target="cpu-cluster",
environment=env,
arguments=[
"--data-path",
ds.as_mount(),
"--learning-rate",
0.003,
"--momentum",
0.92,
"--epochs",
2,
],
)
run = exp.submit(src)
RunDetails(run).show()
###Output
_____no_output_____
|
Experimental/DW_LCAM/[1]_Main_EEG_representation_Giga.ipynb
|
###Markdown
###Code
from google.colab import drive
drive.mount('/content/drive')
# Supporting modules
#-------------------------------------------------------------------------------
import numpy as np
import scipy.io as sio
import pywt
import pandas as pd
import pickle
import os
import matplotlib.pyplot as plt
import cv2
import warnings
import shutil
from scipy.signal import butter, lfilter, lfilter_zi, filtfilt
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.model_selection import StratifiedKFold,train_test_split,ShuffleSplit
warnings.filterwarnings("ignore")
#-------------------------------------------------------------------------------
!pip install mne==0.19
import mne
from mne.decoding import CSP
from mne.io import read_raw_gdf
#-------------------------------------------------------------------------------
# Definitions-------------------------------------------------------------------
def leer_GIGA_data(path_filename,ch,vt,sbj_id):
#--- info ------------------------------------------------------------------
# 2 ---> sample rate
# 7 ---> imaginary_left
# 8 ---> imaginary_right
# 11 ---> imaginary_event
# 14 ---> bad_trials
# class1: left
# class2: right
#---------------------------------------------------------------------------
raw = sio.loadmat(path_filename)
eeg_raw = raw['eeg']
sfreq = np.float(eeg_raw[0][0][2])
id_MI = np.where(eeg_raw[0][0][11]==1)
id_MI = id_MI[1]
raw_c1 = []
raw_c2 = []
y_c1 = []
y_c2 = []
for i in range(len(id_MI)):
l_thr = id_MI[i]-(sfreq*2-1)
h_thr = id_MI[i]+(sfreq*5)
tmp_c1 = eeg_raw[0][0][7][ch,np.int(l_thr):np.int(h_thr)]
tmp_c2 = eeg_raw[0][0][8][ch,np.int(l_thr):np.int(h_thr)]
raw_c1.append(tmp_c1[:,np.int(vt[0]*sfreq):np.int(vt[1]*sfreq)])
raw_c2.append(tmp_c2[:,np.int(vt[0]*sfreq):np.int(vt[1]*sfreq)])
y_c1.append(1.0)
y_c2.append(2.0)
# remove bad trials---------------------------------------------------------
id_bad_tr_voltage_c1 = eeg_raw[0][0][14][0][0][0][0][0]
id_bad_tr_voltage_c2 = eeg_raw[0][0][14][0][0][0][0][1]
id_bad_tr_mi_c1 = eeg_raw[0][0][14][0][0][1][0][0]
id_bad_tr_mi_c2 = eeg_raw[0][0][14][0][0][1][0][1]
ref_axis_c1 = 1
ref_axis_c2 = 1
if id_bad_tr_mi_c1.shape[0]>id_bad_tr_mi_c1.shape[1]:
id_bad_tr_mi_c1 = id_bad_tr_mi_c1.T
if id_bad_tr_mi_c2.shape[0]>id_bad_tr_mi_c2.shape[1]:
id_bad_tr_mi_c2 = id_bad_tr_mi_c2.T
if id_bad_tr_voltage_c1.shape[1] == 0:
id_bad_tr_voltage_c1 = np.reshape(id_bad_tr_voltage_c1, (id_bad_tr_voltage_c1.shape[0], id_bad_tr_mi_c1.shape[1]))
if id_bad_tr_voltage_c2.shape[1] == 0:
id_bad_tr_voltage_c2 = np.reshape(id_bad_tr_voltage_c2, (id_bad_tr_voltage_c2.shape[0], id_bad_tr_mi_c2.shape[1]))
if (id_bad_tr_voltage_c1.shape[1] > id_bad_tr_mi_c1.shape[1]):
if id_bad_tr_mi_c1.shape[0] == 0:
id_bad_tr_mi_c1 = np.reshape(id_bad_tr_mi_c1, (id_bad_tr_mi_c1.shape[0],id_bad_tr_voltage_c1.shape[1]))
ref_axis_c1 = 0
if (id_bad_tr_voltage_c2.shape[1] > id_bad_tr_mi_c2.shape[1]):
if id_bad_tr_mi_c2.shape[0] == 0:
id_bad_tr_mi_c2 = np.reshape(id_bad_tr_mi_c2, (id_bad_tr_mi_c2.shape[0],id_bad_tr_voltage_c2.shape[1]))
ref_axis_c2 = 0
if (id_bad_tr_mi_c1.shape[0] > id_bad_tr_voltage_c1.shape[0]):
ref_axis_c1 = 0
if (id_bad_tr_mi_c2.shape[0] > id_bad_tr_voltage_c2.shape[0]):
ref_axis_c2 = 0
if (id_bad_tr_voltage_c1.shape[0] > id_bad_tr_mi_c1.shape[0]):
ref_axis_c1 = 0
if (id_bad_tr_voltage_c2.shape[0] > id_bad_tr_mi_c2.shape[0]):
ref_axis_c2 = 0
id_bad_tr_c1 = np.concatenate((id_bad_tr_voltage_c1,id_bad_tr_mi_c1),axis=ref_axis_c1)
id_bad_tr_c1 = id_bad_tr_c1.ravel()-1
for ele in sorted(id_bad_tr_c1, reverse = True):
del raw_c1[ele]
del y_c1[ele]
id_bad_tr_c2 = np.concatenate((id_bad_tr_voltage_c2,id_bad_tr_mi_c2),axis=ref_axis_c2)
id_bad_tr_c2= id_bad_tr_c2.ravel()-1
for ele in sorted(id_bad_tr_c2, reverse = True):
del raw_c2[ele]
del y_c2[ele]
Xraw = np.array(raw_c1 + raw_c2)
y = np.array(y_c1 + y_c2)
return Xraw, y, sfreq
#-------------------------------------------------------------------------------
def bank_filter_epochsEEG(Xraw, fs, f_frec):
nf,ff = f_frec.shape
epochs,channels,T = Xraw.shape
Xraw_f = np.zeros((epochs,channels,T,nf))
for f in range(nf):
lfc = f_frec[f,0]
hfc = f_frec[f,1]
b,a = butter_bandpass(lfc, hfc, fs)
zi = lfilter_zi(b, a)
for n in range(epochs):
for c in range(channels):
zi = lfilter_zi(b, a)
Xraw_f[n,c,:,f] = lfilter(b, a, Xraw[n,c,:],zi = zi*Xraw[n,c,0])[0]
return Xraw_f
#-------------------------------------------------------------------------------
def butter_bandpass(lowcut, highcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='band')
return b, a
#-------------------------------------------------------------------------------
def calculo_cwt(x,fs):
wname = 'cmor'
delta = 1/fs
coef,freq = pywt.cwt(x.T,np.arange(1,32),wname,delta)
return coef, freq
#-------------------------------------------------------------------------------
def cwt_feat_extraction(X,fs,freq_ref):
X_cwt = np.zeros((X.shape[0],X.shape[1],2))
for tr in range(X.shape[0]):#loop across trials
for ch in range(X.shape[1]):#loop across channels
coef, freq = calculo_cwt(np.squeeze(X[tr,ch,:,0]),fs)
coef = np.abs(coef)
fb_valavg = []
for fb in range(freq_ref.shape[0]):#loop across filter bands
coef_mat = coef[np.where((freq > freq_ref[fb,0]) & (freq <freq_ref[fb,1])),:]
coef_mat = np.squeeze(coef_mat[0,:,:])
X_cwt[tr,ch,fb] = np.mean(coef_mat.flatten())
return X_cwt
#-------------------------------------------------------------------------------
from sklearn.base import BaseEstimator, TransformerMixin
class CSP_epochs_filter_extractor(TransformerMixin,BaseEstimator):
def __init__(self, fs,f_frec=[4,30], ncomp=4,reg='empirical'):
self.reg = reg
self.fs = fs
self.f_frec = f_frec
self.ncomp = ncomp
def _averagingEEG(self,X):
epochs,channels,T = X.shape
Xc = np.zeros((epochs,channels,T))
for i in range(epochs):
Xc[i,:,:] = X[i,:,:] - np.mean(X[i,:,:])
return Xc
def _bank_filter_epochsEEG(self,X):
nf,ff = self.f_frec.shape
epochs,channels,T = X.shape
X_f = np.zeros((epochs,channels,T,nf))
for f in range(nf):
lfc = self.f_frec[f,0]
hfc = self.f_frec[f,1]
b,a = butter_bandpass(lfc, hfc, self.fs)
X_f[:,:,:,f] = filtfilt(b,a,X,axis=2)
return X_f
def _CSP_epochsEEG(self,Xraw, y,*_):
ncomp = self.ncomp
mne.set_log_level('WARNING')
epochs,channels,T,nf = Xraw.shape
Xcsp = np.zeros((epochs,self.ncomp,nf))
self.filters =np.zeros((self.ncomp,channels,nf))
csp_l = []
for f in range(nf):
csp_l+= [CSP(n_components=ncomp, reg=self.reg, log=False,transform_into='average_power').fit(Xraw[:,:,:,f],y)]
Xcsp[:,:,f] = csp_l[f].transform(Xraw[:,:,:,f])
self.filters[:,:,f] = csp_l[f].filters_[:self.ncomp]
return csp_l, Xcsp
def fit(self,Xraw,y, *_):
Xraw = self._averagingEEG(Xraw)
Xraw_f = self._bank_filter_epochsEEG(Xraw)
self.csp_l, self.Xcsp = self._CSP_epochsEEG(Xraw_f, y)
return self
def transform(self, Xraw, *_):
Xraw = self._averagingEEG(Xraw)
Xraw_f = self._bank_filter_epochsEEG(Xraw)
epochs,channels,T,nf = Xraw_f.shape
ncomp = self.ncomp
result = np.zeros((epochs,ncomp,nf))
for f in range(nf):
result[:,:,f] = self.csp_l[f].transform(Xraw_f[:,:,:,f])
return result
#-------------------------------------------------------------------------------
def csp_feat_extraction(Xtrain,ytrain,Xtest,fs,f_frec):
# Y = W.T * X
# A*Y = X ---- A= pinv(W.T)
XT_train = np.zeros((Xtrain.shape[0],Xtrain.shape[1],2))
XT_test = np.zeros((Xtest.shape[0],Xtest.shape[1],2))
ncomp = 6# Biclass (4-6) -- Multiclass (8-12)
csp_c = CSP_epochs_filter_extractor(fs=fs,f_frec=f_frec, ncomp=ncomp)
XT = csp_c.fit_transform(Xtrain,ytrain)
Filt_ = csp_c.filters
# train/test
for tr in range(Xtrain.shape[0]):#loop across train trials
for fb in range(len(f_frec)):#loop across filter bands
Xpr_tr = []
Xpr_tr = np.dot(Filt_[:,:,fb],Xtrain[tr,:,:])
Xfr_tr = []
Xfr_tr = np.dot(np.linalg.pinv(Filt_[:,:,fb]),Xpr_tr)
XT_train[tr,:,fb] = np.mean(np.abs(Xfr_tr),axis=1)
for tr in range(Xtest.shape[0]):#loop across test trials
for fb in range(len(f_frec)):#loop across filter bands
Xpr_ts = []
Xpr_ts = np.dot(Filt_[:,:,fb],Xtest[tr,:,:])
Xfr_ts = []
Xfr_ts = np.dot(np.linalg.pinv(Filt_[:,:,fb]),Xpr_ts)
XT_test[tr,:,fb] = np.mean(np.abs(Xfr_ts),axis=1)
return XT_train, XT_test
#-------------------------------------------------------------------------------
def topomap_generation(types,time_inf,time_sup,id_sbj,info):
cmap = 'gray'
newX = 40
newY = 40
for itm in range(len(types)): #len(types)
#-----------------------------------------------------------------------
path = '/content/drive/MyDrive/Colab Notebooks/GradCam_Paper/GigaData/data/X_'+types[itm]+'_sbj_'+str(id_sbj)+'_Tw_'+str(time_inf)+'s_'+str(time_sup)+'s.pickle'
with open(path, 'rb') as f:
XT_train, XT_test, y_train, y_test = pickle.load(f)
#-----------------------------------------------------------------------
try:
os.mkdir('figures/'+str(time_inf)+'s-'+str(time_sup)+'s/'+types[itm])
except OSError:
print('Folder exists!')
#-----------------------------------------------------------------------
# train
X = XT_train.copy()
#-----------------------------------------------------------------------
try:
os.mkdir('figures/'+str(time_inf)+'s-'+str(time_sup)+'s/'+types[itm]+'/train')
except OSError:
print('Folder exists!')
#-----------------------------------------------------------------------
X_train_reshape = np.zeros((X.shape[0],X.shape[2],int(newX),int(newY)))
#-----------------------------------------------------------------------
fig_mu = plt.figure(figsize=(10,10))
for tr in range(X.shape[0]):
fig_mu.clear()
image_mu = []
img_mu = []
rho_mu = []
rho_mu = (X[tr,:,0]-np.min(X[tr,:,0]))/(np.max(X[tr,:,0])-np.min(X[tr,:,0]))
mne.viz.plot_topomap(rho_mu, info, sensors=False, show=False, cmap=cmap, contours=0)
path_mu = 'figures/'+str(time_inf)+'s-'+str(time_sup)+'s/'+types[itm]+'/train/sbj_'+str(id_sbj)+'_tr_'+str(tr+1)+'_fb_mu.png'
fig_mu.savefig(fname=path_mu,dpi=40,format='png',facecolor='w')
image_mu = cv2.imread(path_mu,0)
img_mu = cv2.resize(image_mu,(int(newX),int(newY)))
X_train_reshape[tr,0,:,:] = img_mu
#-----------------------------------------------------------------------
fig_beta = plt.figure(figsize=(10,10))
for tr in range(X.shape[0]):#
fig_beta.clear()
image_beta = []
img_beta = []
rho_beta = []
rho_beta = (X[tr,:,1]-np.min(X[tr,:,1]))/(np.max(X[tr,:,1])-np.min(X[tr,:,1]))
mne.viz.plot_topomap(rho_beta, info, sensors=False, show=False, cmap=cmap, contours=0)
path_beta = 'figures/'+str(time_inf)+'s-'+str(time_sup)+'s/'+types[itm]+'/train/sbj_'+str(id_sbj)+'_tr_'+str(tr+1)+'_fb_beta.png'
fig_beta.savefig(fname=path_beta,dpi=40,format='png',facecolor='w')
image_beta = cv2.imread(path_beta,0)
img_beta = cv2.resize(image_beta,(int(newX),int(newY)))
X_train_reshape[tr,1,:,:] = img_beta
#-----------------------------------------------------------------------
X = X_train_reshape.copy()
#-----------------------------------------------------------------------
# test
X1 = XT_test.copy()
#-----------------------------------------------------------------------
try:
os.mkdir('figures/'+str(time_inf)+'s-'+str(time_sup)+'s/'+types[itm]+'/test')
except OSError:
print('Folder exists!')
#-----------------------------------------------------------------------
X_test_reshape = np.zeros((X1.shape[0],X1.shape[2],int(newX),int(newY)))
#-----------------------------------------------------------------------
fig_mu = plt.figure(figsize=(10,10))
for tr in range(X1.shape[0]):
fig_mu.clear()
image_mu = []
img_mu = []
rho_mu = []
rho_mu = (X1[tr,:,0]-np.min(X1[tr,:,0]))/(np.max(X1[tr,:,0])-np.min(X1[tr,:,0]))
mne.viz.plot_topomap(rho_mu, info, sensors=False, show=False, cmap=cmap, contours=0)
path_mu = 'figures/'+str(time_inf)+'s-'+str(time_sup)+'s/'+types[itm]+'/test/sbj_'+str(id_sbj)+'_tr_'+str(tr+1)+'_fb_mu.png'
fig_mu.savefig(fname=path_mu,dpi=40,format='png',facecolor='w')
image_mu = cv2.imread(path_mu,0)
img_mu = cv2.resize(image_mu,(int(newX),int(newY)))
X_test_reshape[tr,0,:,:] = img_mu
#-----------------------------------------------------------------------
fig_beta = plt.figure(figsize=(10,10))
for tr in range(X1.shape[0]):
fig_beta.clear()
image_beta = []
img_beta = []
rho_beta = []
rho_beta = (X1[tr,:,1]-np.min(X1[tr,:,1]))/(np.max(X1[tr,:,1])-np.min(X1[tr,:,1]))
mne.viz.plot_topomap(rho_beta, info, sensors=False, show=False, cmap=cmap, contours=0)
path_beta = 'figures/'+str(time_inf)+'s-'+str(time_sup)+'s/'+types[itm]+'/test/sbj_'+str(id_sbj)+'_tr_'+str(tr+1)+'_fb_beta.png'
fig_beta.savefig(fname=path_beta,dpi=40,format='png',facecolor='w')
image_beta = cv2.imread(path_beta,0)
img_beta = cv2.resize(image_beta,(int(newX),int(newY)))
X_test_reshape[tr,1,:,:] = img_beta
#-----------------------------------------------------------------------
X1 = X_test_reshape.copy()
#-----------------------------------------------------------------------
Xtr = X
Xts = X1
with open('/content/drive/MyDrive/Colab Notebooks/GradCam_Paper/GigaData/data/CWT_CSP_data_mubeta_8_30_Tw_'+str(time_inf)+'s_'+str(time_sup)+'s_subject'+str(id_sbj)+'_'+types[itm]+'_resized_10.pickle', 'wb') as f:
pickle.dump([Xtr, Xts, y_train, y_test], f)
#-----------------------------------------------------------------------
#-------------------------------------------------------------------------------
###Output
_____no_output_____
###Markdown
CWT and CSP feature extraction
###Code
,# Experiment information--------------------------------------------------------
th = np.array([[0.5, 2.5],[1.5, 3.5],[2.5, 4.5],[3.5, 5.5],[4.5, 6.5]])
th_name = np.array([[-1.5, 0.5],[-0.5, 1.5],[0.5, 2.5],[1.5, 3.5],[2.5, 4.5]])
freq_ref = np.array([[8,12],[12,30]])
Nsbj = [1]
#-------------------------------------------------------------------------------
for sbj in range(len(Nsbj)):#loop across subjects
for i in range(th_name.shape[0]):#loop across time windows #
#-----------------------------------------------------------------------
print('Subject - '+str(Nsbj[sbj])+' - Time window '+str(i+1)+' of '+str(th_name.shape[0]))
#-----------------------------------------------------------------------
# load EEG signals------------------------------------------------------
name = '/content/drive/MyDrive/Universidad-2020/CNN_GIGA/GIGAdata/s' + str(Nsbj[sbj])
filename_train = name+'.mat'
ch = np.arange(0,64)
vt = [th[i,0],th[i,1]]
Xraw,y,sfreq = leer_GIGA_data(filename_train,ch,vt,Nsbj[sbj])
fs = sfreq
#-----------------------------------------------------------------------
# Filtering-------------------------------------------------------------
f_frec = np.transpose(np.array([[8],[30]]))
Xraw_filt = bank_filter_epochsEEG(Xraw, fs, f_frec)
#-----------------------------------------------------------------------
# Split in train/test subsets-------------------------------------------
rs = ShuffleSplit(n_splits=1, train_size=0.9, test_size=0.1, random_state=0)
for train_index, test_index in rs.split(y):
X_train, y_train = Xraw_filt[train_index], y[train_index]
X_test, y_test = Xraw_filt[test_index], y[test_index]
#-----------------------------------------------------------------------
if i==0:
with open('/content/drive/MyDrive/Colab Notebooks/GradCam_Paper/GigaData/data/idxs_train_test_'+str(Nsbj[sbj])+'.pickle', 'wb') as f:
pickle.dump([train_index, test_index], f)
#-----------------------------------------------------------------------
# Compute CWT feature extraction----------------------------------------
X_cwt_train = cwt_feat_extraction(X_train,fs,freq_ref)
X_cwt_test = cwt_feat_extraction(X_test,fs,freq_ref)
#-----------------------------------------------------------------------
# Compute CSP feature extraction----------------------------------------
X_csp_train,X_csp_test = csp_feat_extraction(np.squeeze(X_train),y_train,np.squeeze(X_test),fs,freq_ref)
#-----------------------------------------------------------------------
# Save extracted features-----------------------------------------------
with open('/content/drive/MyDrive/Colab Notebooks/GradCam_Paper/GigaData/data/X_cwt_sbj_'+str(Nsbj[sbj])+'_Tw_'+str(th_name[i,0])+'s_'+str(th_name[i,1])+'s.pickle', 'wb') as f:
pickle.dump([X_cwt_train, X_cwt_test, y_train, y_test], f)
with open('/content/drive/MyDrive/Colab Notebooks/GradCam_Paper/GigaData/data/X_csp_sbj_'+str(Nsbj[sbj])+'_Tw_'+str(th_name[i,0])+'s_'+str(th_name[i,1])+'s.pickle', 'wb') as f:
pickle.dump([X_csp_train, X_csp_test, y_train, y_test], f)
#-----------------------------------------------------------------------
print('Feature Extraction Done!!!\n')
###Output
_____no_output_____
###Markdown
Topographic map montage
###Code
# set EEG montage using standard 10-20 system-----------------------------------
channels_names = ['FP1','AF7','AF3','F1','F3','F5','F7','FT7','FC5','FC3','FC1','C1',
'C3','C5','T7','TP7','CP5','CP3','CP1','P1','P3','P5','P7','P9','PO7',
'PO3','O1','Iz','Oz','POz','Pz','CPz','FPz','FP2','AF8','AF4','AFz',
'Fz','F2','F4','F6','F8','FT8','FC6','FC4','FC2','FCz','Cz','C2','C4',
'C6','T8','TP8','CP6','CP4','CP2','P2','P4','P6','P8','P10','PO8',
'PO4','O2']
montage = mne.channels.read_montage('standard_1020', channels_names)
info = mne.create_info(channels_names, sfreq=512, ch_types="eeg",
montage=montage)
f,ax = plt.subplots(1,1,figsize=(3,3))
mne.viz.plot_sensors(info, show_names=True,axes=ax)
#-------------------------------------------------------------------------------
###Output
_____no_output_____
###Markdown
Topoplot generation
###Code
# Load feat data----------------------------------------------------------------
th_name = np.array([[-1.5, 0.5],[-0.5, 1.5],[0.5, 2.5],[1.5, 3.5],[2.5, 4.5]])
types = ['cwt','csp']
Nsbj = [30]
#-------------------------------------------------------------------------------
for sbj in range(len(Nsbj)):#loop across subjects
try:
os.mkdir('figures')
except OSError:
print('Folder exists!')
for i in range(th_name.shape[0]):#loop across time windows
print('Subject - '+str(Nsbj[sbj])+' - Time window '+str(i+1)+' of '+str(th_name.shape[0]))
try:
os.mkdir('figures/'+str(th_name[i,0])+'s-'+str(th_name[i,1])+'s')
except OSError:
print('Folder exists!')
topomap_generation(types,th_name[i,0],th_name[i,1],Nsbj[sbj],info)
shutil.rmtree('figures', ignore_errors=True)
print('Topoplot generation Done!!!\n')
#-------------------------------------------------------------------------------
###Output
_____no_output_____
|
archive/test_phage_reference/0_explore_references_created.ipynb
|
###Markdown
Test references createdHere we want to see how well _pilA_ gene aligns against PAO1+phage reference vs PA14+phage reference. This _pilA_ gene has a low sequence identity with ????
###Code
import os
import pandas as pd
import numpy as np
from Bio import SeqIO
from core_acc_modules import paths
%%bash -s $paths.PAO1_PHAGE_REF $paths.PAO1_PHAGE_DB_DIR
makeblastdb -in $1 -dbtype nucl -parse_seqids -out $2
%%bash -s $paths.PA14_PHAGE_REF $paths.PA14_PHAGE_DB_DIR
makeblastdb -in $1 -dbtype nucl -parse_seqids -out $2
%%bash -s $paths.PILA_QUERY $paths.PAO1_PILA_BLAST_RESULT $paths.PAO1_PHAGE_DB_DIR
blastn -query $1 -out $2 -db $3 -outfmt 6
%%bash -s $paths.PILA_QUERY $paths.PA14_PILA_BLAST_RESULT $paths.PA14_PHAGE_DB_DIR
blastn -query $1 -out $2 -db $3 -outfmt 6
pao1_blast_result = pd.read_csv(paths.PAO1_PILA_BLAST_RESULT, sep="\t", header=None)
pa14_blast_result = pd.read_csv(paths.PA14_PILA_BLAST_RESULT, sep="\t", header=None)
# Add column names described above
col_names = [
"qseqid",
"sseqid",
"pident",
"length",
"mismatch",
"gapopen",
"qstart",
"qend",
"sstart",
"send",
"evalue",
"bitscore",
]
# BLAST results for PAO1
pao1_blast_result.columns = col_names
print(pao1_blast_result.shape)
print(pao1_blast_result["evalue"].max())
pao1_blast_result.head()
# BLAST results for PA14
pa14_blast_result.columns = col_names
print(pa14_blast_result.shape)
print(pa14_blast_result["evalue"].max())
pa14_blast_result.head()
###Output
(2, 12)
2.5100000000000002e-23
|
Chapter03/Activity3.07/Activity3.07.ipynb
|
###Markdown
Displaying multiple images In this activity, we will plot images in a grid.
###Code
# Import statements
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
###Output
_____no_output_____
###Markdown
Load all four images from the subfolder data.
###Code
# Load images
img_filenames = sorted(os.listdir('../../datasets/images'))
imgs = [mpimg.imread(os.path.join('../../datasets/images', img_filename)) for img_filename in img_filenames]
###Output
_____no_output_____
###Markdown
Visualize the images in a 2x2 grid. Remove the axes and give each image a label.
###Code
# Create subplot
fig, axes = plt.subplots(2, 2)
fig.figsize = (6, 6)
fig.dpi = 150
axes = axes.ravel()
# Specify labels
labels = ['coast', 'beach', 'building', 'city at night']
# Plot images
for i in range(len(imgs)):
axes[i].imshow(imgs[i])
axes[i].set_xticks([])
axes[i].set_yticks([])
axes[i].set_xlabel(labels[i])
plt.show()
###Output
_____no_output_____
###Markdown
Activity 3.07: Plotting Multiple Images in a Grid In this activity, we will plot images in a grid.
###Code
# Import statements
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
###Output
_____no_output_____
###Markdown
Load all four images from the subfolder data.
###Code
# Load images
img_filenames = sorted(os.listdir('../../Datasets/images'))
imgs = [mpimg.imread(os.path.join('../../Datasets/images', img_filename)) for img_filename in img_filenames]
###Output
_____no_output_____
###Markdown
Visualize the images in a 2x2 grid. Remove the axes and give each image a label.
###Code
# Create subplot
fig, axes = plt.subplots(2, 2)
fig.figsize = (6, 6)
fig.dpi = 150
axes = axes.ravel()
# Specify labels
labels = ['coast', 'beach', 'building', 'city at night']
# Plot images
for i in range(len(imgs)):
axes[i].imshow(imgs[i])
axes[i].set_xticks([])
axes[i].set_yticks([])
axes[i].set_xlabel(labels[i])
plt.show()
###Output
_____no_output_____
###Markdown
Activity 3.07: Plotting Multiple Images in a Grid In this activity, we will plot images in a grid.
###Code
# Import statements
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
###Output
_____no_output_____
###Markdown
Load all four images from the subfolder data.
###Code
# Load images
img_filenames = sorted(os.listdir('../../Datasets/images'))
imgs = [mpimg.imread(os.path.join('../../Datasets/images', img_filename)) for img_filename in img_filenames]
###Output
_____no_output_____
###Markdown
Visualize the images in a 2x2 grid. Remove the axes and give each image a label.
###Code
# Create subplot
fig, axes = plt.subplots(2, 2)
fig.figsize = (6, 6)
fig.dpi = 150
axes = axes.ravel()
# Specify labels
labels = ['coast', 'beach', 'building', 'city at night']
# Plot images
for i in range(len(imgs)):
axes[i].imshow(imgs[i])
axes[i].set_xticks([])
axes[i].set_yticks([])
axes[i].set_xlabel(labels[i])
plt.show()
###Output
_____no_output_____
|
Lesson03/Exercise42-47/Matplotlib and Descriptive Statistics.ipynb
|
###Markdown
Exercise 22: Intro to Matplotlib through a simple scatter plot
###Code
people = ['Ann','Brandon','Chen','David','Emily','Farook',
'Gagan','Hamish','Imran','Joseph','Katherine','Lily']
age = [21,12,32,45,37,18,28,52,5,40,48,15]
weight = [55,35,77,68,70,60,72,69,18,65,82,48]
height = [160,135,170,165,173,168,175,159,105,171,155,158]
plt.scatter(age,weight)
plt.show()
plt.figure(figsize=(8,6))
plt.title("Plot of Age vs. Weight (in kgs)",fontsize=20)
plt.xlabel("Age (years)",fontsize=16)
plt.ylabel("Weight (kgs)",fontsize=16)
plt.grid (True)
plt.ylim(0,100)
plt.xticks([i*5 for i in range(12)],fontsize=15)
plt.yticks(fontsize=15)
plt.scatter(x=age,y=weight,c='orange',s=150,edgecolors='k')
plt.text(x=20,y=85,s="Weights are more or less similar \nafter 18-20 years of age",fontsize=15)
plt.vlines(x=20,ymin=0,ymax=80,linestyles='dashed',color='blue',lw=3)
plt.legend(['Weight in kgs'],loc=2,fontsize=12)
plt.show()
###Output
_____no_output_____
###Markdown
Exercise 23: Generating random numbers from a Uniform distribution
###Code
x = np.random.randint(1,10)
print(x)
x = np.random.randint(1,10,size=1)
print(x)
x = np.random.randint(1,6,size=10)
print(x)
x = 50+50*np.random.random(size=15)
x= x.round(decimals=2)
print(x)
x = np.random.rand(3,3)
print(x)
###Output
[[0.22970354 0.0465693 0.05575691]
[0.57112177 0.37055141 0.62719709]
[0.82848353 0.06978211 0.39281706]]
###Markdown
Exercise 24: Generating random numbers from a Binomial distribution and Bar plot
###Code
x = np.random.binomial(10,0.6,size=8)
print(x)
plt.figure(figsize=(7,4))
plt.title("Number of successes in coin toss",fontsize=16)
plt.bar(left=np.arange(1,9),height=x)
plt.xlabel("Experiment number",fontsize=15)
plt.ylabel("Number of successes",fontsize=15)
plt.show()
###Output
_____no_output_____
###Markdown
Exercise 25: Generating random numbers from Normal distribution and Histogram
###Code
x = np.random.normal()
print(x)
heights = np.random.normal(loc=155,scale=10,size=100)
plt.figure(figsize=(7,5))
plt.hist(heights,color='orange',edgecolor='k')
plt.title("Histogram of teen aged students's height",fontsize=18)
plt.xlabel("Height in cm",fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.show()
###Output
_____no_output_____
###Markdown
Exercise 26: Calculation of descriptive statistics from a DataFrame
###Code
people_dict={'People':people,'Age':age,'Weight':weight,'Height':height}
people_df=pd.DataFrame(data=people_dict)
people_df
print(people_df['Age'].mean())
print(people_df['Height'].max())
print(people_df['Weight'].std())
np.percentile(people_df['Age'],25)
pcnt_75 = np.percentile(people_df['Age'],75)
pcnt_25 = np.percentile(people_df['Age'],25)
print("Inter-quartile range: ",pcnt_75-pcnt_25)
print(people_df.describe())
###Output
Age Weight Height
count 12.000000 12.000000 12.000000
mean 29.416667 59.916667 157.833333
std 15.329463 18.451205 19.834925
min 5.000000 18.000000 105.000000
25% 17.250000 53.250000 157.250000
50% 30.000000 66.500000 162.500000
75% 41.250000 70.500000 170.250000
max 52.000000 82.000000 175.000000
###Markdown
Exercise 27: DataFrame even has built-in plotting utilities
###Code
people_df['Weight'].hist()
plt.show()
people_df.plot.scatter('Weight','Height',s=150,c='orange',edgecolor='k')
plt.grid(True)
plt.title("Weight vs. Height scatter plot",fontsize=18)
plt.xlabel("Weight (in kg)",fontsize=15)
plt.ylabel("Height (in cm)",fontsize=15)
plt.show()
###Output
_____no_output_____
|
praktikum/backlog/NeuralNetwork.ipynb
|
###Markdown
Neural Network with Raw 28x28 Images
###Code
title = "raw"
if os.path.exists("results/"+FOLDER+"/"+title+".json"):
os.remove("results/"+FOLDER+"/"+title+".json")
for run_number in range(NUMBER_RUNS):
for subset in itertools.combinations([i for i in range(10)],2):
print(run_number, subset)
tf.keras.backend.clear_session()
model = tf.keras.Sequential([
tf.keras.layers.Dense(4, activation='relu'),
tf.keras.layers.Dense(2, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.binary_crossentropy,
metrics=['accuracy'])
x_train_subset = x_train[(y_train == subset[0]) | (y_train == subset[1])] / 255
y_train_subset = y_train[(y_train == subset[0]) | (y_train == subset[1])]
y_train_subset = np.where(y_train_subset == subset[0], 0, y_train_subset)
y_train_subset = np.where(y_train_subset == subset[1], 1, y_train_subset)
x_test_subset = x_test[(y_test == subset[0]) | (y_test == subset[1])] / 255
y_test_subset = y_test[(y_test == subset[0]) | (y_test == subset[1])]
y_test_subset = np.where(y_test_subset == subset[0], 0, y_test_subset)
y_test_subset = np.where(y_test_subset == subset[1], 1, y_test_subset)
hist = model.fit(x_train_subset, y_train_subset, epochs=3, batch_size=32,validation_data=(x_test_subset, y_test_subset),verbose=0 )
h.log_results(filename="results/"+FOLDER+"/"+title+".json",result={
str(run_number): {
str(subset):hist.history["val_accuracy"][-1]
}
})
del model
###Output
0 (0, 1)
|
Python_Standard_Llibrary/Python Standard Library .ipynb
|
###Markdown
A cheatsheet for Python standard library Software License Agreement (MIT License) Copyright (c) 2019, Amirhossein Pakdaman. "Python Standard Library" math
###Code
import math
# constants
print(math.pi)
print(math.e)
print(math.nan)
print(math.inf)
print(-math.inf)
# trigonometry
a1 = math.cos(math.pi / 4) # cos(45) deg
a2 = math.sin(math.pi / 4) # sin(45) deg
print(a1)
print(a2)
# ciel & floor
print(math.ceil(47.3)) # rounds up
print(math.ceil(47.8))
print(math.ceil(48))
print(math.floor(47.3)) # rounds down
print(math.floor(47.8))
print(math.floor(47))
# factorial & square root
print(math.factorial(3))
print(math.sqrt(64))
# GCD : Gratest Common Denominator
print(math.gcd(52,8))
# degrees & radians
print(math.radians(360))
print(math.degrees(math.pi * 2))
###Output
6.283185307179586
360.0
###Markdown
random
###Code
import random
print(random.random()) # float random between 0 & 1
print(random.randrange(2)) # random 0 or 1
print(random.randrange(1,7)) # random between 1 & 6
winners = random.sample(range(100),5) # selects 5 numbers from the range and returns in a list
print(winners)
pets = ['cat', 'dog', 'fish', 'kitten']
print(random.choice(pets))
random.shuffle(pets)
print(pets)
###Output
0.5054766469456693
1
2
[30, 37, 95, 11, 29]
dog
['fish', 'kitten', 'cat', 'dog']
###Markdown
statistics
###Code
import statistics
data = [10, 15, 10, 11, 12, 10, 10, 13, 14]
print(statistics.mean(data)) # avarage
print(statistics.mode(data)) # most frequent value
print(statistics.median(data)) # mid point of data
print(statistics.variance(data)) # variance - the avarage of squared differences from the mean, tells how varied is the data
print(statistics.stdev(data)) # standard diviation - the square root of devience
###Output
11.666666666666666
10
11
3.75
1.9364916731037085
###Markdown
itertools
###Code
import itertools
# infinite count
for x in itertools.count(50,5):
print(x)
if x >= 70: break
# infinite cycle
i = 0
for c in itertools.cycle('RACECAR'):
print(c)
i += 1
if i >= 10: break
# infinite repeat
i = 0
for r in itertools.repeat(True):
print(r)
i += 1
if i >= 10: break
# permutations - all posssible orders of a data
dic1 = {1:'bob' , 2:'john' , 3:'linda'}
for p1 in itertools.permutations(dic1):
print(p1)
for p2 in itertools.permutations(dic1.values()):
print(p2)
# combinations - all posssible orders of a particular number of data
colors = ['red', 'blue', 'green', 'pink']
for c in itertools.combinations(colors, 2):
print(c)
###Output
('red', 'blue')
('red', 'green')
('red', 'pink')
('blue', 'green')
('blue', 'pink')
('green', 'pink')
###Markdown
command line arguments
###Code
# assuming that some arguments are passed as file runs in cmd
import sys
print(sys.argv) # prints the arguments, first argument is file path
sys.argv.remove(sys.argv[0]) # removes the first argument
print(sys.argv)
###Output
['C:\\Users\\Amirhossein\\AppData\\Roaming\\jupyter\\runtime\\kernel-cc794b46-5e36-4946-9832-c6e03807c0ae.json']
###Markdown
tempfile
###Code
import tempfile
tmp = tempfile.TemporaryFile()
tmp.write(b'some data on temp file') # b changes data to byte
tmp.seek(0)
print(tmp.read())
tmp.close()
###Output
b'some data on temp file'
###Markdown
HTML parser
###Code
from html.parser import HTMLParser
class HTMLParser(HTMLParser):
def handle_starttag(self, tag, attrs):
print('Start tag: ', tag)
for atr in attrs:
print('attrs:', atr)
def handle_endtag(self, tag):
print('End tag: ', tag)
def handle_comment(self,com):
print('Comment: ', com)
def handle_data(self, data):
print('Data: ', data)
parser = HTMLParser()
parser.feed('<html><head><title>Code</title></head><body><h1><!--hi!-->I am a Coder!</h1></body></html>')
print()
# html data from consule
inhtml = input('Put soem HTML code: ')
parser.feed(inhtml)
print()
# html data from a file
htmlfile = open('sample_html.html', 'r')
s = ''
for line in htmlfile:
s += line
parser.feed(s)
###Output
Start tag: h2
Data: Welcome
End tag: h2
Data:
Start tag: p
Data: Welcome to my blog
End tag: p
###Markdown
text wrap
###Code
import textwrap
textdata = ''' This is a text data
for testing text wrapper module
in Python standard libaray.'''
print('No Dedent:') # keeps the beginning tab, does not keep the enters
print(textwrap.fill(textdata))
print('-------------')
print('Dedent: ') # removes the beginning spases and keeps our enters
dedtxt = textwrap.dedent(textdata).strip()
print(dedtxt)
print('-------------')
print('Fill: ')
print(textwrap.fill(dedtxt, width=80)) # sets next line by rhe given width
print('')
print(textwrap.fill(dedtxt, width=10))
print('-------------')
print('Controlled indent: ')
print(textwrap.fill(dedtxt, initial_indent=' ' , subsequent_indent=' '))
print('-------------')
print('Shortening text: ')
shr = textwrap.shorten('Some text data for testing', width=20, placeholder='...')
print(shr)
###Output
No Dedent:
This is a text data for testing text wrapper module in Python
standard libaray.
-------------
Dedent:
This is a text data
for testing text wrapper module
in Python standard libaray.
-------------
Fill:
This is a text data for testing text wrapper module in Python standard libaray.
This is a
text data
for
testing
text
wrapper
module in
Python
standard
libaray.
-------------
Controlled indent:
This is a text data for testing text wrapper module in Python
standard libaray.
-------------
Shortening text:
Some text data...
###Markdown
HTTP package, urllib, json
###Code
import urllib.request
import json
import textwrap
with urllib.request.urlopen("https://www.googleapis.com/books/v1/volumes?q=isbn:1101904224") as f:
text = f.read()
decodedtext = text.decode('utf-8')
print(textwrap.fill(decodedtext, width=50))
print('------------------------------------------')
obj = json.loads(decodedtext)
print(obj['kind'])
print('------------------------------------------')
print(obj['items'][0]['searchInfo']['textSnippet'])
###Output
{ "kind": "books#volumes", "totalItems": 1,
"items": [ { "kind": "books#volume", "id":
"1imJDAAAQBAJ", "etag": "I6DIl71MSQo",
"selfLink": "https://www.googleapis.com/books/v1/v
olumes/1imJDAAAQBAJ", "volumeInfo": {
"title": "Dark Matter", "subtitle": "A Novel",
"authors": [ "Blake Crouch" ],
"publisher": "Crown Books", "publishedDate":
"2016", "description": "A mind-bending,
relentlessly paced science-fiction thriller, in
which an ordinary man is kidnapped, knocked
unconscious--and awakens in a world inexplicably
different from the reality he thought he knew.",
"industryIdentifiers": [ { "type":
"ISBN_13", "identifier": "9781101904220"
}, { "type": "ISBN_10",
"identifier": "1101904224" } ],
"readingModes": { "text": false,
"image": false }, "pageCount": 342,
"printType": "BOOK", "categories": [
"Fiction" ], "averageRating": 4.0,
"ratingsCount": 944, "maturityRating":
"NOT_MATURE", "allowAnonLogging": false,
"contentVersion": "0.1.0.0.preview.0",
"panelizationSummary": {
"containsEpubBubbles": false,
"containsImageBubbles": false },
"imageLinks": { "smallThumbnail": "http://boo
ks.google.com/books/content?id=1imJDAAAQBAJ&prints
ec=frontcover&img=1&zoom=5&source=gbs_api",
"thumbnail": "http://books.google.com/books/conten
t?id=1imJDAAAQBAJ&printsec=frontcover&img=1&zoom=1
&source=gbs_api" }, "language": "en",
"previewLink": "http://books.google.com/books?id=1
imJDAAAQBAJ&dq=isbn:1101904224&hl=&cd=1&source=gbs
_api", "infoLink": "http://books.google.com/bo
oks?id=1imJDAAAQBAJ&dq=isbn:1101904224&hl=&source=
gbs_api", "canonicalVolumeLink": "https://book
s.google.com/books/about/Dark_Matter.html?hl=&id=1
imJDAAAQBAJ" }, "saleInfo": { "country":
"IR", "saleability": "NOT_FOR_SALE",
"isEbook": false }, "accessInfo": {
"country": "IR", "viewability": "NO_PAGES",
"embeddable": false, "publicDomain": false,
"textToSpeechPermission": "ALLOWED", "epub": {
"isAvailable": false }, "pdf": {
"isAvailable": false }, "webReaderLink": "
http://play.google.com/books/reader?id=1imJDAAAQBA
J&hl=&printsec=frontcover&source=gbs_api",
"accessViewStatus": "NONE",
"quoteSharingAllowed": false },
"searchInfo": { "textSnippet": "A mind-
bending, relentlessly paced science-fiction
thriller, in which an ordinary man is kidnapped,
knocked unconscious--and awakens in a world
inexplicably different from the reality he thought
he knew." } } ] }
------------------------------------------
books#volumes
------------------------------------------
A mind-bending, relentlessly paced science-fiction thriller, in which an ordinary man is kidnapped, knocked unconscious--and awakens in a world inexplicably different from the reality he thought he knew.
|
Section4.ipynb
|
###Markdown
###Code
! apt update
! apt install openjdk-8-jdk-headless -qq > /dev/null
! wget -q http://archive.apache.org/dist/spark/spark-2.3.1/spark-2.3.1-bin-hadoop2.7.tgz
! tar xf spark-2.3.1-bin-hadoop2.7.tgz
! pip install -q findspark
import os
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
os.environ["SPARK_HOME"] = "/content/spark-2.3.1-bin-hadoop2.7"
! ls
import findspark
findspark.init()
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
spark
from pyspark.sql import types
df = spark.read.csv("challenge.csv", header=True)
df.show()
from pyspark.sql import functions as sqlf
df_mex = df.withColumn("mexico", sqlf.when(df.Country == "Mexico", "YES").otherwise("NO"))
df_mex.show()
df_mex.groupBy("mexico").agg(sqlf.sum(df.Bytes_used)).alias("Bytes_Mexico").show()
df_ip_country = df.groupBy("Country").agg(sqlf.countDistinct("ip_address").alias("ips_per_country"))
df_ip_country.sort(col("ips_per_country").desc()).show()
###Output
_____no_output_____
###Markdown
Section 4: Statistical Observations (via Stats Linear Regression) **Corbett includes his work using stats linear regression here).** Fit a stats linear regression to dfX and describe the statistical findings (R-squared, p-value, F value, etc).
###Code
import statsmodels.formula.api as sm
ols1 = sm.ols(formula='CAISO_HourlyLoad ~ isWeekend + DayofYear + CA_CustomerCount + apparentTemperatureMax + apparentTemperatureMin + dewPoint + uvIndex + GDP + CAPOP + pressure + humidity',
data=dfX).fit()
ols1.summary()
###Output
_____no_output_____
###Markdown
**Interpretation:** We used the stats model package to evaluate our data with linear regression. We started by creating a linear model with all 12 independent variables. The initial linear model has an adjusted R squared of 0.742 which is a good starting point. There are some independent variables with high p-values (CA_CustomerCount, uvIndex, CAPOP) indicating that they are probably not statically significant to our model and our model would perform better without them.
###Code
ols2 = sm.ols(formula='CAISO_HourlyLoad ~ isWeekend + DayofYear + apparentTemperatureMax + apparentTemperatureMin + dewPoint + GDP + pressure + humidity',
data=dfX).fit()
ols2.summary()
###Output
_____no_output_____
###Markdown
**Interpretation:** After removing the variables CA_CustomerCount, uvIndex, and CAPOP the adjusted R squared of our model mainly stayed the same at 0.742. We did see a good increase in the F-stat of the model from 331.5 to 455.4. Looking at the scatter plot matrix it appears that apparentTemperatureMax and apparentTemperatureMin could have more of an exponential rather a linear relationship with our dependent variable.
###Code
ols3 = sm.ols(formula='CAISO_HourlyLoad ~ isWeekend + DayofYear + I(apparentTemperatureMin ** 3)+ I(apparentTemperatureMax ** 3)+ dewPoint + GDP + humidity',
data=dfX).fit()
ols3.summary()
###Output
_____no_output_____
|
notebooks/legacy/lightheavy/fraction-correct-RF.ipynb
|
###Markdown
Random forest fraction correct analysis Table of contents1. [Data preprocessing](Data-preprocessing)2. [Fitting random forest](Fit-random-forest-and-run-10-fold-CV-validation)3. [Feature importance](Feature-importance)
###Code
import sys
sys.path.append('/home/jbourbeau/cr-composition')
print('Added to PYTHONPATH')
import argparse
from collections import defaultdict
import itertools
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn.apionly as sns
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score, StratifiedShuffleSplit, KFold
import composition as comp
import composition.analysis.plotting as plotting
# Plotting-related
sns.set_palette('muted')
sns.set_color_codes()
color_dict = defaultdict()
for i, composition in enumerate(['light', 'heavy', 'total']):
color_dict[composition] = sns.color_palette('muted').as_hex()[i]
%matplotlib inline
###Output
_____no_output_____
###Markdown
Data preprocessing1. Load simulation dataframe and apply specified quality cuts2. Extract desired features from dataframe3. Get separate testing and training datasets
###Code
df, cut_dict = comp.load_sim(return_cut_dict=True)
selection_mask = np.array([True] * len(df))
standard_cut_keys = ['lap_reco_success', 'lap_zenith', 'num_hits_1_30', 'IT_signal',
'max_qfrac_1_30', 'lap_containment', 'energy_range_lap']
for key in standard_cut_keys:
selection_mask *= cut_dict[key]
df = df[selection_mask]
feature_list, feature_labels = comp.get_training_features()
print('training features = {}'.format(feature_list))
X_train, X_test, y_train, y_test, le = comp.get_train_test_sets(df, feature_list, comp_class=True)
print('number training events = ' + str(y_train.shape[0]))
print('number testing events = ' + str(y_test.shape[0]))
###Output
training features = ['lap_log_energy', 'InIce_charge_1_30', 'lap_cos_zenith', 'NChannels_1_30', 'log_s125']
number training events = 145543
number testing events = 62376
###Markdown
Fit random forest and run 10-fold CV validation
###Code
pipeline = comp.get_pipeline('RF')
clf_name = pipeline.named_steps['classifier'].__class__.__name__
print('=' * 30)
print(clf_name)
scores = cross_val_score(estimator=pipeline, X=X_train, y=y_train, cv=10, n_jobs=20)
print('CV score: {:.2%} (+/- {:.2%})'.format(scores.mean(), scores.std()))
print('=' * 30)
def get_frac_correct(X_train, X_test, y_train, y_test, comp_list):
pipeline = comp.get_pipeline('RF')
pipeline.fit(X_train, y_train)
test_predictions = pipeline.predict(X_test)
correctly_identified_mask = (test_predictions == y_test)
# Energy-related variables
energy_bin_width = 0.1
energy_bins = np.arange(6.2, 8.1, energy_bin_width)
energy_midpoints = (energy_bins[1:] + energy_bins[:-1]) / 2
log_energy = X_test[:, 0]
# Construct MC composition masks
MC_comp_mask = {}
for composition in comp_list:
MC_comp_mask[composition] = (le.inverse_transform(y_test) == composition)
# Get number of MC comp in each reco energy bin
num_MC_energy, num_MC_energy_err = {}, {}
for composition in comp_list:
num_MC_energy[composition] = np.histogram(log_energy[MC_comp_mask[composition]],
bins=energy_bins)[0]
num_MC_energy_err[composition] = np.sqrt(num_MC_energy[composition])
num_MC_energy['total'] = np.histogram(log_energy, bins=energy_bins)[0]
num_MC_energy_err['total'] = np.sqrt(num_MC_energy['total'])
# Get number of correctly identified comp in each reco energy bin
num_reco_energy, num_reco_energy_err = {}, {}
for composition in comp_list:
num_reco_energy[composition] = np.histogram(
log_energy[MC_comp_mask[composition] & correctly_identified_mask],
bins=energy_bins)[0]
num_reco_energy_err[composition] = np.sqrt(num_reco_energy[composition])
num_reco_energy['total'] = np.histogram(log_energy[correctly_identified_mask], bins=energy_bins)[0]
num_reco_energy_err['total'] = np.sqrt(num_reco_energy['total'])
# Calculate correctly identified fractions as a function of MC energy
reco_frac, reco_frac_err = {}, {}
for composition in comp_list:
# print(composition)
reco_frac[composition], reco_frac_err[composition] = comp.ratio_error(
num_reco_energy[composition], num_reco_energy_err[composition],
num_MC_energy[composition], num_MC_energy_err[composition])
frac_correct_folds[composition].append(reco_frac[composition])
reco_frac['total'], reco_frac_err['total'] = comp.ratio_error(
num_reco_energy['total'], num_reco_energy_err['total'],
num_MC_energy['total'], num_MC_energy_err['total'])
return reco_frac, reco_frac_err
###Output
_____no_output_____
###Markdown
Compute systematic in fraction correct via CV
###Code
comp_list = ['light', 'heavy']
# Split data into training and test samples
kf = KFold(n_splits=10)
frac_correct_folds = defaultdict(list)
fold_num = 0
for train_index, test_index in kf.split(X_train):
fold_num += 1
print('Fold number {}...'.format(fold_num))
X_train_fold, X_test_fold = X_train[train_index], X_train[test_index]
y_train_fold, y_test_fold = y_train[train_index], y_train[test_index]
reco_frac, reco_frac_err = get_frac_correct(X_train_fold, X_test_fold,
y_train_fold, y_test_fold,
comp_list)
for composition in comp_list:
frac_correct_folds[composition].append(reco_frac[composition])
frac_correct_folds['total'].append(reco_frac['total'])
frac_correct_sys_err = {key: np.std(frac_correct_folds[key], axis=0) for key in frac_correct_folds}
reco_frac, reco_frac_sterr = get_frac_correct(X_train, X_test,
y_train, y_test,
comp_list)
# Energy-related variables
energy_bin_width = 0.1
energy_bins = np.arange(6.2, 8.1, energy_bin_width)
energy_midpoints = (energy_bins[1:] + energy_bins[:-1]) / 2
step_x = energy_midpoints
step_x = np.append(step_x[0]-energy_bin_width/2, step_x)
step_x = np.append(step_x, step_x[-1]+energy_bin_width/2)
# Plot fraction of events vs energy
def plot_steps(x, y, y_err, ax, color, label):
step_x = x
x_widths = x[1:]-x[:-1]
if len(np.unique(x_widths)) != 1:
raise('Unequal bins...')
x_width = np.unique(x_widths)[0]
step_x = np.append(step_x[0]-x_width/2, step_x)
step_x = np.append(step_x, step_x[-1]+x_width/2)
step_y = y
step_y = np.append(step_y[0], step_y)
step_y = np.append(step_y, step_y[-1])
err_upper = y + y_err
err_upper = np.append(err_upper[0], err_upper)
err_upper = np.append(err_upper, err_upper[-1])
err_lower = y - y_err
err_lower = np.append(err_lower[0], err_lower)
err_lower = np.append(err_lower, err_lower[-1])
ax.step(step_x, step_y, where='mid',
marker=None, color=color, linewidth=1,
linestyle='-', label=label, alpha=0.8)
ax.fill_between(step_x, err_upper, err_lower,
alpha=0.15, color=color,
step='mid', linewidth=1)
return step_x, step_y
fig, ax = plt.subplots()
for composition in comp_list + ['total']:
err = np.sqrt(frac_correct_sys_err[composition]**2+reco_frac_sterr[composition]**2)
plot_steps(energy_midpoints, reco_frac[composition], err, ax, color_dict[composition], composition)
plt.xlabel('$\log_{10}(E_{\mathrm{reco}}/\mathrm{GeV})$')
ax.set_ylabel('Fraction correctly identified')
ax.set_ylim([0.0, 1.0])
ax.set_xlim([6.2, 8.0])
ax.grid()
leg = plt.legend(loc='upper center',
bbox_to_anchor=(0.5, # horizontal
1.1),# vertical
ncol=len(comp_list)+1, fancybox=False)
# set the linewidth of each legend object
for legobj in leg.legendHandles:
legobj.set_linewidth(3.0)
# place a text box in upper left in axes coords
textstr = '$\mathrm{\underline{Training \ features}}$: \n'
for i, label in enumerate(feature_labels):
if (i == len(feature_labels)-1):
textstr += '{}) '.format(i+1) + label
else:
textstr += '{}) '.format(i+1) + label + '\n'
props = dict(facecolor='white', linewidth=0)
ax.text(1.025, 0.855, textstr, transform=ax.transAxes, fontsize=8,
verticalalignment='top', bbox=props)
cvstr = '$\mathrm{\underline{CV \ score}}$:\n' + '{:0.2f}\% (+/- {:.2}\%)'.format(scores.mean()*100, scores.std()*100)
print(cvstr)
props = dict(facecolor='white', linewidth=0)
ax.text(1.025, 0.9825, cvstr, transform=ax.transAxes, fontsize=8,
verticalalignment='top', bbox=props)
plt.show()
df, cut_dict = comp.load_sim(return_cut_dict=True)
selection_mask = np.array([True] * len(df))
standard_cut_keys = ['lap_reco_success', 'lap_zenith', 'num_hits_1_30', 'IT_signal',
'max_qfrac_1_30', 'lap_containment', 'energy_range_lap']
for key in standard_cut_keys:
selection_mask *= cut_dict[key]
df = df[selection_mask]
# feature_list, feature_labels = comp.get_training_features()
feature_list = np.array(['lap_log_energy', 'InIce_log_charge_1_30', 'lap_cos_zenith',
'log_NChannels_1_30', 'log_s125', 'StationDensity', 'charge_nchannels_ratio',
'stationdensity_charge_ratio', 'lap_likelihood'])
label_dict = {'reco_log_energy': '$\log_{10}(E_{\mathrm{reco}}/\mathrm{GeV})$',
'lap_log_energy': '$\log_{10}(E_{\mathrm{Lap}}/\mathrm{GeV})$',
'log_s125': '$\log_{10}(S_{\mathrm{125}})$',
'lap_likelihood': '$r\log_{10}(l)$',
'InIce_charge_1_30': 'InIce charge (top 50\%)',
'InIce_log_charge_1_30': '$\log_{10}$(InIce charge (top 50\%))',
'lap_cos_zenith': '$\cos(\\theta_{\mathrm{Lap}})$',
'LLHlap_cos_zenith': '$\cos(\\theta_{\mathrm{Lap}})$',
'LLHLF_cos_zenith': '$\cos(\\theta_{\mathrm{LLH+COG}})$',
'lap_chi2': '$\chi^2_{\mathrm{Lap}}/\mathrm{n.d.f}$',
'NChannels_1_30': 'NChannels (top 50\%)',
'log_NChannels_1_30' : '$\log_{10}$(NChannels (top 50\%))',
'StationDensity': 'StationDensity',
'charge_nchannels_ratio': 'Charge/NChannels',
'stationdensity_charge_ratio': 'StationDensity/Charge',
}
feature_labels = np.array([label_dict[feature] for feature in feature_list])
print('training features = {}'.format(feature_list))
X_train, X_test, y_train, y_test, le = comp.get_train_test_sets(
df, feature_list, train_he=True, test_he=True)
print('number training events = ' + str(y_train.shape[0]))
print('number testing events = ' + str(y_test.shape[0]))
fig, axarr = plt.subplots(2, 2, sharex=True, sharey=True)
max_depth_list = [3, 5, 6, 7]
for max_depth, ax in zip(max_depth_list, axarr.flatten()):
pipeline = comp.get_pipeline('RF')
params = {'classifier__max_depth': max_depth}
pipeline.set_params(**params)
sbs = comp.analysis.SBS(pipeline, k_features=2)
sbs.fit(X_train, y_train)
# plotting performance of feature subsets
k_feat = [len(k) for k in sbs.subsets_]
ax.plot(k_feat, sbs.scores_, marker='.', linestyle=':')
# plt.ylim([0.5, 1.1])
ax.set_xlim([sorted(k_feat)[0]-1, sorted(k_feat)[-1]+1])
ax.set_ylabel('Accuracy')
ax.set_xlabel('Number of features')
ax.set_title('max depth = {}'.format(max_depth))
ax.grid()
plt.tight_layout()
plt.show()
print(sbs.subsets_)
print(len(feature_list))
gen = (feature_list[i] for i in sbs.subsets_)
for i in sbs.subsets_:
print(', '.join(feature_list[np.array(i)]))
print('\n')
feature_list[ np.array((0, 3, 4))]
###Output
_____no_output_____
###Markdown
Feature importance
###Code
num_features = len(feature_list)
pipeline = comp.get_pipeline('RF')
pipeline.fit(X_train, y_train)
importances = pipeline.named_steps['classifier'].feature_importances_
indices = np.argsort(importances)[::-1]
fig, ax = plt.subplots()
for f in range(num_features):
print('{}) {}'.format(f + 1, importances[indices[f]]))
plt.ylabel('Feature Importances')
plt.bar(range(num_features),
importances[indices],
align='center')
plt.xticks(range(num_features),
feature_labels[indices], rotation=90)
plt.xlim([-1, len(feature_list)])
# plt.ylim([0, .40])
plt.show()
probs = pipeline.named_steps['classifier'].predict_proba(X_test)
prob_1 = probs[:, 0][MC_iron_mask]
prob_2 = probs[:, 1][MC_iron_mask]
# print(min(prob_1-prob_2))
# print(max(prob_1-prob_2))
# plt.hist(prob_1-prob_2, bins=30, log=True)
plt.hist(prob_1, bins=np.linspace(0, 1, 50), log=True)
plt.hist(prob_2, bins=np.linspace(0, 1, 50), log=True)
probs = pipeline.named_steps['classifier'].predict_proba(X_test)
dp1 = (probs[:, 0]-probs[:, 1])[MC_proton_mask]
print(min(dp1))
print(max(dp1))
dp2 = (probs[:, 0]-probs[:, 1])[MC_iron_mask]
print(min(dp2))
print(max(dp2))
fig, ax = plt.subplots()
# plt.hist(prob_1-prob_2, bins=30, log=True)
counts, edges, pathes = plt.hist(dp1, bins=np.linspace(-1, 1, 100), log=True, label='Proton', alpha=0.75)
counts, edges, pathes = plt.hist(dp2, bins=np.linspace(-1, 1, 100), log=True, label='Iron', alpha=0.75)
plt.legend(loc=2)
plt.show()
pipeline.named_steps['classifier'].classes_
print(pipeline.named_steps['classifier'].classes_)
le.inverse_transform(pipeline.named_steps['classifier'].classes_)
pipeline.named_steps['classifier'].decision_path(X_test)
comp_list = ['P', 'He', 'O', 'Fe']
# test_probs = defaultdict(list)
fig, ax = plt.subplots()
test_probs = pipeline.predict_proba(X_test)
for class_ in pipeline.classes_:
composition = le.inverse_transform(class_)
plt.hist(test_probs[:, class_], bins=np.linspace(0, 1, 50),
histtype='step', label=composition,
color=color_dict[composition], alpha=0.8, log=True)
plt.ylabel('Counts')
plt.xlabel('Testing set class probabilities')
plt.legend()
plt.grid()
plt.show()
pipeline = comp.get_pipeline('RF')
pipeline.fit(X_train, y_train)
test_predictions = pipeline.predict(X_test)
comp_list = ['P', 'He', 'O', 'Fe']
fig, ax = plt.subplots()
test_probs = pipeline.predict_proba(X_test)
fig, axarr = plt.subplots(2, 2, sharex=True, sharey=True)
for composition, ax in zip(comp_list, axarr.flatten()):
comp_mask = (le.inverse_transform(y_test) == composition)
probs = np.copy(test_probs[comp_mask])
print('probs = {}'.format(probs.shape))
weighted_mass = np.zeros(len(probs))
for class_ in pipeline.classes_:
c = le.inverse_transform(class_)
weighted_mass += comp.simfunctions.comp2mass(c) * probs[:, class_]
print('min = {}'.format(min(weighted_mass)))
print('max = {}'.format(max(weighted_mass)))
ax.hist(weighted_mass, bins=np.linspace(0, 5, 100),
histtype='step', label=None, color='darkgray',
alpha=1.0, log=False)
for c in comp_list:
ax.axvline(comp.simfunctions.comp2mass(c), color=color_dict[c],
marker='None', linestyle='-')
ax.set_ylabel('Counts')
ax.set_xlabel('Weighted atomic number')
ax.set_title('MC {}'.format(composition))
ax.grid()
plt.tight_layout()
plt.show()
pipeline = comp.get_pipeline('RF')
pipeline.fit(X_train, y_train)
test_predictions = pipeline.predict(X_test)
comp_list = ['P', 'He', 'O', 'Fe']
fig, ax = plt.subplots()
test_probs = pipeline.predict_proba(X_test)
fig, axarr = plt.subplots(2, 2, sharex=True, sharey=True)
for composition, ax in zip(comp_list, axarr.flatten()):
comp_mask = (le.inverse_transform(y_test) == composition)
probs = np.copy(test_probs[comp_mask])
weighted_mass = np.zeros(len(probs))
for class_ in pipeline.classes_:
c = le.inverse_transform(class_)
ax.hist(probs[:, class_], bins=np.linspace(0, 1, 50),
histtype='step', label=c, color=color_dict[c],
alpha=1.0, log=True)
ax.legend(title='Reco comp', framealpha=0.5)
ax.set_ylabel('Counts')
ax.set_xlabel('Testing set class probabilities')
ax.set_title('MC {}'.format(composition))
ax.grid()
plt.tight_layout()
plt.show()
comp_list = np.unique(df['MC_comp'])
test_probs = defaultdict(list)
fig, ax = plt.subplots()
# test_probs = pipeline.predict_proba(X_test)
for event in pipeline.predict_proba(X_test):
composition = le.inverse_transform(np.argmax(event))
test_probs[composition].append(np.amax(event))
for composition in comp_list:
plt.hist(test_probs[composition], bins=np.linspace(0, 1, 100),
histtype='step', label=composition,
color=color_dict[composition], alpha=0.8, log=False)
plt.ylabel('Counts')
plt.xlabel('Testing set class probabilities')
plt.legend(title='Reco comp')
plt.grid()
plt.show()
###Output
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|
develop/2019-04-25-mh-titanic.ipynb
|
###Markdown
Intorduction What is in this notebook? Inputs The following are the inputs which the model needs to run, please select one of the below for each input:
###Code
# inputs go here
###Output
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###Markdown
Magics & Versions The below table shows the version of libraries and packages used for running the model.
###Code
# Inline matplotlib
%matplotlib inline
# Interactive matplotlib plot()
#%matplotlib notebook
# Autoreload packages before runs
# https://ipython.org/ipython-doc/dev/config/extensions/autoreload.html
%load_ext autoreload
%autoreload 2
# %install_ext http://raw.github.com/jrjohansson/version_information/master/version_information.py
# ~/anaconda/bin/pip install version_information
%load_ext version_information
%version_information numpy, scipy, matplotlib, pandas
###Output
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###Markdown
Standard imports
###Code
# Standard library
import os
import sys
sys.path.append("../src/")
# Third party imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# Date and time
import datetime
import time
# Ipython imports
from IPython.display import FileLink
###Output
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###Markdown
Other imports
###Code
# Other imports
# Stats models
import statsmodels.api as sm
from statsmodels.nonparametric.kde import KDEUnivariate
from statsmodels.nonparametric import smoothers_lowess
from patsy import dmatrices
from sklearn import datasets, svm
# Sk-learn
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier
from sklearn.feature_selection import SelectKBest
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
###Output
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###Markdown
Customization
###Code
# Customizations
sns.set() # matplotlib defaults
# Any tweaks that normally go in .matplotlibrc, etc., should explicitly go here
plt.rcParams['figure.figsize'] = (12, 12)
# Silent mode
import warnings
warnings.filterwarnings('ignore')
warnings.filterwarnings('ignore', category=DeprecationWarning)
# Find the notebook the saved figures came from
fig_prefix = "../figures/2019-04-25-mh-titanic"
###Output
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###Markdown
Data preprocessing Reading the data
###Code
data = pd.read_csv('../data/training/train.csv')
data.shape
###Output
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###Markdown
We have:* 891 rows* 12 columns Exploring the data
###Code
data.head()
data.dtypes
###Output
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###Markdown
The Survived column is the target variable. If Suvival = 1 the passenger survived, otherwise he's dead. The is the variable we're going to predict.The other variables describe the passengers. They are the features.* PassengerId: and id given to each traveler on the boat* Pclass: the passenger class. It has three possible values: 1,2,3 (first, second and third class)* The Name of the passeger* The Sex* The Age* SibSp: number of siblings and spouses traveling with the passenger* Parch: number of parents and children traveling with the passenger* The ticket number* The ticket Fare* The cabin number* The embarkation. This describe three possible areas of the Titanic from which the people embark. Three possible values S,C,Q Features unique values
###Code
for col in data.columns.values:
print(col, ' :', data[col].nunique())
###Output
_____no_output_____
###Markdown
Pclass, Sex, Embarked are categorical features.
###Code
data.describe()
data.info()
###Output
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###Markdown
Age seems to have 177 missing values. let's impute this using the median age. Missing values
###Code
data['Age'] = data['Age'].fillna(data['Age'].median())
data.describe()
###Output
_____no_output_____
###Markdown
Visualization
###Code
data['Died'] = 1 - data['Survived']
###Output
_____no_output_____
###Markdown
Sex
###Code
# Survival count based on gender
data.groupby('Sex').agg('sum')[['Survived', 'Died']].plot(kind='Bar', figsize=(12, 7),
stacked=True, colors=['g', 'r'])
plt.savefig(fig_prefix + '-Sex', dpi=300)
# Survival ratio based on the gender
data.groupby('Sex').agg('mean')[['Survived', 'Died']].plot(kind='Bar', figsize=(12, 7),
stacked=True, colors=['g', 'r'])
plt.savefig(fig_prefix + '-Sex-ratio', dpi=300)
###Output
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###Markdown
Age
###Code
# Violin plots for correlating the survival with sex and age
fig = plt.figure(figsize=(12, 7))
sns.violinplot(x='Sex', y='Age',
hue='Survived', data=data,
split=True,
palette={0:'r', 1:'g'}
)
plt.savefig(fig_prefix + '-age', dpi=300)
###Output
_____no_output_____
###Markdown
As we saw in the chart above and validate by the following:* Women survive more than men, as depicted by the larger female green histogramNow, we see that:* The age conditions the survival for male passengers: * Younger male tend to survive * A large number of passengers between 20 and 40 succumb* The age doesn't seem to have a direct impact on the female survival Ticket fare
###Code
figure = plt.figure(figsize=(25, 12))
plt.hist([data[data['Survived'] == 1]['Fare'], data[data['Survived'] == 0]['Fare']],
stacked=True, color=['g', 'r'],
bins=50, label = ['Survived', 'Dead'])
plt.xlabel('Fare')
plt.ylabel('Nubmer of passerngers')
plt.legend()
plt.savefig(fig_prefix + '-fare-dist', dpi=300)
plt.figure(figsize=(25, 7))
ax = plt.subplot()
ax.scatter(data[data['Survived'] == 1]['Age'], data[data['Survived'] == 1]['Fare'],
c='g', s=data[data['Survived'] == 1]['Fare'])
ax.scatter(data[data['Survived'] == 0]['Age'], data[data['Survived'] == 0]['Fare'],
c='r', s=data[data['Survived'] == 0]['Fare'])
plt.savefig(fig_prefix + '-fare-scatter', dpi=300)
###Output
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###Markdown
The size of the circles is proportional to the ticket fare.On the x-axis, we have the ages and the y-axis, we consider the ticket fare.We can observe different clusters:1. Large green dots between x=20 and x=45: adults with the largest ticket fares2. Small red dots between x=10 and x=45, adults from lower classes on the boat3. Small greed dots between x=0 and x=7: these are the children that were saved
###Code
ax = plt.subplot()
ax.set_ylabel('Average fare')
(data.groupby('Pclass')['Fare'].mean()).plot(kind='bar', figsize=(25, 7), ax=ax)
plt.savefig(fig_prefix + '-fare-class', dpi=300)
###Output
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###Markdown
Embarked
###Code
fig = plt.figure(figsize=(25, 7))
sns.violinplot(x='Embarked', y='Fare', hue='Survived', data=data,
split=True, palette={0: 'r', 1: 'g'})
plt.savefig(fig_prefix + '-embared-fare', dpi=300)
###Output
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###Markdown
It seems that the embarkation C have a wider range of fare tickets and therefore the passengers who pay the highest prices are those who survive.We also see this happening in embarkation S and less in embarkation Q. Feature engineering
###Code
# Function that asserts whether or not a feature has been processed.
def status(feature):
print('Processing', feature, ': ok')
# Function for combining the train and the test data
def get_combined_data():
# reading the train data
train = pd.read_csv('../data/training/train.csv')
# reading the test data
test = pd.read_csv('../data/training/test.csv')
# extracting and removing the target from the training data
targets = train.Survived
train.drop(['Survived'], 1, inplace=True)
# merging train data and test data for future feature engineering
# we'll also remove the PassengerID since this is not an informative feature
combined = train.append(test)
combined.reset_index(inplace=True)
combined.drop(['index', 'PassengerId'], inplace=True, axis=1)
return combined
combined = get_combined_data()
print(combined.shape)
###Output
_____no_output_____
###Markdown
Passenger titles
###Code
titles = set()
for name in data['Name']:
titles.add(name.split(',')[1].split('.')[0].strip())
print(titles)
title_dic ={
"Capt": "Officer",
"Col": "Officer",
"Major": "Officer",
"Jonkheer": "Royalty",
"Don": "Royalty",
"Sir" : "Royalty",
"Dr": "Officer",
"Rev": "Officer",
"the Countess":"Royalty",
"Mme": "Mrs",
"Mlle": "Miss",
"Ms": "Mrs",
"Mr" : "Mr",
"Mrs" : "Mrs",
"Miss" : "Miss",
"Master" : "Master",
"Lady" : "Royalty"
}
def get_titles():
# We extract title from each name
combined['Title'] = combined['Name'].map(lambda name:name.split(',')[1].split('.')[0].strip())
# a map of more aggregated title
# we map each title
combined['Title'] = combined.Title.map(title_dic)
status('Title')
return combined
combined = get_titles()
combined.head()
# Checking
combined[combined.Title.isnull()]
###Output
_____no_output_____
###Markdown
There is indeed a NaN value in the line 1305. In fact the corresponding name is Oliva y Ocana, Dona. Fermina.This title was not encoutered in the train dataset. Passenger ages
###Code
# Number of missing ages in train set
print(combined.iloc[:891].Age.isnull().sum())
# Number of missing ages in test set
print(combined.iloc[891:].Age.isnull().sum())
# Grouping
grouped_train = combined.iloc[:891].groupby(['Sex', 'Pclass', 'Title'])
grouped_median_train = grouped_train.median()
grouped_median_train = grouped_median_train.reset_index()[['Sex', 'Pclass', 'Title', 'Age']]
grouped_median_train.head()
def fill_age(row):
condition = (
(grouped_median_train['Sex'] == row['Sex']) &
(grouped_median_train['Title'] == row['Title']) &
(grouped_median_train['Pclass'] == row['Pclass'])
)
return grouped_median_train[condition]['Age'].values[0]
def process_age():
global combined
# a function that fills the missing values of the Age variable
combined['Age'] = combined.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)
status('age')
return combined
combined = process_age()
combined.head()
###Output
_____no_output_____
###Markdown
Names
###Code
def process_names():
global combined
# we clean the Name variable
combined.drop('Name', axis=1, inplace=True)
# encoding in dummy variable
titles_dummies = pd.get_dummies(combined['Title'], prefix='Title')
combined = pd.concat([combined, titles_dummies], axis=1)
# removing the title variable
combined.drop('Title', axis=1, inplace=True)
status('names')
return combined
combined = process_names()
combined.head()
###Output
_____no_output_____
###Markdown
Fare
###Code
def process_fares():
global combined
# there's one missing fare value - replacing it with the mean.
combined.Fare.fillna(combined.iloc[:891].Fare.mean(), inplace=True)
status('fare')
return combined
combined = process_fares()
###Output
_____no_output_____
###Markdown
Embarked
###Code
def process_embarked():
global combined
# two missing embarked values - filling them with the most frequent one in the train set(S)
combined.Embarked.fillna('S', inplace=True)
# dummy encoding
embarked_dummies = pd.get_dummies(combined['Embarked'], prefix='Embarked')
combined = pd.concat([combined, embarked_dummies], axis=1)
combined.drop('Embarked', axis=1, inplace=True)
status('embarked')
return combined
combined = process_embarked()
combined.head()
###Output
_____no_output_____
###Markdown
Cabin
###Code
train_cabin, test_cabin = set(), set()
for c in combined.iloc[:891]['Cabin']:
try:
train_cabin.add(c[0])
except:
train_cabin.add('U')
for c in combined.iloc[891:]['Cabin']:
try:
test_cabin.add(c[0])
except:
test_cabin.add('U')
print(train_cabin)
print(test_cabin)
###Output
_____no_output_____
###Markdown
We don't have any cabin letter in the test set that is not present in the train set.
###Code
def process_cabin():
global combined
# replacing missing cabins with U (for Uknown)
combined.Cabin.fillna('U', inplace=True)
# mapping each Cabin value with the cabin letter
combined['Cabin'] = combined['Cabin'].map(lambda c: c[0])
# dummy encoding ...
cabin_dummies = pd.get_dummies(combined['Cabin'], prefix='Cabin')
combined = pd.concat([combined, cabin_dummies], axis=1)
combined.drop('Cabin', axis=1, inplace=True)
status('cabin')
return combined
###Output
_____no_output_____
###Markdown
This function replaces NaN values with U (for Unknow). It then maps each Cabin value to the first letter. Then it encodes the cabin values using dummy encoding again.
###Code
combined = process_cabin()
combined.head()
###Output
_____no_output_____
###Markdown
Sex
###Code
def process_sex():
global combined
# mapping string values to numerical one
combined['Sex'] = combined['Sex'].map({'male':1, 'female':0})
status('Sex')
return combined
combined = process_sex()
###Output
_____no_output_____
###Markdown
Pclass
###Code
def process_pclass():
global combined
# encoding into 3 categories:
pclass_dummies = pd.get_dummies(combined['Pclass'], prefix="Pclass")
# adding dummy variable
combined = pd.concat([combined, pclass_dummies],axis=1)
# removing "Pclass"
combined.drop('Pclass',axis=1,inplace=True)
status('Pclass')
return combined
combined = process_pclass()
###Output
_____no_output_____
###Markdown
Ticket
###Code
def cleanTicket(ticket):
ticket = ticket.replace('.', '')
ticket = ticket.replace('/', '')
ticket = ticket.split()
ticket = map(lambda t : t.strip(), ticket)
ticket = list(filter(lambda t : not t.isdigit(), ticket))
if len(ticket) > 0:
return ticket[0]
else:
return 'XXX'
tickets = set()
for t in combined['Ticket']:
tickets.add(cleanTicket(t))
print(len(tickets))
def process_ticket():
global combined
# a function that extracts each prefix of the ticket, returns 'XXX' if no prefix (i.e the ticket is a digit)
def cleanTicket(ticket):
ticket = ticket.replace('.','')
ticket = ticket.replace('/','')
ticket = ticket.split()
ticket = map(lambda t : t.strip(), ticket)
ticket = list(filter(lambda t : not t.isdigit(), ticket))
if len(ticket) > 0:
return ticket[0]
else:
return 'XXX'
# Extracting dummy variables from tickets:
combined['Ticket'] = combined['Ticket'].map(cleanTicket)
tickets_dummies = pd.get_dummies(combined['Ticket'], prefix='Ticket')
combined = pd.concat([combined, tickets_dummies], axis=1)
combined.drop('Ticket', inplace=True, axis=1)
status('Ticket')
return combined
combined = process_ticket()
###Output
_____no_output_____
###Markdown
FamilyThis part includes creating new variables based on the size of the family (the size is by the way, another variable we create).This creation of new variables is done under a realistic assumption: Large families are grouped together, hence they are more likely to get rescued than people traveling alone.
###Code
def process_family():
global combined
# introducing a new feature : the size of families (including the passenger)
combined['FamilySize'] = combined['Parch'] + combined['SibSp'] + 1
# introducing other features based on the family size
combined['Singleton'] = combined['FamilySize'].map(lambda s: 1 if s == 1 else 0)
combined['SmallFamily'] = combined['FamilySize'].map(lambda s: 1 if 2 <= s <= 4 else 0)
combined['LargeFamily'] = combined['FamilySize'].map(lambda s: 1 if 5 <= s else 0)
status('family')
return combined
combined = process_family()
print(combined.shape)
combined.head()
###Output
_____no_output_____
|
manifold-demo/Manifold demo.ipynb
|
###Markdown
Manifold**Manifold** is a tool for model-agnostic evaluation with visual support developed by Uber — you can find its repo [here](https://github.com/uber/manifold).In this quick demo, the [California housing dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html) (regression) and part of the setup available in this [example](https://scikit-learn.org/stable/auto_examples/inspection/plot_partial_dependence.html) on the [scikit-learn](https://scikit-learn.org/stable/index.html) website are used.The main idea for this demo is to test **Manifold**'s [integration with Jupyter Notebook](https://eng.uber.com/manifold-open-source/) and the [Geo Feature View](https://github.com/uber/manifoldgeo-feature-view) map.
###Code
from sklearn.datasets import fetch_california_housing
import pandas as pd
import numpy as np
# Installation: https://github.com/uber/manifold/tree/master/bindings/jupyter
from mlvis import Manifold
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import QuantileTransformer
from sklearn.pipeline import make_pipeline
from sklearn.neural_network import MLPRegressor
california_housing = fetch_california_housing()
california_housing.data.shape
california_housing.feature_names
california_housing.target.shape
california_housing.keys()
X = pd.DataFrame(california_housing.data, columns=california_housing.feature_names)
y = california_housing.target
# The Latitude feature must be called "lat".
# The Longitude feature must be called "lng".
X.rename(columns={'Latitude':'lat', 'Longitude': 'lng'}, inplace=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
est = make_pipeline(QuantileTransformer(),
MLPRegressor(hidden_layer_sizes=(50, 50),
learning_rate_init=0.01,
early_stopping=True))
est.fit(X_train, y_train)
print("Test R2 score: {:.2f}".format(est.score(X_test, y_test)))
yPred = est.predict(X_test)
# Mapbox access token: https://docs.mapbox.com/help/how-mapbox-works/access-tokens/
# It must be replaced with a valid token.
TOKEN = "pk.eyJ1Ijoiam9hb3BhbG1laXJvIiwiYSI6ImNrM2c0dnJqMjBheTczcHBnbzAxODFjejQifQ.2EztIjx7QEHHf65x-X5qQg"
# Props: https://github.com/uber/manifold/blob/master/bindings/jupyter-modules/jupyter-manifold/src/manifold.js
# Classification: https://github.com/uber/manifold/blob/master/bindings/jupyter/notebooks/manifold.ipynb
Manifold(props={'data': {
'x': X_test[['lat', 'lng']],
'yPred': [pd.DataFrame(yPred, columns=["Target"])], # Each element in this list contains the predictions for a model.
'yTrue': pd.DataFrame(y_test, columns=["Target"])
},
'mapboxAccessToken': TOKEN,
'width': 1000,
'height': 700
})
###Output
_____no_output_____
|
2_Neural_network/Intro_to_NN/StudentAdmissions.ipynb
|
###Markdown
Predicting Student Admissions with Neural NetworksIn this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:- GRE Scores (Test)- GPA Scores (Grades)- Class rank (1-4)The dataset originally came from here: http://www.ats.ucla.edu/ Loading the dataTo load the data and format it nicely, we will use two very useful packages called Pandas and Numpy. You can read on the documentation here:- https://pandas.pydata.org/pandas-docs/stable/- https://docs.scipy.org/
###Code
# Importing pandas and numpy
import pandas as pd
import numpy as np
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
# Printing out the first 10 rows of our data
data[:10]
###Output
_____no_output_____
###Markdown
Plotting the dataFirst let's make a plot of our data to see how it looks. In order to have a 2D plot, let's ingore the rank.
###Code
# Importing matplotlib
import matplotlib.pyplot as plt
# Function to help us plot
def plot_points(data):
X = np.array(data[["gre","gpa"]])
y = np.array(data["admit"])
admitted = X[np.argwhere(y==1)]
rejected = X[np.argwhere(y==0)]
plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'red', edgecolor = 'k')
plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'cyan', edgecolor = 'k')
plt.xlabel('Test (GRE)')
plt.ylabel('Grades (GPA)')
# Plotting the points
plot_points(data)
plt.show()
###Output
_____no_output_____
###Markdown
\Roughly, it looks like the students with high scores in the grades and test passed, while the ones with low scores didn't, but the data is not as nicely separable as we hoped it would. Maybe it would help to take the rank into account? Let's make 4 plots, each one for each rank.
###Code
# Separating the ranks
data_rank1 = data[data["rank"]==1]
data_rank2 = data[data["rank"]==2]
data_rank3 = data[data["rank"]==3]
data_rank4 = data[data["rank"]==4]
# Plotting the graphs
plot_points(data_rank1)
plt.title("Rank 1")
plt.show()
plot_points(data_rank2)
plt.title("Rank 2")
plt.show()
plot_points(data_rank3)
plt.title("Rank 3")
plt.show()
plot_points(data_rank4)
plt.title("Rank 4")
plt.show()
###Output
_____no_output_____
###Markdown
This looks more promising, as it seems that the lower the rank, the higher the acceptance rate. Let's use the rank as one of our inputs. In order to do this, we should one-hot encode it. TODO: One-hot encoding the rankUse the `get_dummies` function in Pandas in order to one-hot encode the data.
###Code
# TODO: Make dummy variables for rank
one_hot_data = pd.concat([data, pd.get_dummies(data['rank'],prefix = 'rank')], axis=1)
# TODO: Drop the previous rank column
one_hot_data = one_hot_data.drop('rank', axis=1)
# Print the first 10 rows of our data
one_hot_data[:10]
# or Alternatively we can use selection
one_hot_data = pd.get_dummies(data, columns=['rank'])
one_hot_data.head()
###Output
_____no_output_____
###Markdown
TODO: Scaling the dataThe next step is to scale the data. We notice that the range for grades is 1.0-4.0, whereas the range for test scores is roughly 200-800, which is much larger. This means our data is skewed, and that makes it hard for a neural network to handle. Let's fit our two features into a range of 0-1, by dividing the grades by 4.0, and the test score by 800.
###Code
# Making a copy of our data
processed_data = one_hot_data[:]
# TODO: Scale the columns
processed_data['gre'] = processed_data['gre']/800
processed_data['gpa'] = processed_data['gpa']/4
# Printing the first 10 rows of our procesed data
processed_data[:10]
###Output
_____no_output_____
###Markdown
Splitting the data into Training and Testing In order to test our algorithm, we'll split the data into a Training and a Testing set. The size of the testing set will be 10% of the total data.
###Code
sample = np.random.choice(processed_data.index, size=int(len(processed_data)*0.9), replace=False)
train_data, test_data = processed_data.iloc[sample], processed_data.drop(sample)
print("Number of training samples is", len(train_data))
print("Number of testing samples is", len(test_data))
print(train_data[:10])
print(test_data[:10])
###Output
Number of training samples is 360
Number of testing samples is 40
admit gre gpa rank_1 rank_2 rank_3 rank_4
250 0 0.825 0.012930 0 0 0 1
151 0 0.500 0.013203 0 1 0 0
341 1 0.700 0.010352 0 0 1 0
60 1 0.775 0.012422 0 1 0 0
340 0 0.625 0.012617 0 0 0 1
266 0 0.700 0.012656 0 0 0 1
260 0 0.850 0.012148 0 1 0 0
242 1 0.850 0.011562 0 0 1 0
249 0 0.800 0.014570 0 0 1 0
75 0 0.900 0.015625 0 0 1 0
admit gre gpa rank_1 rank_2 rank_3 rank_4
2 1 1.000 0.015625 1 0 0 0
10 0 1.000 0.015625 0 0 0 1
19 1 0.675 0.014883 1 0 0 0
22 0 0.750 0.011016 0 0 0 1
37 0 0.650 0.011328 0 0 1 0
56 0 0.700 0.012461 0 0 1 0
57 0 0.475 0.011484 0 0 1 0
73 0 0.725 0.015625 0 1 0 0
116 1 0.550 0.013477 0 1 0 0
118 1 1.000 0.014453 1 0 0 0
###Markdown
Splitting the data into features and targets (labels)Now, as a final step before the training, we'll split the data into features (X) and targets (y).
###Code
features = train_data.drop('admit', axis=1)
targets = train_data['admit']
features_test = test_data.drop('admit', axis=1)
targets_test = test_data['admit']
print(features[:10])
print(targets[:10])
###Output
gre gpa rank_1 rank_2 rank_3 rank_4
250 0.825 0.012930 0 0 0 1
151 0.500 0.013203 0 1 0 0
341 0.700 0.010352 0 0 1 0
60 0.775 0.012422 0 1 0 0
340 0.625 0.012617 0 0 0 1
266 0.700 0.012656 0 0 0 1
260 0.850 0.012148 0 1 0 0
242 0.850 0.011562 0 0 1 0
249 0.800 0.014570 0 0 1 0
75 0.900 0.015625 0 0 1 0
250 0
151 0
341 1
60 1
340 0
266 0
260 0
242 1
249 0
75 0
Name: admit, dtype: int64
###Markdown
Training the 2-layer Neural NetworkThe following function trains the 2-layer neural network. First, we'll write some helper functions.
###Code
# Activation (sigmoid) function
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def sigmoid_prime(x):
return sigmoid(x) * (1-sigmoid(x))
def error_formula(y, output):
return - y*np.log(output) - (1 - y) * np.log(1-output)
###Output
_____no_output_____
###Markdown
TODO: Backpropagate the errorNow it's your turn to shine. Write the error term. Remember that this is given by the equation $$ (y-\hat{y}) \sigma'(x) $$
###Code
# TODO: Write the error term formula
def error_term_formula(x, y, output):
return (y-output)*sigmoid(x)
## alternatively--> we could only use the 'y' and 'output'
def error_term_formula(x,y,output):
return (y-output) * output * (1-output)
# Neural Network hyperparameters
epochs = 5000
learnrate = 0.5
# Training function
def train_nn(features, targets, epochs, learnrate):
# Use to same seed to make debugging easier
np.random.seed(42)
n_records, n_features = features.shape
last_loss = None
# Initialize weights
weights = np.random.normal(scale=1 / n_features**.5, size=n_features)
for e in range(epochs):
del_w = np.zeros(weights.shape)
for x, y in zip(features.values, targets):
# Loop through all records, x is the input, y is the target
# Activation of the output unit
# Notice we multiply the inputs and the weights here
# rather than storing h as a separate variable
output = sigmoid(np.dot(x, weights))
# The error, the target minus the network output
error = error_formula(y, output)
# The error term
error_term = error_term_formula(x, y, output)
# The gradient descent step, the error times the gradient times the inputs
del_w += error_term * x
# Update the weights here. The learning rate times the
# change in weights, divided by the number of records to average
weights += learnrate * del_w / n_records
# Printing out the mean square error on the training set
if e % (epochs / 10) == 0:
out = sigmoid(np.dot(features, weights))
loss = np.mean((out - targets) ** 2)
print("Epoch:", e)
if last_loss and last_loss < loss:
print("Train loss: ", loss, " WARNING - Loss Increasing")
else:
print("Train loss: ", loss)
last_loss = loss
print("=========")
print("Finished training!")
return weights
weights = train_nn(features, targets, epochs, learnrate)
###Output
Epoch: 0
Train loss: 0.281886441691
=========
Epoch: 500
Train loss: 0.208077992841
=========
Epoch: 1000
Train loss: 0.20667229231
=========
Epoch: 1500
Train loss: 0.205550685982
=========
Epoch: 2000
Train loss: 0.204588002534
=========
Epoch: 2500
Train loss: 0.203759019443
=========
Epoch: 3000
Train loss: 0.203045132254
=========
Epoch: 3500
Train loss: 0.202430176835
=========
Epoch: 4000
Train loss: 0.20190008512
=========
Epoch: 4500
Train loss: 0.201442705149
=========
Finished training!
###Markdown
Calculating the Accuracy on the Test Data
###Code
# Calculate accuracy on test data
test_out = sigmoid(np.dot(features_test, weights))
predictions = test_out > 0.5
accuracy = np.mean(predictions == targets_test)
print("Prediction accuracy: {:.3f}".format(accuracy))
###Output
Prediction accuracy: 0.750
|
Python Basics/Control Flow and Functions.ipynb
|
###Markdown
if, elif, else Statementsif Statements in Python allows us to tell the computer to perform alternative actions based on a certain set of results.Verbally, we can imagine we are telling the computer:"Hey if this case happens, perform some action"We can then expand the idea further with elif and else statements, which allow us to tell the computer:"Hey if this case happens, perform some action. Else, if another case happens, perform some other action. Else, if *none* of the above cases happened, perform this action."Let's go ahead and look at the syntax format for if statements to get a better idea of this: if case1: perform action1 elif case2: perform action2 else: perform action3 First ExampleLet's see a quick example of this:
###Code
if True:
print('It was true!')
###Output
_____no_output_____
###Markdown
Let's add in some else logic:
###Code
x = False
if x:
print('x was True!')
else:
print('I will be printed in any case where x is not true')
print('Here it is')
###Output
_____no_output_____
###Markdown
Multiple BranchesLet's get a fuller picture of how far if, elif, and else can take us!We write this out in a nested structure. Take note of how the if, elif, and else line up in the code. This can help you see what if is related to what elif or else statements.We'll reintroduce a comparison syntax for Python.
###Code
loc = 'Bank'
if loc == 'Auto Shop':
print('Welcome to the Auto Shop!')
elif loc == 'Bank':
print('Welcome to the bank!')
else:
print('Where are you?')
###Output
_____no_output_____
###Markdown
Note how the nested if statements are each checked until a True boolean causes the nested code below it to run. You should also note that you can put in as many elif statements as you want before you close off with an else. if-elif-else ExerciseWrite code to check weather condition with following senarios: 1.) Sunny and Hot (temp is higher than 30) 2.) Sunny and Moderate (temp is between 20 and 30) 3.) Rainy and Cold (temp is less than 20) 4.) Rainy and Very Cold (temp is less than 5)
###Code
# Write your code here
temp=int(input('Enter temperature '))
if temp>30:
print('Sunny and Hot ')
elif 20<=temp<=30:
print('Sunny and Moderate ')
elif 5<=temp<20:
print('Rainy and Cold ')
elif temp<5:
print('Rainy and Very Cold ')
###Output
_____no_output_____
###Markdown
for LoopsA for loop acts as an iterator in Python; it goes through items that are in a *sequence* or any other iterable item. Objects that we've learned about that we can iterate over include strings, lists, tuples, and even built-in iterables for dictionaries, such as keys or values.We've already seen the for statement a little bit in past lectures but now let's formalize our understanding.Here's the general format for a for loop in Python: for item in object: statements to do stuff The variable name used for the item is completely up to the coder, so use your best judgment for choosing a name that makes sense and you will be able to understand when revisiting your code. This item name can then be referenced inside your loop, for example if you wanted to use if statements to perform checks.Let's go ahead and work through several example of for loops using a variety of data object types. We'll start simple and build more complexity later on. Example 1Iterating through a list
###Code
# We'll learn how to automate this sort of list in the next lecture
list1 = [1,2,3,4,5,6,7,8,9,10]
list1
for num in list1:
print(num)
###Output
_____no_output_____
###Markdown
Great! Hopefully this makes sense. Now let's add an if statement to check for even numbers. We'll first introduce a new concept here--the modulo. ModuloThe modulo allows us to get the remainder in a division and uses the % symbol. For example:
###Code
17 % 5
###Output
_____no_output_____
###Markdown
This makes sense since 17 divided by 5 is 3 remainder 2. Let's see a few more quick examples:
###Code
# 3 Remainder 1
10 % 3
# 2 no remainder
4 % 2
###Output
_____no_output_____
###Markdown
Notice that if a number is fully divisible with no remainder, the result of the modulo call is 0. We can use this to test for even numbers, since if a number modulo 2 is equal to 0, that means it is an even number!Back to the for loops! Example 2Let's print only the even numbers from that list!
###Code
for num in list1:
if num % 2 == 0:
print(num)
###Output
_____no_output_____
###Markdown
We could have also put an else statement in there:
###Code
for num in list1:
if num % 2 == 0:
print(num)
else:
print('Odd number')
###Output
_____no_output_____
###Markdown
Example 3
###Code
list1=['w','o','r','d']
print(list1)
result=''
for elements in list1:
result +=elements
###Output
_____no_output_____
###Markdown
Please notice here that result +=elements is equivallent to result=result+elements
###Code
print(result)
###Output
_____no_output_____
###Markdown
For loop Exercise
###Code
#create empty list
lit=[]
#write for loop to assign each letter of "result" separately to list
for i in result:
lit=lit+[i]
#print list
print(lit)
###Output
_____no_output_____
###Markdown
while LoopsThe while statement in Python is one of most general ways to perform iteration. A while statement will repeatedly execute a single statement or group of statements as long as the condition is true. The reason it is called a 'loop' is because the code statements are looped through over and over again until the condition is no longer met.The general format of a while loop is: while test: code statements else: final code statementsLet’s look at a few simple while loops in action. Notice how many times the print statements occurred and how the while loop kept going until the True condition was met, which occurred once x==10. It's important to note that once this occurred the code stopped. Also see how we could add an else statement:
###Code
x = 0
while x < 10:
print('x is currently: ',x)
print(' x is still less than 10, adding 1 to x')
x+=1
else:
print('All Done!')
###Output
_____no_output_____
###Markdown
break, continue, passWe can use break, continue, and pass statements in our loops to add additional functionality for various cases. The three statements are defined by: break: Breaks out of the current closest enclosing loop. continue: Goes to the top of the closest enclosing loop. pass: Does nothing at all. Thinking about break and continue statements, the general format of the while loop looks like this: while test: code statement if test: break if test: continue else:break and continue statements can appear anywhere inside the loop’s body, but we will usually put them further nested in conjunction with an if statement to perform an action based on some condition.Let's go ahead and look at some examples!
###Code
x = 0
while x < 10:
print('x is currently: ',x)
print(' x is still less than 10, adding 1 to x')
x+=1
if x==3:
print('x==3')
else:
print('continuing...')
continue #pass
print('is this executed?')
###Output
_____no_output_____
###Markdown
Note how we have a printed statement when x==3, and a continue being printed out as we continue through the outer while loop. Let's put in a break once x ==3 and see if the result makes sense:
###Code
x = 0
while x < 10:
print('x is currently: ',x)
print(' x is still less than 10, adding 1 to x')
x+=1
if x==3:
print('Breaking because x==3')
break
else:
print('continuing...')
continue
###Output
_____no_output_____
###Markdown
Note how the other else statement wasn't reached and continuing was never printed!After these brief but simple examples, you should feel comfortable using while statements in your code.**A word of caution however! It is possible to create an infinitely running loop with while statements.** While Loop ExerciseWrite code to check if input number is greater than, less than or equal to 50. Loop should be continuous and command of **exit** should end the loop Hint: you need to used input() fucntion and cast the value to 'int' for conditions but don't cast when 'exit'. The code for taking input and casting is as following: x=input("Enter a your value: ")
###Code
#write code here
while True:
x=input("Enter a your value: ")
if x=='exit':
print('breaking ')
break
else:
x=int(x)
if x==50:
print('equal to 50 ')
elif x<50:
print('less than 50 ')
else:
print('greater than 50')
continue
###Output
_____no_output_____
###Markdown
MethodsMethods are essentially functions for the objects. Methods perform specific actions on an object and can also take arguments, just like a function. This lecture will serve as just a brief introduction to methods and get you thinking about overall design methods that we will touch back upon when we reach OOP in the course.Methods are in the form: object.method(arg1,arg2,etc...) You'll later see that we can think of methods as having an argument 'self' referring to the object itself. You can't see this argument but we will be using it later on in the course during the OOP lectures.Let's take a quick look at what an example of the various methods a list has:
###Code
# Create a simple list
lst = [1,2,3,4,5]
###Output
_____no_output_____
###Markdown
Fortunately, with iPython and the Jupyter Notebook we can quickly see all the possible methods using the tab key. The common methods for a list are:* append* count* extend* insert* pop* remove* reverse* sortLet's try out a few of them:
###Code
lst.append(6)
lst
lst.clear()
lst
# Check how many times 2 shows up in the list
lst.count(2)
###Output
_____no_output_____
###Markdown
You can always use Tab in the Jupyter Notebook to get list of all methods you use Shift+Tab in the Jupyter Notebook to get help of all methods. The methods for a string are:* lower* upper* replace* split* count* join* find* endswith Home Task Make a string having two words, starting with same letters and separated by a space like **'Abrar Ahmed'** and perform the following tasks using methods
###Code
#declare a string
name='Abrar Ahmad'
###Output
_____no_output_____
###Markdown
Hint: s.split('Letter to split with', maxsplit=no. of splits)
###Code
# split the string
name.split()
# apply upper() method on the string
name.upper()
# apply lower() method on the string
name.lower()
###Output
_____no_output_____
###Markdown
Functions Introduction to Functions**So what is a function?**Formally, a function is a useful device that groups together a set of statements so they can be run more than once. They can also let us specify parameters that can serve as inputs to the functions.On a more fundamental level, functions allow us to not have to repeatedly write the same code again and again. Functions will be one of most basic levels of reusing code in Python, and it will also allow us to start thinking of program design. def StatementsLet's see how to build out a function's syntax in Python. It has the following form:
###Code
def name_of_function(arg1,arg2):
'''
This is where the function's Document String (docstring) goes
'''
name_of_function(1,2)
###Output
_____no_output_____
###Markdown
We begin with def then a space followed by the name of the function. Try to keep names relevant, for example len() is a good name for a length() function. Also be careful with names, you wouldn't want to call a function the same name as a [built-in function in Python](https://docs.python.org/2/library/functions.html) (such as len).Next come a pair of parentheses with a number of arguments separated by a comma. These arguments are the inputs for your function. You'll be able to use these inputs in your function and reference them. After this you put a colon.The important step is that you must indent to begin the code inside your function correctly. Example 1: A simple print 'hello' function
###Code
def say_hello():
print('hello')
###Output
_____no_output_____
###Markdown
**Call the Function**
###Code
say_hello()
###Output
_____no_output_____
###Markdown
Example 2: A simple greeting functionLet's write a function that greets people with their name.
###Code
def greeting(name):
print('Hello',(name))
greeting('Jose')
###Output
_____no_output_____
###Markdown
Using returnLet's see some example that use a return statement. return allows a function to *return* a result that can then be stored as a variable, or used in whatever manner a user wants. Example 3: Addition function
###Code
def add_num(num1,num2):
return num2,num1
# Can save as variable due to return
result = add_num(4,5)
result
###Output
_____no_output_____
###Markdown
Example 4: Function with default arguments
###Code
def my_power(num,n=2):
'''
Method to get power of number
'''
return num**n
my_power(3,3)
###Output
_____no_output_____
###Markdown
Functions exerciseWrite a function to check if a number is even or odd.
###Code
#define the function
def even_odd(num):
return num%2
#call the function
if even_odd(40):
print('odd')
else:
print('even')
###Output
_____no_output_____
###Markdown
Built-in Functions in PythonWe will discuss some built in functions provided to us by Python. They are as following:* Filter* Map* Lambda* Range* Min, Max* EnumerateLet's have a look at the following examples and see how they work map functionThe **map** function allows you to "map" a function to an iterable object. That is to say you can quickly call the same function to every item in an iterable, such as a list. For example:
###Code
def square(num):
return num**2
my_nums = [1,2,3,4,5]
new_num=[]
for i in my_nums:
new_num.append(square(i))
new_num
###Output
_____no_output_____
###Markdown
**Using the map() function:**
###Code
map(square,my_nums)
#printing results
list(map(square,my_nums))
###Output
_____no_output_____
###Markdown
filter functionThe filter function returns an iterator yielding those items of iterable for which function(item)is true. Meaning you need to filter by a function that returns either True or False. Then passing that into filter (along with your iterable) and you will get back only the results that would return True when passed to the function.
###Code
def check_even(num):
return num % 2 == 0
nums = [0,1,2,3,4,5,6,7,8,9,10]
filter(check_even,nums)
list(filter(check_even,nums))
###Output
_____no_output_____
###Markdown
lambda expressionlambda expressions allow us to create "anonymous" functions. This basically means we can quickly make ad-hoc functions without needing to properly define a function using def.**lambda's body is a single expression, not a block of statements.** Consider the following function:
###Code
def square(num):
result = num**2
return result
square(2)
###Output
_____no_output_____
###Markdown
We could actually even write this all on one line.
###Code
lambda num: num ** 2
# for displaying results
square = lambda num: num **2
###Output
_____no_output_____
###Markdown
Let's repeat some of the examples from above with a lambda expression
###Code
list(map(lambda num: num ** 2, my_nums))
###Output
_____no_output_____
###Markdown
TaskRepeat the **filter()** function example with the **lambda()** functions
###Code
# wrtite the code here
number=[1,2,3,4,5]
list(filter(lambda n:n%2==0,number))
###Output
_____no_output_____
###Markdown
rangeThe range function allows you to quickly *generate* a list of integers, this comes in handy a lot, so take note of how to use it! There are 3 parameters you can pass, a start, a stop, and a step size. Let's see some examples:
###Code
range(0,11)
###Output
_____no_output_____
###Markdown
Note that this is a **generator** function, so to actually get a list out of it, we need to cast it to a list with **list()**. What is a generator? Its a special type of function that will generate information and not need to save it to memory.
###Code
list(range(0,11))
list(range(0,11,2))
###Output
_____no_output_____
###Markdown
enumerateenumerate is a very useful function to use with for loops. Let's imagine the following situation:
###Code
index_count = 0
for letter in 'abcde':
print("At index " + str(index_count) +" the letter is " + str(letter))
index_count += 1
###Output
_____no_output_____
###Markdown
Keeping track of how many loops you've gone through is so common, that enumerate was created so you don't need to worry about creating and updating this index_count or loop_count variable
###Code
# Notice the tuple unpacking!
for i,letter in enumerate('abcde'):
print("At index " + str(i) +" the letter is " + str(letter))
###Output
_____no_output_____
###Markdown
min and maxQuickly check the minimum or maximum of a list with these functions.
###Code
mylist = [10,20,30,40,100]
min(mylist)
max(mylist)
###Output
_____no_output_____
###Markdown
randomPython comes with a built in random library. There are a lot of functions included in this random library, so we will only show you two useful functions for now.
###Code
from random import shuffle
shuffle(mylist)
mylist
from random import randint
randint(0,100)
###Output
_____no_output_____
###Markdown
inputThis function is used in python to take input from user.
###Code
a=input('Enter Something into this box: ')
###Output
_____no_output_____
|
src/get_single_family_cost.ipynb
|
###Markdown
Find cost of single family homes with given zip codes.
###Code
import json
import requests
import pandas as pd
import csv
from config_zillow import api_key
import time
datafile1 = "../data/zipcodes.csv"
df1 = pd.read_csv(datafile1)
len(df1)
###Output
_____no_output_____
###Markdown
Grab zipcode information.
###Code
df1.head(10)
###Output
_____no_output_____
###Markdown
Grab information about single family homes from Zillow.
###Code
url = f"http://www.zillow.com/webservice/GetDemographics.htm?zws-id={api_key}&zip="
zip_code = df1["zip"]
new_url = []
for x in zip_code:
n_url = f"{url}{x}"
new_url.append(n_url)
text_container = []
for y in new_url:
time.sleep(0.5)
try:
response = requests.get(y)
response = response.text
text_container.append(response)
except:
print ("Not available")
df = pd.DataFrame({
"Text": text_container
})
df.to_csv("../data/dumpfile.csv")
zip_code = []
bbc = []
for x in text_container:
try:
ts = "</city><zip>"
aa = x.split(ts)
bb = aa[1].split("<")
bbc.append(aa[1])
zip_co = bb[0]
zip_code.append(zip_co)
except:
continue
cost_home = []
for r in bbc:
s = 'Median Single Family Home Value</name><values><nation><value type="USD">'
t = 'Median Single Family Home Value</name><values><zip><value type="USD">'
try:
aaa = r.split(t)
bbb = aaa[1].split("<")
home_co = bbb[0]
cost_home.append(home_co)
except:
aaaa = r.split(s)
bbbb = aaaa[1].split("<")
home_co = bbbb[0]
cost_home.append(home_co)
data = pd.DataFrame({
"Zip Code": zip_code,
"Cost of Single Family Home": cost_home
})
data["Zip Code"] = data["Zip Code"].astype("int")
data["Cost of Single Family Home"] = data["Cost of Single Family Home"].astype("float64")
data.to_csv("../data/Single_Family_Home_Cost.csv")
data.head(20)
###Output
_____no_output_____
###Markdown
Grab information about single family homes from realtor.com csv file.
###Code
datafile2 = "../data/realtor_info.csv"
df2 = pd.read_csv(datafile2)
df2["Median Listing Price"] = df2["Median Listing Price"].astype("float64")
df2["ZipCode"] = df2["ZipCode"].astype("int")
df2 = df2[["ZipCode", "Median Listing Price"]]
df2 = df2.rename(columns ={"ZipCode": "Zip Code"})
df2.head()
###Output
_____no_output_____
###Markdown
Merge the two tables. Find Average.
###Code
data1 = pd.merge(data, df2, on = "Zip Code", how = "left")
data1.head()
data1["Median Listing Price"] = data1["Median Listing Price"].fillna(0)
data1.head(300)
###Output
_____no_output_____
|
week5/day2.ipynb
|
###Markdown
Metryka sukcesu
###Code
print('mean', mae(df.airmiles, linear_func(df.airmiles)))
print('mean', mae(df.airmiles, linear_func(df.airmiles, 1300, -3000)))
best_k = 1300
best_b = -3000
best_mae = mae(df.airmiles, linear_func(df.airmiles, best_k, -best_b))
for k in range(1000, 1400, 50):
for b in (-4000, -2000, 50):
actual = mae(df.airmiles, linear_func(df.airmiles, k, b))
if actual < best_mae:
best_mae = actual
best_k = k
best_b = b
print("Best mea={} for k={} and b={}".format(best_mae, best_k, best_b))
###Output
Best mea=2714.5833333333335 for k=1250 and b=-4000
|
Chapter07/Activity7.01/Activity7_01.ipynb
|
###Markdown
Activity 7.01
###Code
# Import the Libraries
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPool2D
from keras.layers import Flatten
from keras.layers import Dense
import numpy as np
from tensorflow import random
###Output
Using TensorFlow backend.
###Markdown
Set the seed and initialize the CNN model
###Code
seed = 1
np.random.seed(seed)
random.set_seed(seed)
# Initialising the CNN
classifier = Sequential()
###Output
_____no_output_____
###Markdown
Add the convolutional layers
###Code
# Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
# Pooling
classifier.add(MaxPool2D(pool_size = (2, 2)))
# Flattening
classifier.add(Flatten())
###Output
_____no_output_____
###Markdown
Add the dense layers to the network
###Code
# Full ANN Connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(128,activation='relu'))
classifier.add(Dense(128,activation='relu'))
classifier.add(Dense(128,activation='relu'))
classifier.add(Dense(units = 1, activation = 'softmax'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
###Output
_____no_output_____
###Markdown
Create the training and test data generators
###Code
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
###Output
_____no_output_____
###Markdown
Create training and test datasets
###Code
training_set = train_datagen.flow_from_directory('../dataset/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('../dataset/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
###Output
Found 10764 images belonging to 2 classes.
Found 2674 images belonging to 2 classes.
###Markdown
Fit the model to the training data
###Code
classifier.fit_generator(training_set,
steps_per_epoch = 10000,
epochs = 2,
validation_data = test_set,
validation_steps = 2500,
shuffle=False)
###Output
Epoch 1/2
10000/10000 [==============================] - 3576s 358ms/step - loss: 8.1741 - accuracy: 0.4669 - val_loss: 11.4999 - val_accuracy: 0.4695
Epoch 2/2
10000/10000 [==============================] - 3354s 335ms/step - loss: 8.1764 - accuracy: 0.4667 - val_loss: 7.1875 - val_accuracy: 0.4691
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CVND_Exercises/3_6_Matrices_and_transformation_of_state/1_vector_coding.ipynb
|
###Markdown
Vectors in PythonIn the following exercises, you will work on coding vectors in Python.Assume that you have a state vector $$\mathbf{x_0}$$representing the x position, y position, velocity in the x direction, and velocity in the y direction of a car that is driving in front of your vehicle. You are tracking the other vehicle.Currently, the other vehicle is 5 meters ahead of you along your x-axis, 2 meters to your left along your y-axis, driving 10 m/s in the x direction and 0 m/s in the y-direction. How would you represent this in a Python list where the vector contains `` in exactly that order? Vector Assignment: Example 1
###Code
## Practice working with Python vectors
## TODO: Assume the state vector contains values for <x, y, vx, vy>
## Currently, x = 5, y = 2, vx = 10, vy = 0
## Represent this information in a list
x0 = [5, 2, 10, 0]
###Output
_____no_output_____
###Markdown
Test your codeRun the cell below to test your code. The test code uses a Python assert statement. If you have a code statement that resolves to either True or False, an assert statement will either:* do nothing if the statement is True* throw an error if the statement is FalseA Python assert statementwill output an error if the answer was not as expected. If theanswer was as expected, then nothing will be outputted.
###Code
### Test Cases
### Run these test cases to see if your results are as expected
### Running this cell should produce no output if all assertions are True
assert x0 == [5, 2, 10, 0]
###Output
_____no_output_____
###Markdown
Vector Assignment: Example 2The vehicle ahead of you has now moved farther away from you. You know that the vehicle has moved 3 meters forward in the x-direction, 5 meters forward in the y-direction, has increased its x velocity by 2 m/s and has increased its y velocity by 5 m/s. Store the change in position and velocity in a list variable called xdelta
###Code
## TODO: Assign the change in position and velocity to the variable
## xdelta. Remember that the order of the vector is x, y, vx, vy
xdelta = [3, 5, 2, 5]
### Test Case
### Run this test case to see if your results are as expected
### Running this cell should produce no output if all assertions are True
assert xdelta == [3, 5, 2, 5]
###Output
_____no_output_____
###Markdown
Vector Math: AdditionCalculate the tracked vehicle's new position and velocity. Here are the steps you need to carry this out:* initialize an empty list called x1* add xdelta to x0 using a for loop* store your results in x1 as you iterate through the for loop using the append method
###Code
## TODO: Add the vectors together element-wise. For example,
## element-wise addition of [2, 6] and [10, 3] is [12, 9].
## Place the answer in the x1 variable.
##
## Hint: You can use a for loop. The append method might also
## be helpful.
x1 = []
for i in range(len(x0)):
x1.append(xdelta[i] + x0[i])
### Test Case
### Run this test case to see if your results are as expected
### Running this cell should produce no output if all assertions are True
assert x1 == [8, 7, 12, 5]
###Output
_____no_output_____
###Markdown
Vector Math: Scalar MultiplicationYou have your current position in meters and current velocity in meters per second. But you need to report your results at a company meeting where most people will only be familiar with working in feet rather than meters. Convert your position vector x1 to feet and feet/second.This will involve scalar multiplication. The process for coding scalar multiplication is very similar to vector addition. You will need to:* initialize an empty list* use a for loop to access each element in the vector* multiply each element by the scalar* append the result to the empty list
###Code
## TODO: Multiply each element in the x1 vector by the conversion
## factor shown belowand store the results in the variable s.
## Use a for loop
meters_to_feet = 1.0 / 0.3048
x1feet =[]
for val in x1:
x1feet.append(val * meters_to_feet)
### Test Cases
### Run this test case to see if your results are as expected
### Running this cell should produce no output if all assertions are True
x1feet_sol = [8/.3048, 7/.3048, 12/.3048, 5/.3048]
assert(len(x1feet) == len(x1feet_sol))
for response, expected in zip(x1feet, x1feet_sol):
assert(abs(response-expected) < 0.001)
###Output
_____no_output_____
###Markdown
Vector Math: Dot ProductThe tracked vehicle is currently at the state represented by $$\mathbf{x_1} = [8, 7, 12, 5] $$.Where will the vehicle be in two seconds?You could actually solve this problem very quickly using Matrix multiplication, but we have not covered that yet. Instead, think about the x-direction and y-direction separately and how you could do this with the dot product. Solving with the Dot ProductYou know that the tracked vehicle at x1 is 8m ahead of you in the x-direction and traveling at 12m/s. Assuming constant velocity, the new x-position after 2 seconds would be$$8 + 12*2 = 32$$The new y-position would be$$7 + 5*2 = 17$$You could actually solve each of these equations using the dot product:$$x_2 = [8, 7, 12, 5]\cdot[1, 0, 2, 0] \\\ = 8\times1 + 7\times0 + 12\times2 + 5\times0 \\\= 32$$$$y_2 = [8, 7, 12, 5]\cdot[0, 1, 0, 2] \\\ = 8\times0 + 7\times1 + 12\times0 + 5\times2 \\\= 17$$Since you are assuming constant velocity, the final state vector would be $$\mathbf{x_2} = [32, 17, 12, 5]$$ Coding the Dot ProductNow, calculate the state vector $$\mathbf{x_2}$$ but with code. You will need to calculate the dot product of two vectors. Rather than writing the dot product code for the x-direction and then copying the code for the y-direction, write a function that calculates the dot product of two Python lists.Here is an outline of the steps:* initialize an empty list* initialize a variable with value zero to accumulate the sum* use a for loop to iterate through the vectors. Assume the two vectors have the same length* accumulate the sum as you multiply elements togetherYou will see in the starter code that x2 is already being calculated for you based on the results of your dotproduct function
###Code
## TODO: Fill in the dotproduct() function to calculate the
## dot product of two vectors.
##
## Here are the inputs and outputs of the dotproduct() function:
## INPUTS: vector, vector
## OUTPUT: dot product of the two vectors
##
##
## The dot product involves mutliplying the vectors element
## by element and then taking the sum of the results
##
## For example, the dot product of [9, 7, 5] and [2, 3, 4] is
## 9*2+7*3 +5*4 = 59
##
## Hint: You can use a for loop. You will also need to accumulate
## the sum as you iterate through the vectors. In Python, you can accumulate
## sums with syntax like w = w + 1
x2 = []
def dotproduct(vectora, vectorb):
# variable for accumulating the sum
result = 0
# TODO: Use a for loop to multiply the two vectors
# element by element. Accumulate the sum in the result variable
for i in range(len(vectora)):
result += vectora[i] * vectorb[i]
return result
x2 = [dotproduct([8, 7, 12, 5], [1, 0, 2, 0]),
dotproduct([8, 7, 12, 5], [0, 1, 0, 2]),
12,
5]
### Test Case
### Run this test case to see if your results are as expected
### Running this cell should produce no output if all assertions are True
assert x2 == [32, 17, 12, 5]
###Output
_____no_output_____
|
DCGAN_fake_simple_image_creator.ipynb
|
###Markdown
Deep Convolutional GANsIn this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a Deep Convolutional GAN, or DCGAN for short. The DCGAN architecture was first explored in 2016 and has seen impressive results in generating new images; you can read the [original paper, here](https://arxiv.org/pdf/1511.06434.pdf).You'll be training DCGAN on the [Street View House Numbers](http://ufldl.stanford.edu/housenumbers/) (SVHN) dataset. These are color images of house numbers collected from Google street view. SVHN images are in color and much more variable than MNIST. So, our goal is to create a DCGAN that can generate new, realistic-looking images of house numbers. We'll go through the following steps to do this:* Load in and pre-process the house numbers dataset* Define discriminator and generator networks* Train these adversarial networks* Visualize the loss over time and some sample, generated images Deeper Convolutional NetworksSince this dataset is more complex than our MNIST data, we'll need a deeper network to accurately identify patterns in these images and be able to generate new ones. Specifically, we'll use a series of convolutional or transpose convolutional layers in the discriminator and generator. It's also necessary to use batch normalization to get these convolutional networks to train. Besides these changes in network structure, training the discriminator and generator networks should be the same as before. That is, the discriminator will alternate training on real and fake (generated) images, and the generator will aim to trick the discriminator into thinking that its generated images are real!
###Code
# import libraries
import matplotlib.pyplot as plt
import numpy as np
import pickle as pkl
%matplotlib inline
###Output
_____no_output_____
###Markdown
Getting the dataHere you can download the SVHN dataset. It's a dataset built-in to the PyTorch datasets library. We can load in training data, transform it into Tensor datatypes, then create dataloaders to batch our data into a desired size.
###Code
import torch
from torchvision import datasets
from torchvision import transforms
# Tensor transform
transform = transforms.ToTensor()
# SVHN training datasets
svhn_train = datasets.SVHN(root='data/', split='train', download=True, transform=transform)
batch_size = 128
num_workers = 0
# build DataLoaders for SVHN dataset
train_loader = torch.utils.data.DataLoader(dataset=svhn_train,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
# obtain one batch of training images
dataiter = iter(train_loader)
images, labels = dataiter.next()
# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(25, 4))
plot_size=20
for idx in np.arange(plot_size):
ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])
ax.imshow(np.transpose(images[idx], (1, 2, 0)))
# print out the correct label for each image
# .item() gets the value contained in a Tensor
ax.set_title(str(labels[idx].item()))
###Output
_____no_output_____
###Markdown
Pre-processing: scaling from -1 to 1We need to do a bit of pre-processing; we know that the output of our `tanh` activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)
###Code
# current range
img = images[0]
print('Min: ', img.min())
print('Max: ', img.max())
# helper scale function
def scale(x, feature_range=(-1, 1)):
''' Scale takes in an image x and returns that image, scaled
with a feature_range of pixel values from -1 to 1.
This function assumes that the input x is already scaled from 0-1.'''
# assume x is scaled to (0, 1)
# scale to feature_range and return scaled x
min, max = feature_range
x = x * (max-min) + min
return x
# scaled range
scaled_img = scale(img)
print('Scaled min: ', scaled_img.min())
print('Scaled max: ', scaled_img.max())
###Output
Scaled min: tensor(-1.)
Scaled max: tensor(0.8667)
###Markdown
--- Define the ModelA GAN is comprised of two adversarial networks, a discriminator and a generator. DiscriminatorHere you'll build the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. * The inputs to the discriminator are 32x32x3 tensor images* You'll want a few convolutional, hidden layers* Then a fully connected layer for the output; as before, we want a sigmoid output, but we'll add that in the loss function, [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.htmlbcewithlogitsloss), laterFor the depths of the convolutional layers I suggest starting with 32 filters in the first layer, then double that depth as you add layers (to 64, 128, etc.). Note that in the DCGAN paper, they did all the downsampling using only strided convolutional layers with no maxpooling layers.You'll also want to use batch normalization with [nn.BatchNorm2d](https://pytorch.org/docs/stable/nn.htmlbatchnorm2d) on each layer **except** the first convolutional layer and final, linear output layer. Helper `conv` function In general, each layer should look something like convolution > batch norm > leaky ReLU, and so we'll define a function to put these layers together. This function will create a sequential series of a convolutional + an optional batch norm layer. We'll create these using PyTorch's [Sequential container](https://pytorch.org/docs/stable/nn.htmlsequential), which takes in a list of layers and creates layers according to the order that they are passed in to the Sequential constructor.Note: It is also suggested that you use a **kernel_size of 4** and a **stride of 2** for strided convolutions.
###Code
import torch.nn as nn
import torch.nn.functional as F
# helper conv function
def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):
"""Creates a convolutional layer, with optional batch normalization.
"""
layers = []
conv_layer = nn.Conv2d(in_channels, out_channels,
kernel_size, stride, padding, bias=False)
# append conv layer
layers.append(conv_layer)
if batch_norm:
# append batchnorm layer
layers.append(nn.BatchNorm2d(out_channels))
# using Sequential container
return nn.Sequential(*layers)
class Discriminator(nn.Module):
def __init__(self, conv_dim=32):
super(Discriminator, self).__init__()
# complete init function
self.conv_dim = conv_dim
# 32x32 input
self.conv1 = conv(3, conv_dim, 4, batch_norm=False) # first layer, no batch_norm
# 16x16 input
self.conv2 = conv(conv_dim, conv_dim*2, 4)
# 8x8 input
self.conv3 = conv(conv_dim*2, conv_dim*4, 4)
# 4x4 output
# finally, fully connected layer
self.fc = nn.Linear(conv_dim*4*4*4, 1)
def forward(self, x):
# all hidden layers + leaky relu activation
out = F.leaky_relu(self.conv1(x), 0.2)
out = F.leaky_relu(self.conv2(out), 0.2)
out = F.leaky_relu(self.conv3(out), 0.2)
# flatten
out = out.view(-1, self.conv_dim*4*4*4)
# final output layer
out = self.fc(out)
return out
###Output
_____no_output_____
###Markdown
GeneratorNext, you'll build the generator network. The input will be our noise vector `z`, as before. And, the output will be a $tanh$ output, but this time with size 32x32 which is the size of our SVHN images.What's new here is we'll use transpose convolutional layers to create our new images. * The first layer is a fully connected layer which is reshaped into a deep and narrow layer, something like 4x4x512. * Then, we use batch normalization and a leaky ReLU activation. * Next is a series of [transpose convolutional layers](https://pytorch.org/docs/stable/nn.htmlconvtranspose2d), where you typically halve the depth and double the width and height of the previous layer. * And, we'll apply batch normalization and ReLU to all but the last of these hidden layers. Where we will just apply a `tanh` activation. Helper `deconv` functionFor each of these layers, the general scheme is transpose convolution > batch norm > ReLU, and so we'll define a function to put these layers together. This function will create a sequential series of a transpose convolutional + an optional batch norm layer. We'll create these using PyTorch's Sequential container, which takes in a list of layers and creates layers according to the order that they are passed in to the Sequential constructor.Note: It is also suggested that you use a **kernel_size of 4** and a **stride of 2** for transpose convolutions.
###Code
# helper deconv function
def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):
"""Creates a transposed-convolutional layer, with optional batch normalization.
"""
# create a sequence of transpose + optional batch norm layers
layers = []
transpose_conv_layer = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size, stride, padding, bias=False)
# append transpose convolutional layer
layers.append(transpose_conv_layer)
if batch_norm:
# append batchnorm layer
layers.append(nn.BatchNorm2d(out_channels))
return nn.Sequential(*layers)
class Generator(nn.Module):
def __init__(self, z_size, conv_dim=32):
super(Generator, self).__init__()
# complete init function
self.conv_dim = conv_dim
# first, fully-connected layer
self.fc = nn.Linear(z_size, conv_dim*4*4*4)
# transpose conv layers
self.t_conv1 = deconv(conv_dim*4, conv_dim*2, 4)
self.t_conv2 = deconv(conv_dim*2, conv_dim, 4)
self.t_conv3 = deconv(conv_dim, 3, 4, batch_norm=False)
def forward(self, x):
# fully-connected + reshape
out = self.fc(x)
out = out.view(-1, self.conv_dim*4, 4, 4) # (batch_size, depth, 4, 4)
# hidden transpose conv layers + relu
out = F.relu(self.t_conv1(out))
out = F.relu(self.t_conv2(out))
# last layer + tanh activation
out = self.t_conv3(out)
out = F.tanh(out)
return out
###Output
_____no_output_____
###Markdown
Build complete networkDefine your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments.
###Code
# define hyperparams
conv_dim = 32
z_size = 100
# define discriminator and generator
D = Discriminator(conv_dim)
G = Generator(z_size=z_size, conv_dim=conv_dim)
print(D)
print()
print(G)
###Output
Discriminator(
(conv1): Sequential(
(0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
)
(conv2): Sequential(
(0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(conv3): Sequential(
(0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(fc): Linear(in_features=2048, out_features=1, bias=True)
)
Generator(
(fc): Linear(in_features=100, out_features=2048, bias=True)
(t_conv1): Sequential(
(0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(t_conv2): Sequential(
(0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(t_conv3): Sequential(
(0): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
)
)
###Markdown
Training on GPUCheck if you can train on GPU. If you can, set this as a variable and move your models to GPU. > Later, we'll also move any inputs our models and loss functions see (real_images, z, and ground truth labels) to GPU as well.
###Code
train_on_gpu = torch.cuda.is_available()
if train_on_gpu:
# move models to GPU
G.cuda()
D.cuda()
print('GPU available for training. Models moved to GPU')
else:
print('Training on CPU.')
###Output
GPU available for training. Models moved to GPU
###Markdown
--- Discriminator and Generator LossesNow we need to calculate the losses. And this will be exactly the same as before. Discriminator Losses> * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss`. * Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.The losses will by binary cross entropy loss with logits, which we can get with [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.htmlbcewithlogitsloss). This combines a `sigmoid` activation function **and** and binary cross entropy loss in one function.For the real images, we want `D(real_images) = 1`. That is, we want the discriminator to classify the the real images with a label = 1, indicating that these are real. The discriminator loss for the fake data is similar. We want `D(fake_images) = 0`, where the fake images are the _generator output_, `fake_images = G(z)`. Generator LossThe generator loss will look similar only with flipped labels. The generator's goal is to get `D(fake_images) = 1`. In this case, the labels are **flipped** to represent that the generator is trying to fool the discriminator into thinking that the images it generates (fakes) are real!
###Code
def real_loss(D_out, smooth=False):
batch_size = D_out.size(0)
# label smoothing
if smooth:
# smooth, real labels = 0.9
labels = torch.ones(batch_size)*0.9
else:
labels = torch.ones(batch_size) # real labels = 1
# move labels to GPU if available
if train_on_gpu:
labels = labels.cuda()
# binary cross entropy with logits loss
criterion = nn.BCEWithLogitsLoss()
# calculate loss
loss = criterion(D_out.squeeze(), labels)
return loss
def fake_loss(D_out):
batch_size = D_out.size(0)
labels = torch.zeros(batch_size) # fake labels = 0
if train_on_gpu:
labels = labels.cuda()
criterion = nn.BCEWithLogitsLoss()
# calculate loss
loss = criterion(D_out.squeeze(), labels)
return loss
###Output
_____no_output_____
###Markdown
OptimizersNot much new here, but notice how I am using a small learning rate and custom parameters for the Adam optimizers, This is based on some research into DCGAN model convergence. HyperparametersGANs are very sensitive to hyperparameters. A lot of experimentation goes into finding the best hyperparameters such that the generator and discriminator don't overpower each other. Try out your own hyperparameters or read [the DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf) to see what worked for them.
###Code
import torch.optim as optim
# params - These parameters are set based on the DCGAN paper
lr = 0.0002
beta1=0.5
beta2=0.999 # default value
# Create optimizers for the discriminator and generator
d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])
g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])
###Output
_____no_output_____
###Markdown
--- TrainingTraining will involve alternating between training the discriminator and the generator. We'll use our functions `real_loss` and `fake_loss` to help us calculate the discriminator losses in all of the following cases. Discriminator training1. Compute the discriminator loss on real, training images 2. Generate fake images3. Compute the discriminator loss on fake, generated images 4. Add up real and fake loss5. Perform backpropagation + an optimization step to update the discriminator's weights Generator training1. Generate fake images2. Compute the discriminator loss on fake images, using **flipped** labels!3. Perform backpropagation + an optimization step to update the generator's weights Saving SamplesAs we train, we'll also print out some loss statistics and save some generated "fake" samples.**Evaluation mode**Notice that, when we call our generator to create the samples to display, we set our model to evaluation mode: `G.eval()`. That's so the batch normalization layers will use the population statistics rather than the batch statistics (as they do during training), *and* so dropout layers will operate in eval() mode; not turning off any nodes for generating samples.
###Code
import pickle as pkl
# training hyperparams
num_epochs = 50
# keep track of loss and generated, "fake" samples
samples = []
losses = []
print_every = 300
# Get some fixed data for sampling. These are images that are held
# constant throughout training, and allow us to inspect the model's performance
sample_size=16
fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))
fixed_z = torch.from_numpy(fixed_z).float()
# train the network
for epoch in range(num_epochs):
for batch_i, (real_images, _) in enumerate(train_loader):
batch_size = real_images.size(0)
# important rescaling step
real_images = scale(real_images)
# ============================================
# TRAIN THE DISCRIMINATOR
# ============================================
d_optimizer.zero_grad()
# 1. Train with real images
# Compute the discriminator losses on real images
if train_on_gpu:
real_images = real_images.cuda()
D_real = D(real_images)
d_real_loss = real_loss(D_real)
# 2. Train with fake images
# Generate fake images
z = np.random.uniform(-1, 1, size=(batch_size, z_size))
z = torch.from_numpy(z).float()
# move x to GPU, if available
if train_on_gpu:
z = z.cuda()
fake_images = G(z)
# Compute the discriminator losses on fake images
D_fake = D(fake_images)
d_fake_loss = fake_loss(D_fake)
# add up loss and perform backprop
d_loss = d_real_loss + d_fake_loss
d_loss.backward()
d_optimizer.step()
# =========================================
# TRAIN THE GENERATOR
# =========================================
g_optimizer.zero_grad()
# 1. Train with fake images and flipped labels
# Generate fake images
z = np.random.uniform(-1, 1, size=(batch_size, z_size))
z = torch.from_numpy(z).float()
if train_on_gpu:
z = z.cuda()
fake_images = G(z)
# Compute the discriminator losses on fake images
# using flipped labels!
D_fake = D(fake_images)
g_loss = real_loss(D_fake) # use real loss to flip labels
# perform backprop
g_loss.backward()
g_optimizer.step()
# Print some loss stats
if batch_i % print_every == 0:
# append discriminator loss and generator loss
losses.append((d_loss.item(), g_loss.item()))
# print discriminator and generator loss
print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(
epoch+1, num_epochs, d_loss.item(), g_loss.item()))
## AFTER EACH EPOCH##
# generate and save sample, fake images
G.eval() # for generating samples
if train_on_gpu:
fixed_z = fixed_z.cuda()
samples_z = G(fixed_z)
samples.append(samples_z)
G.train() # back to training mode
# Save training generator samples
with open('train_samples.pkl', 'wb') as f:
pkl.dump(samples, f)
###Output
C:\Users\saidu\Anaconda3\lib\site-packages\torch\nn\functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
###Markdown
Training lossHere we'll plot the training losses for the generator and discriminator, recorded after each epoch.
###Code
fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator', alpha=0.5)
plt.plot(losses.T[1], label='Generator', alpha=0.5)
plt.title("Training Losses")
plt.legend()
###Output
_____no_output_____
###Markdown
Generator samples from trainingHere we can view samples of images from the generator. We'll look at the images we saved during training.
###Code
# helper function for viewing a list of passed in sample images
def view_samples(epoch, samples):
fig, axes = plt.subplots(figsize=(16,4), nrows=2, ncols=8, sharey=True, sharex=True)
for ax, img in zip(axes.flatten(), samples[epoch]):
img = img.detach().cpu().numpy()
img = np.transpose(img, (1, 2, 0))
img = ((img +1)*255 / (2)).astype(np.uint8) # rescale to pixel range (0-255)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
im = ax.imshow(img.reshape((32,32,3)))
_ = view_samples(-1, samples)
###Output
_____no_output_____
|
metodos_numericos/zero_de_funcoes/teoria/02_metodo_da_bissecao.ipynb
|
###Markdown
Método da bisseção*** Iniciamos a nossa discussão com o método da bisseção pois este se trata de uma versão mais sofisticada do raciocínio que fizemos anteriormente.* Começamos mudando ligeiramente o nosso problema.* Ao invés de encontrar o valor de x para o qual $f(x) = y_0$, passamos $y_0$ para o lado esquerdo da igualdade e buscamos o valor de $x$ para o qual $g(x) = f(x) - y_0 = 0$.* Em matemática este problema é conhecido como encontrar o zero de uma função.* É lógico que é trivial transformar um problema em que buscamos $x$ que produz um valor arbitrário (ex.: 42) em um problema de zero de funções. Utilizar o zero, no entanto, simplifica alguns cálculos. *** Exemplos*** Importa as bibliotecas
###Code
import numpy
import matplotlib.pyplot as matplot
%matplotlib inline
###Output
_____no_output_____
###Markdown
*** Definimos a nossa função $f(x) = x^2 + 2x + 1$ e o valor de $y_0$Queremos encontrar o valor de $x$ para o qual $f(x) = y_0$
###Code
def f(x):
return x*x + 2*x + 1
y0 = 42
###Output
_____no_output_____
###Markdown
*** Cria uma sequência de números começando em $0$ e terminando em $10$ e inserir os resultados de $f(x)$ em y
###Code
x = numpy.linspace(0, 10)
y = f(x)
###Output
_____no_output_____
###Markdown
*** Definimos $g(x)$, a função que queremos encontrar os zeros
###Code
def g(x):
return f(x) - y0
###Output
_____no_output_____
###Markdown
*** O método da bisseção, assim como o método de força bruta mostrado anteriormente, exige um intervalo de busca inicial. Este intervalo deve ser escolhido para conter o zero da função e deve conter um valor para o qual $g(x)$ seja positivo e outro valor para o qual $g(x)$ seja negativo. Chamamos estes valores de $x_a$ e $x_b$. Pois pelo **teorema de Bolzano** $f(a)\times f(b) < 0$ então existe pelo menos uma raiz entre $a$ e $b$Nosso intervalo de busca inicial é entre $0$ e $10$. No método de força bruta, usamos entre $5$ e $6$. Vamos utilizar um intervalo maior só por segurança.
###Code
x0 = 0
x1 = 10
###Output
_____no_output_____
###Markdown
*** Verificamos os valores de $g(x)$ para descobrir quem é $x_a$ e $x_b$. No nosso caso sabemos que $x_a = x_0$ e $x_b = x_1$, mas caso a função seja decrescente no intervalo, esta relação se inverte.
###Code
if g(x0) < g(x1):
print("Função é crescente.")
# xa = Valores de x para que g(x) seja positivo
# xb = Valores de x para que g(x) seja negativo
xa = x0
xb = x1
ya = g(x0)
yb = g(x1)
else:
print("Função é decrescente.")
# xa = Valores de x para que g(x) seja positivo
# xb = Valores de x para que g(x) seja negativo
xa = x1
xb = x0
ya = g(x1)
yb = g(x0)
print("g(x0) =", g(x0))
print("g(x1) =", g(x1))
###Output
Função é crescente.
g(x0) = -41
g(x1) = 79
###Markdown
*** Fazemos um gráfico dos resultados, vamos plotar o gráfico para cada valor de $x$ um respectivo valor de $y - y_0$ na qual $y_0$ é 42, isso irá permitir que o eixo $y$ fica um pouco acima do inicio do gráfico, além disso temos também uma linha horizontal na qual $y = 0$
###Code
matplot.plot(x, y - y0)
matplot.plot(
[xa, xb], [0, 0],
color='black',
linestyle='-',
marker='o'
)
matplot.show()
###Output
_____no_output_____
###Markdown
*** O próximo passo consiste em avaliar o valor de g(x) no ponto central do intervalo e investigar qual será o novo intervalo
###Code
x_average = (xa + xb) / 2
g_average = g(x_average)
print(g_average)
###Output
-6.0
###Markdown
*** Vimos que o valor no ponto médio é igual à -6.0, e portanto negativo. Deste modo, sabemos que o zero de g(x) deve estar entre este valor e xb. Atualizamos nossas variáveis e mostramos o resultado em um gráfico.
###Code
xa = x_average
ga = g_average
matplot.plot(x, y - y0)
matplot.plot([xa, xb], [0, 0], 'ko-')
matplot.show()
###Output
_____no_output_____
###Markdown
*** Vemos que o intervalo reduziu pela metade. Agora repetimos outra vez o mesmo raciocínio.
###Code
x_average = (xa + xb) / 2
g_average = g(x_average)
print(g_average)
###Output
30.25
###Markdown
*** Observe que desta vez o valor de g(x_average) ficou positivo. Isto significa que devemos substituir $x_b$ e não $x_a$. Novamente, o intervalo de valores aceitáveis para o zero da função reduziu pela metade.
###Code
xb = x_average
yb = g_average
matplot.plot(x, y - y0)
matplot.plot([xa, xb], [0, 0], 'ko-')
matplot.show()
###Output
_____no_output_____
###Markdown
*** Justar tudo em um único passo*** Agora juntamos o raciocínio realizado nas duas etapas anteriores em um único passo em que o computador decide automaticamente qual dos dois valores ($x_a$ ou $x_b$) deve ser atualizado:
###Code
x_average = (xa + xb) / 2
g_average = g(x_average)
if g_average <= 0:
xa = x_average
ya = g_average
else:
xb = x_average
yb = g_average
print("Intervalo:", xa, "e", xb)
matplot.plot(x, y - y0)
matplot.plot([xa, xb], [0, 0], 'ko-')
matplot.grid(True)
###Output
Intervalo: 5.478515625 e 5.4833984375
###Markdown
Observe que o intervalo aceitável diminui, mas sempre contêm o valor do zero da função. Podemos verificar isto executando várias vezes a célula acima.É lógico que quanto mais repetições realizarmos, melhor será a estimativa do intervalo da função. Cada repetição reduz o intervalo pela metade. Deste modo, após 10 repetições teríamos um intervalo $2^{10} = 1024$ vezes menor que o intervalo inicial. Nada mal! *** Não queremos executar a célula acima várias vezes manualmente. Vamos então programar o computador para fazer isto automaticamente.Vemos que o valor de $x$ converge para $5.4807$... e o valor de $g(x)$ se aproxima de zero. vamos fazer o calculo acompanhando o valor do erro, para depois mostrarmos em um gráfico.
###Code
# Reseta o intervalo inicial
x0, x1 = 0, 10
# Calcula xa/ya e xb/yb
if g(x0) < g(x1):
print("Função é crescente.")
xa, xb = x0, x1
ya, yb = g(x0), g(x1)
else:
print("Função é decrescente.")
xa, xb = x1, x0
ya, yb = g(x1), g(x0)
# Atualiza 20 vezes o intervalo
g_result = []
for i in range(20):
x_average = (xa + xb) / 2
g_average = g(x_average)
if g_average <= 0:
xa, ya = x_average, g_average
else:
xb, yb = x_average, g_average
g_result.append(abs(g_average))
print("%2d) centro: %.6f, g(x): %9.6f" % (i, x_average, g_average))
matplot.plot(g_result)
matplot.xlabel('Número de iterações')
matplot.ylabel('|g(x)|')
matplot.show()
###Output
Função é crescente.
0) centro: 5.000000, g(x): -6.000000
1) centro: 7.500000, g(x): 30.250000
2) centro: 6.250000, g(x): 10.562500
3) centro: 5.625000, g(x): 1.890625
4) centro: 5.312500, g(x): -2.152344
5) centro: 5.468750, g(x): -0.155273
6) centro: 5.546875, g(x): 0.861572
7) centro: 5.507812, g(x): 0.351624
8) centro: 5.488281, g(x): 0.097794
9) centro: 5.478516, g(x): -0.028835
10) centro: 5.483398, g(x): 0.034455
11) centro: 5.480957, g(x): 0.002804
12) centro: 5.479736, g(x): -0.013017
13) centro: 5.480347, g(x): -0.005107
14) centro: 5.480652, g(x): -0.001152
15) centro: 5.480804, g(x): 0.000826
16) centro: 5.480728, g(x): -0.000163
17) centro: 5.480766, g(x): 0.000332
18) centro: 5.480747, g(x): 0.000085
19) centro: 5.480738, g(x): -0.000039
###Markdown
Percebe-se que o erro começou a tender a zero a partir da oitava iteração *** Critério de parada*** Vimos no gráfico anterior que o erro claramente reduz com o número de iterações. Mas quantas iterações devemos realizar? A resposta é sempre "depende".O método da bisseção, assim como vários outros métodos numéricos atinge a resposta correta apenas após um número infinito de iterações. Se por um lado é impossível esperar estas infinitas iterações, por outro, raramente precisamos do valor "completo" da solução com todas suas infinitas casas decimais. Na prática precisamos apenas de um valor "próximo o suficiente" do correto e é lógico que o que é "suficiente" depende muito da aplicação.No método da bisseção escolhemos tipicamente dois critérios para definir o que é "bom o suficiente". Eles se traduzem em uma margem de tolerância para $y$ ou para $x$.* $|g(x)| < y_{tol}$* $|x_b - x_a| < x_{tol}$Tipicamente, paramos quando um dos dois critérios for atingido. É lógico que podemos também adotar apenas um dos dois critérios ou, se quisermos ser mais rigorosos, paramos apenas quando os dois critérios forem atingidos. O código abaixo implementa a bisseção com o critério de parada.No nosso caso colocarmos o critério de parada como $1 \times 10^\left(-6\right)$
###Code
# Definir tolerâncias = 1x10^(-6)
x_tolerance = 1e-6
y_tolerance = 1e-6
# Reseta o intervalo inicial
x0, x1 = 0, 10
# Calcula xa/ya e xb/yb
if g(x0) < g(x1):
xa, xb = x0, x1
ya, yb = g(x0), g(x1)
else:
xa, xb = x1, x0
ya, yb = g(x1), g(x0)
# Atualiza o intervalo até atingir o critério de parada
iterations = 0
while True:
iterations += 1
x_average = (xa + xb) / 2
g_average = g(x_average)
if g_average <= 0:
xa, ya = x_average, g_average
else:
xb, yb = x_average, g_average
if abs(xb - xa) < x_tolerance or abs(g_average) < y_tolerance:
break
###Output
_____no_output_____
###Markdown
*** Verificamos o resultado
###Code
x_average = (xa + xb) / 2
print("Número de iterações: %s" % iterations)
print("xa = %.7f, xb = %.7f" % (xa, xb))
print("Meio do intervalo: %.7f" % x_average)
print("g(x) no meio do intervalo: %.7f" % g(x_average))
###Output
Número de iterações: 24
xa = 5.4807407, xb = 5.4807413
Meio do intervalo: 5.4807410
g(x) no meio do intervalo: 0.0000034
###Markdown
*** Criando uma função*** Seria bom reutilizarmos a lógica do método da bisseção com qualquer função arbitrária, não? Isto é fácil fazer em Python. Vamos criar uma função que recebe uma funçao $g$ e um intervalo $x_0$, $x_1$ e a partir disto calcula o zero de $g(x)$ contido neste intervalo.
###Code
def bissect(g, x0, x1, x_tolerance=1e-6, y_tolerance=1e-6):
"""
Calcula o zero de g(x) dentro do intervalo (x0, y0)
Argumentos:
g: um função de um única variável
x0, x1: intervalo inicial para a busca do zero de g(x)
x_tolerance: tolerância em x (retorna quando o intervalo for menor que x_tolerance)
y_tolerance: tolerância em y (retorna quando |g(x)| < y_tolerance)
Retornos:
Retorna o zero da função g(x) (valor de x em que g(x) = 0)
"""
# Calcula xa/ya e xb/yb
# xa = Valores de x para que g(x) seja positivo -> g(xa) = ya
# xb = Valores de x para que g(x) seja negativo -> g(xb) = yb
if g(x0) < g(x1):
# Função é crescente
xa, xb = x0, x1
ya, yb = g(x0), g(x1)
else:
# Função é decrescente
xa, xb = x1, x0
ya, yb = g(x1), g(x0)
# Atualiza o intervalo até atingir o critério de parada
iterations = 0
while True:
iterations += 1
x_average = (xa + xb) / 2
g_average = g(x_average)
# Se o ponto médio de y (g_average) for negativo substituimos o xa/ya caso contrario xb/yb
# Deste modo, sabemos que o zero de g(x) deve estar entre os novos xa e xb.
if g_average < 0:
xa, ya = x_average, g_average
elif g_average > 0:
xb, yb = x_average, g_average
else:
return x_average
# Critério de parada: |xb - xa| < tolerancia em x ou |g(x)| < tolerancia em y
if abs(xb - xa) < x_tolerance or abs(g_average) < y_tolerance:
break
# Retorna o ponto em que g(x) é praticamente zero
if abs(ya) < abs(yb):
return xa
else:
return xb
###Output
_____no_output_____
###Markdown
*** Agora vamos usar a função ***
###Code
# Encontra o zero da função g(x)
print("x =", bissect(g, 0, 10))
###Output
x = 5.480740666389465
###Markdown
***
###Code
# Acha o zero da função cos(x) no intervalo de 0 a 4 (pi/2)
print("x =", bissect(numpy.cos, 0, 4))
###Output
x = 1.5707969665527344
###Markdown
***
###Code
# Encontre o ponto onde cos(x) é igual a 0.5 no intervalo de 0 a 4
def g2(x):
return numpy.cos(x) - 0.5
print("x =", bissect(g2, 0, 4))
###Output
x = 1.0471973419189453
###Markdown
***
###Code
# Encontra o ponto onde cos(x) é igual a sin(x) no intervalo de 0 a 4
# Sabemos que a resposta é pi/4
def g3(x):
return numpy.cos(x) - numpy.sin(x)
print("x =", bissect(g3, 0, 4))
###Output
x = 0.7853984832763672
|
2-mfcc-extractor.ipynb
|
###Markdown
MFCC Extractor
###Code
dataset_path = "data/archive/Data/genres_original"
json_path = "data/gtzan_mfcc_json.json"
sr = 22050
duration = 30 # seconds
total_samples = sr * duration
num_mfcc = 13
n_fft = 2048
hop_length = 512
segments_per_track = 10
###Output
_____no_output_____
###Markdown
We create a dictionary to store labels of all the songs' MFCCs.* `mapping` consists of all the 10 genres.* `labels` consists of the label for each of the 1000 songs. 0 corresponds to blues, 1 for classical, 2 for country and so on. Since each songs has a label, there will be 100 zeroes, 100 ones, 100 twos, and so on.* `mfcc` consists of individual mfcc values (grouped by 13 to be called an MFCC) for every song.Every song consists of `22050 * 30 = 661500` total number of samples,which are divided into 10 segments. So each segment has 66150 samples.The number of MFCCs in each segment would be determined by `hop_length` (`=512`),which would be `ceil(66150 / 512) = 130` MFCCs in each segment.
###Code
# dictionary to store mapping, labels, and MFCCs
data = {
"mapping": [],
"labels": [],
"mfcc": []
}
print("No. of segments: ", segments_per_track)
samples_per_segment = int(total_samples / segments_per_track)
print("No. of samples per segment: ", samples_per_segment)
num_mfcc_vectors_per_segment = math.ceil(samples_per_segment / hop_length)
print("No. of MFCCs per segment: ", num_mfcc_vectors_per_segment)
# loop through all genre sub-folder
for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):
# ensure we're processing a genre sub-folder level
if dirpath is not dataset_path:
# save genre label (i.e., sub-folder name) in the mapping
# For Windows, '\\' is used. For Linux, change to '/'
semantic_label = dirpath.split('\\')[-1]
# print(dirpath)
# print(semantic_label)
data["mapping"].append(semantic_label)
print("Processing:", semantic_label)
# process all audio files in genre sub-dir
for f in filenames:
# load audio file
file_path = os.path.join(dirpath, f)
signal, sample_rate = librosa.load(file_path, sr=sr)
# process all segments of audio file
for d in range(segments_per_track):
# calculate start and finish sample for current segment
start = samples_per_segment * d
finish = start + samples_per_segment
# extract mfcc
mfcc = librosa.feature.mfcc(signal[start:finish], sample_rate, n_mfcc=num_mfcc, n_fft=n_fft,
hop_length=hop_length)
mfcc = mfcc.T
# store only mfcc feature with expected number of vectors
if len(mfcc) == num_mfcc_vectors_per_segment:
data["mfcc"].append(mfcc.tolist())
data["labels"].append(i - 1)
print("\nMFCCs extracted. Saving to JSON file...")
# save MFCCs to json file
with open(json_path, "w") as fp:
json.dump(data, fp, indent=4)
print("Done")
###Output
Processing: blues
Processing: classical
Processing: country
Processing: disco
Processing: hiphop
Processing: jazz
Processing: metal
Processing: pop
Processing: reggae
Processing: rock
MFCCs extracted. Saving to JSON file...
Done
###Markdown
* There are total 1000-1 = 999 songs (one song removed as the file was corrupted)So there should ideally be 9990 total number of segments, which would serveas the input to the training part.The dimensions would be (9990, 130, 13)* The above dimensions are under the assumption that every song is __exactly__ 30 seconds in duration.
###Code
print("Labels:", len(data["labels"]))
print("MFCCs:", len(data["mfcc"]))
###Output
Labels: 9986
MFCCs: 9986
|
6.CHATBOT/6.4.seq2seq.ipynb
|
###Markdown
패키지 불러오기
###Code
!pip install konlpy
import tensorflow as tf
import numpy as np
import os
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import matplotlib.pyplot as plt
from preprocess import *
###Output
_____no_output_____
###Markdown
시각화 함수
###Code
def plot_graphs(history, string):
plt.plot(history.history[string])
plt.plot(history.history['val_'+string], '')
plt.xlabel("Epochs")
plt.ylabel(string)
plt.legend([string, 'val_'+string])
plt.show()
###Output
_____no_output_____
###Markdown
학습 데이터 경로 정의
###Code
DATA_IN_PATH = './data_in/'
DATA_OUT_PATH = './data_out/'
TRAIN_INPUTS = 'train_inputs.npy'
TRAIN_OUTPUTS = 'train_outputs.npy'
TRAIN_TARGETS = 'train_targets.npy'
DATA_CONFIGS = 'data_configs.json'
###Output
_____no_output_____
###Markdown
랜덤 시드 고정
###Code
SEED_NUM = 1234
tf.random.set_seed(SEED_NUM)
###Output
_____no_output_____
###Markdown
파일 로드
###Code
index_inputs = np.load(open(DATA_IN_PATH + TRAIN_INPUTS, 'rb'))
index_outputs = np.load(open(DATA_IN_PATH + TRAIN_OUTPUTS , 'rb'))
index_targets = np.load(open(DATA_IN_PATH + TRAIN_TARGETS , 'rb'))
prepro_configs = json.load(open(DATA_IN_PATH + DATA_CONFIGS, 'r'))
# Show length ## length가 아니라 data 갯수
print(len(index_inputs), len(index_outputs), len(index_targets))
###Output
11823 11823 11823
###Markdown
모델 만들기에 필요한 값 선언
###Code
MODEL_NAME = 'seq2seq_kor'
BATCH_SIZE = 2
MAX_SEQUENCE = 25 ## 이건 EDA에서 나온 것도 아니고 경험적으로 나온 숫자.
EPOCH = 30
UNITS = 1024
EMBEDDING_DIM = 256
VALIDATION_SPLIT = 0.1
char2idx = prepro_configs['char2idx']
idx2char = prepro_configs['idx2char']
std_index = prepro_configs['std_symbol']
end_index = prepro_configs['end_symbol']
vocab_size = prepro_configs['vocab_size']
###Output
_____no_output_____
###Markdown
모델 인코더
###Code
class Encoder(tf.keras.layers.Layer):
def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
super(Encoder, self).__init__()
self.batch_sz = batch_sz
self.enc_units = enc_units
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.embedding = tf.keras.layers.Embedding(self.vocab_size, self.embedding_dim)
self.gru = tf.keras.layers.GRU(self.enc_units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform') # xavier initialization
def call(self, x, hidden):
x = self.embedding(x)
output, state = self.gru(x, initial_state = hidden)
# initial_state: List of initial state tensors to be passed to the first call of the cell.
return output, state
def initialize_hidden_state(self, inp):
return tf.zeros((tf.shape(inp)[0], self.enc_units))
###Output
_____no_output_____
###Markdown
어텐션
###Code
class BahdanauAttention(tf.keras.layers.Layer):
def __init__(self, units):
super(BahdanauAttention, self).__init__()
self.W1 = tf.keras.layers.Dense(units)
self.W2 = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
#W1, W2, V가 학습되는 가중치.
def call(self, query, values):
hidden_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(
self.W1(values) + self.W2(hidden_with_time_axis)))
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * values
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
###Output
_____no_output_____
###Markdown
디코더
###Code
class Decoder(tf.keras.layers.Layer):
def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
super(Decoder, self).__init__()
self.batch_sz = batch_sz
self.dec_units = dec_units
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.embedding = tf.keras.layers.Embedding(self.vocab_size, self.embedding_dim)
self.gru = tf.keras.layers.GRU(self.dec_units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform')
self.fc = tf.keras.layers.Dense(self.vocab_size)
self.attention = BahdanauAttention(self.dec_units)
def call(self, x, hidden, enc_output):
context_vector, attention_weights = self.attention(hidden, enc_output)
x = self.embedding(x)
x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)
output, state = self.gru(x)
output = tf.reshape(output, (-1, output.shape[2]))
x = self.fc(output)
return x, state, attention_weights
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy')
def loss(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0)) ## real에서 <PAD>인 0 은 tf.math.equal 하면 True니까, mask는 False가 됨.
loss_ = loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype) ##True는 1, False는 0 으로 바꿔줌.
loss_ *= mask ## 따라서, <PAD>는 계산하지 않게ㄷ
return tf.reduce_mean(loss_)
def accuracy(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
mask = tf.expand_dims(tf.cast(mask, dtype=pred.dtype), axis=-1)
pred *= mask
acc = train_accuracy(real, pred)
return tf.reduce_mean(acc)
###Output
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###Markdown
시퀀스 투 스퀀스 모델
###Code
class seq2seq(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, enc_units, dec_units, batch_sz, end_token_idx=2):
super(seq2seq, self).__init__()
self.end_token_idx = end_token_idx
self.encoder = Encoder(vocab_size, embedding_dim, enc_units, batch_sz)
self.decoder = Decoder(vocab_size, embedding_dim, dec_units, batch_sz)
def call(self, x):
inp, tar = x #inp: encoder의 입력값, tar: decoder의 입력값.
enc_hidden = self.encoder.initialize_hidden_state(inp)
enc_output, enc_hidden = self.encoder(inp, enc_hidden)
dec_hidden = enc_hidden
predict_tokens = list()
for t in range(0, tar.shape[1]):
dec_input = tf.dtypes.cast(tf.expand_dims(tar[:, t], 1), tf.float32)
predictions, dec_hidden, _ = self.decoder(dec_input, dec_hidden, enc_output)
predict_tokens.append(tf.dtypes.cast(predictions, tf.float32))
return tf.stack(predict_tokens, axis=1)
def inference(self, x): # 사용자의 입력에 대한 모델의 결괏값을 확인하기 위해 테스트 목적으로 만들어진 함수. (하나의 배치만 동작하도록 되어있음.)
inp = x
enc_hidden = self.encoder.initialize_hidden_state(inp)
enc_output, enc_hidden = self.encoder(inp, enc_hidden)
dec_hidden = enc_hidden ## encoder의 last hidden state를 dec_hidden으로 다시 넣어주는 부분.
dec_input = tf.expand_dims([char2idx[std_index]], 1)
predict_tokens = list()
for t in range(0, MAX_SEQUENCE):
predictions, dec_hidden, _ = self.decoder(dec_input, dec_hidden, enc_output)
predict_token = tf.argmax(predictions[0])
if predict_token == self.end_token_idx:
break
predict_tokens.append(predict_token)
dec_input = tf.dtypes.cast(tf.expand_dims([predict_token], 0), tf.float32)
return tf.stack(predict_tokens, axis=0).numpy()
model = seq2seq(vocab_size, EMBEDDING_DIM, UNITS, UNITS, BATCH_SIZE, char2idx[end_index])
model.compile(loss=loss, optimizer=tf.keras.optimizers.Adam(1e-3), metrics=[accuracy])
#model.run_eagerly = True
###Output
_____no_output_____
###Markdown
학습 진행
###Code
PATH = DATA_OUT_PATH + MODEL_NAME
if not(os.path.isdir(PATH)):
os.makedirs(os.path.join(PATH))
checkpoint_path = DATA_OUT_PATH + MODEL_NAME + '/weights.h5'
cp_callback = ModelCheckpoint(
checkpoint_path, monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=True)
earlystop_callback = EarlyStopping(monitor='val_accuracy', min_delta=0.0001, patience=10)
history = model.fit([index_inputs, index_outputs], index_targets,
batch_size=BATCH_SIZE, epochs=EPOCH,
validation_split=VALIDATION_SPLIT, callbacks=[earlystop_callback, cp_callback])
###Output
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###Markdown
결과 플롯
###Code
plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')
###Output
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###Markdown
결과 확인
###Code
SAVE_FILE_NM = "weights.h5"
model.load_weights(os.path.join(DATA_OUT_PATH, MODEL_NAME, SAVE_FILE_NM))
query = "남자친구 승진 선물로 뭐가 좋을까?"
test_index_inputs, _ = enc_processing([query], char2idx)
predict_tokens = model.inference(test_index_inputs)
print(predict_tokens)
print(' '.join([idx2char[str(t)] for t in predict_tokens]))
###Output
_____no_output_____
###Markdown
패키지 불러오기
###Code
import tensorflow as tf
import numpy as np
import os
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import matplotlib.pyplot as plt
from preprocess import *
###Output
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###Markdown
시각화 함수
###Code
def plot_graphs(history, string):
plt.plot(history.history[string])
plt.plot(history.history['val_'+string], '')
plt.xlabel("Epochs")
plt.ylabel(string)
plt.legend([string, 'val_'+string])
plt.show()
###Output
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###Markdown
학습 데이터 경로 정의
###Code
DATA_IN_PATH = './data_in/'
DATA_OUT_PATH = './data_out/'
TRAIN_INPUTS = 'train_inputs.npy'
TRAIN_OUTPUTS = 'train_outputs.npy'
TRAIN_TARGETS = 'train_targets.npy'
DATA_CONFIGS = 'data_configs.json'
###Output
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###Markdown
랜덤 시드 고정
###Code
SEED_NUM = 1234
tf.random.set_seed(SEED_NUM)
###Output
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###Markdown
파일 로드
###Code
index_inputs = np.load(open(DATA_IN_PATH + TRAIN_INPUTS, 'rb'))
index_outputs = np.load(open(DATA_IN_PATH + TRAIN_OUTPUTS , 'rb'))
index_targets = np.load(open(DATA_IN_PATH + TRAIN_TARGETS , 'rb'))
prepro_configs = json.load(open(DATA_IN_PATH + DATA_CONFIGS, 'r'))
# Show length
print(len(index_inputs), len(index_outputs), len(index_targets))
###Output
20 20 20
###Markdown
모델 만들기에 필요한 값 선언
###Code
MODEL_NAME = 'seq2seq_kor'
BATCH_SIZE = 2
MAX_SEQUENCE = 25
EPOCH = 30
UNITS = 1024
EMBEDDING_DIM = 256
VALIDATION_SPLIT = 0.1
char2idx = prepro_configs['char2idx']
idx2char = prepro_configs['idx2char']
std_index = prepro_configs['std_symbol']
end_index = prepro_configs['end_symbol']
vocab_size = prepro_configs['vocab_size']
###Output
_____no_output_____
###Markdown
모델 인코더
###Code
class Encoder(tf.keras.layers.Layer):
def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
super(Encoder, self).__init__()
self.batch_sz = batch_sz
self.enc_units = enc_units
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.embedding = tf.keras.layers.Embedding(self.vocab_size, self.embedding_dim)
self.gru = tf.keras.layers.GRU(self.enc_units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform')
def call(self, x, hidden):
x = self.embedding(x)
output, state = self.gru(x, initial_state = hidden)
return output, state
def initialize_hidden_state(self, inp):
return tf.zeros((tf.shape(inp)[0], self.enc_units))
###Output
_____no_output_____
###Markdown
어텐션
###Code
class BahdanauAttention(tf.keras.layers.Layer):
def __init__(self, units):
super(BahdanauAttention, self).__init__()
self.W1 = tf.keras.layers.Dense(units)
self.W2 = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
def call(self, query, values):
hidden_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(
self.W1(values) + self.W2(hidden_with_time_axis)))
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * values
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
###Output
_____no_output_____
###Markdown
디코더
###Code
class Decoder(tf.keras.layers.Layer):
def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
super(Decoder, self).__init__()
self.batch_sz = batch_sz
self.dec_units = dec_units
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.embedding = tf.keras.layers.Embedding(self.vocab_size, self.embedding_dim)
self.gru = tf.keras.layers.GRU(self.dec_units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform')
self.fc = tf.keras.layers.Dense(self.vocab_size)
self.attention = BahdanauAttention(self.dec_units)
def call(self, x, hidden, enc_output):
context_vector, attention_weights = self.attention(hidden, enc_output)
x = self.embedding(x)
x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)
output, state = self.gru(x)
output = tf.reshape(output, (-1, output.shape[2]))
x = self.fc(output)
return x, state, attention_weights
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy')
def loss(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
loss_ = loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return tf.reduce_mean(loss_)
def accuracy(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
mask = tf.expand_dims(tf.cast(mask, dtype=pred.dtype), axis=-1)
pred *= mask
acc = train_accuracy(real, pred)
return tf.reduce_mean(acc)
###Output
_____no_output_____
###Markdown
시퀀스 투 스퀀스 모델
###Code
class seq2seq(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, enc_units, dec_units, batch_sz, end_token_idx=2):
super(seq2seq, self).__init__()
self.end_token_idx = end_token_idx
self.encoder = Encoder(vocab_size, embedding_dim, enc_units, batch_sz)
self.decoder = Decoder(vocab_size, embedding_dim, dec_units, batch_sz)
def call(self, x):
inp, tar = x
enc_hidden = self.encoder.initialize_hidden_state(inp)
enc_output, enc_hidden = self.encoder(inp, enc_hidden)
dec_hidden = enc_hidden
predict_tokens = list()
for t in range(0, tar.shape[1]):
dec_input = tf.dtypes.cast(tf.expand_dims(tar[:, t], 1), tf.float32)
predictions, dec_hidden, _ = self.decoder(dec_input, dec_hidden, enc_output)
predict_tokens.append(tf.dtypes.cast(predictions, tf.float32))
return tf.stack(predict_tokens, axis=1)
def inference(self, x):
inp = x
enc_hidden = self.encoder.initialize_hidden_state(inp)
enc_output, enc_hidden = self.encoder(inp, enc_hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([char2idx[std_index]], 1)
predict_tokens = list()
for t in range(0, MAX_SEQUENCE):
predictions, dec_hidden, _ = self.decoder(dec_input, dec_hidden, enc_output)
predict_token = tf.argmax(predictions[0])
if predict_token == self.end_token_idx:
break
predict_tokens.append(predict_token)
dec_input = tf.dtypes.cast(tf.expand_dims([predict_token], 0), tf.float32)
return tf.stack(predict_tokens, axis=0).numpy()
model = seq2seq(vocab_size, EMBEDDING_DIM, UNITS, UNITS, BATCH_SIZE, char2idx[end_index])
model.compile(loss=loss, optimizer=tf.keras.optimizers.Adam(1e-3), metrics=[accuracy])
#model.run_eagerly = True
###Output
_____no_output_____
###Markdown
학습 진행
###Code
PATH = DATA_OUT_PATH + MODEL_NAME
if not(os.path.isdir(PATH)):
os.makedirs(os.path.join(PATH))
checkpoint_path = DATA_OUT_PATH + MODEL_NAME + '/weights.h5'
cp_callback = ModelCheckpoint(
checkpoint_path, monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=True)
earlystop_callback = EarlyStopping(monitor='val_accuracy', min_delta=0.0001, patience=10)
history = model.fit([index_inputs, index_outputs], index_targets,
batch_size=BATCH_SIZE, epochs=EPOCH,
validation_split=VALIDATION_SPLIT, callbacks=[earlystop_callback, cp_callback])
###Output
Epoch 1/30
9/9 [==============================] - ETA: 0s - loss: 0.8365 - accuracy: 0.8360
Epoch 00001: val_accuracy improved from -inf to 0.85400, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 5s 599ms/step - loss: 0.8365 - accuracy: 0.8360 - val_loss: 0.6929 - val_accuracy: 0.8540
Epoch 2/30
9/9 [==============================] - ETA: 0s - loss: 0.7576 - accuracy: 0.8527
Epoch 00002: val_accuracy improved from 0.85400 to 0.85600, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 91ms/step - loss: 0.7576 - accuracy: 0.8527 - val_loss: 0.6273 - val_accuracy: 0.8560
Epoch 3/30
9/9 [==============================] - ETA: 0s - loss: 0.6806 - accuracy: 0.8572
Epoch 00003: val_accuracy improved from 0.85600 to 0.85667, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 70ms/step - loss: 0.6806 - accuracy: 0.8572 - val_loss: 0.5928 - val_accuracy: 0.8567
Epoch 4/30
9/9 [==============================] - ETA: 0s - loss: 0.6296 - accuracy: 0.8572
Epoch 00004: val_accuracy improved from 0.85667 to 0.85700, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 88ms/step - loss: 0.6296 - accuracy: 0.8572 - val_loss: 0.5681 - val_accuracy: 0.8570
Epoch 5/30
9/9 [==============================] - ETA: 0s - loss: 0.5831 - accuracy: 0.8582
Epoch 00005: val_accuracy improved from 0.85700 to 0.85800, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 72ms/step - loss: 0.5831 - accuracy: 0.8582 - val_loss: 0.5146 - val_accuracy: 0.8580
Epoch 6/30
9/9 [==============================] - ETA: 0s - loss: 0.5308 - accuracy: 0.8577
Epoch 00006: val_accuracy improved from 0.85800 to 0.85900, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 89ms/step - loss: 0.5308 - accuracy: 0.8577 - val_loss: 0.4513 - val_accuracy: 0.8590
Epoch 7/30
9/9 [==============================] - ETA: 0s - loss: 0.4739 - accuracy: 0.8598
Epoch 00007: val_accuracy improved from 0.85900 to 0.86114, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 72ms/step - loss: 0.4739 - accuracy: 0.8598 - val_loss: 0.4118 - val_accuracy: 0.8611
Epoch 8/30
9/9 [==============================] - ETA: 0s - loss: 0.4216 - accuracy: 0.8632
Epoch 00008: val_accuracy improved from 0.86114 to 0.86450, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 90ms/step - loss: 0.4216 - accuracy: 0.8632 - val_loss: 0.3691 - val_accuracy: 0.8645
Epoch 9/30
8/9 [=========================>....] - ETA: 0s - loss: 0.3744 - accuracy: 0.8667
Epoch 00009: val_accuracy improved from 0.86450 to 0.86933, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 87ms/step - loss: 0.3652 - accuracy: 0.8669 - val_loss: 0.3232 - val_accuracy: 0.8693
Epoch 10/30
9/9 [==============================] - ETA: 0s - loss: 0.3247 - accuracy: 0.8736
Epoch 00010: val_accuracy improved from 0.86933 to 0.87780, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 90ms/step - loss: 0.3247 - accuracy: 0.8736 - val_loss: 0.2706 - val_accuracy: 0.8778
Epoch 11/30
9/9 [==============================] - ETA: 0s - loss: 0.2797 - accuracy: 0.8816
Epoch 00011: val_accuracy improved from 0.87780 to 0.88491, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 89ms/step - loss: 0.2797 - accuracy: 0.8816 - val_loss: 0.2418 - val_accuracy: 0.8849
Epoch 12/30
9/9 [==============================] - ETA: 0s - loss: 0.2250 - accuracy: 0.8882
Epoch 00012: val_accuracy improved from 0.88491 to 0.89117, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 87ms/step - loss: 0.2250 - accuracy: 0.8882 - val_loss: 0.2023 - val_accuracy: 0.8912
Epoch 13/30
9/9 [==============================] - ETA: 0s - loss: 0.1984 - accuracy: 0.8938
Epoch 00013: val_accuracy improved from 0.89117 to 0.89615, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 72ms/step - loss: 0.1984 - accuracy: 0.8938 - val_loss: 0.2193 - val_accuracy: 0.8962
Epoch 14/30
9/9 [==============================] - ETA: 0s - loss: 0.1793 - accuracy: 0.8985
Epoch 00014: val_accuracy improved from 0.89615 to 0.90043, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 88ms/step - loss: 0.1793 - accuracy: 0.8985 - val_loss: 0.1197 - val_accuracy: 0.9004
Epoch 15/30
9/9 [==============================] - ETA: 0s - loss: 0.1552 - accuracy: 0.9023
Epoch 00015: val_accuracy improved from 0.90043 to 0.90400, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 91ms/step - loss: 0.1552 - accuracy: 0.9023 - val_loss: 0.1431 - val_accuracy: 0.9040
Epoch 16/30
9/9 [==============================] - ETA: 0s - loss: 0.1389 - accuracy: 0.9059
Epoch 00016: val_accuracy improved from 0.90400 to 0.90750, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 90ms/step - loss: 0.1389 - accuracy: 0.9059 - val_loss: 0.1279 - val_accuracy: 0.9075
Epoch 17/30
9/9 [==============================] - ETA: 0s - loss: 0.1268 - accuracy: 0.9090
Epoch 00017: val_accuracy improved from 0.90750 to 0.91047, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 77ms/step - loss: 0.1268 - accuracy: 0.9090 - val_loss: 0.1207 - val_accuracy: 0.9105
Epoch 18/30
9/9 [==============================] - ETA: 0s - loss: 0.1297 - accuracy: 0.9119
Epoch 00018: val_accuracy improved from 0.91047 to 0.91322, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 87ms/step - loss: 0.1297 - accuracy: 0.9119 - val_loss: 0.1242 - val_accuracy: 0.9132
Epoch 19/30
9/9 [==============================] - ETA: 0s - loss: 0.1235 - accuracy: 0.9146
Epoch 00019: val_accuracy improved from 0.91322 to 0.91568, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 73ms/step - loss: 0.1235 - accuracy: 0.9146 - val_loss: 0.1305 - val_accuracy: 0.9157
Epoch 20/30
9/9 [==============================] - ETA: 0s - loss: 0.1107 - accuracy: 0.9169
Epoch 00020: val_accuracy improved from 0.91568 to 0.91790, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 92ms/step - loss: 0.1107 - accuracy: 0.9169 - val_loss: 0.1224 - val_accuracy: 0.9179
Epoch 21/30
9/9 [==============================] - ETA: 0s - loss: 0.1014 - accuracy: 0.9190
Epoch 00021: val_accuracy improved from 0.91790 to 0.92010, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 82ms/step - loss: 0.1014 - accuracy: 0.9190 - val_loss: 0.1348 - val_accuracy: 0.9201
Epoch 22/30
9/9 [==============================] - ETA: 0s - loss: 0.0860 - accuracy: 0.9210
Epoch 00022: val_accuracy improved from 0.92010 to 0.92209, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 85ms/step - loss: 0.0860 - accuracy: 0.9210 - val_loss: 0.1393 - val_accuracy: 0.9221
Epoch 23/30
9/9 [==============================] - ETA: 0s - loss: 0.0679 - accuracy: 0.9230
Epoch 00023: val_accuracy improved from 0.92209 to 0.92409, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 75ms/step - loss: 0.0679 - accuracy: 0.9230 - val_loss: 0.1486 - val_accuracy: 0.9241
Epoch 24/30
9/9 [==============================] - ETA: 0s - loss: 0.0564 - accuracy: 0.9254
Epoch 00024: val_accuracy improved from 0.92409 to 0.92642, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 87ms/step - loss: 0.0564 - accuracy: 0.9254 - val_loss: 0.1438 - val_accuracy: 0.9264
Epoch 25/30
9/9 [==============================] - ETA: 0s - loss: 0.0442 - accuracy: 0.9275
Epoch 00025: val_accuracy improved from 0.92642 to 0.92848, saving model to ./data_out/seq2seq_kor/weights.h5
9/9 [==============================] - 1s 72ms/step - loss: 0.0442 - accuracy: 0.9275 - val_loss: 0.1351 - val_accuracy: 0.9285
###Markdown
결과 플롯
###Code
plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')
###Output
_____no_output_____
###Markdown
결과 확인
###Code
SAVE_FILE_NM = "weights.h5"
model.load_weights(os.path.join(DATA_OUT_PATH, MODEL_NAME, SAVE_FILE_NM))
query = "남자친구 승진 선물로 뭐가 좋을까?"
test_index_inputs, _ = enc_processing([query], char2idx)
predict_tokens = model.inference(test_index_inputs)
print(predict_tokens)
print(' '.join([idx2char[str(t)] for t in predict_tokens]))
###Output
[83 79 98 97 21 56]
평소에 필요했던 게 좋을 것 생각해보세요
###Markdown
패키지 불러오기
###Code
import tensorflow as tf
import numpy as np
import os
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import matplotlib.pyplot as plt
from preprocess import *
###Output
_____no_output_____
###Markdown
시각화 함수
###Code
def plot_graphs(history, string):
plt.plot(history.history[string])
plt.plot(history.history['val_'+string], '')
plt.xlabel("Epochs")
plt.ylabel(string)
plt.legend([string, 'val_'+string])
plt.show()
###Output
_____no_output_____
###Markdown
학습 데이터 경로 정의
###Code
DATA_IN_PATH = './data_in/'
DATA_OUT_PATH = './data_out/'
TRAIN_INPUTS = 'train_inputs.npy'
TRAIN_OUTPUTS = 'train_outputs.npy'
TRAIN_TARGETS = 'train_targets.npy'
DATA_CONFIGS = 'data_configs.json'
###Output
_____no_output_____
###Markdown
랜덤 시드 고정
###Code
SEED_NUM = 1234
tf.random.set_seed(SEED_NUM)
###Output
_____no_output_____
###Markdown
파일 로드
###Code
index_inputs = np.load(open(DATA_IN_PATH + TRAIN_INPUTS, 'rb'))
index_outputs = np.load(open(DATA_IN_PATH + TRAIN_OUTPUTS , 'rb'))
index_targets = np.load(open(DATA_IN_PATH + TRAIN_TARGETS , 'rb'))
prepro_configs = json.load(open(DATA_IN_PATH + DATA_CONFIGS, 'r'))
# Show length
print(len(index_inputs), len(index_outputs), len(index_targets))
###Output
20 20 20
###Markdown
모델 만들기에 필요한 값 선언
###Code
MODEL_NAME = 'seq2seq_kor'
BATCH_SIZE = 2
MAX_SEQUENCE = 25
EPOCH = 30
UNITS = 1024
EMBEDDING_DIM = 256
VALIDATION_SPLIT = 0.1
char2idx = prepro_configs['char2idx']
idx2char = prepro_configs['idx2char']
std_index = prepro_configs['std_symbol']
end_index = prepro_configs['end_symbol']
vocab_size = prepro_configs['vocab_size']
###Output
_____no_output_____
###Markdown
모델 인코더
###Code
class Encoder(tf.keras.layers.Layer):
def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
super(Encoder, self).__init__()
self.batch_sz = batch_sz
self.enc_units = enc_units
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.embedding = tf.keras.layers.Embedding(self.vocab_size, self.embedding_dim)
self.gru = tf.keras.layers.GRU(self.enc_units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform')
def call(self, x, hidden):
x = self.embedding(x)
output, state = self.gru(x, initial_state = hidden)
return output, state
def initialize_hidden_state(self, inp):
return tf.zeros((tf.shape(inp)[0], self.enc_units))
###Output
_____no_output_____
###Markdown
어텐션
###Code
class BahdanauAttention(tf.keras.layers.Layer):
def __init__(self, units):
super(BahdanauAttention, self).__init__()
self.W1 = tf.keras.layers.Dense(units)
self.W2 = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
def call(self, query, values):
hidden_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(
self.W1(values) + self.W2(hidden_with_time_axis)))
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * values
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
###Output
_____no_output_____
###Markdown
디코더
###Code
class Decoder(tf.keras.layers.Layer):
def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
super(Decoder, self).__init__()
self.batch_sz = batch_sz
self.dec_units = dec_units
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.embedding = tf.keras.layers.Embedding(self.vocab_size, self.embedding_dim)
self.gru = tf.keras.layers.GRU(self.dec_units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform')
self.fc = tf.keras.layers.Dense(self.vocab_size)
self.attention = BahdanauAttention(self.dec_units)
def call(self, x, hidden, enc_output):
context_vector, attention_weights = self.attention(hidden, enc_output)
x = self.embedding(x)
x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)
output, state = self.gru(x)
output = tf.reshape(output, (-1, output.shape[2]))
x = self.fc(output)
return x, state, attention_weights
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy')
def loss(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
loss_ = loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return tf.reduce_mean(loss_)
def accuracy(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
mask = tf.expand_dims(tf.cast(mask, dtype=pred.dtype), axis=-1)
pred *= mask
acc = train_accuracy(real, pred)
return tf.reduce_mean(acc)
###Output
_____no_output_____
###Markdown
시퀀스 투 스퀀스 모델
###Code
class seq2seq(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, enc_units, dec_units, batch_sz, end_token_idx=2):
super(seq2seq, self).__init__()
self.end_token_idx = end_token_idx
self.encoder = Encoder(vocab_size, embedding_dim, enc_units, batch_sz)
self.decoder = Decoder(vocab_size, embedding_dim, dec_units, batch_sz)
def call(self, x):
inp, tar = x
enc_hidden = self.encoder.initialize_hidden_state(inp)
enc_output, enc_hidden = self.encoder(inp, enc_hidden)
dec_hidden = enc_hidden
predict_tokens = list()
for t in range(0, tar.shape[1]):
dec_input = tf.dtypes.cast(tf.expand_dims(tar[:, t], 1), tf.float32)
predictions, dec_hidden, _ = self.decoder(dec_input, dec_hidden, enc_output)
predict_tokens.append(tf.dtypes.cast(predictions, tf.float32))
return tf.stack(predict_tokens, axis=1)
def inference(self, x):
inp = x
enc_hidden = self.encoder.initialize_hidden_state(inp)
enc_output, enc_hidden = self.encoder(inp, enc_hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([char2idx[std_index]], 1)
predict_tokens = list()
for t in range(0, MAX_SEQUENCE):
predictions, dec_hidden, _ = self.decoder(dec_input, dec_hidden, enc_output)
predict_token = tf.argmax(predictions[0])
if predict_token == self.end_token_idx:
break
predict_tokens.append(predict_token)
dec_input = tf.dtypes.cast(tf.expand_dims([predict_token], 0), tf.float32)
return tf.stack(predict_tokens, axis=0).numpy()
model = seq2seq(vocab_size, EMBEDDING_DIM, UNITS, UNITS, BATCH_SIZE, char2idx[end_index])
model.compile(loss=loss, optimizer=tf.keras.optimizers.Adam(1e-3), metrics=[accuracy])
#model.run_eagerly = True
###Output
_____no_output_____
###Markdown
학습 진행
###Code
PATH = DATA_OUT_PATH + MODEL_NAME
if not(os.path.isdir(PATH)):
os.makedirs(os.path.join(PATH))
checkpoint_path = DATA_OUT_PATH + MODEL_NAME + '/weights.h5'
cp_callback = ModelCheckpoint(
checkpoint_path, monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=True)
earlystop_callback = EarlyStopping(monitor='val_accuracy', min_delta=0.0001, patience=10)
history = model.fit([index_inputs, index_outputs], index_targets,
batch_size=BATCH_SIZE, epochs=EPOCH,
validation_split=VALIDATION_SPLIT, callbacks=[earlystop_callback, cp_callback])
###Output
Train on 18 samples, validate on 2 samples
Epoch 1/30
16/18 [=========================>....] - ETA: 1s - loss: 0.9376 - accuracy: 0.8322
Epoch 00001: val_accuracy improved from -inf to 0.85400, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 527ms/sample - loss: 0.9562 - accuracy: 0.8343 - val_loss: 0.7050 - val_accuracy: 0.8540
Epoch 2/30
16/18 [=========================>....] - ETA: 1s - loss: 0.7962 - accuracy: 0.8541
Epoch 00002: val_accuracy improved from 0.85400 to 0.85800, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 535ms/sample - loss: 0.7904 - accuracy: 0.8544 - val_loss: 0.6005 - val_accuracy: 0.8580
Epoch 3/30
16/18 [=========================>....] - ETA: 0s - loss: 0.6852 - accuracy: 0.8557
Epoch 00003: val_accuracy did not improve from 0.85800
18/18 [==============================] - 9s 494ms/sample - loss: 0.6769 - accuracy: 0.8559 - val_loss: 0.5785 - val_accuracy: 0.8580
Epoch 4/30
16/18 [=========================>....] - ETA: 0s - loss: 0.6097 - accuracy: 0.8579
Epoch 00004: val_accuracy did not improve from 0.85800
18/18 [==============================] - 9s 515ms/sample - loss: 0.6246 - accuracy: 0.8579 - val_loss: 0.5722 - val_accuracy: 0.8580
Epoch 5/30
16/18 [=========================>....] - ETA: 1s - loss: 0.5821 - accuracy: 0.8579
Epoch 00005: val_accuracy improved from 0.85800 to 0.85840, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 557ms/sample - loss: 0.5787 - accuracy: 0.8579 - val_loss: 0.5162 - val_accuracy: 0.8584
Epoch 6/30
16/18 [=========================>....] - ETA: 1s - loss: 0.5554 - accuracy: 0.8581
Epoch 00006: val_accuracy improved from 0.85840 to 0.85900, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 537ms/sample - loss: 0.5397 - accuracy: 0.8581 - val_loss: 0.4701 - val_accuracy: 0.8590
Epoch 7/30
16/18 [=========================>....] - ETA: 0s - loss: 0.4891 - accuracy: 0.8597
Epoch 00007: val_accuracy improved from 0.85900 to 0.86114, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 522ms/sample - loss: 0.4897 - accuracy: 0.8599 - val_loss: 0.4284 - val_accuracy: 0.8611
Epoch 8/30
16/18 [=========================>....] - ETA: 0s - loss: 0.4208 - accuracy: 0.8624
Epoch 00008: val_accuracy improved from 0.86114 to 0.86425, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 533ms/sample - loss: 0.4337 - accuracy: 0.8626 - val_loss: 0.3885 - val_accuracy: 0.8643
Epoch 9/30
16/18 [=========================>....] - ETA: 0s - loss: 0.3834 - accuracy: 0.8656
Epoch 00009: val_accuracy improved from 0.86425 to 0.86844, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 518ms/sample - loss: 0.3849 - accuracy: 0.8658 - val_loss: 0.3584 - val_accuracy: 0.8684
Epoch 10/30
16/18 [=========================>....] - ETA: 1s - loss: 0.3153 - accuracy: 0.8713
Epoch 00010: val_accuracy improved from 0.86844 to 0.87300, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 533ms/sample - loss: 0.3375 - accuracy: 0.8715 - val_loss: 0.3542 - val_accuracy: 0.8730
Epoch 11/30
16/18 [=========================>....] - ETA: 0s - loss: 0.2682 - accuracy: 0.8764
Epoch 00011: val_accuracy improved from 0.87300 to 0.88036, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 527ms/sample - loss: 0.2905 - accuracy: 0.8767 - val_loss: 0.2194 - val_accuracy: 0.8804
Epoch 12/30
16/18 [=========================>....] - ETA: 0s - loss: 0.2369 - accuracy: 0.8835
Epoch 00012: val_accuracy improved from 0.88036 to 0.88700, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 533ms/sample - loss: 0.2514 - accuracy: 0.8838 - val_loss: 0.2265 - val_accuracy: 0.8870
Epoch 13/30
16/18 [=========================>....] - ETA: 0s - loss: 0.2007 - accuracy: 0.8896
Epoch 00013: val_accuracy improved from 0.88700 to 0.89231, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 524ms/sample - loss: 0.2121 - accuracy: 0.8899 - val_loss: 0.1900 - val_accuracy: 0.8923
Epoch 14/30
16/18 [=========================>....] - ETA: 0s - loss: 0.1865 - accuracy: 0.8943
Epoch 00014: val_accuracy improved from 0.89231 to 0.89657, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 529ms/sample - loss: 0.1895 - accuracy: 0.8945 - val_loss: 0.1510 - val_accuracy: 0.8966
Epoch 15/30
16/18 [=========================>....] - ETA: 0s - loss: 0.1597 - accuracy: 0.8985
Epoch 00015: val_accuracy improved from 0.89657 to 0.90067, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 525ms/sample - loss: 0.1558 - accuracy: 0.8987 - val_loss: 0.1406 - val_accuracy: 0.9007
Epoch 16/30
16/18 [=========================>....] - ETA: 0s - loss: 0.1419 - accuracy: 0.9023
Epoch 00016: val_accuracy improved from 0.90067 to 0.90425, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 523ms/sample - loss: 0.1449 - accuracy: 0.9025 - val_loss: 0.1578 - val_accuracy: 0.9043
Epoch 17/30
16/18 [=========================>....] - ETA: 0s - loss: 0.1294 - accuracy: 0.9058
Epoch 00017: val_accuracy improved from 0.90425 to 0.90729, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 523ms/sample - loss: 0.1368 - accuracy: 0.9059 - val_loss: 0.1259 - val_accuracy: 0.9073
Epoch 18/30
16/18 [=========================>....] - ETA: 0s - loss: 0.1321 - accuracy: 0.9084
Epoch 00018: val_accuracy improved from 0.90729 to 0.90989, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 524ms/sample - loss: 0.1313 - accuracy: 0.9085 - val_loss: 0.1138 - val_accuracy: 0.9099
Epoch 19/30
16/18 [=========================>....] - ETA: 1s - loss: 0.1259 - accuracy: 0.9110
Epoch 00019: val_accuracy improved from 0.90989 to 0.91232, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 537ms/sample - loss: 0.1266 - accuracy: 0.9111 - val_loss: 0.1255 - val_accuracy: 0.9123
Epoch 20/30
16/18 [=========================>....] - ETA: 1s - loss: 0.1252 - accuracy: 0.9132
Epoch 00020: val_accuracy improved from 0.91232 to 0.91450, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 533ms/sample - loss: 0.1220 - accuracy: 0.9134 - val_loss: 0.1191 - val_accuracy: 0.9145
Epoch 21/30
16/18 [=========================>....] - ETA: 0s - loss: 0.1095 - accuracy: 0.9156
Epoch 00021: val_accuracy improved from 0.91450 to 0.91676, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 526ms/sample - loss: 0.1247 - accuracy: 0.9157 - val_loss: 0.1318 - val_accuracy: 0.9168
Epoch 22/30
16/18 [=========================>....] - ETA: 1s - loss: 0.1128 - accuracy: 0.9178
Epoch 00022: val_accuracy improved from 0.91676 to 0.91882, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 538ms/sample - loss: 0.1082 - accuracy: 0.9179 - val_loss: 0.1220 - val_accuracy: 0.9188
Epoch 23/30
16/18 [=========================>....] - ETA: 0s - loss: 0.0918 - accuracy: 0.9198
Epoch 00023: val_accuracy improved from 0.91882 to 0.92096, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 529ms/sample - loss: 0.0944 - accuracy: 0.9199 - val_loss: 0.1226 - val_accuracy: 0.9210
Epoch 24/30
16/18 [=========================>....] - ETA: 0s - loss: 0.0833 - accuracy: 0.9219
Epoch 00024: val_accuracy improved from 0.92096 to 0.92292, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 9s 517ms/sample - loss: 0.0858 - accuracy: 0.9220 - val_loss: 0.1138 - val_accuracy: 0.9229
Epoch 25/30
16/18 [=========================>....] - ETA: 1s - loss: 0.0690 - accuracy: 0.9238
Epoch 00025: val_accuracy improved from 0.92292 to 0.92480, saving model to ./data_out/seq2seq_kor/weights.h5
18/18 [==============================] - 10s 537ms/sample - loss: 0.0713 - accuracy: 0.9239 - val_loss: 0.1090 - val_accuracy: 0.9248
###Markdown
결과 플롯
###Code
plot_graphs(history, 'accuracy')
plot_graphs(history, 'loss')
###Output
_____no_output_____
###Markdown
결과 확인
###Code
SAVE_FILE_NM = "weights.h5"
model.load_weights(os.path.join(DATA_OUT_PATH, MODEL_NAME, SAVE_FILE_NM))
query = "남자친구 승진 선물로 뭐가 좋을까?"
test_index_inputs, _ = enc_processing([query], char2idx)
predict_tokens = model.inference(test_index_inputs)
print(predict_tokens)
print(' '.join([idx2char[str(t)] for t in predict_tokens]))
###Output
[41 27 10 26 78 70]
평소에 필요했던 게 좋을 것 같아요
|
Week 4 - Classification/SpaceX_Machine Learning Prediction_Part_5.ipynb
|
###Markdown
**Space X Falcon 9 First Stage Landing Prediction** Assignment: Machine Learning Prediction Estimated time needed: **60** minutes Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch. In this lab, you will create a machine learning pipeline to predict if the first stage will land given the data from the preceding labs.  Several examples of an unsuccessful landing are shown here:  Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans. Objectives Perform exploratory Data Analysis and determine Training Labels* create a column for the class* Standardize the data* Split into training data and test data\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression* Find the method performs best using test data *** Import Libraries and Define Auxiliary Functions We will import the following libraries for the lab
###Code
# Pandas is a software library written for the Python programming language for data manipulation and analysis.
import pandas as pd
# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays
import numpy as np
# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.
import matplotlib.pyplot as plt
#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics
import seaborn as sns
# Preprocessing allows us to standarsize our data
from sklearn import preprocessing
# Allows us to split our data into training and testing data
from sklearn.model_selection import train_test_split
# Allows us to test parameters of classification algorithms and find the best one
from sklearn.model_selection import GridSearchCV
# Logistic Regression classification algorithm
from sklearn.linear_model import LogisticRegression
# Support Vector Machine classification algorithm
from sklearn.svm import SVC
# Decision Tree classification algorithm
from sklearn.tree import DecisionTreeClassifier
# K Nearest Neighbors classification algorithm
from sklearn.neighbors import KNeighborsClassifier
###Output
_____no_output_____
###Markdown
This function is to plot the confusion matrix.
###Code
def plot_confusion_matrix(y,y_predict):
"this function plots the confusion matrix"
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y, y_predict)
ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix');
ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])
###Output
_____no_output_____
###Markdown
Load the dataframe Load the data
###Code
data = pd.read_csv("https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv")
# If you were unable to complete the previous lab correctly you can uncomment and load this csv
# data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')
data.head()
X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv')
# If you were unable to complete the previous lab correctly you can uncomment and load this csv
# X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_3.csv')
X.head(100)
###Output
_____no_output_____
###Markdown
TASK 1 Create a NumPy array from the column Class in data, by applying the method to_numpy() thenassign it to the variable Y,make sure the output is a Pandas series (only one bracket df\['name of column']).
###Code
Y = data['Class'].to_numpy()
###Output
_____no_output_____
###Markdown
TASK 2 Standardize the data in X then reassign it to the variable X using the transform provided below.
###Code
# students get this
transform = preprocessing.StandardScaler()
X = transform.fit_transform(X)
###Output
_____no_output_____
###Markdown
We split the data into training and testing data using the function train_test_split. The training data is divided into validation data, a second set used for training data; then the models are trained and hyperparameters are selected using the function GridSearchCV. TASK 3 Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to 0.2 and random_state to 2. The training data and test data should be assigned to the following labels. X_train, X_test, Y_train, Y_test
###Code
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)
###Output
_____no_output_____
###Markdown
we can see we only have 18 test samples.
###Code
Y_test.shape
###Output
_____no_output_____
###Markdown
TASK 4 Create a logistic regression object then create a GridSearchCV object logreg_cv with cv = 10. Fit the object to find the best parameters from the dictionary parameters.
###Code
parameters ={'C':[0.01,0.1,1],
'penalty':['l2'],
'solver':['lbfgs']}
parameters ={"C":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge
lr=LogisticRegression()
logreg_cv = GridSearchCV(lr, parameters, cv=10)
logreg_cv.fit(X_train, Y_train)
###Output
_____no_output_____
###Markdown
We output the GridSearchCV object for logistic regression. We display the best parameters using the data attribute best_params\_ and the accuracy on the validation data using the data attribute best_score\_.
###Code
print("tuned hpyerparameters :(best parameters) ",logreg_cv.best_params_)
print("accuracy :",logreg_cv.best_score_)
###Output
tuned hpyerparameters :(best parameters) {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}
accuracy : 0.8464285714285713
###Markdown
TASK 5 Calculate the accuracy on the test data using the method score:
###Code
logreg_cv.score(X_test, Y_test)
###Output
_____no_output_____
###Markdown
Lets look at the confusion matrix:
###Code
yhat=logreg_cv.predict(X_test)
plot_confusion_matrix(Y_test,yhat)
###Output
_____no_output_____
###Markdown
Examining the confusion matrix, we see that logistic regression can distinguish between the different classes. We see that the major problem is false positives. TASK 6 Create a support vector machine object then create a GridSearchCV object svm_cv with cv - 10. Fit the object to find the best parameters from the dictionary parameters.
###Code
parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),
'C': np.logspace(-3, 3, 5),
'gamma':np.logspace(-3, 3, 5)}
svm = SVC()
svm_cv = GridSearchCV(svm, parameters, cv=10)
svm_cv.fit(X_train, Y_train)
print("tuned hpyerparameters :(best parameters) ",svm_cv.best_params_)
print("accuracy :",svm_cv.best_score_)
###Output
tuned hpyerparameters :(best parameters) {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}
accuracy : 0.8482142857142856
###Markdown
TASK 7 Calculate the accuracy on the test data using the method score:
###Code
svm_cv.score(X_test, Y_test)
###Output
_____no_output_____
###Markdown
We can plot the confusion matrix
###Code
yhat=svm_cv.predict(X_test)
plot_confusion_matrix(Y_test,yhat)
###Output
_____no_output_____
###Markdown
TASK 8 Create a decision tree classifier object then create a GridSearchCV object tree_cv with cv = 10. Fit the object to find the best parameters from the dictionary parameters.
###Code
parameters = {'criterion': ['gini', 'entropy'],
'splitter': ['best', 'random'],
'max_depth': [2*n for n in range(1,10)],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 4],
'min_samples_split': [2, 5, 10]}
tree = DecisionTreeClassifier()
tree_cv = GridSearchCV(tree, parameters, cv=10)
tree_cv.fit(X_train, Y_train)
print("tuned hpyerparameters :(best parameters) ",tree_cv.best_params_)
print("accuracy :",tree_cv.best_score_)
###Output
tuned hpyerparameters :(best parameters) {'criterion': 'entropy', 'max_depth': 16, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'splitter': 'random'}
accuracy : 0.8892857142857145
###Markdown
TASK 9 Calculate the accuracy of tree_cv on the test data using the method score:
###Code
tree_cv.score(X_test, Y_test)
###Output
_____no_output_____
###Markdown
We can plot the confusion matrix
###Code
yhat = svm_cv.predict(X_test)
plot_confusion_matrix(Y_test,yhat)
###Output
_____no_output_____
###Markdown
TASK 10 Create a k nearest neighbors object then create a GridSearchCV object knn_cv with cv = 10. Fit the object to find the best parameters from the dictionary parameters.
###Code
parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],
'p': [1,2]}
KNN = KNeighborsClassifier()
knn_cv = GridSearchCV(KNN, parameters, cv=10)
knn_cv.fit(X_train, Y_train)
print("tuned hpyerparameters :(best parameters) ",knn_cv.best_params_)
print("accuracy :",knn_cv.best_score_)
###Output
tuned hpyerparameters :(best parameters) {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}
accuracy : 0.8482142857142858
###Markdown
TASK 11 Calculate the accuracy of tree_cv on the test data using the method score:
###Code
tree_cv.score(X_test, Y_test)
###Output
_____no_output_____
###Markdown
We can plot the confusion matrix
###Code
yhat = knn_cv.predict(X_test)
plot_confusion_matrix(Y_test,yhat)
###Output
_____no_output_____
|
Identifying Fraudulent Activities.ipynb
|
###Markdown
Load Data
###Code
fraud_df = pd.read_csv("../Collection of DS take home challenges/data collection-Product dataset数据挑战数据集/ML Identifying Fraudulent Activities with solution/Fraud_Data.csv")
ipaddress_df = pd.read_csv("../Collection of DS take home challenges/data collection-Product dataset数据挑战数据集/ML Identifying Fraudulent Activities with solution/IpAddress_to_Country.csv")
fraud_df.head()
ipaddress_df.head()
fraud_df.info()
ipaddress_df.info()
countries = []
for i in range(len(fraud_df)):
country = ipaddress_df[(ipaddress_df["lower_bound_ip_address"] <= fraud_df["ip_address"][i]) & (ipaddress_df["upper_bound_ip_address"] >= fraud_df["ip_address"][i])]["country"].values
if len(country) == 1:
countries.append(country[0])
else:
countries.append("NA")
fraud_df["country"] = countries
fraud_df.describe()
fraud_df.info()
fraud_df["signup_time"] = pd.to_datetime(fraud_df["signup_time"])
fraud_df["purchase_time"] = pd.to_datetime(fraud_df["purchase_time"])
fraud_df.isnull().sum()
columns = ["source", "browser", "country"]
for i in columns:
uniques = sorted(fraud_df[i].unique())
print("{0:10s} {1:10d}\t".format(i, len(uniques)), uniques[:5])
###Output
source 3 ['Ads', 'Direct', 'SEO']
browser 5 ['Chrome', 'FireFox', 'IE', 'Opera', 'Safari']
country 182 ['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Antigua and Barbuda']
###Markdown
Feature Engineering
###Code
fraud_df.head()
# time interval
def time_interval(x):
if x.hour >= 6 and x.hour <= 12:
return("Morning")
elif x.hour > 12 and x.hour <= 16:
return("Afternoon")
elif x.hour > 16 and x.hour <= 23:
return("Evening/Night")
elif x.hour >= 0 and x.hour < 6:
return("Midnight")
fraud_df["signup_interval"] = fraud_df["signup_time"].apply(time_interval)
fraud_df["purchase_interval"] = fraud_df["purchase_time"].apply(time_interval)
# signup and purchase diff
fraud_df["difference"] = fraud_df["purchase_time"]-fraud_df["signup_time"]
fraud_df["difference"] = fraud_df["difference"].apply(lambda x: x.seconds)
# how many user_id associated with the device_id
fraud_df["num_user_id"] = fraud_df["device_id"].apply(lambda x: len(fraud_df[fraud_df["device_id"] == x]))
# lambda function is really slow, try to use merge next time
# how many user_id associated with the ip_address
ip_count = fraud_df.groupby("ip_address").size().reset_index().rename(columns = {0:"num_ip_address"})
fraud_df = fraud_df.merge(ip_count, how = "left", on = "ip_address")
# day of week
fraud_df["signup_day"] = fraud_df["signup_time"].apply(lambda x: x.strftime('%A'))
fraud_df["purchase_day"] = fraud_df["purchase_time"].apply(lambda x: x.strftime('%A'))
fraud_df = pd.read_csv("fraud_df.csv")
fraud_df.head()
###Output
_____no_output_____
###Markdown
Model Building
###Code
# select features and target
df = fraud_df[["purchase_value", "source", "browser", "sex", "age", "country", "difference", "num_user_id", "num_ip_address", "signup_day", "purchase_day", "class"]]
h2o.init()
h2o.remove_all()
h2o_df = H2OFrame(df)
for i in ["source", "browser", "sex", "country", "signup_day", "purchase_day", "class"]:
h2o_df[i] = h2o_df[i].asfactor()
# train test split
strat_split = h2o_df["class"].stratified_split(test_frac= 0.3)
train = h2o_df[strat_split == "train"]
test = h2o_df[strat_split == "test"]
features = ["purchase_value", "source", "browser", "sex", "age", "country", "difference", "num_user_id", "num_ip_address", "signup_day", "purchase_day"]
target = "class"
clf = H2ORandomForestEstimator(balance_classes = True, stopping_rounds=5, stopping_metric='auc', score_each_iteration=True)
clf.train(x = features, y=target, training_frame=train, validation_frame=test)
clf.varimp_plot()
# predict
train_true = train.as_data_frame()['class'].values
test_true = test.as_data_frame()['class'].values
train_pred = clf.predict(train).as_data_frame()['p1'].values
test_pred = clf.predict(test).as_data_frame()['p1'].values
train_fpr, train_tpr, _ = roc_curve(train_true, train_pred)
test_fpr, test_tpr, _ = roc_curve(test_true, test_pred)
train_auc = np.round(auc(train_fpr, train_tpr), 3)
test_auc = np.round(auc(test_fpr, test_tpr), 3)
# Classification report
print(classification_report(y_true=test_true, y_pred=(test_pred > 0.5).astype(int)))
fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(train_fpr, train_tpr, label='Train AUC: ' + str(train_auc))
ax.plot(test_fpr, test_tpr, label='Test AUC: ' + str(test_auc))
ax.plot(train_fpr, train_fpr, 'k--', label='Chance Curve')
ax.set_xlabel('False Positive Rate', fontsize=12)
ax.set_ylabel('True Positive Rate', fontsize=12)
ax.grid(True)
ax.legend(fontsize=12)
plt.show()
cols = ['num_user_id', 'difference', 'country', 'num_ip_address']
_ = clf.partial_plot(data=train, cols=cols, nbins=200, figsize=(18, 20))
h2o.cluster().shutdown()
###Output
H2O session _sid_8c8e closed.
|
02-solutions.ipynb
|
###Markdown
Solutions exercise 02===
###Code
# 1.a
sentence = ['I', 'want', 'to', 'go', 'home']
# 1.b
new_string = ''
for word in sentence:
new_string = new_string + word
print(new_string)
# alternatively (nicer):
new_string = ''
for word in sentence:
new_string = new_string + word + ' '
print(new_string)
# 1.c
numbers = [1,2,3,4,5,6,7,8,9,10]
# even numbers
print(numbers[1::2])
# uneven numbers
print(numbers[::2])
# the first three numbers
print(numbers[:3])
# the last three numbers
print(numbers[-3:])
# 1.d
my_list = [6, 9, 1, 2, 15]
new_list = []
for number in my_list:
new_list.append(number + sum(my_list))
print(new_list)
# 1.e
new_list[1::2] = [0] * int(len(new_list)/2)
print(new_list)
###Output
_____no_output_____
|
lectures/06 Machine Learning Part 1 and Midterm Review/1_PythonExamples.ipynb
|
###Markdown
Loading packages
###Code
import numpy as np
import matplotlib.pylab as py
import pandas as pa
import scipy.stats as st
np.set_printoptions(precision=2)
%matplotlib inline
###Output
_____no_output_____
###Markdown
Discrete Random Variables In this section we show a few example of discrete random variables using Python. The documentation for these routines can be found at:http://docs.scipy.org/doc/scipy-0.14.0/reference/stats.html
###Code
X=st.bernoulli(p=0.3)
X.rvs(100)
# Note that "high" is not included.
X=st.randint(low=1,high=5)
X.rvs(100)
###Output
_____no_output_____
###Markdown
Continuous Random Variables The documentation for these routines can be found at:http://docs.scipy.org/doc/scipy-0.14.0/reference/stats.html
###Code
XUniform=st.uniform(loc=0.7,scale=0.3);
# "bins" tells you how many bars to use
# "normed" says to turn the counts into probability densities
py.hist(XUniform.rvs(1000000),bins=20,normed=True);
x = np.linspace(-0.1,1.1,100)
py.plot(x,XUniform.pdf(x))
#py.savefig('Figures/uniformPDF.png')
py.plot(XUniform.cdf(x))
#py.savefig('Figures/uniformCDF.png')
XNormal=st.norm(loc=0,scale=1);
# "bins" tells you how many bars to use
# "normed" says to turn the counts into probability densities
py.hist(XNormal.rvs(1000),bins=100,normed=True);
x = np.linspace(-3,3,100)
py.plot(x,XNormal.pdf(x))
#py.savefig('Figures/normalPDF.png')
###Output
_____no_output_____
###Markdown
http://en.wikipedia.org/wiki/Carl_Friedrich_Gauss
###Code
py.plot(XNormal.cdf(x))
#py.savefig('Figures/normalCDF.png')
###Output
_____no_output_____
###Markdown
Now we can look at the histograms of some of our data from Case Study 2.
###Code
data = pa.read_hdf('data.h5','movies')
data
data['title'][100000]
X=data.pivot_table('rating',index='timestamp',aggfunc='count')
X.plot()
# Warning: Some versions of Pandas use "index" and "columns", some use "rows" and "cols"
X=data.pivot_table('rating',index='title',aggfunc='sum')
#X=data.pivot_table('rating',rows='title',aggfunc='sum')
X
X.hist()
# Warning: Some versions of Pandas use "index" and "columns", some use "rows" and "cols"
X=data.pivot_table('rating',index='occupation',aggfunc='sum')
#X=data.pivot_table('rating',rows='occupation',aggfunc='sum')
X
###Output
_____no_output_____
###Markdown
Central limit theorem Here we show an example of the central limit theorem. You can play around with "numberOfDistributions" and "numberOfSamples" to see how quickly this converges to something that looks Gaussian.
###Code
numberOfDistributions = 100
numberOfSamples = 1000
XTest = st.uniform(loc=0,scale=1);
# The same thing works with many distributions.
#XTest = st.lognorm(s=1.0);
XCLT=np.zeros([numberOfSamples])
for i in range(numberOfSamples):
for j in range(numberOfDistributions):
XCLT[i] += XTest.rvs()
XCLT[i] = XCLT[i]/numberOfDistributions
py.hist(XCLT,normed=True)
###Output
_____no_output_____
###Markdown
Linear Algebra Some basic ideas in Linear Algebra and how you can use them in Python.
###Code
import numpy as np
a=np.array([1,2,3])
a
A=np.matrix(np.random.randint(1,10,size=[3,3]))
A
x=np.matrix([[1],[2],[3]])
print x
print x.T
a*a
np.dot(a,a)
x.T*x
A*x
b = np.matrix([[5],[6],[7]])
b
Ai = np.linalg.inv(A)
print A
print Ai
A*Ai
Ai*A
xHat = Ai*b
xHat
print A*xHat
print b
###Output
[[ 5.]
[ 6.]
[ 7.]]
[[5]
[6]
[7]]
###Markdown
But matrix inversion can be very expensive.
###Code
sizes = range(100,1000,200)
times = np.zeros(len(sizes))
for i in range(len(sizes)):
A = np.random.random(size=[sizes[i],sizes[i]])
x = %timeit -o np.linalg.inv(A)
times[i] = x.best
py.plot(sizes,times)
###Output
The slowest run took 15.27 times longer than the fastest. This could mean that an intermediate result is being cached
1000 loops, best of 3: 345 µs per loop
100 loops, best of 3: 6.5 ms per loop
10 loops, best of 3: 21.9 ms per loop
10 loops, best of 3: 56.1 ms per loop
10 loops, best of 3: 110 ms per loop
###Markdown
Something slightly more advanced: Sparse matrices. Sparse matrices (those with lots of 0s) can often be worked with much more efficiently than general matrices than standard methods.
###Code
from scipy.sparse.linalg import spsolve
from scipy.sparse import rand,eye
mySize = 1000
A=rand(mySize,mySize,0.001)+eye(mySize)
b=np.random.random(size=[mySize])
###Output
_____no_output_____
###Markdown
The sparsity structure of A.
###Code
py.spy(A,markersize=0.1)
dense = %timeit -o np.linalg.solve(A.todense(),b)
sparse = %timeit -o spsolve(A,b)
dense.best/sparse.best
###Output
_____no_output_____
###Markdown
Descriptive statistics Pandas provides many routines for computing statistics.
###Code
XNormal=st.norm(loc=0.7,scale=2);
x = XNormal.rvs(1000)
print np.mean(x)
print np.std(x)
print np.var(x)
###Output
0.741294831708
1.95243659129
3.81200864301
###Markdown
But empirical measures are not always good approximations of the true properties of the distribution.
###Code
sizes = np.arange(16)+1
errors = np.zeros(16)
for i in range(16):
x = XNormal.rvs(2**i)
errors[i] = np.abs(0.7-np.mean(x))
py.plot(sizes,errors)
py.plot(sizes,2/np.sqrt(sizes))
py.plot(sizes,2*2/np.sqrt(sizes),'r')
#py.savefig('Figures/errorInMean.png')
###Output
_____no_output_____
###Markdown
Playing around with data
###Code
data.pivot_table?
X=data.pivot_table('rating',index='title',aggfunc='mean')
#X=data.pivot_table('rating',rows='title',aggfunc='mean')
hist(X)
X=data.pivot_table('rating',index='title',columns='gender',aggfunc='mean')
#X=data.pivot_table('rating',rows='title',cols='gender',aggfunc='mean')
py.subplot(1,2,1)
X['M'].hist()
py.subplot(1,2,2)
X['F'].hist()
py.plot(X['M'],X['F'],'.')
X.cov()
X.corr()
X=data.pivot_table('rating',index='occupation',columns='gender',aggfunc='mean')
#X=data.pivot_table('rating',rows='occupation',cols='gender',aggfunc='mean')
X
###Output
_____no_output_____
|
comparison/.ipynb_checkpoints/lora-ucb-vs-ts-checkpoint.ipynb
|
###Markdown
LoRa Data Analysis - UCB vs. TS We first declare a fixed parameters.Those parameters are not changed during the experiments.Fixed communication parameters are listed below:- Code Rate: 4/5- Frequency: 866.1 MHz- Bandwidth: 125 kHzEnd nodes:- were sending different types of uplink messages- were sending a single message each 2 minutes- comparison of upper confidence bound algorithm (UCB) and Thompson sampling (TS)Access points:- only a single access point was used- capture effect was also considered Initial declaration
###Code
%matplotlib inline
import pandas as pd # import pandas
import numpy as np # import numpy
import matplotlib as mpl # import matplotlib
import matplotlib.pyplot as plt # import plotting module
import statistics
import math
import base64
from IPython.display import set_matplotlib_formats # module for svg export
# Set output format for png figures
output_format = 'png'
set_matplotlib_formats(output_format) # set export to svg file
ts_uplink_file = 'ts_uplink_messages.csv'
ucb_uplink_file = 'ucb_uplink_messages.csv'
###Output
_____no_output_____
###Markdown
Analysis of Uplink Messages We read a csv file with uplink messages
###Code
ts_uplink_messages = pd.read_csv(ts_uplink_file, delimiter=',')
ucb_uplink_messages = pd.read_csv(ucb_uplink_file, delimiter=',')
###Output
_____no_output_____
###Markdown
Let us have a look at various columns that are present and can be evaluated.
###Code
ts_uplink_messages.head()
ucb_uplink_messages.head()
###Output
_____no_output_____
###Markdown
Remove all columns that have fixed values or there is no point in their analysis.
###Code
try:
del ts_uplink_messages['id']
del ts_uplink_messages['msg_group_number']
del ts_uplink_messages['is_primary']
del ts_uplink_messages['coderate']
del ts_uplink_messages['bandwidth']
del ts_uplink_messages['receive_time']
except KeyError:
print('Columns have already been removed')
try:
del ucb_uplink_messages['id']
del ucb_uplink_messages['msg_group_number']
del ucb_uplink_messages['is_primary']
del ucb_uplink_messages['coderate']
del ucb_uplink_messages['bandwidth']
del ucb_uplink_messages['receive_time']
except KeyError:
print('Columns have already been removed')
###Output
_____no_output_____
###Markdown
Payload Length
###Code
adr_uplink_messages['payload_len'] = adr_uplink_messages.app_data.apply(len)
ucb_uplink_messages['payload_len'] = ucb_uplink_messages.app_data.apply(len)
adr_payload_len = round(statistics.mean(adr_uplink_messages.payload_len))
ucb_payload_len = round(statistics.mean(ucb_uplink_messages.payload_len))
print(f'Mean value of payload length for ADR is {adr_payload_len} B')
print(f'Mean value of payload length for UCB is {ucb_payload_len} B')
###Output
Mean value of payload length for ADR is 47 B
Mean value of payload length for UCB is 43 B
###Markdown
Spreading Factor
###Code
sf1 = adr_uplink_messages.spf.value_counts()
sf2 = ucb_uplink_messages.spf.value_counts()
diff = abs(sf1 - sf2)
diff.fillna(0)
sf_adr = [sf1, diff]
sf_adr = pd.concat(sf_adr, axis=1, sort=False).sum(axis=1)
sf_adr.sort_index(ascending=False, inplace=True)
diff = abs(sf2 - sf1)
diff.fillna(0)
sf_ucb = [sf2, diff]
sf_ucb = pd.concat(sf_ucb, axis=1, sort=False).sum(axis=1)
sf_ucb.sort_index(ascending=False, inplace=True)
# Create a grouped bar chart, with job as the x-axis
# and gender as the variable we're grouping on so there
# are two bars per job.
fig, ax = plt.subplots(figsize=(10, 4))
# Define bar width. We need this to offset the second bar.
bar_width = 0.3
index = np.arange(len(sf_adr))
ax.bar(index, sf_adr, width=bar_width, color='green', label = 'ADR')
# Same thing, but offset the x.
ax.bar(index + bar_width, sf_ucb, width=bar_width, color='blue', label = 'UCB')
# Fix the x-axes.
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(sf_ucb.index)
# Add legend.
ax.legend()
# Axis styling.
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
ax.tick_params(bottom=False, left=False)
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
# Add axis and chart labels.
ax.set_xlabel('Spreading Factor', labelpad=15)
ax.set_ylabel('Number of Messages', labelpad=15)
ax.set_title('Utilization of Spreading Factor', pad=15)
fig.tight_layout()
# For each bar in the chart, add a text label.
for bar in ax.patches:
# The text annotation for each bar should be its height.
bar_value = round(bar.get_height())
# Format the text with commas to separate thousands. You can do
# any type of formatting here though.
text = f'{bar_value:,}'
# This will give the middle of each bar on the x-axis.
text_x = bar.get_x() + bar.get_width() / 2
# get_y() is where the bar starts so we add the height to it.
text_y = bar.get_y() + bar_value
# If we want the text to be the same color as the bar, we can
# get the color like so:
bar_color = bar.get_facecolor()
# If you want a consistent color, you can just set it as a constant, e.g. #222222
ax.text(text_x, text_y, text, ha='center', va='bottom', color=bar_color, size=10)
fig.savefig(f'adr-ucb-sf.{output_format}', dpi=300)
###Output
_____no_output_____
###Markdown
All nodes used the same frequency to increase a probability of collisions. We have only a single Access Point. Analysis of End Nodes Analysis of certain aspects (active time, sleep time and collisions) of end devices.
###Code
adr_unique_ens = adr_uplink_messages.node_id.nunique()
ucb_unique_ens = ucb_uplink_messages.node_id.nunique()
print(f'Number of end nodes participating for ADR is {adr_unique_ens}.')
print(f'Number of end nodes participating for UCB is {ucb_unique_ens}.')
adr_end_nodes = pd.read_csv(f'adr_end_nodes.csv', delimiter=',')
ucb_end_nodes = pd.read_csv(f'ucb_end_nodes.csv', delimiter=',')
###Output
_____no_output_____
###Markdown
Collision Ratio
###Code
adr_collisions = adr_end_nodes.collisions
ucb_collisions = ucb_end_nodes.collisions
adr_max_collisions = max(adr_end_nodes.collisions)
adr_min_collisions = min(adr_end_nodes.collisions)
ucb_max_collisions = max(ucb_end_nodes.collisions)
ucb_min_collisions = min(ucb_end_nodes.collisions)
max_collisions = max(adr_max_collisions, ucb_max_collisions)
min_collisions = min(adr_min_collisions, ucb_min_collisions)
range_collisions = max_collisions - min_collisions
buckets = 8
increment = range_collisions / buckets
print(f'Max number of collisions for ADR: {adr_max_collisions}')
print(f'Min number of collisions for ADR: {adr_min_collisions}')
print(f'Max number of collisions for UCB: {ucb_max_collisions}')
print(f'Min number of collisions for UCB: {ucb_min_collisions}')
fig, ax = plt.subplots(figsize=(10, 4))
bar_width = 0.4
index = np.arange(buckets)
bins = []
for i in range(buckets + 1):
bins.append(round(min_collisions + i * increment))
out_adr = pd.cut(adr_collisions, bins=bins)
adr_values = out_adr.value_counts(sort=False).iloc[::-1]
out_ucb = pd.cut(ucb_collisions, bins=bins)
ucb_values = out_ucb.value_counts(sort=False).iloc[::-1]
ax.bar(index, adr_values, width=bar_width, color='green', label='ADR')
ax.bar(index + bar_width, ucb_values, width=bar_width, color='blue', label='UCB')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(adr_values.index, rotation=45)
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
ax.tick_params(bottom=False, left=False)
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
ax.set_xlabel('Number of Collisions', labelpad=15)
ax.set_ylabel('Number of Devices', labelpad=15)
ax.set_title('Collision Rate', pad=15)
fig.tight_layout()
for bar in ax.patches:
bar_value = round(bar.get_height())
text = f'{bar_value:,}'
text_x = bar.get_x() + bar.get_width() / 2
text_y = bar.get_y() + bar_value
bar_color = bar.get_facecolor()
ax.text(text_x, text_y, text, ha='center', va='bottom', color=bar_color, size=10)
fig.savefig(f'adr-ucb-collisions.{output_format}', dpi=300)
print(f'Mean collision number for ADR is {round(statistics.mean(adr_collisions))}')
print(f'Mean collision number for UCB is {round(statistics.mean(ucb_collisions))}')
###Output
Mean collision number for ADR is 212
Mean collision number for UCB is 4
###Markdown
Ration between active time and total nodes uptime
###Code
adr_energy = (adr_end_nodes.active_time / adr_end_nodes.uptime)
adr_active_time = round(statistics.mean(adr_energy) * 100, 2)
ucb_energy = (ucb_end_nodes.active_time / ucb_end_nodes.uptime)
ucb_active_time = round(statistics.mean(ucb_energy) * 100, 2)
print(f'ADR nodes spent {adr_active_time}% of their uptime in active mode')
print(f'UCB nodes spent {ucb_active_time}% of their uptime in active mode')
###Output
ADR nodes spent 23.15% of their uptime in active mode
UCB nodes spent 2.5% of their uptime in active mode
###Markdown
Packet Delivery Ratio (PDR) Evaluation of packet delivery ratio for end nodes. Add message count from uplink data and collisions.
###Code
adr_data = adr_uplink_messages.node_id.value_counts()
adr_nodes = pd.DataFrame({}, columns = ['dev_id', 'collisions', 'messages'])
collisions = []
messages = []
dev_id = []
for index,value in adr_data.items():
dev_id.append(index)
collision_count = adr_end_nodes.loc[adr_end_nodes.dev_id == index].collisions.values[0]
collisions.append(collision_count)
messages.append(value + collision_count)
adr_nodes['dev_id'] = dev_id
adr_nodes['collisions'] = collisions
adr_nodes['messages'] = messages
# Make the same for another algorithm
ucb_data = ucb_uplink_messages.node_id.value_counts()
ucb_nodes = pd.DataFrame({}, columns = ['dev_id', 'collisions', 'messages'])
collisions = []
messages = []
dev_id = []
for index,value in ucb_data.items():
dev_id.append(index)
collision_count = ucb_end_nodes.loc[ucb_end_nodes.dev_id == index].collisions.values[0]
collisions.append(collision_count)
messages.append(value + collision_count)
ucb_nodes['dev_id'] = dev_id
ucb_nodes['collisions'] = collisions
ucb_nodes['messages'] = messages
adr_nodes['pdr'] = round((1 - (adr_nodes.collisions / adr_nodes.messages))*100, 2)
adr_mean_pdr = round(statistics.mean(adr_nodes.pdr), 2)
ucb_nodes['pdr'] = round((1 - (ucb_nodes.collisions / ucb_nodes.messages))*100, 2)
ucb_mean_pdr = round(statistics.mean(ucb_nodes.pdr), 2)
print(f'Mean value of PDR for ADR is {adr_mean_pdr}%')
print(f'Mean value of PDR for UCB is {ucb_mean_pdr}%')
adr_max_pdr = max(adr_nodes.pdr)
adr_min_pdr = min(adr_nodes.pdr)
ucb_max_pdr = max(ucb_nodes.pdr)
ucb_min_pdr = min(ucb_nodes.pdr)
max_pdr = max(adr_max_pdr, ucb_max_pdr)
min_pdr = min(adr_min_pdr, ucb_max_pdr)
range_pdr = max_pdr - min_pdr
buckets = 8
increment = math.ceil(range_pdr / buckets)
print(f'Max PDR for ADR: {adr_max_pdr}%')
print(f'Min PDR for ADR: {adr_min_pdr}%')
print(f'Max PDR for UCB: {ucb_max_pdr}%')
print(f'Min PDR for UCB: {ucb_min_pdr}%')
fig, ax = plt.subplots(figsize=(10, 4))
bins = []
bar_width = 0.4
index = np.arange(buckets)
for i in range(buckets + 1):
bins.append(round(min_pdr + i * increment))
out_adr = pd.cut(adr_nodes.pdr, bins=bins)
adr_values = out_adr.value_counts(sort=False).iloc[::-1]
out_ucb = pd.cut(ucb_nodes.pdr, bins=bins)
ucb_values = out_ucb.value_counts(sort=False).iloc[::-1]
ax.bar(index, adr_values, width=bar_width, color='green', label='ADR')
ax.bar(index + bar_width, ucb_values, width=bar_width, color='blue', label='UCB')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(adr_values.index, rotation=45)
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
ax.tick_params(bottom=False, left=False)
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
ax.set_xlabel('Packet Delivery Ratio [%]', labelpad=15)
ax.set_ylabel('Number of Devices', labelpad=15)
ax.set_title('Comparison of PDR', pad=15)
fig.tight_layout()
for bar in ax.patches:
bar_value = round(bar.get_height())
text = f'{bar_value:,}'
text_x = bar.get_x() + bar.get_width() / 2
text_y = bar.get_y() + bar_value
bar_color = bar.get_facecolor()
ax.text(text_x, text_y, text, ha='center', va='bottom', color=bar_color, size=10)
fig.savefig(f'adr-ucb-pdr.{output_format}', dpi=300)
###Output
_____no_output_____
###Markdown
Path of Each End Node Data about position are encoded as base64. Decode base64, extract position and save the results to original data frame.
###Code
# Extracting X and Y coordinates from payload
adr_app_data = adr_uplink_messages.app_data.apply(base64.b64decode)
adr_app_data = adr_app_data.astype(str)
adr_app_data = adr_app_data.str.split(',')
df = pd.DataFrame({}, columns = ['node_id', 'x', 'y'])
x = []
y = []
for row in adr_app_data:
x.append(round(float(row[1].split('\'')[0]), 2) / 1000)
y.append(round(float(row[0].split('\'')[1]), 2) / 1000)
adr_uplink_messages['x'] = x
adr_uplink_messages['y'] = y
# Same for the second algorithm
ucb_app_data = ucb_uplink_messages.app_data.apply(base64.b64decode)
ucb_app_data = ucb_app_data.astype(str)
ucb_app_data = ucb_app_data.str.split(',')
df = pd.DataFrame({}, columns = ['node_id', 'x', 'y'])
x = []
y = []
for row in ucb_app_data:
x.append(round(float(row[1].split('\'')[0]), 2) / 1000)
y.append(round(float(row[0].split('\'')[1]), 2) / 1000)
ucb_uplink_messages['x'] = x
ucb_uplink_messages['y'] = y
###Output
_____no_output_____
###Markdown
Now, we draw a path for each end node based on the received coordinates.
###Code
adr_unique_ens = len(adr_uplink_messages.node_id.unique())
ucb_unique_ens = len(ucb_uplink_messages.node_id.unique())
adr_cmap = mpl.cm.summer
ucb_cmap = mpl.cm.get_cmap('PuBu')
xlim = 10
ylim = 10
fig, axis = plt.subplots(nrows=1, ncols=2, figsize=(10,5))
for i in range(0, adr_unique_ens):
adr_data = adr_uplink_messages[adr_uplink_messages.node_id == adr_uplink_messages.node_id[i]]
axis[0].plot(adr_data.x, adr_data.y, color=adr_cmap(i / unique_ens))
for i in range(0, ucb_unique_ens):
ucb_data = ucb_uplink_messages[ucb_uplink_messages.node_id == ucb_uplink_messages.node_id[i]]
axis[1].plot(ucb_data.x, ucb_data.y, color=ucb_cmap(i / unique_ens))
# Add Access Point
axis[0].plot(xlim / 2, ylim / 2, '+', mew=10, ms=2, color='black')
axis[1].plot(xlim / 2, ylim / 2, '+', mew=10, ms=2, color='black')
# ax.plot(xlim / 2 + 5, ylim / 2 - 5, 'X', mew=10, ms=2, color='black')
for i in range(2):
axis[i].set_xlim([0,xlim])
axis[i].set_ylim([0,ylim])
axis[i].spines['top'].set_visible(False)
axis[i].spines['right'].set_color('#dddddd')
axis[i].spines['left'].set_visible(False)
axis[i].spines['bottom'].set_color('#dddddd')
axis[i].tick_params(bottom=False, left=False)
axis[i].set_axisbelow(True)
axis[i].yaxis.grid(True, color='#eeeeee')
axis[i].xaxis.grid(True, color='#eeeeee')
axis[i].set_xlabel('X [km]', labelpad=15)
axis[i].set_ylabel('Y [km]', labelpad=15)
axis[0].set_title('Paths of ADR Nodes', pad=15)
axis[1].set_title('Paths of UCB Nodes', pad=15)
fig.tight_layout()
fig.savefig(f'adr-ucb-paths.{output_format}', dpi=300)
###Output
_____no_output_____
|
Example_User_Analysis.ipynb
|
###Markdown
Example User Analysis
###Code
import numpy as np
import pandas as pd
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import plotly.tools as tls
import cufflinks as cf
import plotly.figure_factory as ff
import plotly.tools as tls
name='LA'
plotly.tools.set_credentials_file(username='xxxxxxxx', api_key='xxxxxxxxxxxx')
gradebook = pd.read_csv('Data\gradebook.csv', low_memory=False)
users = pd.read_csv('Data\\users.csv')
users = users.set_index('imperial_user_id')
country_codes = pd.read_csv('../../../Data/ISO_country_codes.csv')
country_codes = country_codes.set_index('alpha2')
## this sets the index of the country_codes table to the two letter code, making it easy to join with the 'country_cd' column in df
### Join the user table to the gradebook so that we can link attainment at each stage of the module to demographics
df = gradebook.join(users, 'Anonymized Coursera ID (imperial_user_id)', how = 'left')
### Strip out unnecessary info
df.columns = df.columns.str.replace('Assessment Grade: ','')
cols = [c for c in df.columns if c.lower()[:10] != 'submission']
df = df[cols]
### Pull in additional country information from ISO data so that we have full country name and alpha3 code which is required for the worldview Plotly map
df = df.join(country_codes,'country_cd', how = 'left')
total = df['Anonymized Coursera ID (imperial_user_id)'].nunique()
print(total)
### simply for easy viewing of fields:
pretty = df.iloc[1].transpose()
pretty
def countries (df, column):
df1 = df.groupby('alpha3').count()
data = [dict(
type = 'choropleth',
locations = df1.index.values,
z = df1[column],
text = df1[column]/total*100,
colorscale = [[0,"rgb(5, 10, 172)"],[0.35,"rgb(40, 60, 190)"],[0.5,"rgb(70, 100, 245)"],\
[0.6,"rgb(90, 120, 245)"],[0.7,"rgb(106, 137, 247)"],[1,"rgb(220, 220, 220)"]],
autocolorscale = False,
reversescale = True,
marker = dict(
line = dict (
color = 'rgb(180,180,180)',
width = 0.5
) ),
colorbar = dict(
autotick = False),
) ]
layout = dict(
geo = dict(
showframe = False,
showcoastlines = False,
projection = dict(
type = 'Mercator'
)
)
)
fig = dict( data=data, layout=layout )
plot = py.iplot( fig, validate=False, filename=name + 'learner-world-map2' )
return plot
def countries_ratio (df, column1, column2):
df2 = df.groupby('alpha3').count()
data = [dict(
type = 'choropleth',
locations = df2.index.values,
z = df2[column1]/df2[column2]*100,
colorscale = [[0,"rgb(5, 10, 172)"],[0.35,"rgb(40, 60, 190)"],[0.5,"rgb(70, 100, 245)"],\
[0.6,"rgb(90, 120, 245)"],[0.7,"rgb(106, 137, 247)"],[1,"rgb(220, 220, 220)"]],
autocolorscale = False,
reversescale = True,
marker = dict(
line = dict (
color = 'rgb(180,180,180)',
width = 0.5
) ),
colorbar = dict(
autotick = False),
) ]
layout = dict(
geo = dict(
showframe = False,
showcoastlines = False,
projection = dict(
type = 'Mercator'
)
)
)
fig = dict( data=data, layout=layout )
plot = py.iplot( fig, validate=False, filename=name + 'learner-world-map2' )
return plot
def progress(df, column,x):
df3 = df.drop(columns = ['Course Grade', 'Course Passed', 'Completed with CC'])
df3 = df3.groupby(column).count()
df3 = df3[df3['Anonymized Coursera ID (imperial_user_id)'] > total*(x/100)]
# print (df3.iloc[0])
progress = df3.transpose()
progress = progress[:-11]
breakdown = progress.iloc[0]
print (breakdown)
# plot1 = breakdown.iplot(kind='bar', sharing='private')
progress = (progress/progress.iloc[0]) * 100
plot = progress.iplot(kind='line', sharing='private')
return plot
def learningCurve(df, column, x):
# df3 = df.groupby(column).count()
# df3 = df3[df3['Anonymized Coursera ID (imperial_user_id)'] > total*(x/100)]
# df3 = df3.iloc[0]
df = df.drop(columns = ['Course Grade', 'Course Passed', 'Completed with CC'])
df = df.groupby(column).mean()
progress = df.transpose()
progress = progress[:-1]
breakdown = progress.iloc[1]
print (breakdown)
# plot1 = breakdown.iplot(kind='bar', sharing='private')
plot = progress.iplot(kind='line', sharing='private')
return plot
countries (df, 'Anonymized Coursera ID (imperial_user_id)')
countries_ratio (df, 'Eigenvalues and eigenvectors','Anonymized Coursera ID (imperial_user_id)')
progress(df,'browser_language_cd', 1)
learningCurve(df,'educational_attainment',1)
progress(df,'reported_or_inferred_gender')
learningCurve(df,'reported_or_inferred_gender')
progress(df,'browser_language_cd',1)
learningCurve(df,'browser_language_cd')
###Output
_____no_output_____
|
Computer Vision/6 Data Augmentation/data-augmentation.ipynb
|
###Markdown
Introduction Now that you've learned the fundamentals of convolutional classifiers, you're ready to move on to more advanced topics.In this lesson, you'll learn a trick that can give a boost to your image classifiers: it's called **data augmentation**. The Usefulness of Fake Data The best way to improve the performance of a machine learning model is to train it on more data. The more examples the model has to learn from, the better it will be able to recognize which differences in images matter and which do not. More data helps the model to *generalize* better.One easy way of getting more data is to use the data you already have. If we can transform the images in our dataset in ways that preserve the class, we can teach our classifier to ignore those kinds of transformations. For instance, whether a car is facing left or right in a photo doesn't change the fact that it is a *Car* and not a *Truck*. So, if we **augment** our training data with flipped images, our classifier will learn that "left or right" is a difference it should ignore.And that's the whole idea behind data augmentation: add in some extra fake data that looks reasonably like the real data and your classifier will improve. Using Data Augmentation Typically, many kinds of transformation are used when augmenting a dataset. These might include rotating the image, adjusting the color or contrast, warping the image, or many other things, usually applied in combination. Here is a sample of the different ways a single image might be transformed.Data augmentation is usually done *online*, meaning, as the images are being fed into the network for training. Recall that training is usually done on mini-batches of data. This is what a batch of 16 images might look like when data augmentation is used.Each time an image is used during training, a new random transformation is applied. This way, the model is always seeing something a little different than what it's seen before. This extra variance in the training data is what helps the model on new data.It's important to remember though that not every transformation will be useful on a given problem. Most importantly, whatever transformations you use should not mix up the classes. If you were training a [digit recognizer](https://www.kaggle.com/c/digit-recognizer), for instance, rotating images would mix up '9's and '6's. In the end, the best approach for finding good augmentations is the same as with most ML problems: try it and see! Example - Training with Data Augmentation Keras lets you augment your data in two ways. The first way is to include it in the data pipeline with a function like [`ImageDataGenerator`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator). The second way is to include it in the model definition by using Keras's **preprocessing layers**. This is the approach that we'll take. The primary advantage for us is that the image transformations will be computed on the GPU instead of the CPU, potentially speeding up training.In this exercise, we'll learn how to improve the classifier from Lesson 1 through data augmentation. This next hidden cell sets up the data pipeline.
###Code
# Imports
import os, warnings
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing import image_dataset_from_directory
# Reproducability
def set_seed(seed=31415):
np.random.seed(seed)
tf.random.set_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
set_seed()
# Set Matplotlib defaults
plt.rc('figure', autolayout=True)
plt.rc('axes', labelweight='bold', labelsize='large',
titleweight='bold', titlesize=18, titlepad=10)
plt.rc('image', cmap='magma')
warnings.filterwarnings("ignore") # to clean up output cells
# Load training and validation sets
ds_train_ = image_dataset_from_directory(
'../input/car-or-truck/train',
labels='inferred',
label_mode='binary',
image_size=[128, 128],
interpolation='nearest',
batch_size=64,
shuffle=True,
)
ds_valid_ = image_dataset_from_directory(
'../input/car-or-truck/valid',
labels='inferred',
label_mode='binary',
image_size=[128, 128],
interpolation='nearest',
batch_size=64,
shuffle=False,
)
# Data Pipeline
def convert_to_float(image, label):
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
return image, label
AUTOTUNE = tf.data.experimental.AUTOTUNE
ds_train = (
ds_train_
.map(convert_to_float)
.cache()
.prefetch(buffer_size=AUTOTUNE)
)
ds_valid = (
ds_valid_
.map(convert_to_float)
.cache()
.prefetch(buffer_size=AUTOTUNE)
)
###Output
Found 5117 files belonging to 2 classes.
Found 5051 files belonging to 2 classes.
###Markdown
Step 2 - Define Model To illustrate the effect of augmentation, we'll just add a couple of simple transformations to the model from Tutorial 1.
###Code
from tensorflow import keras
from tensorflow.keras import layers
# these are a new feature in TF 2.2
from tensorflow.keras.layers.experimental import preprocessing
pretrained_base = tf.keras.models.load_model(
'../input/cv-course-models/cv-course-models/vgg16-pretrained-base',
)
pretrained_base.trainable = False
model = keras.Sequential([
# Preprocessing
preprocessing.RandomFlip('horizontal'), # flip left-to-right
preprocessing.RandomContrast(0.5), # contrast change by up to 50%
# Base
pretrained_base,
# Head
layers.Flatten(),
layers.Dense(6, activation='relu'),
layers.Dense(1, activation='sigmoid'),
])
###Output
_____no_output_____
###Markdown
Step 3 - Train and Evaluate And now we'll start the training!
###Code
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['binary_accuracy'],
)
history = model.fit(
ds_train,
validation_data=ds_valid,
epochs=30,
verbose=0,
)
import pandas as pd
history_frame = pd.DataFrame(history.history)
history_frame.loc[:, ['loss', 'val_loss']].plot()
history_frame.loc[:, ['binary_accuracy', 'val_binary_accuracy']].plot();
###Output
_____no_output_____
|
coffee_tuning_blog用.ipynb
|
###Markdown
###Code
!pip install optuna gspread pandas
# Google スプレッドシートの承認
from google.colab import auth
from oauth2client.client import GoogleCredentials
import gspread
auth.authenticate_user()
gc = gspread.authorize(GoogleCredentials.get_application_default())
# スプレッドシートからデータを取ってくる
import pandas as pd
ss_name = "shu65コーヒーデータ"
workbook = gc.open(ss_name)
worksheet = workbook.get_worksheet(1)
df = pd.DataFrame(worksheet.get_all_records())
df = df.set_index('淹れた日')
df
# 探索空間の定義
import optuna
search_space={
"豆の量(g)": optuna.distributions.IntUniformDistribution(8, 12),
"ミルの時間 (sec)": optuna.distributions.IntUniformDistribution(3, 15),
"トータルの時間 (sec)": optuna.distributions.IntUniformDistribution(180, 300),
"蒸らし時間 (sec)": optuna.distributions.IntUniformDistribution(20, 40),
}
score_column = 'スコア'
# 現在までのデータをstudyに登録
import optuna
sampler = optuna.samplers.TPESampler(multivariate=True)
study = optuna.create_study(direction='maximize', sampler=sampler)
for record_i, record in df.iterrows():
print(record.to_dict())
params = {}
for key in search_space.keys():
params[key] = record[key]
trial = optuna.trial.create_trial(
params=params,
distributions=search_space,
value=record[score_column])
study.add_trial(trial)
# 次のパラメータの出力
trial = study._ask()
new_params = {}
for key, space in search_space.items():
new_params[key] = trial._suggest(key, space)
for key in ["豆の量(g)", "ミルの時間 (sec)", "トータルの時間 (sec)", "蒸らし時間 (sec)"]:
print(key, new_params[key])
###Output
豆の量(g) 9
ミルの時間 (sec) 13
トータルの時間 (sec) 199
蒸らし時間 (sec) 33
|
Classification/NaiveBayes/NaiveBayes_HF.ipynb
|
###Markdown
Naive BayesNaive Bayes is one of the most famous classification techniques, one of the most simplest ones, and the easiest to apply. Evaluation Metricshttps://developers.google.com/machine-learning/crash-course/classification/precision-and-recall Accuracy_Accuracy is one metric for evaluating classification models. Informally, accuracy is the fraction of predictions our model got right._**A = (TP+TN)/(TP+TN+FP+FN)** Recall_What proportion of actual positives was identified correctly?_**R = TP/(TP+FN)** Precision_What proportion of positive identifications was actually correct?_**P = TP/(TP+FP)** Heart Failure DatasetWe will be using the Heart Failure Dataset with the out of the box values. Imports and data loading
###Code
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import sklearn.metrics as metrics
from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
import ds_functions as ds
data: pd.DataFrame = pd.read_csv('../../datasets/heart_failure_clinical_records_dataset.csv')
data.head()
###Output
_____no_output_____
###Markdown
Prepare and split data
###Code
y: np.ndarray = data.pop('DEATH_EVENT').values # Target Variable
X: np.ndarray = data.values # Values of each feature on each record
labels = pd.unique(y)
if(labels[0] == 1):
temp = labels[0]
labels[0] = labels[1]
labels[1] = temp
train_size = 0.7 # % of records used for train (the remainder will be left for test)
trnX, tstX, trnY, tstY = train_test_split(X, y, train_size=train_size, stratify=y)
###Output
_____no_output_____
###Markdown
Gaussian Naive Bayes Estimator
###Code
clf = GaussianNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Multinomial Naive Bayes Estimator
###Code
clf = MultinomialNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Bernoulli Naive Bayes Estimator
###Code
clf = BernoulliNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Comparison
###Code
estimators = {'GaussianNB': GaussianNB(),
'MultinomialNB': MultinomialNB(),
'BernoulliNB': BernoulliNB()}
# Accuracy
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.accuracy_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Accuracy', percentage=True)
plt.show()
'''
# Precision
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.precision_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Precision', percentage=True)
plt.show()
# Recall
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.recall_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Recall', percentage=True)
plt.show()
'''
###Output
_____no_output_____
###Markdown
Heart Failure Dataset (Standardized)We will be using the Heart Failure Dataset with the standardized values produced in the Scaling section. Imports and data loading
###Code
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import sklearn.metrics as metrics
from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
import ds_functions as ds
data: pd.DataFrame = pd.read_csv('../../datasets/hf_scaled/HF_standardized.csv')
data.head()
###Output
_____no_output_____
###Markdown
Prepare and split data
###Code
y: np.ndarray = data.pop('DEATH_EVENT').values # Target Variable
X: np.ndarray = data.values # Values of each feature on each record
labels = pd.unique(y)
if(labels[0] == 1):
temp = labels[0]
labels[0] = labels[1]
labels[1] = temp
train_size = 0.7 # % of records used for train (the remainder will be left for test)
trnX, tstX, trnY, tstY = train_test_split(X, y, train_size=train_size, stratify=y)
###Output
_____no_output_____
###Markdown
Gaussian Naive Bayes Estimator
###Code
clf = GaussianNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Multinomial Naive Bayes Estimator MultinomialNB assumes that features have multinomial distribution which is a generalization of the binomial distribution. Neither binomial nor multinomial distributions can contain negative values. Bernoulli Naive Bayes Estimator
###Code
clf = BernoulliNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Comparison
###Code
estimators = {'GaussianNB': GaussianNB(),
'BernoulliNB': BernoulliNB()}
# Accuracy
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.accuracy_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Accuracy', percentage=True)
plt.show()
'''
# Precision
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.precision_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Precision', percentage=True)
plt.show()
# Recall
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.recall_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Recall', percentage=True)
plt.show()
'''
###Output
_____no_output_____
###Markdown
**Which distribution is more adequate to model our data??**- Regardless of the dataset used (with standardization, normalization or without any scaling) we noted that the Gaussian Naive Bayes is the one that obtained the best results (which makes sense since our data follows a Gaussian distribution)**Is the accuracy achieved good enough??**- Most accuracies obtained using the Gaussian Naive Bayes estimator are around 80%, which is not an absolutely amazing score, but not as bad as one would think taking into account how simple this model is**What is the largest kind of errors??**- Most of our problems lied in False NegativesIt should also be noted that the best accuracy obtained was when using the non-scaled dataset and with the Gaussian Naive Bayes estimator. Heart Failure Dataset (Normalized)We will be using the Heart Failure Dataset with the normalized values produced in the Scaling section. Imports and data loading
###Code
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import sklearn.metrics as metrics
from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
import ds_functions as ds
data: pd.DataFrame = pd.read_csv('../../datasets/hf_scaled/HF_normalized.csv')
data.head()
###Output
_____no_output_____
###Markdown
Prepare and split data
###Code
y: np.ndarray = data.pop('DEATH_EVENT').values # Target Variable
X: np.ndarray = data.values # Values of each feature on each record
labels = pd.unique(y)
if(labels[0] == 1):
temp = labels[0]
labels[0] = labels[1]
labels[1] = temp
train_size = 0.7 # % of records used for train (the remainder will be left for test)
trnX, tstX, trnY, tstY = train_test_split(X, y, train_size=train_size, stratify=y)
###Output
_____no_output_____
###Markdown
Gaussian Naive Bayes Estimator
###Code
clf = GaussianNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Multinomial Naive Bayes Estimator
###Code
clf = MultinomialNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Bernoulli Naive Bayes Estimator
###Code
clf = BernoulliNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Comparison
###Code
estimators = {'GaussianNB': GaussianNB(),
'MultinomialNB': MultinomialNB(),
'BernoulliNB': BernoulliNB()}
# Accuracy
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.accuracy_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Accuracy', percentage=True)
plt.show()
'''
# Precision
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.precision_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Precision', percentage=True)
plt.show()
# Recall
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.recall_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Recall', percentage=True)
plt.show()
'''
###Output
_____no_output_____
###Markdown
Summary **Which distribution is more adequate to model our data??**- Regardless of the dataset used (with standardization, normalization or without any scaling) we noted that the Gaussian Naive Bayes is the one that obtained the best results (which makes sense since our data follows a Gaussian distribution)**Is the accuracy achieved good enough??**- Most accuracies obtained using the Gaussian Naive Bayes estimator are around 80%, which is not an absolutely amazing score, but not as bad as one would think taking into account how simple this model is**What is the largest kind of errors??**- Most of our problems lied in False NegativesIt should also be noted that the best accuracy obtained was when using the non-scaled dataset and with the Gaussian Naive Bayes estimator. Heart Failure Dataset - BalancedWe will be using the Balanced Heart Failure Dataset with the out of the box values. Imports and data loading
###Code
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import sklearn.metrics as metrics
from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
import ds_functions as ds
data: pd.DataFrame = pd.read_csv('../../datasets/hf_balanced/HF_balanced.csv')
data.head()
###Output
_____no_output_____
###Markdown
Prepare and split data
###Code
y: np.ndarray = data.pop('DEATH_EVENT').values # Target Variable
X: np.ndarray = data.values # Values of each feature on each record
labels = pd.unique(y)
if(labels[0] == 1):
temp = labels[0]
labels[0] = labels[1]
labels[1] = temp
train_size = 0.7 # % of records used for train (the remainder will be left for test)
trnX, tstX, trnY, tstY = train_test_split(X, y, train_size=train_size, stratify=y)
###Output
_____no_output_____
###Markdown
Gaussian Naive Bayes Estimator
###Code
clf = GaussianNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Multinomial Naive Bayes Estimator
###Code
clf = MultinomialNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Bernoulli Naive Bayes Estimator
###Code
clf = BernoulliNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Comparison
###Code
estimators = {'GaussianNB': GaussianNB(),
'MultinomialNB': MultinomialNB(),
'BernoulliNB': BernoulliNB()}
# Accuracy
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.accuracy_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Accuracy', percentage=True)
plt.show()
'''
# Precision
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.precision_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Precision', percentage=True)
plt.show()
# Recall
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.recall_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Recall', percentage=True)
plt.show()
'''
###Output
_____no_output_____
###Markdown
Heart Failure Dataset (Standardized) - BalancedWe will be using the Balanced Heart Failure Dataset with the standardized values produced in the Scaling section. Imports and data loading
###Code
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import sklearn.metrics as metrics
from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
import ds_functions as ds
data: pd.DataFrame = pd.read_csv('../../datasets/HF_balanced_standardized.csv')
data.head()
###Output
_____no_output_____
###Markdown
Prepare and split data
###Code
y: np.ndarray = data.pop('DEATH_EVENT').values # Target Variable
X: np.ndarray = data.values # Values of each feature on each record
labels = pd.unique(y)
if(labels[0] == 1):
temp = labels[0]
labels[0] = labels[1]
labels[1] = temp
train_size = 0.7 # % of records used for train (the remainder will be left for test)
trnX, tstX, trnY, tstY = train_test_split(X, y, train_size=train_size, stratify=y)
###Output
_____no_output_____
###Markdown
Gaussian Naive Bayes Estimator
###Code
clf = GaussianNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Multinomial Naive Bayes Estimator MultinomialNB assumes that features have multinomial distribution which is a generalization of the binomial distribution. Neither binomial nor multinomial distributions can contain negative values. Bernoulli Naive Bayes Estimator
###Code
clf = BernoulliNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Comparison
###Code
estimators = {'GaussianNB': GaussianNB(),
'BernoulliNB': BernoulliNB()}
# Accuracy
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.accuracy_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Accuracy', percentage=True)
plt.show()
'''
# Precision
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.precision_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Precision', percentage=True)
plt.show()
# Recall
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.recall_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Recall', percentage=True)
plt.show()
'''
###Output
_____no_output_____
###Markdown
Heart Failure Dataset (Normalized) - BalancedWe will be using the Balanced Heart Failure Dataset with the normalized values produced in the Scaling section. Imports and data loading
###Code
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import sklearn.metrics as metrics
from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
import ds_functions as ds
data: pd.DataFrame = pd.read_csv('../../datasets/hf_scaled/HF_balanced_normalized.csv')
data.head()
###Output
_____no_output_____
###Markdown
Prepare and split data
###Code
y: np.ndarray = data.pop('DEATH_EVENT').values # Target Variable
X: np.ndarray = data.values # Values of each feature on each record
labels = pd.unique(y)
if(labels[0] == 1):
temp = labels[0]
labels[0] = labels[1]
labels[1] = temp
train_size = 0.7 # % of records used for train (the remainder will be left for test)
trnX, tstX, trnY, tstY = train_test_split(X, y, train_size=train_size, stratify=y)
###Output
_____no_output_____
###Markdown
Gaussian Naive Bayes Estimator
###Code
clf = GaussianNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Multinomial Naive Bayes Estimator
###Code
clf = MultinomialNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Bernoulli Naive Bayes Estimator
###Code
clf = BernoulliNB()
clf.fit(trnX, trnY)
prd_trn = clf.predict(trnX)
prd_tst = clf.predict(tstX)
ds.plot_evaluation_results(labels, trnY, prd_trn, tstY, prd_tst)
###Output
_____no_output_____
###Markdown
Comparison
###Code
estimators = {'GaussianNB': GaussianNB(),
'MultinomialNB': MultinomialNB(),
'BernoulliNB': BernoulliNB()}
# Accuracy
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.accuracy_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Accuracy', percentage=True)
plt.show()
'''
# Precision
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.precision_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Precision', percentage=True)
plt.show()
# Recall
xvalues = []
yvalues = []
for clf in estimators:
xvalues.append(clf)
estimators[clf].fit(trnX, trnY)
prdY = estimators[clf].predict(tstX)
yvalues.append(metrics.recall_score(tstY, prdY))
plt.figure()
ds.bar_chart(xvalues, yvalues, title='Comparison of Naive Bayes Models', ylabel='Recall', percentage=True)
plt.show()
'''
###Output
_____no_output_____
|
03_data_preparation/5_attorney_tables.ipynb
|
###Markdown
Disambiguation of Attorney Names 1. Creating a table of raw attorney namesIn this step, we first remove any non-alphabetic charactersand then we create a table containing raw attorney names and the processed ones.
###Code
client = bigquery.Client()
job_config = bigquery.QueryJobConfig()
job_config.use_query_cache = False
job_config.write_disposition = 'WRITE_TRUNCATE'
# Set Destination
dataset_id = 'data_preparation'
table_id = '5_attorney_raw'
table_ref = client.dataset(dataset_id).table(table_id)
job_config.destination = table_ref
query="""
WITH t1 AS(
SELECT *
FROM(
SELECT UPPER(REGEXP_REPLACE(
REGEXP_REPLACE(
REGEXP_REPLACE(correspondence_name_line_1, r'[^a-zA-Z\s]+', ''),
r'[\s]+', ' '),
r'(^\s+)|(\s+$)', ''
)
) AS lawyer,
correspondence_name_line_1 AS raw_lawyer
FROM `patents-public-data.uspto_oce_pair.correspondence_address`
GROUP BY correspondence_name_line_1
)
GROUP BY raw_lawyer, lawyer
ORDER BY lawyer DESC
)
SELECT *
FROM t1
"""
query_job = client.query(query, location='US', job_config=job_config)
print('Query job has {} started!'.format(query_job.job_id))
query_job.result()
print('Job has finished!')
###Output
Query job has b311b384-530a-436f-921d-e27effcd44d2 started!
Job has finished!
###Markdown
Extracting the table into a CSV file
###Code
# Exctracting table
client = bigquery.Client()
# Set Source table
project_id = 'usptobias'
dataset_id = 'data_preparation'
table_id = '5_attorney_raw'
table_ref = client.dataset(dataset_id, project=project_id).table(table_id)
# Set Destination
dest_bucket = 'uspto-data'
dest_folder = 'data_preparation'
dest_file_name = '5_attorney_raw.csv'
dest_uri = "gs://{0}/{1}/{2}".format(dest_bucket, dest_folder, dest_file_name)
extract_job = client.extract_table(table_ref, dest_uri, location='US')
print('Extract job has {} started!'.format(extract_job.job_id))
extract_job.result()
print('Job has finished and table {} has been exported to {} bucket!'.format(dest_file_name, dest_bucket))
###Output
_____no_output_____
###Markdown
2. Disambiguating Using Standardization Rules ***Source***: The standardization rules has been downloaded from the following link: https://sites.google.com/site/patentdataproject/Home/posts/namestandardizationroutinesuploadedWe then preprocessed the rules to prepare them for our purpose. The preprocessed rules can be found in the `./stdname_rules/` directory.
###Code
# Loading "5_attorney_raw" table in a Pandas dataframe
## You need to first download "5_attorney_raw.csv" into the './data/' folder (located in the current path) ...
## ... from "uspto-data/data_preparation" GCP Bucket
data_folder = './data/'
df_rawlawyer = pd.read_csv(data_folder+'5_attorney_raw.csv', low_memory=False)
print('Number of records: {:,}'.format(df_rawlawyer.shape[0]))
df_rawlawyer.head(2)
# Adding trailing and ending space (for using the rule-based disambiguation)
df_rawlawyer = df_rawlawyer.dropna()
df_rawlawyer.lawyer = df_rawlawyer.lawyer.apply(lambda x: " " + x + " ")
df_rawlawyer.shape
# Extracting the standard rule files
from zipfile import ZipFile
data_folder = './data/'
with ZipFile(data_folder+'stdname_rules.zip', 'r') as file_ref:
file_ref.extractall(data_folder+'stdname_rules/')
files = sorted(os.listdir(data_folder+'stdname_rules/'))
# Loading standard rules into a dictionary
pattern = r'^.*\"(.*?)\".*?\"(.*?)\"'
std_mapper = dict()
decoding = [(2, 1),
(1, 2),
(1, 2),
(2, 1),
(1, 2),
(1, 2)]
for dec, file in zip(decoding, files):
encoding = chardet.detect(open(data_folder+'stdname_rules/'+file, "rb").read())['encoding']
with codecs.open(data_folder+'stdname_rules/'+file, 'r', encoding=encoding) as text_file:
lines = text_file.readlines()
for line in lines:
key = (re.match(pattern, line)[dec[0]]).rstrip()
value = (re.match(pattern, line)[dec[1]]).rstrip()
std_mapper[key] = value
df_mapper = pd.DataFrame(std_mapper, index=['mapped']).T.reset_index(drop=False).rename(columns={'index':'initial'})
df_mapper.mapped = ' '
df_mapper.initial = df_mapper.initial.apply(lambda x: x+' ')
std_mapper = df_mapper.dropna().set_index('initial')['mapped'].to_dict()
df_mapper.head(3)
# Starting standardization
start_t = time.perf_counter()
df_rawlawyer.lawyer = df_rawlawyer.lawyer.replace(std_mapper, regex=True).replace(std_mapper, regex=True)
end_t = time.perf_counter()
diff_t = end_t - start_t
print('Total running time was {:,.0f} hours and {:.0f} minutes!'.format(diff_t//3600, (diff_t%3600)//60))
# Stripping the spaces
df_rawlawyer.lawyer = df_rawlawyer.lawyer.str.strip()
# Getting unique disambiguated lawyers
df_lawyer_id = df_rawlawyer[['lawyer']].drop_duplicates().reset_index(drop=True).copy()
# Adding unique ID to each lawyer
df_lawyer_id = df_lawyer_id.reset_index(drop=False).rename(columns={'index':'lawyer_id'})
df_lawyer_id.lawyer_id = df_lawyer_id.lawyer_id + 100000
print('Number of unique lawyers: {:,}'.format(df_lawyer_id.shape[0]))
df_lawyer_id.head(2)
df_lawyer_merger = pd.merge(df_rawlawyer, df_lawyer_id, on=['lawyer'], how='left')
print('Number of records: {:,}'.format(df_lawyer_merger.shape[0]))
df_lawyer_merger.head(3)
# Saving the resulting dataframes
df_lawyer_id.to_csv('./data/5_attorneyId.csv', encoding='utf-8', index=False)
df_lawyer_merger.to_csv('./data/5_attorney_disambiguated.csv', encoding='utf-8', index=False)
###Output
_____no_output_____
###Markdown
3. Creating the BigQuery tables using the disambiguated attorney names
###Code
# Creating "5_attorneyID" table
# Creating "lawyer_id_fung" table
bq_client = bigquery.Client()
schema = [
bigquery.SchemaField('attorney_id', 'STRING', 'NULLABLE', None, ()),
bigquery.SchemaField('attorney', 'STRING', 'NULLABLE', None, ())
]
dataset_id = 'data_preparation'
dataset_ref = bq_client.dataset(dataset_id)
dest_table_name = '5_attorneyID'
job_config = bigquery.LoadJobConfig()
job_config.schema = schema
job_config.skip_leading_rows = 1
job_config.source_format = bigquery.SourceFormat.CSV
uri = "gs://uspto-data/data_preparation/5_attorneyId.csv"
load_job = bq_client.load_table_from_uri(
uri, dataset_ref.table(dest_table_name), job_config=job_config
)
print("Starting job {}".format(load_job.job_id))
load_job.result()
print('Job has finished!')
# Creating "5_attorney_disambiguated" table
# Creating "lawyer_id_fung" table
bq_client = bigquery.Client()
schema = [
bigquery.SchemaField('attorney', 'STRING', 'NULLABLE', None, ()),
bigquery.SchemaField('raw_attorney', 'STRING', 'NULLABLE', None, ()),
bigquery.SchemaField('attorney_id', 'STRING', 'NULLABLE', None, ())
]
# Setting the destination table path
dataset_id = 'data_preparation'
dataset_ref = bq_client.dataset(dataset_id)
dest_table_name = '5_attorney_disambiguated'
job_config = bigquery.LoadJobConfig()
job_config.schema = schema
job_config.skip_leading_rows = 1
job_config.source_format = bigquery.SourceFormat.CSV
uri = "gs://uspto-data/data_preparation/5_attorney_disambiguated.csv"
load_job = bq_client.load_table_from_uri(
uri, dataset_ref.table(dest_table_name), job_config=job_config
)
print("Starting job {}".format(load_job.job_id))
load_job.result()
print('Job has finished!')
###Output
Starting job 8b558291-172d-486e-8700-5a78986c6323
Job has finished!
###Markdown
4. Creating the final table: `5_appln_attorney`
###Code
client = bigquery.Client()
job_config = bigquery.QueryJobConfig()
job_config.use_query_cache = False
job_config.write_disposition = 'WRITE_TRUNCATE'
# Set Destination
project_id = 'usptobias'
dataset_id = 'data_preparation'
table_id = '5_appln_attorney'
table_ref = client.dataset(dataset_id).table(table_id)
job_config.destination = table_ref
query="""
WITH rlawyerAppln_table AS(
SELECT
application_number AS appln_nr,
correspondence_name_line_1 AS raw_attorney,
correspondence_region_code AS attorney_region_code,
correspondence_country_code AS attorney_country_code
FROM `patents-public-data.uspto_oce_pair.correspondence_address`
), lawyerMerger_table AS(
SELECT attorney, raw_attorney, attorney_id
FROM `{0}.{1}.5_attorney_disambiguated`
)
SELECT appln_nr, attorney, attorney_id, attorney_region_code, attorney_country_code
FROM rlawyerAppln_table
LEFT JOIN lawyerMerger_table USING(raw_attorney)
WHERE attorney IS NOT NULL
""".format(project_id, dataset_id)
query_job = client.query(query, location='US', job_config=job_config)
print('Query job has {} started!'.format(query_job.job_id))
query_job.result()
print('Job has finished!')
###Output
Query job has 5ba0d91a-70bd-43c4-8a0a-77a2fc725415 started!
Job has finished!
|
colab_notebooks/federated_query_demo.ipynb
|
###Markdown
Querying Multiple Data Formats In this demo we join a CSV, a Parquet File, and a GPU DataFrame(GDF) in a single query using BlazingSQL.In this notebook, we will cover: - How to set up [BlazingSQL](https://blazingsql.com) and the [RAPIDS AI](https://rapids.ai/) suite.- How to create and then join BlazingSQL tables from CSV, Parquet, and GPU DataFrame (GDF) sources.  Setup Environment Sanity Check RAPIDS packages (BlazingSQL included) require Pascal+ architecture to run. For Colab, this translates to a T4 GPU instance. The cell below will let you know what type of GPU you've been allocated, and how to proceed.
###Code
!wget https://github.com/BlazingDB/bsql-demos/raw/master/utils/colab_env.py
!python colab_env.py
###Output
***********************************
GPU = b'Tesla T4'
Woo! You got the right kind of GPU!
***********************************
###Markdown
Installs The cell below pulls our Google Colab install script from the `bsql-demos` repo then runs it. The script first installs miniconda, then uses miniconda to install BlazingSQL and RAPIDS AI. This takes a few minutes to run.
###Code
!wget https://github.com/BlazingDB/bsql-demos/raw/master/utils/bsql-colab.sh
!bash bsql-colab.sh
import sys, os, time
sys.path.append('/usr/local/lib/python3.6/site-packages/')
os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'
os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'
import subprocess
subprocess.Popen(['blazingsql-orchestrator', '9100', '8889', '127.0.0.1', '8890'],
stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
subprocess.Popen(['java', '-jar', '/usr/local/lib/blazingsql-algebra.jar', '-p', '8890'])
import pyblazing.apiv2.context as cont
cont.runRal()
time.sleep(1)
###Output
_____no_output_____
###Markdown
Grab DataThe data is in our public S3 bucket so we can use wget to grab it
###Code
!wget 'https://blazingsql-colab.s3.amazonaws.com/cancer_data/cancer_data_00.csv'
!wget 'https://blazingsql-colab.s3.amazonaws.com/cancer_data/cancer_data_01.parquet'
!wget 'https://blazingsql-colab.s3.amazonaws.com/cancer_data/cancer_data_02.csv'
###Output
_____no_output_____
###Markdown
Import packages and create Blazing ContextYou can think of the BlazingContext much like a Spark Context (i.e. where information such as FileSystems you have registered and Tables you have created will be stored). If you have issues running this cell, restart runtime and try running it again.
###Code
from blazingsql import BlazingContext
# start up BlazingSQL
bc = BlazingContext()
###Output
BlazingContext ready
###Markdown
Create Table from CSVHere we create a BlazingSQL table directly from a comma-separated values (CSV) file.
###Code
# define column names and types
column_names = ['diagnosis_result', 'radius', 'texture', 'perimeter']
column_types = ['float32', 'float32', 'float32', 'float32']
# create table from CSV file
bc.create_table('data_00', '/content/cancer_data_00.csv', dtype=column_types, names=column_names)
###Output
_____no_output_____
###Markdown
Create Table from ParquetHere we create a BlazingSQL table directly from an Apache Parquet file.
###Code
# create table from Parquet file
bc.create_table('data_01', '/content/cancer_data_01.parquet')
###Output
_____no_output_____
###Markdown
Create Table from GPU DataFrameHere we use cuDF to create a GPU DataFrame (GDF), then use BlazingSQL to create a table from that GDF.The GDF is the standard memory representation for the RAPIDS AI ecosystem.
###Code
import cudf
# define column names and types
column_names = ['compactness', 'symmetry', 'fractal_dimension']
column_types = ['float32', 'float32', 'float32', 'float32']
# make GDF with cuDF
gdf_02 = cudf.read_csv('/content/cancer_data_02.csv', dtype=column_types, names=column_names)
# create BlazingSQL table from GDF
bc.create_table('data_02', gdf_02)
###Output
_____no_output_____
###Markdown
Join Tables Together Now we can use BlazingSQL to join all three data formats in a single federated query.
###Code
# define a query
sql = '''
SELECT
a.*,
b.area, b.smoothness,
c.*
FROM data_00 as a
LEFT JOIN data_01 as b
ON (a.perimeter = b.perimeter)
LEFT JOIN data_02 as c
ON (b.compactness = c.compactness)
'''
# join the tables together
gdf = bc.sql(sql)
# display results
gdf
###Output
_____no_output_____
###Markdown
Querying Multiple Data Formats In this demo we join a CSV, a Parquet File, and a GPU DataFrame(GDF) in a single query using BlazingSQL.In this notebook, we will cover: - How to set up [BlazingSQL](https://blazingsql.com) and the [RAPIDS AI](https://rapids.ai/) suite.- How to create and then join BlazingSQL tables from CSV, Parquet, and GPU DataFrame (GDF) sources.  Setup Environment Sanity Check RAPIDS packages (BlazingSQL included) require Pascal+ architecture to run. For Colab, this translates to a T4 GPU instance. The cell below will let you know what type of GPU you've been allocated, and how to proceed.
###Code
!wget https://github.com/BlazingDB/bsql-demos/raw/master/utils/colab_env.py
!python colab_env.py
###Output
***********************************
GPU = b'Tesla T4'
Woo! You got the right kind of GPU!
***********************************
###Markdown
Installs The cell below pulls our Google Colab install script from the `bsql-demos` repo then runs it. The script first installs miniconda, then uses miniconda to install BlazingSQL and RAPIDS AI. This takes a few minutes to run.
###Code
!wget https://github.com/BlazingDB/bsql-demos/raw/master/utils/bsql-colab.sh
!bash bsql-colab.sh
import sys, os, time
sys.path.append('/usr/local/lib/python3.6/site-packages/')
os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'
os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'
import subprocess
subprocess.Popen(['blazingsql-orchestrator', '9100', '8889', '127.0.0.1', '8890'],stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
subprocess.Popen(['java', '-jar', '/usr/local/lib/blazingsql-algebra.jar', '-p', '8890'])
import pyblazing.apiv2.context as cont
cont.runRal()
time.sleep(1)
###Output
_____no_output_____
###Markdown
Grab DataThe data is in our public S3 bucket so we can use wget to grab it
###Code
!wget 'https://blazingsql-colab.s3.amazonaws.com/cancer_data/cancer_data_00.csv'
!wget 'https://blazingsql-colab.s3.amazonaws.com/cancer_data/cancer_data_01.parquet'
!wget 'https://blazingsql-colab.s3.amazonaws.com/cancer_data/cancer_data_02.csv'
###Output
_____no_output_____
###Markdown
Import packages and create Blazing ContextYou can think of the BlazingContext much like a Spark Context (i.e. where information such as FileSystems you have registered and Tables you have created will be stored). If you have issues running this cell, restart runtime and try running it again.
###Code
# Import RAPIDS AI stack
from blazingsql import BlazingContext
import cudf
bc = BlazingContext()
###Output
BlazingContext ready
###Markdown
Create Table from CSVHere we create a BlazingSQL table directly from a comma-separated values (CSV) file.
###Code
# define column names and types
column_names = ['diagnosis_result', 'radius', 'texture', 'perimeter']
column_types = ['float32', 'float32', 'float32', 'float32']
# create table from CSV file
bc.create_table('data_00', '/content/cancer_data_00.csv', delimiter=',', dtype=column_types, names=column_names)
###Output
_____no_output_____
###Markdown
Create Table from ParquetHere we create a BlazingSQL table directly from an Apache Parquet file.
###Code
# create table from Parquet file
bc.create_table('data_01', '/content/cancer_data_01.parquet')
###Output
_____no_output_____
###Markdown
Create Table from GPU DataFrameHere we use cuDF to create a GPU DataFrame (GDF), then use BlazingSQL to create a table from that GDF.The GDF is the standard memory representation for the RAPIDS AI ecosystem.
###Code
# define column names and types
column_names = ['compactness', 'symmetry', 'fractal_dimension']
column_types = ['float32', 'float32', 'float32', 'float32']
# make GDF with cuDF
gdf_02 = cudf.read_csv('/content/cancer_data_02.csv',delimiter=',', dtype=column_types, names=column_names)
# create BlazingSQL table from GDF
bc.create_table('data_02', gdf_02)
###Output
_____no_output_____
###Markdown
Join Tables Together Now we can use BlazingSQL to join all three data formats in a single federated query.
###Code
# define a query
sql = '''
SELECT a.*, b.area, b.smoothness, c.* from main.data_00 as a
LEFT JOIN main.data_01 as b
ON (a.perimeter = b.perimeter)
LEFT JOIN main.data_02 as c
ON (b.compactness = c.compactness)
'''
# join the tables together
join = bc.sql(sql).get()
# extract dataframe
result = join.columns
# display results
result
###Output
_____no_output_____
|
notebooks/01_train_local.ipynb
|
###Markdown
Copyright (c) Microsoft Corporation. All rights reserved.Licensed under the MIT License.
###Code
import azureml.core
from azureml.core import Workspace, Experiment, Run, Dataset
from azureml.core import ScriptRunConfig
ws = Workspace.from_config()
#TODO: load variables from config file
exp_name = ""
dataset_name = ""
dataset = Dataset.get_by_name(ws,dataset_name)
exp = Experiment(workspace=ws, name=exp_name)
with exp.start_logging() as run:
run.log(name="message", value="my message")
run.log('Accuracy',0.99)
###Output
_____no_output_____
|
Day 3 Assignnment.ipynb
|
###Markdown
Question 1
###Code
altitude = input("Enter ant distance: ")
alt = int(altitude)
if alt<=1000:
print("You are safe to land")
elif 1000<alt<=5000:
print("Bring down the plane to 1000")
else:
print("Turn around, Better luck next time")
###Output
You are safe to land
###Markdown
Question 2
###Code
for num in range(200):
if num > 1:
for i in range(2,num):
if (num % i) == 0:
break
else:
print(num)
###Output
2
3
5
7
11
13
17
19
23
29
31
37
41
43
47
53
59
61
67
71
73
79
83
89
97
101
103
107
109
113
127
131
137
139
149
151
157
163
167
173
179
181
191
193
197
199
|
01 - The Basics/01 - Types.ipynb
|
###Markdown
Workbook 1 - TypesIn programming, data, held in variables, have *types* associated with them. Here we shall explore a few of these and what this means for everyday programming. Assigning variables valuesBefore going over variable *types* let's go over what a variable is and how to assign a value to one:
###Code
variable = 'value'
###Output
_____no_output_____
###Markdown
In Python assigning a value to a variable is as simple as writing the above. In this case the variable we have created has been assigned the value of 'value', we can retrieve this value either by entering this into the interpreter
###Code
variable
###Output
_____no_output_____
###Markdown
or by letting a Python method (or command) act upon the variable. Here we will use the *print()* command to print out the value of the variable to the *console*
###Code
print(variable)
###Output
_____no_output_____
###Markdown
for now we shall omit as to why a programmer may pick the latter method to display a variable's value when running a program, however, the use of *print()* is likely to be the mechanism you commonly adopt. TypesNow that we know how to assign variables values, let us look at the *type()* method. Simply put, entering in *type(variable)* will let you know what type of data is being held by the variable.There are quite a few different types of data that can be stored within a variable, however, the most common are: The booleanTrue or False, one or zero the boolean can only be in one of two states
###Code
boolean = True
type(boolean)
###Output
_____no_output_____
###Markdown
The integerA whole number
###Code
integer = 3
type(integer)
###Output
_____no_output_____
###Markdown
The floating-point numberAny number containing a decimal point
###Code
floating_point_number = 3.141
type(floating_point_number)
###Output
_____no_output_____
###Markdown
The stringIndividual letters are referred to as characters, collections of characters are 'strung' together to form strings of text
###Code
text_string = 'text'
type(text_string)
###Output
_____no_output_____
###Markdown
The listJust like a shopping list is a list of items, a list in Python is a list of variables. Each variable has an *index* value associated with it, making recall of an individual, specific, variables possible.
###Code
list_of_things = ['text', 'more text', 'even more text']
type(list_of_things)
###Output
_____no_output_____
###Markdown
Once a list, such as the one above has been created it is possible to retrieve an indivdual variable using the syntax *list[n]* where you replace the letter *n* with the index of the variable. Indices start at zero where the first variable in the list (the value on the left hand side, closest to the equals sign) has the zero value; *e.g.*:
###Code
list_of_things[0]
###Output
_____no_output_____
###Markdown
The dictionaryThe final type that we will cover here is the dictionary, it is very similar to the list but has a neat nuance. When using lists you have to know the index of the variable you're trying to recall, usually as you create the list you will know this, however, it is possible to append and remove entries from a list which can start to cause headaches when tracking where everything is.In a dictionary you call variables not by index but by their *key* and when you call a key you are returned it's value. This approach of variable storage has been given the imaginative name of *key, value* pairs.
###Code
key_value_pair_dictionary = {'key' : 'value', 'key_two' : 3.141}
type(key_value_pair_dictionary)
###Output
_____no_output_____
###Markdown
Above I have created a simple dictionary containing two variables, *key* and *key_two*, to retrieve one I would simply have to enter
###Code
key_value_pair_dictionary['key']
###Output
_____no_output_____
###Markdown
This retrieves the value from the dictionary, interestingly although the variable *key_value_pair_dictionary* has a type of dictionary the values retain their own types
###Code
type(key_value_pair_dictionary['key_two'])
###Output
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|
analysis/.ipynb_checkpoints/milestone3-checkpoint.ipynb
|
###Markdown
IntroductionThis dataset includes the list of games with sales recorded over 100,000 copies over different platforms and continents. It contains data for over 16,500 games from 1980 to 2016. This dataset was generated by scrape from [VGChartz](https://www.vgchartz.com/). QuestionTo narrow down to answering my main question, I have to explore what other factors in my dataset affects sale records of a game?- Factors such as: 1. Platform 2. Genre 3. Publisher Find out which factors has done better overall in terms of sale records will help me answer the main questions:**Does publishing more games in any given genre leads to a high sale records?** DatasetThis dataset has 11 unique columns.- Rank : Rank the games according to best overall sales records- Name : Name of the game- Platform : Name of the platform (i.e. PS2, PS3, PC,..) on which the game was released.- Year : The year in which the games was released- Genre : Genre of the game- Publisher : Publisher of the game- NA_Sales : Game's sale record in North America (in millions)- EU_Sales : Game's sale record in Europe (in millions)- JP_Sales : Game's sale record in Japan (in millions)- Other_Sales : Game's sale record in Other parts of the world (in millions)- Global_Sales : Game's sale record worldwide (in millions)
###Code
## why are we using boxplot and countplot?
from Scripts import project_functions as pf
from Scripts import groupby_function as gf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# This will load data, remove the missing data and reset the index
df = pf.load_and_process("../data/raw/vgsales.csv")
# exporting the process data to a folder
df.to_csv("../data/processed/processed_vgsales.csv")
df
###Output
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###Markdown
Exploratory Data Analysis with observations **Which platform has the best total global sales?**
###Code
col_list = ['Platform', 'Global_Sales']
df2 = (
df.groupby(['Platform']).sum()
.sort_values('Global_Sales', ascending = False)
.reset_index(col_level = 1)
)
df2 = df2[col_list]
fig, ax = plt.subplots(figsize=(6,8))
sns.barplot(x='Global_Sales', y= 'Platform', data = df, estimator = np.sum)
plt.xlabel('Global_Sales (in millions)')
df2.head(11)
###Output
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###Markdown
Observations:- As we can see the PS2 has highest total game sales globally **Let's also see which platform has highest sale record in other part of the worlds?**
###Code
df3 = df.groupby(['Platform']).sum()
columns = ['Rank', 'Year', 'Global_Sales']
df3.drop(columns, inplace = True, axis=1)
ax = df3.plot(kind = "barh", figsize=(15,20), width = 1, stacked = True, colormap = "tab20b")
ax.set_xlabel("Sales (in million)")
df3
###Output
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###Markdown
Observation:- Xbox has the highest total game sales in North America.- PS3 has the highest total game sales in Europe.- DS has the highest total game sales in Japan.- PS2 has the highest total game sales in other parts of the world. PC has been used worldwide but yet it is not the best perfroming platform. One of the reason I think is that PC games have been facing problem with pirating. Other platform like PS, GB, NES and Xbox it is tough to pirate games.Therefore, many games company sales record are not too high for PC. **Which publisher has good sales record throughout years globally? (As there many publisher! let's take top 15)**
###Code
df3 = (df.groupby(['Publisher']).sum()
.sort_values('Global_Sales', ascending = False)
.reset_index(col_level = 1)
)
top15 = df3.head(15)
fig, ax = plt.subplots(figsize=(10,20))
sns.barplot(x='Global_Sales', y='Publisher', data = top15)
plt.xlabel('Global_Sales (in millions)')
col = ['Publisher', 'Global_Sales']
top15[col]
###Output
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###Markdown
Observation:- As we can see that Nintendo has the highest global sales **Which Genre has been dominating the gaming industry globally?**
###Code
fig, ax = plt.subplots(figsize=(10,20))
sns.barplot(x='Global_Sales', y='Genre', data = df, estimator = np.sum)
plt.xlabel('Global_Sales (in millions)')
###Output
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###Markdown
Observation:- Action has been taking over the gaming industry. AnalysisAs of now we have seen which platform, publisher and Genre has the most game sales record. Now, we are going to focus on the main question Does pubishing more games in a dominating genre leads to higher sale of games?**Let's count the number of games published by each publisher**
###Code
df5 = df.groupby(['Publisher']).count()
df5 = df5.sort_values('Global_Sales', ascending = False)
df5 = df5.rename(columns={"Rank": "No. of Games Published"})
col = ['No. of Games Published']
df5[col].head(15)
fig, ax = plt.subplots(figsize=(18,12))
sns.countplot(y='Publisher', data=df,order=df.Publisher.value_counts().iloc[:15].index)
###Output
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###Markdown
As we can see that Electronic Arts has the most number of game published. **Let's see which publisher has released the most amount of action games?**As we saw before, Action genre has the highest sales recorded. Therefore, we are trying to find if which publisher has published the most action genre games. Does that publisher has the highest sale records?
###Code
col = ['Name', 'Publisher', 'Genre']
df6 = df[col]
df6 = df6[df6['Genre'].str.match('Action')]
df6 = ( df6.reset_index(col_level = 1)
.drop(['index'], axis = 1)
)
df6
fig, ax = plt.subplots(figsize=(18,15))
sns.countplot(y='Publisher', data=df6,order=df6.Publisher.value_counts().iloc[:15].index)
###Output
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###Markdown
As from the countplot above, we can observe that **Activision** has the most amount of action games published. However, they are third on the list of highest global sales.As of now, we know that in terms of sales record:- **PS2** is best platform - Best publisher is **Nintendo** - Best Genre is **Action** - Most game is published by **EA**- Most action games published by **Activision**One of the fun fact which will help us answer the main question is that almost all of the games made by Nintendo was on avaliable to only consoles by Nintendo themselves (i.e. Wii U, Wii, SNES, ...).Therefore, we lead to one more question:**How many Action based games were published by Activision on PS2?**As our main question is **Does publishing more games in a given genre leads to a high sale records?**As Activison as published the most action games, we will check what is the sale records of Activision publishing action based games on PS2 which is the best performing platform.
###Code
df7 = df[df['Genre'].str.match('Action')]
df7 = df7[df7['Platform'].str.match('PS2')]
df7 = df7[df7['Publisher'].str.match('Activision')]
df7 = df7.groupby(['Platform']).sum()
col = ['Global_Sales']
df7[col]
df8 = df[df['Genre'].str.match('Action')]
df8 = df8[df8['Publisher'].str.match('Activision')]
df8 = df8[df8['Platform'].str.match('PS2')]
df8 = df8.groupby(['Platform']).count()
df8 = df8.rename(columns={"Name": "No. of Action Games"})
col = ['No. of Action Games']
df8[col]
df10 = df[df['Genre'].str.match('Action')]
df10 = df10[df10['Publisher'].str.match('Activision')]
df10 = df10.groupby(['Publisher']).sum()
col = ['Global_Sales']
df10[col].head(1)
###Output
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###Markdown
Activision has global sales of 141.82 (in millions) of action based games across all platforms.Activision has published 310 action games. Out of 310, 28 were released on PS2.
###Code
(28/310)*100
###Output
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###Markdown
That's 9%. That 28 games on PS2 has made 22.34 (in million).
###Code
(22.34/141.82)*100
###Output
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###Markdown
That's 15.75%.We have to find how many action games have Activision has published on other platforms and their global sale record and compare it with PS2 sales record.
###Code
df11 = df[df['Genre'].str.match('Action')]
df11 = df11[df11['Publisher'].str.match('Activision')]
df11 = df11.groupby(['Platform']).count()
col = ['Rank']
df11 = df11[col]
df11 = df11.rename(columns={'Rank':'No. of the game published'})
df11
fig, ax = plt.subplots(figsize=(10,10))
line = df11.plot(kind = "bar", ax=ax, color=(0.2, 0.4, 0.6, 0.6))
df14 = df[df['Genre'].str.match('Action')]
df14 = df14[df14['Publisher'].str.match('Activision')]
df14 = df14.groupby(['Platform']).sum()
df14 = df14.rename(columns={'Global_Sales':'Global_Sales (in millions)'})
col = ['Global_Sales (in millions)']
df14 = df14[col]
df14
fig, ax = plt.subplots(figsize=(12,10))
line1 = df14.plot(kind = "bar",ax =ax,)
line = df11.plot(kind = "bar", ax=ax,color=(0.2, 0.4, 0.6, 0.6))
###Output
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###Markdown
**Process Data for Tableau**---
###Code
df.head()
###Output
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###Markdown
- No changes need to be made - This may change - Data can be exported to processed
###Code
df.to_csv("../data/processed/music_processed.csv")
###Output
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###Markdown
Introduction data set intro The data was created by university of california irvine, The datas main goal is to Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset. the Extraction of data was done by Barry Becker from the 1994 Census database. The pupose of the data collected is mainliy for public intrest.The data goes into detail about income in America giving different information about each individual and their income. the information it gives on each individual is age, workclass, education level, marital status occupation realationship, race, sex, hours per week of work, and what country an individual is from. The main prediction the data makes based on the related information is whether or not the individual has an income greater than or less than 50k a year. I hope to use this dataset to find information about income in america as well as the different factors and correlations I can make in the data set. I am interested in the opportunities that the data set presents in relation to the different categories that are available to interpret. Reserch Questions RQ 1: What race on average works the most hours per week?RQ 2: Is there a pattern in relation to age on whether or not an age group is predicted to make less than 50k?
###Code
from scripts.project_functions import *
df = project_functions.load_and_process(r"..\data\raw\adult.csv")
df
###Output
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###Markdown
Data Set this data set has 10 unique collums 1. **age** represents the individuals age.2. **workclass**education** represent the highest level of education an indivual has completed3. **marital.status** represents the last know status of an indivduals relationship in relation to mariage 4. **occupation** represents
###Code
df.plot(kind='scatter', x='hours.per.week', y='race')
###Output
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###Markdown
Analysis Graph 1 analysis This graph is a scatter plot that shows the relationship between hours.per.week which represents the average hours worked in a week for an individual andrace of the indivvidual that is
###Code
df['hours.per.week'].plot(kind='hist', bins=10, figsize=(12,6), facecolor='green',edgecolor='black')
sns.displot(df['age'], kde=False, bins=72)
sns.catplot(x="income", y="age", data=df)
df.groupby(['race']).mean().sort_values('hours.per.week', ascending=False)
###Output
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###Markdown
Milestone 2/3 - EDA, Data Wrangling, and Data Analysis About the data**poll_averages:** - State - Date Analyzed - Candidate Name - pct_estimate - Average percent of voters for the specified candidate based on polling data - pct_trend_adjusted - Expected trend of the poll averages **economy:** - Date analyzed - current_zscore - Std. deviations from the previous 2-year average for the indicator - Positive is for Trump, negative is for Biden - projected_zscore - Expected Z score on Election day - projected_hi - Highest expected Z score on Election day - projected_lo - Lowest expected Z score on Election day - category - Economic area for the specified indicator - indicator - Actual indicator used for the category - Timestamp - Simulations - Number of trials done for the expected Z Scores
###Code
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas_profiling as pp
import matplotlib.ticker as ticker
### Loading data
poll_averages = pd.read_csv("../data/raw/election-forecasts-2020/presidential_poll_averages_2020.csv")
economy = pd.read_csv("../data/raw/election-forecasts-2020/economic_index.csv")
###Output
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###Markdown
EDA
###Code
poll_averages
economy
poll_averages.describe().T
economy.describe().T
poll_averages.groupby('candidate_name').describe().T
economy.groupby('category', as_index=False).describe().T
#Check for missing values
poll_averages.isnull().sum()
economy.isnull().sum()
#Exploration of z-score vs projected z-score over time for average of all six indicators
explore1 = economy.copy()
#Flip dataframe to start on proper date
explore1 = explore1.iloc[::-1]
#1.Get the average of all indicators
explore1 = explore1.loc[lambda x: x['indicator'].str.contains("Average of all six indicators")]
#2. Calculate z difference
explore1 = explore1.assign(z_difference=lambda x: abs(x['current_zscore'] - x['projected_zscore']))
#3. Reset index so we can access modeldate
explore1 = explore1.reset_index()
explore1.to_csv("../data/processed/exploration_1.csv")
fig = plt.gcf()
fig.set_size_inches(25, 5)
plot1 = sns.lineplot(data=explore1, x='modeldate', y='z_difference')
plot1.set_title('Daily (Z Score - Projected Z Score) for all six indicators')
plot1.set_ylabel('Z Difference')
plot1.set_xlabel('Date')
plot1.xaxis.set_major_locator(ticker.MultipleLocator(12))
###Output
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###Markdown
This graph provides a way of showing how uncertainty about the projected z-score dropped off as it got closer to election day.
###Code
#Exploration of pct_estimate over time
explore2 = poll_averages.copy()
#Flip dataframe to start on proper date
explore2 = explore2.iloc[::-1]
trumppct = explore2.loc[lambda x: x['candidate_name'].str.contains("Trump")]
bidenpct = explore2.loc[lambda x: x['candidate_name'].str.contains("Biden")]
explore2.to_csv("../data/processed/exploration_2.csv")
fig = plt.gcf()
fig.set_size_inches(25, 5)
plot1 = sns.lineplot(data=bidenpct, x='modeldate', y='pct_estimate')
plot1.xaxis.set_major_locator(ticker.MultipleLocator(12))
plot2 = sns.lineplot(data=trumppct, x='modeldate', y='pct_estimate')
plot2.set_title('Daily Percent Estimate for each candidate')
plot2.set_ylabel('Percent Estimate')
plot2.set_xlabel('Date')
plt.legend(labels=['Joe Biden', 'Donald Trump'])
###Output
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###Markdown
Something interesting must have happened around August 20th as each candidate had a large dropoff. This is probably an issue with the data provided (adjustments made for error and such) Exploration Poll Averages- The pct_estimate skews lower than 50% for each candidate.- The minimum and maximum pct_estimates are very different for each candidate.- There is a large dropoff around August 20th, 2020 Economy Estimates- The overall mean for the Z-Score (Std. deviations from the previous 2-year average for the indicator) is negative, giving a large advantage to the challenging candidate. FiveThirtyEight has [shared](https://fivethirtyeight.com/features/measuring-the-effect-of-the-economy-on-elections/) that a score below 2% generally means the incumbent president becomes the underdog.- The projected high is still in the negatives, meaning even with a full rebound the incumbent would still be at a disadvantage according to the economy. Outliers, Missing Values, or Human Error- Past August 20th in the Poll Averages data may be considered as outliers because the data has an extreme shift.- Human error is not likely to be present as the projected data was generated with computer simulations. Data Wrangling
###Code
### Drop unused columns from each dataset
economy.drop(['cycle', 'branch', 'model', 'candidate_3rd', 'simulations', 'candidate_inc', 'candidate_chal', 'category', 'timestamp', 'projected_lo', 'projected_hi'], axis=1, inplace=True)
poll_averages.drop(['cycle', 'pct_trend_adjusted'], axis=1, inplace=True)
### Drop duplicate rows if any
economy.drop_duplicates(inplace=True)
poll_averages.drop_duplicates(inplace=True)
###Output
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###Markdown
Research Questions- Were there any days in which the mean polling pct_estimate for Joe Biden jumped - This is interesting because there may be a few more days that are similar to the jump seen but aren't shown on the plot. - Which economic indicator has been the most stable over time? - Other than the average of all six, which indicator has been the most stable helps confirm the general trend as it will not change much over time. This should give a relative accurate picture of the current estiamtes.- Has the minimum polling pct_estimate state changed over time for Donald Trump? - See if there has been any drastic change in a particular state flipping over time. Maybe Trump loses support in some key states and the trend could be pinpointed. Data Analysis
###Code
### RQ 1
# Map out each date and the pct estimate on a plot
# Calculate the change vs the previous day for Joe biden
# Find the biggest change
research1 = poll_averages.copy()
#1. Drop all Trump rows
research1 = research1.loc[lambda x: ~x['candidate_name'].str.contains("Trump")]
#2. Group by date and mean
research1 = research1.groupby("modeldate").mean()
#3. Calculate daily difference in pct_estimate (absolute value because we only care about total change)
research1 = research1.assign(daily_difference=lambda x: abs(x - x.shift(1)))
#4. Reset index so we can access modeldate
research1 = research1.reset_index()
#Save to file
research1.to_csv("../data/processed/research_question_1.csv")
fig = plt.gcf()
fig.set_size_inches(25, 5)
plot1 = sns.lineplot(data=research1, x='modeldate', y='daily_difference')
plot1.set_title('Daily Difference of the Overall Mean for Joe Biden')
plot1.set_ylabel('Daily Difference')
plot1.set_xlabel('Date')
plot1.xaxis.set_major_locator(ticker.MultipleLocator(12))
### RQ 2
# Calculate the total change in each indicator
research2 = economy.copy()
#Flip dataframe to start on proper date
research2 = research2.iloc[::-1]
#For each indicator: calculate the change since the previous day for that indicator
research2 = research2.assign(daily_difference=lambda x: abs(x['current_zscore'] - x.shift(7)['current_zscore']))
research2.to_csv("../data/processed/research_question_2.csv")
#Find which indicator had the loweresearch2.to_csv("../data/processed/research_question_2.csv")st mean change for every day.
research2 = research2.groupby('indicator').mean()
#Reset index so we can access indicator
research2 = research2.reset_index()
plot2 = sns.barplot(data=research2, y='indicator', x='daily_difference', palette='colorblind')
plot2.set_title('Mean Daily Difference for Each Economic Indicator')
plot2.set_ylabel('Indicator Used')
plot2.set_xlabel('Mean Daily Difference')
### RQ 3
#Map out the minimum polling estimate for each day for Trump.
#Remove all Joe Biden rows
research3 = poll_averages.loc[lambda x: ~x['candidate_name'].str.contains("Biden")]
#Group by date and state, find the minimum pct_estimate, and convert back to a dataframe
research3 = research3.groupby(['modeldate', 'state'])['pct_estimate'].min().to_frame()
#Sort ascending by pct_estimate
research3 = research3.sort_values(by='pct_estimate')
#Save to file
research3.to_csv("../data/processed/research_question_3.csv")
fig = plt.gcf()
fig.set_size_inches(5, 15)
plot3 = sns.histplot(data=research3, y='state', x='pct_estimate')
plot3.set_xlabel('Minimum Mean pct_estimate')
plot3.set_ylabel('US State')
plot3.set_title('Minimum Mean Percent Estimate of Each State for Donald Trump')
###Output
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###Markdown
Data Analysis **The description of the data source is avaiable in the main README file for the project.****The 3 research questions that the analysis aims to answer are as follows:**>***1*** *Based on the frequenct of voting, realization of the importance of voting, and the reasons provided by non-frequent voters for not voting, do Americans seem to have a mistaken impression about the concept of voting?*>>***1.1*** *What is the distribution of Americans based on their frequency of voting?*>>>>***1.2*** *What views do they have about the importance of voting based on their age and gender?*>>>>***1.3*** *For Americans who rarely or never vote, what were the reasons for not voting?*>***2*** *How difficult do Americans think it is to vote in the national elections? Does age play a role in this thinking?*>***3*** *Do Americans think that the design and structure of the American Government need change? Does age play a role in this thinking?**The first question is the key research question, the results for which have also been portrayed in the form of a Tableau dashboard which can be found as a separate file in this project.***The analysis has been divided into sections based on the particular research question it corresponds to and the part of the complete raw data that it studies to answer it.****Each section contains the following sub-sections:**- ***Dataset*** *(describing the attributes of the filtered data used for the particular section and basic profiling/analysis of the data)*- ***Visualization*** *(containing the plots and the description of its elements)*- ***Analysis*** *(providing the reasoning for the selection of a particular plot and the inferences that can be drawn from the plotted data)*- ***Conclusion*** *(summarizes how the analysis answers a research question or a part of it)*
###Code
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas_profiling
from scripts import project_functions
###Output
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###Markdown
*Section 1 (Research Question 1)* Dataset This section consists of two dataframes. These first dataset corresponds to the first two subcomponents of Research Question 1 i.e. 1_1 and 1_2 while the second dataset corresponds to subcomponent 3 i.e. 1_3. The analysis of this data provides the answer to the first research question. The dataframes are returned by a python script which filters/wrangles the data based on the needs for a particular section. It can be viewed under the scripts directory. **Dataset 1** This dataset includes the data filtered specifically for help to answer the first research question. Basic descriptive analysis of the numeric and non-numeric columns has also been conducted. The description of each column is as follows:- id: the unique ID of each participant- ppage: the age of a participant- gender: the gender of a participant- educ: the education level of a participant- race: the race of a participant- cat: the voter category of a participant which refers to the frequent of voting (3 values) - always votes - votes sometimes - rarely or never votes- voting_imp: the level of importance given to voting by each participant (values 1 to 4) - 1: very important - 2: somewhat important - 3: not so important - 4: not at all important
###Code
sns.set_theme(style = 'darkgrid', font_scale = 1.4)
df1 = project_functions.load_and_process("../data/raw/nonvoters_data.csv", 1)
df1
df1.describe(include = [np.number]).T
df1.describe(exclude = [np.number]).T
###Output
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###Markdown
**Dataset 2** This dataset includes the data filtered specifically for help to answer the first research question. Basic descriptive analysis of the numeric and non-numeric columns has also been conducted. The description of each column is as follows:- ppage: the age of a participant- gender: the gender of a participant- cat: the voter category of a participant which refers to the frequent of voting (3 values) - always votes - votes sometimes - rarely or never votes- Q29 and its sub-divisions: Considering the last few elections where the participant did not vote, what was the reason for deciding not to vote? The participants chose from the following options. The value of 1 under these columns indicated a choice. The NaN values can be ignored for now. They need to be separately removed for each of the 9 alternatives separately which has been done further during the visual analysis - Q29_1: I didn’t like any of the candidates - Q29_2: Because of where I live, my vote doesn’t matter - Q29_3: No matter who wins, nothing will change for people like me - Q29_4: Our system is too broken to be fixed by voting - Q29_5: I wanted to vote, but I didn’t have time, couldn’t get off work, something came up, or I forgot - Q29_6: I’m not sure if I can vote - Q29_7: Nobody talks about the issues that are important to me personally - Q29_8: All the candidates are the same - Q29_9: I don’t believe in voting
###Code
df2 = project_functions.load_and_process("../data/raw/nonvoters_data.csv", 2)
df2
df2.describe(include = [np.number]).T
df2.describe(exclude = [np.number]).T
###Output
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###Markdown
Visualization This section consists of 11 plots. The first 2 plots utilize dataset 1 while the other make use of dataset 2. The description of the elements of each plot has been provided ahead. Plots 3-11 use a similar format; therefore, a single description has been provided for these plots. **Plot 1** This pie plot represents the distribution of the voters based on their frequency of participation in voting. The blue section corresponds to 'always', green to ' sporadic' (or sometimes), and orange to 'rarely/never'. This helps to understand the distribution and proportion of participants out of all participants that belong to each category.
###Code
group = df1.groupby('cat').size()
plot1 = group.plot.pie(figsize=(10, 10)).set(ylabel = 'Voter category')
###Output
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###Markdown
**Plot 2** This box plot represents the distribution of the level of importance of voting along the age and gender of participants. The blue section corresponds to 'female' and orange to 'male'. The y-axis represents the age and the x-axis represents the responses from 1 - 4 given by the participants portraying the importance of voting. This helps to understand the how important do participants seem to think voting is, and how does this thinking differ on the basis of age and gender.
###Code
plt.figure(figsize=(8,10))
plot2 = sns.boxplot(x = 'voting_imp', y = 'ppage', hue = 'gender', data = df1, palette = 'RdBu').set(xlabel = 'How Important Is Voting (1-High, 4-Low)', ylabel = 'Age')
plt.legend(loc='upper center')
###Output
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###Markdown
**Plots 3-11** These count plots 3-11 each refer to an alternative as described abode under the dataset description. Sub-dataframes have been created for each plot to remove the large number of NaN values separately for each question/alternative. These NaN values are not missing data, instead, they imply that the current alternative was not chosen as a reason for not voting. They show the distribution of participants who chose the current alternative on the basis of their frequency of voting and gender.- x-axis: the category of voters- y-axis: the count of participants who chose the current alternative - the y-axis also describes what the alternative is- further classification based on gender
###Code
df2_1 = df2.drop(df2.loc[df2['Q29_1'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot3 = sns.countplot(x="cat", hue="gender", data=df2_1).set(xlabel = 'Voter Category', ylabel = 'Count (Did not like any candidates)')
df2_2 = df2.drop(df2.loc[df2['Q29_2'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot4 = sns.countplot(x="cat", hue="gender", data=df2_2).set(xlabel = 'Voter Category', ylabel = 'Count (My vote does not matter because of where I live)')
df2_3 = df2.drop(df2.loc[df2['Q29_3'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot5 = sns.countplot(x="cat", hue="gender", data=df2_3).set(xlabel = 'Voter Category', ylabel = 'Count (Nothing changes for me whoever wins)')
df2_4 = df2.drop(df2.loc[df2['Q29_4'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot6 = sns.countplot(x="cat", hue="gender", data=df2_4).set(xlabel = 'Voter Category', ylabel = 'Count (System is too broken to be fixed by voting)')
df2_5 = df2.drop(df2.loc[df2['Q29_5'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot7 = sns.countplot(x="cat", hue="gender", data=df2_5).set(xlabel = 'Voter Category', ylabel = 'Count (Wanted to but no time/had work/forgot)')
df2_6 = df2.drop(df2.loc[df2['Q29_6'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot8 = sns.countplot(x="cat", hue="gender", data=df2_6).set(xlabel = 'Voter Category', ylabel = 'Count (Not sure if I can vote)')
plt.legend(loc='upper right')
df2_7 = df2.drop(df2.loc[df2['Q29_7'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot9 = sns.countplot(x="cat", hue="gender", data=df2_7).set(xlabel = 'Voter Category', ylabel = 'Count (Nobody talks about issues important to me)')
df2_8 = df2.drop(df2.loc[df2['Q29_8'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot10 = sns.countplot(x="cat", hue="gender", data=df2_8).set(xlabel = 'Voter Category', ylabel = 'Count (All candidates are same)')
df2_9 = df2.drop(df2.loc[df2['Q29_9'] < 1].index).dropna()
plt.figure(figsize=(8,10))
plot11 = sns.countplot(x="cat", hue="gender", data=df2_9).set(xlabel = 'Voter Category', ylabel = 'Count (I do not believe in voting)')
###Output
_____no_output_____
###Markdown
Analysis This analysis corresponds to research question 1 and details the following:- the reason behind selection of the particular plots- the inferences that can be drawn from the visual data in the form of pointers.The formation and description of a conclusion on the basis of the analysis is detailed further in the conclusion section.***Selection of specific plots***- *Pie plot*: It is one of the best choices when we have only one categoric variable. This plot was chosed to represent the distribution of voters based on the frequency of their participation in voting because it provides the best visual representation of how many participants of each type are present. It is easy to know the proportion of the total participants that each of the three categories hold.- *Box plot*: It is one of the best choices when we have a numeric and a categoric variable with several numeric observations per category. This can further by subclassified on the basis of gender. It makes the distinction between distribution of the choices made on the basis of age and gender easier.- *Count plot*: This is one of the best choices when the count of a specific category needs to be put under analysis. It was chosed for the 9 subsequent plots because it provides an easy way to distinguish between the number of voters, based on their category (further based on gender), who chose not to vote due to a specific reason. It allows us to study the difference between the selections based on what category the participants belong to.***Inferences drawn from the plots (in the form of pointers):***- Plot 1 - The maximum number of participants are the ones who vote sometimes (not rarely or always) - There is practically not much difference between the participants who vote rarely and who always vote. - These inferences were drawn by looking at the proportion of the whole pie that each category covers.- Plot 2 - The average age of the participants who consider voting important is in the higher 50s. - It can be clearly observed, and quite interestingly, that older people tend to realize the importance of voting more than the young ones as the average age shown by the boxplot is clearly the highest for selection 1 (very important). - There is practically not much observable difference based on the gender of the participants. While the age spread for females is higher in case of option 4 (no importance), the overall distribution seems quite similar. - The impact of age on this distribution is clearly visible and has been clearly described.- Plot 3-11 - The maximum number of participants for each of the plots/alternatives are the ones who rarely or never vote. - This is good and in line with the practicality of the data as the ones who vote less should be the ones to most likely choose one of the options. - The different plots were created for a visual impact when put on a dashboard or single sheet to compare the choices made by the participants. - The bar corresponding to females can be seen to be higher in most of the plots implying that females tend to vote lesser than men due to these particular reasons. - Not being sure of their ability to vote was the least popular choice. - Thinking that nothing changes for the participant whichever candidate wins was the most popular choice. - This can be figured out based on the maximum count of each of the plots. - The participants who almost vote sometimes have significantly lower count for each plot/alternative which is good and implies the trust in system that still exists within them. - The count of voters who almost always vote is quite negligible when compared to the other two categories. Conclusion >***1*** *Based on the frequenct of voting, realization of the importance of voting, and the reasons provided by non-frequent voters for not voting, do Americans seem to have a mistaken impression about the concept of voting?*Based on the analysis, the Americans who rarely vote seem to have mistaken impression about the concept of voting. While the 'sporadic' voters form the maximum part of the whole sample and the older people tend to realize the importance of voting better, the ones who rarely or never vote show significantly higher count in response to the provided alternatives as reasons not to vote. All the provided reasons are genuine feelings however can be rebutted with proper information and education.***Many Americans who rarely/never vote, do not trust the concept of voting and have misconceptions about the same.*** *Section 2 (Research Question 2)* Dataset The dataframe is returned by a python script which filters/wrangles the data based on the needs for this particular section. It can be viewed under the scripts directory. This dataset includes the data filtered specifically for answering the second research question. Basic descriptive analysis of the numeric and non-numeric columns has also been conducted. The description of each column is as follows:- ppage: the age of a participant- gender: the gender of a participant- difficulty: the level of difficulty participants find to vote in the national elections (values 1 to 4) - 1: very easy - 2: somewhat easy - 3: somewhat difficult - 4: very difficult
###Code
df3 = project_functions.load_and_process("../data/raw/nonvoters_data.csv", 3)
df3
df3.describe(include = [np.number]).T
df3.describe(exclude = [np.number]).T
###Output
_____no_output_____
###Markdown
Visualization This section consists of 2 plots. These two plots allow for answering research question 2. The description of the elements of each plot has been provided ahead. **Plot 1** This count plot represents the choices made by the participants answering how difficult it is to vote in the national election. The distribution shows the count of participants who chose a particular option which has also been sub-classified based on their gender.- x-axis: the count of voters who chose a particular alternative- y-axis: the 4 provided alternatives - from 1 (easiest) to 4 (most difficult)- further classification based on gender
###Code
plt.figure(figsize=(8,7))
plot11 = sns.countplot(y="difficulty", hue="gender", data=df3).set(ylabel = 'Difficulty in voting - (1 - Easiest, 4 - Most difficult)', xlabel = 'Count of chosen alternative')
###Output
_____no_output_____
###Markdown
**Plot 2** This violin plot represents the choices made by the participants answering how difficult it is to vote in the national election based on the age of the participants. The plot shows the distribution of participants for each choice based on their age and gender. This helps to understand how the thinking and choices of the participants differ based on age and gender.- x-axis: the 4 provided alternatives - from 1 (easiest) to 4 (most difficult)- y-axis: the age of the voters- further classification based on gender
###Code
plt.rc("axes.spines", top=False, right=False)
plt.figure(figsize=(8,10))
plot12 = sns.violinplot(x = 'difficulty', y = 'ppage', hue = 'gender', data = df3, palette = 'muted', split = True).set(xlabel = 'Difficulty in voting - (1 - Easiest, 4 - Most difficult)', ylabel = 'Age')
plt.legend(loc='upper center')
###Output
_____no_output_____
###Markdown
Analysis This analysis corresponds to research question 2 and details the following:- the reason behind selection of the particular plots- the inferences that can be drawn from the visual data in the form of pointers.The formation and description of a conclusion on the basis of the analysis is detailed further in the conclusion section.***Selection of specific plots***- *Count plot*: This is one of the best choices when the count of a specific category needs to be put under analysis. It was chosed because it provides an easy way to distinguish between the number of voters for each alternative, based on the difficulty level of their choice (further based on gender). It allows us to study the difference between the selections based on gender. These selections show how difficult they find to vote in the national elections.- *Violin plot*: These plots contain all the data points unline some others with mean and error bars. Also, they work well for both quantitative and qualitative data even when the data does not follow a normal distribution. In the current case though, it is one of the best choices when the plot consists of a categoric variable and a numeric variable with multiple numeric values corresponding to a cetegory. It allows us to study the difference between the selection made by the participants for the difficulty of voting, on the basis of their age and gender.***Inferences drawn from the plots (in the form of pointers):***- Plot 1 - The maximum number of participants find it easy to vote in the national elections. - The least number of participants find it the most difficult to vote in the national elections. - The count can be seen to be dropping almost uniformly as we go from the least difficult to the most difficult. - It can be seen that the bar for males is slightly higher in the case of option 1 (easiest) and slightly lower in all other cases. - This shows that compared to females, males tend to find the process slightly easier. - While the difference is not fairly large to consider this as a fact, the slight different can be interpreted as a slight difference in how difficult they find it based on their gender.- Plot 2 - The average age is higher for the participants who find voting to be the easiest. - The difference based on age is only visible clearly in the case of option 1 (easiest) while the age distribution is almost similar for the other three alternatives. - Again, the difference based on gender is not very visible. - We can stick to the analysis of the previous plot leading to a very minute difference based on gender as this graph does not state otherwise. - People of higher age can be visible seen to find it easier. - This is visible in the high average age for choices corresponding to easy and the larger spread (area-wise) of the plot for higher ages in case of choice 1 (easiest). - The maximum spread in age distribution can be seen for choice 4 (most difficult). Conclusion >***2*** *How difficult do Americans think it is to vote in the national elections? Does age play a role in this thinking?*Based of the analysis of the two plots, it can be concluded that participants generally seem to find it easier to vote in the national elections and age can be seen to have a very minor role in this choice. A possible explanation of why older people find it easier to vote is the higher amount of life experience. This answers our research question. ***Americans do not find it difficult to vote in the national elections and age has a fairly minor role to play in the same.*** *Section 3 (Research Question 3)* Dataset The dataframe is returned by a python script which filters/wrangles the data based on the needs for this particular section. It can be viewed under the scripts directory. This dataset includes the data filtered specifically for answering the second research question. Basic descriptive analysis of the numeric and non-numeric columns has also been conducted. The description of each column is as follows:- id: the unique ID of each participant- ppage: the age of a participant- gender: the gender of a participant- cat: the voter category of a participant which refers to the frequent of voting (3 values) - always votes - votes sometimes - rarely or never votes- need_change: the answer to whether the design and structure of the American government need change - 1: yes (a lot of changes are needed) - 2: no (changes are not really needed)
###Code
df4 = project_functions.load_and_process("../data/raw/nonvoters_data.csv", 4)
df4
df4.describe(include = [np.number]).T
df4.describe(exclude = [np.number]).T
###Output
_____no_output_____
###Markdown
Visualization This section consists of 2 plots. These two plots allow for answering research question 3. The description of the elements of each plot has been provided ahead. **Plot 1** This count plot represents the choices made by the participants answering whether the design and structure of the American government need change. The distribution shows the count of participants who chose a particular option which has also been sub-classified based on their gender.- x-axis: the count of voters who chose a particular alternative- y-axis: the 2 provided alternatives - 1: Yes - 2: No- further classification based on gender
###Code
plt.figure(figsize=(8,5))
plot11 = sns.countplot(y="need_change", hue="gender", data=df4).set(ylabel = 'Need change - (1 - Yes, 2 - No)', xlabel = 'Count of chosen alternative')
###Output
_____no_output_____
###Markdown
**Plot 2** This violin plot represents the choices made by the participants answering whether the design and structure of the American government need change, based on the age of the participants. The plot shows the distribution of participants for each choice based on their age and gender. This helps to understand how the thinking and choices of the participants differ based on age and gender.- x-axis: the category of voters- y-axis: the age of the voters- legend: the 2 provided alternatives - 1: Yes - 2: No
###Code
plt.rc("axes.spines", top=False, right=False)
plt.figure(figsize=(8,10))
violin_plot_2 = sns.violinplot(x = 'cat', y = 'ppage', hue = 'need_change', data = df4, palette = 'muted', split = True).set(xlabel = 'Voter Category; Legend: (1-Yes, 2-No)', ylabel = 'Age')
plt.legend(loc='upper center')
###Output
_____no_output_____
###Markdown
Analysis This analysis corresponds to research question 2 and details the following:- the reason behind selection of the particular plots- the inferences that can be drawn from the visual data in the form of pointers.The formation and description of a conclusion on the basis of the analysis is detailed further in the conclusion section.***Selection of specific plots***- *Count plot*: This is one of the best choices when the count of a specific category needs to be put under analysis. It was chosed because it provides an easy way to see how many participants seek change in the design and structure of the American government. It allows us to study the difference between the selections (yes or no) based on their voter category too. Study of this plot is the easiest way to see visible difference, and its extent, between the two choices.- *Violin plot*: These plots contain all the data points unline some others with mean and error bars. Also, they work well for both quantitative and qualitative data even when the data does not follow a normal distribution. In the current case though, it is one of the best choices when the plot consists of a categoric variable and a numeric variable with multiple numeric values corresponding to a cetegory. It allows us to study the difference between the selection made by the participants for whether change is required or not, on the basis of their age and voter category.***Inferences drawn from the plots (in the form of pointers):***- Plot 1 - The bars for alternative 1 (Yes, a lot of changes are reuqired) are significantly higher than for its counterpart. - This trend is similar for both males and females. - This implies that there are, in general, more men and women who think that change is required than men and women who think otherwise respectively. - Note that the above point compares males to males and females to females (not males to females). - The bar for males is higher is case of 'no change' and lower in case of 'a lot of change' as compared to females. - This implies that, as compared to females, males find the need for change to be lower. - Consequently, the opposite is true for females. - The count of participants who seek change is more than 4 times the count of participants seeking no change.- Plot 2 - An inference which is helpful in general, but not much for the current research question is that the participants of higher age can again be seen to be the ones who vote more frequently. - Here, it can be seen that the choice is not clearly dependent of the category of voters except for the voters who rarely or never vote. - More of these non-frequent voters, who are of a younger age, seek change - The choices are not really age dependent either. - The only age dependence again can be seen in the case of participants who rarely/never vote. - The distribution of the choices on bases of age is quite uniform in the case of participants who vote sometimes or almost always. Conclusion >***3*** *Do Americans think that the design and structure of the American Government need change? Does age play a role in this thinking?*Based of the analysis of the two plots, it can be concluded that a fairly larger number of participants think that the design and structure of the American government needs change. This could possibly be because of the lack of trust by people in the current practices. The thinking for change is case of females can be viewed as slightly higher compared to males. This answers our research question. ***Americans think that the design and structure of the American Government need change and age plays only a minor role in it (that too in the case of participants who rarely or never vote).*** End of Data Analysis ***Exporting processed data for dashboard***
###Code
df_proc = project_functions.load_and_process("../data/raw/nonvoters_data.csv", 5)
df_proc
df_proc.to_csv (r'../data/processed/processed_data.csv', index = True, header=True)
###Output
_____no_output_____
|
hypocycloid-online.ipynb
|
###Markdown
Hypocycloid definition and animation Deriving the parametric equations of a hypocycloid On May 11 @fermatslibrary posted a gif file, https://twitter.com/fermatslibrary/status/862659602776805379, illustrating the motion of eight cocircular points. The Fermat's Library followers found it so fascinating that the tweet picked up more than 1000 likes and 800 retweets. Soon after I saw the gif I created a similar Python Plotly animation although the tweet did not mention how it was generated. @plotlygraphs tweeted a link to my [Jupyter notebook](http://nbviewer.jupyter.org/github/empet/Math/blob/master/fermat-circle-moving-online.ipynb) presenting the animation code. How I succeeded to reproduce it so fast? Here I explain the secret: At the first sight you can think that the gif displays an illusory rectiliniar motion of the eight points, but it is a real one. I noticed that the moving points lie on a rolling circle along another circle, and I knew that a fixed point on a rolling circle describes a curve called hypocycloid. In the particular case when the ratio of the two radii is 2 the hypocycloid degenerates to a diameter in the base (fixed) circle. In this Jupyter notebook I deduce the parametric equations of a hypoycyloid, animate its construction and explain why when $R/r=2$ any point on the rolling circle runs a diameter in the base circle.
###Code
from IPython.display import Image
Image(filename='generate-hypocycloid.png')
###Output
_____no_output_____
###Markdown
We refer to the figure in the above cell to explain how we get the parameterization of the hypocycloid generated by a fixed point of a circle of center $O'_0$ and radius r, rolling without slipping along the circleof center O and radius $R>r$.Suppose that initially the hypocycloid generating point, $P$, is located at $(R,0)$.After the small circle was rolling along the greater circle a length corresponding to an angle of measure, $t$, it reaches the point $P'$ on the circle $C(O'_t, r)$.Rolling without slipping means that the length the arc $\stackrel{\frown}{PQ}$ of the greater circle equals the length of the arc $\stackrel{\frown}{P'Q}$ on the smaller one, i.e $Rt=r\omega$, where $\omega$ is the measure of the non-oriented angle $\widehat{P'O'_tQ}$ (i.e. we consider $\omega>0$) . Thus $\omega=(R/r)t$The center $O'_t$ has the coordinates $x=(R-r)\cos(t), (R-r)\sin(t)$. The clockwise parameterization of the circle $C(O'_t,r)$ with respect to the coordinate system $x'O'_ty'$ is as follows:$$\begin{array}{llr}x'(\tau)&=&r\cos(\tau)\\y'(\tau)&=&-r\sin(\tau),\end{array}$$ $\tau\in[0,2\pi]$. Hence the point $P'$ on the hypocycloid has the coordinates:$x'=r\cos(\omega-t), y'=-r\sin(\omega-t)$, and with respect to $xOy$, the coordinates:$x=(R-r)\cos(t)+r\cos(\omega-t), y=(R-r)\sin(t)-r\sin(\omega-t)$.Replacing $\omega=(R/r)t$ we get the parameterization of the hypocycloid generated by the initial point $P$:$$\begin{array}{lll}x(t)&=&(R-r)\cos(t)+r\cos(t(R-r)/r)\\y(t)&=&(R-r)\sin(t)-r\sin(t(R-r)/r), \quad t\in[0,2\pi]\end{array}$$ If $R/r=2$ the parametric equations of the corresponding hypocycloid are:$$\begin{array}{lll}x(t)&=&2r\cos(t)\\y(t)&=&0\end{array}$$i.e. the moving point $P$ runs the diameter $y=0$, from the position $(R=2r, 0)$ to $(-R,0)$ when $t\in[0,\pi]$,and back to $(R,0)$, for $t\in[\pi, 2\pi]$. What about the trajectory of any other point, $A$, on the rolling circle that at $t=0$ has the angular coordinate $\varphi$ with respect to the center $O'_0$? We show that it is also a diameter in the base circle, referring to the figure in the next cell that is a particularization ofthe above figure to the case $R=2r$.
###Code
Image(filename='hypocycloid-2r.png')
###Output
_____no_output_____
###Markdown
The arbitrary point $A$ on the rolling circle has, for t=0, the coordinates:$x=r+r\cos(\varphi), y=r\sin(\varphi)$. The angle $\widehat{QO'_tP'}=\omega$ is in this case $2t$, and $\widehat{B'O'_tP'}=t$. Since $\widehat{A'O'_tP'}=\varphi$, we get that the position of the fixed point on the smaller circle, after rolling along an arc of length $r(2t-\varphi)$,is $A'(x(t)=r\cos(t)+r\cos(t-\varphi), y(t)=r\sin(t)-r\sin(t-\varphi))$, with $\varphi$ constant, and $t$ variable in the interval $[\varphi, 2\pi+\varphi]$.Let us show that $y(t)/x(t)=$constant for all $t$, i.e. the generating point of the hypocycloid lies on a segment of line (diameter in the base circle):$$\displaystyle\frac{y(t)}{x(t)}=\frac{r\sin(t)-r\sin(t-\varphi)}{r\cos(t)+r\cos(t-\varphi)}=\left\{\begin{array}{ll}\tan(\varphi/2)& \mbox{if}\:\: t=\varphi/2\\\displaystyle\frac{2\cos(t-\varphi/2)\sin(\varphi/2)}{2\cos(t-\varphi/2)\cos(\varphi/2)}=\tan(\varphi/2)& \mbox{if}\:\: t\neq\varphi/2 \end{array}\right.$$Hence the @fermatslibrary animation, illustrated by a Python Plotly code in my [Jupyter notebook](http://nbviewer.jupyter.org/github/empet/Math/blob/master/fermat-circle-moving-online.ipynb), displays the motion of the eight points placed on the rollingcircle of radius $r=R/2$, along the corresponding diameters in the base circle. Animating the hypocycloid generation
###Code
import numpy as np
from numpy import pi, cos, sin
import copy
import plotly.plotly as py
from plotly.grid_objs import Grid, Column
import time
###Output
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###Markdown
Set the layout of the plot:
###Code
axis=dict(showline=False,
zeroline=False,
showgrid=False,
showticklabels=False,
range=[-1.1,1.1],
autorange=False,
title=''
)
layout=dict(title='',
font=dict(family='Balto'),
autosize=False,
width=600,
height=600,
showlegend=False,
xaxis=dict(axis),
yaxis=dict(axis),
hovermode='closest',
shapes=[],
updatemenus=[dict(type='buttons',
showactive=False,
y=1,
x=1.2,
xanchor='right',
yanchor='top',
pad=dict(l=10),
buttons=[dict(label='Play',
method='animate',
args=[None, dict(frame=dict(duration=90, redraw=False),
transition=dict(duration=0),
fromcurrent=True,
mode='immediate'
)]
)]
)]
)
###Output
_____no_output_____
###Markdown
Define the base circle:
###Code
layout['shapes'].append(dict(type= 'circle',
layer= 'below',
xref= 'x',
yref='y',
fillcolor= 'rgba(245,245,245, 0.95)',
x0= -1.005,
y0= -1.005,
x1= 1.005,
y1= 1.005,
line= dict(color= 'rgb(40,40,40)', width=2
)
)
)
def circle(C, rad):
#C=center, rad=radius
theta=np.linspace(0,1,100)
return C[0]+rad*cos(2*pi*theta), C[1]-rad*sin(2*pi*theta)
###Output
_____no_output_____
###Markdown
Prepare data for animation to be uploaded to Plotly cloud:
###Code
def set_my_columns(R=1.0, ratio=3):
#R=the radius of base circle
#ratio=R/r, where r=is the radius of the rolling circle
r=R/float(ratio)
xrol, yrol=circle([R-r, 0], 0)
my_columns=[Column(xrol, 'xrol'), Column(yrol, 'yrol')]
my_columns.append(Column([R-r, R], 'xrad'))
my_columns.append(Column([0,0], 'yrad'))
my_columns.append(Column([R], 'xstart'))
my_columns.append(Column([0], 'ystart'))
a=R-r
b=(R-r)/float(r)
frames=[]
t=np.linspace(0,1,50)
xpts=[]
ypts=[]
for k in range(t.shape[0]):
X,Y=circle([a*cos(2*pi*t[k]), a*sin(2*pi*t[k])], r)
my_columns.append(Column(X, 'xrcirc{}'.format(k+1)))
my_columns.append(Column(Y, 'yrcirc{}'.format(k+1)))
#The generator point has the coordinates(xp,yp)
xp=a*cos(2*pi*t[k])+r*cos(2*pi*b*t[k])
yp=a*sin(2*pi*t[k])-r*sin(2*pi*b*t[k])
xpts.append(xp)
ypts.append(yp)
my_columns.append(Column([a*cos(2*pi*t[k]), xp], 'xrad{}'.format(k+1)))
my_columns.append(Column([a*sin(2*pi*t[k]), yp], 'yrad{}'.format(k+1)))
my_columns.append(Column(copy.deepcopy(xpts), 'xpt{}'.format(k+1)))
my_columns.append(Column(copy.deepcopy(ypts), 'ypt{}'.format(k+1)))
return t, Grid(my_columns)
def set_data(grid):
return [dict(xsrc=grid.get_column_reference('xrol'),#rolling circle
ysrc= grid.get_column_reference('yrol'),
mode='lines',
line=dict(width=2, color='blue'),
name='',
),
dict(xsrc=grid.get_column_reference('xrad'),#radius in the rolling circle
ysrc= grid.get_column_reference('yrad'),
mode='markers+lines',
line=dict(width=1.5, color='blue'),
marker=dict(size=4, color='blue'),
name=''),
dict(xsrc=grid.get_column_reference('xstart'),#starting point on the hypocycloid
ysrc= grid.get_column_reference('ystart'),
mode='marker+lines',
line=dict(width=2, color='red', shape='spline'),
name='')
]
###Output
_____no_output_____
###Markdown
Set data for each animation frame:
###Code
def set_frames(t, grid):
return [dict(data=[dict(xsrc=grid.get_column_reference('xrcirc{}'.format(k+1)),#update rolling circ position
ysrc=grid.get_column_reference('yrcirc{}'.format(k+1))
),
dict(xsrc=grid.get_column_reference('xrad{}'.format(k+1)),#update the radius
ysrc=grid.get_column_reference('yrad{}'.format(k+1))#of generating point
),
dict(xsrc=grid.get_column_reference('xpt{}'.format(k+1)),#update hypocycloid arc
ysrc=grid.get_column_reference('ypt{}'.format(k+1))
)
],
traces=[0,1,2]) for k in range(t.shape[0])
]
###Output
_____no_output_____
###Markdown
Animate the generation of a hypocycloid with 3 cusps(i.e. $R/r=3$):
###Code
py.sign_in('empet', 'my_api_key')#access my Plotly account
t, grid=set_my_columns(R=1, ratio=3)
py.grid_ops.upload(grid, 'animdata-hypo3'+str(time.time()), auto_open=False)#upload data to Plotly cloud
data1=set_data(grid)
frames1=set_frames(t, grid)
title='Hypocycloid with '+str(3)+' cusps, '+'<br>generated by a fixed point of a circle rolling inside another circle; R/r=3'
layout.update(title=title)
fig1=dict(data=data1, layout=layout, frames=frames1)
py.icreate_animations(fig1, filename='anim-hypocycl3'+str(time.time()))
###Output
_____no_output_____
###Markdown
Hypocycloid with four cusps (astroid):
###Code
t, grid=set_my_columns(R=1, ratio=4)
py.grid_ops.upload(grid, 'animdata-hypo4'+str(time.time()), auto_open=False)#upload data to Plotly cloud
data2=set_data(grid)
frames2=set_frames(t, grid)
title2='Hypocycloid with '+str(4)+' cusps, '+'<br>generated by a fixed point of a circle rolling inside another circle; R/r=4'
layout.update(title=title2)
fig2=dict(data=data2, layout=layout, frames=frames2)
py.icreate_animations(fig2, filename='anim-hypocycl4'+str(time.time()))
###Output
_____no_output_____
###Markdown
Degenerate hypocycloid (R/r=2):
###Code
t, grid=set_my_columns(R=1, ratio=2)
py.grid_ops.upload(grid, 'animdata-hypo2'+str(time.time()), auto_open=False)#upload data to Plotly cloud
data3=set_data(grid)
frames3=set_frames(t, grid)
title3='Degenerate Hypocycloid; R/r=2'
layout.update(title=title3)
fig3=dict(data=data3, layout=layout, frames=frames3)
py.icreate_animations(fig3, filename='anim-hypocycl2'+str(time.time()))
from IPython.core.display import HTML
def css_styling():
styles = open("./custom.css", "r").read()
return HTML(styles)
css_styling()
###Output
_____no_output_____
|
.ipynb_checkpoints/laborempnovsprod-checkpoint.ipynb
|
###Markdown
Replacing the mispelling of the value indepedent in Company Type
###Code
df['Company Type'].replace(to_replace='Indepedent Producer Operator',
value = 'Independent Producer Operator', inplace=True)
df.dtypes
###Output
_____no_output_____
###Markdown
Renaming columns by underscore to remove the spaces between the Column names
###Code
df.rename(columns=lambda x: x.replace(" ", "_"), inplace=True)
df.head()
df.hist(bins=50,figsize=(7,7))
correlation = df.corr()['Production_(short_tons)']
correlation
correlation = df.corr()
correlation
fig = plt.subplots(figsize=(8,8))
sns.heatmap(correlation,annot=True)
plt.scatter(df.Average_Employees, df.Labor_Hours)
plt.xlabel('Total Number of Employees')
plt.ylabel('Total Number of Hours worked')
plt.savefig('Employee_vs_Labourhrs_Scatter.png', dpi=300, bbox_inches='tight')
###Output
_____no_output_____
###Markdown
We used basic scatter plot to show the relationship between Employees vs Number of Hours Work. Here we can see that as the number of emplyees goes up, the total number of hours worked increases at the mines in a linear relationship.
###Code
#using seaborn regression plot
sns_plot=sns.regplot(df.Average_Employees, df.Labor_Hours, ci=95, n_boot=1000,scatter_kws={"color": "orange"}, line_kws={"color": "red"})
fig = sns_plot.get_figure()
fig.savefig("AverageEmp_vs_labourhrs.png")
###Output
C:\Users\SHREE\AppData\Roaming\Python\Python38\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
warnings.warn(
###Markdown
Here using seaborn we showed the same trend, but by fitting a line in the data which gave it a bootstrapping in the middle of the line.
###Code
plt.scatter(df.Labor_Hours, df['Production_(short_tons)'], color='#ff7f0e')
df['Production_(short_tons)'].hist(bins=10)
#wanted to see the minimum value
min(df['Production_(short_tons)'])
#look at where the production shorttons is eqaul to zero
df['Production_(short_tons)'] == 0
#where is the production equal to zero
df[df['Production_(short_tons)'] == 0]
###Output
_____no_output_____
###Markdown
There are quite a number of mines that had no production values. Next trying to look at the mines that produced at least 1 tons or more. Slicing the Data
###Code
#where is the production is at least 1 tonne
df = df[df['Production_(short_tons)'] >1 ]
df.head()
len(df)
#May be predictive variable: A good predictor in how productive the mine is.
df.Mine_Status.unique()
df[['Mine_Status','Production_(short_tons)']].groupby('Mine_Status').mean()
df[['Mine_Status','Production_(short_tons)']].groupby('Mine_Status').mean()
###Output
_____no_output_____
###Markdown
Predict the production of coal mines in 2015What caused high or lower production Feature Engineering
###Code
for columns in df:
print(columns)
df.Union_Code.unique()
###Output
_____no_output_____
###Markdown
Of the above features on two are number: labour hours and Average_employee--Split into numerical features; and categorical variables
###Code
pd.get_dummies(df["Company_Type"]).sample(50).head()
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
df['Coal_Supply_Region']= le.fit_transform(df['Coal_Supply_Region'])
df['Mine_State']= le.fit_transform(df['Mine_State'])
df['Mine_County']= le.fit_transform(df['Mine_County'])
df['Mine_Status']= le.fit_transform(df['Mine_Status'])
df['Mine_Type']= le.fit_transform(df['Mine_Type'])
df['Company_Type']= le.fit_transform(df['Company_Type'])
df['Operation_Type']= le.fit_transform(df['Operation_Type'])
df.drop(['Union_Code'],axis='columns',inplace=True)
df.head()
###Output
_____no_output_____
###Markdown
Build our Model
###Code
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
#len(dummy_categoricals)
X = df.iloc[:,[12,13]]
y = df.iloc[:,14]
feature_list = list(X.columns)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = RandomForestRegressor(n_estimators=100, oob_score=True)
model.fit(X_train,y_train)
model.score(X_test,y_test)
y_pred=model.predict(X_test)
from sklearn import model_selection
kfold = model_selection.KFold(n_splits=10, random_state=7, shuffle=True)
scoring = 'r2'
results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print("R^2: %.3f (%.3f)" % (results.mean(), results.std()))
scoring = 'neg_mean_squared_error'
results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print("MSE: %.3f (%.3f)" % (results.mean(), results.std()))
###Output
MSE: -956351105.655 (141924512.116)
###Markdown
Make Predictions on the Test Set
###Code
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print("MAE: %.3f (%.3f)" % (results.mean(), results.std()))
# Calculate the absolute errors
errors = abs(y_pred - y_test)
# Print out the mean absolute error (mae)
print('Mean Absolute Error:', round(np.mean(errors), 2))
###Output
Mean Absolute Error: 15104.57
###Markdown
Determine Performance Metrics
###Code
# Calculate mean absolute percentage error (MAPE)
mape = 100 * (errors / y_test)
# Calculate and display accuracy
accuracy = 100 - np.mean(mape)
print('Accuracy:', round(accuracy, 2), '%.')
###Output
Accuracy: 46.3 %.
###Markdown
Interpret Model and Report Results Visualizing a Single Decision Tree
###Code
!pip install pydot
# Import tools needed for visualization
from sklearn.tree import export_graphviz
import pydot
# Pull out one tree from the forest
tree = model.estimators_[5]
# Import tools needed for visualization
from sklearn.tree import export_graphviz
import pydot
# Pull out one tree from the forest
tree = model.estimators_[5]
# Export the image to a dot file
export_graphviz(tree, out_file = 'tree.dot', feature_names = feature_list, rounded = True, precision = 1)
# Use dot file to create a graph
(graph, ) = pydot.graph_from_dot_file('tree.dot')
# Write graph to a png file
graph.write_png('tree.png')
# Limit depth of tree to 3 levels
rf_small = RandomForestRegressor(n_estimators=10, max_depth = 3)
rf_small.fit(X_train,y_train)
# Extract the small tree
tree_small = rf_small.estimators_[5]
# Save the tree as a png image
export_graphviz(tree_small, out_file = 'small_tree.dot', feature_names = feature_list, rounded = True, precision = 1)
(graph, ) = pydot.graph_from_dot_file('small_tree.dot')
graph.write_png('small_tree.png');
# Get numerical feature importances
importances = list(model.feature_importances_)
# List of tuples with variable and importance
feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]
# Sort the feature importances by most important first
feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)
# Print out the feature and importances
[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];
# Import matplotlib for plotting and use magic command for Jupyter Notebooks
import matplotlib.pyplot as plt
%matplotlib inline
# Set the style
plt.style.use('fivethirtyeight')
# list of x locations for plotting
x_values = list(range(len(importances)))
# Make a bar chart
plt.bar(x_values, importances, orientation = 'vertical')
# Tick labels for x axis
plt.xticks(x_values, feature_list, rotation='vertical')
# Axis labels and title
plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');
import pickle
# Saving model to disk
pickle.dump(model, open('model.pkl','wb'))
# Loading model to compare the results
modell = pickle.load(open('model.pkl','rb'))
print("We need 28774319 (short tinnes) production in 2022 with 20 number of employees.\nSo Our 20 employees have to work total :",model.predict([[28774319,20]]),"hrs in 2022")
###Output
We need 28774319 (short tinnes) production in 2022 with 20 number of employees.
So Our 20 employees have to work total : [44085.46] hrs in 2022
|
discrete_systems/eigenfunctions.ipynb
|
###Markdown
Characterization of Discrete Systems*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Comunications Engineering, Universität Rostock. Please direct questions and suggestions to [[email protected]](mailto:[email protected]).* EigenfunctionsAn [eigenfunction](https://en.wikipedia.org/wiki/Eigenfunction) of a discrete system is defined as the input signal $x[k]$ which produces the output signal $y[k] = \mathcal{H}\{ x[k] \} = \lambda \cdot x[k]$ with $\lambda \in \mathbb{C}$. The weight $\lambda$ associated with $x[k]$ is known as scalar eigenvalue of the system. Hence besides a weighting factor, an eigenfunction is not modified by passing through the system.[Complex exponential signals](../discrete_signals/standard_signals.ipynbComplex-Exponential-Signal) $z^k$ with $z \in \mathbb{C}$ are eigenfunctions of discrete linear time-invariant (LTI) systems. Let's assume a generic LTI system with input signal $x[k] = z^k$ and output signal $y[k] = \mathcal{H}\{ x[k] \}$. Due to the time-invariance of the system, the response to a shifted input signal $x(k-\kappa) = z^{k - \kappa}$ reads\begin{equation}y[k- \kappa] = \mathcal{H}\{ x[k - \kappa] \} = \mathcal{H}\{ z^{- \kappa} \cdot z^k \}\end{equation}Due to the linearity of the system this can be reformulated as\begin{equation}y[k- \kappa] = z^{- \kappa} \cdot \mathcal{H}\{ z^k \} = z^{- \kappa} \cdot y[k]\end{equation}If the complex exponential signal $z^k$ is an eigenfunction of the LTI system, the output signal is a weighted exponential signal $y[k] = \lambda \cdot z^k$. Introducing $y[k]$ into the left- and right-hand side of above equation yields\begin{equation}\lambda z^k z^{- \kappa} = z^{- \kappa} \lambda z^k\end{equation}which obviously is fulfilled. This proves that the exponential signal $z^k$ is an eigenfunction of LTI systems. **Example**The output signal of the previously introduced [second-order recursive LTI system](difference_equation.ipynbSecond-Order-System) with the difference equation\begin{equation}y[k] - y[k-1] + \frac{1}{2} y[k-2] = x[k]\end{equation}is computed for a complex exponential signal $x[k] = z^k$ at the input. The output signal should be a weighted complex exponential due to above reasoning.
###Code
%matplotlib inline
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
a = [1.0, -1.0, 1/2]
b = [1.0]
z = np.exp(0.02 + .5j)
k = np.arange(30)
x = z**k
y = signal.lfilter(b, a, x)
###Output
_____no_output_____
###Markdown
The real and imaginary part of the input and output signal is plotted.
###Code
plt.figure(figsize=(10,8))
plt.subplot(221)
plt.stem(k, np.real(x))
plt.xlabel('$k$')
plt.ylabel(r'$\Re \{ x[k] \}$')
plt.subplot(222)
plt.stem(k, np.imag(x))
plt.xlabel('$k$')
plt.ylabel(r'$\Im \{ x[k] \}$')
plt.tight_layout()
plt.subplot(223)
plt.stem(k, np.real(y))
plt.xlabel('$k$')
plt.ylabel(r'$\Re \{ y[k] \}$')
plt.subplot(224)
plt.stem(k, np.imag(y))
plt.xlabel('$k$')
plt.ylabel(r'$\Im \{ y[k] \}$')
plt.tight_layout()
###Output
_____no_output_____
|
Machine Learning/Linear Regression.ipynb
|
###Markdown
Generate a Toy dataset for linear regression. Assume that the model follows a line $(Y = mx + c)$ to generate the $1000$ point and Add white noise to all the points generated. Take $70%$ generated points at random and use it to find the model (line in this example). Plot original line and the computed line along with data points. What is the MSE for the training and test data used
###Code
import numpy as np
import matplotlib.pyplot as plot
###Output
_____no_output_____
###Markdown
Let slope $(M) = 1$ and Y-intercept $(C) = 0$ :
###Code
x_points = np.random.rand(1000,1)
m = 1
c = 0
y_points = m*x_points + c
###Output
_____no_output_____
###Markdown
Design of Gaussian noise:
###Code
y_noise = np.random.randn(1000, 1)
plot.hist(y_noise, bins=50)
plot.xlabel('X-Axis')
plot.ylabel('Y-Axis')
plot.title('Gaussian Noise', fontweight ="bold")
plot.show()
###Output
_____no_output_____
###Markdown
Adding above noise to y:
###Code
y_points = y_points + y_noise
plot.figure(figsize=(20,10))
plot.scatter(x_points, y_points, s=10)
plot.title('y-axis with Gussian Noise', fontweight ="bold")
plot.xlabel('X-Axis')
plot.ylabel('Y-Axis')
###Output
_____no_output_____
###Markdown
Take 70% of data for training and remaining data for testing:
###Code
Data_Train = np.hstack((np.expand_dims(x_points,axis=1),np.expand_dims(y_points,axis=1)))
N = len(Data_Train)
Train_Test = int(N * (70/100)) # i,e Train_Test = 700
Train_Data, Test_Data = Data_Train[:Train_Test], Data_Train[Train_Test:]
x_Data, y_Data = Data_Train[:,0], Data_Train[:,1]
Train_x, Train_y = x_Data[:Train_Test], y_Data[:Train_Test]
Test_x, Test_y = x_Data[Train_Test:], y_Data[Train_Test:]
plot.figure(figsize = (20,10))
plot.scatter(Train_x,Train_y, s=10)
plot.title('Train Data', fontweight ="bold")
plot.xlabel('X-Axis')
plot.ylabel('Y-Axis')
plot.figure(figsize = (20,10))
plot.scatter(Test_x,Test_y, s=10)
plot.title('Test Data', fontweight ="bold")
plot.xlabel('X-Axis')
plot.ylabel('Y-Axis')
###Output
_____no_output_____
###Markdown
Convert X to (X and ones matrix):
###Code
x_oneTrain, x_oneTest = np.append(Train_x,np.ones((len(Train_x),1)),axis=1), np.append(Test_x,np.ones((len(Test_x),1)),axis=1)
x_oneTest
###Output
_____no_output_____
###Markdown
Setting up initial thetta matrix:
###Code
thetta = np.array([[0], [0]])
###Output
_____no_output_____
###Markdown
Find cost function or MSE:
###Code
def cost_function(X, Y, Thetta):
J = np.sum((X.dot(thetta)-Y)**2)*1/len(Y)
return J
###Output
_____no_output_____
###Markdown
Apply Gradient descent:
###Code
def gradient_descent(X, Y, thetta, alpha, iteration):
Cost_history = [0] * iteration
n = len(Y)
for iteration in range(iteration):
y_predict = X.dot(thetta)
Gradient = (1/n)*(X.T.dot(y_predict - Y))
thetta = thetta - (alpha*Gradient)
Cost = cost_function(X, Y, thetta)
Cost_history[iteration] = Cost
if (Cost>Cost_history[iteration-1]):
alpha = alpha*0.01
return thetta, y_predict
###Output
_____no_output_____
###Markdown
Training:
###Code
Thetta_Train, y_predict = gradient_descent(x_oneTrain, Train_y, thetta, 0.5, 10000)
m, c = Thetta_Train
Y_new = y_predict
print("Value of slope, M: ",m ,"\nValue of Y intercept, C: ", c, "\n\n")
###Output
Value of slope, M: [0.94334614]
Value of Y intercept, C: [-0.00335048]
###Markdown
Mean Squared Error or MSE of training:
###Code
MSE = np.sum((Train_y - Y_new)**2)/len(Train_y)
print("Mean squared error or MSE of Training: ", MSE, "\n\n")
###Output
Mean squared error or MSE of Training: 0.98217080729045
###Markdown
Plotting graph of training:
###Code
Train_Y = m*Train_x + c
plot.figure(figsize = (20, 10))
plot.scatter(Train_x,Train_y, s=10)
plot.title('Train', fontweight ="bold")
plot.xlabel('X-Axis')
plot.ylabel('Y-Axis')
plot.plot(Train_Y, Train_x, color='g')
###Output
_____no_output_____
###Markdown
Testing:
###Code
Thetta_Test, y_predict = gradient_descent(x_oneTest, Test_y, thetta, 0.5, 10000)
Y_New = y_predict
m_Test, c_Test = Thetta_Test
print("Value of slope, M: ",m ,"\nValue of Y intercept, C: ", c, "\n\n")
###Output
Value of slope, M: [0.94334614]
Value of Y intercept, C: [-0.00335048]
###Markdown
Mean Squared Error or MSE of testing:
###Code
MSE = np.sum((Test_y - Y_New)**2)/len(Test_y)
print("Mean squared error or MSE of Testing: ", MSE)
###Output
Mean squared error or MSE of Testing: 0.9007985017926133
###Markdown
Plotting graph of testing:
###Code
Test_Y = m*Test_x + c
plot.figure(figsize = (20,10))
plot.scatter(Test_x,Test_y, s=10)
plot.title('Test', fontweight ="bold")
plot.xlabel('X-Axis')
plot.ylabel('Y-Axis')
plot.plot(Test_Y, Test_x, color='r')
###Output
_____no_output_____
###Markdown
Regresion del la funcion Base Funciones de base Polinomica
###Code
polynomial=PolynomialFeatures(include_bias=False)
#para manejar datos en las cuales las varibles no tiens relaciones lineales
from sklearn.pipeline import make_pipeline,Pipeline
poly_model=Pipeline([
('polynomial',polynomial),
('linear',LinearRegression())
])
x=10*rng.rand(1000)
y=np.sin(x)+0.1*rng.randn(1000)
plt.scatter(x,y)
from sklearn.model_selection import GridSearchCV
grid=GridSearchCV(
estimator=poly_model,
param_grid=dict(
polynomial__degree=list(range(1,20)),
#polynomial__include_bias=[True,False],
linear__fit_intercept=[True,False]
),
cv=7
)
grid.fit(x[:,np.newaxis],y);
grid.best_params_
poly_model=make_pipeline(PolynomialFeatures(degree=8),LinearRegression(fit_intercept=True))
poly_model.fit(x[:,np.newaxis],y);
xtest=np.linspace(0,10,1000)
yresult=poly_model.predict(xtest[:,np.newaxis])
plt.scatter(x,y,color='red',alpha=0.3)
plt.plot(xtest,yresult,color='black')
plt.xlim(0,10)
poly_model.steps[1][1].coef_
###Output
_____no_output_____
###Markdown
Prediccion de trafico de bicicletas
###Code
import pandas as pd
counts=pd.read_csv('FremontBridge.csv',index_col='Date',parse_dates=True)
counts.head(10)
weather=pd.read_csv('BicycleWeather0.csv',index_col='DATE',parse_dates=True)
weather=weather.drop('Unnamed: 0',axis=1)
weather.head()
daily=counts.resample('1D').sum()
daily['Total']=daily.sum(axis=1)
daily=daily[['Total']]
daily.head()
(daily.index.dayofweek==4).astype(float)
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
for i,day in enumerate(days):
daily[day]=(daily.index.dayofweek==i).astype(float)
daily.head()
###Output
_____no_output_____
###Markdown
(미니 배치) 확률적 경사 하강법 대용량의 데이터일 경우 효과적
###Code
def stochastic_gradient_descent_steps(X, y, batch_size=10, iters =1000):
w0 = np.zeros((1, 1))
w1 = np.zeros((1, 1))
prev_cost = 100000
iter_index = 0
for ind in range(iters):
np.random.seed(ind)
# 전체 X, y 데이터에서 랜덤하게 batch_size만큼 데이터를 추출해 sample_X, sample_y로 저장
stochastic_random_index = np.random.permutation(X.shape[0])
sample_X = X[stochastic_random_index[0:batch_size]]
sample_y = y[stochastic_random_index[0:batch_size]]
# 랜덤하게 batch_size만큼 추출된 데이터 기반으로 w1_update, w0_update 계산 후 업데이트
w1_update, w0_update = get_weight_updates(w1, w0, sample_X, sample_y, learning_rate=0.01)
w1 = w1 - w1_update
w0 = w0 - w0_update
return w1, w0
w1, w0 = stochastic_gradient_descent_steps(X, y, iters=1000)
print("w1 :", round(w1[0,0], 3), "w0:", round(w0[0, 0], 3))
y_pred = w1[0, 0] * X + w0
print('Stochastic Gradient Descent Total Cost:{0:.4f}'.format(get_cost(y, y_pred)))
###Output
w1 : 4.028 w0: 6.156
Stochastic Gradient Descent Total Cost:0.9937
###Markdown
사이킷런 LinearRegression을 이용한 보스턴 주택 가격 예측 Crim : 지역별 범죄 발생률ZN : 25,000평방피트를 초과하는 거주 지역의 비율INDUS : 비상업 지역 넓이 비율CHAS : 찰스강에 대한 더미 변수(강의 경계에 위치한 경우는 1, 아니면 0)NOX : 일산화질소 농도RM : 거주할 수 있는 방 개수AGE : 1940년 이전에 건축된 소유 주택의 비율DIS : 5개 주요 고용센터까지의 가중 거리RAD : 고속도로 접근 용이도TAX : 10,000 달러당 재산세율PTRATIO : 지역의 교사와 학생 수 비율B : 지역의 교사와 학생 수 비율LSTAT : 하위 계층의 비율MEDV : 본인 소유의 주택 가격(중앙값)
###Code
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from scipy import stats
from sklearn.datasets import load_boston
%matplotlib inline
# boston 데이터 세트 로드
boston = load_boston()
# boston 데이터 세트 DataFrame 변환
bostonDF = pd.DataFrame(boston.data, columns = boston.feature_names)
# boston 데이터 세트의 target 배열은 주택 가격임. 이를 PRICE 칼럼으로 DataFrame에 추가
bostonDF['PRICE'] = boston.target
print('Boston 데이터 세트 크기 :', bostonDF.shape)
bostonDF.head()
# 2개의 행과 4개의 열을 가진 subplots를 사용. axs는 4x2개의 ax를 가짐.
fig, axs = plt.subplots(figsize=(16,8), ncols=4, nrows=2)
lm_features = ['RM', 'ZN', 'INDUS', 'NOX', 'AGE', 'PTRATIO', 'LSTAT', 'RAD']
for i, feature in enumerate(lm_features):
row = int(i/4)
col = i%4
# 시본의 regplot을 이용해 산점도와 선형 회귀 직선을 함께 표현
sns.regplot(x=feature, y='PRICE', data=bostonDF, ax=axs[row][col])
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
y_target = bostonDF['PRICE']
X_data = bostonDF.drop(['PRICE'], axis=1, inplace=False)
X_train, X_test, y_train, y_test = train_test_split(X_data, y_target, test_size = 0.3, random_state=156)
# 선형 회귀 OLS로 학습/예측/평가 수행.
lr = LinearRegression()
lr.fit(X_train, y_train)
y_preds = lr.predict(X_test)
mse = mean_squared_error(y_test, y_preds)
rmse = np.sqrt(mse)
print('MSE : {0: .3f}, RMSE : {1:.3F}'.format(mse, rmse))
print('Variance score : {0:.3f}'.format(r2_score(y_test, y_preds)))
print('절편 값:', lr.intercept_)
print('회귀 계수값:', np.round(lr.coef_, 1))
# 회귀 계수를 큰 값 순으로 정렬하기 위해 Series로 생성. 인덱스 칼럼명에 유의
coeff = pd.Series(data = np.round(lr.coef_, 1), index=X_data.columns )
coeff.sort_values(ascending=False)
from sklearn.model_selection import cross_val_score
y_target = bostonDF['PRICE']
X_data = bostonDF.drop(['PRICE'], axis=1, inplace=False)
lr = LinearRegression()
# cross_val_score()로 폴드 세트로 MSE를 구한 뒤 이를 기반으로다시 RMSE 구함.
neg_mse_scores = cross_val_score(lr, X_data, y_target, scoring="neg_mean_squared_error", cv = 5)
rmse_scores = np.sqrt(-1 * neg_mse_scores)
avg_rmse = np.mean(rmse_scores)
# cross_val_score(scoring="neg_mean_squared_error"로 반환된 값은 모두 음수
print(' 5 folds 의 개별 Negative MSE scores: ', np.round(neg_mse_scores, 2))
print(' 5 folds 의 개별 RMSE scores : ',np.round(rmse_scores, 2))
print(' 5 folds 의 평균 RMSE : {0:.3f} '.format(avg_rmse))
###Output
5 folds 의 개별 Negative MSE scores: [-12.46 -26.05 -33.07 -80.76 -33.31]
5 folds 의 개별 RMSE scores : [3.53 5.1 5.75 8.99 5.77]
5 folds 의 평균 RMSE : 5.829
###Markdown
PolynomialFeatures를 이용해 단항값 [x1, x2]를 2차 다항값으로 [1, x1, x2, x1^2, x1x2, x2^2]로 변환하는 예제
###Code
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
# 다항식으로 변환한 단항식 생성, [[0, 1], [2, 3]]의 2x2 행렬 생성
X = np.arange(4).reshape(2, 2)
print('일차 단항식 계수 피처:\n', X)
# degree = 2인 , 2차 다항식으로 변환하기 위해 PolynomialFeatures를 이용해 변환
poly = PolynomialFeatures(degree=2)
poly.fit(X)
poly_ftr = poly.transform(X)
print('변환된 2차 다항식 계수 피처:\n', poly_ftr)
def polynomial_func(X):
y = 1 + 2*X[:,0] + 3*X[:,0]**2 + 4*X[:,1]**3
return y
X = np.arange(4).reshape(2,2)
print('일차 단항식 계수 feature: \n', X)
y = polynomial_func(X)
print('삼차 다항식 결정값: \n', y)
# 3차 다항식 변환
poly_ftr = PolynomialFeatures(degree=3).fit_transform(X)
print('3차 다항식 계수 feature: \n', poly_ftr)
# Linear Regression에 3차 다항식 계수 feature와 3차 다항식 결정값으로 학습 후 회귀 계수 확인
model = LinearRegression()
model.fit(poly_ftr, y)
print('Polynomial 회귀계수\n', np.round(model.coef_, 2))
print('Polynomial 회귀 Shape :', model.coef_.shape)
###Output
3차 다항식 계수 feature:
[[ 1. 0. 1. 0. 0. 1. 0. 0. 0. 1.]
[ 1. 2. 3. 4. 6. 9. 8. 12. 18. 27.]]
Polynomial 회귀계수
[0. 0.18 0.18 0.36 0.54 0.72 0.72 1.08 1.62 2.34]
Polynomial 회귀 Shape : (10,)
###Markdown
사이킷런의 Pipeline 객체를 이용해 한 번에 다항 회귀를 구현
###Code
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
import numpy as np
def polynomial_func(X):
y = 1 + 2*X[:,0] + 3*X[:,0]**2 + 4*X[:,1]**3
return y;
# Pipeline 객체로 Streamline하게 Polynomial Feature 변환과 Linear Regression을 연결
model = Pipeline([('poly', PolynomialFeatures(degree=3)),
('linear', LinearRegression())])
X = np.arange(4).reshape(2, 2)
y = polynomial_func(X)
model = model.fit(X, y)
print('Polynomial 회귀 계수 \n', np.round(model.named_steps['linear'].coef_, 2))
###Output
Polynomial 회귀 계수
[0. 0.18 0.18 0.36 0.54 0.72 0.72 1.08 1.62 2.34]
###Markdown
피처 X와 target y가 잡음(Noise)이 포함된 다항식의 코사인(Cosine) 그래프 관계를 가지는 데이터 세트에서다항 회귀의 차수를 변화시키면서 그에 따른 회귀 예측 곡선과 예측 정확도를 비교하는 예제
###Code
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
%matplotlib inline
# 임의의 값으로 구성된 X값에 대해 코사인 변한 값을 반환.
def true_fun(X):
return np.cos(1.5 * np.pi * X)
# X는 0부터 1까지 30개의 임의의 값을 순서대로 샘플링한 데이터입니다.
np.random.seed(0)
n_samples= 30
X = np.sort(np.random.rand(n_samples))
# y 값은 코사인 기반의 true_fun()에서 약간의 노이즈 변동 값을 더한 값이다.
y = true_fun(X) + np.random.randn(n_samples) * 0.1
plt.figure(figsize=(14, 5))
degrees = [1, 4, 15]
# 다항 회귀의 차수(degree)를 1, 4, 15로 변화시키면서 비교
for i in range(len(degrees)):
ax = plt.subplot(1, len(degrees), i + 1)
plt.setp(ax, xticks=(), yticks=())
# 개별 degree 별로 Polynomial 변환
polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline([("polynomial_features", polynomial_features),
("linear_regression", linear_regression)])
pipeline.fit(X.reshape(-1, 1), y)
# 교차 검증으로 다항 회귀를 평가
scores = cross_val_score(pipeline, X.reshape(-1,1), y, scoring="neg_mean_squared_error", cv=10)
# Pipeline을 구성하는 세부 객체를 접근하는 named_steps['객체명']을 이용해 회귀계수 추출
coefficients = pipeline.named_steps['linear_regression'].coef_
print('\nDegree {0} 회귀 계수는 {1} 입니다.'.format(degrees[i], np.round(coefficients, 2)))
print('Degree {0} MSE 는 {1} 입니다. '.format(degrees[1], -1*np.mean(scores)))
# 0부터 1까지 테스트 데이터 세트를 100개로 나눠 예측을 수행한다.
# 테스트 데이터 세트에 회귀 예측을 수행하고 예측 곡선과 실제 곡선을 구려서 비교한다.
X_test = np.linspace(0, 1, 100)
# 예측값 곡선
plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label="Model")
# 실제값 곡선
plt.plot(X_test, true_fun(X_test), '-', label="True function")
plt.scatter(X, y, edgecolor='b', s=20, label="Samples")
plt.xlabel("x"); plt.ylabel("y"); plt.xlim((0, 1)); plt.ylim((-2, 2)); plt.legend(loc="best")
plt.title("Degree {}\nMSE = {:.2e}(+/- {:.2e})".format(degrees[i], -scores.mean(), scores.std()))
plt.show()
###Output
Degree 1 회귀 계수는 [-1.61] 입니다.
Degree 4 MSE 는 0.40772896250986845 입니다.
Degree 4 회귀 계수는 [ 0.47 -17.79 23.59 -7.26] 입니다.
Degree 4 MSE 는 0.043208749872317626 입니다.
Degree 15 회귀 계수는 [-2.98292000e+03 1.03899180e+05 -1.87415809e+06 2.03715961e+07
-1.44873157e+08 7.09315008e+08 -2.47065753e+09 6.24561150e+09
-1.15676562e+10 1.56895047e+10 -1.54006170e+10 1.06457389e+10
-4.91378211e+09 1.35919860e+09 -1.70381087e+08] 입니다.
Degree 4 MSE 는 182493841.77304456 입니다.
###Markdown
릿지(Ridge) 회귀
###Code
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
# alpha = 10으로 설정해 릿지 회귀 수행.
ridge = Ridge(alpha = 10)
neg_mse_scores = cross_val_score(ridge, X_data, y_target, scoring = "neg_mean_squared_error", cv = 5 )
rmse_scores = np.sqrt(-1 * neg_mse_scores)
avg_rmse = np.mean(rmse_scores)
print(' 5 folds 의 개별 Negative MSE scores: ', np.round(neg_mse_scores, 3))
print(' 5 folds 의 개별 RMSE scores: ', np.round(rmse_scores, 3))
print(' 5 folds 의 평균 RMSE: {0:.3f}'.format(avg_rmse))
###Output
5 folds 의 개별 Negative MSE scores: [-11.422 -24.294 -28.144 -74.599 -28.517]
5 folds 의 개별 RMSE scores: [3.38 4.929 5.305 8.637 5.34 ]
5 folds 의 평균 RMSE: 5.518
###Markdown
릿지의 alpha 값을 0, 0.1, 1, 10, 100으로 변화시키면서 RMSE와 회귀 계수 값의 변화 보기
###Code
# 릿지에 사용될 alpha 파라미터의 값을 정의
alphas = [0, 0.1, 1, 10, 100]
# alphas list 값을 반복하면서 alpha에 따른 평균 rmse를 구함.
for alpha in alphas :
ridge = Ridge(alpha = alpha)
# cross_val_score를 이용해 5 폴드의 평균 RMSE를 계산
neg_mse_scores = cross_val_score(ridge, X_data, y_target, scoring="neg_mean_squared_error", cv = 5)
avg_rmse = np.mean(np.sqrt(-1 * neg_mse_scores))
print('alpha {0} 일 때 5 folds 의 평균 RMSE : {1:.3f}'.format(alpha, avg_rmse))
# 각 alpha에 따른 회귀 계수 값을 시각화하기 위해 5개의 열로 된 matplotlib 축 생성
fig, axs = plt.subplots(figsize=(18, 6), nrows=1, ncols=5)
# 각 alpha에 따른 회귀 계수 값을 데이터로 저장하기 위한 DataFrame 생성
coeff_df = pd.DataFrame()
# alphas 리스트 값을 차례로 입력해 회귀 계수 값 시각화 및 데이터 저장 . pos는 axis의 위치 지정
for pos, alpha in enumerate(alphas) :
ridge = Ridge(alpha = alpha)
ridge.fit(X_data, y_target)
# alpha에 따른 피처별로 회귀 계수를 Series로 변환하고 이를 DataFrame의 칼럼으로 추가.
coeff = pd.Series(data=ridge.coef_, index=X_data.columns )
colname = 'alpha:'+str(alpha)
coeff_df[colname] = coeff
# 막대 그래프로 각 alpha 값에서의 회귀 계수를 시각화. 회귀 계수값이 높은 순으로 표현
coeff = coeff.sort_values(ascending=False)
axs[pos].set_title(colname)
axs[pos].set_xlim(-3, 6)
sns.barplot(x=coeff.values, y=coeff.index, ax=axs[pos])
# for 문 바깥에서 matplotlib의 show 호출 및 alpha에 따른 회귀 계수를 DataFrame으로 표시
plt.show()
ridge_alphas = [0, 0.1, 1, 10, 100]
sort_column = 'alpha:' + str(ridge_alphas[0])
coeff_df.sort_values(by=sort_column, ascending=False)
###Output
_____no_output_____
###Markdown
라쏘 회귀 (Lasso)
###Code
from sklearn.linear_model import Lasso, ElasticNet
# alpha값에 따른 회귀 모델의 평균 RMSE를 출력하고 회귀 계수값들을 DataFrame으로 반환
def get_linear_reg_eval(model_name, params = None, X_data_n=None, y_target_n=None, verbose=True):
coeff_df = pd.DataFrame()
if verbose : print('#######', model_name, '#######')
for param in params:
if model_name == 'Ridge' :
model = Ridge(alpha=param)
elif model_name =='Lasso':
model = Lasso(alpha=param)
elif model_name =='ElasticNet':
model = ElasticNet(alpha=param, l1_ratio=0.7)
neg_mse_scores = cross_val_score(model, X_data_n, y_target_n, scoring="neg_mean_squared_error", cv = 5)
avg_rmse = np.mean(np.sqrt(-1 * neg_mse_scores))
print('alpha {0}일 때 5 폴드 세트의 평균 RMSE : {1:.3f}'.format(param, avg_rmse))
# cross_val_score 는 evaluation metric만 반환하므로 모델을 다시 학습하여 회귀 계수 추출
model.fit(X_data, y_target)
# alpha에 따른 피처별 회귀 계수를 Series로 변환하고 이를 DataFrame의 칼럼으로 추가.
coeff = pd.Series(data=model.coef_, index=X_data.columns )
colname='alpha:'+str(param)
coeff_df[colname] = coeff
return coeff_df
# end of get_linear_regre_eval
# 라쏘에 사용될 alpha 파라미터의 값을 정의하고 get_linear_reg_eval() 함수 호출
lasso_alphas = [0.07, 0.1, 0.5 , 1, 3]
coeff_lasso_df = get_linear_reg_eval('Lasso', params=lasso_alphas, X_data_n=X_data, y_target_n = y_target)
# 반환된 coeff_lasso_df를 첫 번째 칼럼순으로 내림차순 정렬해 회귀계수 DataFrame 출력
sort_column = 'alpha:'+str(lasso_alphas[0])
coeff_lasso_df.sort_values(by=sort_column, ascending=False)
###Output
_____no_output_____
###Markdown
엘라스틱넷 회귀
###Code
# 엘라스틱넷을 사용될 alpha 파라미터의 값들을 정의하고 get_linear_reg_eval() 함수 호출
# l1_ratio는 0.7로 고정
elastic_alphas = [ 0.07, 0.1, 0.5, 1, 3]
coeff_elastic_df = get_linear_reg_eval('ElasticNet', params = elastic_alphas,
X_data_n = X_data, y_target_n= y_target)
# 반환된 coeff_elastic_df를 첫 번째 칼럼순으로 내림차순 정렬해 회귀계수 DataFrame 출력
sort_column = 'alpha:' + str(elastic_alphas[0])
coeff_elastic_df.sort_values(by=sort_column, ascending=False)
###Output
_____no_output_____
###Markdown
표준 정규 분포 변환, 최댓값/최솟값 정규화로 그 변환을 차례로 적용한 후 RMSE로 각 경우별 예측 성능 측정
###Code
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
# method는 표준 정규 분포 변환 (Standard), 최댓값/최솟값 정규화(MinMax), 로그변환(Log) 결정
# p_degree는 다항식 특성을 추가할 때 적용, p_degree는 2이상 부여하지 않음.
def get_scaled_data(method='None', p_degree=None, input_data = None):
if method == 'Standard':
scaled_data = StandardScaler().fit_transform(input_data)
elif method == 'MinMax':
scaled_data = MinMaxScaler().fit_transform(input_data)
elif method == 'Log':
scaled_data = np.log1p(input_data)
else :
scaled_data = input_data
if p_degree != None :
scaled_data = PolynomialFeatures(degree=p_degree,
include_bias = False).fit_transform(scaled_data)
return scaled_data
# Ridge의 alpha 값을 다르게 적용하고 다양한 데이터 변환 방법에 따른 RMSE 추출.
alphas = [0.1, 1, 10, 100]
# 5개 방식으로 변환, 먼저 원본 그대로, 표준 정규 분포 + 다항식 특성
# 최대/최소 정규화, 최대/최소 정규화 + 다항식 특성, 로그변환
scale_methods=[(None, None), ('Standard', None), ('Standard',2),
('MinMax', None), ('MinMax', 2), ('Log', None)]
for scale_method in scale_methods:
X_data_scaled = get_scaled_data(method=scale_method[0], p_degree =scale_method[1],
input_data=X_data)
print('\n## 변환 유형:{0}, Polynomial Degree:{1}'.format(scale_method[0], scale_method[1]))
get_linear_reg_eval('Ridge', params=alphas, X_data_n=X_data_scaled,
y_target_n = y_target, verbose=False)
###Output
## 변환 유형:None, Polynomial Degree:None
alpha 0.1일 때 5 폴드 세트의 평균 RMSE : 5.788
alpha 1일 때 5 폴드 세트의 평균 RMSE : 5.653
alpha 10일 때 5 폴드 세트의 평균 RMSE : 5.518
alpha 100일 때 5 폴드 세트의 평균 RMSE : 5.330
## 변환 유형:Standard, Polynomial Degree:None
alpha 0.1일 때 5 폴드 세트의 평균 RMSE : 5.826
alpha 1일 때 5 폴드 세트의 평균 RMSE : 5.803
alpha 10일 때 5 폴드 세트의 평균 RMSE : 5.637
alpha 100일 때 5 폴드 세트의 평균 RMSE : 5.421
## 변환 유형:Standard, Polynomial Degree:2
alpha 0.1일 때 5 폴드 세트의 평균 RMSE : 8.827
alpha 1일 때 5 폴드 세트의 평균 RMSE : 6.871
alpha 10일 때 5 폴드 세트의 평균 RMSE : 5.485
alpha 100일 때 5 폴드 세트의 평균 RMSE : 4.634
## 변환 유형:MinMax, Polynomial Degree:None
alpha 0.1일 때 5 폴드 세트의 평균 RMSE : 5.764
alpha 1일 때 5 폴드 세트의 평균 RMSE : 5.465
alpha 10일 때 5 폴드 세트의 평균 RMSE : 5.754
alpha 100일 때 5 폴드 세트의 평균 RMSE : 7.635
## 변환 유형:MinMax, Polynomial Degree:2
alpha 0.1일 때 5 폴드 세트의 평균 RMSE : 5.298
alpha 1일 때 5 폴드 세트의 평균 RMSE : 4.323
alpha 10일 때 5 폴드 세트의 평균 RMSE : 5.185
alpha 100일 때 5 폴드 세트의 평균 RMSE : 6.538
## 변환 유형:Log, Polynomial Degree:None
alpha 0.1일 때 5 폴드 세트의 평균 RMSE : 4.770
alpha 1일 때 5 폴드 세트의 평균 RMSE : 4.676
alpha 10일 때 5 폴드 세트의 평균 RMSE : 4.836
alpha 100일 때 5 폴드 세트의 평균 RMSE : 6.241
###Markdown
Linear RegressionThis notebook follows the tutorial [pythonprogramming.net](https://pythonprogramming.net/machine-learning-tutorial-python-introduction/) I just added some comments.
###Code
import pandas as pd
import quandl, math,datetime
import numpy as np
from sklearn import preprocessing, model_selection, svm
from sklearn.linear_model import LinearRegression
import matplotlib.pylab as plt
import matplotlib as mpl
from matplotlib import style
import pickle
style.use("ggplot")
import time
mpl.rcParams['mathtext.fontset'] = 'cm'
%config InlineBackend.figure_format = 'retina'
df=quandl.get("WIKI/GOOGL")
df.head()
df=df[["Adj. Open","Adj. Close","Adj. High","Adj. Low","Adj. Volume"]]
df["HL_PCT"] = (df["Adj. High"]-df["Adj. Low"])/df["Adj. Low"] * 100.0
df["PCT_change"] = (df["Adj. Close"]-df["Adj. Open"])/df["Adj. Open"] * 100.0
df = df[["Adj. Close","HL_PCT","PCT_change","Adj. Volume"]]
df.head()
forecast_col="Adj. Close"
df.fillna(-99999, inplace=True)
forecast_out=int(math.ceil(0.01*len(df)))
df["label"]=df[forecast_col].shift(-forecast_out)
df.head()
df.tail()
df.dropna(inplace=True)
X = np.array(df.drop(["label"],1))
X=preprocessing.scale(X)
y=np.array(df["label"])
print(np.shape(X),np.shape(y))
X_train,X_test,y_train,y_test=model_selection.train_test_split(X,y,test_size=0.2)
print(np.shape(X_train),np.shape(X_test))
print(np.shape(y_train),np.shape(y_test))
print(np.shape(X_train)[0]+np.shape(X_test)[0])
print(np.shape(y_train)[0]+np.shape(y_test)[0])
clf=LinearRegression()
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)
print(accuracy)
clf=svm.SVR(gamma='auto')
#clf.fit(X_train,y_train)
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)
print(accuracy)
clf=svm.SVR(kernel='poly',gamma='auto')
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)
print(accuracy)
for i in range(1,11):
begin=time.time()
clf=LinearRegression(n_jobs=i)
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)
dt=time.time()-begin
print(i,dt*1000000)
plt.plot(i,dt*1000000,'.')
plt.show()
print(begin)
df=quandl.get("WIKI/GOOGL")
df=df[["Adj. Open","Adj. Close","Adj. High","Adj. Low","Adj. Volume"]]
df["HL_PCT"] = (df["Adj. High"]-df["Adj. Close"])/df["Adj. Close"] * 100.0
df["PCT_change"] = (df["Adj. Close"]-df["Adj. Open"])/df["Adj. Open"] * 100.0
df = df[["Adj. Close","HL_PCT","PCT_change","Adj. Volume"]]
df.fillna(-99999, inplace=True)
df["label"]=df[forecast_col].shift(-forecast_out)
X = np.array(df.drop(["label"],1))
X=preprocessing.scale(X)
X= X[:-forecast_out]
X_lately=X[-forecast_out:]
df.dropna(inplace=True)
y=np.array(df["label"])
X_train,X_test,y_train,y_test=model_selection.train_test_split(X,y,test_size=0.2)
clf=LinearRegression()
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)
#print(accuracy)
forecast_set=clf.predict(X_lately)
df["Forecast"]=np.nan
last_date=df.iloc[-1].name
last_unix=last_date.timestamp()
one_day=86400
next_unix=last_unix+one_day
plt.figure(figsize=(12,5))
for i in forecast_set:
next_date=datetime.datetime.fromtimestamp(next_unix)
next_unix+=one_day
df.loc[next_date]= [np.nan for _ in range(len(df.columns)-1)]+ [i]
df["Adj. Close"].plot()
df["Forecast"].plot()
plt.legend(loc=4)
plt.xlabel("Date")
plt.ylabel("Price")
plt.show()
X_train,X_test,y_train,y_test=model_selection.train_test_split(X,y,test_size=0.2)
clf=LinearRegression(n_jobs=-1)
clf.fit(X_train,y_train)
with open ('linearregession.pickle','wb') as f:
pickle.dump(clf,f)
pickle_in = open('linearregession.pickle','rb')
clf2=pickle.load(pickle_in)
accuracy=clf2.score(X_test,y_test)
forecast_set=clf2.predict(X_lately)
df["Forecast"]=np.nan
last_date=df.iloc[-1].name
last_unix=last_date.timestamp()
one_day=86400
next_unix=last_unix+one_day
plt.figure(figsize=(12,5))
for i in forecast_set:
next_date=datetime.datetime.fromtimestamp(next_unix)
next_unix+=one_day
df.loc[next_date]= [np.nan for _ in range(len(df.columns)-1)]+ [i]
df["Adj. Close"].plot()
df["Forecast"].plot()
plt.legend(loc=4)
plt.xlabel("Date")
plt.ylabel("Price")
plt.show()
###Output
_____no_output_____
|
examples/ch07/snippets_ipynb/07_02selfcheck.ipynb
|
###Markdown
 7.2 Self Check **2. _(IPython Session)_** Create a one-dimensional `array` from a list comprehension that produces the even integers from 2 through 20. **Answer:**
###Code
import numpy as np
np.array([x for x in range(2, 21, 2)])
###Output
_____no_output_____
###Markdown
**3. _(IPython Session)_** Create a 2-by-5 `array` containing the even integers from 2 through 10 in the first row and the odd integers from 1 through 9 in the second row. **Answer:**
###Code
np.array([[2, 4, 6, 8, 10], [1, 3, 5, 7, 9]])
##########################################################################
# (C) Copyright 2019 by Deitel & Associates, Inc. and #
# Pearson Education, Inc. All Rights Reserved. #
# #
# DISCLAIMER: The authors and publisher of this book have used their #
# best efforts in preparing the book. These efforts include the #
# development, research, and testing of the theories and programs #
# to determine their effectiveness. The authors and publisher make #
# no warranty of any kind, expressed or implied, with regard to these #
# programs or to the documentation contained in these books. The authors #
# and publisher shall not be liable in any event for incidental or #
# consequential damages in connection with, or arising out of, the #
# furnishing, performance, or use of these programs. #
##########################################################################
###Output
_____no_output_____
|
notebooks/B1-exploration.ipynb
|
###Markdown
Dataset Cleaning and ExplorationTo prepare for our ultimate task of using molecular embeddings to predict abundances and detectability, we need to put our dataset into working order. This is typically referred to as "cleaning", where we're making sure that the data will be valid (free of missing data, for example) and duplicate entries are removed. We will also need to inspect the data to make sure that entries we expect to be there are present, as well as observe some general trends where we can. Since we're looking at such a large dataset, we need to be able to inspect it from a microscopic and a macroscopic level. Combining both perspectives gives you an overview of what the dataset looks like, and in turn may give you insight into why/how a machine learning model behaves the way it does.The first part of this notebook will be combining the three datasets: QM9, ZINC15, and KIDA. The latter is actually obtained by scraping their website, i.e. extracting the relevant information from tables in websites.
###Code
from pathlib import Path
from tempfile import NamedTemporaryFile
import fileinput
import os
import numpy as np
import pandas as pd
from mol2vec import features
from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import Descriptors
from gensim.models import word2vec
###Output
Traceback (most recent call last):
File "/home/kelvin/anaconda3/envs/rdkit/lib/python3.6/site-packages/rdkit/Chem/PandasTools.py", line 130, in <module>
if 'display.width' in pd.core.config._registered_options:
AttributeError: module 'pandas.core' has no attribute 'config'
###Markdown
Loading and combining SMILES from the two datasets
###Code
qm9 = pd.read_csv("../data/gdb9_prop_smiles.csv.tar.gz")
smi_list = qm9["smiles"].dropna().to_list()
for smi_file in Path("../data/").rglob("*/*.smi"):
temp = smi_file.read_text().split("\n")[1:]
for line in temp:
if len(line) != 0:
smi_list.append(line.split(" ")[0])
print(f"Number of molecules: {len(smi_list)}")
###Output
Number of molecules: 1543543
###Markdown
Extracting SMILES from KIDASince our database contains mostly terrestrial/stable molecules, we need to augment this set with astronomically relevant molecules. KIDA is one of the biggest reaction networks used in astrochemistry, and is therefore a nice collection molecules that may or may not be found in space (at least of interest).To perform this, we'll scrape the KIDA website below.
###Code
import requests
import datetime
from bs4 import BeautifulSoup
url = requests.get("http://kida.astrophy.u-bordeaux.fr/species.html")
print(f"""Last retrieved: {url.headers["Date"]}""")
RERUN = False
if RERUN:
date = url.headers["Date"]
# cut the date, replace spaces with underscores for naming
date = date[5:16].replace(" ", "_")
# save the webpage for reference. If KIDA decides to go bottom up we
# will always have a copy of this data
with open(f"../data/kida-site_{date}.html", "w+") as write_file:
write_file.write(str(url.content))
soup = BeautifulSoup(url.content)
# the first and only table on the page corresponds to the molecules
molecule_table = soup.find_all("table", "table")[0]
if RERUN:
map_dict = dict()
for row in soup.find_all("tr"):
# some InChI are missing, this sets a default value
inchi = None
for index, column in enumerate(row.find_all("td")):
# loop over columns in each row, and grab the second and
# third columns which are formulae and InChI
if index == 1:
# strip twice because the first header is parsed funnily
name = column.text.strip().strip()
if index == 2:
inchi = column.text.strip()
map_dict[name] = inchi
# Just for reference, dump the KIDA mapping as a dataframe
kida_df = pd.DataFrame.from_dict(map_dict, orient="index").reset_index()
kida_df.columns = ["Formula", "InChI"]
kida_df.to_csv(f"../data/kida-molecules_{date}.csv", index=False)
else:
kida_df = pd.read_csv("../data/kida-molecules_05_Jul_2020.csv")
def inchi_to_smiles(inchi: str):
inchi = str(inchi)
if len(inchi) != 0:
mol = Chem.MolFromInchi(inchi, sanitize=False, removeHs=False)
if mol:
smiles = Chem.MolToSmiles(mol, canonical=True)
return smiles
else:
return ""
###Output
_____no_output_____
###Markdown
Now we convert all the InChI codes from KIDA into SMILES through RDKit. Initially I was most worried about this because KIDA has strange molecules, and as we see below RDKit has plenty to complain about. The attitude we're taking here is to ignore the ones that don't play by the rules, and we'll worry about them some other time.
###Code
# This applies our filtering function we defined above
kida_df["SMILES"] = kida_df["InChI"].apply(inchi_to_smiles)
# Extract only those with SMILES strings
kida_smiles = kida_df.loc[(kida_df["SMILES"].str.len() != 0.)].dropna()
# append all the KIDA entries to our full list
smi_list.extend(kida_smiles["SMILES"].to_list())
print(f"Number of molecules with KIDA: {len(smi_list)}")
###Output
Number of molecules with KIDA: 1544121
###Markdown
Adding extra SMILES from Jacqueline's analysisTurns out we're missing some molecules (no surprise) that are known in TMC-1, but not inlcuded in our list. The code below will take data directly from the Google Sheets Jacqueline's set up.
###Code
!pip install xlrd
jac_df = pd.read_excel("../data/ChemicalCollection3.xlsx")
jac_df2 = pd.read_excel("../data/ChemicalCollection4.xlsx")
combined_jac = pd.concat([jac_df, jac_df2])
# Missing molecules
missing = combined_jac.loc[~combined_jac["Notation"].isin(smi_list)]
jac_missing = missing["Notation"].to_list()
combined_jac.to_csv("../data/jacqueline_tmc1_combined.csv", index=False)
###Output
_____no_output_____
###Markdown
Microscopic inspectionIn this section, we're going to put certain aspects of the dataset under the microscope: for example, we want to check that certain molecules are contained in the set. Here, we'll be using our chemical intuition; the idea is to pick out a few molecules, and check if: (a) they are contained in our list, and (b) what their most similar molecules are.
###Code
mol_df = pd.DataFrame(data=smi_list)
mol_df.columns = ["Raw"]
def canonicize_smi(smi: str):
"""
Function to convert any SMILES string into its canonical counterpart.
This ensures that all comparisons made subsequently are made with the
same SMILES representation, if it exists.
"""
return Chem.MolToSmiles(Chem.MolFromSmiles(smi, sanitize=False), canonical=True)
checklist = [
"CC=O", # acetaldehyde
"c1ccccc1", # benzene
"c1ccc(cc1)C#N", # benzonitrile
"N#CC=C", # vinyl cyanide
"CC#N", # methyl cyanide
"C#CC#CC#N",
"C#N",
"C#CC#CC#CC#N",
"C#CC#CC#CC#CC#N",
]
checklist = [canonicize_smi(smi) for smi in checklist]
mol_df.loc[:, "Check"] = mol_df["Raw"].isin(checklist)
mol_df.loc[mol_df["Check"] == True]
molecular_weights = list()
for smi in smi_list:
mol = Chem.MolFromSmiles(smi, sanitize=False)
mol.UpdatePropertyCache(strict=False)
molecular_weights.append(Descriptors.ExactMolWt(mol))
###Output
_____no_output_____
###Markdown
Calculate descriptors
###Code
mol_df["MW"] = molecular_weights
###Output
_____no_output_____
###Markdown
Drop duplicate entries, and save the data to diskOur dataset actually contains a lot of duplicate entries. This step removes them, which would otherwise just waste our computation time.
###Code
mol_df.drop_duplicates("Raw", inplace=True)
mol_df.to_pickle("../data/combined_smiles.pkl.bz2")
###Output
_____no_output_____
###Markdown
Tasks for data exploration Distribution of molecular weightPlot a histogram of the molecular weight in our dataset. Counting functional group examples
###Code
# For example, number of carbonyls
mol_df["Raw"].str.contains("C=O").sum()
###Output
_____no_output_____
###Markdown
Dumping the SMILES data for mol2vec useThis only takes the SMILES column, and dumps it into a list of SMILES formatted and ready for `mol2vec` usage. Every SMILES is separated by a new line, and we don't include a header.
###Code
mol_df["Raw"].to_csv("./collected_smiles.smi", sep="\n", index=False, header=None)
###Output
_____no_output_____
|
exgapi/kucoin/kucoin-exchange-api-private-authentication.ipynb
|
###Markdown
Video tutorial[How to write a Crypto Exchange API using Python (Authentication)](https://youtu.be/KSD7s6I2J10)
###Code
import requests
import json
import time
import hashlib
import hmac
import base64
###Output
_____no_output_____
###Markdown
Global Variables
###Code
kchost = "https://api.kucoin.com"
###Output
_____no_output_____
###Markdown
keysecretpassphrase
###Code
with open("../../../keys/kc-key") as f:
api_key, api_secret, api_passphrase = f.read().split()
###Output
_____no_output_____
###Markdown
Util function
###Code
def authentication(endpoint, method):
now = int(time.time() * 1000)
str_to_sign = str(now) + method.upper() + endpoint
signature = base64.b64encode(
hmac.new(api_secret.encode('utf-8'), str_to_sign.encode('utf-8'), hashlib.sha256).digest())
passphrase = base64.b64encode(hmac.new(api_secret.encode('utf-8'), api_passphrase.encode('utf-8'), hashlib.sha256).digest())
headers = {
"KC-API-SIGN": signature,
"KC-API-TIMESTAMP": str(now),
"KC-API-KEY": api_key,
"KC-API-PASSPHRASE": passphrase,
"KC-API-KEY-VERSION": "2"
}
return headers
def paramURL(params):
items = params.items()
t = ["%s=%s" % (k, v) for k, v in items]
return "&".join(t)
def getPrivate(endpoint, params):
parmurl = paramURL(params)
if len(parmurl) > 0:
parmurl = "?" + parmurl
url = "%s%s%s" % (kchost, endpoint, parmurl)
headers = authentication(endpoint, "get")
return requests.get(url, headers=headers, timeout=5).json()
###Output
_____no_output_____
###Markdown
get the balances
###Code
endpoint = "/api/v1/accounts"
getPrivate(endpoint, {})
###Output
_____no_output_____
|
neural_style_tutorial.ipynb
|
###Markdown
Neural Transfer Using PyTorch=============================**Author**: `Alexis Jacq `_ **Edited by**: `Winston Herring `_Introduction------------This tutorial explains how to implement the `Neural-Style algorithm `__developed by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.Neural-Style, or Neural-Transfer, allows you to take an image andreproduce it with a new artistic style. The algorithm takes three images,an input image, a content-image, and a style-image, and changes the input to resemble the content of the content-image and the artistic style of the style-image. .. figure:: /_static/img/neural-style/neuralstyle.png :alt: content1 Underlying Principle--------------------The principle is simple: we define two distances, one for the content($D_C$) and one for the style ($D_S$). $D_C$ measures how different the contentis between two images while $D_S$ measures how different the style isbetween two images. Then, we take a third image, the input, andtransform it to minimize both its content-distance with thecontent-image and its style-distance with the style-image. Now we canimport the necessary packages and begin the neural transfer.Importing Packages and Selecting a Device-----------------------------------------Below is a list of the packages needed to implement the neural transfer.- ``torch``, ``torch.nn``, ``numpy`` (indispensables packages for neural networks with PyTorch)- ``torch.optim`` (efficient gradient descents)- ``PIL``, ``PIL.Image``, ``matplotlib.pyplot`` (load and display images)- ``torchvision.transforms`` (transform PIL images into tensors)- ``torchvision.models`` (train or load pre-trained models)- ``copy`` (to deep copy the models; system package)
###Code
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models
import copy
###Output
_____no_output_____
###Markdown
Next, we need to choose which device to run the network on and import thecontent and style images. Running the neural transfer algorithm on largeimages takes longer and will go much faster when running on a GPU. We canuse ``torch.cuda.is_available()`` to detect if there is a GPU available.Next, we set the ``torch.device`` for use throughout the tutorial. Also the ``.to(device)``method is used to move tensors or modules to a desired device.
###Code
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
###Output
_____no_output_____
###Markdown
Loading the Images------------------Now we will import the style and content images. The original PIL images have values between 0 and 255, but whentransformed into torch tensors, their values are converted to be between0 and 1. The images also need to be resized to have the same dimensions.An important detail to note is that neural networks from thetorch library are trained with tensor values ranging from 0 to 1. If youtry to feed the networks with 0 to 255 tensor images, then the activatedfeature maps will be unable sense the intended content and style.However, pre-trained networks from the Caffe library are trained with 0to 255 tensor images. .. Note:: Here are links to download the images required to run the tutorial: `picasso.jpg `__ and `dancing.jpg `__. Download these two images and add them to a directory with name ``images`` in your current working directory.
###Code
# desired size of the output image
imsize = 512 if torch.cuda.is_available() else 128 # use small size if no gpu
loader = transforms.Compose([
transforms.Resize(imsize), # scale imported image
transforms.CenterCrop(imsize),
transforms.ToTensor()]) # transform it into a torch tensor
def image_loader(image_name):
image = Image.open(image_name)
# fake batch dimension required to fit network's input dimensions
image = loader(image).unsqueeze(0)
#image = image.to(device, torch.float)
return image.to(device, torch.float)
style_img = image_loader("botw.jpg")
content_img = image_loader("oot.png")
assert style_img.size() == content_img.size(), \
"we need to import style and content images of the same size"
###Output
_____no_output_____
###Markdown
Now, let's create a function that displays an image by reconverting a copy of it to PIL format and displaying the copy using ``plt.imshow``. We will try displaying the content and style images to ensure they were imported correctly.
###Code
unloader = transforms.ToPILImage() # reconvert into PIL image
plt.ion()
def imshow(tensor, title=None):
image = tensor.cpu().clone() # we clone the tensor to not do changes on it
image = image.squeeze(0) # remove the fake batch dimension
image = unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
plt.figure()
imshow(style_img, title='Style Image')
plt.figure()
imshow(content_img, title='Content Image')
###Output
_____no_output_____
###Markdown
Loss Functions--------------Content Loss~~~~~~~~~~~~The content loss is a function that represents a weighted version of thecontent distance for an individual layer. The function takes the featuremaps $F_{XL}$ of a layer $L$ in a network processing input $X$ and returns theweighted content distance $w_{CL}.D_C^L(X,C)$ between the image $X$ and thecontent image $C$. The feature maps of the content image($F_{CL}$) must beknown by the function in order to calculate the content distance. Weimplement this function as a torch module with a constructor that takes$F_{CL}$ as an input. The distance $\|F_{XL} - F_{CL}\|^2$ is the mean square errorbetween the two sets of feature maps, and can be computed using ``nn.MSELoss``.We will add this content loss module directly after the convolutionlayer(s) that are being used to compute the content distance. This wayeach time the network is fed an input image the content losses will becomputed at the desired layers and because of auto grad, all thegradients will be computed. Now, in order to make the content loss layertransparent we must define a ``forward`` method that computes the contentloss and then returns the layer’s input. The computed loss is saved as aparameter of the module.
###Code
class ContentLoss(nn.Module):
def __init__(self, target,):
super(ContentLoss, self).__init__()
# we 'detach' the target content from the tree used
# to dynamically compute the gradient: this is a stated value,
# not a variable. Otherwise the forward method of the criterion
# will throw an error.
self.target = target.detach()
def forward(self, input):
self.loss = F.mse_loss(input, self.target)
return input
###Output
_____no_output_____
###Markdown
.. Note:: **Important detail**: although this module is named ``ContentLoss``, it is not a true PyTorch Loss function. If you want to define your content loss as a PyTorch Loss function, you have to create a PyTorch autograd function to recompute/implement the gradient manually in the ``backward`` method. Style Loss~~~~~~~~~~The style loss module is implemented similarly to the content lossmodule. It will act as a transparent layer in anetwork that computes the style loss of that layer. In order tocalculate the style loss, we need to compute the gram matrix $G_{XL}$. A grammatrix is the result of multiplying a given matrix by its transposedmatrix. In this application the given matrix is a reshaped version ofthe feature maps $F_{XL}$ of a layer $L$. $F_{XL}$ is reshaped to form $\hat{F}_{XL}$, a $K$\ x\ $N$matrix, where $K$ is the number of feature maps at layer $L$ and $N$ is thelength of any vectorized feature map $F_{XL}^k$. For example, the first lineof $\hat{F}_{XL}$ corresponds to the first vectorized feature map $F_{XL}^1$.Finally, the gram matrix must be normalized by dividing each element bythe total number of elements in the matrix. This normalization is tocounteract the fact that $\hat{F}_{XL}$ matrices with a large $N$ dimension yieldlarger values in the Gram matrix. These larger values will cause thefirst layers (before pooling layers) to have a larger impact during thegradient descent. Style features tend to be in the deeper layers of thenetwork so this normalization step is crucial.
###Code
def gram_matrix(input):
a, b, c, d = input.size() # a=batch size(=1)
# b=number of feature maps
# (c,d)=dimensions of a f. map (N=c*d)
features = input.view(a * b, c * d) # resise F_XL into \hat F_XL
G = torch.mm(features, features.t()) # compute the gram product
# we 'normalize' the values of the gram matrix
# by dividing by the number of element in each feature maps.
return G.div(a * b * c * d)
###Output
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###Markdown
Now the style loss module looks almost exactly like the content lossmodule. The style distance is also computed using the mean squareerror between $G_{XL}$ and $G_{SL}$.
###Code
class StyleLoss(nn.Module):
def __init__(self, target_feature):
super(StyleLoss, self).__init__()
self.target = gram_matrix(target_feature).detach()
def forward(self, input):
G = gram_matrix(input)
self.loss = F.mse_loss(G, self.target)
return input
###Output
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###Markdown
Importing the Model-------------------Now we need to import a pre-trained neural network. We will use a 19layer VGG network like the one used in the paper.PyTorch’s implementation of VGG is a module divided into two child``Sequential`` modules: ``features`` (containing convolution and pooling layers),and ``classifier`` (containing fully connected layers). We will use the``features`` module because we need the output of the individualconvolution layers to measure content and style loss. Some layers havedifferent behavior during training than evaluation, so we must set thenetwork to evaluation mode using ``.eval()``.
###Code
cnn = models.vgg19(pretrained=True).features.to(device).eval()
###Output
Downloading: "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth" to /home/toby/.torch/models/vgg19-dcbb9e9d.pth
100%|██████████| 574673361/574673361 [00:17<00:00, 32684680.10it/s]
###Markdown
Additionally, VGG networks are trained on images with each channelnormalized by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].We will use them to normalize the image before sending it into the network.
###Code
cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
# create a module to normalize input image so we can easily put it in a
# nn.Sequential
class Normalization(nn.Module):
def __init__(self, mean, std):
super(Normalization, self).__init__()
# .view the mean and std to make them [C x 1 x 1] so that they can
# directly work with image Tensor of shape [B x C x H x W].
# B is batch size. C is number of channels. H is height and W is width.
self.mean = torch.tensor(mean).view(-1, 1, 1)
self.std = torch.tensor(std).view(-1, 1, 1)
def forward(self, img):
# normalize img
return (img - self.mean) / self.std
###Output
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###Markdown
A ``Sequential`` module contains an ordered list of child modules. Forinstance, ``vgg19.features`` contains a sequence (Conv2d, ReLU, MaxPool2d,Conv2d, ReLU…) aligned in the right order of depth. We need to add ourcontent loss and style loss layers immediately after the convolutionlayer they are detecting. To do this we must create a new ``Sequential``module that has content loss and style loss modules correctly inserted.
###Code
# desired depth layers to compute style/content losses :
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
style_img, content_img,
content_layers=content_layers_default,
style_layers=style_layers_default):
cnn = copy.deepcopy(cnn)
# normalization module
normalization = Normalization(normalization_mean, normalization_std).to(device)
# just in order to have an iterable access to or list of content/syle
# losses
content_losses = []
style_losses = []
# assuming that cnn is a nn.Sequential, so we make a new nn.Sequential
# to put in modules that are supposed to be activated sequentially
model = nn.Sequential(normalization)
i = 0 # increment every time we see a conv
for layer in cnn.children():
if isinstance(layer, nn.Conv2d):
i += 1
name = 'conv_{}'.format(i)
elif isinstance(layer, nn.ReLU):
name = 'relu_{}'.format(i)
# The in-place version doesn't play very nicely with the ContentLoss
# and StyleLoss we insert below. So we replace with out-of-place
# ones here.
layer = nn.ReLU(inplace=False)
elif isinstance(layer, nn.MaxPool2d):
name = 'pool_{}'.format(i)
elif isinstance(layer, nn.BatchNorm2d):
name = 'bn_{}'.format(i)
else:
raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
model.add_module(name, layer)
if name in content_layers:
# add content loss:
target = model(content_img).detach()
content_loss = ContentLoss(target)
model.add_module("content_loss_{}".format(i), content_loss)
content_losses.append(content_loss)
if name in style_layers:
# add style loss:
target_feature = model(style_img).detach()
style_loss = StyleLoss(target_feature)
model.add_module("style_loss_{}".format(i), style_loss)
style_losses.append(style_loss)
# now we trim off the layers after the last content and style losses
for i in range(len(model) - 1, -1, -1):
if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
break
model = model[:(i + 1)]
return model, style_losses, content_losses
###Output
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###Markdown
Next, we select the input image. You can use a copy of the content imageor white noise.
###Code
input_img = content_img.clone()
# if you want to use white noise instead uncomment the below line:
# input_img = torch.randn(content_img.data.size(), device=device)
# add the original input image to the figure:
plt.figure()
imshow(input_img, title='Input Image')
###Output
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###Markdown
Gradient Descent----------------As Leon Gatys, the author of the algorithm, suggested `here `__, we will useL-BFGS algorithm to run our gradient descent. Unlike training a network,we want to train the input image in order to minimise the content/stylelosses. We will create a PyTorch L-BFGS optimizer ``optim.LBFGS`` and passour image to it as the tensor to optimize.
###Code
def get_input_optimizer(input_img):
# this line to show that input is a parameter that requires a gradient
optimizer = optim.LBFGS([input_img.requires_grad_()])
return optimizer
###Output
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###Markdown
Finally, we must define a function that performs the neural transfer. Foreach iteration of the networks, it is fed an updated input and computesnew losses. We will run the ``backward`` methods of each loss module todynamicaly compute their gradients. The optimizer requires a “closure”function, which reevaluates the modul and returns the loss.We still have one final constraint to address. The network may try tooptimize the input with values that exceed the 0 to 1 tensor range forthe image. We can address this by correcting the input values to bebetween 0 to 1 each time the network is run.
###Code
def run_style_transfer(cnn, normalization_mean, normalization_std,
content_img, style_img, input_img, num_steps=300,
style_weight=1000000, content_weight=1):
"""Run the style transfer."""
print('Building the style transfer model..')
model, style_losses, content_losses = get_style_model_and_losses(cnn,
normalization_mean, normalization_std, style_img, content_img)
optimizer = get_input_optimizer(input_img)
print('Optimizing..')
run = [0]
while run[0] <= num_steps:
def closure():
# correct the values of updated input image
input_img.data.clamp_(0, 1)
optimizer.zero_grad()
model(input_img)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.loss
for cl in content_losses:
content_score += cl.loss
style_score *= style_weight
content_score *= content_weight
loss = style_score + content_score
loss.backward()
run[0] += 1
if run[0] % 50 == 0:
print("run {}:".format(run))
print('Style Loss : {:4f} Content Loss: {:4f}'.format(
style_score.item(), content_score.item()))
print()
return style_score + content_score
optimizer.step(closure)
# a last correction...
input_img.data.clamp_(0, 1)
return input_img
###Output
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###Markdown
Finally, we can run the algorithm.
###Code
output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std,
content_img, style_img, input_img)
plt.figure()
imshow(output, title='Output Image')
# sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()
from skimage.io import imsave
with torch.no_grad():
imsave('style-transfer.jpg', output.cpu().detach().squeeze().permute(1,2,0).numpy())
###Output
/home/toby/anaconda3/lib/python3.6/site-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float32 to uint8
.format(dtypeobj_in, dtypeobj_out))
###Markdown
Neural Transfer with PyTorch============================**Author**: `Alexis Jacq `_Introduction------------Welcome! This tutorial explains how to impletment the`Neural-Style `__ algorithm developedby Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.Neural what?~~~~~~~~~~~~The Neural-Style, or Neural-Transfer, is an algorithm that takes asinput a content-image (e.g. a tortle), a style-image (e.g. artisticwaves) and return the content of the content-image as if it was'painted' using the artistic style of the style-image:.. figure:: /_static/img/neural-style/neuralstyle.png :alt: content1How does it work?~~~~~~~~~~~~~~~~~The principle is simple: we define two distances, one for the content($D_C$) and one for the style ($D_S$). $D_C$ measureshow different the content is between two images, while $D_S$measures how different the style is between two images. Then, we take athird image, the input, (e.g. a with noise), and we transform it inorder to both minimize its content-distance with the content-image andits style-distance with the style-image.OK. How does it work?^^^^^^^^^^^^^^^^^^^^^Well, going further requires some mathematics. Let $C_{nn}$ be apre-trained deep convolutional neural network and $X$ be anyimage. $C_{nn}(X)$ is the network fed by $X$ (containingfeature maps at all layers). Let $F_{XL} \in C_{nn}(X)$ be thefeature maps at depth layer $L$, all vectorized and concatenatedin one single vector. We simply define the content of $X$ at layer$L$ by $F_{XL}$. Then, if $Y$ is another image of samethe size than $X$, we define the distance of content at layer$L$ as follow:\begin{align}D_C^L(X,Y) = \|F_{XL} - F_{YL}\|^2 = \sum_i (F_{XL}(i) - F_{YL}(i))^2\end{align}Where $F_{XL}(i)$ is the $i^{th}$ element of $F_{XL}$.The style is a bit less trivial to define. Let $F_{XL}^k$ with$k \leq K$ be the vectorized $k^{th}$ of the $K$feature maps at layer $L$. The style $G_{XL}$ of $X$at layer $L$ is defined by the Gram produce of all vectorizedfeature maps $F_{XL}^k$ with $k \leq K$. In other words,$G_{XL}$ is a $K$\ x\ $K$ matrix and the element$G_{XL}(k,l)$ at the $k^{th}$ line and $l^{th}$ columnof $G_{XL}$ is the vectorial produce between $F_{XL}^k$ and$F_{XL}^l$ :\begin{align}G_{XL}(k,l) = \langle F_{XL}^k, F_{XL}^l\rangle = \sum_i F_{XL}^k(i) . F_{XL}^l(i)\end{align}Where $F_{XL}^k(i)$ is the $i^{th}$ element of$F_{XL}^k$. We can see $G_{XL}(k,l)$ as a measure of thecorrelation between feature maps $k$ and $l$. In that way,$G_{XL}$ represents the correlation matrix of feature maps of$X$ at layer $L$. Note that the size of $G_{XL}$ onlydepends on the number of feature maps, not on the size of $X$.Then, if $Y$ is another image *of any size*, we define thedistance of style at layer $L$ as follow:\begin{align}D_S^L(X,Y) = \|G_{XL} - G_{YL}\|^2 = \sum_{k,l} (G_{XL}(k,l) - G_{YL}(k,l))^2\end{align}In order to minimize in one shot $D_C(X,C)$ between a variableimage $X$ and target content-image $C$ and $D_S(X,S)$between $X$ and target style-image $S$, both computed atseveral layers , we compute and sum the gradients (derivative withrespect to $X$) of each distance at each wanted layer:\begin{align}\nabla_{ extit{total}}(X,S,C) = \sum_{L_C} w_{CL_C}.\nabla_{ extit{content}}^{L_C}(X,C) + \sum_{L_S} w_{SL_S}.\nabla_{ extit{style}}^{L_S}(X,S)\end{align}Where $L_C$ and $L_S$ are respectivement the wanted layers(arbitrary stated) of content and style and $w_{CL_C}$ and$w_{SL_S}$ the weights (arbitrary stated) associated with thestyle or the content at each wanted layer. Then, we run a gradientdescent over $X$:\begin{align}X \leftarrow X - \alpha \nabla_{ extit{total}}(X,S,C)\end{align}Ok. That's enough with maths. If you want to go deeper (how to computethe gradients) **we encourage you to read the original paper** by LeonA. Gatys and AL, where everything is much better and much clearerexplained.For our implementation in PyTorch, we already have everythingwe need: indeed, with PyTorch, all the gradients are automatically anddynamically computed for you (while you use functions from the library).This is why the implementation of this algorithm becomes verycomfortable with PyTorch.PyTorch implementation----------------------If you are not sure to understand all the mathematics above, you willprobably get it by implementing it. If you are discovering PyTorch, werecommend you to first read this :doc:`Introduction toPyTorch `.Packages~~~~~~~~We will have recourse to the following packages:- ``torch``, ``torch.nn``, ``numpy`` (indispensables packages for neural networks with PyTorch)- ``torch.optim`` (efficient gradient descents)- ``PIL``, ``PIL.Image``, ``matplotlib.pyplot`` (load and display images)- ``torchvision.transforms`` (treat PIL images and transform into torch tensors)- ``torchvision.models`` (train or load pre-trained models)- ``copy`` (to deep copy the models; system package)
###Code
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models
import copy
###Output
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###Markdown
Cuda~~~~If you have a GPU on your computer, it is preferable to run thealgorithm on it, especially if you want to try larger networks (likeVGG). For this, we have ``torch.cuda.is_available()`` that returns``True`` if you computer has an available GPU. Then, we can set the``torch.device`` that will be used in this script. Then, we will usethe method ``.to(device)`` that moves a tensor or a module to the desireddevice. When we want to move back this tensor or module to theCPU (e.g. to use numpy), we can use the ``.cpu()`` method.
###Code
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
###Output
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###Markdown
Load images~~~~~~~~~~~In order to simplify the implementation, let's start by importing astyle and a content image of the same dimentions. We then scale them tothe desired output image size (128 or 512 in the example, depending on gpuavailablity) and transform them into torch tensors, ready to feeda neural network:.. Note:: Here are links to download the images required to run the tutorial: `picasso.jpg `__ and `dancing.jpg `__. Download these two images and add them to a directory with name ``images``
###Code
# desired size of the output image
imsize = 512 if torch.cuda.is_available() else 128 # use small size if no gpu
loader = transforms.Compose([
transforms.Resize(imsize), # scale imported image
transforms.ToTensor()]) # transform it into a torch tensor
def image_loader(image_name):
image = Image.open(image_name)
# fake batch dimension required to fit network's input dimensions
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
style_img = image_loader("images/picasso.jpg")
content_img = image_loader("images/dancing.jpg")
assert style_img.size() == content_img.size(), \
"we need to import style and content images of the same size"
###Output
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###Markdown
Imported PIL images has values between 0 and 255. Transformed into torchtensors, their values are between 0 and 1. This is an important detail:neural networks from torch library are trained with 0-1 tensor image. Ifyou try to feed the networks with 0-255 tensor images the activatedfeature maps will have no sense. This is not the case with pre-trainednetworks from the Caffe library: they are trained with 0-255 tensorimages.Display images~~~~~~~~~~~~~~We will use ``plt.imshow`` to display images. So we need to firstreconvert them into PIL images:
###Code
unloader = transforms.ToPILImage() # reconvert into PIL image
plt.ion()
def imshow(tensor, title=None):
image = tensor.cpu().clone() # we clone the tensor to not do changes on it
image = image.squeeze(0) # remove the fake batch dimension
image = unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
plt.figure()
imshow(style_img, title='Style Image')
plt.figure()
imshow(content_img, title='Content Image')
###Output
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###Markdown
Content loss~~~~~~~~~~~~The content loss is a function that takes as input the feature maps$F_{XL}$ at a layer $L$ in a network fed by $X$ andreturn the weigthed content distance $w_{CL}.D_C^L(X,C)$ betweenthis image and the content image. Hence, the weight $w_{CL}$ andthe target content $F_{CL}$ are parameters of the function. Weimplement this function as a torch module with a constructor that takesthese parameters as input. The distance $\|F_{XL} - F_{YL}\|^2$ isthe Mean Square Error between the two sets of feature maps, that can becomputed using a criterion ``nn.MSELoss`` stated as a third parameter.We will add our content losses at each desired layer as additive modulesof the neural network. That way, each time we will feed the network withan input image $X$, all the content losses will be computed at thedesired layers and, thanks to autograd, all the gradients will becomputed. For that, we just need to make the ``forward`` method of ourmodule returning the input: the module becomes a ''transparent layer''of the neural network. The computed loss is saved as a parameter of themodule.Finally, we define a fake ``backward`` method, that just call thebackward method of ``nn.MSELoss`` in order to reconstruct the gradient.This method returns the computed loss: this will be useful when runningthe gradient descent in order to display the evolution of style andcontent losses.
###Code
class ContentLoss(nn.Module):
def __init__(self, target,):
super(ContentLoss, self).__init__()
# we 'detach' the target content from the tree used
# to dynamically compute the gradient: this is a stated value,
# not a variable. Otherwise the forward method of the criterion
# will throw an error.
self.target = target.detach()
def forward(self, input):
self.loss = F.mse_loss(input, self.target)
return input
###Output
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###Markdown
.. Note:: **Important detail**: this module, although it is named ``ContentLoss``, is not a true PyTorch Loss function. If you want to define your content loss as a PyTorch Loss, you have to create a PyTorch autograd Function and to recompute/implement the gradient by the hand in the ``backward`` method.Style loss~~~~~~~~~~For the style loss, we need first to define a module that compute thegram produce $G_{XL}$ given the feature maps $F_{XL}$ of theneural network fed by $X$, at layer $L$. Let$\hat{F}_{XL}$ be the re-shaped version of $F_{XL}$ into a$K$\ x\ $N$ matrix, where $K$ is the number of featuremaps at layer $L$ and $N$ the lenght of any vectorizedfeature map $F_{XL}^k$. The $k^{th}$ line of$\hat{F}_{XL}$ is $F_{XL}^k$. We let you check that$\hat{F}_{XL} \cdot \hat{F}_{XL}^T = G_{XL}$. Given that, itbecomes easy to implement our module:
###Code
def gram_matrix(input):
a, b, c, d = input.size() # a=batch size(=1)
# b=number of feature maps
# (c,d)=dimensions of a f. map (N=c*d)
features = input.view(a * b, c * d) # resise F_XL into \hat F_XL
G = torch.mm(features, features.t()) # compute the gram product
# we 'normalize' the values of the gram matrix
# by dividing by the number of element in each feature maps.
return G.div(a * b * c * d)
###Output
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###Markdown
The longer is the feature maps dimension $N$, the bigger are thevalues of the Gram matrix. Therefore, if we don't normalize by $N$,the loss computed at the first layers (before pooling layers) will havemuch more importance during the gradient descent. We dont want that,since the most interesting style features are in the deepest layers!Then, the style loss module is implemented exactly the same way than thecontent loss module, but it compares the difference in Gram matrices of targetand input
###Code
class StyleLoss(nn.Module):
def __init__(self, target_feature):
super(StyleLoss, self).__init__()
self.target = gram_matrix(target_feature).detach()
def forward(self, input):
G = gram_matrix(input)
self.loss = F.mse_loss(G, self.target)
return input
###Output
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###Markdown
Load the neural network~~~~~~~~~~~~~~~~~~~~~~~Now, we have to import a pre-trained neural network. As in the paper, weare going to use a pretrained VGG network with 19 layers (VGG19).PyTorch's implementation of VGG is a module divided in two child``Sequential`` modules: ``features`` (containing convolution and poolinglayers) and ``classifier`` (containing fully connected layers). We arejust interested by ``features``:Some layers have different behavior in training and in evaluation. Since weare using it as a feature extractor. We will use ``.eval()`` to set thenetwork in evaluation mode.
###Code
cnn = models.vgg19(pretrained=True).features.to(device).eval()
###Output
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###Markdown
Additionally, VGG networks are trained on images with each channel normalizedby mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225]. We will use themto normalize the image before sending into the network.
###Code
cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
# create a module to normalize input image so we can easily put it in a
# nn.Sequential
class Normalization(nn.Module):
def __init__(self, mean, std):
super(Normalization, self).__init__()
# .view the mean and std to make them [C x 1 x 1] so that they can
# directly work with image Tensor of shape [B x C x H x W].
# B is batch size. C is number of channels. H is height and W is width.
self.mean = torch.tensor(mean).view(-1, 1, 1)
self.std = torch.tensor(std).view(-1, 1, 1)
def forward(self, img):
# normalize img
return (img - self.mean) / self.std
###Output
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###Markdown
A ``Sequential`` module contains an ordered list of child modules. Forinstance, ``vgg19.features`` contains a sequence (Conv2d, ReLU,MaxPool2d, Conv2d, ReLU...) aligned in the right order of depth. As wesaid in *Content loss* section, we wand to add our style and contentloss modules as additive 'transparent' layers in our network, at desireddepths. For that, we construct a new ``Sequential`` module, in which weare going to add modules from ``vgg19`` and our loss modules in theright order:
###Code
# desired depth layers to compute style/content losses :
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
style_img, content_img,
content_layers=content_layers_default,
style_layers=style_layers_default):
cnn = copy.deepcopy(cnn)
# normalization module
normalization = Normalization(normalization_mean, normalization_std).to(device)
# just in order to have an iterable access to or list of content/syle
# losses
content_losses = []
style_losses = []
# assuming that cnn is a nn.Sequential, so we make a new nn.Sequential
# to put in modules that are supposed to be activated sequentially
model = nn.Sequential(normalization)
i = 0 # increment every time we see a conv
for layer in cnn.children():
if isinstance(layer, nn.Conv2d):
i += 1
name = 'conv_{}'.format(i)
elif isinstance(layer, nn.ReLU):
name = 'relu_{}'.format(i)
# The in-place version doesn't play very nicely with the ContentLoss
# and StyleLoss we insert below. So we replace with out-of-place
# ones here.
layer = nn.ReLU(inplace=False)
elif isinstance(layer, nn.MaxPool2d):
name = 'pool_{}'.format(i)
elif isinstance(layer, nn.BatchNorm2d):
name = 'bn_{}'.format(i)
else:
raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
model.add_module(name, layer)
if name in content_layers:
# add content loss:
target = model(content_img).detach()
content_loss = ContentLoss(target)
model.add_module("content_loss_{}".format(i), content_loss)
content_losses.append(content_loss)
if name in style_layers:
# add style loss:
target_feature = model(style_img).detach()
style_loss = StyleLoss(target_feature)
model.add_module("style_loss_{}".format(i), style_loss)
style_losses.append(style_loss)
# now we trim off the layers after the last content and style losses
for i in range(len(model) - 1, -1, -1):
if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
break
model = model[:(i + 1)]
return model, style_losses, content_losses
###Output
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###Markdown
.. Note:: In the paper they recommend to change max pooling layers into average pooling. With AlexNet, that is a small network compared to VGG19 used in the paper, we are not going to see any difference of quality in the result. However, you can use these lines instead if you want to do this substitution: :: avgpool = nn.AvgPool2d(kernel_size=layer.kernel_size, stride=layer.stride, padding = layer.padding) model.add_module(name,avgpool) Input image~~~~~~~~~~~Again, in order to simplify the code, we take an image of the samedimensions than content and style images. This image can be a whitenoise, or it can also be a copy of the content-image.
###Code
input_img = content_img.clone()
# if you want to use a white noise instead uncomment the below line:
# input_img = torch.randn(content_img.data.size(), device=device)
# add the original input image to the figure:
plt.figure()
imshow(input_img, title='Input Image')
###Output
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###Markdown
Gradient descent~~~~~~~~~~~~~~~~As Leon Gatys, the author of the algorithm, suggested`here `__,we will use L-BFGS algorithm to run our gradient descent. Unliketraining a network, we want to train the input image in order tominimise the content/style losses. We would like to simply create aPyTorch L-BFGS optimizer ``optim.LBFGS``, passing our image as theTensor to optimize. We use ``.requires_grad_()`` to make sure that thisimage requires gradient.
###Code
def get_input_optimizer(input_img):
# this line to show that input is a parameter that requires a gradient
optimizer = optim.LBFGS([input_img.requires_grad_()])
return optimizer
###Output
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###Markdown
**Last step**: the loop of gradient descent. At each step, we must feedthe network with the updated input in order to compute the new losses,we must run the ``backward`` methods of each loss to dynamically computetheir gradients and perform the step of gradient descent. The optimizerrequires as argument a "closure": a function that reevaluates the modeland returns the loss.However, there's a small catch. The optimized image may take its valuesbetween $-\infty$ and $+\infty$ instead of staying between 0and 1. In other words, the image might be well optimized and have absurdvalues. In fact, we must perform an optimization under constraints inorder to keep having right vaues into our input image. There is a simplesolution: at each step, to correct the image to maintain its values intothe 0-1 interval.
###Code
def run_style_transfer(cnn, normalization_mean, normalization_std,
content_img, style_img, input_img, num_steps=300,
style_weight=1000000, content_weight=1):
"""Run the style transfer."""
print('Building the style transfer model..')
model, style_losses, content_losses = get_style_model_and_losses(cnn,
normalization_mean, normalization_std, style_img, content_img)
optimizer = get_input_optimizer(input_img)
print('Optimizing..')
run = [0]
while run[0] <= num_steps:
def closure():
# correct the values of updated input image
input_img.data.clamp_(0, 1)
optimizer.zero_grad()
model(input_img)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.loss
for cl in content_losses:
content_score += cl.loss
style_score *= style_weight
content_score *= content_weight
loss = style_score + content_score
loss.backward()
run[0] += 1
if run[0] % 50 == 0:
print("run {}:".format(run))
print('Style Loss : {:4f} Content Loss: {:4f}'.format(
style_score.item(), content_score.item()))
print()
return style_score + content_score
optimizer.step(closure)
# a last correction...
input_img.data.clamp_(0, 1)
return input_img
###Output
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###Markdown
Finally, run the algorithm
###Code
output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std,
content_img, style_img, input_img)
plt.figure()
imshow(output, title='Output Image')
# sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()
###Output
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###Markdown
Neural Transfer with PyTorch============================**Author**: `Alexis Jacq `_Introduction------------Welcome! This tutorial explains how to impletment the`Neural-Style `__ algorithm developedby Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.Neural what?~~~~~~~~~~~~The Neural-Style, or Neural-Transfer, is an algorithm that takes asinput a content-image (e.g. a tortle), a style-image (e.g. artisticwaves) and return the content of the content-image as if it was'painted' using the artistic style of the style-image:.. figure:: /_static/img/neural-style/neuralstyle.png :alt: content1How does it work?~~~~~~~~~~~~~~~~~The principle is simple: we define two distances, one for the content($D_C$) and one for the style ($D_S$). $D_C$ measureshow different the content is between two images, while $D_S$measures how different the style is between two images. Then, we take athird image, the input, (e.g. a with noise), and we transform it inorder to both minimize its content-distance with the content-image andits style-distance with the style-image.OK. How does it work?^^^^^^^^^^^^^^^^^^^^^Well, going further requires some mathematics. Let $C_{nn}$ be apre-trained deep convolutional neural network and $X$ be anyimage. $C_{nn}(X)$ is the network fed by $X$ (containingfeature maps at all layers). Let $F_{XL} \in C_{nn}(X)$ be thefeature maps at depth layer $L$, all vectorized and concatenatedin one single vector. We simply define the content of $X$ at layer$L$ by $F_{XL}$. Then, if $Y$ is another image of samethe size than $X$, we define the distance of content at layer$L$ as follow:\begin{align}D_C^L(X,Y) = \|F_{XL} - F_{YL}\|^2 = \sum_i (F_{XL}(i) - F_{YL}(i))^2\end{align}Where $F_{XL}(i)$ is the $i^{th}$ element of $F_{XL}$.The style is a bit less trivial to define. Let $F_{XL}^k$ with$k \leq K$ be the vectorized $k^{th}$ of the $K$feature maps at layer $L$. The style $G_{XL}$ of $X$at layer $L$ is defined by the Gram produce of all vectorizedfeature maps $F_{XL}^k$ with $k \leq K$. In other words,$G_{XL}$ is a $K$\ x\ $K$ matrix and the element$G_{XL}(k,l)$ at the $k^{th}$ line and $l^{th}$ columnof $G_{XL}$ is the vectorial produce between $F_{XL}^k$ and$F_{XL}^l$ :\begin{align}G_{XL}(k,l) = \langle F_{XL}^k, F_{XL}^l\rangle = \sum_i F_{XL}^k(i) . F_{XL}^l(i)\end{align}Where $F_{XL}^k(i)$ is the $i^{th}$ element of$F_{XL}^k$. We can see $G_{XL}(k,l)$ as a measure of thecorrelation between feature maps $k$ and $l$. In that way,$G_{XL}$ represents the correlation matrix of feature maps of$X$ at layer $L$. Note that the size of $G_{XL}$ onlydepends on the number of feature maps, not on the size of $X$.Then, if $Y$ is another image *of any size*, we define thedistance of style at layer $L$ as follow:\begin{align}D_S^L(X,Y) = \|G_{XL} - G_{YL}\|^2 = \sum_{k,l} (G_{XL}(k,l) - G_{YL}(k,l))^2\end{align}In order to minimize in one shot $D_C(X,C)$ between a variableimage $X$ and target content-image $C$ and $D_S(X,S)$between $X$ and target style-image $S$, both computed atseveral layers , we compute and sum the gradients (derivative withrespect to $X$) of each distance at each wanted layer:\begin{align}\nabla_{ extit{total}}(X,S,C) = \sum_{L_C} w_{CL_C}.\nabla_{ extit{content}}^{L_C}(X,C) + \sum_{L_S} w_{SL_S}.\nabla_{ extit{style}}^{L_S}(X,S)\end{align}Where $L_C$ and $L_S$ are respectivement the wanted layers(arbitrary stated) of content and style and $w_{CL_C}$ and$w_{SL_S}$ the weights (arbitrary stated) associated with thestyle or the content at each wanted layer. Then, we run a gradientdescent over $X$:\begin{align}X \leftarrow X - \alpha \nabla_{ extit{total}}(X,S,C)\end{align}Ok. That's enough with maths. If you want to go deeper (how to computethe gradients) **we encourage you to read the original paper** by LeonA. Gatys and AL, where everything is much better and much clearerexplained. For our implementation in PyTorch, we already have everythingwe need: indeed, with PyTorch, all the gradients are automatically anddynamically computed for you (while you use functions from the library).This is why the implementation of this algorithm becomes verycomfortable with PyTorch.PyTorch implementation----------------------If you are not sure to understand all the mathematics above, you willprobably get it by implementing it. If you are discovering PyTorch, werecommend you to first read this :doc:`Introduction toPyTorch `.Packages~~~~~~~~We will have recourse to the following packages:- ``torch``, ``torch.nn``, ``numpy`` (indispensables packages for neural networks with PyTorch)- ``torch.autograd.Variable`` (dynamic computation of the gradient wrt a variable)- ``torch.optim`` (efficient gradient descents)- ``PIL``, ``PIL.Image``, ``matplotlib.pyplot`` (load and display images)- ``torchvision.transforms`` (treat PIL images and transform into torch tensors)- ``torchvision.models`` (train or load pre-trained models)- ``copy`` (to deep copy the models; system package)
###Code
from __future__ import print_function
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models
import copy
###Output
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###Markdown
Cuda~~~~If you have a GPU on your computer, it is preferable to run thealgorithm on it, especially if you want to try larger networks (likeVGG). For this, we have ``torch.cuda.is_available()`` that returns``True`` if you computer has an available GPU. Then, we can use method``.cuda()`` that moves allocated proccesses associated with a modulefrom the CPU to the GPU. When we want to move back this module to theCPU (e.g. to use numpy), we use the ``.cpu()`` method. Finally,``.type(dtype)`` will be use to convert a ``torch.FloatTensor`` into``torch.cuda.FloatTensor`` to feed GPU processes.
###Code
use_cuda = torch.cuda.is_available()
dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
###Output
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###Markdown
Load images~~~~~~~~~~~In order to simplify the implementation, let's start by importing astyle and a content image of the same dimentions. We then scale them tothe desired output image size (128 or 512 in the example, depending on gpuavailablity) and transform them into torch tensors, ready to feeda neural network:.. Note:: Here are links to download the images required to run the tutorial: `picasso.jpg `__ and `dancing.jpg `__. Download these two images and add them to a directory with name ``images``
###Code
# desired size of the output image
imsize = 512 if use_cuda else 128 # use small size if no gpu
loader = transforms.Compose([
transforms.Scale(imsize), # scale imported image
transforms.ToTensor()]) # transform it into a torch tensor
def image_loader(image_name):
image = Image.open(image_name)
image = Variable(loader(image))
# fake batch dimension required to fit network's input dimensions
image = image.unsqueeze(0)
return image
style_img = image_loader("images/picasso.jpg").type(dtype)
content_img = image_loader("images/dancing.jpg").type(dtype)
assert style_img.size() == content_img.size(), \
"we need to import style and content images of the same size"
###Output
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###Markdown
Imported PIL images has values between 0 and 255. Transformed into torchtensors, their values are between 0 and 1. This is an important detail:neural networks from torch library are trained with 0-1 tensor image. Ifyou try to feed the networks with 0-255 tensor images the activatedfeature maps will have no sense. This is not the case with pre-trainednetworks from the Caffe library: they are trained with 0-255 tensorimages.Display images~~~~~~~~~~~~~~We will use ``plt.imshow`` to display images. So we need to firstreconvert them into PIL images:
###Code
unloader = transforms.ToPILImage() # reconvert into PIL image
plt.ion()
def imshow(tensor, title=None):
image = tensor.clone().cpu() # we clone the tensor to not do changes on it
image = image.view(3, imsize, imsize) # remove the fake batch dimension
image = unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
plt.figure()
imshow(style_img.data, title='Style Image')
plt.figure()
imshow(content_img.data, title='Content Image')
###Output
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###Markdown
Content loss~~~~~~~~~~~~The content loss is a function that takes as input the feature maps$F_{XL}$ at a layer $L$ in a network fed by $X$ andreturn the weigthed content distance $w_{CL}.D_C^L(X,C)$ betweenthis image and the content image. Hence, the weight $w_{CL}$ andthe target content $F_{CL}$ are parameters of the function. Weimplement this function as a torch module with a constructor that takesthese parameters as input. The distance $\|F_{XL} - F_{YL}\|^2$ isthe Mean Square Error between the two sets of feature maps, that can becomputed using a criterion ``nn.MSELoss`` stated as a third parameter.We will add our content losses at each desired layer as additive modulesof the neural network. That way, each time we will feed the network withan input image $X$, all the content losses will be computed at thedesired layers and, thanks to autograd, all the gradients will becomputed. For that, we just need to make the ``forward`` method of ourmodule returning the input: the module becomes a ''transparent layer''of the neural network. The computed loss is saved as a parameter of themodule.Finally, we define a fake ``backward`` method, that just call thebackward method of ``nn.MSELoss`` in order to reconstruct the gradient.This method returns the computed loss: this will be useful when runningthe gradient descent in order to display the evolution of style andcontent losses.
###Code
class ContentLoss(nn.Module):
def __init__(self, target, weight):
super(ContentLoss, self).__init__()
# we 'detach' the target content from the tree used
self.target = target.detach() * weight
# to dynamically compute the gradient: this is a stated value,
# not a variable. Otherwise the forward method of the criterion
# will throw an error.
self.weight = weight
self.criterion = nn.MSELoss()
def forward(self, input):
self.loss = self.criterion(input * self.weight, self.target)
self.output = input
return self.output
def backward(self, retain_graph=True):
self.loss.backward(retain_graph=retain_graph)
return self.loss
###Output
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###Markdown
.. Note:: **Important detail**: this module, although it is named ``ContentLoss``, is not a true PyTorch Loss function. If you want to define your content loss as a PyTorch Loss, you have to create a PyTorch autograd Function and to recompute/implement the gradient by the hand in the ``backward`` method.Style loss~~~~~~~~~~For the style loss, we need first to define a module that compute thegram produce $G_{XL}$ given the feature maps $F_{XL}$ of theneural network fed by $X$, at layer $L$. Let$\hat{F}_{XL}$ be the re-shaped version of $F_{XL}$ into a$K$\ x\ $N$ matrix, where $K$ is the number of featuremaps at layer $L$ and $N$ the lenght of any vectorizedfeature map $F_{XL}^k$. The $k^{th}$ line of$\hat{F}_{XL}$ is $F_{XL}^k$. We let you check that$\hat{F}_{XL} \cdot \hat{F}_{XL}^T = G_{XL}$. Given that, itbecomes easy to implement our module:
###Code
class GramMatrix(nn.Module):
def forward(self, input):
a, b, c, d = input.size() # a=batch size(=1)
# b=number of feature maps
# (c,d)=dimensions of a f. map (N=c*d)
features = input.view(a * b, c * d) # resise F_XL into \hat F_XL
G = torch.mm(features, features.t()) # compute the gram product
# we 'normalize' the values of the gram matrix
# by dividing by the number of element in each feature maps.
return G.div(a * b * c * d)
###Output
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###Markdown
The longer is the feature maps dimension $N$, the bigger are thevalues of the gram matrix. Therefore, if we don't normalize by $N$,the loss computed at the first layers (before pooling layers) will havemuch more importance during the gradient descent. We dont want that,since the most interesting style features are in the deepest layers!Then, the style loss module is implemented exactly the same way than thecontent loss module, but we have to add the ``gramMatrix`` as aparameter:
###Code
class StyleLoss(nn.Module):
def __init__(self, target, weight):
super(StyleLoss, self).__init__()
self.target = target.detach() * weight
self.weight = weight
self.gram = GramMatrix()
self.criterion = nn.MSELoss()
def forward(self, input):
self.output = input.clone()
self.G = self.gram(input)
self.G.mul_(self.weight)
self.loss = self.criterion(self.G, self.target)
return self.output
def backward(self, retain_graph=True):
self.loss.backward(retain_graph=retain_graph)
return self.loss
###Output
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###Markdown
Load the neural network~~~~~~~~~~~~~~~~~~~~~~~Now, we have to import a pre-trained neural network. As in the paper, weare going to use a pretrained VGG network with 19 layers (VGG19).PyTorch's implementation of VGG is a module divided in two child``Sequential`` modules: ``features`` (containing convolution and poolinglayers) and ``classifier`` (containing fully connected layers). We arejust interested by ``features``:
###Code
cnn = models.vgg19(pretrained=True).features
# move it to the GPU if possible:
if use_cuda:
cnn = cnn.cuda()
###Output
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###Markdown
A ``Sequential`` module contains an ordered list of child modules. Forinstance, ``vgg19.features`` contains a sequence (Conv2d, ReLU,Maxpool2d, Conv2d, ReLU...) aligned in the right order of depth. As wesaid in *Content loss* section, we wand to add our style and contentloss modules as additive 'transparent' layers in our network, at desireddepths. For that, we construct a new ``Sequential`` module, in wich weare going to add modules from ``vgg19`` and our loss modules in theright order:
###Code
# desired depth layers to compute style/content losses :
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
def get_style_model_and_losses(cnn, style_img, content_img,
style_weight=1000, content_weight=1,
content_layers=content_layers_default,
style_layers=style_layers_default):
cnn = copy.deepcopy(cnn)
# just in order to have an iterable access to or list of content/syle
# losses
content_losses = []
style_losses = []
model = nn.Sequential() # the new Sequential module network
gram = GramMatrix() # we need a gram module in order to compute style targets
# move these modules to the GPU if possible:
if use_cuda:
model = model.cuda()
gram = gram.cuda()
i = 1
for layer in list(cnn):
if isinstance(layer, nn.Conv2d):
name = "conv_" + str(i)
model.add_module(name, layer)
if name in content_layers:
# add content loss:
target = model(content_img).clone()
content_loss = ContentLoss(target, content_weight)
model.add_module("content_loss_" + str(i), content_loss)
content_losses.append(content_loss)
if name in style_layers:
# add style loss:
target_feature = model(style_img).clone()
target_feature_gram = gram(target_feature)
style_loss = StyleLoss(target_feature_gram, style_weight)
model.add_module("style_loss_" + str(i), style_loss)
style_losses.append(style_loss)
if isinstance(layer, nn.ReLU):
name = "relu_" + str(i)
model.add_module(name, layer)
if name in content_layers:
# add content loss:
target = model(content_img).clone()
content_loss = ContentLoss(target, content_weight)
model.add_module("content_loss_" + str(i), content_loss)
content_losses.append(content_loss)
if name in style_layers:
# add style loss:
target_feature = model(style_img).clone()
target_feature_gram = gram(target_feature)
style_loss = StyleLoss(target_feature_gram, style_weight)
model.add_module("style_loss_" + str(i), style_loss)
style_losses.append(style_loss)
i += 1
if isinstance(layer, nn.MaxPool2d):
name = "pool_" + str(i)
model.add_module(name, layer) # ***
return model, style_losses, content_losses
###Output
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###Markdown
.. Note:: In the paper they recommend to change max pooling layers into average pooling. With AlexNet, that is a small network compared to VGG19 used in the paper, we are not going to see any difference of quality in the result. However, you can use these lines instead if you want to do this substitution: :: avgpool = nn.AvgPool2d(kernel_size=layer.kernel_size, stride=layer.stride, padding = layer.padding) model.add_module(name,avgpool) Input image~~~~~~~~~~~Again, in order to simplify the code, we take an image of the samedimensions than content and style images. This image can be a whitenoise, or it can also be a copy of the content-image.
###Code
input_img = content_img.clone()
# if you want to use a white noise instead uncomment the below line:
# input_img = Variable(torch.randn(content_img.data.size())).type(dtype)
# add the original input image to the figure:
plt.figure()
imshow(input_img.data, title='Input Image')
###Output
_____no_output_____
###Markdown
Gradient descent~~~~~~~~~~~~~~~~As Leon Gatys, the author of the algorithm, suggested`here `__,we will use L-BFGS algorithm to run our gradient descent. Unliketraining a network, we want to train the input image in order tominimise the content/style losses. We would like to simply create aPyTorch L-BFGS optimizer, passing our image as the variable to optimize.But ``optim.LBFGS`` takes as first argument a list of PyTorch``Variable`` that require gradient. Our input image is a ``Variable``but is not a leaf of the tree that requires computation of gradients. Inorder to show that this variable requires a gradient, a possibility isto construct a ``Parameter`` object from the input image. Then, we justgive a list containing this ``Parameter`` to the optimizer'sconstructor:
###Code
def get_input_param_optimizer(input_img):
# this line to show that input is a parameter that requires a gradient
input_param = nn.Parameter(input_img.data)
optimizer = optim.LBFGS([input_param])
return input_param, optimizer
###Output
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###Markdown
**Last step**: the loop of gradient descent. At each step, we must feedthe network with the updated input in order to compute the new losses,we must run the ``backward`` methods of each loss to dynamically computetheir gradients and perform the step of gradient descent. The optimizerrequires as argument a "closure": a function that reevaluates the modeland returns the loss.However, there's a small catch. The optimized image may take its valuesbetween $-\infty$ and $+\infty$ instead of staying between 0and 1. In other words, the image might be well optimized and have absurdvalues. In fact, we must perform an optimization under constraints inorder to keep having right vaues into our input image. There is a simplesolution: at each step, to correct the image to maintain its values intothe 0-1 interval.
###Code
def run_style_transfer(cnn, content_img, style_img, input_img, num_steps=300,
style_weight=1000, content_weight=1):
"""Run the style transfer."""
print('Building the style transfer model..')
model, style_losses, content_losses = get_style_model_and_losses(cnn,
style_img, content_img, style_weight, content_weight)
input_param, optimizer = get_input_param_optimizer(input_img)
print('Optimizing..')
run = [0]
while run[0] <= num_steps:
def closure():
# correct the values of updated input image
input_param.data.clamp_(0, 1)
optimizer.zero_grad()
model(input_param)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.backward()
for cl in content_losses:
content_score += cl.backward()
run[0] += 1
if run[0] % 50 == 0:
print("run {}:".format(run))
print('Style Loss : {:4f} Content Loss: {:4f}'.format(
style_score.data[0], content_score.data[0]))
print()
return style_score + content_score
optimizer.step(closure)
# a last correction...
input_param.data.clamp_(0, 1)
return input_param.data
###Output
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###Markdown
Finally, run the algorithm
###Code
output = run_style_transfer(cnn, content_img, style_img, input_img)
plt.figure()
imshow(output, title='Output Image')
# sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()
###Output
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###Markdown
Neural Transfer with PyTorch============================**Author**: `Alexis Jacq `_Introduction------------Welcome! This tutorial explains how to impletment the`Neural-Style `__ algorithm developedby Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.Neural what?~~~~~~~~~~~~The Neural-Style, or Neural-Transfer, is an algorithm that takes asinput a content-image (e.g. a tortle), a style-image (e.g. artisticwaves) and return the content of the content-image as if it was'painted' using the artistic style of the style-image:.. figure:: /_static/img/neural-style/neuralstyle.png :alt: content1How does it work?~~~~~~~~~~~~~~~~~The principle is simple: we define two distances, one for the content($D_C$) and one for the style ($D_S$). $D_C$ measureshow different the content is between two images, while $D_S$measures how different the style is between two images. Then, we take athird image, the input, (e.g. a with noise), and we transform it inorder to both minimize its content-distance with the content-image andits style-distance with the style-image.OK. How does it work?^^^^^^^^^^^^^^^^^^^^^Well, going further requires some mathematics. Let $C_{nn}$ be apre-trained deep convolutional neural network and $X$ be anyimage. $C_{nn}(X)$ is the network fed by $X$ (containingfeature maps at all layers). Let $F_{XL} \in C_{nn}(X)$ be thefeature maps at depth layer $L$, all vectorized and concatenatedin one single vector. We simply define the content of $X$ at layer$L$ by $F_{XL}$. Then, if $Y$ is another image of samethe size than $X$, we define the distance of content at layer$L$ as follow:\begin{align}D_C^L(X,Y) = \|F_{XL} - F_{YL}\|^2 = \sum_i (F_{XL}(i) - F_{YL}(i))^2\end{align}Where $F_{XL}(i)$ is the $i^{th}$ element of $F_{XL}$.The style is a bit less trivial to define. Let $F_{XL}^k$ with$k \leq K$ be the vectorized $k^{th}$ of the $K$feature maps at layer $L$. The style $G_{XL}$ of $X$at layer $L$ is defined by the Gram produce of all vectorizedfeature maps $F_{XL}^k$ with $k \leq K$. In other words,$G_{XL}$ is a $K$\ x\ $K$ matrix and the element$G_{XL}(k,l)$ at the $k^{th}$ line and $l^{th}$ columnof $G_{XL}$ is the vectorial produce between $F_{XL}^k$ and$F_{XL}^l$ :\begin{align}G_{XL}(k,l) = \langle F_{XL}^k, F_{XL}^l\rangle = \sum_i F_{XL}^k(i) . F_{XL}^l(i)\end{align}Where $F_{XL}^k(i)$ is the $i^{th}$ element of$F_{XL}^k$. We can see $G_{XL}(k,l)$ as a measure of thecorrelation between feature maps $k$ and $l$. In that way,$G_{XL}$ represents the correlation matrix of feature maps of$X$ at layer $L$. Note that the size of $G_{XL}$ onlydepends on the number of feature maps, not on the size of $X$.Then, if $Y$ is another image *of any size*, we define thedistance of style at layer $L$ as follow:\begin{align}D_S^L(X,Y) = \|G_{XL} - G_{YL}\|^2 = \sum_{k,l} (G_{XL}(k,l) - G_{YL}(k,l))^2\end{align}In order to minimize in one shot $D_C(X,C)$ between a variableimage $X$ and target content-image $C$ and $D_S(X,S)$between $X$ and target style-image $S$, both computed atseveral layers , we compute and sum the gradients (derivative withrespect to $X$) of each distance at each wanted layer:\begin{align}\nabla_{ extit{total}}(X,S,C) = \sum_{L_C} w_{CL_C}.\nabla_{ extit{content}}^{L_C}(X,C) + \sum_{L_S} w_{SL_S}.\nabla_{ extit{style}}^{L_S}(X,S)\end{align}Where $L_C$ and $L_S$ are respectivement the wanted layers(arbitrary stated) of content and style and $w_{CL_C}$ and$w_{SL_S}$ the weights (arbitrary stated) associated with thestyle or the content at each wanted layer. Then, we run a gradientdescent over $X$:\begin{align}X \leftarrow X - \alpha \nabla_{ extit{total}}(X,S,C)\end{align}Ok. That's enough with maths. If you want to go deeper (how to computethe gradients) **we encourage you to read the original paper** by LeonA. Gatys and AL, where everything is much better and much clearerexplained. For our implementation in PyTorch, we already have everythingwe need: indeed, with PyTorch, all the gradients are automatically anddynamically computed for you (while you use functions from the library).This is why the implementation of this algorithm becomes verycomfortable with PyTorch.PyTorch implementation----------------------If you are not sure to understand all the mathematics above, you willprobably get it by implementing it. If you are discovering PyTorch, werecommend you to first read this :doc:`Introduction toPyTorch `.Packages~~~~~~~~We will have recourse to the following packages:- ``torch``, ``torch.nn``, ``numpy`` (indispensables packages for neural networks with PyTorch)- ``torch.autograd.Variable`` (dynamic computation of the gradient wrt a variable)- ``torch.optim`` (efficient gradient descents)- ``PIL``, ``PIL.Image``, ``matplotlib.pyplot`` (load and display images)- ``torchvision.transforms`` (treat PIL images and transform into torch tensors)- ``torchvision.models`` (train or load pre-trained models)- ``copy`` (to deep copy the models; system package)
###Code
from __future__ import print_function
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models
import copy
###Output
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###Markdown
Cuda~~~~If you have a GPU on your computer, it is preferable to run thealgorithm on it, especially if you want to try larger networks (likeVGG). For this, we have ``torch.cuda.is_available()`` that returns``True`` if you computer has an available GPU. Then, we can use method``.cuda()`` that moves allocated proccesses associated with a modulefrom the CPU to the GPU. When we want to move back this module to theCPU (e.g. to use numpy), we use the ``.cpu()`` method. Finally,``.type(dtype)`` will be use to convert a ``torch.FloatTensor`` into``torch.cuda.FloatTensor`` to feed GPU processes.
###Code
use_cuda = torch.cuda.is_available()
dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
###Output
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###Markdown
Load images~~~~~~~~~~~In order to simplify the implementation, let's start by importing astyle and a content image of the same dimentions. We then scale them tothe desired output image size (128 or 512 in the example, depending on gpuavailablity) and transform them into torch tensors, ready to feeda neural network:.. Note:: Here are links to download the images required to run the tutorial: `picasso.jpg `__ and `dancing.jpg `__. Download these two images and add them to a directory with name ``images``
###Code
# desired size of the output image
imsize = 512 if use_cuda else 128 # use small size if no gpu
loader = transforms.Compose([
transforms.Scale(imsize), # scale imported image
transforms.ToTensor()]) # transform it into a torch tensor
def image_loader(image_name):
image = Image.open(image_name)
image = Variable(loader(image))
# fake batch dimension required to fit network's input dimensions
image = image.unsqueeze(0)
return image
style_img = image_loader("images/picasso.jpg").type(dtype)
content_img = image_loader("images/dancing.jpg").type(dtype)
assert style_img.size() == content_img.size(), \
"we need to import style and content images of the same size"
###Output
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###Markdown
Imported PIL images has values between 0 and 255. Transformed into torchtensors, their values are between 0 and 1. This is an important detail:neural networks from torch library are trained with 0-1 tensor image. Ifyou try to feed the networks with 0-255 tensor images the activatedfeature maps will have no sense. This is not the case with pre-trainednetworks from the Caffe library: they are trained with 0-255 tensorimages.Display images~~~~~~~~~~~~~~We will use ``plt.imshow`` to display images. So we need to firstreconvert them into PIL images:
###Code
unloader = transforms.ToPILImage() # reconvert into PIL image
plt.ion()
def imshow(tensor, title=None):
image = tensor.clone().cpu() # we clone the tensor to not do changes on it
image = image.view(3, imsize, imsize) # remove the fake batch dimension
image = unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
plt.figure()
imshow(style_img.data, title='Style Image')
plt.figure()
imshow(content_img.data, title='Content Image')
###Output
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###Markdown
Content loss~~~~~~~~~~~~The content loss is a function that takes as input the feature maps$F_{XL}$ at a layer $L$ in a network fed by $X$ andreturn the weigthed content distance $w_{CL}.D_C^L(X,C)$ betweenthis image and the content image. Hence, the weight $w_{CL}$ andthe target content $F_{CL}$ are parameters of the function. Weimplement this function as a torch module with a constructor that takesthese parameters as input. The distance $\|F_{XL} - F_{YL}\|^2$ isthe Mean Square Error between the two sets of feature maps, that can becomputed using a criterion ``nn.MSELoss`` stated as a third parameter.We will add our content losses at each desired layer as additive modulesof the neural network. That way, each time we will feed the network withan input image $X$, all the content losses will be computed at thedesired layers and, thanks to autograd, all the gradients will becomputed. For that, we just need to make the ``forward`` method of ourmodule returning the input: the module becomes a ''transparent layer''of the neural network. The computed loss is saved as a parameter of themodule.Finally, we define a fake ``backward`` method, that just call thebackward method of ``nn.MSELoss`` in order to reconstruct the gradient.This method returns the computed loss: this will be useful when runningthe gradient descent in order to display the evolution of style andcontent losses.
###Code
class ContentLoss(nn.Module):
def __init__(self, target, weight):
super(ContentLoss, self).__init__()
# we 'detach' the target content from the tree used
self.target = target.detach() * weight
# to dynamically compute the gradient: this is a stated value,
# not a variable. Otherwise the forward method of the criterion
# will throw an error.
self.weight = weight
self.criterion = nn.MSELoss()
def forward(self, input):
self.loss = self.criterion(input * self.weight, self.target)
self.output = input
return self.output
def backward(self, retain_graph=True):
self.loss.backward(retain_graph=retain_graph)
return self.loss
###Output
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###Markdown
.. Note:: **Important detail**: this module, although it is named ``ContentLoss``, is not a true PyTorch Loss function. If you want to define your content loss as a PyTorch Loss, you have to create a PyTorch autograd Function and to recompute/implement the gradient by the hand in the ``backward`` method.Style loss~~~~~~~~~~For the style loss, we need first to define a module that compute thegram produce $G_{XL}$ given the feature maps $F_{XL}$ of theneural network fed by $X$, at layer $L$. Let$\hat{F}_{XL}$ be the re-shaped version of $F_{XL}$ into a$K$\ x\ $N$ matrix, where $K$ is the number of featuremaps at layer $L$ and $N$ the lenght of any vectorizedfeature map $F_{XL}^k$. The $k^{th}$ line of$\hat{F}_{XL}$ is $F_{XL}^k$. We let you check that$\hat{F}_{XL} \cdot \hat{F}_{XL}^T = G_{XL}$. Given that, itbecomes easy to implement our module:
###Code
class GramMatrix(nn.Module):
def forward(self, input):
a, b, c, d = input.size() # a=batch size(=1)
# b=number of feature maps
# (c,d)=dimensions of a f. map (N=c*d)
features = input.view(a * b, c * d) # resise F_XL into \hat F_XL
G = torch.mm(features, features.t()) # compute the gram product
# we 'normalize' the values of the gram matrix
# by dividing by the number of element in each feature maps.
return G.div(a * b * c * d)
###Output
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###Markdown
The longer is the feature maps dimension $N$, the bigger are thevalues of the gram matrix. Therefore, if we don't normalize by $N$,the loss computed at the first layers (before pooling layers) will havemuch more importance during the gradient descent. We dont want that,since the most interesting style features are in the deepest layers!Then, the style loss module is implemented exactly the same way than thecontent loss module, but we have to add the ``gramMatrix`` as aparameter:
###Code
class StyleLoss(nn.Module):
def __init__(self, target, weight):
super(StyleLoss, self).__init__()
self.target = target.detach() * weight
self.weight = weight
self.gram = GramMatrix()
self.criterion = nn.MSELoss()
def forward(self, input):
self.output = input.clone()
self.G = self.gram(input)
self.G.mul_(self.weight)
self.loss = self.criterion(self.G, self.target)
return self.output
def backward(self, retain_graph=True):
self.loss.backward(retain_graph=retain_graph)
return self.loss
###Output
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###Markdown
Load the neural network~~~~~~~~~~~~~~~~~~~~~~~Now, we have to import a pre-trained neural network. As in the paper, weare going to use a pretrained VGG network with 19 layers (VGG19).PyTorch's implementation of VGG is a module divided in two child``Sequential`` modules: ``features`` (containing convolution and poolinglayers) and ``classifier`` (containing fully connected layers). We arejust interested by ``features``:
###Code
cnn = models.vgg19(pretrained=True).features
# move it to the GPU if possible:
if use_cuda:
cnn = cnn.cuda()
###Output
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###Markdown
A ``Sequential`` module contains an ordered list of child modules. Forinstance, ``vgg19.features`` contains a sequence (Conv2d, ReLU,Maxpool2d, Conv2d, ReLU...) aligned in the right order of depth. As wesaid in *Content loss* section, we wand to add our style and contentloss modules as additive 'transparent' layers in our network, at desireddepths. For that, we construct a new ``Sequential`` module, in wich weare going to add modules from ``vgg19`` and our loss modules in theright order:
###Code
# desired depth layers to compute style/content losses :
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
def get_style_model_and_losses(cnn, style_img, content_img,
style_weight=1000, content_weight=1,
content_layers=content_layers_default,
style_layers=style_layers_default):
cnn = copy.deepcopy(cnn)
# just in order to have an iterable access to or list of content/syle
# losses
content_losses = []
style_losses = []
model = nn.Sequential() # the new Sequential module network
gram = GramMatrix() # we need a gram module in order to compute style targets
# move these modules to the GPU if possible:
if use_cuda:
model = model.cuda()
gram = gram.cuda()
i = 1
for layer in list(cnn):
if isinstance(layer, nn.Conv2d):
name = "conv_" + str(i)
model.add_module(name, layer)
if name in content_layers:
# add content loss:
target = model(content_img).clone()
content_loss = ContentLoss(target, content_weight)
model.add_module("content_loss_" + str(i), content_loss)
content_losses.append(content_loss)
if name in style_layers:
# add style loss:
target_feature = model(style_img).clone()
target_feature_gram = gram(target_feature)
style_loss = StyleLoss(target_feature_gram, style_weight)
model.add_module("style_loss_" + str(i), style_loss)
style_losses.append(style_loss)
if isinstance(layer, nn.ReLU):
name = "relu_" + str(i)
model.add_module(name, layer)
if name in content_layers:
# add content loss:
target = model(content_img).clone()
content_loss = ContentLoss(target, content_weight)
model.add_module("content_loss_" + str(i), content_loss)
content_losses.append(content_loss)
if name in style_layers:
# add style loss:
target_feature = model(style_img).clone()
target_feature_gram = gram(target_feature)
style_loss = StyleLoss(target_feature_gram, style_weight)
model.add_module("style_loss_" + str(i), style_loss)
style_losses.append(style_loss)
i += 1
if isinstance(layer, nn.MaxPool2d):
name = "pool_" + str(i)
model.add_module(name, layer) # ***
return model, style_losses, content_losses
###Output
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###Markdown
.. Note:: In the paper they recommend to change max pooling layers into average pooling. With AlexNet, that is a small network compared to VGG19 used in the paper, we are not going to see any difference of quality in the result. However, you can use these lines instead if you want to do this substitution: :: avgpool = nn.AvgPool2d(kernel_size=layer.kernel_size, stride=layer.stride, padding = layer.padding) model.add_module(name,avgpool) Input image~~~~~~~~~~~Again, in order to simplify the code, we take an image of the samedimensions than content and style images. This image can be a whitenoise, or it can also be a copy of the content-image.
###Code
input_img = content_img.clone()
# if you want to use a white noise instead uncomment the below line:
# input_img = Variable(torch.randn(content_img.data.size())).type(dtype)
# add the original input image to the figure:
plt.figure()
imshow(input_img.data, title='Input Image')
###Output
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###Markdown
Gradient descent~~~~~~~~~~~~~~~~As Leon Gatys, the author of the algorithm, suggested`here `__,we will use L-BFGS algorithm to run our gradient descent. Unliketraining a network, we want to train the input image in order tominimise the content/style losses. We would like to simply create aPyTorch L-BFGS optimizer, passing our image as the variable to optimize.But ``optim.LBFGS`` takes as first argument a list of PyTorch``Variable`` that require gradient. Our input image is a ``Variable``but is not a leaf of the tree that requires computation of gradients. Inorder to show that this variable requires a gradient, a possibility isto construct a ``Parameter`` object from the input image. Then, we justgive a list containing this ``Parameter`` to the optimizer'sconstructor:
###Code
def get_input_param_optimizer(input_img):
# this line to show that input is a parameter that requires a gradient
input_param = nn.Parameter(input_img.data)
optimizer = optim.LBFGS([input_param])
return input_param, optimizer
###Output
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###Markdown
**Last step**: the loop of gradient descent. At each step, we must feedthe network with the updated input in order to compute the new losses,we must run the ``backward`` methods of each loss to dynamically computetheir gradients and perform the step of gradient descent. The optimizerrequires as argument a "closure": a function that reevaluates the modeland returns the loss.However, there's a small catch. The optimized image may take its valuesbetween $-\infty$ and $+\infty$ instead of staying between 0and 1. In other words, the image might be well optimized and have absurdvalues. In fact, we must perform an optimization under constraints inorder to keep having right vaues into our input image. There is a simplesolution: at each step, to correct the image to maintain its values intothe 0-1 interval.
###Code
def run_style_transfer(cnn, content_img, style_img, input_img, num_steps=300,
style_weight=1000, content_weight=1):
"""Run the style transfer."""
print('Building the style transfer model..')
model, style_losses, content_losses = get_style_model_and_losses(cnn,
style_img, content_img, style_weight, content_weight)
input_param, optimizer = get_input_param_optimizer(input_img)
print('Optimizing..')
run = [0]
while run[0] <= num_steps:
def closure():
# correct the values of updated input image
input_param.data.clamp_(0, 1)
optimizer.zero_grad()
model(input_param)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.backward()
for cl in content_losses:
content_score += cl.backward()
run[0] += 1
if run[0] % 50 == 0:
print("run {}:".format(run))
print('Style Loss : {:4f} Content Loss: {:4f}'.format(
style_score.data[0], content_score.data[0]))
print()
return style_score + content_score
optimizer.step(closure)
# a last correction...
input_param.data.clamp_(0, 1)
return input_param.data
###Output
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###Markdown
Finally, run the algorithm
###Code
output = run_style_transfer(cnn, content_img, style_img, input_img)
plt.figure()
imshow(output, title='Output Image')
# sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()
###Output
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|
notebooks/compile.ipynb
|
###Markdown
Compile hello and fizzbuzz
###Code
mod_useless = mods.ModCell()
mod_useless._import('hello')
mod_useless._import('fizzbuzz')
with open('../module/useless.py', mode='w') as f:
mod_useless.compile(out=f, source_info = source_info)
###Output
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###Markdown
Compile game and animmal (with sellective import)
###Code
mod_funny = mods.ModCell()
mod_funny._import('game')
mod_funny._import('animal', tag='Mammals')
mod_funny._import('animal', tag='Birds')
with open('../module/funny.py', mode='w') as f:
mod_funny.compile(out=f, source_info = source_info)
###Output
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|
task1_modelling.ipynb
|
###Markdown
load train data
###Code
pd_df_train = pd.read_csv("data/preprocessing_data/task1_preprocess_training_data.csv")
pd_df_train.head()
###Output
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###Markdown
Grouping and creating one hot encoding for intersection_id and time_window for train data
###Code
intersection_id_index = pd_df_train.groupby('intersection_id').ngroup()
intersection_id_class_vec = pd.get_dummies(intersection_id_index, prefix= "intesection_id_index")
time_window_index = pd_df_train.groupby('time_window').ngroup()
time_window_class_vec = pd.get_dummies(time_window_index)
pd_df_train = pd.concat([pd_df_train, intersection_id_index, intersection_id_class_vec,
time_window_index, time_window_class_vec], axis=1)
pd_df_train.head()
train_set_X = pd_df_train.drop(["intersection_id", "time_window"], axis=1)
train_set_X = train_set_X.rename(columns={"average_travl_time":"label"})
train_set_X.head()
train_set_X = train_set_X.loc[:,~train_set_X.columns.duplicated()]
train_set_y = train_set_X.label
pca = PCA(n_components=10)
features_pca=pca.fit_transform(train_set_X.drop("label", axis=1))
scaler=StandardScaler()
features_scled=scaler.fit_transform(features_pca)
features_scled.shape
###Output
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###Markdown
load test data
###Code
pd_df_test = pd.read_csv("data/preprocessing_data/task1_preprocess_test_data.csv")
pd_df_test.head()
###Output
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###Markdown
Grouping and creating one hot encoding for intersection_id and time_window for test data
###Code
intersection_id_index_test = pd_df_test.groupby('intersection_id').ngroup()
intersection_id_class_vec_test = pd.get_dummies(intersection_id_index_test, prefix= "intesection_id_index")
time_window_index_test = pd_df_test.groupby('time_window').ngroup()
time_window_class_vec_test = pd.get_dummies(time_window_index_test)
pd_df_test = pd.concat([pd_df_test, intersection_id_index_test, intersection_id_class_vec_test,
time_window_index_test, time_window_class_vec_test], axis=1)
pd_df_test.tail()
test_set = pd_df_test.drop(["intersection_id", "time_window"], axis=1)
pca = PCA(n_components=10)
test_features_pca=pca.fit_transform(test_set.drop("average_travl_time", axis=1))
scaler=StandardScaler()
test_features_scled=scaler.fit_transform(test_features_pca)
test_features_scled.shape
###Output
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###Markdown
Split data train set for train and cross validation
###Code
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features_scled,
train_set_y,
test_size=0.30)
###Output
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###Markdown
Implementing Random Forest Regressor
###Code
from sklearn.ensemble import RandomForestRegressor
r_model = RandomForestRegressor(n_estimators=10)
r_model.fit(X_train, y_train)
y_pred = r_model.predict(X_test)
###Output
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###Markdown
Evaluation metrics
###Code
from sklearn.metrics import mean_squared_error
from math import sqrt
# MAE,R^2:
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
###Output
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###Markdown
MAPE Calculation method
###Code
def Mean_Absolute_Percentage_Error(labl,predction):
labl, predction = np.array(labl), np.array(predction)
return np.mean(np.abs((labl - predction) / labl))
###Output
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###Markdown
MAPE for Random Forest Regressor
###Code
print("MAPE for RF", Mean_Absolute_Percentage_Error(y_test, y_pred))
###Output
MAPE for RF 0.25916502549085335
###Markdown
Mean Abosute error for Random Forest Regressor
###Code
mae_rf=mean_absolute_error(y_test, y_pred)
print("MAE for RF", mae_rf)
###Output
MAE for RF 27.737714113366522
###Markdown
r2_score for Random Forest Regressor
###Code
r2_rf=r2_score(y_test,y_pred)
print("r2_score for RF", r2_rf)
###Output
r2_score for RF 0.49803893021280965
###Markdown
Root Mean Squared Error For Random Forest Regressor
###Code
#root mean square error metric:
rms = np.sqrt(mean_squared_error(y_test, y_pred))
print("RMSE for RF", rms)
#Not needed, unusually large value to compare the error performance metric.
mse = mean_squared_error(y_test, y_pred)
print("MSE for RF", mse)
###Output
MSE for RF 1645.682338155616
###Markdown
Implementing Gradient Boosting Regressor
###Code
from sklearn.ensemble import GradientBoostingRegressor
gb_reg = GradientBoostingRegressor()
gb_reg.fit(X_train, y_train)
gb_pred = gb_reg.predict(X_test)
###Output
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###Markdown
Evaluation metrics
###Code
# mean squared error
mse = mean_squared_error(y_test, gb_pred)
print("MSE for GBR", mse)
# root mean squared error
rms_gbt=np.sqrt(mean_squared_error(y_test, gb_pred))
print("RMSE for GBR", rms_gbt)
###Output
RMSE for GBR 38.244084334846534
###Markdown
MAPE for Gradient Boosting Regressor
###Code
print("MAPE for GBR", Mean_Absolute_Percentage_Error(y_test, gb_pred))
###Output
MAPE for GBR 0.23597822282799474
###Markdown
MAE for Gradient Boosting Regressor
###Code
mae_gbr=mean_absolute_error(y_test, gb_pred)
print("MAE for GBR", mae_gbr)
###Output
MAE for GBR 25.572250855541068
###Markdown
r_2 score for Gradient Boosting Regressor
###Code
r2_gb=r2_score(y_test,gb_pred)
print("r2_score for GBR", r2_gb)
###Output
r2_score for GBR 0.5538791074446174
###Markdown
Implementing XGBOOST
###Code
import xgboost as xgb
xgb_model = xgb.XGBRegressor()
xgb_model.fit(X_train, y_train)
y_pred_xgb = xgb_model.predict(X_test)
###Output
_____no_output_____
###Markdown
Evaluation of XGBoost performance
###Code
print("MAPE for XGB", Mean_Absolute_Percentage_Error(y_test, y_pred_xgb))
print("MAE for XBG", mean_absolute_error(y_test, y_pred_xgb))
print("RMSE for XGB", np.sqrt(mean_squared_error(y_test, y_pred_xgb)))
print("Mean Squared Error XGB", mean_squared_error(y_test, y_pred_xgb))
print("r_2 score XGB", r2_score(y_test,y_pred_xgb))
###Output
r_2 score XGB 0.5392683884207605
###Markdown
Conclusion Based on the above experiment we choose Gradient Boosting Regressor as the best model as it has the lowest MAPE score Predicting on test data
###Code
pred = gb_reg.predict(test_features_scled)
pd_df_test["avg_travel_time"] = pred
f_data = pd_df_test[["intersection_id","tollgate_id","time_window","avg_travel_time"]]
f_data.head()
f_data.to_csv(path_or_buf="data/task1_submission.csv", index=False)
###Output
_____no_output_____
|
docs/40_tabular_data_wrangling/handling_NaNs.ipynb
|
###Markdown
Handling NaN valuesWhen analysing tabular data, sometimes table cells are present that does not contain data. In Python this typically means the value is _Not a Number_ ([NaN](https://en.wikipedia.org/wiki/NaN)). We cannot assume these values are `0` or `-1` or any other value because that would distort descriptive statistics, for example. We need to deal with these NaN entries differently and this notebook will introduce how.To get a first view where NaNs play a role, we load again an example table and sort it.
###Code
import pandas as pd
data = pd.read_csv('../../data/Results.csv', index_col=0, delimiter=';')
data.sort_values(by = "Area", ascending=False)
###Output
_____no_output_____
###Markdown
As you can see, there are rows at the bottom containing NaNs. These are at the bottom of the table because pandas cannot sort them. A quick check if there are NaNs anywhere in a DataFrame is an important quality check and good scientific practice.
###Code
data.isnull().values.any()
###Output
_____no_output_____
###Markdown
We can also get some deeper insights in which columns these NaN values are located.
###Code
data.isnull().sum()
###Output
_____no_output_____
###Markdown
For getting a glimpse about if we can further process that tabel, we may want to know the percentage of NaNs for each column?
###Code
data.isnull().mean().sort_values(ascending=False) *100
###Output
_____no_output_____
###Markdown
Dropping rows that containt NaNsDepending on what kind of data analysis should be performed, it might make sense to just ignore columns that contain NaN values. Alternatively, it is possible to delete rows that contain NaNs.It depends on your project and what is important or not for the analysis. Its not an easy answer.
###Code
data_no_nan = data.dropna(how="any")
data_no_nan
###Output
_____no_output_____
###Markdown
On the bottom of that table, you can see that it still contains 374 of the original 391 columns. If you remove rows, you should document in your later scientific publication, home many out of how many datasets were analysed.We can now also check again if NaNs are present.
###Code
data_no_nan.isnull().values.any()
###Output
_____no_output_____
###Markdown
(tabular_data_wrangling.handling_nan_values)= Handling NaN valuesWhen analysing tabular data, sometimes table cells are present that does not contain data. In Python this typically means the value is _Not a Number_ ([NaN](https://en.wikipedia.org/wiki/NaN)). We cannot assume these values are `0` or `-1` or any other value because that would distort descriptive statistics, for example. We need to deal with these NaN entries differently and this notebook will introduce how.To get a first view where NaNs play a role, we load again an example table and sort it.
###Code
import numpy as np
import pandas as pd
data = pd.read_csv('../../data/Results.csv', index_col=0, delimiter=';')
data.sort_values(by = "Area", ascending=False)
###Output
_____no_output_____
###Markdown
As you can see, there are rows at the bottom containing NaNs. These are at the bottom of the table because pandas cannot sort them. A quick check if there are NaNs anywhere in a DataFrame is an important quality check and good scientific practice.
###Code
data.isnull().values.any()
###Output
_____no_output_____
###Markdown
We can also get some deeper insights in which columns these NaN values are located.
###Code
data.isnull().sum()
###Output
_____no_output_____
###Markdown
For getting a glimpse about if we can further process that tabel, we may want to know the percentage of NaNs for each column?
###Code
data.isnull().mean().sort_values(ascending=False) *100
###Output
_____no_output_____
###Markdown
Dropping rows that contain NaNsDepending on what kind of data analysis should be performed, it might make sense to just ignore columns that contain NaN values. Alternatively, it is possible to delete rows that contain NaNs.It depends on your project and what is important or not for the analysis. Its not an easy answer.
###Code
data_no_nan = data.dropna(how="any")
data_no_nan
###Output
_____no_output_____
###Markdown
On the bottom of that table, you can see that it still contains 374 of the original 391 columns. If you remove rows, you should document in your later scientific publication, home many out of how many datasets were analysed.We can now also check again if NaNs are present.
###Code
data_no_nan.isnull().values.any()
###Output
_____no_output_____
###Markdown
Determining rows that contain NaNsIn some use-cases it might be useful to have a list of row-indices where there are NaN values.
###Code
data = {
'A': [0, 1, 22, 21, 12, 23],
'B': [2, 3, np.nan, 2, 12, 22],
'C': [2, 3, 44, 2, np.nan, 52],
}
table = pd.DataFrame(data)
table
np.max(table.isnull().values, axis=1)
###Output
_____no_output_____
|
1_Neural Networks and Deep Learning/Week 2/Logistic Regression as a Neural Network/Logistic Regression with a Neural Network mindset v3.ipynb
|
###Markdown
Logistic Regression with a Neural Network mindsetWelcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.**Instructions:**- Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.**You will learn to:**- Build the general architecture of a learning algorithm, including: - Initializing parameters - Calculating the cost function and its gradient - Using an optimization algorithm (gradient descent) - Gather all three functions above into a main model function, in the right order. 1 - Packages First, let's run the cell below to import all the packages that you will need during this assignment. - [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end.
###Code
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
###Output
_____no_output_____
###Markdown
2 - Overview of the Problem set **Problem Statement**: You are given a dataset ("data.h5") containing: - a training set of m_train images labeled as cat (y=1) or non-cat (y=0) - a test set of m_test images labeled as cat or non-cat - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.Let's get more familiar with the dataset. Load the data by running the following code.
###Code
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
###Output
_____no_output_____
###Markdown
We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images.
###Code
# Example of a picture
index = 25
plt.imshow(train_set_x_orig[index])
print ("y = " + str(train_set_y[:, index]) + ", it's a '" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") + "' picture.")
###Output
y = [1], it's a 'cat' picture.
###Markdown
Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs. **Exercise:** Find the values for: - m_train (number of training examples) - m_test (number of test examples) - num_px (= height = width of a training image)Remember that `train_set_x_orig` is a numpy-array of shape (m_train, num_px, num_px, 3). For instance, you can access `m_train` by writing `train_set_x_orig.shape[0]`.
###Code
### START CODE HERE ### (≈ 3 lines of code)
m_train = train_set_x_orig.shape[0]
m_test = test_set_x_orig.shape[0]
num_px = test_set_y.shape[1]
### END CODE HERE ###
print ("Number of training examples: m_train = " + str(m_train))
print ("Number of testing examples: m_test = " + str(m_test))
print ("Height/Width of each image: num_px = " + str(num_px))
print ("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)")
print ("train_set_x shape: " + str(train_set_x_orig.shape))
print ("train_set_y shape: " + str(train_set_y.shape))
print ("test_set_x shape: " + str(test_set_x_orig.shape))
print ("test_set_y shape: " + str(test_set_y.shape))
###Output
Number of training examples: m_train = 209
Number of testing examples: m_test = 50
Height/Width of each image: num_px = 50
Each image is of size: (50, 50, 3)
train_set_x shape: (209, 64, 64, 3)
train_set_y shape: (1, 209)
test_set_x shape: (50, 64, 64, 3)
test_set_y shape: (1, 50)
###Markdown
**Expected Output for m_train, m_test and num_px**: **m_train** 209 **m_test** 50 **num_px** 64 For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px $*$ num_px $*$ 3, 1). After this, our training (and test) dataset is a numpy-array where each column represents a flattened image. There should be m_train (respectively m_test) columns.**Exercise:** Reshape the training and test data sets so that images of size (num_px, num_px, 3) are flattened into single vectors of shape (num\_px $*$ num\_px $*$ 3, 1).A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b$*$c$*$d, a) is to use: ```pythonX_flatten = X.reshape(X.shape[0], -1).T X.T is the transpose of X```
###Code
# Reshape the training and test examples
### START CODE HERE ### (≈ 2 lines of code)
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T
### END CODE HERE ###
print ("train_set_x_flatten shape: " + str(train_set_x_flatten.shape))
print ("train_set_y shape: " + str(train_set_y.shape))
print ("test_set_x_flatten shape: " + str(test_set_x_flatten.shape))
print ("test_set_y shape: " + str(test_set_y.shape))
print ("sanity check after reshaping: " + str(train_set_x_flatten[0:5,0]))
###Output
train_set_x_flatten shape: (12288, 209)
train_set_y shape: (1, 209)
test_set_x_flatten shape: (12288, 50)
test_set_y shape: (1, 50)
sanity check after reshaping: [17 31 56 22 33]
###Markdown
**Expected Output**: **train_set_x_flatten shape** (12288, 209) **train_set_y shape** (1, 209) **test_set_x_flatten shape** (12288, 50) **test_set_y shape** (1, 50) **sanity check after reshaping** [17 31 56 22 33] To represent color images, the red, green and blue channels (RGB) must be specified for each pixel, and so the pixel value is actually a vector of three numbers ranging from 0 to 255.One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel). Let's standardize our dataset.
###Code
train_set_x = train_set_x_flatten/255.
test_set_x = test_set_x_flatten/255.
###Output
_____no_output_____
###Markdown
**What you need to remember:**Common steps for pre-processing a new dataset are:- Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...)- Reshape the datasets such that each example is now a vector of size (num_px \* num_px \* 3, 1)- "Standardize" the data 3 - General Architecture of the learning algorithm It's time to design a simple algorithm to distinguish cat images from non-cat images.You will build a Logistic Regression, using a Neural Network mindset. The following Figure explains why **Logistic Regression is actually a very simple Neural Network!****Mathematical expression of the algorithm**:For one example $x^{(i)}$:$$z^{(i)} = w^T x^{(i)} + b \tag{1}$$$$\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\tag{2}$$ $$ \mathcal{L}(a^{(i)}, y^{(i)}) = - y^{(i)} \log(a^{(i)}) - (1-y^{(i)} ) \log(1-a^{(i)})\tag{3}$$The cost is then computed by summing over all training examples:$$ J = \frac{1}{m} \sum_{i=1}^m \mathcal{L}(a^{(i)}, y^{(i)})\tag{6}$$**Key steps**:In this exercise, you will carry out the following steps: - Initialize the parameters of the model - Learn the parameters for the model by minimizing the cost - Use the learned parameters to make predictions (on the test set) - Analyse the results and conclude 4 - Building the parts of our algorithm The main steps for building a Neural Network are:1. Define the model structure (such as number of input features) 2. Initialize the model's parameters3. Loop: - Calculate current loss (forward propagation) - Calculate current gradient (backward propagation) - Update parameters (gradient descent)You often build 1-3 separately and integrate them into one function we call `model()`. 4.1 - Helper functions**Exercise**: Using your code from "Python Basics", implement `sigmoid()`. As you've seen in the figure above, you need to compute $sigmoid( w^T x + b) = \frac{1}{1 + e^{-(w^T x + b)}}$ to make predictions. Use np.exp().
###Code
# GRADED FUNCTION: sigmoid
def sigmoid(z):
"""
Compute the sigmoid of z
Arguments:
z -- A scalar or numpy array of any size.
Return:
s -- sigmoid(z)
"""
### START CODE HERE ### (≈ 1 line of code)
s = 1./ (1.+np.exp(-z))
### END CODE HERE ###
return s
print ("sigmoid([0, 2]) = " + str(sigmoid(np.array([0,2]))))
###Output
sigmoid([0, 2]) = [ 0.5 0.88079708]
###Markdown
**Expected Output**: **sigmoid([0, 2])** [ 0.5 0.88079708] 4.2 - Initializing parameters**Exercise:** Implement parameter initialization in the cell below. You have to initialize w as a vector of zeros. If you don't know what numpy function to use, look up np.zeros() in the Numpy library's documentation.
###Code
# GRADED FUNCTION: initialize_with_zeros
def initialize_with_zeros(dim):
"""
This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.
Argument:
dim -- size of the w vector we want (or number of parameters in this case)
Returns:
w -- initialized vector of shape (dim, 1)
b -- initialized scalar (corresponds to the bias)
"""
### START CODE HERE ### (≈ 1 line of code)
w = np.zeros((dim, 1))
b = 0
### END CODE HERE ###
assert(w.shape == (dim, 1))
assert(isinstance(b, float) or isinstance(b, int))
return w, b
dim = 2
w, b = initialize_with_zeros(dim)
print ("w = " + str(w))
print ("b = " + str(b))
###Output
w = [[ 0.]
[ 0.]]
b = 0
###Markdown
**Expected Output**: ** w ** [[ 0.] [ 0.]] ** b ** 0 For image inputs, w will be of shape (num_px $\times$ num_px $\times$ 3, 1). 4.3 - Forward and Backward propagationNow that your parameters are initialized, you can do the "forward" and "backward" propagation steps for learning the parameters.**Exercise:** Implement a function `propagate()` that computes the cost function and its gradient.**Hints**:Forward Propagation:- You get X- You compute $A = \sigma(w^T X + b) = (a^{(0)}, a^{(1)}, ..., a^{(m-1)}, a^{(m)})$- You calculate the cost function: $J = -\frac{1}{m}\sum_{i=1}^{m}y^{(i)}\log(a^{(i)})+(1-y^{(i)})\log(1-a^{(i)})$Here are the two formulas you will be using: $$ \frac{\partial J}{\partial w} = \frac{1}{m}X(A-Y)^T\tag{7}$$$$ \frac{\partial J}{\partial b} = \frac{1}{m} \sum_{i=1}^m (a^{(i)}-y^{(i)})\tag{8}$$
###Code
# GRADED FUNCTION: propagate
def propagate(w, b, X, Y):
"""
Implement the cost function and its gradient for the propagation explained above
Arguments:
w -- weights, a numpy array of size (num_px * num_px * 3, 1)
b -- bias, a scalar
X -- data of size (num_px * num_px * 3, number of examples)
Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)
Return:
cost -- negative log-likelihood cost for logistic regression
dw -- gradient of the loss with respect to w, thus same shape as w
db -- gradient of the loss with respect to b, thus same shape as b
Tips:
- Write your code step by step for the propagation. np.log(), np.dot()
"""
m = X.shape[1]
# FORWARD PROPAGATION (FROM X TO COST)
### START CODE HERE ### (≈ 2 lines of code)
A = sigmoid(np.dot(w.T, X) + b) # compute activation
cost = np.sum(Y * np.log(A) + (1 - Y) * np.log(1 - A))/(-m) # compute cost
### END CODE HERE ###
# BACKWARD PROPAGATION (TO FIND GRAD)
### START CODE HERE ### (≈ 2 lines of code)
dw = np.dot(X, (A - Y).T) / m
db = np.sum(A - Y) / m
### END CODE HERE ###
assert(dw.shape == w.shape)
assert(db.dtype == float)
cost = np.squeeze(cost)
assert(cost.shape == ())
grads = {"dw": dw,
"db": db}
return grads, cost
w, b, X, Y = np.array([[1],[2]]), 2, np.array([[1,2],[3,4]]), np.array([[1,0]])
grads, cost = propagate(w, b, X, Y)
print ("dw = " + str(grads["dw"]))
print ("db = " + str(grads["db"]))
print ("cost = " + str(cost))
###Output
dw = [[ 0.99993216]
[ 1.99980262]]
db = 0.499935230625
cost = 6.00006477319
###Markdown
**Expected Output**: ** dw ** [[ 0.99993216] [ 1.99980262]] ** db ** 0.499935230625 ** cost ** 6.000064773192205 d) Optimization- You have initialized your parameters.- You are also able to compute a cost function and its gradient.- Now, you want to update the parameters using gradient descent.**Exercise:** Write down the optimization function. The goal is to learn $w$ and $b$ by minimizing the cost function $J$. For a parameter $\theta$, the update rule is $ \theta = \theta - \alpha \text{ } d\theta$, where $\alpha$ is the learning rate.
###Code
# GRADED FUNCTION: optimize
def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):
"""
This function optimizes w and b by running a gradient descent algorithm
Arguments:
w -- weights, a numpy array of size (num_px * num_px * 3, 1)
b -- bias, a scalar
X -- data of shape (num_px * num_px * 3, number of examples)
Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)
num_iterations -- number of iterations of the optimization loop
learning_rate -- learning rate of the gradient descent update rule
print_cost -- True to print the loss every 100 steps
Returns:
params -- dictionary containing the weights w and bias b
grads -- dictionary containing the gradients of the weights and bias with respect to the cost function
costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.
Tips:
You basically need to write down two steps and iterate through them:
1) Calculate the cost and the gradient for the current parameters. Use propagate().
2) Update the parameters using gradient descent rule for w and b.
"""
costs = []
for i in range(num_iterations):
# Cost and gradient calculation (≈ 1-4 lines of code)
### START CODE HERE ###
grads, cost = propagate(w, b, X, Y)
### END CODE HERE ###
# Retrieve derivatives from grads
dw = grads["dw"]
db = grads["db"]
# update rule (≈ 2 lines of code)
### START CODE HERE ###
w = w - learning_rate*dw
b = b - learning_rate*db
### END CODE HERE ###
# Record the costs
if i % 100 == 0:
costs.append(cost)
# Print the cost every 100 training examples
if print_cost and i % 100 == 0:
print ("Cost after iteration %i: %f" %(i, cost))
params = {"w": w,
"b": b}
grads = {"dw": dw,
"db": db}
return params, grads, costs
params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)
print ("w = " + str(params["w"]))
print ("b = " + str(params["b"]))
print ("dw = " + str(grads["dw"]))
print ("db = " + str(grads["db"]))
###Output
w = [[ 0.1124579 ]
[ 0.23106775]]
b = 1.55930492484
dw = [[ 0.90158428]
[ 1.76250842]]
db = 0.430462071679
###Markdown
**Expected Output**: **w** [[ 0.1124579 ] [ 0.23106775]] **b** 1.55930492484 **dw** [[ 0.90158428] [ 1.76250842]] **db** 0.430462071679 **Exercise:** The previous function will output the learned w and b. We are able to use w and b to predict the labels for a dataset X. Implement the `predict()` function. There is two steps to computing predictions:1. Calculate $\hat{Y} = A = \sigma(w^T X + b)$2. Convert the entries of a into 0 (if activation 0.5), stores the predictions in a vector `Y_prediction`. If you wish, you can use an `if`/`else` statement in a `for` loop (though there is also a way to vectorize this).
###Code
# GRADED FUNCTION: predict
def predict(w, b, X):
'''
Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)
Arguments:
w -- weights, a numpy array of size (num_px * num_px * 3, 1)
b -- bias, a scalar
X -- data of size (num_px * num_px * 3, number of examples)
Returns:
Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X
'''
m = X.shape[1]
Y_prediction = np.zeros((1,m))
w = w.reshape(X.shape[0], 1)
# Compute vector "A" predicting the probabilities of a cat being present in the picture
### START CODE HERE ### (≈ 1 line of code)
A = np.dot(w.T, X)
### END CODE HERE ###
for i in range(A.shape[1]):
# Convert probabilities A[0,i] to actual predictions p[0,i]
### START CODE HERE ### (≈ 4 lines of code)
if (A[0, i] > 0.5):
Y_prediction[0][i] = 1
else:
Y_prediction[0][i] = 0
### END CODE HERE ###
assert(Y_prediction.shape == (1, m))
return Y_prediction
print ("predictions = " + str(predict(w, b, X)))
###Output
predictions = [[ 1. 1.]]
###Markdown
**Expected Output**: **predictions** [[ 1. 1.]] **What to remember:**You've implemented several functions that:- Initialize (w,b)- Optimize the loss iteratively to learn parameters (w,b): - computing the cost and its gradient - updating the parameters using gradient descent- Use the learned (w,b) to predict the labels for a given set of examples 5 - Merge all functions into a model You will now see how the overall model is structured by putting together all the building blocks (functions implemented in the previous parts) together, in the right order.**Exercise:** Implement the model function. Use the following notation: - Y_prediction for your predictions on the test set - Y_prediction_train for your predictions on the train set - w, costs, grads for the outputs of optimize()
###Code
# GRADED FUNCTION: model
def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
"""
Builds the logistic regression model by calling the function you've implemented previously
Arguments:
X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)
Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)
X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)
Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)
num_iterations -- hyperparameter representing the number of iterations to optimize the parameters
learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()
print_cost -- Set to true to print the cost every 100 iterations
Returns:
d -- dictionary containing information about the model.
"""
### START CODE HERE ###
# initialize parameters with zeros (≈ 1 line of code)
w, b = initialize_with_zeros(X_train.shape[0])
# Gradient descent (≈ 1 line of code)
parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost = False)
# Retrieve parameters w and b from dictionary "parameters"
w = parameters["w"]
b = parameters["b"]
# Predict test/train set examples (≈ 2 lines of code)
Y_prediction_test = predict(w, b, X_test)
Y_prediction_train = predict(w, b, X_train)
### END CODE HERE ###
# Print train/test Errors
print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))
d = {"costs": costs,
"Y_prediction_test": Y_prediction_test,
"Y_prediction_train" : Y_prediction_train,
"w" : w,
"b" : b,
"learning_rate" : learning_rate,
"num_iterations": num_iterations}
return d
###Output
_____no_output_____
###Markdown
Run the following cell to train your model.
###Code
d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)
###Output
train accuracy: 98.08612440191388 %
test accuracy: 70.0 %
###Markdown
**Expected Output**: **Train Accuracy** 99.04306220095694 % **Test Accuracy** 70.0 % **Comment**: Training accuracy is close to 100%. This is a good sanity check: your model is working and has high enough capacity to fit the training data. Test error is 68%. It is actually not bad for this simple model, given the small dataset we used and that logistic regression is a linear classifier. But no worries, you'll build an even better classifier next week!Also, you see that the model is clearly overfitting the training data. Later in this specialization you will learn how to reduce overfitting, for example by using regularization. Using the code below (and changing the `index` variable) you can look at predictions on pictures of the test set.
###Code
# Example of a picture that was wrongly classified.
index = 1
plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))
print ("y = " + str(test_set_y[0,index]) + ", you predicted that it is a \"" + classes[d["Y_prediction_test"][0,index]].decode("utf-8") + "\" picture.")
###Output
_____no_output_____
###Markdown
Let's also plot the cost function and the gradients.
###Code
# Plot learning curve (with costs)
costs = np.squeeze(d['costs'])
plt.plot(costs)
plt.ylabel('cost')
plt.xlabel('iterations (per hundreds)')
plt.title("Learning rate =" + str(d["learning_rate"]))
plt.show()
###Output
_____no_output_____
###Markdown
**Interpretation**:You can see the cost decreasing. It shows that the parameters are being learned. However, you see that you could train the model even more on the training set. Try to increase the number of iterations in the cell above and rerun the cells. You might see that the training set accuracy goes up, but the test set accuracy goes down. This is called overfitting. 6 - Further analysis (optional/ungraded exercise) Congratulations on building your first image classification model. Let's analyze it further, and examine possible choices for the learning rate $\alpha$. Choice of learning rate **Reminder**:In order for Gradient Descent to work you must choose the learning rate wisely. The learning rate $\alpha$ determines how rapidly we update the parameters. If the learning rate is too large we may "overshoot" the optimal value. Similarly, if it is too small we will need too many iterations to converge to the best values. That's why it is crucial to use a well-tuned learning rate.Let's compare the learning curve of our model with several choices of learning rates. Run the cell below. This should take about 1 minute. Feel free also to try different values than the three we have initialized the `learning_rates` variable to contain, and see what happens.
###Code
learning_rates = [0.01, 0.001, 0.0001]
models = {}
for i in learning_rates:
print ("learning rate is: " + str(i))
models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)
print ('\n' + "-------------------------------------------------------" + '\n')
for i in learning_rates:
plt.plot(np.squeeze(models[str(i)]["costs"]), label= str(models[str(i)]["learning_rate"]))
plt.ylabel('cost')
plt.xlabel('iterations')
legend = plt.legend(loc='upper center', shadow=True)
frame = legend.get_frame()
frame.set_facecolor('0.90')
plt.show()
###Output
learning rate is: 0.01
train accuracy: 99.52153110047847 %
test accuracy: 68.0 %
-------------------------------------------------------
learning rate is: 0.001
train accuracy: 81.81818181818181 %
test accuracy: 52.0 %
-------------------------------------------------------
learning rate is: 0.0001
train accuracy: 65.55023923444976 %
test accuracy: 34.0 %
-------------------------------------------------------
###Markdown
**Interpretation**: - Different learning rates give different costs and thus different predictions results.- If the learning rate is too large (0.01), the cost may oscillate up and down. It may even diverge (though in this example, using 0.01 still eventually ends up at a good value for the cost). - A lower cost doesn't mean a better model. You have to check if there is possibly overfitting. It happens when the training accuracy is a lot higher than the test accuracy.- In deep learning, we usually recommend that you: - Choose the learning rate that better minimizes the cost function. - If your model overfits, use other techniques to reduce overfitting. (We'll talk about this in later videos.) 7 - Test with your own image (optional/ungraded exercise) Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that: 1. Click on "File" in the upper bar of this notebook, then click "Open" to go on your Coursera Hub. 2. Add your image to this Jupyter Notebook's directory, in the "images" folder 3. Change your image's name in the following code 4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!
###Code
## START CODE HERE ## (PUT YOUR IMAGE NAME)
my_image = "lala.jpg" # change this to the name of your image file
## END CODE HERE ##
# We preprocess the image to fit your algorithm.
fname = "images/" + my_image
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T
my_predicted_image = predict(d["w"], d["b"], my_image)
plt.imshow(image)
print("y = " + str(np.squeeze(my_predicted_image)) + ", your algorithm predicts a \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") + "\" picture.")
###Output
_____no_output_____
|
Day_25/college_salaries.ipynb
|
###Markdown
Starting Working on Pandas and Data Analysis
###Code
import pandas
# reading the files
data = pandas.read_csv("salaries_by_college_major.csv")
data.head(5)
data.columns
data.shape
data.tail().isna()
#remove na values
cleaned_data = data.dropna()
cleaned_data.tail()
# find the row with highest starting median salary
cleaned_data["Starting Median Salary"].idxmax()
# get that entire row
cleaned_data.loc[43]
# What college major has the highest mid-career salary? How much do
# graduates with this major earn? (Mid-career is defined as having 10+ years of experience).
cleaned_data.loc[cleaned_data["Mid-Career Median Salary"].idxmax()]
# Which college major has the lowest starting salary
# and how much do graduates earn after university?
cleaned_data.loc[cleaned_data["Starting Median Salary"].idxmin()]
# Which college major has the lowest mid-career
# salary and how much can people expect to earn with this degree?
cleaned_data.loc[cleaned_data["Mid-Career Median Salary"].idxmin()]
# How would we calculate the
# difference between the earnings of the
# 10th and 90th percentile?
#Calculate
spread_col = cleaned_data['Mid-Career 90th Percentile Salary'] - cleaned_data['Mid-Career 10th Percentile Salary']
#insert new column
cleaned_data.insert(1, 'Spread', spread_col)
#display the data
cleaned_data.head()
# Sort by lowest risk
low_risk = cleaned_data.sort_values('Spread')
low_risk[['Undergraduate Major', 'Spread']].head()
# Using the .sort_values() method, can you find the degrees with
# the highest potential? Find the top 5 degrees
# with the highest values in the 90th percentile.
high_value =cleaned_data.sort_values("Mid-Career 90th Percentile Salary", ascending=False)
high_value.head(5)
###Output
_____no_output_____
###Markdown
Grouping and Pivoting Data with Pandas
###Code
cleaned_data.groupby("Group").count()
pandas.options.display.float_format = '{:,.2f}'.format
cleaned_data.groupby("Group").mean()
###Output
_____no_output_____
|
jaffe-with-resnet50.ipynb
|
###Markdown
Existing model is forked From [https://github.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE](http://) . But I have removed the labeling error and changed the CNN Model . I have also updated the existing model that gives better accuracy than previous.
###Code
import os,cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from pylab import rcParams
rcParams['figure.figsize'] = 20, 10
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
import keras
from keras.utils import np_utils
from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD,RMSprop,adam
from keras.preprocessing.image import ImageDataGenerator
data_path = '../input/jaffefacialexpression/jaffe/jaffe'
data_dir_list = os.listdir(data_path)
img_rows=256
img_cols=256
num_channel=1
num_epoch=10
img_data_list=[]
for dataset in data_dir_list:
img_list=os.listdir(data_path+'/'+ dataset)
print ('Loaded the images of dataset-'+'{}\n'.format(dataset))
for img in img_list:
input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img )
#input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)
input_img_resize=cv2.resize(input_img,(128,128))
img_data_list.append(input_img_resize)
img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data = img_data/255
img_data.shape
num_classes = 7
num_of_samples = img_data.shape[0]
labels = np.ones((num_of_samples,),dtype='int64')
labels[0:29]=0 #30
labels[30:58]=1 #29
labels[59:90]=2 #32
labels[91:121]=3 #31
labels[122:151]=4 #30
labels[152:182]=5 #31
labels[183:]=6 #30
names = ['ANGRY','DISGUST','FEAR','HAPPY','NEUTRAL','SAD','SURPRISE']
def getLabel(id):
return ['ANGRY','DISGUST','FEAR','HAPPY','NEUTRAL','SAD','SURPRISE'][id]
Y = np_utils.to_categorical(labels, num_classes)
#Shuffle the dataset
x,y = shuffle(img_data,Y, random_state=2)
# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=2)
x_test=X_test
#X_train=X_train.reshape(X_train.shape[0],128,128,1)
#X_test=X_test.reshape(X_test.shape[0],128,128,1)
x_test.shape
from keras.models import Sequential
from keras.layers import Dense , Activation , Dropout ,Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.metrics import categorical_accuracy
from keras.models import model_from_json
from keras.callbacks import ModelCheckpoint
from keras.optimizers import *
from keras.layers.normalization import BatchNormalization
input_shape=(128,128,3)
model = Sequential()
model.add(Conv2D(6, (5, 5), input_shape=input_shape, padding='same', activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(16, (5, 5), padding='same', activation = 'relu'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation = 'softmax'))
# Classification
# model.add(Flatten())
# model.add(Dense(64))
# model.add(Activation('relu'))
# model.add(Dropout(0.5))
# model.add(Dense(num_classes))
# model.add(Activation('softmax'))
#Compile Model
model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=["accuracy"])
model.summary()
model.get_config()
model.layers[0].get_config()
model.layers[0].input_shape
model.layers[0].output_shape
model.layers[0].get_weights()
np.shape(model.layers[0].get_weights()[0])
model.layers[0].trainable
from keras import callbacks
filename='model_train_new.csv'
filepath="Best-weights-my_model-{epoch:03d}-{loss:.4f}-{acc:.4f}.hdf5"
csv_log=callbacks.CSVLogger(filename, separator=',', append=False)
checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [csv_log,checkpoint]
callbacks_list = [csv_log]
hist = model.fit(X_train, y_train, batch_size=7, epochs=50, verbose=1, validation_data=(X_test, y_test),callbacks=callbacks_list)
#Model Save
model.save_weights('model_weights.h5')
model.save('model_keras.h5')
# visualizing losses and accuracy
%matplotlib inline
train_loss=hist.history['loss']
val_loss=hist.history['val_loss']
train_acc=hist.history['acc']
val_acc=hist.history['val_acc']
epochs = range(len(train_acc))
plt.plot(epochs,train_loss,'r', label='train_loss')
plt.plot(epochs,val_loss,'b', label='val_loss')
plt.title('train_loss vs val_loss')
plt.legend()
plt.figure()
plt.plot(epochs,train_acc,'r', label='train_acc')
plt.plot(epochs,val_acc,'b', label='val_acc')
plt.title('train_acc vs val_acc')
plt.legend()
plt.figure()
# Evaluating the model
score = model.evaluate(X_test, y_test, verbose=0)
print('Test Loss:', score[0])
print('Test accuracy:', score[1])
test_image = X_test[0:1]
print (test_image.shape)
print(model.predict(test_image))
print(model.predict_classes(test_image))
print(y_test[0:1])
res = model.predict_classes(X_test[9:18])
plt.figure(figsize=(10, 10))
for i in range(0, 9):
plt.subplot(330 + 1 + i)
plt.imshow(x_test[i],cmap=plt.get_cmap('gray'))
plt.gca().get_xaxis().set_ticks([])
plt.gca().get_yaxis().set_ticks([])
plt.ylabel('prediction = %s' % getLabel(res[i]), fontsize=14)
# show the plot
plt.show()
from sklearn.metrics import confusion_matrix
results = model.predict_classes(X_test)
cm = confusion_matrix(np.where(y_test == 1)[1], results)
plt.matshow(cm)
plt.title('Confusion Matrix')
plt.colorbar()
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.show()
testimg_data_list=[]
test_img=cv2.imread('../input/facial-expression/facial/facial/Shawon.jpg',True)
test_img_resize=cv2.resize(test_img,(128,128))
testimg_data_list.append(test_img_resize)
testimg_data = np.array(testimg_data_list)
testimg_data = testimg_data.astype('float32')
testimg_data = testimg_data/255
testimg_data.shape
print("test image original shape",testimg_data[0].shape)
print("image original shape",img_data[0].shape)
results = model.predict_classes(testimg_data)
plt.imshow(test_img,cmap=plt.get_cmap('Set2'))
plt.gca().get_xaxis().set_ticks([])
plt.gca().get_yaxis().set_ticks([])
plt.xlabel('prediction = %s' % getLabel(results[0]), fontsize=25)
###Output
_____no_output_____
|
Pulling and Processing PDFs .ipynb
|
###Markdown
author: Ben P. Meredith, Ed.D., February 2019 *Pulling and Processing PDFs *This is a KEY piece of code. This code 1. pulls a .pdf from a directory1. rejects the .pdf if it has "._"_ in the title1. pulls the text from the .pdf1. cleans the text1. tokenizes the text by sentence1. searches each sentence for our designated list of keywords1. saves the sentences that contain one of our keywords in a dataframe - saves the name of the file for reference - saves the keyword in the same row for reference - saves the sentence in the same row Load Libraries`__nlp_init_nsv__.py` is a local library that I produced. It is necessary to run this program. It will take a moment to run as it is rather long. It is loading a lot of things.
###Code
%run lib/__nlp_init_nsv__.py
###Output
Welcome to our Natural Language Processing Library. I am loading at the moment and will be done in a nanosecond...
********************************************************************************
nltk imported
nltk SnowballStemmer imported
nltk WordNetLemmatizer imported
spacy imported
Pattern parse imported
Pattern imported
Pandas imported as pd
Numpy imported as np
re imported
BeautifulSoup imported
CONTRACTION_MAP imported
Unicodedata imported.
PyPDF2 imported.
Gensim's summarize imported.
Textract imported.
Gensim's keywords imported
Pyphen imported.
Textblob imported.
Logging imported logging for summarizing and reading text from PDFs
sys imported.
Counter imported.
NLTK's sent_tokenize imported.
NLTK's word_tokenize imported.
__________________________________________________
Our libraries are loaded correctly.
__________________________________________________
DONE! Phew, I am good! Record Time!
I also loaded the following functions:
pull_all_text(df)
add_to_library(df)
clean_the_text(raw_text)
drop_nonenglish_words(text)
remove_puntuation(text)
strip_html_tags(text)
remove_accented_characters(text)
expand_contractions(text, contraction_mapping=CONTRACTION_MAP
remove_special_characters(text, remove_digits=False)
simple_stemmer(text)
lemmatize_text(text)
remove_stopwords(text, is_lower_case=False)
normalize_corpus(corpus, html_stripping=True, contraction_expansion=True, accented_char_removal=True, text
_lower_case=True, text_lemmatization=True, special_char_removal=True, stopword_removal=True,
remove_digits=True)
import_pdf(file_path)
tokenize_by_sentences(text)
tokenize_by_words(text)
find_keywords(text)
pos_tag(text)
normalize_corpus(text)
import_pdf(file_path)
word_count(string)
countwords(words, length_of_word_to_find)
avgwordspersentence(words)
noofsyllables(words)
********************************************************************************
To learn more about each of these functions, please type 'Tell_me_more(the function's name' and I will happily tell you more about each of these functions.
********************************************************************************
###Markdown
The Real Work of the Program
###Code
# Pulling only those files with a .pdf extention
import os
df = pd.DataFrame(columns=['1'])
i = 0
# directory is where you point Python to where the .pdfs are stored in a blob
directory = 'test_pdfs/'
#keywords are those words or parts of words for which we are searching
keywords = ['correlat',
'male',
'race',
'ethn',
'will be used',
'female',
'youth',
'weapon',
'adolescent',
'gun',
'knife',
' caus',
]
for root, dirs, files in os.walk(directory):
for file in files:
if file.endswith(".pdf"):
# print(os.path.join(root, file)) ## This line is only used to check our work
if '._' in file:
# This line tells us which files do not make the criteria for being processed
# This printed line is NOT saved.
print('\n\n',(os.path.join(root, file)),'ignored.', '\n\n')
pass
else:
#if there is a unicodedecode error because of the .pdf, this will skip the bad file.
try:
s = str(directory+file)
# This line tells us which files are being processed. It is not saved.
print('\n\n',s, 'being processed.','\n\n')
# import the text from the .pdfs, convert it to a string, and clean it
text = import_pdf(s)
text = str(text)
text = clean_the_text(text) #uses my internal library from __nlp_init_nsv__.py
#Where the real work is done
for term in keywords:
for sentence in sent_tokenize(text):
if term in sentence:
sentence = clean_the_text(sentence)
print(term, '\n', sentence)
df.loc[i, 'Article'] = s
df.loc[i, 'Keyword'] = term
df.loc[i, 'Sentence'] = sentence
i += 1
except UnicodeDecodeError:
print('\n\n',(os.path.join(root, file)),'ignored.', '\n\n')
continue
df
###Output
test_pdfs/The Revenge Of The Lost Boys.pdf being processed.
CLEANING THE TEXT
CLEANING THE TEXT
male
Although mass killers understandably seize our imaginations and dominate the media, and not all dysfunctional young males are violent and not all of them gain the publicity they crave.
CLEANING THE TEXT
male
What they all have in common is their gender (male), their race (most are white), and their youth (almost all under 30 at their peak destructiveness).
CLEANING THE TEXT
male
Some of them kill, but others lash out in other, more creative http://thefederalist.com/2015/07/09/the-revenge-of-the-lost-boys/ 1/9 3/24/2018 The Revenge Of The Lost Boys ways: whether its Edward Snowden deciding only he could save America from the scourge of surveillance, or Bowe Bergdahl walking away from his post to personally solve the war in Afghanistan, the combination of immaturity and grandiosity among these young males is jaw-dropping in its scale even when it is not expressed through the barrel of a gun.
CLEANING THE TEXT
male
They turn into what German writer Hans Enzensberger called "the http://thefederalist.com/2015/07/09/the-revenge-of-the-lost-boys/ 2/9 3/24/2018 The Revenge Of The Lost Boys radicalized losers," the unsuccessful males who channel their blunted male social impulses toward destruction.
CLEANING THE TEXT
male
Jihadis, of course, are the object lesson in this kind of deformed male identity.
CLEANING THE TEXT
male
It narcissism among seems unarguable, however, that young black males who younger people are prey on their own society share one essential trait in creating a new kind common with the white losers who act out and harm of lashing out.
CLEANING THE TEXT
male
Like their white brethren, dangerous black males are angry and childish, but their effect usually does not reach beyond their own neighborhoods.
CLEANING THE TEXT
male
Likewise, the media and the public, for a variety of tragic reasons, simply do not respond to the daily violence among young blacks the same way they respond to the showy productions executed by angry white males.
CLEANING THE TEXT
male
Very few Americans serve in the military, yet among this small sample of alienated losers, many either joined or tried to join the Army, including McVeigh, Snowden, Bergdahl, and, while he identified as a male, a young Bradley Manning.
CLEANING THE TEXT
male
A Failure to Mature Out of Social Confusion Intelligence analyst John Schindler has identified these males as a generational "insider threat" to the security of the United States.
CLEANING THE TEXT
male
Nonetheless, social isolation is an important, even key, factor in the paths chosen by these males.
CLEANING THE TEXT
male
And this should matter to all of us, because when society breeds too many narcissistic males determined to get even with a world that denies them their due-the fame, recognition, or sexual mate they think they deserve-were all in danger.
CLEANING THE TEXT
male
Young males no longer live in a world where theres a Jack for every Jill, or where social institutions like schools, the police, churches, or the military-all decimated by repeated social attack since the 1960s-provide some kind of equalizing effect among men, protecting and building up the weaker boys while disciplining and maturing the stronger ones.
CLEANING THE TEXT
male
The result is that today American youth, and especially the males, live in a kind of "Lord of the Flies" domain where the Wild Boys act without restraint and the weak kids fall off the ledge, without even a noble Ralph to mourn them.
CLEANING THE TEXT
male
The alreadyanarchic environment of adolescence has been turned completely toxic by the absence of responsible adults and especially of male role models.
CLEANING THE TEXT
male
I dont know how to do that: the multiple horses of promiscuity, affluence (even among "poor" kids), permissiveness, violent and ghettoized teen culture, and perpetual immaturity are so far out of the barn now, and so entrenched in American life, that I have no idea how to stop their corrosive influence on the weaker or less competitive males who are plowed under a society that moves faster than they, and we, can manage.
CLEANING THE TEXT
male
The traditional venues for male socialization adolescence has (including marriage) have mostly vanished or, in the case been turned of schools, been rendered safe havens from the normal completely toxic by behavior of males in need of discipline and maturing.
CLEANING THE TEXT
male
the absence of Nor can mentors or schools fight the epidemic of divorce, responsible adults pop culture, the media, and the overall assaults on the and especially of creation of the kind of family life that channels men male role models.
CLEANING THE TEXT
race
What they all have in common is their gender (male), their race (most are white), and their youth (almost all under 30 at their peak destructiveness).
CLEANING THE TEXT
race
In a darker but similar vein, former Federal Bureau of Investigation agent Dave Gomez noted after the Roof case that many of these young men "are guys with anger issues about race and unfairness and loneliness and inadequacy, and they find this stuff online and start copying the rhetoric.
CLEANING THE TEXT
youth
What they all have in common is their gender (male), their race (most are white), and their youth (almost all under 30 at their peak destructiveness).
CLEANING THE TEXT
youth
The result is that today American youth, and especially the males, live in a kind of "Lord of the Flies" domain where the Wild Boys act without restraint and the weak kids fall off the ledge, without even a noble Ralph to mourn them.
CLEANING THE TEXT
adolescent
For all their faux piety and supposed distaste for Western immorality, the young men from North America and Europe who gravitate toward jihadism are often gleeful consumers of forbidden Western delights, and they have a particular obsession with rape, pornography, and an adolescent fixation on the subjugation of women.
CLEANING THE TEXT
adolescent
With adolescent fantasies of grandeur, the young Wolfking, the "TrueHOOHA" (as he was known in another of his identities on the Internet) also headed for the Special Forces-every little boy wants to be the bravest soldier in the Army, apparently-only to find that things like standing up straight and getting out of bed in the morning were skills hed have to master before he could jump out of airplanes with a knife in his teeth.
CLEANING THE TEXT
adolescent
If it hadnt been one of these causes, it would have been something else: I have no doubt that at some point we will see a mass murder or http://thefederalist.com/2015/07/09/the-revenge-of-the-lost-boys/ 6/9 3/24/2018 The Revenge Of The Lost Boys anti-government attack committed by a spelling reformer or a raw-milk advocate, if thats what it takes for a screwed-up adolescent to act out his rage against a world that refuses to acknowledges his specialness.
CLEANING THE TEXT
gun
Amidst the wreckage, we look for reasons that already fit our preconceptions about violence, and we blame racism, guns, unemployment, drugs, a bad family, or whatever else helps us to make sense of the tragedy.
CLEANING THE TEXT
gun
Some of them kill, but others lash out in other, more creative http://thefederalist.com/2015/07/09/the-revenge-of-the-lost-boys/ 1/9 3/24/2018 The Revenge Of The Lost Boys ways: whether its Edward Snowden deciding only he could save America from the scourge of surveillance, or Bowe Bergdahl walking away from his post to personally solve the war in Afghanistan, the combination of immaturity and grandiosity among these young males is jaw-dropping in its scale even when it is not expressed through the barrel of a gun.
CLEANING THE TEXT
gun
Many of these young misfits are mesmerized, as boys transitioning to men often are, with symbols of sex and power: guns, the military, and heroic medieval myths.
CLEANING THE TEXT
knife
With adolescent fantasies of grandeur, the young Wolfking, the "TrueHOOHA" (as he was known in another of his identities on the Internet) also headed for the Special Forces-every little boy wants to be the bravest soldier in the Army, apparently-only to find that things like standing up straight and getting out of bed in the morning were skills hed have to master before he could jump out of airplanes with a knife in his teeth.
CLEANING THE TEXT
caus
A young man-a "loner" and "adrift," as usual-seizes a vile cause and attacks innocent people.
CLEANING THE TEXT
caus
If it hadnt been one of these causes, it would have been something else: I have no doubt that at some point we will see a mass murder or http://thefederalist.com/2015/07/09/the-revenge-of-the-lost-boys/ 6/9 3/24/2018 The Revenge Of The Lost Boys anti-government attack committed by a spelling reformer or a raw-milk advocate, if thats what it takes for a screwed-up adolescent to act out his rage against a world that refuses to acknowledges his specialness.
test_pdfs/._The Revenge Of The Lost Boys.pdf ignored.
test_pdfs/The School Shootings of 2018_ What's Behind the Numbers - Education Week.pdf being processed.
###Markdown
Save to FileI included a copy of the output to the file just to give you an idea of what it should come out looking like.
###Code
# Save the file
df.to_csv('KeywordExtract.csv')
###Output
_____no_output_____
|
FactorizacionDiagonalizacion.ipynb
|
###Markdown
[View in Colaboratory](https://colab.research.google.com/github/zronyj/matematica/blob/master/FactorizacionDiagonalizacion.ipynb) ¡Te volvemos a dar la bienvenida a un cuaderno interactivo de Colaboratory!En esta ocasión vamos a ir directamente al grano y comenzar a trabajar con matrices de Hückel. Según la práctica de laboratorio, una matriz de Hückel se construye para moléculas con enlaces $\pi$ en resonancia. Para ello vamos a comenzar con un ejemplo sencillo: una molécula de **benceno**.La matriz para el benceno va a tener 6 columnas y 6 filas, pues son solo 6 los átomos que cuentan con enlaces $\pi$ en resonancia. Siguiendo con lo propuesto, la diagonal principal se debe llenar con valores $\alpha$ que representan la energía de ionización del orbital **p**, y luego se deben de colocar $\beta$ en aquella combinación de fila-columna en donde se encuentre un enlace.Por ejemplo, la fila 1, columna 2 representa al átomo de carbono 1 y el átomo de carbono 2 en la molécula de benceno. De estos podemos esperar que haya un enlace, por lo que colocamos un $\beta$ en esa entrada de la matriz. Siguiendo así terminaríamos con la siguiente matriz:$$\mathbf{H} = \left [ \begin{matrix}\alpha & \beta & 0 & 0 & 0 & \beta\\ \beta & \alpha & \beta & 0 & 0 & 0\\ 0 & \beta & \alpha & \beta & 0 & 0\\ 0 & 0 & \beta & \alpha & \beta & 0\\ 0 & 0 & 0 & \beta & \alpha & \beta\\ \beta & 0 & 0 & 0 & \beta & \alpha\end{matrix} \right ]$$ Sin embargo, al colocar esto dentro de la ecuación de valores propios, resulta que se puede manipular más este problema para facilitar su resolución.$$\left [ \begin{matrix}\alpha & \beta & 0 & 0 & 0 & \beta\\ \beta & \alpha & \beta & 0 & 0 & 0\\ 0 & \beta & \alpha & \beta & 0 & 0\\ 0 & 0 & \beta & \alpha & \beta & 0\\ 0 & 0 & 0 & \beta & \alpha & \beta\\ \beta & 0 & 0 & 0 & \beta & \alpha\end{matrix} \right ] \cdot\left [ \begin{matrix}c_1\\ c_2\\ c_3\\ c_4\\ c_5\\ c_6\end{matrix} \right ] =\left [ \begin{matrix}E_1 & 0 & 0 & 0 & 0 & 0\\ 0 & E_2 & 0 & 0 & 0 & 0\\ 0 & 0 & E_3 & 0 & 0 & 0\\ 0 & 0 & 0 & E_4 & 0 & 0\\ 0 & 0 & 0 & 0 & E_5 & 0\\ 0 & 0 & 0 & 0 & 0 & E_6\end{matrix} \right ] \cdot\left [ \begin{matrix}c_1\\ c_2\\ c_3\\ c_4\\ c_5\\ c_6\end{matrix} \right ]$$ Entonces, factorizando un poco y dividiendo entre $\beta$, se puede tener la siguiente situación:$$\left [ \begin{matrix}\frac{\left ( \alpha - E \right )}{\beta}& 1 & 0 & 0 & 0 & 1\\ 1 & \frac{\left ( \alpha - E \right )}{\beta} & 1 & 0 & 0 & 0\\ 0 & 1 & \frac{\left ( \alpha - E \right )}{\beta} & 1 & 0 & 0\\ 0 & 0 & 1 & \frac{\left ( \alpha - E \right )}{\beta} & 1 & 0\\ 0 & 0 & 0 & 1 & \frac{\left ( \alpha - E \right )}{\beta} & 1\\ 1 & 0 & 0 & 0 & 1 & \frac{\left ( \alpha - E \right )}{\beta}\end{matrix} \right ] \cdot\left [ \begin{matrix}c_1\\ c_2\\ c_3\\ c_4\\ c_5\\ c_6\end{matrix} \right ] = 0$$ Si definimos $x_i = \frac{E_i - \alpha}{\beta}$, entonces es posible reescribir la anterior ecuación como:$$\left [ \begin{matrix}-x_1& 1 & 0 & 0 & 0 & 1\\ 1 & -x_2 & 1 & 0 & 0 & 0\\ 0 & 1 & -x_3 & 1 & 0 & 0\\ 0 & 0 & 1 & -x_4 & 1 & 0\\ 0 & 0 & 0 & 1 & -x_5 & 1\\ 1 & 0 & 0 & 0 & 1 & -x_6\end{matrix} \right ] \cdot\left [ \begin{matrix}c_1\\ c_2\\ c_3\\ c_4\\ c_5\\ c_6\end{matrix} \right ] = 0$$ Lo cual es simplemente el problema de valores propios del que partimos, pero con $E_i = \alpha + x_i \beta$.$$\left [ \begin{matrix}0 & 1 & 0 & 0 & 0 & 1\\ 1 & 0 & 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 1 & 0 & 0\\ 0 & 0 & 1 & 0 & 1 & 0\\ 0 & 0 & 0 & 1 & 0 & 1\\ 1 & 0 & 0 & 0 & 1 & 0\end{matrix} \right ] \cdot\left [ \begin{matrix}c_1\\ c_2\\ c_3\\ c_4\\ c_5\\ c_6\end{matrix} \right ] =x_i \cdot\left [ \begin{matrix}c_1\\ c_2\\ c_3\\ c_4\\ c_5\\ c_6\end{matrix} \right ]$$ Ahora vamos a proceder a resolver el problema de valores propios, y así encontrar los valores de energía del sistema. Lo primero que vamos a intentar será descomponer la matriz $\mathbf{H}$ en matrices $Q$ y $R$.
###Code
# Ejecuta esta celda sin realizar cambios en ella
import numpy as np
import numpy.linalg as LA
import matplotlib.pyplot as plt
import matplotlib.cm as cm
np.set_printoptions(suppress=True)
# Creamos la matriz H de la molecula de benceno
benceno = np.array([[0,1,0,0,0,1],
[1,0,1,0,0,0],
[0,1,0,1,0,0],
[0,0,1,0,1,0],
[0,0,0,1,0,1],
[1,0,0,0,1,0]])
# Calculamos las matrices Q y R a partir de la matriz de Huckel del benceno
q, r = LA.qr(benceno)
np.around(q, decimals=4)
np.around(r, decimals=4)
###Output
_____no_output_____
###Markdown
En este punto es probable que surja la pregunta: ¿Y para qué me sirve poder realizar la descomposición QR? Pues esta es la parte más interesante de todo lo que se hará hoy. Hasta ahora, solo sabíamos calcular los valores y vectores propios de una matriz mediante su polinomio característico. Sin embargo, hace unos momentos vimos cómo es que diagonalizar una matriz lleva al mismo resultado. Entonces, recordando en principio cómo se diagonaliza una matriz $A$ (se busca una matriz $P$ tal que $P^{-1} A P = D$ ) y recordando cómo se halla la matriz $R$ en una descomposición QR ($R = Q^{-1} A$), vamos a realizar el siguiente análisis:$$A = QR$$$$R Q = Q^{-1} A Q = A'$$Entonces, ¡multiplicar $R$ por $Q$ es equivalente a diagonalizar $A$! El problema es que esto no sucede inmediatamente. $A'$ se aproxima a la forma diagonalizada de $A$, pero no es la matriz diagonal. ¿Entonces qué? Pues repetimos este procedimiento. Esto se repite tantas veces como sea necesario hasta que los valores de $Q$ y de $R$ ya no cambien.
###Code
# Primero vamos a volver a la matriz H del benceno; a la larga es Q por R
b = np.dot(q,r)
np.around(b, decimals=4)
# Ahora vamos a repetir el proceso de factorizar la matriz
# y generar una nueva matriz 200 veces
for i in xrange(200):
q, r = LA.qr(b)
b = np.dot(np.dot(np.transpose(q),b), q)
# Finalmente vamos a volver a hallar las matrices Q y R
q, r = LA.qr(b)
# Y es la matriz R la contiene los valores propios en la diagonal principal
np.around(r, decimals=4)
###Output
_____no_output_____
###Markdown
A este punto vale la pena decir que los valores propios de $\mathbf{H}$ son `[-2, -1, -1, 1, 1, 2]`. Entonces, ¿existe algún error? Lo que sucede es que nuestros ordenadores están trabajando con pocos decimales relativamente y este problema requiere mucho mayor precisión. Por la misma razón, se han tenido que buscar alternativas para este tipo de problemas. Sin embargo, vale la pena resaltar que nuestros ordenadores **sí** utilizan el algoritmo QR para este tipo de problemas, solo que este cuenta con otro par de modificaciones para evitar estos errores. A continuación se presenta la manera en que lo hace NumPy.
###Code
# Calculamos los valores y vectores propios de la matriz H en una linea
w, v = LA.eigh(benceno)
# Luego mostramos los valores propios
np.around(w, decimals=4)
# Y finalmente, observamos los vectores propios
np.around(v, decimals=4)
# Ejecuta esta celda sin hacer cambios en ella
# Funcion de onda para orbital 2p de un atomo de carbono
def Psi(x, y, z, x0, y0, z0):
r = ((x - x0)**2 + (y - y0)**2 + (z - z0)**2)**0.5
A = (3.25**5 / (32*np.pi))**0.5
ex = -3.25 * r / 2
return A * (z - z0) * np.e**ex
# Ejecuta esta celda sin hacer cambios en ella
# Funcion para graficar los orbitales
def orbital2D(coords, constantes, z=1, orbital=0, delta=0.05):
if len(coords) != len(constantes):
raise ValueError, """El numero de coordenadas no coincide
con el numero de constantes"""
n = len(coords)
lx = min([c[0] for c in coords])
ux = max([c[0] for c in coords])
ly = min([c[1] for c in coords])
uy = max([c[1] for c in coords])
domx = np.arange(lx - 2, ux + 2 + delta, delta)
domy = np.arange(ly - 2, uy + 2 + delta, delta)
densmap = [[0]*len(domx)]*len(domy)
for iy in range(len(domy)):
for ix in range(len(domx)):
densmap[iy][ix] = 0
for i in range(n):
p = Psi(domx[ix], domy[iy], z, coords[i][0], coords[i][1], coords[i][2])
densmap[iy][ix] += ( constantes[i][n-orbital-1] * p )
densmap[iy] = tuple(densmap[iy])
dm = np.array(densmap, dtype=np.float32)
plt.imshow(dm, cmap=cm.viridis)
plt.grid(False)
plt.axis('equal')
plt.axis('off')
plt.show()
# Ahora vamos a necesitar las coordenadas de los atomos de carbono en el benceno
benceno_coords = [[2.6,0,0], [1.3,2.2517,0],[-1.3,2.2517,0],
[-2.6,0,0],[-1.3,-2.2517,0],[1.3,-2.2517,0]]
# Finalmente, graficamos los orbitales pi del benceno
# Para cambiar el orbital, cambia el numero aqui abajo y corre otra vez la celda
# En el benceno hallamos 6 niveles de energia
# Entonces hay orbitales del 0 al 5
orbital2D(benceno_coords, v, orbital=0)
###Output
_____no_output_____
###Markdown
Este momento es para detenerse y reflexionar: ¿Qué acabamos de hacer? Con una simple **función** y un poco de **álgebra de matrices** acabamos de encontrar la ubicación de los electrones que se hallan en resonancia en el benceno. No solo eso, ¡hallamos cómo se comportan a diferentes niveles de energía! Es importante recordar que solo estamos hablando de los electrones en los orbitales $\pi$. Sin embargo, esto ya complementa la intuición química sobre el comportamiento de los electrones en moléculas con resonancia.Otras observaciones que quizá vale la pena mencionar, a pesar de que no son tema de esta práctica directamente son las siguientes:1. Aquellos orbitales que tienen la misma energía (i.e. el mismo valor propio) son llamados *degenerados*2. Los orbitales negativos de mayor energía son llamados **orbitales moleculares ocupados de más energía**, u **HOMO** por sus siglas en inglés.3. Los orbitales positivos de menor energía son llamados **orbitales moleculares desocupados de menor energía**, u **LUMO** por sus siglas en inglés.4. Es conveniente poner atención a las simetrías que se presentan entre los orbitales degenerados. Los planos de simetría suelen ser peculiares cuando se trata de este tipo de orbital.---Ahora que ya hemos visto cómo hacer esto para el benceno, vamos a repetir el mismo ejercicio con el naftaleno, el estireno, el azuleno o el coroneno. **¡Solo es necesario hacer uno!**El procedimiento se desarrollará de la siguiente manera:* Crear la matriz de Hückel de 0s y 1s* Factorizar la matriz en Q y R* Encontrar los valores propios utilizando el algoritmo QR* Encontrar los valores y vectores propios utilizando la función de NumPy: `LA.eigh()`* Graficar los orbitales utilizando la función `orbital2D()`* Guardar el cuaderno interactivo e imprimir un PDF del mismo |Azuleno|Coroneno|Estireno|Naftaleno|---|---|---|---||||||
###Code
# Coordenadas para las moleculas
azuleno_coords = [[-1.21800105,-3.02633781,0],[-0.0819981,-3.97084248,0],
[1.21800105,-3.02633781,0],[0.72144557,-1.49809715,0],
[-0.72144557,-1.49809715,0],[-1.62107347,-0.37,0],
[-1.3,1.0367154,0],[0,1.6627624,0],
[1.3,1.0367154,0],[1.62107347,-0.37,0]]
coroneno_coords = [[0,2.6,0],[-2.2517,1.3,0],[-2.2517,-1.3,0],
[0,-2.6,0],[2.2517,-1.3,0],[2.2517,1.3,0],
[-4.5033,2.6,0],[-6.7551,1.3,0],[-6.7551,-1.3,0],
[-4.5033,-2.6,0],[4.5033,2.6,0],[4.5033,-2.6,0],
[6.7551,-1.3,0],[6.7551,1.3,0],[2.2517,6.5,0],
[0,5.2,0],[4.5033,5.2,0],[-2.2517,6.5,0],
[-4.5033,5.2,0],[-4.5033,-5.2,0],[-2.2517,-6.5,0],
[0,-5.2,0],[2.2517,-6.5,0],[4.5033,-5.2,0]]
estireno_coords = [[-2.2517,2.6,0],[-4.5033,1.3,0],[-4.5033,-1.3,0],
[-2.2517,-2.6,0],[0,-1.3,0],[0,1.3,0],
[2.2517,2.6,0],[4.5033,1.3,0]]
naftaleno_coords = [[-2.2517,2.6,0],[-4.5033,1.3,0],[-4.5033,-1.3,0],
[-2.2517,-2.6,0],[0,-1.3,0],[0,1.3,0],
[2.2517,2.6,0],[2.2517,-2.6,0],[4.5033,-1.3,0],
[4.5033,1.3,0]]
###Output
_____no_output_____
|
util-code/01.notebook/1_get_data_sql.ipynb
|
###Markdown
Extrai informações da base de dados do EPM
###Code
import pyodbc
import pandas as pd
from tqdm import tqdm
import os
###Output
_____no_output_____
###Markdown
> Parâmetros de conexão na base de dados.
###Code
server = '20.186.181.130'
database = 'EPM_Database1'
username = 'epasa'
password = 'ep@s@#2020'
###Output
_____no_output_____
###Markdown
> Busca da base de dados a tags que foram cadastradas no sistema.
###Code
cnxn = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server};SERVER='+server+';DATABASE='+database+';UID='+username+';PWD='+ password)
df_tags = pd.read_sql_query("select * from Epm_Tags", cnxn)
tags_list = df_tags['Name'].unique()
cnxn.close()
###Output
_____no_output_____
###Markdown
> Lista de tags para serem extraidas da base de dados
###Code
tags_generating_unit = ['1ST1000','1TE6575','1TE6180A','1TE6180B','2PT6180A','2PT6180B','1ST1004A','1ST1004B','2GT1022','1PT5070','1TE5070','ANALOG_164']
tags_factory = ['FQ003']
###Output
_____no_output_____
###Markdown
> Monta um dicionário com todas as tags que tem de extrair para cada unidade geradora
###Code
# tag_per_machine = {}
# for i in range(1,41):
# machine_number = 'UGD{}'.format(i)
# filtered_tags = []
# for tag in tags_generating_unit:
# filtered_tags = filtered_tags + list(filter(lambda x: machine_number+"_" in x and tag in x, tags_list))
# tag_per_machine[machine_number] = filtered_tags
###Output
_____no_output_____
###Markdown
> Busca as tags e escreve os arquivos, um arquivo para cada tag
###Code
# for key, item in tqdm(tag_per_machine.items()):
# if key not in ['UGD1','UGD2','UGD3','UGD4','UGD5']:
# os.mkdir(+key)
# for i in range(len(item)):
# cnxn = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server};SERVER='+server+';DATABASE='+database+';UID='+username+';PWD='+ password)
# query = "SELECT CAST(A.Timestamp as smalldatetime) as DataHora, CAST(A.Value as numeric) AS {} FROM dbo.EpmQueryRawFunction(-3,'01/01/2018 00:00:00','05/30/2020 00:00:00',0,0,'{}') AS A".format(item[i], item[i])
# unidade_geradora = pd.read_sql_query(query, cnxn)
# unidade_geradora.to_csv('./../../dataset/01.real/epm/{}/{}_{}.tar.xz'.format(key,key,item[i]), chunksize=10000, compression='xz')
# cnxn.close()
###Output
_____no_output_____
###Markdown
> Escreve um arquivo exlusivo para uma tag.
###Code
# # pd.read_csv('UGD1_Main_1ST1000.tar.xz', compression='xz')
# tag = "UGD1_Main_1ST1000"
# cnxn = pyodbc.connect('DRIVER={ODBC Driver 17 for SQL Server};SERVER='+server+';DATABASE='+database+';UID='+username+';PWD='+ password)
# query = "SELECT CAST(A.Timestamp as smalldatetime) as DataHora, CAST(A.Value as numeric) AS {} FROM dbo.EpmQueryRawFunction(-3,'01/01/2018 00:00:00','02/02/2018 00:00:00',0,0,'{}') AS A".format(tag, tag)
# unidade_geradora = pd.read_sql_query(query, cnxn)
# unidade_geradora.to_csv('{}.tar.xz'.format('UGD1_Main_1ST1000'), chunksize=10000, compression='xz')
# cnxn.close()
def get_tag_from_database(tag):
print(tag)
###Output
_____no_output_____
|
Notebooks cidades/Guarulhos_Durante.ipynb
|
###Markdown
PRÉ-PROCESSAMENTO
###Code
Durante['CS_GESTANT'].replace({1.0: 1, 2.0: 1, 3.0 :1, 4.0 : 1}, inplace= True)
Durante['CS_GESTANT'].replace({5.0: 0, 6.0:0, 9.0:0}, inplace= True)
Durante['CS_RACA'].fillna(9,inplace= True)
Durante['CS_ESCOL_N'].fillna(9,inplace= True)
Durante['SURTO_SG'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['SURTO_SG'].fillna(0,inplace= True)
Durante['NOSOCOMIAL'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['NOSOCOMIAL'].fillna(0,inplace= True)
Durante['FEBRE'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['FEBRE'].fillna(0,inplace= True)
Durante['TOSSE'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['TOSSE'].fillna(0,inplace= True)
Durante['GARGANTA'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['GARGANTA'].fillna(0,inplace= True)
Durante['DISPNEIA'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['DISPNEIA'].fillna(0,inplace= True)
Durante['DESC_RESP'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['DESC_RESP'].fillna(0,inplace= True)
Durante['SATURACAO'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['SATURACAO'].fillna(0,inplace= True)
Durante['DIARREIA'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['DIARREIA'].fillna(0,inplace= True)
Durante['VOMITO'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['VOMITO'].fillna(0,inplace= True)
Durante['PUERPERA'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['PUERPERA'].fillna(0,inplace= True)
Durante['CARDIOPATI'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['CARDIOPATI'].fillna(0,inplace= True)
Durante['HEMATOLOGI'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['HEMATOLOGI'].fillna(0,inplace= True)
Durante['SIND_DOWN'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['SIND_DOWN'].fillna(0,inplace= True)
Durante['HEPATICA'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['HEPATICA'].fillna(0,inplace= True)
Durante['ASMA'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['ASMA'].fillna(0,inplace= True)
Durante['DIABETES'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['DIABETES'].fillna(0,inplace= True)
Durante['NEUROLOGIC'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['NEUROLOGIC'].fillna(0,inplace= True)
Durante['PNEUMOPATI'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['PNEUMOPATI'].fillna(0,inplace= True)
Durante['IMUNODEPRE'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['IMUNODEPRE'].fillna(0,inplace= True)
Durante['RENAL'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['RENAL'].fillna(0,inplace= True)
Durante['OBESIDADE'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['OBESIDADE'].fillna(0,inplace= True)
Durante['ASMA'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['ASMA'].fillna(0,inplace= True)
Durante['ANTIVIRAL'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['ANTIVIRAL'].fillna(0,inplace= True)
Durante['UTI'].replace({2.0: 0, 9.0: 0}, inplace= True)
Durante['UTI'].fillna(0,inplace= True)
Durante['SUPORT_VEN'].replace({3.0: 0, 9.0: 0}, inplace= True)
Durante['SUPORT_VEN'].fillna(0,inplace= True)
Durante['PCR_RESUL'].fillna(4,inplace= True)
Durante['HISTO_VGM'].replace({0: 2}, inplace= True)
Durante['DOR_ABD'].replace({9.0: 0, 2.0 :0}, inplace= True)
Durante['DOR_ABD'].fillna(0,inplace= True)
Durante['FADIGA'].replace({9.0: 0, 2.0 :0}, inplace= True)
Durante['FADIGA'].fillna(0,inplace= True)
Durante['PERD_OLFT'].replace({9.0: 0, 2.0 :0}, inplace= True)
Durante['PERD_OLFT'].fillna(0,inplace= True)
Durante['PERD_PALA'].replace({9.0: 0, 2.0 :0}, inplace= True)
Durante['PERD_PALA'].fillna(0,inplace= True)
Durante['VACINA'].fillna(0,inplace= True)
Durante['FATOR_RISC'].replace({'S': 1, 'N':2, '1':1, '2':2}, inplace= True)
Durante['FATOR_RISC'].fillna(0,inplace= True)
###Output
_____no_output_____
###Markdown
- Resetando o Index novamente.
###Code
Durante=Durante.reset_index(drop=True)
Durante.head()
###Output
_____no_output_____
###Markdown
- Aplicação da Dummy nas Features Categóricas
###Code
Durante=pd.get_dummies(Durante, columns=['CS_SEXO', 'CS_GESTANT', 'CS_RACA', 'CS_ESCOL_N',
'SURTO_SG', 'NOSOCOMIAL', 'FEBRE', 'TOSSE', 'GARGANTA', 'DISPNEIA',
'DESC_RESP', 'SATURACAO', 'DIARREIA', 'VOMITO', 'PUERPERA',
'FATOR_RISC', 'CARDIOPATI', 'HEMATOLOGI', 'SIND_DOWN', 'HEPATICA',
'ASMA', 'DIABETES', 'NEUROLOGIC', 'PNEUMOPATI', 'IMUNODEPRE', 'RENAL',
'OBESIDADE', 'VACINA', 'ANTIVIRAL', 'UTI', 'SUPORT_VEN', 'PCR_RESUL',
'HISTO_VGM', 'DOR_ABD', 'FADIGA', 'PERD_OLFT', 'PERD_PALA'], drop_first=True)
Durante.head()
###Output
_____no_output_____
###Markdown
Verificando o Balanceamento
###Code
Durante["EVOLUCAO"].value_counts(normalize=True)
X = Durante[['IDADE_ANOS','CS_SEXO_M','CS_RACA_4.0','FEBRE_1.0','DISPNEIA_1.0','SATURACAO_1.0','UTI_1.0',
'SUPORT_VEN_1.0', 'SUPORT_VEN_2.0', 'PCR_RESUL_2.0','TOSSE_1.0','DESC_RESP_1.0', 'FATOR_RISC_2']]
y = Durante['EVOLUCAO']
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3, random_state=42)
Xtrain.shape, Xtest.shape, ytrain.shape, ytest.shape
Xtrain.columns
smote = SMOTE(sampling_strategy = 'minority', random_state = 42)
Xtrain_over, ytrain_over = smote.fit_resample(Xtrain,ytrain)
Xtest_over, ytest_over = smote.fit_resample(Xtest,ytest)
Xtrain_over.shape, ytrain_over.shape, Xtest_over.shape, ytest_over.shape
###Output
_____no_output_____
###Markdown
Aplicação do Modelo Escolhido
###Code
random_state=42
BCG=BaggingClassifier()
BCG.fit(Xtrain_over, ytrain_over)
previsoes = BCG.predict(Xtest_over)
previsoes
accuracy_score(ytest_over, previsoes)
# Testar Modelo
idade = 43.0
sexo = 1
raca = 0
febre = 1
dispneia = 1
saturacao = 0
uti = 1
suport1 = 1
suport2 = 0
pcr = 1
tosse = 1
descresp = 0
frisc = 0
prediction = BCG.predict(np.array([idade, sexo, raca, febre, dispneia, saturacao, uti, suport1, suport2, pcr, tosse, descresp, frisc]).reshape(1, -1))
print(prediction)
###Output
[1.]
|
colab/NMT-Seq2SeqWithAttention-de2en.ipynb
|
###Markdown
Seq2Seq with Attention for German-English Neural Machine Translation- Network architecture based on this [paper](https://arxiv.org/abs/1409.0473)- Fit to run on Google Colaboratory
###Code
import os
import io
import math
import time
import random
import spacy
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchtext.data import Field, BucketIterator
from torchtext.datasets import TranslationDataset, Multi30k
###Output
_____no_output_____
###Markdown
Set random seed
###Code
SEED = 2015010720
random.seed(SEED)
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
###Output
_____no_output_____
###Markdown
Check Spacy (이미 설치되어 있음)
###Code
# 설치가 되어있는지 확인
!pip show spacy
# 설치가 되어있는지 확인 (없다면 자동설치됨)
!python -m spacy download en
!python -m spacy download de
import spacy
spacy_en = spacy.load('en')
spacy_de = spacy.load('de')
###Output
_____no_output_____
###Markdown
1. Define Tokenizing Functions문장을 받아 그보다 작은 어절 혹은 형태소 단위의 리스트로 반환해주는 함수를 각 언어에 대해 작성 German Tokenizer
###Code
def tokenize_de(text):
return [t.text for t in spacy_de.tokenizer(text)]
# Usage example
print(tokenize_de("Was ich nicht erschaffen kann, verstehe ich nicht."))
###Output
['Was', 'ich', 'nicht', 'erschaffen', 'kann', ',', 'verstehe', 'ich', 'nicht', '.']
###Markdown
English Tokenizer
###Code
def tokenize_en(text):
return [t.text for t in spacy_en.tokenizer(text)]
# Usage example
print(tokenize_en("What I cannot create, I don't understand."))
###Output
['What', 'I', 'can', 'not', 'create', ',', 'I', 'do', "n't", 'understand', '.']
###Markdown
2. Data Preprocessing Define Fields
###Code
GERMAN = Field(
tokenize = tokenize_de,
init_token='<sos>',
eos_token='<eos>',
lower=True,
include_lengths=True,
batch_first=False # time first
)
ENGLISH = Field(
tokenize=tokenize_en,
init_token='<sos>',
eos_token='<eos>',
lower=True,
include_lengths=True,
batch_first=False # time first
)
###Output
_____no_output_____
###Markdown
Load Data
###Code
train_set, valid_set, test_set = Multi30k.splits(
exts=('.de', '.en'),
fields=(GERMAN, ENGLISH)
)
print('#. train examples:', len(train_set.examples))
print('#. valid examples:', len(valid_set.examples))
print('#. test examples:', len(test_set.examples))
# Training example (GERMAN, source language)
train_set.examples[50].src
# Training example (ENGLISH, target language)
train_set.examples[50].trg
###Output
_____no_output_____
###Markdown
Build Vocabulary- 각 언어별 생성: `Field`의 인스턴스를 활용- 최소 빈도수(`MIN_FREQ`) 값을 작게 하면 vocabulary의 크기가 커짐.- 최소 빈도수(`MIN_FREQ`) 값을 크게 하면 vocabulary의 크기가 작아짐.
###Code
MIN_FREQ = 2 # TODO: try different values
###Output
_____no_output_____
###Markdown
German vocab
###Code
# Build vocab for German
GERMAN.build_vocab(train_set, min_freq=MIN_FREQ) # de
print('Size of source vocab (de):', len(GERMAN.vocab))
GERMAN.vocab.freqs.most_common(10)
# Check indices of some important tokens
tokens = ['<unk>', '<pad>', '<sos>', '<eos>']
for token in tokens:
print(f"{token} -> {GERMAN.vocab.stoi[token]}")
###Output
<unk> -> 0
<pad> -> 1
<sos> -> 2
<eos> -> 3
###Markdown
English vocab
###Code
# Build vocab for English
ENGLISH.build_vocab(train_set, min_freq=MIN_FREQ) # en
print('Size of target vocab (en):', len(ENGLISH.vocab))
ENGLISH.vocab.freqs.most_common(10)
# Check indices of some important tokens
tokens = ['<unk>', '<pad>', '<sos>', '<eos>']
for token in tokens:
print(f"{token} -> {ENGLISH.vocab.stoi[token]}")
###Output
<unk> -> 0
<pad> -> 1
<sos> -> 2
<eos> -> 3
###Markdown
Configure Device- *'런타임' -> '런타임 유형변경'* 에서 하드웨어 가속기로 **GPU** 선택
###Code
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device to use:', device)
###Output
Device to use: cuda
###Markdown
Create Data Iterators- 데이터를 미니배치(mini-batch) 단위로 반환해주는 역할- `train_set`, `dev_set`, `test_set`에 대해 개별적으로 정의해야 함- `BATCH_SIZE`를 정의해주어야 함- `torchtext.data.BucketIterator`는 하나의 미니배치를 서로 비슷한 길이의 관측치들로 구성함- [Bucketing](https://medium.com/@rashmi.margani/how-to-speed-up-the-training-of-the-sequence-model-using-bucketing-techniques-9e302b0fd976)의 효과: 하나의 미니배치 내 padding을 최소화하여 연산의 낭비를 줄여줌
###Code
BATCH_SIZE = 128
#from torchtext.data import BucketIterator
# Train iterator
train_iterator = BucketIterator(
train_set,
batch_size=BATCH_SIZE,
train=True,
sort_within_batch=True,
sort_key=lambda x: len(x.src),
device=device,
)
print(f'Number of minibatches per epoch: {len(train_iterator)}')
#from torchtext.data import BucketIterator
# Dev iterator
valid_iterator = BucketIterator(
valid_set,
batch_size=BATCH_SIZE,
train=False,
sort_within_batch=True,
sort_key=lambda x: len(x.src),
device=device
)
print(f'Number of minibatches per epoch: {len(valid_iterator)}')
#from torchtext.data import BucketIterator
# Test iterator
test_iterator = BucketIterator(
test_set,
batch_size=BATCH_SIZE,
train=False,
sort_within_batch=True,
sort_key=lambda x: len(x.src),
device=device
)
print(f'Number of minibatches per epoch: {len(test_iterator)}')
train_batch = next(iter(train_iterator))
src, src_len = train_batch.src
trg, trg_len = train_batch.trg
print('a batch of source examples has shape:', src.size()) # (source_seq_len, batch_size)
print('a batch of target examples has shape:', trg.size()) # (target_seq_len, batch_size)
src
src_len # sorted in descending order
trg
trg_len
# Checking last sample in mini-batch (GERMAN, source lang)
src, src_len = train_batch.src
de_indices = src[:, -1]
de_tokens = [GERMAN.vocab.itos[i] for i in de_indices]
for t, i in zip(de_tokens, de_indices):
print(f"{t} ({i})")
del de_indices, de_tokens
# Checking last sample in mini-batch (EN, target lang)
trg, trg_len = train_batch.trg
en_indices = trg[:, -1]
en_tokens = [ENGLISH.vocab.itos[i] for i in en_indices]
for t, i in zip(en_tokens, en_indices):
print(f"{t} ({i})")
del en_indices, en_tokens
###Output
<sos> (2)
a (4)
dj (1880)
is (10)
spinning (1448)
records (4334)
at (20)
a (4)
club (1116)
full (298)
of (12)
people (19)
. (5)
<eos> (3)
<pad> (1)
<pad> (1)
<pad> (1)
<pad> (1)
<pad> (1)
<pad> (1)
###Markdown
3. Building Seq2Seq Model Hyperparameters
###Code
# Hyperparameters
INPUT_DIM = len(GERMAN.vocab)
OUTPUT_DIM = len(ENGLISH.vocab)
ENC_EMB_DIM = DEC_EMB_DIM = 256
ENC_HID_DIM = DEC_HID_DIM = 512
USE_BIDIRECTIONAL = False
print('source vocabulary size:', INPUT_DIM)
print('source word embedding size:', ENC_EMB_DIM)
print(f'encoder RNN hidden size: {ENC_HID_DIM} ({ENC_HID_DIM * 2} if bidirectional)')
print('-' * 50)
print('target vocabulary size:', OUTPUT_DIM)
print('target word embedding size:', ENC_EMB_DIM)
print('decoder RNN hidden size:', ENC_HID_DIM)
###Output
source vocabulary size: 7855
source word embedding size: 256
encoder RNN hidden size: 512 (1024 if bidirectional)
--------------------------------------------------
target vocabulary size: 5893
target word embedding size: 256
decoder RNN hidden size: 512
###Markdown
Encoder
###Code
class Encoder(nn.Module):
"""
Learns an embedding for the source text.
Arguments:
input_dim: int, size of input language vocabulary.
emb_dim: int, size of embedding layer output.
enc_hid_dim: int, size of encoder hidden state.
dec_hid_dim: int, size of decoder hidden state.
bidirectional: uses bidirectional RNNs if True. default is False.
"""
def __init__(self, input_dim, emb_dim, enc_hid_dim, dec_hid_dim, bidirectional=False):
super(Encoder, self).__init__()
self.input_dim = input_dim
self.emb_dim = emb_dim
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
self.bidirectional = bidirectional
self.embedding = nn.Embedding(
num_embeddings=self.input_dim,
embedding_dim=self.emb_dim
)
self.rnn = nn.GRU(
input_size=self.emb_dim,
hidden_size=self.enc_hid_dim,
bidirectional=self.bidirectional,
batch_first=False
)
self.rnn_output_dim = self.enc_hid_dim
if self.bidirectional:
self.rnn_output_dim *= 2
self.fc = nn.Linear(self.rnn_output_dim, self.dec_hid_dim)
self.dropout = nn.Dropout(.2)
def forward(self, src, src_len):
"""
Arguments:
src: 2d tensor of shape (S, B)
src_len: 1d tensor of shape (B).
Returns:
outputs: 3d tensor of shape (input_seq_len, batch_size, num_directions * enc_h)
hidden: 2d tensor of shape (b, dec_h). This tensor will be used as the initial
hidden state value of the decoder (h0 of decoder).
"""
assert len(src.size()) == 2, 'Input requires dimension (input_seq_len, batch_size).'
# Shape: (b, s, h)
embedded = self.embedding(src)
embedded = self.dropout(embedded)
packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, src_len)
packed_outputs, hidden = self.rnn(packed_embedded)
outputs, _ = nn.utils.rnn.pad_packed_sequence(packed_outputs)
if self.bidirectional:
# (2, b, enc_h) -> (b, 2 * enc_h)
hidden = torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)
else:
# (1, b, enc_h) -> (b, enc_h)
hidden = hidden.squeeze(0)
# (b, num_directions * enc_h) -> (b, dec_h)
hidden = self.fc(hidden)
hidden = torch.tanh(hidden)
return outputs, hidden # (S, B, enc_h * num_directions), (B, dec_h)
###Output
_____no_output_____
###Markdown
Attention
###Code
class Attention(nn.Module):
def __init__(self, enc_hid_dim, dec_hid_dim, encoder_is_bidirectional=False):
super(Attention, self).__init__()
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
self.encoder_is_bidirectional = encoder_is_bidirectional
self.attention_input_dim = enc_hid_dim + dec_hid_dim
if self.encoder_is_bidirectional:
self.attention_input_dim += enc_hid_dim # 2 * h_enc + h_dec
self.linear = nn.Linear(self.attention_input_dim, dec_hid_dim)
self.v = nn.Parameter(torch.rand(dec_hid_dim))
def forward(self, hidden, encoder_outputs, mask):
"""
Arguments:
hidden: 2d tensor with shape (batch_size, dec_hid_dim).
encoder_outputs: 3d tensor with shape (input_seq_len, batch_size, enc_hid_dim * num_directions).
mask: 2d tensor with shape(batch_size, input_seq_len)
"""
# Shape check
assert hidden.dim() == 2
assert encoder_outputs.dim() == 3
seq_len, batch_size, _ = encoder_outputs.size()
# (b, dec_h) -> (b, s, dec_h)
hidden = hidden.unsqueeze(1).expand(-1, seq_len, -1)
# (s, b, enc_h * num_directions) -> (b, s, enc_h * num_directions)
encoder_outputs = encoder_outputs.permute(1, 0, 2)
# concat; shape results in (b, s, enc_h + dec_h).
# if encoder is bidirectional, (b, s, 2 * h_enc + h_dec).
concat = torch.cat((hidden, encoder_outputs), dim=2)
# energy; shape is (b, s, dec_h)
energy = torch.tanh(self.linear(concat))
# tile v; (dec_h, ) -> (b, dec_h) -> (b, dec_h, 1)
v = self.v.unsqueeze(0).expand(batch_size, -1).unsqueeze(2)
# attn; (b, s, dec_h) @ (b, dec_h, 1) -> (b, s, 1) -> (b, s)
attn_scores = torch.bmm(energy, v).squeeze(-1)
# mask padding indices
attn_scores = attn_scores.masked_fill(mask == 0, -1e10)
assert attn_scores.dim() == 2 # Final shape check: (b, s)
return F.softmax(attn_scores, dim=1)
###Output
_____no_output_____
###Markdown
Decoder
###Code
class Decoder(nn.Module):
"""
Unlike the encoder, a single forward pass of
a `Decoder` instance is defined for only a single timestep.
Arguments:
output_dim: int,
emb_dim: int,
enc_hid_dim: int,
dec_hid_dim: int,
attention_module: torch.nn.Module,
encoder_is_bidirectional: False
"""
def __init__(self, output_dim, emb_dim, enc_hid_dim, dec_hid_dim, attention_module, encoder_is_bidirectional=False):
super(Decoder, self).__init__()
self.emb_dim = emb_dim
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
self.output_dim = output_dim
self.encoder_is_bidirectional = encoder_is_bidirectional
if isinstance(attention_module, nn.Module):
self.attention_module = attention_module
else:
raise ValueError
self.rnn_input_size = enc_hid_dim + emb_dim # enc_h + dec_emb_dim
if self.encoder_is_bidirectional:
self.rnn_input_size += enc_hid_dim # 2 * enc_h + dec_emb_dim
self.embedding = nn.Embedding(output_dim, emb_dim)
self.rnn = nn.GRU(
input_size=self.rnn_input_size,
hidden_size=dec_hid_dim,
bidirectional=False,
batch_first=False,
)
self.out_input_size = emb_dim + dec_hid_dim + enc_hid_dim
if self.encoder_is_bidirectional:
self.out_input_size += enc_hid_dim
self.out = nn.Linear(self.out_input_size, output_dim)
self.dropout = nn.Dropout(.2)
def forward(self, inp, hidden, encoder_outputs, mask, temperature=1.):
"""
Arguments:
inp: 1d tensor with shape (batch_size, )
hidden: 2d tensor with shape (batch_size, dec_hid_dim).
This `hidden` tensor is the hidden state vector from the previous timestep.
encoder_outputs: 3d tensor with shape (input_seq_len, batch_size, enc_hid_dim * num_directions).
mask: 2d tensor of shape (batch_size, input_seq_len).
"""
assert inp.dim() == 1
assert hidden.dim() == 2
assert encoder_outputs.dim() == 3
# (b, ) -> (1, b)
inp = inp.unsqueeze(0)
# (1, b) -> (1, b, emb)
embedded = self.embedding(inp)
embedded = self.dropout(embedded)
# attention probabilities; (b, s)
attn_probs = self.attention_module(hidden, encoder_outputs, mask)
# (b, 1, s)
attn_probs = attn_probs.unsqueeze(1)
# (s, b, ~) -> (b, s, ~)
encoder_outputs = encoder_outputs.permute(1, 0, 2)
# (b, 1, s) @ (b, s, ~) -> (b, 1, enc_h * num_directions)
weighted = torch.bmm(attn_probs, encoder_outputs)
# (1, b, ~)
weighted = weighted.permute(1, 0, 2)
# (b, 1, emb + enc_h * num_directions)
rnn_input = torch.cat((embedded, weighted), dim=2)
# output; (b, 1, dec_h)
# new_hidden; (1, b, dec_h)
output, new_hidden = self.rnn(rnn_input, hidden.unsqueeze(0))
assert (output == new_hidden).all()
embedded = embedded.squeeze(0) # (1, b, emb) -> (b, emb)
output = output.squeeze(0) # (1, b, dec_h) -> (b, dec_h)
weighted = weighted.squeeze(0) # (1, b, enc_h * num_d) -> (b, enc_h * num_d)
# output; (b, emb + enc_h + dec_h) -> (b, output_dim)
# if encoder is bidirectional, (b, emb + 2 * enc_h + dec_h) -> (b, output_dim)
output = self.out(torch.cat((output, weighted, embedded), dim=1))
output = output / temperature
return output, new_hidden.squeeze(0), attn_probs.squeeze(1)
###Output
_____no_output_____
###Markdown
Seq2Seq
###Code
class Seq2Seq(nn.Module):
def __init__(self, encoder, decoder, pad_idx, sos_idx, eos_idx, device):
super(Seq2Seq, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.pad_idx = pad_idx # 1
self.sos_idx = sos_idx # 2
self.eos_idx = eos_idx # 3
self.device = device
def create_mask(self, src):
mask = (src != self.pad_idx).permute(1, 0) # (b, s)
return mask
def forward(self, src, src_len, trg=None, teacher_forcing_ratio=.5):
batch_size = src.size(1)
max_seq_len = trg.size(0) if trg is not None else 100
trg_vocab_size = self.decoder.output_dim
if trg is None:
assert teacher_forcing_ratio == 0., "Must be zero during inference."
inference = True
trg = torch.zeros(max_seq_len, batch_size).long().fill_(self.sos_idx).to(self.device)
else:
inference = False
# An empty tensor to store decoder outputs (time index first for faster indexing)
outputs_shape = (max_seq_len, batch_size, trg_vocab_size)
outputs = torch.zeros(outputs_shape).to(self.device)
# empty tensor to store attention probs
attns_shape = (max_seq_len, batch_size, src.size(0))
attns = torch.zeros(attns_shape).to(self.device)
encoder_outputs, hidden = self.encoder(src, src_len)
mask = self.create_mask(src)
# first input to the decoder is '<sos>'
# trg; shape (batch_size, seq_len)
initial_dec_input = output = trg[0, :] # get first timestep token
for t in range(1, max_seq_len):
output, hidden, attn = self.decoder(output, hidden, encoder_outputs, mask)
outputs[t] = output # Save output for timestep t, for 1 <= t <= max_len
attns[t] = attn
_, idx = output.max(dim=1)
teacher_force = torch.rand(1).item() < teacher_forcing_ratio
new_dec_input = output = trg[t] if teacher_force else idx
if inference and output.item() == self.eos_idx:
return outputs[:t], attns[:t]
return outputs, attns
###Output
_____no_output_____
###Markdown
Build Model
###Code
# Define encoder
enc = Encoder(
input_dim=INPUT_DIM,
emb_dim=ENC_EMB_DIM,
enc_hid_dim=ENC_HID_DIM,
dec_hid_dim=DEC_HID_DIM,
bidirectional=USE_BIDIRECTIONAL
)
print(enc)
# Define attention layer
attn = Attention(
enc_hid_dim=ENC_HID_DIM,
dec_hid_dim=DEC_HID_DIM,
encoder_is_bidirectional=USE_BIDIRECTIONAL
)
print(attn)
# Define decoder
dec = Decoder(
output_dim=OUTPUT_DIM,
emb_dim=DEC_EMB_DIM,
enc_hid_dim=ENC_HID_DIM,
dec_hid_dim=DEC_HID_DIM,
attention_module=attn,
encoder_is_bidirectional=USE_BIDIRECTIONAL
)
print(dec)
PAD_IDX = GERMAN.vocab.stoi['<pad>']
SOS_IDX = ENGLISH.vocab.stoi['<sos>']
EOS_IDX = ENGLISH.vocab.stoi['<eos>']
print('PAD INDEX:', PAD_IDX)
print('SOS INDEX:', SOS_IDX)
print('EOS INDEX:', EOS_IDX)
model = Seq2Seq(enc, dec, PAD_IDX, SOS_IDX, EOS_IDX, device).to(device)
print(model)
###Output
Seq2Seq(
(encoder): Encoder(
(embedding): Embedding(7855, 256)
(rnn): GRU(256, 512)
(fc): Linear(in_features=512, out_features=512, bias=True)
(dropout): Dropout(p=0.2)
)
(decoder): Decoder(
(attention_module): Attention(
(linear): Linear(in_features=1024, out_features=512, bias=True)
)
(embedding): Embedding(5893, 256)
(rnn): GRU(768, 512)
(out): Linear(in_features=1280, out_features=5893, bias=True)
(dropout): Dropout(p=0.2)
)
)
###Markdown
Count trainable parameters
###Code
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {count_parameters(model):,} trainable parameters.')
###Output
The model has 15,008,261 trainable parameters.
###Markdown
Initialize trainable parameters
###Code
def init_parameters(model):
for name, param in model.named_parameters():
if 'weight' in name:
nn.init.normal_(param.data, mean=0., std=0.01)
else:
nn.init.constant_(param.data, 0.)
model.apply(init_parameters)
###Output
_____no_output_____
###Markdown
4. Train Optimizer- Use `optim.Adam` or `optim.RMSprop`.
###Code
optimizer = optim.Adam(model.parameters(), lr=0.001)
#optimizer = optim.RMSprop(model.parameters(), lr=0.01)
###Output
_____no_output_____
###Markdown
Loss function
###Code
criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)
print(f"<pad> index in target vocab (en): '{PAD_IDX}' will be ignored when loss is calculated.")
###Output
<pad> index in target vocab (en): '1' will be ignored when loss is calculated.
###Markdown
Train function
###Code
def train(seq2seq_model, iterator, optimizer, criterion, grad_clip=1.0):
seq2seq_model.train()
epoch_loss = .0
for i, batch in enumerate(iterator):
print('.', end='')
src, src_len = batch.src
trg, _ = batch.trg
optimizer.zero_grad()
decoder_outputs, _ = seq2seq_model(src, src_len, trg, teacher_forcing_ratio=.5)
trg_seq_len, batch_size, trg_vocab_size = decoder_outputs.size() # (s, b, trg_vocab)
# (s-1, b, trg_vocab)
decoder_outputs = decoder_outputs[1:]
# (s-1 * b, trg_vocab)
decoder_outputs = decoder_outputs.view(-1, trg_vocab_size)
# (s, b) -> (s-1 * b, )
trg = trg[1:].view(-1)
loss = criterion(decoder_outputs, trg)
loss.backward()
# Gradient clipping; remedy for exploding gradients
torch.nn.utils.clip_grad_norm_(seq2seq_model.parameters(), grad_clip)
optimizer.step()
epoch_loss += loss.item()
return epoch_loss / len(iterator)
###Output
_____no_output_____
###Markdown
Evaluate function
###Code
def evaluate(seq2seq_model, iterator, criterion):
seq2seq_model.eval()
epoch_loss = 0.
with torch.no_grad():
for i, batch in enumerate(iterator):
print('.', end='')
src, src_len = batch.src
trg, _ = batch.trg
decoder_outputs, _ = seq2seq_model(src, src_len, trg, teacher_forcing_ratio=0.)
trg_seq_len, batch_size, trg_vocab_size = decoder_outputs.size() # (s, b, trg_vocab)
# (s-1, b, trg_vocab)
decoder_outputs = decoder_outputs[1:]
# (s-1 * b, trg_vocab)
decoder_outputs = decoder_outputs.view(-1, trg_vocab_size)
# (s, b) -> (s-1 * b, )
trg = trg[1:].view(-1)
loss = criterion(decoder_outputs, trg)
epoch_loss += loss.item()
return epoch_loss / len(iterator)
###Output
_____no_output_____
###Markdown
Epoch time measure function
###Code
def epoch_time(start_time, end_time):
"""Returns elapsed time in mins & secs."""
elapsed_time = end_time - start_time
elapsed_mins = int(elapsed_time / 60)
elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
return elapsed_mins, elapsed_secs
###Output
_____no_output_____
###Markdown
Train for multiple epochs
###Code
NUM_EPOCHS = 10
best_valid_loss = float('inf')
for epoch in range(NUM_EPOCHS):
start_time = time.time()
train_loss = train(model, train_iterator, optimizer, criterion)
valid_loss = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), './best_model_de2en.pt')
print("\n")
print(f"Epoch: {epoch + 1:>02d} | Time: {epoch_mins}m {epoch_secs}s")
print(f"Train Loss: {train_loss:>.4f} | Train Perplexity: {math.exp(train_loss):7.3f}")
print(f"Valid Loss: {valid_loss:>.4f} | Valid Perplexity: {math.exp(valid_loss):7.3f}")
###Output
...........................................................................................................................................................................................................................................
Epoch: 01 | Time: 1m 18s
Train Loss: 5.1055 | Train Perplexity: 164.919
Valid Loss: 4.9291 | Valid Perplexity: 138.261
...........................................................................................................................................................................................................................................
Epoch: 02 | Time: 1m 18s
Train Loss: 4.2797 | Train Perplexity: 72.219
Valid Loss: 4.5825 | Valid Perplexity: 97.757
...........................................................................................................................................................................................................................................
Epoch: 03 | Time: 1m 18s
Train Loss: 3.6402 | Train Perplexity: 38.099
Valid Loss: 3.8618 | Valid Perplexity: 47.551
...........................................................................................................................................................................................................................................
Epoch: 04 | Time: 1m 18s
Train Loss: 3.0178 | Train Perplexity: 20.447
Valid Loss: 3.5828 | Valid Perplexity: 35.972
...........................................................................................................................................................................................................................................
Epoch: 05 | Time: 1m 18s
Train Loss: 2.5803 | Train Perplexity: 13.202
Valid Loss: 3.3126 | Valid Perplexity: 27.458
...........................................................................................................................................................................................................................................
Epoch: 06 | Time: 1m 18s
Train Loss: 2.2584 | Train Perplexity: 9.568
Valid Loss: 3.2681 | Valid Perplexity: 26.260
...........................................................................................................................................................................................................................................
Epoch: 07 | Time: 1m 18s
Train Loss: 1.9874 | Train Perplexity: 7.297
Valid Loss: 3.2186 | Valid Perplexity: 24.994
...........................................................................................................................................................................................................................................
Epoch: 08 | Time: 1m 18s
Train Loss: 1.7838 | Train Perplexity: 5.952
Valid Loss: 3.2528 | Valid Perplexity: 25.861
...........................................................................................................................................................................................................................................
Epoch: 09 | Time: 1m 18s
Train Loss: 1.5771 | Train Perplexity: 4.841
Valid Loss: 3.2394 | Valid Perplexity: 25.520
...........................................................................................................................................................................................................................................
Epoch: 10 | Time: 1m 17s
Train Loss: 1.4479 | Train Perplexity: 4.254
Valid Loss: 3.2979 | Valid Perplexity: 27.057
###Markdown
Save last model (overfitted)
###Code
torch.save(model.state_dict(), './last_model_de2en.pt')
###Output
_____no_output_____
###Markdown
5. Test Evaluate on test data
###Code
model.load_state_dict(torch.load('best_model_de2en.pt'))
test_loss = evaluate(model, test_iterator, criterion)
print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |')
###Output
........| Test Loss: 3.216 | Test PPL: 24.939 |
###Markdown
Function to convert indices to original text strings (translate)
###Code
def translate_sentence(seq2seq_model, sentence):
seq2seq_model.eval()
# Tokenize sentence
tokenized = tokenize_de(sentence)
# lower tokens
tokenized = [t.lower() for t in tokenized]
# Add <sos> & <eos> tokens to the front and back of the sentence
tokenized = ['<sos>'] + tokenized + ['<eos>']
# tokens -> indices
numericalized = [GERMAN.vocab.stoi[s] for s in tokenized]
sent_length = torch.tensor([len(numericalized)]).long().to(device)
tensor = torch.LongTensor(numericalized).unsqueeze(1).to(device)
translation_logits, attention = seq2seq_model(tensor, sent_length, None, 0.)
translation_tensor = torch.argmax(translation_logits.squeeze(1), dim=1)
translation = [ENGLISH.vocab.itos[i] for i in translation_tensor]
translation, attention = translation[1:], attention[1:]
#assert translation.__len__() == 1 # (sequence_length, )
#assert attention.dim() == 2
return translation, attention
def display_attention(candidate, translation, attention):
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111)
attention = attention.squeeze(1).cpu().detach().numpy()
cax = ax.matshow(attention, cmap='bone')
ax.tick_params(labelsize=15)
ax.set_xticklabels([''] + ['<sos>'] + [t.lower() for t in tokenize_de(candidate)] + ['<eos>'],
rotation=45)
ax.set_yticklabels([''] + translation)
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
plt.show()
plt.close()
example_idx = 4
src = ' '.join(train_set.examples[example_idx].src)
trg = ' '.join(train_set.examples[example_idx].trg)
print(f'src = {src}')
print(f'trg = {trg}')
translation, attention = translate_sentence(model, src)
print(f'predicted = {translation}')
###Output
predicted = ['two', 'men', 'are', 'at', 'the', 'stove', 'preparing', 'food', '.']
###Markdown
Alignment
###Code
display_attention(src, translation, attention)
example_idx = 35
src = ' '.join(valid_set.examples[example_idx].src)
trg = ' '.join(valid_set.examples[example_idx].trg)
print(f'src = {src}')
print(f'trg = {trg}')
translation, attention = translate_sentence(model, src)
print(f'predicted trg = {translation}')
display_attention(src, translation, attention)
example_idx = 7
src = ' '.join(test_set.examples[example_idx].src)
trg = ' '.join(test_set.examples[example_idx].trg)
print(f'src = {src}')
print(f'trg = {trg}')
translation, attention = translate_sentence(model, src)
print(f'predicted trg = {translation}')
display_attention(src, translation, attention)
###Output
src = ein junge in einem roten trikot versucht , die home base zu erreichen , während der catcher im blauen trikot versucht , ihn zu fangen .
trg = a boy in a red uniform is attempting to avoid getting out at home plate , while the catcher in the blue uniform is attempting to catch him .
predicted trg = ['a', 'boy', 'in', 'a', 'red', 'uniform', 'trying', 'to', 'catch', 'a', 'baseball', 'bat', 'to', 'the', 'the', 'blue', 'tries', 'to', 'block', 'blue', '.']
###Markdown
6. Download Model
###Code
!ls -al
from google.colab import files
print('Downloading best model...') # Known bug; if using Firefox, a print statement in the same cell is necessary.
files.download('./best_model_de2en.pt')
print('Downloading last model...') # Known bug; if using Firefox, a print statement in the same cell is necessary.
files.download('./last_model_de2en.pt')
###Output
_____no_output_____
|
Rastrigin_check.ipynb
|
###Markdown
The Rastrigin function: plotting resultsWe are going to make the following plots for the results of the evolution stored in the database:* Error bars graph (raw scores).* Error bars graph (fitness scores).* Max/min/avg/std. dev. graph (raw scores).* Max/min/avg/std. dev. graph (fitness scores).* Raw and Fitness min/max difference graph.* Heat map of population raw score distribution.
###Code
%matplotlib inline
from pyevolve_plot import plot_errorbars_raw, plot_errorbars_fitness, \
plot_maxmin_raw, plot_maxmin_fitness, \
plot_diff_raw, plot_pop_heatmap_raw
plot_errorbars_raw('rastrigin.db','ex1')
plot_errorbars_fitness('rastrigin.db','ex1')
plot_maxmin_raw('rastrigin.db','ex1')
plot_maxmin_raw('rastrigin.db','ex1')
plot_diff_raw('rastrigin.db','ex1')
plot_pop_heatmap_raw('rastrigin.db','ex1')
###Output
Loading database...
123 generations found !
###Markdown
The Rastrigin function: plotting resultsWe are going to make the following plots for the results of the evolution stored in the database:* Error bars graph (raw scores).* Error bars graph (fitness scores).* Max/min/avg/std. dev. graph (raw scores).* Max/min/avg/std. dev. graph (fitness scores).* Raw and Fitness min/max difference graph.* Heat map of population raw score distribution.
###Code
%matplotlib inline
from pyevolve_plot import plot_errorbars_raw, plot_errorbars_fitness, \
plot_maxmin_raw, plot_maxmin_fitness, \
plot_diff_raw, plot_pop_heatmap_raw
plot_errorbars_raw('rastrigin.db','ex1')
plot_errorbars_fitness('rastrigin.db','ex1')
plot_maxmin_raw('rastrigin.db','ex1')
plot_maxmin_raw('rastrigin.db','ex1')
plot_diff_raw('rastrigin.db','ex1')
plot_pop_heatmap_raw('rastrigin.db','ex1')
###Output
_____no_output_____
###Markdown
The Rastrigin function: plotting resultsWe are going to make the following plots for the results of the evolution stored in the database:* Error bars graph (raw scores).* Error bars graph (fitness scores).* Max/min/avg/std. dev. graph (raw scores).* Max/min/avg/std. dev. graph (fitness scores).* Raw and Fitness min/max difference graph.* Heat map of population raw score distribution.
###Code
%matplotlib inline
from pyevolve_plot import plot_errorbars_raw, plot_errorbars_fitness, \
plot_maxmin_raw, plot_maxmin_fitness, \
plot_diff_raw, plot_pop_heatmap_raw
plot_errorbars_raw('rastrigin.db','ex1')
plot_errorbars_fitness('rastrigin.db','ex1')
plot_maxmin_raw('rastrigin.db','ex1')
plot_maxmin_raw('rastrigin.db','ex1')
plot_diff_raw('rastrigin.db','ex1')
plot_pop_heatmap_raw('rastrigin.db','ex1')
###Output
_____no_output_____
|
aou_workbench_pooled_analyses/phenotype_exploration/compare_sql_phenotype_to_r_phenotype.ipynb
|
###Markdown
Compare SQL phenotype to R phenotype For AoU there are some logic changes that will affect which of a person's measurements is used. And which measurement is used will affect the age, since its age at time of measurement, and the statin use indicator, since the measurment must occur with in the statin use interval to be true. AoU: We now retain only measurements where value_as_number IS NOT NULL AND value_as_number > 0. AoU: Previously the R code was modifying LDL during the lipids adjustment. Now LDL is the original value from the measurements table. Adjustments only occur within LDL_adjusted. AoU: A single age and statin use indicator was previously chosen per person, even though those values could vary between a person's different lipid measurements. Now each measurement is retaining the age and statin use flag associated with the datetime of the measurment. AoU: When choosing the "most recent" measurement, the SQL code goes to greater lengths to make the result reproducible by sorting not only by measurement date, but also by measurement time, and measurement id in the case of ties. AoU: The SQL JOIN logic for measurements and statin use intervals uses the datetime instead of the date. UKB: 148 UKB samples were getting dropped incorrectly. I narrowed it down to the na.omit command being used to keep only people with all four lipids. Since na.omit is run on the entire dataframe, it checks other columns for NAs too such as the european ancestry column. UKB: the lipids adjustment is not the same formula, specifically the rule If TG > 400, then LDL = NA` was not applied to to ldladj in the natarajan dataframe provided. Setup
###Code
lapply(c('hexbin', 'hrbrthemes', 'skimr', 'viridis'),
function(pkg) { if(! pkg %in% installed.packages()) { install.packages(pkg)} } )
library(hexbin)
library(hrbrthemes)
library(skimr)
library(tidyverse)
ORIG_R_PHENO <- c(
HDL = 'gs://fc-secure-fd6786bf-6c28-4f33-ac30-3860fbeee5bb/data/MergedData_HDL_Iteration2_ForGWAS.csv',
LDL = 'gs://fc-secure-fd6786bf-6c28-4f33-ac30-3860fbeee5bb/data/MergedData_LDL_Iteration2_ForGWAS.csv',
TC = 'gs://fc-secure-fd6786bf-6c28-4f33-ac30-3860fbeee5bb/data/MergedData_TC_Iteration2_ForGWAS.csv',
TG = 'gs://fc-secure-fd6786bf-6c28-4f33-ac30-3860fbeee5bb/data/MergedData_TG_Iteration2_ForGWAS.csv'
)
NEW_SQL_PHENO <- 'gs://fc-secure-fd6786bf-6c28-4f33-ac30-3860fbeee5bb/data/pooled/phenotypes/20211224/aou_alpha2_ukb_pooled_lipids_phenotype.tsv'
# Set some visualiation defaults.
theme_set(theme_ipsum(base_size = 16)) # Default theme for plots.
#' Returns a data frame with a y position and a label, for use annotating ggplot boxplots.
#'
#' @param d A data frame.
#' @return A data frame with column y as max and column label as length.
get_boxplot_fun_data <- function(df) {
return(data.frame(y = max(df), label = stringr::str_c('N = ', length(df))))
}
###Output
_____no_output_____
###Markdown
Load data
###Code
orig_hdl <- read_csv(pipe(str_glue('gsutil cat {ORIG_R_PHENO[["HDL"]]}')))
orig_ldl <- read_csv(pipe(str_glue('gsutil cat {ORIG_R_PHENO[["LDL"]]}')))
orig_tc <- read_csv(pipe(str_glue('gsutil cat {ORIG_R_PHENO[["TC"]]}')))
orig_tg <- read_csv(pipe(str_glue('gsutil cat {ORIG_R_PHENO[["TG"]]}')))
orig_pheno_wide <- orig_hdl %>%
full_join(orig_ldl) %>%
full_join(orig_tc) %>%
full_join(orig_tg) %>%
mutate(
FID = paste0(sampleid, '_', cohort),
IID = FID
)
nrow(orig_pheno_wide)
length(unique(orig_pheno_wide$IID))
stopifnot(nrow(orig_pheno_wide) == length(unique(orig_pheno_wide$IID)))
colnames(orig_pheno_wide)
new_pheno_wide = read_tsv(pipe(str_glue('gsutil cat {NEW_SQL_PHENO}')))
colnames(new_pheno_wide)
###Output
_____no_output_____
###Markdown
Compare data
###Code
dim(orig_pheno_wide)
dim(new_pheno_wide)
###Output
_____no_output_____
###Markdown
We've retained more non-zero and non-null measurements.
###Code
length(unique(orig_pheno_wide$IID))
length(unique(new_pheno_wide$IID))
nrow(new_pheno_wide) - nrow(orig_pheno_wide)
new_pheno_wide %>%
filter(!IID %in% orig_pheno_wide$IID) %>%
group_by(cohort) %>%
summarize(count = n())
###Output
_____no_output_____
###Markdown
We've also included more genomes.
###Code
pheno_versions <- inner_join(
new_pheno_wide,
orig_pheno_wide,
suffix = c('_sql_phenotypes', '_r_phenotypes'),
by = c('FID', 'IID')
)
dim(pheno_versions)
stopifnot(nrow(orig_pheno_wide) == nrow(pheno_versions))
colnames(pheno_versions)
###Output
_____no_output_____
###Markdown
Check age
###Code
sum(abs(pheno_versions$age_sql_phenotypes - pheno_versions$age_r_phenotypes) > 2)
pheno_versions %>%
select(IID, age_r_phenotypes, age_sql_phenotypes) %>%
filter(age_sql_phenotypes - age_r_phenotypes > 2)
###Output
_____no_output_____
###Markdown
Check cohort
###Code
table(pheno_versions$cohort_r_phenotypes, pheno_versions$cohort_sql_phenotypes)
###Output
_____no_output_____
###Markdown
The results are identical. Check sex_at_birth
###Code
table(pheno_versions$sex, pheno_versions$sex_at_birth)
###Output
_____no_output_____
###Markdown
The results are identical. Check PCs
###Code
skim(pheno_versions %>%
select(pc1, PC1, pc2, PC2, pc3, PC3, pc4, PC4, pc5, PC5, pc6, PC6, pc7, PC7, pc8, PC8, pc9, PC9, pc10, PC10))
###Output
_____no_output_____
###Markdown
The results are identical. Check raw lipids
###Code
skim(pheno_versions %>%
filter(cohort_r_phenotypes == 'AOU') %>%
select(HDLraw, HDL, LDLraw, LDL,
TCraw, TC, TGraw, TG))
skim(pheno_versions %>%
filter(cohort_r_phenotypes == 'UKB') %>%
select(HDLraw, HDL, LDLraw, LDL,
TCraw, TC, TGraw, TG))
###Output
_____no_output_____
###Markdown
The results have minor differences, but no major differences. Check adjusted lipids
###Code
skim(pheno_versions %>%
filter(cohort_r_phenotypes == 'AOU') %>%
select(HDLadj, HDL, LDLadj, LDL_adjusted,
TCadj, TC_adjusted, TGadj, TG_adjusted))
skim(pheno_versions %>%
filter(cohort_r_phenotypes == 'UKB') %>%
select(HDLadj, HDL, LDLadj, LDL_adjusted,
TCadj, TC_adjusted, TGadj, TG_adjusted))
###Output
_____no_output_____
###Markdown
The results have minor differences, but no unexpected major differences. (It is expected that we have more NA values for LDL_adjusted.) Check normalized lipids
###Code
skim(pheno_versions %>%
filter(cohort_r_phenotypes == 'AOU') %>%
select(HDLnorm, HDL_norm, LDLnorm, LDL_adjusted_norm,
TCnorm, TC_adjusted_norm, TGnorm, TG_adjusted_norm))
skim(pheno_versions %>%
filter(cohort_r_phenotypes == 'UKB') %>%
select(HDLnorm, HDL_norm, LDLnorm, LDL_adjusted_norm,
TCnorm, TC_adjusted_norm, TGnorm, TG_adjusted_norm))
###Output
_____no_output_____
###Markdown
The results have minor differences, but no major differences. Provenance
###Code
devtools::session_info()
###Output
_____no_output_____
|
exploratory/PlayerLogsRebounds.ipynb
|
###Markdown
Player Logs
###Code
%config IPCompleter.greedy=True
%matplotlib inline
# Import the dependencies.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Path
from pathlib import Path
# NBA
import nba_api
from nba_api.stats.static import players
from nba_api.stats.static import teams
from nba_api.stats.endpoints import leaguegamefinder,boxscoretraditionalv2, boxscoreadvancedv2, playergamelog
import requests
###Output
_____no_output_____
###Markdown
Look Up Tables> We want to create a couple of look up tables so when we pull the previous day's games we could use team id or player id to filter and display the correct information.
###Code
# This gets all the teams put in a dictionary
nba_teams = teams.get_teams()
print(nba_teams)
# This gets all the teams put in a dictionary
nba_players = players.get_players()
print(nba_players)
# Need to get team ids to a csv
nba_team_ids = pd.DataFrame(nba_teams)
nba_team_ids.to_csv('nba_team_ids.csv', index=False)
# Need to get team ids to a csv
nba_player_ids = pd.DataFrame(nba_players)
nba_player_ids.to_csv('nba_player_ids.csv', index=False)
# From our Team ID look up table we want to go through all teams and grab all played games
# Create list of Team Ids from the nba_team_ids dataframe
# list_of_team_ids = nba_team_ids['id'].unique().tolist()
games_list = []
for value in nba_team_ids['id']:
gamefinder = leaguegamefinder.LeagueGameFinder(team_id_nullable=value, season_nullable="2020-21")
games = gamefinder.get_data_frames()[-1]
games_list.append(games)
games_df = pd.concat(df)
games_df
print(games)
###Output
_____no_output_____
|
lecture15/02_More_Python.ipynb
|
###Markdown
More on pythonPython has many high-level builtin features, time to learn some more! 3.02 FunctionsFunctions can be defined using a lambda expression or via `def`. Python provides for functions both positional and keyword-based arguments.
###Code
square = lambda x: x * x
square(10)
# roots of ax^2 + bx + c
quadratic_root = lambda a, b, c: ((-b - (b * b - 4 * a * c) ** .5) / (2 * a), (-b + (b * b - 4 * a * c) ** .5) / (2 * a))
quadratic_root(1, 5.5, -10.5)
# a clearer function using def
def quadratic_root(a, b, c):
d = (b * b - 4 * a * c) ** .5
coeff = .5 / a
return (coeff * (-b - d), coeff * (-b + d))
quadratic_root(1, 5.5, -10.5)
###Output
_____no_output_____
###Markdown
Functions can have positional arguments and keyword based arguments. Positional arguments have to be declared before keyword args
###Code
# name is a positional argument, message a keyword argument
def greet(name, message='Hello {}, how are you today?'):
print(message.format(name))
greet('Tux')
greet('Tux', 'Hi {}!')
greet('Tux', message='What\'s up {}?')
# this doesn't work
greet(message="Hi {} !", 'Tux')
###Output
_____no_output_____
###Markdown
keyword arguments can be used to define default values
###Code
import math
def log(num, base=math.e):
return math.log(num) / math.log(base)
log(math.e)
log(10)
log(1000, 10)
###Output
_____no_output_____
###Markdown
3.03 builtin functions, attributesPython provides a rich standard library with many builtin functions. Also, bools/ints/floats/strings have many builtin methods allowing for concise code.One of the most useful builtin function is `help`. Call it on any object to get more information, what methods it supports.
###Code
s = 'This is a test string!'
print(s.lower())
print(s.upper())
print(s.startswith('This'))
print('string' in s)
print(s.isalnum())
###Output
this is a test string!
THIS IS A TEST STRING!
True
True
False
###Markdown
For casting objects, python provides several functions closely related to the constructors`bool, int, float, str, list, tuple, dict, ...`
###Code
tuple([1, 2, 3, 4])
str((1, 2, 3))
str([1, 4.5])
###Output
_____no_output_____
###Markdown
4.01 DictionariesDictionaries (or associate arrays) provide a structure to lookup values based on keys. I.e. they're a collection of k->v pairs.
###Code
list(zip(['brand', 'model', 'year'], ['Ford', 'Mustang', 1964])) # creates a list of tuples by "zipping" two list
# convert a list of tuples to a dictionary
D = dict(zip(['brand', 'model', 'year'], ['Ford', 'Mustang', 1964]))
D
D['brand']
D = dict([('brand', 'Ford'), ('model', 'Mustang')])
D['model']
###Output
_____no_output_____
###Markdown
Dictionaries can be also directly defined using `{ ... : ..., ...}` syntax
###Code
D = {'brand' : 'Ford', 'model' : 'Mustang', 'year' : 1964}
D
# dictionaries have serval useful functions implemented
# help(dict)
# adding a new key
D['price'] = '48k'
D
# removing a key
del D['year']
D
# checking whether a key exists
'brand' in D
# returning a list of keys
D.keys()
# casting to a list
list(D.keys())
D
# iterating over a dictionary
for k in D.keys():
print(k)
for v in D.values():
print(v)
for k, v in D.items():
print('{}: {}'.format(k, v))
###Output
brand: Ford
model: Mustang
price: 48k
###Markdown
4.02 Calling functions with tuples/dictsPython provides two special operators `*` and `**` to call functions with arguments specified through a tuple or dictionary. I.e. `*` unpacks a tuple into positional args, whereas `**` unpacks a dictionary into keyword arguments.
###Code
quadratic_root(1, 5.5, -10.5)
args=(1, 5.5, -10.5)
quadratic_root(*args)
args=('Tux',) # to create a tuple with one element, need to append , !
kwargs={'message' : 'Hi {}!'}
greet(*args, **kwargs)
###Output
Hi Tux!
###Markdown
4.03 Setspython has builtin support for sets (i.e. an unordered list without duplicates). Sets can be defined using `{...}`.\**Note:** `x={}` defines an empty dictionary! To define an empty set, use
###Code
S = set()
type(S)
S = {1, 2, 3, 1, 4}
S
2 in S
# casting can be used to get unique elements from a list!
L = [1, 2, 3, 4, 3, 2, 5, 3, 65, 19]
list(set(L))
###Output
_____no_output_____
###Markdown
set difference via `-` or `difference`
###Code
{1, 2, 3} - {2, 3}, {1, 2, 3}.difference({2, 3})
###Output
_____no_output_____
###Markdown
set union via `+` or `union`
###Code
{1, 2, 3} | {4, 5}, {1, 2, 3}.union({4, 5})
###Output
_____no_output_____
###Markdown
set intersection via `&` or `intersection`
###Code
{1, 5, 3, 4} & {2, 3}
{1, 5, 3, 4}.intersection({2, 3})
###Output
_____no_output_____
###Markdown
4.04 ComprehensionsInstead of creating list, dictionaries or sets via explicit extensional declaration, you can use a comprehension expression. This is especially useful for conversions.
###Code
# list comprehension
L = ['apple', 'pear', 'banana', 'cherry']
[(1, x) for x in L]
# special case: use if in comprehension for additional condition
[(len(x), x) for x in L if len(x) > 5]
# if else must come before for
# ==> here ... if ... else ... is an expression!
[(len(x), x) if len(x) % 2 == 0 else None for x in L]
###Output
_____no_output_____
###Markdown
The same works also for sets AND dictionaries. The collection to iterate over doesn't need to be of the same type.
###Code
L = ['apple', 'pear', 'banana', 'cherry']
length_dict = {k : len(k) for k in L}
length_dict
import random
[random.randint(0, 10) for i in range(10)]
{random.randint(0, 10) for _ in range(20)}
[(k, v) for k, v in length_dict.items()]
# filter out elements from dict based on condition
{k : v for k,v in length_dict.items() if k[0] < 'c'}
###Output
_____no_output_____
###Markdown
5.01 More on functionsNested functions + decorators==> Functions are first-class citizens in python, i.e. we can return them
###Code
def make_plus_one(f):
def inner(x):
return f(x) + 1
return inner
fun = make_plus_one(lambda x: x)
fun(2), fun(3), fun(4)
###Output
_____no_output_____
###Markdown
A more complicated function can be created to create functions to evaluate a polynomial defined through a vectpr $p = (p_1, ..., p_n)^T$$$ f(x) = \sum_{i=1}^n p_i x^i$$
###Code
def make_polynomial(p):
def f(x):
if 0 == len(p):
return 0.
y = 0
xq = 1
for a in p:
y += a * xq
xq *= x
return y
return f
poly = make_polynomial([1])
poly(2)
quad_poly = make_polynomial([1, 2, 1])
quad_poly(1)
###Output
_____no_output_____
###Markdown
Basic idea is that when declaring nested functions, the inner ones have access to the enclosing functions scope. When returning them, a closure is created.We can use this to change the behavior of functions by wrapping them with another!==> we basically decorate the function with another, thus the name decorator
###Code
def greet(name):
return 'Hello {}!'.format(name)
greet('Tux')
###Output
_____no_output_____
###Markdown
Let's say we want to shout the string, we could do:
###Code
greet('Tux').upper()
###Output
_____no_output_____
###Markdown
==> however, we would need to change this everywhere However, what if we want to apply uppercase to another function?
###Code
def state_an_important_fact():
return 'The one and only answer to ... is 42!'
state_an_important_fact().upper()
###Output
_____no_output_____
###Markdown
with a wrapper we could create an upper version
###Code
def make_upper(f):
def inner(*args, **kwargs):
return f(*args, **kwargs).upper()
return inner
GREET = make_upper(greet)
STATE_AN_IMPORTANT_FACT = make_upper(state_an_important_fact)
GREET('tux')
STATE_AN_IMPORTANT_FACT()
###Output
_____no_output_____
###Markdown
Instead of explicitly having to create the decoration via make_upper, we can also use python's builtin support for this via the @ statement. I.e.
###Code
@make_upper
def say_hi(name):
return 'Hi ' + name + '.'
say_hi('sealion')
###Output
_____no_output_____
###Markdown
It's also possible to use multiple decorators
###Code
def split(function):
def wrapper():
res = function()
return res.split()
return wrapper
@split
@make_upper
def state_an_important_fact():
return 'The one and only answer to ... is 42!'
state_an_important_fact()
###Output
_____no_output_____
###Markdown
More on decorators here: .==> Flask (the framework we'll learn next week) uses decorators extensively, therefore they're included here. **Summary**: What is a decorator?A decorator is a design pattern to add/change behavior to an individual object. In python decorators are typically used for functions (later: also for classes) 6.01 GeneratorsAssume we want to generate all square numbers. We could do so using a list comprehension:
###Code
[x * x for x in range(10)]
###Output
_____no_output_____
###Markdown
However, this will create a list of all numbers. Sometimes, we just want to consume the number. I.e. we could do this via a function
###Code
square = lambda x: x * x
print(square(1))
print(square(2))
print(square(3))
###Output
1
4
9
###Markdown
However, what about a more complicated sequence? I.e. fibonnaci numbers?
###Code
def fib(n):
if n <= 0:
return 0
if n <= 1:
return 1
a, b = 0, 1
for i in range(n):
a, b = b, a + b
return a
n = 10
for i in range(n):
print(fib(i))
###Output
0
1
1
2
3
5
8
13
21
34
###Markdown
Complexity is n^2! However, with generators we can stop execution.The pattern is to call basically generator.next()
###Code
def fib():
a, b = 0, 1
yield a
while True:
a, b = b, a + b
yield a
fib()
g = fib()
for i in range(5):
print(next(g))
###Output
0
1
1
2
3
###Markdown
`enumerate` and `zip` are both generator objects!
###Code
L = ['a', 'b', 'c', 'd']
g = enumerate(L)
print(next(g))
print(next(g))
print(next(g))
print(next(g))
# stop iteration exception will be done
print(next(g))
g = range(3)
g
L = ['a', 'b', 'c', 'd']
i = list(range(len(L)))[::-1]
g = zip(L, i)
for el in g:
print(el)
###Output
('a', 3)
('b', 2)
('c', 1)
('d', 0)
###Markdown
**Note**: There is no hasNext in python. Use a loop with `in` to iterate over the full generator.
###Code
for i, n in enumerate(fib()):
if i > 10:
break
print(n)
###Output
0
1
1
2
3
5
8
13
21
34
55
###Markdown
7.01 Higher order functionspython provides two builitn higher order functions: `map` and `filter`. A higher order function is a function which takes another function as argument or returns a function (=> decorators).In python3, `map` and `filter` yield a generator object.
###Code
map(lambda x: x * x, range(7))
for x in map(lambda x: x * x, range(7)):
print(x)
# display squares which end with 1
list(filter(lambda x: x % 10 == 1, map(lambda x: x * x, range(25))))
###Output
_____no_output_____
###Markdown
8.01 Basic I/OPython has builtin support to handle files
###Code
f = open('file.txt', 'w')
f.write('Hello world')
f.close()
###Output
_____no_output_____
###Markdown
Because a file needs to be closed (i.e. the file object destructed), python has a handy statement to deal with auto-closing/destruction: The `with` statement.
###Code
with open('file.txt', 'r') as f:
lines = f.readlines()
print(lines)
###Output
['Hello world']
###Markdown
Again, `help` is useful to understand what methods a file object has
###Code
# uncomment here to get the full help
# help(f)
###Output
_____no_output_____
###Markdown
7.01 classesIn python you can define compound types using `class`
###Code
class Animal:
def __init__(self, name, weight):
self.name = name
self.weight = weight
def print(self):
print('{} ({} kg)'.format(self.name, self.weight))
def __str__(self):
return '{} ({} kg)'.format(self.name, self.weight)
dog = Animal('dog', 20)
dog
print(dog)
dog.print()
###Output
dog (20 kg)
###Markdown
Basic inheritance is supported in python
###Code
class Elephant(Animal):
def __init__(self):
Animal.__init__(self, 'elephant', 1500)
#alternative:
# super().__init__(...)
e = Elephant()
print(e)
###Output
elephant (1500 kg)
###Markdown
8.01 Modules and packagesMore on this at . A good explanation of relative imports can be found here .==> Each file represents a module in python. One or more modules make up a package.Let's say we want to package our `quad_root` function into a separate module `solver`
###Code
!rm -r solver*
!ls
%%file solver.py
# a clearer function using def
def quadratic_root(a, b, c):
d = (b * b - 4 * a * c) ** .5
coeff = .5 / a
return (coeff * (-b - d), coeff * (-b + d))
!cat solver.py
import solver
solver.quadratic_root(1, 1, -2)
###Output
_____no_output_____
###Markdown
Alternative is to import the name quadratic_root directly into the current scope
###Code
from solver import quadratic_root
quadratic_root(1, 1, -2)
###Output
_____no_output_____
###Markdown
To import everything, you can use `from ... import *`. To import multiple specific functions, use `from ... import a, b`.E.g. `from flask import render_template, request, abort, jsonify, make_response`.To organize modules in submodules, subsubmodules, ... you can use folders.I.e. to import a function from a submodule, use `from solver.algebraic import quadratic_root`.There's a special file `__init__.py` that is added at each level, which gets executed when `import folder` is run.
###Code
!rm *.py
!mkdir -p solver/algebraic
%%file solver/__init__.py
# this file we run when import solver is executed
print('import solver executed!')
%%file solver/algebraic/__init__.py
# run when import solver.algebraic is used
print('import solver.algebraic executed!')
%%file solver/algebraic/quadratic.py
print('solver.algebraic.quadratic executed!')
# a clearer function using def
def quadratic_root(a, b, c):
d = (b * b - 4 * a * c) ** .5
coeff = .5 / a
return (coeff * (-b - d), coeff * (-b + d))
%%file test.py
import solver
!python3 test.py
%%file test.py
import solver.algebraic
!python3 test.py
%%file test.py
import solver.algebraic.quadratic
%%file test.py
import solver.algebraic.quadratic
!python3 test.py
%%file test.py
from solver.algebraic.quadratic import *
print(quadratic_root(1, 1, -2))
!python3 test.py
###Output
import solver executed!
import solver.algebraic executed!
solver.algebraic.quadratic executed!
(-2.0, 1.0)
###Markdown
One can also use relative imports to import from other files via `.` or `..`!
###Code
%%file solver/version.py
__version__ = "1.0"
!tree solver
%%file solver/algebraic/quadratic.py
from ..version import __version__
print('solver.algebraic.quadratic executed!')
print('package version is {}'.format(__version__))
# a clearer function using def
def quadratic_root(a, b, c):
d = (b * b - 4 * a * c) ** .5
coeff = .5 / a
return (coeff * (-b - d), coeff * (-b + d))
!python3 test.py
###Output
import solver executed!
import solver.algebraic executed!
solver.algebraic.quadratic executed!
package version is 1.0
(-2.0, 1.0)
###Markdown
This can be also used to bring certain functions into scope!
###Code
%%file solver/algebraic/__init__.py
from .quadratic import *
# use this to restrict what functions to "export"
__all__ = [quadratic_root.__name__]
%%file test.py
from solver.algebraic import *
print(quadratic_root(1, 1, -2))
!python3 test.py
###Output
import solver executed!
solver.algebraic.quadratic executed!
package version is 1.0
(-2.0, 1.0)
|
statsmodels/benchmark_linreg.ipynb
|
###Markdown
Linear regression performance benchmark: scikit-learn vs. statsmodelsWu Sun2019-05-03This notebook tests the performance of running ordinary least square (OLS) linear regression on samples of an intermediate size (*n* ~ 102–104) using [Scikit-learn](https://scikit-learn.org/stable/) vs. using [Statsmodels](https://www.statsmodels.org/stable/index.html).
###Code
import numpy as np
import pandas as pd
import statsmodels.api as sm
from scipy.stats import pearsonr
from sklearn import datasets
from sklearn.linear_model import LinearRegression
###Output
_____no_output_____
###Markdown
Version information
###Code
import pkg_resources
for pkg in ["numpy", "scipy", "pandas", "statsmodels", "scikit-learn"]:
version = pkg_resources.get_distribution(pkg).version
print(f"{pkg} version = {version}")
###Output
numpy version = 1.16.3
scipy version = 1.2.1
pandas version = 0.24.2
statsmodels version = 0.9.0
scikit-learn version = 0.19.2
###Markdown
Load the datasetLoad the Boston house prices dataset from Scikit-learn.
###Code
dataset = datasets.load_boston()
X = dataset.data
y = dataset.target
# add constants
X_with_const = np.hstack([np.ones((X.shape[0], 1)), X])
###Output
_____no_output_____
###Markdown
Benchmark regressions Baseline: normal equation
###Code
coefs = np.linalg.solve(X_with_const.T @ X_with_const, X_with_const.T @ y)
r2 = pearsonr(X_with_const @ coefs, y)[0] ** 2
print(f"""Linear regression results from the normal equation
* coefs = {coefs}
* R^2 = {r2}""")
%%timeit -n 1000
coefs = np.linalg.solve(X_with_const.T @ X_with_const, X_with_const.T @ y)
r2 = pearsonr(X_with_const @ coefs, y)[0] ** 2
###Output
113 µs ± 3.24 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
###Markdown
Statsmodels OLS
###Code
lm_sm = sm.OLS(y, X_with_const).fit()
print(lm_sm.summary())
%%timeit -n 1000
lm_sm = sm.OLS(y, X_with_const).fit()
###Output
613 µs ± 10.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
###Markdown
Scikit-learn linear model
###Code
sk_ols = LinearRegression(fit_intercept=False)
lm_sk = sk_ols.fit(X_with_const, y)
score_sk = lm_sk.score(X_with_const, y) # this calculates the R^2
print(f"""Scikit-learn linear regression results
* coefs = {lm_sk.coef_}
* R^2 = {score_sk}""")
%%timeit -n 500
lm_sk = sk_ols.fit(X_with_const, y)
score_sk = lm_sk.score(X_with_const, y)
###Output
642 µs ± 19.5 µs per loop (mean ± std. dev. of 7 runs, 500 loops each)
|
old_versions/2018.11.02_ref/1main_time_series-v2.ipynb
|
###Markdown
2018.10.27: Multiple states: Time series
###Code
import sys,os
import numpy as np
from scipy import linalg
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
%matplotlib inline
# setting parameter:
np.random.seed(1)
n = 10 # number of positions
m = 4 # number of values at each position
l = 1000 # number of samples
g = 1.
def itab(n,m):
i1 = np.zeros(n)
i2 = np.zeros(n)
for i in range(n):
i1[i] = i*m
i2[i] = (i+1)*m
return i1.astype(int),i2.astype(int)
i1tab,i2tab = itab(n,m)
# generate coupling matrix w0:
def generate_coupling(n,m,g):
nm = n*m
w = np.random.normal(0.0,g/np.sqrt(nm),size=(nm,nm))
for i in range(n):
i1,i2 = i1tab[i],i2tab[i]
w[i1:i2,:] -= w[i1:i2,:].mean(axis=0)
for i in range(n):
i1,i2 = i1tab[i],i2tab[i]
w[:,i1:i2] -= w[:,i1:i2].mean(axis=1)[:,np.newaxis]
return w
w0 = generate_coupling(n,m,g)
"""
plt.figure(figsize=(3,3))
plt.title('actual coupling matrix')
plt.imshow(w0,cmap='rainbow',origin='lower')
plt.xlabel('j')
plt.ylabel('i')
plt.clim(-0.3,0.3)
plt.colorbar(fraction=0.045, pad=0.05,ticks=[-0.3,0,0.3])
plt.show()
"""
# 2018.10.27: generate sequences: time series
def generate_sequences(w,n,m,l):
#print(i1tab,i2tab)
# initial s (categorical variables)
s_ini = np.random.randint(0,m,size=(l,n)) # integer values
#print(s_ini)
# onehot encoder
enc = OneHotEncoder(n_values=m)
s = enc.fit_transform(s_ini).toarray()
#print(s)
ntrial = 100
for t in range(l-1):
h = np.sum(s[t,:]*w[:,:],axis=1)
for i in range(n):
i1,i2 = i1tab[i],i2tab[i]
p1 = np.exp(h[i1:i2])
p = p1/p1.sum()
for itrial in range(ntrial):
k = np.random.randint(0,m)
if p[k] > np.random.rand():
s[t+1,i1:i2] = 0.
s[t+1,i1+k] = 1.
break
"""
if p[0] > np.random.rand():
s[t+1,i1] = 1.
s[t+1,i1+1] = 0.
else:
s[t+1,i1] = 0.
s[t+1,i1] = 1.
"""
return s
s = generate_sequences(w0,n,m,l)
print(s[:10])
# convert variable from {1,0} to {1,-1}
#s = 2*s - 1
s_av = np.mean(s[:-1],axis=0)
ds = s[:-1] - s_av
c = np.cov(ds,rowvar=False,bias=True)
#print(c)
c_inv = linalg.pinv(c,rcond=1e-15)
#print(c_inv)
nm = n*m
wini = np.random.normal(0.0,g/np.sqrt(nm),size=(nm,nm))
#print(w)
nloop = 100
w_infer = np.zeros((nm,nm))
for i in range(n):
#print(i)
i1,i2 = i1tab[i],i2tab[i]
#s1 = np.copy(s[1:,i1:i2])
w = wini[i1:i2,:]
h = s[1:,i1:i2]
for iloop in range(nloop):
h_av = h.mean(axis=0)
dh = h - h_av
dhds = dh[:,:,np.newaxis]*ds[:,np.newaxis,:]
dhds_av = dhds.mean(axis=0)
w = np.dot(dhds_av,c_inv)
h = np.dot(s[:-1],w.T)
p = np.exp(h)
p_sum = p.sum(axis=1)
for k in range(m):
p[:,k] = p[:,k]/p_sum[:]
h += s[1:,i1:i2] - p
#h += s[1:,i1:i2]/p
#cost = ((s[1:,i1:i2]-p)**2).mean(axis=0)
#print(i,iloop,cost)
#w = w - w.mean(axis=0)
w_infer[i1:i2,:] = w
plt.scatter(w0,w_infer)
plt.plot([-0.3,0.3],[-0.3,0.3],'r--')
###Output
_____no_output_____
|
05_Transformada_Fourier.ipynb
|
###Markdown
DefiniciónLa [transformada de Fourier](https://en.wikipedia.org/wiki/Fourier_transform) está definida por\begin{equation}X(j \omega) = \int_{-\infty}^{\infty} x(t) \, e^{-j \omega t} \; dt\end{equation}donde $X(j \omega) = \mathcal{F} \{ x(t) \}$ se usa como notación de la Transformada de Fourier de la señal $x(t)$. $X(j \omega)$ es el espectro de la señal $x(t)$. El argumento $j \omega$, como exponente de la exponencial, encierra el comportamiento de todas las señales oscilatorias $cos(\omega t)$.Observe que la forma de la transformada de Fourier corresponde a la forma de la correlación. De esta manera, podría interpretarse como el "parecido" entre la señal $x(t)$ y $e^{j \omega t}$, es decir entre $x(t)$ y $cos(\omega t)$La transformada inversa de Fourier $x(t) = \mathcal{F}^{-1} \{ X(j \omega) \}$ se define como\begin{equation}x(t) = \frac{1}{2 \pi} \int_{-\infty}^{\infty} X(j \omega) \, e^{j \omega t} \; d\omega\end{equation} Propiedades Invertible\begin{equation}x(t) = \mathcal{F}^{-1} \left\{ \mathcal{F} \{ x(t) \} \right\}\end{equation}Tomando las expresiones de la Transformada de Fourier y la Transformada Inversa de Fourier, se obtiene:\begin{equation}\begin{split}x(t) &= \frac{1}{2 \pi} \int_{-\infty}^{\infty} \underbrace{\int_{-\infty}^{\infty} x(\tau) e^{-j \omega \tau} d\tau}_{X(j \omega)} \; e^{j \omega t} d\omega \\&= \int_{-\infty}^{\infty} x(\tau) \left( \frac{1}{2 \pi} \int_{-\infty}^{\infty} e^{-j \omega \tau} e^{j \omega t} d\omega \right) d\tau \\&= \int_{-\infty}^{\infty} x(\tau) \delta(t - \tau) d\tau = x(t)\end{split}\end{equation} Linealidad\begin{equation}\mathcal{F} \{ A \cdot x_1(t) + B \cdot x_2(t) \} = A \cdot X_1(j \omega) + B \cdot X_2(j \omega)\end{equation}Tomando la expresión de la Transformada de Fourier se obtiene:\begin{equation}\begin{split}&= \int_{-\infty}^{\infty} (A \cdot x_1(t) + B \cdot x_2(t)) \, e^{-j \omega t} \; dt \\&= \int_{-\infty}^{\infty} A \cdot x_1(t) \, e^{-j \omega t} \; dt + \int_{-\infty}^{\infty} B \cdot x_2(t) \, e^{-j \omega t} \; dt \\&= A \cdot \int_{-\infty}^{\infty} x_1(t) \, e^{-j \omega t} \; dt + B \cdot\int_{-\infty}^{\infty} x_2(t) \, e^{-j \omega t} \; dt\end{split}\end{equation} **Ejemplo - Transformada de Fourier de una señal exponencial causal**\begin{equation}x(t) = e^{- \alpha t} \cdot \epsilon(t)\end{equation}con $\alpha \in \mathbb{R}^+$
###Code
t,w = sym.symbols('t omega', real=True)
a = 4
x = sym.exp(-a * t)*sym.Heaviside(t)
x
X = fourier_transform(x)
X
plt.rcParams['figure.figsize'] = 5, 2
sym.plot(x, (t,-1,10), ylabel=r'Amp',line_color='blue',legend=True, label = 'x(t)')
sym.plot(sym.re(X), (w,-20,20), ylabel=r'Real',line_color='blue',legend=True, label = 'X(w)')
sym.plot(sym.im(X), (w,-20,20), ylabel=r'Imag',line_color='blue',legend=True, label = 'X(w)')
sym.plot(sym.sqrt(
(sym.im(X)*sym.im(X)) + (sym.re(X)*sym.re(X))),
(w,-20,20), ylabel=r'Mag',line_color='blue',legend=True, label = 'X(w)')
###Output
_____no_output_____
###Markdown
Observe que:- $X(\omega)$ es una función definida para todos los valores de $\omega$ y no solamente para los múltiplos enteros de un valor determinado $\omega_0$.- $X(\omega)$ es una función compleja, es decir que tiene parte imaginaria y parte real. Así, puede expresarse de forma cartesiana ($real + j \cdot imaginario$) o de forma polar ($magnitud \angle ángulo$). El "parecido" entre la señal $x(t)$ con $sin(\omega t)$ se puede apreciar en la magnitud de $X(\omega)$.- $|X(\omega)|$ tiene un valor máximo en $\omega=0$ y un decaimiento a medida que aumenta $\omega$ Analizando la magnitud de $X(\omega)$
###Code
X
X_real = sym.re(X)
X_real
X_imag = sym.im(X)
X_imag
X_magn = sym.sqrt(X_real*X_real + X_imag*X_imag).simplify()
X_magn
###Output
_____no_output_____
###Markdown
La magnitud de $X(\omega)$ es simétrica respecto a $\omega = 0$. Así, será suficiente analizar solamente un lado del espectro de una señal de tiempo continuo. **Ejemplo - Transformada de Fourier de una señal exponencial por una senoidal**\begin{equation}x(t) = sin(\omega_0 t) \cdot e^{- \alpha t} \cdot \epsilon(t)\end{equation}con $\omega \in \mathbb{R}^+$
###Code
t,w = sym.symbols('t omega', real=True)
w0 = 10
x1 = sym.sin(w0 * t)*sym.exp(-2*t)*sym.Heaviside(t)
x1
X1 = fourier_transform(x1)
X1
plt.rcParams['figure.figsize'] = 5, 2
sym.plot(x1, (t,-2,5), ylabel=r'Amp',line_color='blue',legend=True, label = 'x1(t)')
#sym.plot(sym.re(X), (w,-20,20), ylabel=r'Real',line_color='blue',legend=True, label = 'X(w)')
#sym.plot(sym.im(X), (w,-20,20), ylabel=r'Imag',line_color='blue',legend=True, label = 'X(w)')
sym.plot(sym.sqrt(
(sym.im(X1)*sym.im(X1)) + (sym.re(X1)*sym.re(X1))),
(w,-60,60), ylabel=r'Mag',line_color='blue',legend=False, label = 'X1(w)')
###Output
_____no_output_____
###Markdown
Observe que:- $x1(t)$ corresponde a $x(t)$ multiplicada con una función senoidal de frecuencia angular de $10$ rad/seg.- Al igual que con la magnitud de $X(\omega)$, la magnitud de $X1(\omega)$ decae con $\omega$, sin embargo, hay un pico en $\omega=10$ que se relaciona con la senoidal. **Ejemplo - Transformada de Fourier de senoidales y combinaciones causales**\begin{equation}x(t) = sin(\omega_0 t) \cdot \epsilon(t)\end{equation}con $\omega \in \mathbb{R}^+$
###Code
w1 = 10
w2 = 5
x2_1 = sym.sin(w1 * t)*sym.Heaviside(t)
x2_2 = sym.sin(w2 * t)*sym.Heaviside(t)
x2 = x2_1 + x2_2
x2
X2_1 = fourier_transform(x2_1)
X2_1
X2_2 = fourier_transform(x2_2)
X2_2
(X2_1+X2_2).simplify()
X2 = fourier_transform(x2)
X2
plt.rcParams['figure.figsize'] = 5, 2
gt2_1 = sym.plot(x2_1, (t,-1,5), ylabel=r'Amp',line_color='blue',legend=True, label = 'x2_1(t)', show = False)
gt2_2 = sym.plot(x2_2, (t,-1,5), ylabel=r'Amp',line_color='green',legend=True, label = 'x2_2(t)', show = False)
gt2 = sym.plot(x2, (t,-1,5), ylabel=r'Amp',line_color='red',legend=True, label = 'x2(t)', show = False)
gt2.extend(gt2_1)
gt2.extend(gt2_2)
gt2.show()
plt.rcParams['figure.figsize'] = 6, 3
gw2_1 = sym.plot(sym.sqrt(
(sym.im(X2_1)*sym.im(X2_1)) + (sym.re(X2_1)*sym.re(X2_1))),
(w,0,14), ylabel=r'Mag',line_color='blue',legend=False, label = 'X2_1(w)',show = False)
gw2_2 = sym.plot(sym.sqrt(
(sym.im(X2_2)*sym.im(X2_2)) + (sym.re(X2_2)*sym.re(X2_2))),
(w,0,14), ylabel=r'Mag',line_color='green',legend=False, label = 'X2_2(w)',show = False)
gw2 = sym.plot(sym.sqrt(
(sym.im(X2)*sym.im(X2)) + (sym.re(X2)*sym.re(X2))),
(w,0,14), ylabel=r'Mag',line_color='red',legend=False, label = 'X2(w)',show = False)
gw2.extend(gw2_1)
gw2.extend(gw2_2)
gw2.show()
X2
###Output
_____no_output_____
###Markdown
En la gráfica anterior se observa el efecto de la superposición lineal de los espectros de las dos señales senoidales. **Ejercicio**Analice el espectro de\begin{equation}x(t) = (sin(\omega_0 t) + e^{-2t}) \cdot \epsilon(t)\end{equation}con $\omega_0 \in \mathbb{R}^+$
###Code
t,w = sym.symbols('t omega', real=True)
w0 = 15
a = 2
x4 = (sym.sin(w0 * t) + sym.exp(-a*t))*sym.Heaviside(t)
x4
###Output
_____no_output_____
###Markdown
DualidadObserve que la **Transformada de Fourier** y la **Transformada Inversa de Fourier** tienen formas parecidas.\begin{align}X(\omega) &= \int_{-\infty}^{\infty} x(t) \, e^{-j \omega t} \; dt \\x(t) &= \frac{1}{2 \pi} \int_{-\infty}^{\infty} X(j \omega) \, e^{j \omega t} \; d\omega\end{align}La principal diferencia está en el factor de normalización $2 \pi$ y el signo de la exponencial.Suponga que:\begin{equation}x_2(\omega) = \mathcal{F} \{ x_1(t) \}\end{equation}Puede pensarse que: \begin{equation}x_2(t) = x_2(\omega) \big\vert_{\omega=t}\end{equation}Entonces\begin{equation}\mathcal{F} \{ x_2(t) \} = \int_{-\infty}^{\infty} x_2(\omega) \big\vert_{\omega=t} \, e^{-j \omega t} \; dt\end{equation}Esta tiene la forma de **Transformada de Fourier**, pero la función interna tiene a $\omega$ como variable, esto indica que la integral se trata como una **Transformada inversa**. Así, para volver a la **Transformada de Fourier**, se debe multiplicar por $2\pi$ y quitar el signo de la exponencial del kernel de transformación.\begin{equation}\mathcal{F} \{ x_2(t) \} = 2 \pi \cdot x_1(- \omega)\end{equation}Esta propiedad permite llevar los análisis desde el dominio de las frecuencias al dominio del tiempo y viceversa. TeoremasRetomando la transformada\begin{equation}X(j \omega) = \int_{-\infty}^{\infty} x(t) \, e^{-j \omega t} \; dt\end{equation} DerivadasDadas una señal $x(t)$ y su derivada respecto al tiempo $\frac{d x(t)}{dt}$, y conocida la **Transformada de Fourier** $X(\omega)$ :\begin{equation}\mathcal{F} \left\{ \frac{d x(t)}{dt} \right\} = \int_{-\infty}^{\infty} \frac{d x(t)}{dt} \, e^{-j \omega t} \; dt\end{equation}La integral se puede resolver por partes:\begin{equation}\begin{split}\mathcal{F} \left\{ \frac{d x(t)}{dt} \right\} &= x(t) \cdot e^{-j \omega t} \big\vert_{-\infty}^{\infty} - \int_{-\infty}^{\infty} x(t) (-j \omega) e^{-j \omega t} \; dt \\&= j \omega \int_{-\infty}^{\infty} x(t) e^{-j \omega t} \; dt \\&= j \omega X(\omega) \\end{split}\end{equation}\begin{equation}\frac{d x(t)}{dt} = \frac{d \delta(t)}{dt} * x(t)\end{equation}La principal aplicación está en la transformación de ecuaciones diferenciales.** Ejemplo**\begin{equation}2y(t) + 2 \frac{dy}{dt} - x(t) = 0 \end{equation}Aplicando la **Transformada de Fourier** con sus propiedades se obtiene:\begin{equation}2Y(\omega) + 2 j \omega Y(\omega) - X(\omega) = 0 \end{equation}Observe que en el modelo en el dominio del tiempo (ecuación diferencial) no es posible despejar una expresión equivalente a $\frac{x(t)}{y(t)}$. Por su parte, usando el modelo en el dominio de la frecuencia, se obtiene:\begin{equation}Y(\omega)(2 + 2 j \omega ) = X(\omega) \end{equation}\begin{equation}\frac{Y(\omega)}{X(\omega)} = \frac{1}{2+2j\omega} = F(\omega)\end{equation}Esta relación es conocida como **Función de transferencia** y representa el efecto que tiene el sistema sobre una señal de entrada que en el caso de la transformada de Fourier es senoidal.
###Code
# La función de transferencia
F = 1 / (2 +1j*2*w )
F
plt.rcParams['figure.figsize'] = 5, 2
sym.plot(sym.Abs(F), (w,0,10), ylabel=r'Mag',line_color='blue',legend=True, label = 'F(w)', show = True)
###Output
_____no_output_____
###Markdown
La respuesta de un sistema ante una entrada senoidal de frecuencia específica $\omega$ se determina por el valor complejo que toma $F(\omega)$. Por ejemplo, en la frecuencia $\omega = 1$, $F(1) = \frac{1}{2+2j}$
###Code
F1 = F.subs(w,1)
F1
magF1 = sym.Abs(F1)
magF1
###Output
_____no_output_____
###Markdown
Así, si el sistema es excitado con un seno de frecuencia 1, con amplitud 1, la salida será un seno de amplitud 0.35 y tendrá un desfase definido por el ángulo de la función de transferencia.\begin{equation}0.35 sin(1t + ang)\end{equation} EjercicioDeduzca cómo se calcula el ángulo de fase que introduce el sistema.
###Code
F
F.subs(w,3)
sym.re(F)
sym.im(F)
sym.sqrt(sym.re(F)**2 +
sym.im(F)**2 )
sym.atan(sym.im(F)/sym.re(F))
sym.Abs(F)
sym.arg(F)
plt.rcParams['figure.figsize'] = 5, 2
sym.plot(sym.Abs(F), (w,0,10), ylabel=r'Mag',line_color='blue',legend=True, label = 'F(w)', show = True)
sym.plot(sym.arg(F), (w,0,10), ylabel=r'arg',line_color='blue',legend=True, label = 'F(w)', show = True)
###Output
_____no_output_____
|
Notebook-Class-Assignment-Answers/Step-4-Develop-Model-Task-6-Connect-the-Dots-Class-Assignment.ipynb
|
###Markdown
Install Pygeohash libraryThis library provides functions for computing geohash Step 5 - Develop Model - Task 6 - Connect the dots & Task 7 - Graph Analytics - CLASS ASSIGNMENTS
###Code
!pip install pygeohash
###Output
Collecting pygeohash
Downloading https://files.pythonhosted.org/packages/2c/33/c912fa4476cedcd3ed9cd25c44c163583b92d319860438e6b632f7f42d0c/pygeohash-1.2.0.tar.gz
Building wheels for collected packages: pygeohash
Building wheel for pygeohash (setup.py) ... [?25l[?25hdone
Created wheel for pygeohash: filename=pygeohash-1.2.0-py2.py3-none-any.whl size=6162 sha256=3094842273f60a8a8bb2e706f2eb8583cbb5bf7e12c0cc2b25707e2b34b904ae
Stored in directory: /root/.cache/pip/wheels/3f/5f/14/989d83a271207dda28232746d63e737a2dbd88ea7f7a9db807
Successfully built pygeohash
Installing collected packages: pygeohash
Successfully installed pygeohash-1.2.0
###Markdown
Import pygeohash, networkx and Pandas librariesPygeohash - functions for converting latitude, longitude to geohash and related distance measurement utilitiesNetworkx - functions for creating, manipulating and querying open source network graphs Pandas - Python functions for table manipuation
###Code
import pygeohash as pgh
import networkx as nx
import pandas as pd
###Output
_____no_output_____
###Markdown
Connect to datasets using Google drive or local files
###Code
using_Google_colab = True
using_Anaconda_on_Mac_or_Linux = False
using_Anaconda_on_windows = False
if using_Google_colab:
from google.colab import drive
drive.mount('/content/drive')
###Output
Mounted at /content/drive
###Markdown
DM6.1 Open Notebook, read Lat, Long and compute Geohash - Activity 1
###Code
if using_Google_colab:
state_location = pd.read_csv('/content/drive/MyDrive/COVID_Project/input/state_lat_long.csv')
if using_Anaconda_on_Mac_or_Linux:
state_location = pd.read_csv('../input/state_lat_long.csv')
if using_Anaconda_on_windows:
state_location = pd.read_csv(r'..\input\state_lat_long.csv')
state_location.loc[0:5,]
###Output
_____no_output_____
###Markdown
Apply a function call to convert Lat, Long to Geohash
###Code
def lat_long_to_geohash(lat_long):
return pgh.encode(lat_long[0], lat_long[1])
state_location['geohash'] = state_location[['latitude',
'longitude']].apply(lat_long_to_geohash,
axis=1)
state_location.iloc[0:10,]
###Output
_____no_output_____
###Markdown
Truncate geohash to first two characters
###Code
state_location['geohash'] = state_location.geohash.str.slice(stop=2)
state_location.iloc[0:10,]
###Output
_____no_output_____
###Markdown
DM6.2 - Design Graph representaing States and Geohash Find neighbors by sorting the states by 2 character geohash codes attached to each state Initialize Graph and create state and geohash concepts as nodes
###Code
GRAPH_ID = nx.DiGraph()
GRAPH_ID.add_node('state')
GRAPH_ID.add_node('geohash')
###Output
_____no_output_____
###Markdown
Create a node for each state
###Code
state_list = state_location.state.values
for state in state_list:
GRAPH_ID.add_node(state)
GRAPH_ID.add_edge('state', state, label='instance')
###Output
_____no_output_____
###Markdown
Create a list of unique geohash codes and create a node for each geohash
###Code
geohash_list = state_location.geohash.values
for geohash in geohash_list:
GRAPH_ID.add_node(geohash)
GRAPH_ID.add_edge('geohash', geohash, label='instance')
df_state_geohash = state_location[['state', 'geohash']]
for state_geohash in df_state_geohash.itertuples():
GRAPH_ID.add_edge(state_geohash.state, state_geohash.geohash,
label='located_at')
GRAPH_ID.add_edge(state_geohash.geohash, state_geohash.state,
label='locates',
distance=0.0)
###Output
_____no_output_____
###Markdown
DM6.3 - Which states are in Geohash 9q Find geohash associated with California and Naveda
###Code
list(GRAPH_ID.neighbors('CA'))
list(GRAPH_ID.neighbors('NV'))
###Output
_____no_output_____
###Markdown
Find States locsted with geohash '9q'
###Code
list(GRAPH_ID.neighbors('9q'))
###Output
_____no_output_____
###Markdown
DM6.4 Sort the data and find neighbors sharing geohash Find states located with geohah for all geohashes
###Code
for geohash in GRAPH_ID['geohash']:
print("Geohash: ", geohash, "States: ", list(GRAPH_ID.neighbors(geohash)))
###Output
Geohash: be States: ['AK']
Geohash: dj States: ['AL', 'GA', 'MS']
Geohash: 9y States: ['AR', 'KS', 'MO', 'OK']
Geohash: 9w States: ['AZ', 'NM', 'UT']
Geohash: 9q States: ['CA', 'NV']
Geohash: 9x States: ['CO', 'WY']
Geohash: dr States: ['CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT']
Geohash: dq States: ['DC', 'DE', 'MD', 'VA']
Geohash: dh States: ['FL']
Geohash: 8e States: ['HI']
Geohash: 9z States: ['IA', 'NE', 'SD']
Geohash: 9r States: ['ID', 'OR']
Geohash: dp States: ['IL', 'IN', 'MI', 'OH', 'WI']
Geohash: dn States: ['KY', 'NC', 'SC', 'TN', 'WV']
Geohash: 9v States: ['LA', 'TX']
Geohash: f2 States: ['ME']
Geohash: cb States: ['MN', 'ND']
Geohash: c8 States: ['MT']
Geohash: de States: ['PR']
Geohash: c2 States: ['WA']
###Markdown
DM6.5 Use Graph to find Geohash associated with NY - CLASS ASSIGNMENT
###Code
list(GRAPH_ID.neighbors('NY'))
###Output
_____no_output_____
###Markdown
DM6.6 Use Graph to find which states are in Geohash 'dr' - CLASS ASSIGNMENT
###Code
list(GRAPH_ID.neighbors('dr'))
###Output
_____no_output_____
###Markdown
Step 4 - Develop Model - Task 7 - Graph Analytics - DM7.1 Activity 1 - Find number of state and geohash nodes in a graph
###Code
len(list (GRAPH_ID.neighbors('geohash')))
len(list (GRAPH_ID.neighbors('state')))
###Output
_____no_output_____
###Markdown
DM7.2 - Find all neighboring states for NY Connect neighboring geohash codes if the distance is less than 1,000 km
###Code
for geohash_1 in geohash_list:
for geohash_2 in geohash_list:
if geohash_1 != geohash_2:
distance = pgh.geohash_haversine_distance(geohash_1, geohash_2)
if distance < 1000000:
GRAPH_ID.add_edge(geohash_1, geohash_2, label='near')
###Output
_____no_output_____
###Markdown
Find path length from NY to all nodes (states and geohashes)
###Code
neighbor_path_length = nx.single_source_dijkstra_path_length(GRAPH_ID, 'NY', weight='distance')
neighbor_path_length
###Output
_____no_output_____
###Markdown
Make a list of all nodes covered in the path length and then find those nodes which are states and less than or equal to 3 hops
###Code
neighbor_states = neighbor_path_length.keys()
state_list = (list (GRAPH_ID.neighbors('state')))
for state in state_list:
if state in neighbor_states:
if neighbor_path_length[state] <= 3:
print(state)
###Output
CT
DC
DE
IL
IN
MA
MD
ME
MI
NH
NJ
NY
OH
PA
RI
VA
VT
WI
###Markdown
DM7.3 - Find all neighboring states for each state
###Code
for state_1 in state_list:
neighbor_path_length = nx.single_source_dijkstra_path_length(GRAPH_ID, state_1)
neighbor_state_list = neighbor_path_length.keys()
next_door_list = []
for state_2 in neighbor_state_list:
if state_1 != state_2:
if state_2 in state_list:
if neighbor_path_length[state_2] <=3:
next_door_list.append(state_2)
if next_door_list:
print(state_1, next_door_list)
###Output
AL ['GA', 'MS', 'FL', 'KY', 'NC', 'SC', 'TN', 'WV']
AR ['KS', 'MO', 'OK', 'AZ', 'NM', 'UT', 'IA', 'NE', 'SD', 'LA', 'TX']
AZ ['NM', 'UT', 'AR', 'KS', 'MO', 'OK', 'CA', 'NV', 'CO', 'WY']
CA ['NV', 'AZ', 'NM', 'UT', 'ID', 'OR']
CO ['WY', 'AZ', 'NM', 'UT', 'IA', 'NE', 'SD', 'ID', 'OR', 'MT']
CT ['MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI', 'ME']
DC ['DE', 'MD', 'VA', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'KY', 'NC', 'SC', 'TN', 'WV']
DE ['DC', 'MD', 'VA', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'KY', 'NC', 'SC', 'TN', 'WV']
FL ['AL', 'GA', 'MS']
GA ['AL', 'MS', 'FL', 'KY', 'NC', 'SC', 'TN', 'WV']
IA ['NE', 'SD', 'AR', 'KS', 'MO', 'OK', 'CO', 'WY', 'IL', 'IN', 'MI', 'OH', 'WI', 'MN', 'ND']
ID ['OR', 'CA', 'NV', 'CO', 'WY', 'WA']
IL ['IN', 'MI', 'OH', 'WI', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'IA', 'NE', 'SD', 'KY', 'NC', 'SC', 'TN', 'WV']
IN ['IL', 'MI', 'OH', 'WI', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'IA', 'NE', 'SD', 'KY', 'NC', 'SC', 'TN', 'WV']
KS ['AR', 'MO', 'OK', 'AZ', 'NM', 'UT', 'IA', 'NE', 'SD', 'LA', 'TX']
KY ['NC', 'SC', 'TN', 'WV', 'AL', 'GA', 'MS', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI']
LA ['TX', 'AR', 'KS', 'MO', 'OK']
MA ['CT', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI', 'ME']
MD ['DC', 'DE', 'VA', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'KY', 'NC', 'SC', 'TN', 'WV']
ME ['CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT']
MI ['IL', 'IN', 'OH', 'WI', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'IA', 'NE', 'SD', 'KY', 'NC', 'SC', 'TN', 'WV']
MN ['ND', 'IA', 'NE', 'SD', 'MT']
MO ['AR', 'KS', 'OK', 'AZ', 'NM', 'UT', 'IA', 'NE', 'SD', 'LA', 'TX']
MS ['AL', 'GA', 'FL', 'KY', 'NC', 'SC', 'TN', 'WV']
MT ['CO', 'WY', 'MN', 'ND', 'WA']
NC ['KY', 'SC', 'TN', 'WV', 'AL', 'GA', 'MS', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI']
ND ['MN', 'IA', 'NE', 'SD', 'MT']
NE ['IA', 'SD', 'AR', 'KS', 'MO', 'OK', 'CO', 'WY', 'IL', 'IN', 'MI', 'OH', 'WI', 'MN', 'ND']
NH ['CT', 'MA', 'NJ', 'NY', 'PA', 'RI', 'VT', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI', 'ME']
NJ ['CT', 'MA', 'NH', 'NY', 'PA', 'RI', 'VT', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI', 'ME']
NM ['AZ', 'UT', 'AR', 'KS', 'MO', 'OK', 'CA', 'NV', 'CO', 'WY']
NV ['CA', 'AZ', 'NM', 'UT', 'ID', 'OR']
NY ['CT', 'MA', 'NH', 'NJ', 'PA', 'RI', 'VT', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI', 'ME']
OH ['IL', 'IN', 'MI', 'WI', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'IA', 'NE', 'SD', 'KY', 'NC', 'SC', 'TN', 'WV']
OK ['AR', 'KS', 'MO', 'AZ', 'NM', 'UT', 'IA', 'NE', 'SD', 'LA', 'TX']
OR ['ID', 'CA', 'NV', 'CO', 'WY', 'WA']
PA ['CT', 'MA', 'NH', 'NJ', 'NY', 'RI', 'VT', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI', 'ME']
RI ['CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'VT', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI', 'ME']
SC ['KY', 'NC', 'TN', 'WV', 'AL', 'GA', 'MS', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI']
SD ['IA', 'NE', 'AR', 'KS', 'MO', 'OK', 'CO', 'WY', 'IL', 'IN', 'MI', 'OH', 'WI', 'MN', 'ND']
TN ['KY', 'NC', 'SC', 'WV', 'AL', 'GA', 'MS', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI']
TX ['LA', 'AR', 'KS', 'MO', 'OK']
UT ['AZ', 'NM', 'AR', 'KS', 'MO', 'OK', 'CA', 'NV', 'CO', 'WY']
VA ['DC', 'DE', 'MD', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'KY', 'NC', 'SC', 'TN', 'WV']
VT ['CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI', 'ME']
WA ['ID', 'OR', 'MT']
WI ['IL', 'IN', 'MI', 'OH', 'CT', 'MA', 'NH', 'NJ', 'NY', 'PA', 'RI', 'VT', 'IA', 'NE', 'SD', 'KY', 'NC', 'SC', 'TN', 'WV']
WV ['KY', 'NC', 'SC', 'TN', 'AL', 'GA', 'MS', 'DC', 'DE', 'MD', 'VA', 'IL', 'IN', 'MI', 'OH', 'WI']
WY ['CO', 'AZ', 'NM', 'UT', 'IA', 'NE', 'SD', 'ID', 'OR', 'MT']
###Markdown
DM7.4 - Find path between two states
###Code
nx.dijkstra_path(GRAPH_ID, 'NY', 'CA', weight='distance')
nx.dijkstra_path(GRAPH_ID, 'OR', 'CA', weight='distance')
GRAPH_ID.nodes()
nx.single_source_dijkstra_path_length(GRAPH_ID, 'NY')
###Output
_____no_output_____
|
Week3_gaia_nasa_exoplanet_archive/GaiaTutorial_KEY.ipynb
|
###Markdown
Welcome to the Intro to Astronomy Research Gaia Data Release 2 (DR2) Tutorial. Written by Sarah Blunt, 2018 In this tutorial, you will:- learn about the Astronomical Data Query Language (ADQL)- use the Gaia DR2 Database and the NASA Exoplanet Archive to get Gaia parameters for the 10,000 closest stars- plot a color-magnitude diagram Notes:- This tutorial is challenging! If you spend more than 15 minutes stuck on a task, post about it on Piazza (you can post anonymously if you want). An instructor will help you out! Don't struggle needlessly.- Make sure you complete the pandas and matplotlib tutorials before attempting this tutorial. Learn About ADQL- Navigate to the Gaia ADQL interface. First, go [here](https://gea.esac.esa.int/archive/). Click "Search," then click "Advanced (ADQL)" (in the top left corner of the page). - Read [this webpage](https://gea.esac.esa.int/archive-help/adql/index.html). - Read slides 6-17 in [this powerpoint](https://www.cosmos.esa.int/documents/915837/915858/ADQL_handson_slides.pdf/652b9120-a3fe-4857-b5eb-933b476687ad). Try out some of the commands for yourself in the Gaia ADQL search bar you opened in step 1. Using the buttons that pop up to the right of your query results, you can download the results or view them in your browser. Hover over the buttons to see what they do. Don't worry if you don't understand everything in the powerpoint! Pick out the 7 most important slides and summarize them for yourself. ** Note: wherever the powerpoint uses "gaiadr1.tgas_source," replace with "gaiadr2.gaia_source." Use the Gaia DR2 Database and the NASA Exoplanet Archive to Get Gaia Parameters for the 10,000 Closest Stars Write an ADQL query to get parameters of the 10,000 closest stars. Your query should return the following parameters:- BP - RP color (bp_rp in the Gaia database)- absolute g-band photometric magnitude - distanceADQL QUERY COMMAND:```SELECT TOP 10000 phot_g_mean_mag + 5 * log10(parallax/1000) + 5 AS g_abs, bp_rp, 1/parallax AS distFROM gaiadr2.gaia_source WHERE parallax > 0ORDER BY parallax DESC``` Download your Query Results as a csv file. Hints:- Distance is the inverse of parallax. - You can calculate absolute photometric magnitude in the gband using this formula: phot_g_mean_mag + 5 + 5 * log10(parallax/1000)- You'll need to use "ORDER BY" in your ADQL command.- Some Gaia sources have negative parallaxes due to instrumental imperfections. You'll need to add a line to your query specifying that parallax must be greater than 0. - Using the buttons that pop up to the right of your query results, you can download the results or view them in your browser. Hover over the buttons to see what they do. Plot a Color-Magnitude Diagram of the 10,000 Closest Stars 1. Using [pandas.read_csv](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html), read your downloaded csv file into a pandas DataFrame
###Code
import pandas as pd
import matplotlib.pyplot as plt
%pylab inline
# Type your pd.read_csv command here:
data = pd.read_csv('gaia-query-results.csv')
# HINTS:
# - make sure the jupyter notebook and your csv file are in the same directory
# - your read_csv command should be 1 line of code
###Output
Populating the interactive namespace from numpy and matplotlib
###Markdown
2. Using matplotlib.pyplot, make a scatterplot of BP-RP color vs absolute g-band magnitude. This is a [color-magnitude diagram](https://en.wikipedia.org/wiki/Hertzsprung%E2%80%93Russell_diagram)!
###Code
plt.figure()
# Type your plotting code here:
plt.scatter(data.bp_rp, data.g_abs, s=.1, color='red')
# More Fun Things to Try if You're Interested:
# - use plt.ylim to reverse the direction of the y axis.
plt.ylim(30,-7)
# - give your plot x and y labels.
plt.xlabel('G$_{BP}$ - G$_{RP}$')
plt.ylabel('M$_G$')
# - make the points red
# (changed in initial plotting command)
# - make the 10 closest stars red
data = data.sort_values(by = 'dist')
data_no_nans = data.dropna() # remove NaN values
plt.scatter(
data_no_nans.bp_rp.iloc[0:10],
data_no_nans.g_abs.iloc[0:10],
color='blue', # blue instead of red for clarity in answer key
s=10. # make these points bigger for clarity in answer key
)
# - compare your results against Figure 1 in this paper: https://arxiv.org/pdf/1804.09378.pdf.
# What similarities and differences do you notice?
"""
In the paper, a heat map is presented, so relative density is easier to see. They also have more datapoints
than we do. I see some of the same features, though, notably thick diagonal line going from top left
to bottom right.
"""
# Challenge: read section 2.1 of this paper and try to reproduce their plot exactly.
# To make the plot below, I used the ADQL query shown in the below jupyter notebook cell.
# Note that I only selected the top 100,000 results for simplicity, not all 13,000,000 as the paper does.
challenge_data = pd.read_csv('gaia-query-challenge-results.csv')
# make a density heatmap
plt.figure()
heatmap, xedges, yedges = np.histogram2d(
challenge_data.bp_rp.values,
challenge_data.g_abs.values,
bins=100
)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
heatmap = np.ma.masked_where(heatmap == 0.0, heatmap)
color_map = plt.cm.hot
color_map.set_bad(color='white')
plt.imshow(
np.sqrt(heatmap.T),
extent=extent,
cmap=color_map,
aspect=(extent[1]-extent[0])/(extent[3]-extent[2]),
origin='lower'
)
plt.colorbar()
plt.xlabel('G$_{BP}$ - G$_{RP}$')
plt.ylabel('M$_G$')
plt.gca().invert_yaxis()
# To make this look exactly like the figure from the paper, we'd just need to plot all stars in the Gaia catalogue
###Output
_____no_output_____
|
examples/interruptible-optimization.ipynb
|
###Markdown
Interruptible optimization runs with checkpointsChristian Schell, Mai 2018
###Code
import numpy as np
np.random.seed(777)
###Output
_____no_output_____
###Markdown
Problem statementOptimization runs can take a very long time and even run for multiple days. If for some reason the process has to be interrupted results are irreversibly lost, and the routine has to start over from the beginning.With the help of the `CheckpointSaver` callback the optimizer's current state can be saved after each iteration, allowing to restart from that point at any time.This is useful, for example,* if you don't know how long the process will take and cannot hog computational resources forever* if there might be system failures due to shaky infrastructure (or colleagues...)* if you want to adjust some parameters and continue with the already obtained results Simple exampleWe will use pretty much the same optimization problem as in the [`bayesian-optimization.ipynb`](https://github.com/scikit-optimize/scikit-optimize/blob/master/examples/bayesian-optimization.ipynb) notebook. Additionaly we will instantiate the `CheckpointSaver` and pass it to the minimizer:
###Code
from skopt import gp_minimize
from skopt import callbacks
from skopt.callbacks import CheckpointSaver
noise_level = 0.1
def obj_fun(x, noise_level=noise_level):
return np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) + np.random.randn() * noise_level
checkpoint_saver = CheckpointSaver("./checkpoint.pkl", compress=9) # keyword arguments will be passed to `skopt.dump`
gp_minimize(obj_fun, # the function to minimize
[(-20.0, 20.0)], # the bounds on each dimension of x
x0=[-20.], # the starting point
acq_func="LCB", # the acquisition function (optional)
n_calls=10, # the number of evaluations of f including at x0
n_random_starts=0, # the number of random initialization points
callback=[checkpoint_saver], # a list of callbacks including the checkpoint saver
random_state=777);
###Output
_____no_output_____
###Markdown
Now let's assume this did not finish at once but took some long time: you started this on Friday night, went out for the weekend and now, Monday morning, you're eager to see the results. However, instead of the notebook server you only see a blank page and your colleague Garry tells you that he had had an update scheduled for Sunday noon – who doesn't like updates?TL;DR: `gp_minimize` did not finish, and there is no `res` variable with the actual results! Restoring the last checkpointLuckily we employed the `CheckpointSaver` and can now restore the latest result with `skopt.load` (see [store and load results](./store-and-load-results.ipynb) for more information on that)
###Code
from skopt import load
res = load('./checkpoint.pkl')
res.fun
###Output
_____no_output_____
###Markdown
Continue the searchThe previous results can then be used to continue the optimization process:
###Code
x0 = res.x_iters
y0 = res.func_vals
gp_minimize(obj_fun, # the function to minimize
[(-20.0, 20.0)], # the bounds on each dimension of x
x0=x0, # already examined values for x
y0=y0, # observed values for x0
acq_func="LCB", # the acquisition function (optional)
n_calls=10, # the number of evaluations of f including at x0
n_random_starts=0, # the number of random initialization points
callback=[checkpoint_saver],
random_state=777);
###Output
_____no_output_____
|
deeplearning2/nbs/rossman.ipynb
|
###Markdown
This notebook contains an implementation of the third place result in the Rossman Kaggle competition as detailed in Guo/Berkhahn's [Entity Embeddings of Categorical Variables](https://arxiv.org/abs/1604.06737). The motivation behind exploring this architecture is it's relevance to real-world application. Much of our focus has been computer-vision and NLP tasks, which largely deals with unstructured data.However, most of the data informing KPI's in industry are structured, time-series data. Here we explore the end-to-end process of using neural networks with practical structured data problems.
###Code
%matplotlib inline
import math, keras, datetime, pandas as pd, numpy as np, keras.backend as K
import matplotlib.pyplot as plt, xgboost, operator, random, pickle
from utils2 import *
np.set_printoptions(threshold=50, edgeitems=20)
limit_mem()
from isoweek import Week
from pandas_summary import DataFrameSummary
%cd ./data/rossman/
###Output
/home/kaushik/Jupyter/Keras/Fastai/2016/deeplearning2/nbs/data/rossman
###Markdown
Create datasets In addition to the provided data, we will be using external datasets put together by participants in the Kaggle competition. You can download all of them [here](http://files.fast.ai/part2/lesson14/rossmann.tgz).For completeness, the implementation used to put them together is included below.
###Code
def concat_csvs(dirname):
os.chdir(dirname)
filenames=glob.glob("*.csv")
wrote_header = False
with open("../"+dirname+".csv","w") as outputfile:
for filename in filenames:
name = filename.split(".")[0]
with open(filename) as f:
line = f.readline()
if not wrote_header:
wrote_header = True
outputfile.write("file,"+line)
for line in f:
outputfile.write(name + "," + line)
outputfile.write("\n")
os.chdir("..")
# concat_csvs('googletrend')
# concat_csvs('weather')
###Output
_____no_output_____
###Markdown
Feature Space:* train: Training set provided by competition* store: List of stores* store_states: mapping of store to the German state they are in* List of German state names* googletrend: trend of certain google keywords over time, found by users to correlate well w/ given data* weather: weather* test: testing set
###Code
table_names = ['train', 'store', 'store_states', 'state_names',
'googletrend', 'weather', 'test']
###Output
_____no_output_____
###Markdown
We'll be using the popular data manipulation framework pandas.Among other things, pandas allows you to manipulate tables/data frames in python as one would in a database. We're going to go ahead and load all of our csv's as dataframes into a list `tables`.
###Code
tables = [pd.read_csv(fname+'.csv', low_memory=False) for fname in table_names]
from IPython.display import HTML
###Output
_____no_output_____
###Markdown
We can use `head()` to get a quick look at the contents of each table:* train: Contains store information on a daily basis, tracks things like sales, customers, whether that day was a holdiay, etc.* store: general info about the store including competition, etc.* store_states: maps store to state it is in* state_names: Maps state abbreviations to names* googletrend: trend data for particular week/state* weather: weather conditions for each state* test: Same as training table, w/o sales and customers
###Code
for t in tables: display(t.head())
###Output
_____no_output_____
###Markdown
This is very representative of a typical industry dataset. The following returns summarized aggregate information to each table accross each field.
###Code
for t in tables: display(DataFrameSummary(t).summary())
###Output
_____no_output_____
###Markdown
Data Cleaning / Feature Engineering As a structured data problem, we necessarily have to go through all the cleaning and feature engineering, even though we're using a neural network.
###Code
train, store, store_states, state_names, googletrend, weather, test = tables
len(train),len(test)
###Output
_____no_output_____
###Markdown
Turn state Holidays to Bool
###Code
train.StateHoliday = train.StateHoliday!='0'
test.StateHoliday = test.StateHoliday!='0'
###Output
_____no_output_____
###Markdown
Define function for joining tables on specific fields.By default, we'll be doing a left outer join of `right` on the `left` argument using the given fields for each table.Pandas does joins using the `merge` method. The `suffixes` argument describes the naming convention for duplicate fields. We've elected to leave the duplicate field names on the left untouched, and append a "_y" to those on the right.
###Code
def join_df(left, right, left_on, right_on=None):
if right_on is None: right_on = left_on
return left.merge(right, how='left', left_on=left_on, right_on=right_on,
suffixes=("", "_y"))
###Output
_____no_output_____
###Markdown
Join weather/state names.
###Code
weather = join_df(weather, state_names, "file", "StateName")
###Output
_____no_output_____
###Markdown
In pandas you can add new columns to a dataframe by simply defining it. We'll do this for googletrends by extracting dates and state names from the given data and adding those columns.We're also going to replace all instances of state name 'NI' with the usage in the rest of the table, 'HB,NI'. This is a good opportunity to highlight pandas indexing. We can use `.loc[rows, cols]` to select a list of rows and a list of columns from the dataframe. In this case, we're selecting rows w/ statename 'NI' by using a boolean list `googletrend.State=='NI'` and selecting "State".
###Code
googletrend['Date'] = googletrend.week.str.split(' - ', expand=True)[0]
googletrend['State'] = googletrend.file.str.split('_', expand=True)[2]
googletrend.loc[googletrend.State=='NI', "State"] = 'HB,NI'
###Output
_____no_output_____
###Markdown
The following extracts particular date fields from a complete datetime for the purpose of constructing categoricals.You should always consider this feature extraction step when working with date-time. Without expanding your date-time into these additional fields, you can't capture any trend/cyclical behavior as a function of time at any of these granularities.
###Code
def add_datepart(df):
df.Date = pd.to_datetime(df.Date)
df["Year"] = df.Date.dt.year
df["Month"] = df.Date.dt.month
df["Week"] = df.Date.dt.week
df["Day"] = df.Date.dt.day
###Output
_____no_output_____
###Markdown
We'll add to every table w/ a date field.
###Code
add_datepart(weather)
add_datepart(googletrend)
add_datepart(train)
add_datepart(test)
trend_de = googletrend[googletrend.file == 'Rossmann_DE']
###Output
_____no_output_____
###Markdown
Now we can outer join all of our data into a single dataframe.Recall that in outer joins everytime a value in the joining field on the left table does not have a corresponding value on the right table, the corresponding row in the new table has Null values for all right table fields.One way to check that all records are consistent and complete is to check for Null values post-join, as we do here.*Aside*: Why not just do an inner join?If you are assuming that all records are complete and match on the field you desire, an inner join will do the same thing as an outer join. However, in the event you are wrong or a mistake is made, an outer join followed by a null-check will catch it. (Comparing before/after of rows for inner join is equivalent, but requires keeping track of before/after row 's. Outer join is easier.)
###Code
store = join_df(store, store_states, "Store")
len(store[store.State.isnull()])
joined = join_df(train, store, "Store")
len(joined[joined.StoreType.isnull()])
joined = join_df(joined, googletrend, ["State","Year", "Week"])
len(joined[joined.trend.isnull()])
joined = joined.merge(trend_de, 'left', ["Year", "Week"], suffixes=('', '_DE'))
len(joined[joined.trend_DE.isnull()])
joined = join_df(joined, weather, ["State","Date"])
len(joined[joined.Mean_TemperatureC.isnull()])
joined_test = test.merge(store, how='left', left_on='Store', right_index=True)
len(joined_test[joined_test.StoreType.isnull()])
###Output
_____no_output_____
###Markdown
Next we'll fill in missing values to avoid complications w/ na's.
###Code
joined.CompetitionOpenSinceYear = joined.CompetitionOpenSinceYear.fillna(1900).astype(np.int32)
joined.CompetitionOpenSinceMonth = joined.CompetitionOpenSinceMonth.fillna(1).astype(np.int32)
joined.Promo2SinceYear = joined.Promo2SinceYear.fillna(1900).astype(np.int32)
joined.Promo2SinceWeek = joined.Promo2SinceWeek.fillna(1).astype(np.int32)
###Output
_____no_output_____
###Markdown
Next we'll extract features "CompetitionOpenSince" and "CompetitionDaysOpen". Note the use of `apply()` in mapping a function across dataframe values.
###Code
joined["CompetitionOpenSince"] = pd.to_datetime(joined.apply(lambda x: datetime.datetime(
x.CompetitionOpenSinceYear, x.CompetitionOpenSinceMonth, 15), axis=1).astype(pd.datetime))
joined["CompetitionDaysOpen"] = joined.Date.subtract(joined["CompetitionOpenSince"]).dt.days
###Output
_____no_output_____
###Markdown
We'll replace some erroneous / outlying data.
###Code
joined.loc[joined.CompetitionDaysOpen<0, "CompetitionDaysOpen"] = 0
joined.loc[joined.CompetitionOpenSinceYear<1990, "CompetitionDaysOpen"] = 0
###Output
_____no_output_____
###Markdown
Added "CompetitionMonthsOpen" field, limit the maximum to 2 years to limit number of unique embeddings.
###Code
joined["CompetitionMonthsOpen"] = joined["CompetitionDaysOpen"]//30
joined.loc[joined.CompetitionMonthsOpen>24, "CompetitionMonthsOpen"] = 24
joined.CompetitionMonthsOpen.unique()
###Output
_____no_output_____
###Markdown
Same process for Promo dates.
###Code
joined["Promo2Since"] = pd.to_datetime(joined.apply(lambda x: Week(
x.Promo2SinceYear, x.Promo2SinceWeek).monday(), axis=1).astype(pd.datetime))
joined["Promo2Days"] = joined.Date.subtract(joined["Promo2Since"]).dt.days
joined.loc[joined.Promo2Days<0, "Promo2Days"] = 0
joined.loc[joined.Promo2SinceYear<1990, "Promo2Days"] = 0
joined["Promo2Weeks"] = joined["Promo2Days"]//7
joined.loc[joined.Promo2Weeks<0, "Promo2Weeks"] = 0
joined.loc[joined.Promo2Weeks>25, "Promo2Weeks"] = 25
joined.Promo2Weeks.unique()
###Output
_____no_output_____
###Markdown
Durations It is common when working with time series data to extract data that explains relationships across rows as opposed to columns, e.g.:* Running averages* Time until next event* Time since last eventThis is often difficult to do with most table manipulation frameworks, since they are designed to work with relationships across columns. As such, we've created a class to handle this type of data.
###Code
columns = ["Date", "Store", "Promo", "StateHoliday", "SchoolHoliday"]
###Output
_____no_output_____
###Markdown
We've defined a class `elapsed` for cumulative counting across a sorted dataframe.For e.g., on any given date, how many days it is before the next state holiday, and how many days it is after the previous state holiday.Given a particular field `fld` to monitor, this object will start tracking time since the last occurrence of that field. When the field is seen again, the counter is set to zero.Upon initialization, this will result in datetime na's until the field is encountered. This is reset every time a new store is seen.We'll see how to use this shortly.
###Code
class elapsed(object):
def __init__(self, fld):
self.fld = fld
self.last = pd.to_datetime(np.nan)
self.last_store = 0
def get(self, row):
if row.Store != self.last_store:
self.last = pd.to_datetime(np.nan)
self.last_store = row.Store
if (row[self.fld]): self.last = row.Date
return row.Date-self.last
df = train[columns]
###Output
_____no_output_____
###Markdown
And a function for applying said class across dataframe rows and adding values to a new column.
###Code
def add_elapsed(fld, prefix):
tmp_el = elapsed(fld)
df[prefix+fld] = df.apply(tmp_el.get, axis=1)
###Output
_____no_output_____
###Markdown
Let's walk through an example.Say we're looking at School Holiday. We'll first sort by Store, then Date, and then call `add_elapsed('SchoolHoliday', 'After')`:This will generate an instance of the `elapsed` class for School Holiday:* Instance applied to every row of the dataframe in order of store and date* Will add to the dataframe the days since seeing a School Holiday* If we sort in the other direction, this will count the days until another promotion.
###Code
fld = 'SchoolHoliday'
df = df.sort_values(['Store', 'Date'])
add_elapsed(fld, 'After')
df = df.sort_values(['Store', 'Date'], ascending=[True, False])
add_elapsed(fld, 'Before')
###Output
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###Markdown
We'll do this for two more fields.
###Code
fld = 'StateHoliday'
df = df.sort_values(['Store', 'Date'])
add_elapsed(fld, 'After')
df = df.sort_values(['Store', 'Date'], ascending=[True, False])
add_elapsed(fld, 'Before')
fld = 'Promo'
df = df.sort_values(['Store', 'Date'])
add_elapsed(fld, 'After')
df = df.sort_values(['Store', 'Date'], ascending=[True, False])
add_elapsed(fld, 'Before')
###Output
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###Markdown
We're going to set the active index to Date.
###Code
df = df.set_index("Date")
###Output
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###Markdown
Then set null values from elapsed field calculations to 0.
###Code
columns = ['SchoolHoliday', 'StateHoliday', 'Promo']
for o in ['Before', 'After']:
for p in columns:
a = o+p
df[a] = df[a].fillna(pd.Timedelta(0)).dt.days
###Output
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###Markdown
Next we'll demonstrate window functions in pandas to calculate rolling quantities.The idea is to calculate, for e.g., given any date, how many state holidays were there in the previous week, and how many in the next week.Here we're sorting by date (`sort_index()`) and counting the number of events of interest (`sum()`) defined in `columns` in the following week (`rolling()`), grouped by Store (`groupby()`). We also do the same in the opposite direction for similar windowing over the next week.
###Code
bwd = df[['Store']+columns].sort_index().groupby("Store").rolling(7, min_periods=1).sum()
fwd = df[['Store']+columns].sort_index(ascending=False
).groupby("Store").rolling(7, min_periods=1).sum()
###Output
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###Markdown
Next we want to drop the Store indices grouped together in the window function.Often in pandas, there is an option to do this in place. This is time and memory efficient when working with large datasets.
###Code
bwd.drop('Store',1,inplace=True)
bwd.reset_index(inplace=True)
fwd.drop('Store',1,inplace=True)
fwd.reset_index(inplace=True)
df.reset_index(inplace=True)
###Output
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###Markdown
Now we'll merge these values onto the df.
###Code
df = df.merge(bwd, 'left', ['Date', 'Store'], suffixes=['', '_bw'])
df = df.merge(fwd, 'left', ['Date', 'Store'], suffixes=['', '_fw'])
df.drop(columns,1,inplace=True)
df.head()
###Output
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###Markdown
It's usually a good idea to back up large tables of extracted / wrangled features before you join them onto another one, that way you can go back to it easily if you need to make changes to it.
###Code
df.to_csv('df.csv')
df = pd.read_csv('df.csv', index_col=0)
df["Date"] = pd.to_datetime(df.Date)
df.columns
joined = join_df(joined, df, ['Store', 'Date'])
###Output
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###Markdown
We'll back this up as well.
###Code
joined.to_csv('joined.csv')
###Output
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###Markdown
We now have our final set of engineered features.
###Code
joined = pd.read_csv('joined.csv', index_col=0)
joined["Date"] = pd.to_datetime(joined.Date)
joined.columns
###Output
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/lib/arraysetops.py:395: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
mask |= (ar1 == a)
###Markdown
While these steps were explicitly outlined in the paper, these are all fairly typical feature engineering steps for dealing with time series data and are practical in any similar setting. Create features Now that we've engineered all our features, we need to convert to input compatible with a neural network.This includes converting categorical variables into contiguous integers or one-hot encodings, normalizing continuous features to standard normal, etc...
###Code
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import LabelEncoder, Imputer, StandardScaler
###Output
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###Markdown
This dictionary maps categories to embedding dimensionality. In generally, categories we might expect to be conceptually more complex have larger dimension.
###Code
cat_var_dict = {'Store': 50, 'DayOfWeek': 6, 'Year': 2, 'Month': 6,
'Day': 10, 'StateHoliday': 3, 'CompetitionMonthsOpen': 2,
'Promo2Weeks': 1, 'StoreType': 2, 'Assortment': 3, 'PromoInterval': 3,
'CompetitionOpenSinceYear': 4, 'Promo2SinceYear': 4, 'State': 6,
'Week': 2, 'Events': 4, 'Promo_fw': 1,
'Promo_bw': 1, 'StateHoliday_fw': 1,
'StateHoliday_bw': 1, 'SchoolHoliday_fw': 1,
'SchoolHoliday_bw': 1}
###Output
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###Markdown
Name categorical variables
###Code
cat_vars = [o[0] for o in
sorted(cat_var_dict.items(), key=operator.itemgetter(1), reverse=True)]
"""cat_vars = ['Store', 'DayOfWeek', 'Year', 'Month', 'Day', 'StateHoliday',
'StoreType', 'Assortment', 'Week', 'Events', 'Promo2SinceYear',
'CompetitionOpenSinceYear', 'PromoInterval', 'Promo', 'SchoolHoliday', 'State']"""
###Output
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###Markdown
Likewise for continuous
###Code
# mean/max wind; min temp; cloud; min/mean humid;
contin_vars = ['CompetitionDistance',
'Max_TemperatureC', 'Mean_TemperatureC', 'Min_TemperatureC',
'Max_Humidity', 'Mean_Humidity', 'Min_Humidity', 'Max_Wind_SpeedKm_h',
'Mean_Wind_SpeedKm_h', 'CloudCover', 'trend', 'trend_DE',
'AfterStateHoliday', 'BeforeStateHoliday', 'Promo', 'SchoolHoliday']
"""contin_vars = ['CompetitionDistance', 'Max_TemperatureC', 'Mean_TemperatureC',
'Max_Humidity', 'trend', 'trend_DE', 'AfterStateHoliday', 'BeforeStateHoliday']"""
###Output
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###Markdown
Replace nulls w/ 0 for continuous, "" for categorical.
###Code
for v in contin_vars: joined.loc[joined[v].isnull(), v] = 0
for v in cat_vars: joined.loc[joined[v].isnull(), v] = ""
###Output
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###Markdown
Here we create a list of tuples, each containing a variable and an instance of a transformer for that variable.For categoricals, we use a label encoder that maps categories to continuous integers. For continuous variables, we standardize them.
###Code
cat_maps = [(o, LabelEncoder()) for o in cat_vars]
contin_maps = [([o], StandardScaler()) for o in contin_vars]
###Output
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###Markdown
The same instances need to be used for the test set as well, so values are mapped/standardized appropriately.DataFrame mapper will keep track of these variable-instance mappings.
###Code
cat_mapper = DataFrameMapper(cat_maps)
cat_map_fit = cat_mapper.fit(joined)
cat_cols = len(cat_map_fit.features)
cat_cols
contin_mapper = DataFrameMapper(contin_maps)
contin_map_fit = contin_mapper.fit(joined)
contin_cols = len(contin_map_fit.features)
contin_cols
###Output
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.
warnings.warn(msg, _DataConversionWarning)
###Markdown
Example of first five rows of zeroth column being transformed appropriately.
###Code
cat_map_fit.transform(joined)[0,:5], contin_map_fit.transform(joined)[0,:5]
###Output
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.
warnings.warn(msg, _DataConversionWarning)
###Markdown
We can also pickle these mappings, which is great for portability!
###Code
pickle.dump(contin_map_fit, open('contin_maps.pickle', 'wb'))
pickle.dump(cat_map_fit, open('cat_maps.pickle', 'wb'))
[len(o[1].classes_) for o in cat_map_fit.features]
###Output
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###Markdown
Sample data Next, the authors removed all instances where the store had zero sale / was closed.
###Code
joined_sales = joined[joined.Sales!=0]
n = len(joined_sales)
###Output
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###Markdown
We speculate that this may have cost them a higher standing in the competition. One reason this may be the case is that a little EDA reveals that there are often periods where stores are closed, typically for refurbishment. Before and after these periods, there are naturally spikes in sales that one might expect. Be ommitting this data from their training, the authors gave up the ability to leverage information about these periods to predict this otherwise volatile behavior.
###Code
n
###Output
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###Markdown
We're going to run on a sample.
###Code
samp_size = 100000
np.random.seed(42)
idxs = sorted(np.random.choice(n, samp_size, replace=False))
joined_samp = joined_sales.iloc[idxs].set_index("Date")
samp_size = n
joined_samp = joined_sales.set_index("Date")
###Output
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###Markdown
In time series data, cross-validation is not random. Instead, our holdout data is always the most recent data, as it would be in real application. We've taken the last 10% as our validation set.
###Code
train_ratio = 0.9
train_size = int(samp_size * train_ratio)
train_size
joined_valid = joined_samp[train_size:]
joined_train = joined_samp[:train_size]
len(joined_valid), len(joined_train)
###Output
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###Markdown
Here's a preprocessor for our categoricals using our instance mapper.
###Code
def cat_preproc(dat):
return cat_map_fit.transform(dat).astype(np.int64)
cat_map_train = cat_preproc(joined_train)
cat_map_valid = cat_preproc(joined_valid)
###Output
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###Markdown
Same for continuous.
###Code
def contin_preproc(dat):
return contin_map_fit.transform(dat).astype(np.float32)
contin_map_train = contin_preproc(joined_train)
contin_map_valid = contin_preproc(joined_valid)
###Output
/home/bckenstler/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.
warnings.warn(msg, _DataConversionWarning)
###Markdown
Grab our targets.
###Code
y_train_orig = joined_train.Sales
y_valid_orig = joined_valid.Sales
###Output
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###Markdown
Finally, the authors modified the target values by applying a logarithmic transformation and normalizing to unit scale by dividing by the maximum log value.Log transformations are used on this type of data (because sales data typically has lot of skews) frequently to attain a nicer shape. Further by scaling to the unit interval we can now use a sigmoid output in our neural network. Then we can multiply by the maximum log value to get the original log value and transform back.However, since log loss values for probability of 1 penalizes heavily, it might be prudent to scale by (1.25 * max_log_y) instead of max_log_y.** Aside: ** If we take logs of the target variable, MAE should be used in the loss function instead of MSE.
###Code
max_log_y = np.max(np.log(joined_samp.Sales))
y_train = np.log(y_train_orig)/max_log_y
y_valid = np.log(y_valid_orig)/max_log_y
###Output
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###Markdown
Note: Some testing shows this doesn't make a big difference.
###Code
"""#y_train = np.log(y_train)
ymean=y_train_orig.mean()
ystd=y_train_orig.std()
y_train = (y_train_orig-ymean)/ystd
#y_valid = np.log(y_valid)
y_valid = (y_valid_orig-ymean)/ystd"""
###Output
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###Markdown
Root-mean-squared percent error is the metric Kaggle used for this competition.
###Code
def rmspe(y_pred, targ = y_valid_orig):
pct_var = (targ - y_pred)/targ
return math.sqrt(np.square(pct_var).mean())
###Output
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###Markdown
These undo the target transformations.
###Code
def log_max_inv(preds, mx = max_log_y):
return np.exp(preds * mx)
def normalize_inv(preds):
return preds * ystd + ymean
###Output
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###Markdown
Create models Now we're ready to put together our models. Much of the following code has commented out portions / alternate implementations.
###Code
"""
1 97s - loss: 0.0104 - val_loss: 0.0083
2 93s - loss: 0.0076 - val_loss: 0.0076
3 90s - loss: 0.0071 - val_loss: 0.0076
4 90s - loss: 0.0068 - val_loss: 0.0075
5 93s - loss: 0.0066 - val_loss: 0.0075
6 95s - loss: 0.0064 - val_loss: 0.0076
7 98s - loss: 0.0063 - val_loss: 0.0077
8 97s - loss: 0.0062 - val_loss: 0.0075
9 95s - loss: 0.0061 - val_loss: 0.0073
0 101s - loss: 0.0061 - val_loss: 0.0074
"""
def split_cols(arr): return np.hsplit(arr,arr.shape[1])
map_train = split_cols(cat_map_train) + [contin_map_train]
map_valid = split_cols(cat_map_valid) + [contin_map_valid]
len(map_train)
map_train = split_cols(cat_map_train) + split_cols(contin_map_train)
map_valid = split_cols(cat_map_valid) + split_cols(contin_map_valid)
###Output
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###Markdown
Helper function for getting categorical name and dim.
###Code
def cat_map_info(feat): return feat[0], len(feat[1].classes_)
cat_map_info(cat_map_fit.features[1])
def my_init(scale):
return lambda shape, name=None: initializations.uniform(shape, scale=scale, name=name)
def emb_init(shape, name=None):
return initializations.uniform(shape, scale=2/(shape[1]+1), name=name)
###Output
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###Markdown
Helper function for constructing embeddings. Notice commented out codes, several different ways to compute embeddings at play.Also, note we're flattening the embedding. Embeddings in Keras come out as an element of a sequence like we might use in a sequence of words; here we just want to concatenate them so we flatten the 1-vector sequence into a vector.
###Code
def get_emb(feat):
name, c = cat_map_info(feat)
#c2 = cat_var_dict[name]
c2 = (c+1)//2
if c2>50: c2=50
inp = Input((1,), dtype='int64', name=name+'_in')
# , W_regularizer=l2(1e-6)
u = Flatten(name=name+'_flt')(Embedding(c, c2, input_length=1, init=emb_init)(inp))
# u = Flatten(name=name+'_flt')(Embedding(c, c2, input_length=1)(inp))
return inp,u
###Output
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###Markdown
Helper function for continuous inputs.
###Code
def get_contin(feat):
name = feat[0][0]
inp = Input((1,), name=name+'_in')
return inp, Dense(1, name=name+'_d', init=my_init(1.))(inp)
###Output
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###Markdown
Let's build them.
###Code
contin_inp = Input((contin_cols,), name='contin')
contin_out = Dense(contin_cols*10, activation='relu', name='contin_d')(contin_inp)
#contin_out = BatchNormalization()(contin_out)
###Output
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###Markdown
Now we can put them together. Given the inputs, continuous and categorical embeddings, we're going to concatenate all of them.Next, we're going to pass through some dropout, then two dense layers w/ ReLU activations, then dropout again, then the sigmoid activation we mentioned earlier.** Aside: ** Please note that we have used MAE in the loss function instead of MSE, since we have taken logs of the target variable.
###Code
embs = [get_emb(feat) for feat in cat_map_fit.features]
#conts = [get_contin(feat) for feat in contin_map_fit.features]
#contin_d = [d for inp,d in conts]
x = concatenate([emb for inp,emb in embs] + [contin_out])
#x = merge([emb for inp,emb in embs] + contin_d, mode='concat')
x = Dropout(0.02)(x)
x = Dense(1000, activation='relu', init='uniform')(x)
x = Dense(500, activation='relu', init='uniform')(x)
x = Dropout(0.2)(x)
x = Dense(1, activation='sigmoid')(x)
model = Model([inp for inp,emb in embs] + [contin_inp], x)
#model = Model([inp for inp,emb in embs] + [inp for inp,d in conts], x)
model.compile('adam', 'mean_absolute_error')
#model.compile(Adam(), 'mse')
###Output
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###Markdown
Start training
###Code
%%time
hist = model.fit(map_train, y_train, batch_size=128, epochs=25,
verbose=0, validation_data=(map_valid, y_valid))
hist.history
plot_train(hist)
preds = np.squeeze(model.predict(map_valid, 1024))
###Output
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###Markdown
Result on validation data: 0.1678 (samp 150k, 0.75 trn)
###Code
log_max_inv(preds)
normalize_inv(preds)
###Output
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###Markdown
Using 3rd place data
###Code
pkl_path = '/data/jhoward/github/entity-embedding-rossmann/'
def load_pickle(fname):
return pickle.load(open(pkl_path+fname + '.pickle', 'rb'))
[x_pkl_orig, y_pkl_orig] = load_pickle('feature_train_data')
max_log_y_pkl = np.max(np.log(y_pkl_orig))
y_pkl = np.log(y_pkl_orig)/max_log_y_pkl
pkl_vars = ['Open', 'Store', 'DayOfWeek', 'Promo', 'Year', 'Month', 'Day',
'StateHoliday', 'SchoolHoliday', 'CompetitionMonthsOpen', 'Promo2Weeks',
'Promo2Weeks_L', 'CompetitionDistance',
'StoreType', 'Assortment', 'PromoInterval', 'CompetitionOpenSinceYear',
'Promo2SinceYear', 'State', 'Week', 'Max_TemperatureC', 'Mean_TemperatureC',
'Min_TemperatureC', 'Max_Humidity', 'Mean_Humidity', 'Min_Humidity', 'Max_Wind_SpeedKm_h',
'Mean_Wind_SpeedKm_h', 'CloudCover','Events', 'Promo_fw', 'Promo_bw',
'StateHoliday_fw', 'StateHoliday_bw', 'AfterStateHoliday', 'BeforeStateHoliday',
'SchoolHoliday_fw', 'SchoolHoliday_bw', 'trend_DE', 'trend']
x_pkl = np.array(x_pkl_orig)
gt_enc = StandardScaler()
gt_enc.fit(x_pkl[:,-2:])
x_pkl[:,-2:] = gt_enc.transform(x_pkl[:,-2:])
x_pkl.shape
x_pkl = x_pkl[idxs]
y_pkl = y_pkl[idxs]
x_pkl_trn, x_pkl_val = x_pkl[:train_size], x_pkl[train_size:]
y_pkl_trn, y_pkl_val = y_pkl[:train_size], y_pkl[train_size:]
x_pkl_trn.shape
xgb_parms = {'learning_rate': 0.1, 'subsample': 0.6,
'colsample_bylevel': 0.6, 'silent': True, 'objective': 'reg:linear'}
xdata_pkl = xgboost.DMatrix(x_pkl_trn, y_pkl_trn, feature_names=pkl_vars)
xdata_val_pkl = xgboost.DMatrix(x_pkl_val, y_pkl_val, feature_names=pkl_vars)
xgb_parms['seed'] = random.randint(0,1e9)
model_pkl = xgboost.train(xgb_parms, xdata_pkl)
model_pkl.eval(xdata_val_pkl)
#0.117473
importance = model_pkl.get_fscore()
importance = sorted(importance.items(), key=operator.itemgetter(1))
df = pd.DataFrame(importance, columns=['feature', 'fscore'])
df['fscore'] = df['fscore'] / df['fscore'].sum()
df.plot(kind='barh', x='feature', y='fscore', legend=False, figsize=(6, 10))
plt.title('XGBoost Feature Importance')
plt.xlabel('relative importance');
###Output
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###Markdown
Neural net
###Code
#np.savez_compressed('vars.npz', pkl_cats, pkl_contins)
#np.savez_compressed('deps.npz', y_pkl)
pkl_cats = np.stack([x_pkl[:,pkl_vars.index(f)] for f in cat_vars], 1)
pkl_contins = np.stack([x_pkl[:,pkl_vars.index(f)] for f in contin_vars], 1)
co_enc = StandardScaler().fit(pkl_contins)
pkl_contins = co_enc.transform(pkl_contins)
pkl_contins_trn, pkl_contins_val = pkl_contins[:train_size], pkl_contins[train_size:]
pkl_cats_trn, pkl_cats_val = pkl_cats[:train_size], pkl_cats[train_size:]
y_pkl_trn, y_pkl_val = y_pkl[:train_size], y_pkl[train_size:]
def get_emb_pkl(feat):
name, c = cat_map_info(feat)
c2 = (c+2)//3
if c2>50: c2=50
inp = Input((1,), dtype='int64', name=name+'_in')
u = Flatten(name=name+'_flt')(Embedding(c, c2, input_length=1, init=emb_init)(inp))
return inp,u
n_pkl_contin = pkl_contins_trn.shape[1]
contin_inp = Input((n_pkl_contin,), name='contin')
contin_out = BatchNormalization()(contin_inp)
map_train_pkl = split_cols(pkl_cats_trn) + [pkl_contins_trn]
map_valid_pkl = split_cols(pkl_cats_val) + [pkl_contins_val]
def train_pkl(bs=128, ne=10):
return model_pkl.fit(map_train_pkl, y_pkl_trn, batch_size=bs, nb_epoch=ne,
verbose=0, validation_data=(map_valid_pkl, y_pkl_val))
def get_model_pkl():
conts = [get_contin_pkl(feat) for feat in contin_map_fit.features]
embs = [get_emb_pkl(feat) for feat in cat_map_fit.features]
x = merge([emb for inp,emb in embs] + [contin_out], mode='concat')
x = Dropout(0.02)(x)
x = Dense(1000, activation='relu', init='uniform')(x)
x = Dense(500, activation='relu', init='uniform')(x)
x = Dense(1, activation='sigmoid')(x)
model_pkl = Model([inp for inp,emb in embs] + [contin_inp], x)
model_pkl.compile('adam', 'mean_absolute_error')
#model.compile(Adam(), 'mse')
return model_pkl
model_pkl = get_model_pkl()
train_pkl(128, 10).history['val_loss']
K.set_value(model_pkl.optimizer.lr, 1e-4)
train_pkl(128, 5).history['val_loss']
"""
1 97s - loss: 0.0104 - val_loss: 0.0083
2 93s - loss: 0.0076 - val_loss: 0.0076
3 90s - loss: 0.0071 - val_loss: 0.0076
4 90s - loss: 0.0068 - val_loss: 0.0075
5 93s - loss: 0.0066 - val_loss: 0.0075
6 95s - loss: 0.0064 - val_loss: 0.0076
7 98s - loss: 0.0063 - val_loss: 0.0077
8 97s - loss: 0.0062 - val_loss: 0.0075
9 95s - loss: 0.0061 - val_loss: 0.0073
0 101s - loss: 0.0061 - val_loss: 0.0074
"""
plot_train(hist)
preds = np.squeeze(model_pkl.predict(map_valid_pkl, 1024))
y_orig_pkl_val = log_max_inv(y_pkl_val, max_log_y_pkl)
rmspe(log_max_inv(preds, max_log_y_pkl), y_orig_pkl_val)
###Output
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###Markdown
XGBoost Xgboost is extremely quick and easy to use. Aside from being a powerful predictive model, it gives us information about feature importance.
###Code
X_train = np.concatenate([cat_map_train, contin_map_train], axis=1)
X_valid = np.concatenate([cat_map_valid, contin_map_valid], axis=1)
all_vars = cat_vars + contin_vars
xgb_parms = {'learning_rate': 0.1, 'subsample': 0.6,
'colsample_bylevel': 0.6, 'silent': True, 'objective': 'reg:linear'}
xdata = xgboost.DMatrix(X_train, y_train, feature_names=all_vars)
xdata_val = xgboost.DMatrix(X_valid, y_valid, feature_names=all_vars)
xgb_parms['seed'] = random.randint(0,1e9)
model = xgboost.train(xgb_parms, xdata)
model.eval(xdata_val)
model.eval(xdata_val)
###Output
_____no_output_____
###Markdown
Easily, competition distance is the most important, while events are not important at all.In real applications, putting together a feature importance plot is often a first step. Oftentimes, we can remove hundreds of thousands of features from consideration with importance plots.
###Code
importance = model.get_fscore()
importance = sorted(importance.items(), key=operator.itemgetter(1))
df = pd.DataFrame(importance, columns=['feature', 'fscore'])
df['fscore'] = df['fscore'] / df['fscore'].sum()
df.plot(kind='barh', x='feature', y='fscore', legend=False, figsize=(6, 10))
plt.title('XGBoost Feature Importance')
plt.xlabel('relative importance');
###Output
_____no_output_____
|
practice_tensorflow_wine.ipynb
|
###Markdown
###Code
from sklearn import datasets
wine = datasets.load_wine()
wine.keys()
x_data = wine['data']
y_data = wine['target']
x_data.shape , y_data.shape
import numpy as np
np.unique(y_data)
import pandas as pd
df_wine = pd.DataFrame(wine.data)
df_wine.info()
import sqlite3
connect = sqlite3.connect('./db.sqlite3')
df_wine.to_sql('wine_resource',connect,if_exists='append',index=False)
df_load = pd.read_sql_query('select * from wine_resource', connect)
df_load.head(4)
x_data = df_load.to_numpy()
x_data.shape
y_data = wine.target
y_data , np.unique(y_data)
x_data , x_data.shape
import tensorflow as tf
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(13,))) # input layer
model.add(tf.keras.layers.Dense(64,activation='relu')) # hidden layer
model.add(tf.keras.layers.Dense(32,activation='relu')) # hidden layer
model.add(tf.keras.layers.Dense(3,activation='softmax'))
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['acc'])
hist = model.fit( x_data , y_data , epochs=50 , validation_split=0.3 )
model.evaluate(x_data ,y_data)
x_data[25],y_data[25]
hist.history.keys()
import matplotlib.pyplot as plt
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.show()
plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])
plt.show()
pred = model.predict([[1.305e+01, 2.050e+00, 3.220e+00, 2.500e+01, 1.240e+02, 2.630e+00,
2.680e+00, 4.700e-01, 1.920e+00, 3.580e+00, 1.130e+00, 3.200e+00,
8.300e+02]])
pred , np.argmax(pred)
###Output
_____no_output_____
|
3_2_anneal_comparison/toshiba_sbm/SBM.ipynb
|
###Markdown
Annealing by Toshiba SBM
###Code
import subprocess
import joblib
ip="ip address for server"
cmd=f"""
curl -i -H "Content-Type: application/octet-stream" -X POST "{ip}:8000/solver/ising?steps=0&loops=10" --data-binary "@test.mm"
"""
cmd=cmd.replace("\n","")
#test job
%time res=subprocess.check_output(cmd, shell=True)
res
###Output
_____no_output_____
###Markdown
Make qubo for SBM
###Code
#make sparse dict from array-type qubo
original_qubo=joblib.load("../data/rbm_J.bin")
scale=10**5
dim=original_qubo.shape[0]
sparse_dict={}
for i in range(dim):
for j in range(dim):
val=-original_qubo[i][j]*scale
if val!=0:
sparse_dict[f"{j+1} {i+1}"]=int(val)
#save as mm file
num_non_zero_params=len(sparse_dict.keys())
output=f"""%%MatrixMarket matrix coordinate real symmetric
{dim} {dim} {num_non_zero_params}
"""
#output
for k,v in sparse_dict.items():
output+=f"{k} {v}\n"
with open("qubo.mm","w") as f:
f.write(output[:-1])
###Output
_____no_output_____
###Markdown
Annealing by SBM
###Code
timeout=60
cmd=f"""
curl -i -H "Content-Type: application/octet-stream" -X POST "{ip}:8000/solver/ising?steps=0&loops=10&timeout={timeout}" --data-binary "@qubo.mm"
"""
cmd=cmd.replace("\n","")
#%time res=subprocess.check_output(cmd, shell=True)
print(res)
###Output
b'HTTP/1.1 100 Continue\r\n\r\nHTTP/1.1 200 OK\r\nContent-Type: application/json; charset=utf-8\r\nContent-Length: 4136\r\nETag: W/"1028-MwZccDhIkqhiMFV4+Jh9KwaZ5qo"\r\nDate: Thu, 19 Aug 2021 08:28:46 GMT\r\nProxy-Connection: Keep-Alive\r\nConnection: Keep-Alive\r\n\r\n{"id":"r2814050749","time":60.07,"wait":0,"runs":2240,"steps":8001,"C":0.0000066,"dt":1,"message":"timeout","value":-3961787,"result":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,1,1,0,0,1,0,1,1,0,0,1,1,0,1,1,0,0,0,0,0,1,1,1,0,0,1,0,0,1,1,0,0,0,0,1,1,0,0,1,0,1,0,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,1,1,0,0,0,0,0,0,1,1,1,1,0,1,0,0,1,0,0,1,1,1,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,1,1,1,0,0,0,0,0,1,1,0,1,1,0,0,0,0,1,1,1,0,0,1,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,1,0,1,0,0,1,0,1,0,0,1,1,1,1,1,0,1,0,1,0,1,0,1,0,0,1,1,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,1,0,0,1,1,0,1,1,1,1,0,1,1,0,1,0,1,0,1,1,0,0,1,1,1,0,1,0,1,0,1,1,1,1,0,1,0,1,0,1,1,0,1,0,1,1,0,0,1,1,1,0,1,0,0,0,1,1,1,0,0,0,0,1,1,0,1,0,0,1,1,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,1,1,1,0,0,1,1,1,1,1,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,0,1,0,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0,1,1,1,1,0,0,1,1,0,1,0,1,0,1,0,1,0,1,1,1,0,1,1,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,1,0,1,1,0,1,0,1,1,0,1,0,0,0,1,1,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,0,1,0,1,0,1,1,1,0,1,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,1,0,0,1,1,0,1,1,0,0,0,0,0,0,1,0,1,1,0,0,1,1,0,0,1,0,0,1,1,1,0,0,1,1,0,1,0,0,0,1,0,0,1,0,0,1,1,1,0,1,0,0,1,1,1,0,1,0,0,1,1,1,0,0,1,1,0,1,0,0,0,1,0,0,1,1,1,1,0,0,0,1,1,0,0,0,0,1,1,1,0,1,1,1,1,0,0,1,1,1,0,0,1,0,0,1,0,0,1,1,1,0,1,1,1,1,1,0,0,1,0,1,0,1,1,1,0,1,0,1,0,0,1,1,1,1,0,1,1,1,0,1,1,0,1,1,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,1,1,0,0,0,1,1,0,0,0,1,1,1,0,1,0,0,1,0,0,0,0,1,1,1,0,0,0,1,1,0,0,0,1,0,0,1,1,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,1,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,0,1,1,0,0,1,0,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,0,0,0,1,1,0,1,0,1,1,0,0,0,0,1,0,0,0,1,1,1,0,0,1,0,1,1,0,0,0,1,0,0,0,1,1,1,0,1,0,1,1,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,1,0,1,1,0,1,1,1,1,0,0,1,1,0,0,0,1,1,1,0,1,1,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,1,1,1,1,0,0,0,0,0,0,1,1,1,0,0,1,1,1,0,1,1,1,1,0,1,0,0,1,0,1,1,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,1,0,0,1,1,1,1,0,0,1,1,0,1,0,1,0,0,1,0,0,0,1,0,0,1,1,1,1,1,0,0,0,1,1,0,1,0,0,0,0,0,0,1,1,1,0,0,1,1,1,1,0,0,1,0,0,0,0,1,1,1,0,1,1,1,1,1,1,0,0,0,1,0,1,1,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,0,1,1,0,0,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,1,1,1,0,0,1,1,0,0,1,1,0,0,0,1,0,0,1,1,1,0,0,1,1,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,1,1,1,0,1,0,0,0,0,1,1,1,0,0,1,1,0,0,1,1,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,1,1,0,1,0,1,0,0,1,1,0,1,0,0,1,1,0,1,0,1,1,1,1,0,0,1,1,0,1,0,0,1,0,0,1,0,1,1,0,0,0,0,1,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,0,1,0,1,1,1,1,1,0,1,0,0,0,1,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,1,1,0,1,1,1,0,1,1,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,1,1,0,1,0,0,1,1,1,1,1,1,1,0,1,0,1,1,0,0,1,0,1,0,0,1,0,0,1,0,1,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,1,0,1,1,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,1,0,0,0,1,0,1,0,0,0,1,0,1,0,1,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,1,0,1,1,0,1,1,1,1,0,0,0,0,1,1,1,0,0,1,0,1,1,1,0,0,0,1,1,1,0,1,0,1,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0]}'
|
starbucks_analyze_a_coffee.ipynb
|
###Markdown
Starbucks: Analyze-a-CoffeeStarbucks possesses a unique way of connecting with and rewarding its customers who purchase its products. Starbucks Rewards program allows the company to create a Loyalty Program where it awards loyal customers by offering incentives for buying products with special offers. It utilizes many channels to market its products from social media to TV spots and ads. Starbucks executes its extraordinary marketing strategy by deploying a combination of marketing media channels, where it creates brand recognition. Starbucks does not only understand its products and customers, but also keeps up with how its customers use technology. Starbucks App enables customers to keep track of the available offers and happy hour deals at participating stores. It allows customers to earn and collect stars (collect two stars per $1) that can be redeemed in-store or via the app.Here, we are going to investigate and analysis three files that simulate how people make purchasing decisions and how promotional offers influence those decisions. The analysis and its findings are only observational and not the result of a formal study. General business questions are listed below to guide us through the analysis to develop a set of heuristics where the findings are not guaranteed to be optimal or rational. Business QuestionsThe purpose of the analysis is to examine how Starbucks’ customers respond to an offer whether a BOGO or Discount is the offer. Not all customers have the same incentives to view an offer and then make a transaction to complete the offer. Many factors play an important role in impacting how customers make purchasing decisions; for instance, some customers prefer offers that allow them to collect more and more stars toward getting exclusive perks or even free products. Sometimes, customers at a particular age group, prefer an offer different than what another group prefers. Moreover, we should keep in mind that female customers may react to an offer is different than how male customers do. Many aspects can be investigated and analyzed to find answers to such questions. All of that would help Starbucks to target its customers, and then personalizes and customizes the offers it sends depending on who are the audience. Many questions can be asked; here is some of what we are going to investigate:1. What is the number of customers who received at least one offer? 2. Who usually spend more at Starbucks, female or male?3. For the customers who spend more; Who makes more income per year?4. How old are most of Starbucks customers with respect to gender?5. How much do customers spend at any time since the start of an offer?6. Can we find the most popular offer by an age group or a gender, then compare it to other offers, or even another age group?7. Which offer has made the most for Starbucks? Is there a difference between BOGO offers and Discount offers? If so, Do male customers react the same as female customers do for any of the two offer types?
###Code
# importing libraries
import pandas as pd
import numpy as np
import json
import matplotlib.pyplot as plt
# magic word for producing visualizations in notebook
%matplotlib inline
import plotly.plotly as py #for creating interactive data visualizations
import plotly.graph_objs as go
import plotly.tools as tls
py.sign_in('', '') #API key has been removed for security
from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot #to work with data visualization offline
init_notebook_mode(connected=True)
import cufflinks as cf #connects Plotly with pandas to produce the interactive data visualizations
cf.go_offline()
from IPython.display import Image
# reading the json files
portfolio = pd.read_json('portfolio.json', orient='records', lines=True)
profile = pd.read_json('profile.json', orient='records', lines=True)
transcript = pd.read_json('transcript.json', orient='records', lines=True)
###Output
_____no_output_____
###Markdown
--------- 1. Data WranglingMany alterations and preparation were applied to the three files to get a final clean dataset contains all the information needed for each variable available from offer types to gender and age groups. 1.1 Data SetsThe data is contained in three files:* portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.)* profile.json - demographic data for each customer* transcript.json - records for transactions, offers received, offers viewed, and offers completed 1.1.1 Portfolio
###Code
# printing the portfolio data
print(f"Number of offers: {portfolio.shape[0]}")
print(f"Number of variables: {portfolio.shape[1]}")
portfolio
###Output
Number of offers: 10
Number of variables: 6
###Markdown
Here is the schema and explanation of each variable in the file:**portfolio.json*** id (string) - offer id* offer_type (string) - type of offer ie BOGO, discount, informational* difficulty (int) - minimum required spend to complete an offer* reward (int) - reward given for completing an offer* duration (int) - time for offer to be open, in days* channels (list of strings)
###Code
# overall info about the portfolio data
portfolio.info()
portfolio.describe()
###Output
_____no_output_____
###Markdown
The first look at the portfolio indicates that we will have to split the channels in the cleaning process. --- 1.1.2 Profile
###Code
# printing the profile data
print(f"Number of customers: {profile.shape[0]}")
print(f"Number of variables: {profile.shape[1]}")
profile.sample(10)
###Output
Number of customers: 17000
Number of variables: 5
###Markdown
Here is the schema and explanation of each variable in the file:**profile.json*** age (int) - age of the customer * became_member_on (int) - date when customer created an app account* gender (str) - gender of the customer (note some entries contain 'O' for other rather than M or F)* id (str) - customer id* income (float) - customer's income
###Code
# overall info about the profile data
profile.info()
###Output
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17000 entries, 0 to 16999
Data columns (total 5 columns):
age 17000 non-null int64
became_member_on 17000 non-null int64
gender 14825 non-null object
id 17000 non-null object
income 14825 non-null float64
dtypes: float64(1), int64(2), object(2)
memory usage: 664.1+ KB
###Markdown
We can see that we have 17000 customers in our data. However, gender and income variables have some nulls values which will be investigated next.
###Code
profile.describe()
###Output
_____no_output_____
###Markdown
The age variable contains a maximum value of 118 years old, which is considered an unusual value that will be investigated even further.
###Code
# checking the number of null values
profile.isnull().sum()
# printing the number of nulls values for the gender column by age
print(f"Number of null values: {profile.age[profile.gender.isnull()].value_counts().iloc[0]}")
print(f"Number of unqiue customers with null values: {profile.age[profile.gender.isnull()].nunique()}")
print(f"The age of the unique customers where there are nulls: {profile.age[profile.gender.isnull()][0]}")
###Output
Number of null values: 2175
Number of unqiue customers with null values: 1
The age of the unique customers where there are nulls: 118
###Markdown
As expected, all the nulls in the profile data are part of the customers who have '118' as an age value. Therefore, the rows that have the value 118 will be dropped as a part of the cleaning process of the profile file. --- 1.1.3 Transcript
###Code
# printing the transcript data
print(f"Number of transcripts: {transcript.shape[0]}")
print(f"Number of variables: {transcript.shape[1]}")
transcript.sample(5)
###Output
Number of transcripts: 306534
Number of variables: 4
###Markdown
Here is the schema and explanation of each variable in the files:**transcript.json*** event (str) - record description (ie transaction, offer received, offer viewed, etc.)* person (str) - customer id* time (int) - time in hours since start of test. The data begins at time t=0* value - (dict of strings) - either an offer id or transaction amount depending on the record
###Code
# overall info about the transcript data
transcript.info()
transcript.time.describe()
###Output
_____no_output_____
###Markdown
The transcripts data includes an important variable, **time**, that will be used to make sure each event, or transaction was made within the **duration** of an offer. All of these cleaning process will be applied next. --------- 1.2 Data Cleaning 1.2.1 Portfolio
###Code
# creating a new copy for cleaning purposes
portfolio_clean = portfolio.copy()
# overall view of the portfolio data before cleaning
portfolio_clean.sample(3)
# showing the channels available before splitting them to 4 columns
portfolio_clean.channels.sample(5)
# splitting the channels
def col_split(df, column):
splits = []
for s in df[column]:
for i in s:
if i not in splits:
splits.append(i)
for split in splits:
df[split] = df[column].apply(lambda x: 1 if split in x else 0)
df.drop([column], axis=1, inplace=True)
return splits
col_split(portfolio_clean ,'channels')
# overall view of the portfolio data after the split
portfolio_clean.sample(3)
###Output
_____no_output_____
###Markdown
We have just split the channels where each channel become a variable and has the value 1 if it has been used as a channel for a specific offer whereas the value 0 means it hasn't been used.
###Code
# showing the type of each column
portfolio_clean.dtypes
# converting the duration column to be in hours instead of days
# to be compared with the times columns in the transcript file
portfolio_clean['duration'] = portfolio_clean['duration'] * 24
###Output
_____no_output_____
###Markdown
As mentioned eariler, the duration variable will play a major role in the cleaning process where it will be compared to the time of each transcript provided. Thus, it will be converted to be in hours as the time variables does.
###Code
# renaming the columns for the portfolio data
portfolio_clean.rename(columns={'difficulty': 'difficulty($)',
'duration': 'duration(hours)',
'id': 'offer_id'}, inplace=True)
# overall view of the portfolio data after renaming the columns
portfolio_clean.head(3)
# ordering the columns for the final look at the portfolio data after being clean
portfolio_clean = portfolio_clean[['offer_id',
'offer_type',
'difficulty($)',
'duration(hours)',
'reward',
'email',
'mobile',
'social',
'web']]
portfolio_clean.head(3)
###Output
_____no_output_____
###Markdown
--- 1.2.2 Profile
###Code
# creating a new copy for cleaning purposes
profile_clean = profile.copy()
# overall view of the profile data before cleaning
profile_clean.head(3)
# showing the type of each column
profile_clean.dtypes
# converting the 'became_member_on' column to date
# adding a period of the membership by month
profile_clean['membership_start'] = profile_clean.became_member_on.apply(lambda x: pd.to_datetime(str(x), format='%Y%m%d'))
profile_clean['membership_period'] = profile_clean['membership_start'].dt.to_period('M')
profile_clean.drop(['became_member_on'], axis=1, inplace=True)
###Output
_____no_output_____
###Markdown
The variable "became_member_on" could be used for finding trends with respect to the time most customers have joined Starbucks App. Therefore, the column was converted to be a date instead of int and then a membership_period by Month was created where it shows the month and year when the customer become a member.
###Code
# overall view of the profile data after applying some cleaning
profile_clean.head(3)
# renaming the id column for the profile data
profile_clean.rename(columns={'id': 'customer_id'}, inplace=True)
# listing the customers_ids whose age value is '118',
# which accounts for the all the nulls values
age_118 = profile_clean[profile_clean['age'] == 118]
list(age_118.customer_id)
# making sure the profile data doesn't contains the customers_ids whose age value is '118',
# which accounts for the all the nulls values
profile_clean = profile_clean[profile_clean.age != 118]
profile_clean.head(3)
print(f"Number of customers after cleaning: {profile_clean.shape[0]}")
print(f"Number of variables: {profile_clean.shape[1]}")
###Output
Number of customers after cleaning: 14825
Number of variables: 6
###Markdown
We talked about the customers who have nulls, and then found out that all nulls can be dropped when the rows with age = 118 are dropped, which what we have just done. We recreated the profile data to exclude the rows with age 118. As a result, the prfile dataset contain information about 14825 instead of 17000 customers
###Code
# checking the number of null values after dropping the customers ids with nulls income and gender
profile_clean.isnull().sum()
# ordering the columns for the final look at the profile data after being clean
profile_clean = profile_clean[['customer_id',
'age',
'gender',
'income',
'membership_start',
'membership_period']]
profile_clean.head(3)
###Output
_____no_output_____
###Markdown
--- 1.2.3 Transcript
###Code
# creating a new copy for cleaning purposes
transcript_clean = transcript.copy()
# overall view of the transcript data before cleaning
transcript_clean.head(3)
# showing the type of each column
transcript_clean.dtypes
# showing the values available before splitting them to
# 2 columns: record (offer id or transactions) and record_value (amount or id)
transcript_clean.value.sample(10)
###Output
_____no_output_____
###Markdown
While the time variable would play an important role during the analysis, we should first split the values we have either offer_id with its record or transaction with the amount. The next function will do the magic!
###Code
# splitting the value column
def df_values(df=transcript_clean):
df['record'] = df.value.apply(lambda x: list(x.keys())[0])
df['record_value'] = df.value.apply(lambda x: list(x.values())[0])
df.drop(['value'], axis=1, inplace=True)
return None
df_values()
# renaming the person and time columns for the transcript data
transcript_clean.rename(columns={'person': 'customer_id',
'time': 'time(hours)'}, inplace=True)
transcript_clean.head(3)
# ordering the columns for the final look at the transcript data after being clean
transcript_clean = transcript_clean[['customer_id',
'event',
'record',
'record_value',
'time(hours)']]
transcript_clean = transcript_clean[~transcript_clean.customer_id.isin(age_118.customer_id)]
transcript_clean.head(3)
###Output
_____no_output_____
###Markdown
After splitting the ids and the transactions, we are goining to create two datasets: offers that contains the information where the record is offer_id and the record value is the id, and transactions which include the amount of each transaction made by a customer.
###Code
print(f"Number of transcripts after cleaning: {transcript_clean.shape[0]}")
print(f"Number of variables: {transcript_clean.shape[1]}")
# splitting the transcript dataset to 2 datasets; offers and transactions
transactions = transcript_clean[transcript_clean.event == 'transaction']
offers = transcript_clean[transcript_clean.event != 'transaction']
# overall view of the offers data
offers.sample(3)
# renaming the record_value
offers.rename(columns={'record_value': 'offer_id'}, inplace=True)
offers.drop(['record'], axis=1, inplace=True)
print(f"Number of offers after cleaning: {offers.shape[0]}")
print(f"Number of variables: {offers.shape[1]}")
offers.sample(3)
# converting the record value for the transactions to be numerical
transactions['record_value'] = pd.to_numeric(transactions['record_value'])
transactions.dtypes
# overall view of the transactions data
transactions.sample(3)
# renaming the record_value
transactions.rename(columns={'record_value': 'transaction_amount'}, inplace=True)
transactions.drop(['record', 'event'], axis=1, inplace=True)
print(f"Number of transactions after cleaning: {transactions.shape[0]}")
print(f"Number of variables: {transactions.shape[1]}")
transactions.sample(3)
###Output
Number of transactions after cleaning: 123957
Number of variables: 3
###Markdown
--- 2. Data Exploration 2.1 Part 1 2.1.1 Profile
###Code
# overall view of the profile data after being clean
profile_clean.sample(3)
# describing the values of the income column
profile_clean.income.describe()
# describing the values of the age column
profile_clean.age.describe()
# plotting the numbers and percentages of customers by gender
gender = profile_clean.gender.value_counts()
labels = gender.index
values = gender
colors = ['cornflowerblue', 'pink', 'mediumblue']
trace1 = go.Pie(labels=labels, values=round(100*values/values.sum(), 2),
text=values,
textfont=dict(size=20),
hoverinfo='skip',
hole=0.9,
showlegend=False,
opacity=0.6,
marker=dict(colors=colors,
line=dict(color='#000000', width=2)))
trace2 = go.Bar(
x=['Male'],
y=[values[0]],
name='Male',
marker=dict(
color='cornflowerblue',
line=dict(
color='cornflowerblue',
width=1.5,
)
),
opacity=0.6
)
trace3 = go.Bar(
x=['Female'],
y=[values[1]],
name='Female',
marker=dict(
color='pink',
line=dict(
color='pink',
width=1.5,
)
),
opacity=0.6
)
trace4 = go.Bar(
x=['Other'],
y=[values[2]],
name='Other',
marker=dict(
color='mediumblue',
line=dict(
color='mediumblue',
width=1.5,
)
),
opacity=0.6
)
data1 = go.Data([trace1, trace2, trace3, trace4])
layout = go.Layout(
title='Number of Customers by Gender',
xaxis=dict(
title='Gender',
domain=[0.4, 0.6]
),
yaxis=dict(
title='Count',
domain=[0.4, 0.7]
)
)
fig = go.Figure(data=data1, layout=layout)
py.iplot(fig, filename='percentage of customer by gender')
###Output
/Users/Saleh/anaconda3/lib/python3.7/site-packages/plotly/graph_objs/_deprecations.py:39: DeprecationWarning:
plotly.graph_objs.Data is deprecated.
Please replace it with a list or tuple of instances of the following types
- plotly.graph_objs.Scatter
- plotly.graph_objs.Bar
- plotly.graph_objs.Area
- plotly.graph_objs.Histogram
- etc.
/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:
Consider using IPython.display.IFrame instead
###Markdown
The donut chart illustrates that female customers account for 41.30% and male customers account for 57.20% of the data overall whether they all completed an offer, made a transaction before the end of an offer or not. Also, customers who chose other as gender account only for 1.43%. Can we assume that having more male customers would indicate that they make more transactions amount than female customers do? Next, we will investigate the numbers even further.
###Code
# plotting a gender comparsion with respect to income and age
def array(df, gen, col):
arr = df[df.gender == gen]
arr = np.array(arr[col])
return arr
def gender_comparsion(df, col, plot_name, avg):
x_data = ['Male', 'Female', 'Other']
y0 = array(df, 'M', col)
y1 = array(df, 'F', col)
y2 = array(df, 'O', col)
y_data = [y0,y1,y2]
colors = ['cornflowerblue', 'pink', 'mediumblue']
traces = []
for xd, yd, cls in zip(x_data, y_data, colors):
traces.append(go.Box(
y=yd,
name=xd,
boxmean=True,
boxpoints='all',
jitter=0.5,
whiskerwidth=0.2,
marker=dict(
size=2,
color=cls,
),
line=dict(color=cls,
width=1),
))
layout = {
'title': plot_name,
'shapes': [
# Line Horizontal, average
{
'type': 'line',
'xref': 'paper',
'x0': 0,
'y0': avg,
'x1': 1,
'y1': avg,
'line': {
'color': 'black',
'width': 1,
'dash': 'dashdot',
}
}]}
layout.update(dict(
annotations=[go.Annotation(text="Overall Average", y=avg)]))
fig = go.Figure(data=traces, layout=layout)
plot = py.iplot(fig, filename=plot_name)
return plot
gender_comparsion(profile_clean, 'income', 'Income Comparsion', 64000)
###Output
/Users/Saleh/anaconda3/lib/python3.7/site-packages/plotly/graph_objs/_deprecations.py:144: DeprecationWarning:
plotly.graph_objs.Annotation is deprecated.
Please replace it with one of the following more specific types
- plotly.graph_objs.layout.Annotation
- plotly.graph_objs.layout.scene.Annotation
###Markdown
We can see that, on average, male customers earn less than female customers do in a year. In fact, female customers earn more than the average income per year for all gender types. While female customers make, on average, around 71k a year, male customers make, on average, around 61k.
###Code
gender_comparsion(profile_clean, 'age', 'Age Comparsion', 55)
###Output
_____no_output_____
###Markdown
The box plots describe a summary of the age distribution by gender. They show that, on average, female customers are older than male customers.
###Code
# grouping the data by income and then reporting the average number of customers per icnome
print(f"The average number of customers per icnome rate per year: {round(profile_clean.groupby('income')['customer_id'].count().mean(),0)}")
# plotting the Income Distribution by Gender
dis = profile_clean.drop_duplicates(subset='customer_id', keep='first')
female_income = dis[dis['gender'] == 'F']
male_income = dis[dis['gender'] == 'M']
other_income = dis[dis['gender'] == 'O']
x1 = female_income['income']
x2 = male_income['income']
x3 = other_income['income']
trace1 = go.Histogram(
x=x1,
name='Female',
opacity=0.6,
nbinsx = 91,
marker=dict(
color='pink')
)
trace2 = go.Histogram(
x=x2,
name='Male',
opacity=0.6,
nbinsx = 91,
marker=dict(
color='cornflowerblue')
)
trace3 = go.Histogram(
x=x3,
name='Other',
opacity=0.6,
nbinsx = 91,
marker=dict(
color='mediumblue')
)
data3 = [trace1, trace2, trace3]
updatemenus = list([
dict(active=0,
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True]},
{'title': 'Income Distribution by Gender'}]),
dict(label = 'Female',
method = 'update',
args = [{'visible': [True, False, False]},
{'title': 'Income Distribution of Male Customers'}]),
dict(label = 'Male',
method = 'update',
args = [{'visible': [False, True, False]},
{'title': 'Income Distribution of Female Customers'}])
]),
)
])
layout = go.Layout(dict(updatemenus=updatemenus,
barmode='stack',
bargap=0.2,
title = 'Income by Gender',
xaxis=dict(
title='Income($)'),
yaxis=dict(
title='Number of Customers')))
fig = go.Figure(data=data3, layout=layout)
py.iplot(fig, filename='Income Distribution by Gender')
###Output
_____no_output_____
###Markdown
Here, the plot clarifies that, overall, the majority of our customers make between 50k and 75k. The average number of customer per income rate is 163 customers. Thus, while the minority are making more than 100k per year, some customers earn less than 50k and the other earn between 76k and 100k. Overall, we can see that most of the female customers make between 60k — 75k, and most of the make customers earn 45k— 60k. Next, we will confirm the difference between female and male customers income rates. --- 2.1.2 Portfolio
###Code
# overall view of the portfolio data after being clean
portfolio_clean
# showing the number of offers by offer_type
portfolio_clean.groupby('offer_type')['offer_id'].count()
###Output
_____no_output_____
###Markdown
To be used for further analysis, we can that 4 of the offers are BOGO, 4 are Discounts, and 2 are informational that don't require any compleation by customers as well as don't provide any rewards. Hence, we will focus only on the BOGO and Discounts. --- 2.1.3 Transcript
###Code
# overall view of the transcript data after being clean
transcript_clean.sample(3)
# showing the number of transcripts by event
transcript_clean.event.value_counts()
# showing the percentage of transcripts by event
transcript_clean['event'].value_counts(normalize = True)
###Output
_____no_output_____
###Markdown
In the transcripts, customers made 123957 transactions overall whether they are considerd on time (made before the end of an offer or not). Therefore, we will clean the transcript dataset even further by making sure we have the transaction made on time while the customer viewed and completed the offer.
###Code
# functions to return 3 datasets by event
def offers_event1(df, ev):
offers = df[df.event == ev]
return offers
offer_received = offers_event1(offers, 'offer received')
offers_viewed = offers_event1(offers, 'offer viewed')
offers_completed = offers_event1(offers, 'offer completed')
def offers_event2(df):
df_offers = pd.DataFrame(df.offer_id.value_counts()).reset_index().rename(columns={'index': 'offer_id', 'offer_id': 'count'})
return df_offers
df_received = offers_event2(offer_received)
df_viewed = offers_event2(offers_viewed)
df_completed = offers_event2(offers_completed)
# merging the datasets for each event created from the previous functions to calculate
# the numbers and percentages of the customers for each offer
offers_count = pd.merge(pd.merge(df_received, df_viewed, on='offer_id'), df_completed, on='offer_id')
offers_count = offers_count.rename(columns={'count_x': 'received', 'count_y': 'viewed','count': 'completed'})
offers_count['offer_views(%)'] = offers_count['viewed']/offers_count['received']*100
offers_count['offer_completion(%)'] = offers_count['completed']/offers_count['received']*100
offers_count['completed(not_viewed)'] = offers_count['completed']-offers_count['viewed']
offers_count
print(f"The offer with the most completion percentage: {offers_count.loc[offers_count['offer_completion(%)'] == offers_count['offer_completion(%)'].max(), 'offer_id'].iloc[0]}")
print(f"Its completion percentage: {round(offers_count['offer_completion(%)'].max(), 2)}")
###Output
The offer with the most completion percentage: fafdcd668e3743c1bb461111dcafc2a4
Its completion percentage: 75.21
###Markdown
An overall analysis of each offer before cleaning the data even more to meet our requirements; a customer viewed and completed an offer by making a transaction. Here we can see than the offer "fafdcd668e3743c1bb461111dcafc2a4" is the most popular offer with around a 75% compleation rate for the customers who received an offer following by "2298d6c36e964ae4a3e7e9706d1fb8c2" with a 73% compleation rate. Also, the less popular offer is "0b1e1539f2cc45b7b9fa7c272da2e1d7" as only 33% of the customers who recevied it have viewed the offer and 50% of the customers who recevied that offer have completed it.
###Code
# merging the offers_count data with portfolio data to have a complete info about each offer
offers_comparsion = pd.merge(offers_count, portfolio_clean, on='offer_id')
offers_comparsion
###Output
_____no_output_____
###Markdown
Final look at the offers analysis with even more details about each offer. It shows that the two most popular offers that have just been discovered are discounts where all four channels were used reuiqre a mininum of $10 per transaction In fact, more than 95% of the customers who received these offers have viewed the offer through one of the channels.
###Code
offers_comparsion.sample(2)
###Output
_____no_output_____
###Markdown
2.2 Part 2
###Code
# merging all three datasets to have a complete, clean dataset
full_data = pd.merge(pd.merge(pd.merge(profile_clean,
transactions, on='customer_id'),
offers, on='customer_id'),
offers_comparsion, on='offer_id')
# locking at the full datasets
full_data
# renaming the time hours of the transactions to be distinguished from the time of offers
full_data.rename(columns={'time(hours)_x': 'time_of_transaction(hours)'}, inplace=True)
# a function to recreate the ids columns to be easy ids to use and communicate
def id_mapper(df, column):
coded_dict = dict()
cter = 1
id_encoded = []
for val in df[column]:
if val not in coded_dict:
coded_dict[val] = cter
cter+=1
id_encoded.append(coded_dict[val])
return id_encoded
cid_encoded = id_mapper(full_data, 'customer_id')
full_data['customer_id'] = cid_encoded
oid_encoded = id_mapper(full_data, 'offer_id')
full_data['offer_id'] = oid_encoded
###Output
_____no_output_____
###Markdown
To make even easier to communicate the customers and offers ids, they have been mapped to be represented by numbers instead of hashes or codes.
###Code
# locking at the full datasets after recreating the ids
full_data
# making sure the dataset contains only the transactions that appear before the end of an offer
full_data = full_data[full_data['time_of_transaction(hours)'] <= full_data['duration(hours)']]
full_data
print(f"The number of transactions in the full data: {full_data.shape[0]}")
print(f"The number of variables: {full_data.shape[1]}")
###Output
The number of transactions in the full data: 238356
The number of variables: 25
###Markdown
As mentioned eariler, our goal is to have a dataset that contains only viewed and completed offers where transactions made by anu customers took a place before the end of the duration of an offer. That is, the above code assure that transactions made after the end of an offer are not included in the final, clean data.
###Code
# showing the number of transactions by offer_type for the final and clean data
full_data['offer_type'].value_counts()
###Output
_____no_output_____
###Markdown
We talked about the popularity of the 10 offers we have, and we found that two discount offers were the most popular. Here, we can see than most transactions were made for discount offers.
###Code
# functions to return the individual datasets with respect to each column in the full_data
def full_dataset(df, column, ev):
data = df[df[column] == ev]
return data
def offer_dataset(df, offer_num):
offer_num = full_dataset(df, 'offer_id', offer_num)
return offer_num
###Output
_____no_output_____
###Markdown
2.2.1 Events
###Code
# creating datasets of each event for further analysis
df_received = full_dataset(full_data, 'event', 'offer received')
df_viewed = full_dataset(full_data, 'event', 'offer viewed')
df_completed = full_dataset(full_data, 'event', 'offer completed')
print(f"The number of received offers: {df_received.shape[0]}")
df_received.sample(3)
# overview of the transaction where the customers did view an offer
print(f"The number of viewed offers: {df_viewed.shape[0]}")
df_viewed
# overview of the transaction where the customers did complete an offer
print(f"The number of completed offers: {df_completed.shape[0]}")
df_completed
###Output
The number of completed offers: 68627
###Markdown
After writting some functions that return the full data with respect to a specific variable, we will look next to top_customers customers who completed any offer and paid the most amount.
###Code
# making sure we have only unique_customers even if the customer made transaction more than once for an offer
unique_customers = df_completed.drop_duplicates(subset=['customer_id', 'time_of_transaction(hours)', 'offer_id'], keep='first')
# finding the top customers based on the sum of their transaction_amount of all offers completed
top_customers = unique_customers.groupby('customer_id')['transaction_amount'].sum().sort_values(ascending=False).head(10)
top_customers
###Output
_____no_output_____
###Markdown
It looks like some customers paid thousands of dollars for Starbucks. Next, we will see the total amount of money made for each offer.
###Code
# finding the top offers based on the sum of the transaction_amount made by all customers who completed the offer
top_offers = unique_customers.groupby('offer_id')['transaction_amount'].sum().sort_values(ascending=False).head(10)
top_offers
###Output
_____no_output_____
###Markdown
Offers 5 and 7 have the most sum of transactions amount made by all customers.
###Code
# finding the average transaction_amount over all transactions and offer where customers completed the offers
df_completed['transaction_amount'].mean()
# creating a dataframe grouped by the time when each transaction takes a place from the start of an offer
# showing a description of the amount spent by that time
transcation_by_time = df_completed.groupby('time_of_transaction(hours)').describe()['transaction_amount'].reset_index()
transcation_by_time = transcation_by_time.drop(['std', '25%', '75%'], axis=1)
transcation_by_time
# a function to split the data by column and then returns a description of the all transcations grouped by their time
def transcation_by_time(df, col, target):
transcations = df[df[col] == target]
transcations = transcations.groupby('time_of_transaction(hours)').describe()['transaction_amount'].reset_index()
transcations = transcations.drop(['std', '25%', '75%'], axis=1)
return transcations
# plotting a trend that shows the average Amount Spent since a Start of an Offer at a specific time
f_transcations = transcation_by_time(df_completed, 'gender', 'F')
m_transcations = transcation_by_time(df_completed, 'gender', 'M')
trace_mean1 = go.Scatter(
x=f_transcations['time_of_transaction(hours)'],
y=f_transcations['mean'],
name = "Female",
line = dict(color = 'pink'),
opacity = 0.6)
trace_mean2 = go.Scatter(
x=m_transcations['time_of_transaction(hours)'],
y=m_transcations['mean'],
name = "Male",
line = dict(color = 'cornflowerblue'),
opacity = 0.6)
data1 = [trace_mean1, trace_mean2]
layout = {
'title': 'Average Amount Spent Since a Start of an Offer Trend',
'xaxis': {'title': 'Time Since a Start of an Offer (hours)'},
'yaxis': {'title': 'Average Amount ($)',
"range": [
10,
30
]},
'shapes': [
# Line Horizontal, average
{
'type': 'line',
'x0': 0,
'y0': 16.55,
'x1': 243,
'y1': 16.55,
'line': {
'color': 'black',
'width': 1,
'dash': 'dashdot',
}
},
# 1st highlight above average amount
{
'type': 'rect',
# x-reference is assigned to the x-values
'xref': 'paper',
# y-reference is assigned to the plot [0,1]
'yref': 'y',
'x0': 0,
'y0': 16.55,
'x1': 1,
'y1': 30,
'fillcolor': 'olive',
'opacity': 0.1,
'line': {
'width': 0,
}
},
# 3nd highlight below average months
{
'type': 'rect',
'xref': 'paper',
'yref': 'y',
'x0': 0,
'y0': 16.55,
'x1': 1,
'y1': 0,
'fillcolor': 'tomato',
'opacity': 0.1,
'line': {
'width': 0,
}
}
]
}
layout.update(dict(annotations=[go.Annotation(text="Overall Average Amount ($16.55) Spent After the Start of an Offer",
x=150,
y=16.55,
ax=10,
ay=-120)]))
fig = dict(data=data1, layout=layout)
py.iplot(fig, filename = "Amount Spent since a Start of an Offer Trend")
###Output
_____no_output_____
###Markdown
The trend of the transactions made since a start of an offer shows that, on average, female customers paid more than male customers did at any time from the beginning of an offer up to 10 days. Back to our concern in the first chart; even though the number of male customers is higher, female customers paid more than male customers have paid. The only exception is transactions made after 228 hours from the start of an offer. On average, all customers paid an overall amount of $16.55 at any time since an offer has started. We can see female customers paid more than the average at any time! Whereas male customers most the time paid less than the average. Also, we can observe some peaks over time for both gender where they, on average, paid more than usual. That could be during the weekends or specific times during the day. 2.2.2 Offer Type
###Code
# creating datasets of each offer_type for further analysis
df_bogo = full_dataset(full_data, 'offer_type', 'bogo')
df_discount = full_dataset(full_data, 'offer_type', 'discount')
# overview of the transaction where the offer is BOGO
df_bogo.sample(3)
df_discount.sample(3)
# function to return top customers or offers for a specific offer type
# based on the sum of their transaction_amount of all offers completed
def tops(df, col):
# making sure we have only unique_customers who completed an offer even if the customer made transaction more than once for that offer
completed_offers = df[df['event'] == 'offer completed']
unique_customers = completed_offers.drop_duplicates(subset=['customer_id', 'time_of_transaction(hours)', 'offer_id'], keep='first')
# finding the tops based on the sum of their transaction_amount of all offers completed for that specific offer type
top1 = unique_customers.groupby(col)['transaction_amount'].sum().sort_values(ascending=False).head(10)
return top1
def tops_by_gender(df):
# making sure we have only unique_customers who completed an offer even if the customer made transaction more than once for that offer
completed_offers = df[df['event'] == 'offer completed']
unique_customers = completed_offers.drop_duplicates(subset=['customer_id', 'time_of_transaction(hours)', 'offer_id'], keep='first')
# finding the amount of all completed offer by gender
amount = unique_customers.groupby(['offer_id', 'gender'])['transaction_amount'].sum()
return amount
def customer_report(cid, df=df_discount):
report = df[df.event == 'offer completed']
report = report[report.customer_id == cid]
return report
# finding the top customers based on the sum of their transaction_amount of all BOGO offers completed
tops(df_bogo, 'customer_id')
###Output
_____no_output_____
###Markdown
As we now focus on transactions made for only the BOGO offers, we can see the top 10 customers who paid the most for all BOGO offers
###Code
# finding the top customers based on the sum of their transaction_amount of all discount offers completed
tops(df_discount, 'customer_id')
###Output
_____no_output_____
###Markdown
Here, we find the top 10 customers with respect to the discount offers.
###Code
# reporting more info about a customer
customer_report(8658, df_discount)
# finding the top offers based on the sum of the transaction_amount made by all customers who completed the offer
tops(df_bogo, 'offer_id')
###Output
_____no_output_____
###Markdown
As mentioned eariler, we have 4 BOGO offers and 4 Discount offers. Here we see that offer 2 had the most total transactions amount over the other BOGO offers.
###Code
# finding the top offers based on the sum of the transaction_amount made by all customers who completed the offer
tops(df_discount, 'offer_id')
###Output
_____no_output_____
###Markdown
Offer 5 made the most among other Discount offers, and overall!
###Code
# finding the total amount for each BOGO offer by gender
tops_by_gender(df_bogo)
###Output
_____no_output_____
###Markdown
More details about the total transactions amount made for each BOGO offer by customers with respect to their gender. Overall, female customers paid more than male customers. Both, Female and male customers paid most for offer 2.
###Code
# finding the total amount for each discount offer by gender
tops_by_gender(df_discount)
###Output
_____no_output_____
###Markdown
With respect to Discount offers, again, femal customers paid more than male customers. Both paid the most for offer 5. 2.2.3 Gender
###Code
# creating datasets for each gender for further analysis
df_male = full_dataset(full_data, 'gender', 'M')
df_female = full_dataset(full_data, 'gender', 'F')
df_other = full_dataset(full_data, 'gender', 'O')
# overview of the transactions made by male customers
df_male.sample(3)
# overview of the transactions made by female customers
df_female.sample(3)
# finding the top male customers based on the sum of their transaction_amount
tops(df_male, 'customer_id')
###Output
_____no_output_____
###Markdown
Eariler, we looked at the overall top customers. Here, we find the top 10 male customers based on the total amount they paid for all offer.
###Code
# finding the top female customers based on the sum of their transaction_amount
tops(df_female, 'customer_id')
###Output
_____no_output_____
###Markdown
The top 10 female customers.
###Code
# reporting more info about a customer
customer_report(9904, df_female)
# finding the top offers based on the sum of the transaction_amount made by male customers who completed that offer
tops(df_male, 'offer_id')
# finding the top offers based on the sum of the transaction_amount made by male customers who completed that offer
tops(df_female, 'offer_id')
# function to return plots with respect to different distributions by gender
def age_distributions(df, df2, color, color2):
df = df.drop_duplicates(subset='customer_id', keep='first')
df2 = df2.drop_duplicates(subset='customer_id', keep='first')
x = df.age
x2 = df2.age
trace1 = go.Histogram(
x=x,
name='Female',
opacity=0.6,
nbinsx = 7,
marker=dict(
color=color)
)
trace2 = go.Histogram(
x=x2,
name='Male',
opacity=0.6,
nbinsx = 7,
marker=dict(
color=color2)
)
data1 = [trace1, trace2]
layout = go.Layout(
barmode='stack',
bargap=0.1,
title = 'Age by Gender',
xaxis=dict(
title='Age'),
yaxis=dict(
title='Total number of Customers'))
updatemenus = list([
dict(active=0,
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True]},
{'title': 'Age Distribution by Gender'}]),
dict(label = 'Female',
method = 'update',
args = [{'visible': [True, False]},
{'title': 'Age Distribution of Male Customers'}]),
dict(label = 'Male',
method = 'update',
args = [{'visible': [False, True]},
{'title': 'Age Distribution of Female Customers'}])
]),
)
])
layout.update(dict(updatemenus=updatemenus))
fig = go.Figure(data=data1, layout=layout)
plot = py.iplot(fig, filename='Age by Gender')
return plot
def income_distributions(df, df2, color, color2):
df = df.drop_duplicates(subset='customer_id', keep='first')
df2 = df2.drop_duplicates(subset='customer_id', keep='first')
x = df.income
x2 = df2.income
trace1 = go.Histogram(
x=x,
name='Female',
opacity=0.6,
nbinsx = 7,
marker=dict(
color=color)
)
trace2 = go.Histogram(
x=x2,
name='Male',
opacity=0.6,
nbinsx = 7,
marker=dict(
color=color2)
)
data2 = [trace1, trace2]
layout = go.Layout(
barmode='stack',
bargap=0.1,
title = 'Income by Gender',
xaxis=dict(
title='Income'),
yaxis=dict(
title='Total number of Customers'))
updatemenus = list([
dict(active=0,
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True]},
{'title': 'Income Distribution by Gender'}]),
dict(label = 'Female',
method = 'update',
args = [{'visible': [True, False]},
{'title': 'Income Distribution of Male Customers'}]),
dict(label = 'Male',
method = 'update',
args = [{'visible': [False, True]},
{'title': 'Income Distribution of Female Customers'}])
]),
)
])
layout.update(dict(updatemenus=updatemenus))
fig = go.Figure(data=data2, layout=layout)
plot = py.iplot(fig, filename='Income by Gender')
return plot
# function to return an analysis of the numbers and percentage of all transaction by each event
def analysis(df1, col1, col2, col3):
v = df1[df1['event'] == 'offer viewed']
v = v.drop_duplicates(subset=['customer_id', 'offer_id'], keep='first')
c = df1[df1['event'] == 'offer completed']
c = c.drop_duplicates(subset=['customer_id', 'offer_id'], keep='first')
received = df1.drop_duplicates(subset=['customer_id', 'offer_id'], keep='first')
viewed = pd.Series(v.offer_id).value_counts().reset_index().rename(columns={'index': 'offer_id_v', 'offer_id': 'viewed'})
completed = pd.Series(c.offer_id).value_counts().reset_index().rename(columns={'index': 'offer_id_c', 'offer_id': 'completed'})
received = pd.Series(received.offer_id).value_counts().reset_index().rename(columns={'index': 'offer_id_r', 'offer_id': 'received'})
analysis = pd.concat([received, viewed, completed], axis=1).rename(columns={'offer_id_r': 'offer_id', 'viewed': col2, 'completed': col3, 'received': col1})
analysis[col2+'(%)'] = round(analysis[col2]/analysis[col1]*100, 2)
analysis[col3+'(%)'] = round(analysis[col3]/analysis[col1]*100, 2)
analysis = analysis.drop(['offer_id_c', 'offer_id_v'], axis=1)
return analysis
# plotting the age_distributions
age_distributions(df_female, df_male, 'pink', 'cornflowerblue')
###Output
_____no_output_____
###Markdown
Another look at the age distribution by gender. The box plots confirm that the majority of the customers for both genders are between 40 and 60 years old.
###Code
# plotting the income_distributions
income_distributions(df_female, df_male, 'pink', 'cornflowerblue')
###Output
_____no_output_____
###Markdown
Here, we confirm the previous findings of the income distributions. The plot confirms that most customers for all gender types make between 40k and 60k. Most of the female customers make between 60k and 80k while most male customers make between 40k and 60k a year.
###Code
# showing an analysis of each offer for both genders; male and female customers
overall_analysis = analysis(full_data, 'received', 'overall_views', 'overall_completion')
overall_analysis
# showing an analysis of each offer for male customers
male_analysis = analysis(df_male, 'received', 'male_views', 'male_completion')
male_analysis
# showing an analysis of each offer for female customers
female_analysis = analysis(df_female, 'received', 'female_views', 'female_completion')
female_analysis
# preparing a dataframe shows the completion percentages of each offer by gender
completion_perc = pd.merge(pd.merge(female_analysis,
male_analysis, on='offer_id'),
overall_analysis, on='offer_id')
col_list = ['offer_id', 'female_completion(%)', 'male_completion(%)', 'overall_completion(%)']
completion_perc = completion_perc[col_list].sort_values(by='offer_id').set_index('offer_id')
completion_perc
###Output
_____no_output_____
###Markdown
After the data has been cleaned even further during the previous processes, here is a completion rate comparison of the genders for each offer. The overall completion rate account for customers, again, offer 5 is the most popular offer. Oviously, female customers reponse to offer more than male customers. The following chart illustrates the number where we can see a clear difference between female customers actions toward offers and the male customers response.
###Code
# plotting the Percentages of Completed offers by Gender
f_completed = completion_perc['female_completion(%)']
m_completed = completion_perc['male_completion(%)']
overall_completed = completion_perc['overall_completion(%)']
offers = completion_perc.index
x = offers
y = f_completed
y2 = m_completed
y3 = overall_completed
trace1 = go.Bar(
x=x,
y=y,
name='Female',
#hoverinfo = 'y',
hovertemplate = '<i>Percentage of all Female Customers who completed the Offer</i>: %{y:.2f}%'
'<br><b>Offer</b>: %{x}<br>',
marker=dict(
color='pink',
line=dict(
color='grey',
width=1.5),
),
opacity=0.6
)
trace2 = go.Bar(
x=x,
y=y2,
name='Male',
#hoverinfo = 'y',
hovertemplate = '<i>Percentage of all Male Customers who completed the Offer</i>: %{y:.2f}%'
'<br><b>Offer</b>: %{x}<br>',
marker=dict(
color='cornflowerblue',
line=dict(
color='grey',
width=1.5),
),
opacity=0.6
)
trace3 = go.Scatter(
x=x,
y=y3,
name='Overall',
#hoverinfo= 'y',
hovertemplate = '<i>Percentage of all Customers, Male, Female, and Other who completed the Offer</i>: %{y:.2f}%'
'<br><b>Offer</b>: %{x}<br>',
marker=dict(
color='grey',
)
)
data1 = [trace1, trace2, trace3]
layout = go.Layout(
title = "Percentage of Completed offers by Gender",
xaxis=dict(title = 'Offers',
type='category'),
barmode='group',
yaxis = dict(title = 'Percentage of Completed offers'
#hoverformat = '.2f'
)
)
fig = go.Figure(data=data1, layout=layout)
py.iplot(fig, filename='Percentage of Completed offers by Gender')
###Output
/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:
Consider using IPython.display.IFrame instead
###Markdown
The bar chart illustrates the numbers found earlier where we can see a clear difference between female customers’ behavior toward offers and the male customers’ behavior. Overall, offer 8 is the least popular offer, and offer 5 is the most popular offer for both gender. Offer 8 is the least popular offer for female customers and offer 3 is the least popular for male customers. 2.2.4 Offers
###Code
offers_list = full_data.drop_duplicates(subset=['offer_id'], keep='first')
offers_list = offers_list.drop(['customer_id',
'age',
'gender',
'membership_start', 'membership_period',
'transaction_amount', 'time_of_transaction(hours)',
'event',
'income',
'time(hours)_y',
'offer_views(%)',
'completed(not_viewed)'], axis=1)
offers_list.set_index('offer_id')
offers_list[offers_list.offer_type == 'bogo'].set_index('offer_id')
offers_list[offers_list.offer_type == 'discount'].set_index('offer_id')
# function to return an analysis of the numbers and percentage of all transaction by offer
def analysis2(df1, col1, col2, col3, offer):
v = df1[df1['event'] == 'offer viewed']
v = v.drop_duplicates(subset=['customer_id', 'offer_id'], keep='first')
c = df1[df1['event'] == 'offer completed']
c = c.drop_duplicates(subset=['customer_id', 'offer_id'], keep='first')
received = df1.drop_duplicates(subset=['customer_id', 'offer_id'], keep='first')
viewed = pd.Series(v.age).value_counts().reset_index().rename(columns={'index': 'age_v', 'age': 'viewed'})
completed = pd.Series(c.age).value_counts().reset_index().rename(columns={'index': 'age_c', 'age': 'completed'})
received = pd.Series(received.age).value_counts().reset_index().rename(columns={'index': 'age_r', 'age': 'received'})
analysis2 = pd.concat([viewed, completed, received], axis=1).rename(columns={'age_v': 'age', 'viewed': col2, 'completed': col3, 'received': col1})
analysis2['offer'+offer+'_'+col2+'(%)'] = round(analysis2[col2]/analysis2[col1]*100, 2)
analysis2['offer'+offer+'_'+col3+'(%)'] = round(analysis2[col3]/analysis2[col1]*100, 2)
analysis2 = analysis2.drop(['age_r', 'age_c'], axis=1).sort_values(by='age')
return analysis2
# function to return dataframe grouped by the time when each transaction takes a place from the start of an offer
# showing a description of the amount spent by that time
def offer_transcations(offer):
offer_trans = transcation_by_time(df_completed, 'offer_id', offer)
return offer_trans
# function to plot a trend of any two offers that shows the average Amount Spent since a Start of an Offer at a specific time
def plot2(offer1, offer2):
offer_trans1 = offer_transcations(offer1)
offer_trans2 = offer_transcations(offer2)
trace_mean1 = go.Scatter(
x=offer_trans1['time_of_transaction(hours)'],
y=offer_trans1['mean'],
name = offer1,
opacity = 0.6)
trace_mean2 = go.Scatter(
x=offer_trans2['time_of_transaction(hours)'],
y=offer_trans2['mean'],
name = offer2,
opacity = 0.6)
data1 = [trace_mean1, trace_mean2]
layout = {
'title': 'Average Amount Spent Since a Start of an Offer Trend',
'xaxis': {'title': 'Time Since a Start of an Offer (hours)'},
'yaxis': {'title': 'Average Amount ($)',
"range": [
10,
35
]}}
fig = dict(data=data1, layout=layout)
plot2 = py.iplot(fig, filename = "Amount Spent since a Start of an Offer Trend by Offer")
return plot2
# showing a description of the amount spent by the time of transactions
offer_transcations(1)
# plotting and comparing the trend of any two offers
plot2(5, 7)
###Output
/Users/Saleh/anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:
Consider using IPython.display.IFrame instead
###Markdown
The bar chart illustrates the numbers found earlier where we can see a clear difference between female customers’ behavior toward offers and the male customers’ behavior. Overall, offer 8 is the least popular offer, and offer 5 is the most popular offer for both gender. Offer 8 is the least popular offer for female customers and offer 3 is the least popular for male customers.
###Code
# creating a dataframe that contains only transactions by a specific offer
offer1 = offer_dataset(full_data, 1)
offer1.sample(3)
# showing the numbers and percentages of all transactions made by a specific age for a specific offer
offer1_analysis = analysis2(offer1, 'received', 'viewed', 'completed', '1')
offer1_analysis
offer2 = offer_dataset(full_data, 2)
offer2.sample(3)
offer2_analysis = analysis2(offer2, 'received', 'viewed', 'completed', '2')
offer2_analysis
offer3 = offer_dataset(full_data, 3)
offer3_analysis = analysis2(offer3, 'received', 'viewed', 'completed', '3')
offer3_analysis
offer4 = offer_dataset(full_data, 4)
offer4_analysis = analysis2(offer4, 'received', 'viewed', 'completed', '4')
offer4_analysis
offer5 = offer_dataset(full_data, 5)
offer5_analysis = analysis2(offer5, 'received', 'viewed', 'completed', '5')
offer5_analysis
offer6 = offer_dataset(full_data, 6)
offer6_analysis = analysis2(offer6, 'received', 'viewed', 'completed', '6')
offer6_analysis
offer7 = offer_dataset(full_data, 7)
offer7_analysis = analysis2(offer7, 'received', 'viewed', 'completed', '7')
offer7_analysis
offer8 = offer_dataset(full_data, 8)
offer8_analysis = analysis2(offer8, 'received', 'viewed', 'completed', '8')
offer8_analysis
# merging all the analyses created for each offer and then create a dataframe shows the completion percentage
# of each offer based on the age group
completion_perc_o = pd.merge(pd.merge(pd.merge(pd.merge(pd.merge(pd.merge(pd.merge(offer1_analysis, offer2_analysis, on='age'),
offer3_analysis, on='age'),
offer4_analysis, on='age'),
offer5_analysis, on='age'),
offer6_analysis, on='age'),
offer7_analysis, on='age'),
offer8_analysis, on='age')
col_list = ['age',
'offer1_completed(%)',
'offer2_completed(%)',
'offer3_completed(%)',
'offer4_completed(%)',
'offer5_completed(%)',
'offer6_completed(%)',
'offer7_completed(%)',
'offer8_completed(%)']
completion_perc_o = completion_perc_o[col_list]
completion_perc_o
###Output
_____no_output_____
###Markdown
A complete analysis of each offer was created to show the completion rate of each age group with respect to an offer. The following function will allow us to report the completion rates per age group. Then, we will plot a trend of the completion rates by each age group for each offer.
###Code
# a function to return a report of an age group that contains all completion percentages by offer
def age_report(a, df=completion_perc_o):
report = df[df.age == a]
return report
age_report(35)
age_report(40)
age_report(25)
# creating a copy of the completion reports
plot1 = completion_perc_o.copy()
# plotting the Percentages of Completed offers by each Age group for each offer
plot1 = plot1.set_index('age')
trace1 = go.Scatter(
x=plot1.index,
y=plot1['offer1_completed(%)'],
name = "Offer 1",
opacity = 0.8)
trace2 = go.Scatter(
x=plot1.index,
y=plot1['offer2_completed(%)'],
name = "Offer 2",
opacity = 0.8)
trace3 = go.Scatter(
x=plot1.index,
y=plot1['offer3_completed(%)'],
name = "Offer 3",
opacity = 0.8)
trace4 = go.Scatter(
x=plot1.index,
y=plot1['offer4_completed(%)'],
name = "Offer 4",
opacity = 0.8)
trace5 = go.Scatter(
x=plot1.index,
y=plot1['offer5_completed(%)'],
name = "Offer 5",
opacity = 0.8)
trace6 = go.Scatter(
x=plot1.index,
y=plot1['offer6_completed(%)'],
name = "Offer 6",
opacity = 0.8)
trace7 = go.Scatter(
x=plot1.index,
y=plot1['offer7_completed(%)'],
name = "Offer 7",
opacity = 0.8)
trace8 = go.Scatter(
x=plot1.index,
y=plot1['offer8_completed(%)'],
name = "Offer 8",
opacity = 0.8)
data1 = [trace1, trace2, trace3, trace4, trace5, trace6, trace7, trace8]
layout = {
'title': 'Percentage of Completed offers by Age',
'xaxis': {'title': 'Age'},
'yaxis': {'title': 'Percentage Completed (%)'}}
layout.update(dict(xaxis=dict(rangeslider=dict(visible = True),type='linear')))
updatemenus = list([
dict(active=0,
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True, True, True, True]},
{'title': 'Percentage of Each Completed offers by Age'}]),
dict(label = 'Offer 1',
method = 'update',
args = [{'visible': [True, False, False, False, False, False, False, False]},
{'title': 'Percentage of Completed offers by Age for Offer 1'}]),
dict(label = 'Offer 2',
method = 'update',
args = [{'visible': [False, True, False, False, False, False, False, False]},
{'title': 'Percentage of Completed offers by Age for Offer 2'}]),
dict(label = 'Offer 3',
method = 'update',
args = [{'visible': [False, False, True, False, False, False, False, False]},
{'title': 'Percentage of Completed offers by Age for Offer 3'}]),
dict(label = 'Offer 4',
method = 'update',
args = [{'visible': [False, False, False, True, False, False, False, False]},
{'title': 'Percentage of Completed offers by Age for Offer 4'}]),
dict(label = 'Offer 5',
method = 'update',
args = [{'visible': [False, False, False, False, True, False, False, False]},
{'title': 'Percentage of Completed offers by Age for Offer 5'}]),
dict(label = 'Offer 6',
method = 'update',
args = [{'visible': [False, False, False, False, False, True, False, False]},
{'title': 'Percentage of Completed offers by Age for Offer 6'}]),
dict(label = 'Offer 7',
method = 'update',
args = [{'visible': [False, False, False, False, False, False, True, False]},
{'title': 'Percentage of Completed offers by Age for Offer 7'}]),
dict(label = 'Offer 8',
method = 'update',
args = [{'visible': [False, False, False, False, False, False, False, True]},
{'title': 'Percentage of Completed offers by Age for Offer 8'}])
]),
)
])
layout.update(dict(updatemenus=updatemenus))
fig = go.Figure(data=data1, layout=layout)
py.iplot(fig, filename = "Percentage of Completed offers by Age")
###Output
_____no_output_____
###Markdown
The plot illustrates the Percentages of Completed offers by each age group that can be filtered by offer. For example, at the age group 35 years old, offer 6 has the highest completion rate 82.61%. While the age group 25 years old prefer offer 5 the most with a completion rate of 77.55%.
###Code
fig.update_layout(
updatemenus=[
go.layout.Updatemenu(
buttons=list([
dict(
args=["colorscale", "Viridis"],
label="Viridis",
method="restyle"
),
dict(
args=["colorscale", "Cividis"],
label="Cividis",
method="restyle"
),
dict(
args=["colorscale", "Blues"],
label="Blues",
method="restyle"
),
dict(
args=["colorscale", "Greens"],
label="Greens",
method="restyle"
),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=button_layer_1_height,
yanchor="top"
),
go.layout.Updatemenu(
buttons=list([
dict(
args=["reversescale", False],
label="False",
method="restyle"
),
dict(
args=["reversescale", True],
label="True",
method="restyle"
)
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.37,
xanchor="left",
y=button_layer_1_height,
yanchor="top"
),
go.layout.Updatemenu(
buttons=list([
dict(
args=[{"contours.showlines": False, "type": "contour"}],
label="Hide lines",
method="restyle"
),
dict(
args=[{"contours.showlines": True, "type": "contour"}],
label="Show lines",
method="restyle"
),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.58,
xanchor="left",
y=button_layer_1_height,
yanchor="top"
),
]
)
###Output
_____no_output_____
###Markdown
Bonus
###Code
# plotting a Sunburst Charts shows the numbers of customers
# with respect to all transactions where the the customers completed an offer
trace = go.Sunburst(
labels=["Transactions",
"BOGO", "Discount",
"Offer 1", "Offer 2", "Offer 3", "Offer 4", "Offer 5",
"Offer 6", "Offer 7", "Offer 8",
"Female", "Male", "Other",
"Female", "Male", "Other",
"Female", "Male", "Other",
"Female", "Male", "Other",
"Female", "Male", "Other",
"Female", "Male", "Other",
"Female", "Male", "Other",
"Female", "Male", "Other"],
parents=["",
"Transactions", "Transactions",
"BOGO", "BOGO", "BOGO", "Discount", "Discount",
"Discount", "Discount", "BOGO",
"Offer 1", "Offer 1", "Offer 1",
"Offer 2", "Offer 2", "Offer 2",
"Offer 3", "Offer 3", "Offer 3",
"Offer 4", "Offer 4", "Offer 4",
"Offer 5", "Offer 5", "Offer 5",
"Offer 6", "Offer 6", "Offer 6",
"Offer 7", "Offer 7", "Offer 7",
"Offer 8", "Offer 8", "Offer 8"],
values=[26226,
9937, 12089,
2573, 2773, 2374, 2499, 3729,
2592, 3269, 1917,
1197, 1324, 52,
1281, 1441, 51,
1134, 917, 323,
1247, 1252, 0,
1626, 2052, 51,
1188, 1354, 50,
1371, 1755, 141,
872, 1045, 0],
branchvalues="total",
outsidetextfont = {"size": 15, "color": "#377eb8"},
marker = {"line": {"width": 2}})
layout = go.Layout(
title = 'test',
margin = go.layout.Margin(t=0, l=0, r=0, b=0))
py.iplot(go.Figure([trace], layout), filename='basic_sunburst_chart_total_branchvalues')
###Output
_____no_output_____
|
Assigments/Assignment_4.ipynb
|
###Markdown
Fourth Assignment 1) Suppose that in this term you have only two courses: Calculus II and Programming Languages. Create a function **grades()** that receives two tuples (or lists) of three elements representing the grades of the assigments A1, A2 and A3 in that order. The first tuple (or list) refers to the grades of Calculus II and the second to the grades of Programming Languages. Return a dictionary whose keys are the names of the course and the values are another dict with keys 'A1', 'A2' and 'A3' and values being the corresponding grades. See example below.>```python>>>> grades((4,8,7),(9,8,0))>{'Calculus II': {'A1': 4, 'A2': 8, 'A3': 7}, 'Programming Languages': {'A1': 9, 'A2': 8, 'A3': 0}}``` 2) Create the function **sorts()**, which takes as a single argument a dictionary **d**, and sorts that dictionary based on its values, returning the ordered dictionary. Test cases will not have equal values. See examples:>```python>>>> sorts({1: 2, 3: 4, 4: 3, 2: 1, 0: 0})>{0: 0, 2: 1, 1: 2, 4: 3, 3: 4}>>>>> sorts({"fish":1, "chicken":10, "beef":5, "pork":8})>{"fish":1, "cow":5, "pork":8, "chicken":10}``` 3) Create the function **concatenate()** so that it receives as arguments an indefinite number of dictionaries. Hence, concatenate them in the given order, generating a single dictionary that must be returned as a function. Test cases will not have keys in common. See the examples:>```python>>>> concatenate({1:'a',3:'c'},{2:'b',4:'d'},{5:'e',6:'f'})>{1:'a',3:'c',2:'b',4:'d',5:'e',6:'f'}>>>>> concatenate({'a':1,'b':2,'e':5},{'d':4,'c':3,'f':6})>{'a':1,'b':2,'e':5,'d':4,'c':3,'f':6}``` 4) Create a class called **Triangle**, whose constructor receives the measurements from its three sides (in any order). Implement a method called `type`, which returns:- 0 if the measurements do not form a triangle- 1 if the triangle is equilateral- 2 if the triangle is isosceles- 3 if the triangle is scaleneSee the example:>```python>>>> tri = Triangle(5,5,5) Equilateral>>>> tri.type() >1``` 5) Create the `Point` class representing a point object in the Cartesian plane, with methods `show()`, `move()` and `dist()`. The first must return a tuple with the coordinates of the point. The second must take as arguments the displacements in each of the axes and update the position of the object. The latter, on the other hand, must take another point as an argument and return the Euclidean distance between the two points. Obs: The unitary tests for `dist()` will accept a margin of error of 0.1. Do not change the existing lines in this question and do not create or remove functions or classes. With the class properly implemented, it should be possible to execute the following series of commands:>```python>>>> p1 = Point(2, 3)>>>> p2 = Point(3, 3)>>>> p1.show()>(2, 3)>>>> p2.show()>(3, 3)>>>> p1.move(10, -10)>>>> p1.show()>(12, -7)>>>> p2.show()>(3, 3)>>>> p1.dist(p2)>13.45362404707371``` 6) Create two classes `employee` and `manager` so that manager is a subclass of employee. The employee attributes are name, ssid, salary and department. In addition to these, the manager class must also include the password and the number of employees he/she manages. Make sure your constructors are consistent. In addition, the employee class must have the method `bonus()`, which does not receive parameters and increases the employee's salary by 10%. The manager class must have the methods `authenticate_password(password)`, which returns a Boolean resulting from the validation of the password against the entry, and the method `bonus()`, which increases your salary by 15%. Do not change the existing lines in this question and do not create or remove functions or classes.```python>>> f1=exployee("John",12345678900,2500,"TI")>>> f2=employee("Paul",12345678901,1800,"TI")>>> f3=gerente("Marta",23456789012,6000,"TI",101101,2)>>> f1.name()John>>> f2.ssid()12345678901>>> f3.departament()IT>>> f2.bonus()>>> f2.salary()1980.00>>> f3.bonus()6900.00>>> f3.authenticate_password(101101)True>>> f3.authenticate_password(123456)False``` Challenge 7) There is a file called "alice.txt", which is the first chapter of the book Alice in Wonderland. The text has already been properly cleaned, punctuation was removed, as well as special characters and unnecessary spacing. There is a semi-ready function that reads the file and loads the text into the string-type variable called "alice"; you have to modify this function to return a dictionary whose keys are the unique words in the text, and the values are the number of times each word is repeated in the chapter (frequency distribution) - do not use the method collections.Counter. Extra: Try to discover the top 10 most used words. See the image below to get an idea of the answer (The bigger the word, the more often it is repeated).Book: http://www.gutenberg.org/files/11/11-0.txtImage: https://pypi.org/project/wordcloud/
###Code
def read_text():
with open('../Data/TXT/alice.txt','r') as f:
alice = f.read()
###Output
_____no_output_____
###Markdown
Fourth Assignment 1) Suppose that in this term you have only two courses: Calculus II and Programming Languages. Create a function **grades()** that receives two tuples (or lists) of three elements representing the grades of the assigments A1, A2 and A3 in that order. The first tuple (or list) refers to the grades of Calculus II and the second to the grades of Programming Languages. Return a dictionary whose keys are the names of the course and the values are another dict with keys 'A1', 'A2' and 'A3' and values being the corresponding grades. See example below.>```python>>>> grades((4,8,7),(9,8,0))>{'Calculus II': {'A1': 4, 'A2': 8, 'A3': 7}, 'Programming Languages': {'A1': 9, 'A2': 8, 'A3': 0}}``` 2) Create the function **sorts()**, which takes as a single argument a dictionary **d**, and sorts that dictionary based on its values, returning the ordered dictionary. Test cases will not have equal values. See examples:>```python>>>> sorts({1: 2, 3: 4, 4: 3, 2: 1, 0: 0})>{0: 0, 2: 1, 1: 2, 4: 3, 3: 4}>>>>> sorts({"fish":1, "chicken":10, "beef":5, "pork":8})>{"fish":1, "cow":5, "pork":8, "chicken":10}``` 3) Create the function **concatenate()** so that it receives as arguments an indefinite number of dictionaries. Hence, concatenate them in the given order, generating a single dictionary that must be returned as the function result. Test cases will not have keys in common. See the examples:>```python>>>> concatenate({1:'a',3:'c'},{2:'b',4:'d'},{5:'e',6:'f'})>{1:'a',3:'c',2:'b',4:'d',5:'e',6:'f'}>>>>> concatenate({'a':1,'b':2,'e':5},{'d':4,'c':3,'f':6})>{'a':1,'b':2,'e':5,'d':4,'c':3,'f':6}``` 4) Create a class called **Triangle**, whose constructor receives the measurements from its three sides (in any order). Implement a method called `type`, which returns:- 0 if the measurements do not form a triangle- 1 if the triangle is equilateral- 2 if the triangle is isosceles- 3 if the triangle is scaleneSee the example:>```python>>>> tri = Triangle(5,5,5) Equilateral>>>> tri.type() >1``` 5) Create the `Point` class representing a point object in the Cartesian plane, with methods `show()`, `move()` and `dist()`. The first must return a tuple with the coordinates of the point. The second must take as arguments the displacements in each of the axes and update the position of the object. The latter, on the other hand, must take another point as an argument and return the Euclidean distance between the two points. Obs: The unitary tests for `dist()` will accept a margin of error of 0.1. Do not change the existing lines in this question and do not create or remove functions or classes. With the class properly implemented, it should be possible to execute the following series of commands:>```python>>>> p1 = Point(2, 3)>>>> p2 = Point(3, 3)>>>> p1.show()>(2, 3)>>>> p2.show()>(3, 3)>>>> p1.move(10, -10)>>>> p1.show()>(12, -7)>>>> p2.show()>(3, 3)>>>> p1.dist(p2)>13.45362404707371``` 6) Create two classes `employee` and `manager` so that manager is a subclass of employee. The employee attributes are name, ssid, salary and department. In addition to these, the manager class must also include the password and the number of employees he/she manages. Make sure your constructors are consistent. In addition, the employee class must have the method `bonus()`, which does not receive parameters and increases the employee's salary by 10%. The manager class must have the methods `authenticate_password(password)`, which returns a Boolean resulting from the validation of the password against the entry, and the method `bonus()`, which increases your salary by 15%. Do not change the existing lines in this question and do not create or remove functions or classes.```python>>> f1=employee("John",12345678900,2500,"TI")>>> f2=employee("Paul",12345678901,1800,"TI")>>> f3=employee("Marta",23456789012,6000,"TI",101101,2)>>> f1.name()John>>> f2.ssid()12345678901>>> f3.department()IT>>> f2.bonus()>>> f2.salary()1980.00>>> f3.bonus()6900.00>>> f3.authenticate_password(101101)True>>> f3.authenticate_password(123456)False``` Challenge 7) There is a file called "alice.txt", which is the first chapter of the book Alice in Wonderland. The text has already been properly cleaned, punctuation was removed, as well as special characters and unnecessary spacing. There is a semi-ready function that reads the file and loads the text into the string-type variable called "alice"; you have to modify this function to return a dictionary whose keys are the unique words in the text, and the values are the number of times each word is repeated in the chapter (frequency distribution) - do not use the method collections.Counter. Extra: Try to discover the top 10 most used words. See the image below to get an idea of the answer (The bigger the word, the more frequent it appears).Book: http://www.gutenberg.org/files/11/11-0.txtImage: https://pypi.org/project/wordcloud/
###Code
def read_text():
with open('../Data/TXT/alice.txt','r') as f:
alice = f.read()
###Output
_____no_output_____
###Markdown
Fourth Assignment 1) Suppose that in this term you have only two courses: Calculus II and Programming Languages. Create a function **grades()** that receives two tuples (or lists) of three elements representing the grades of the assigments A1, A2 and A3 in that order. The first tuple (or list) refers to the grades of Calculus II and the second to the grades of Programming Languages. Return a dictionary whose keys are the names of the course and the values are another dict with keys 'A1', 'A2' and 'A3' and values being the corresponding grades. See example below.>```python>>>> grades((4,8,7),(9,8,0))>{'Calculus II': {'A1': 4, 'A2': 8, 'A3': 7}, 'Programming Languages': {'A1': 9, 'A2': 8, 'A3': 0}}```
###Code
a, b = (4,8,7),(9,8,0) # very complicated solution
def grades(a,b):
nit = [a,b]
c = ['Calculus II', 'Programming Languages']
d = ['A1', 'A2', 'A3']
#init = { c[x]: {str(d[x]): str(a[x])} for x in range(len(d))}
# init = {c[x]:{k: v for k, v in zip(d, a)}, c[x]:{k: v for k, v in zip(d, b)}}
init_a = {k: v for k, v in zip(d, a)}
init_b = {k: v for k, v in zip(d, b)}
init_c = [init_a, init_b]
#init = {key: dict(A1=init_a.get(key), A2=init_b.get(key), A3= init)
# for key in c}
# init = {c:dict(zip('A1','A2'),b) for a, b in nit}
init_d = {i:dic for i, dic in enumerate(init_c)}
init = dict(zip(c,list(init_d.values())))
return init
x = grades(a,b)
print(x)
###Output
{'Calculus II': {'A1': 4, 'A2': 8, 'A3': 7}, 'Programming Languages': {'A1': 9, 'A2': 8, 'A3': 0}}
###Markdown
2) Create the function **sorts()**, which takes as a single argument a dictionary **d**, and sorts that dictionary based on its values, returning the ordered dictionary. Test cases will not have equal values. See examples:>```python>>>> sorts({1: 2, 3: 4, 4: 3, 2: 1, 0: 0})>{0: 0, 2: 1, 1: 2, 4: 3, 3: 4}>>>>> sorts({"fish":1, "chicken":10, "beef":5, "pork":8})>{"fish":1, "cow":5, "pork":8, "chicken":10}```
###Code
d = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
d = {"fish":1, "chicken":10, "beef":5, "pork":8}
def sort(d):
d_sorted = {k: v for k, v in sorted(d.items(), key=lambda x: x[1])}
print(d_sorted)
sort(d)
###Output
{'fish': 1, 'beef': 5, 'pork': 8, 'chicken': 10}
###Markdown
3) Create the function **concatenate()** so that it receives as arguments an indefinite number of dictionaries. Hence, concatenate them in the given order, generating a single dictionary that must be returned as a function. Test cases will not have keys in common. See the examples:>```python>>>> concatenate({1:'a',3:'c'},{2:'b',4:'d'},{5:'e',6:'f'})>{1:'a',3:'c',2:'b',4:'d',5:'e',6:'f'}>>>>> concatenate({'a':1,'b':2,'e':5},{'d':4,'c':3,'f':6})>{'a':1,'b':2,'e':5,'d':4,'c':3,'f':6}```
###Code
a, b, c, = {1:'a',3:'c'},{2:'b',4:'d'},{5:'e',6:'f'}
t, f = {'a':1,'b':2,'e':5},{'d':4,'c':3,'f':6}
def concadenate(*args):
init = {}
for i in [*args]:
init.update(i)
return init
x = concadenate(a,b,c)
print(x)
y = concadenate(t,f)
print(y)
###Output
{1: 'a', 3: 'c', 2: 'b', 4: 'd', 5: 'e', 6: 'f'}
{'a': 1, 'b': 2, 'e': 5, 'd': 4, 'c': 3, 'f': 6}
###Markdown
4) Create a class called **Triangle**, whose constructor receives the measurements from its three sides (in any order). Implement a method called `type`, which returns:- 0 if the measurements do not form a triangle- 1 if the triangle is equilateral- 2 if the triangle is isosceles- 3 if the triangle is scaleneSee the example:>```python>>>> tri = Triangle(5,5,5) Equilateral>>>> tri.type() >1```
###Code
class Triangle:
def __init__(self, side1, side2, side3):
self.side1 = side1
self.side2 = side2
self.side3 = side3
print('running __init__\n')
self.type_of_triangle()
def __repr__(self):
return f"I am a Triangle with sides {self.side1}, {self.side2} and {self.side3}"
#def __repr__(self):
# return "42"
def type_of_triangle(self):
if self.side1 == self.side2 and self.side1 == self.side3:
print('1')## equilaterian
self.mytype = 'equilateral'
elif self.side1 == self.side2 or \
self.side1 == self.side3 or \
self.side2 == self.side3:
print('2') #isosceles
self.mytype = 'isosceles'
elif self.side1**2 + self.side2**2 != self.side3**2:
print('0')
self.mytype = 'no triangle'
else:
print('3')
self.mytype = 'scalene'
tri= Triangle(5,5,5)
###Output
running __init__
1
###Markdown
5) Create the `Point` class representing a point object in the Cartesian plane, with methods `show()`, `move()` and `dist()`. The first must return a tuple with the coordinates of the point. The second must take as arguments the displacements in each of the axes and update the position of the object. The latter, on the other hand, must take another point as an argument and return the Euclidean distance between the two points. Obs: The unitary tests for `dist()` will accept a margin of error of 0.1. Do not change the existing lines in this question and do not create or remove functions or classes. With the class properly implemented, it should be possible to execute the following series of commands:>```python>>>> p1 = Point(2, 3)>>>> p2 = Point(3, 3)>>>> p1.show()>(2, 3)>>>> p2.show()>(3, 3)>>>> p1.move(10, -10)>>>> p1.show()>(12, -7)>>>> p2.show()>(3, 3)>>>> p1.dist(p2)>13.45362404707371```
###Code
class Point: # now it works, don´t know why...
def __init__(self, x, y):
self.x = x
self.y = y
def show(p):
print("({0},{1})".format(p.x, p.y))
def move(self, dx, dy):
self.x += dx
self.y += dy
def dist(self,x):
return ((self.x ** 2) + (self.y ** 2)) ** 0.5
p1 = Point(2, 3)
p2 = Point(3, 3)
p1.show()
p2.show()
p1.move(10, -10)
p1.show()
p2.show()
p1.dist(p2)
###Output
(2,3)
(3,3)
(12,-7)
(3,3)
###Markdown
6) Create two classes `employee` and `manager` so that manager is a subclass of employee. The employee attributes are name, ssid, salary and department. In addition to these, the manager class must also include the password and the number of employees he/she manages. Make sure your constructors are consistent. In addition, the employee class must have the method `bonus()`, which does not receive parameters and increases the employee's salary by 10%. The manager class must have the methods `authenticate_password(password)`, which returns a Boolean resulting from the validation of the password against the entry, and the method `bonus()`, which increases your salary by 15%. Do not change the existing lines in this question and do not create or remove functions or classes.```python>>> f1=exployee("John",12345678900,2500,"TI")>>> f2=employee("Paul",12345678901,1800,"TI")>>> f3=gerente("Marta",23456789012,6000,"TI",101101,2)>>> f1.name()John>>> f2.ssid()12345678901>>> f3.departament()IT>>> f2.bonus()>>> f2.salary()1980.00>>> f3.bonus()6900.00>>> f3.authenticate_password(101101)True>>> f3.authenticate_password(123456)False```
###Code
class employee:
def __init__(self, name, ssid, salary, department):
self.name= name
self.ssid= ssid
self.salary = salary
self.department = department
print(name)
# self.bonus()
def bonus(self, salary):
self.salary = salary
self.newsalary = salary*1,1
class manager(employee):
def __init__(self, name, ssid, salary, department, password, numberOfEmployees):
self.name= name
self.ssid= ssid
self.salary = salary
self.department = department
# self.bonus()
# self.password = password
self.numberOfEmployees = numberOfEmployees
# self.authenticate_password =
def bonus():
self.newsalary = salary*1,15
# def authenticate_password(password1):
# x = input("Enter your password: ")
# if password1 != password:
# False
# else:
# True
f1=employee("John",12345678900,2500,"TI")
f2=employee("Paul",12345678901,1800,"TI")
f3=manager("Marta",23456789012,6000,"TI",101101,2)
dir(f1)
#f1.name
#f2.ssid()
#f3.departament()
#f2.bonus()
#f2.salary()
#f3.bonus()
#f3.authenticate_password(101101)
#f3.authenticate_password(123456)
###Output
John
Paul
###Markdown
Challenge 7) There is a file called "alice.txt", which is the first chapter of the book Alice in Wonderland. The text has already been properly cleaned, punctuation was removed, as well as special characters and unnecessary spacing. There is a semi-ready function that reads the file and loads the text into the string-type variable called "alice"; you have to modify this function to return a dictionary whose keys are the unique words in the text, and the values are the number of times each word is repeated in the chapter (frequency distribution) - do not use the method collections.Counter. Extra: Try to discover the top 10 most used words. See the image below to get an idea of the answer (The bigger the word, the more often it is repeated).Book: http://www.gutenberg.org/files/11/11-0.txtImage: https://pypi.org/project/wordcloud/
###Code
from itertools import islice
def read_text():
d = {}
with open('../Data/TXT/alice.txt','r') as f:
alice = f.read()
f.close()
# print(alice)
for word in alice.split():
if word in d:
d[word] += 1
else:
d[word] = 1
g = {k:v for k,v in sorted(d.items(), key = lambda x:x[1], reverse= True)}
return g
x = read_text()
print(x)
###Output
{'the': 92, 'she': 80, 'to': 75, 'it': 67, 'and': 65, 'was': 53, 'a': 52, 'of': 43, 'i': 35, 'alice': 28, 'that': 27, 'her': 26, 'in': 26, 'down': 23, 'very': 23, 'but': 22, 'for': 21, 'had': 20, 'you': 19, 'not': 16, 'on': 15, 'little': 15, 'so': 14, 'as': 14, 'be': 13, 'out': 13, 'way': 13, 'this': 13, 'herself': 13, 'or': 12, 'up': 12, 'there': 12, 'me': 12, 'no': 11, 'with': 11, 'think': 11, 'at': 11, 'like': 11, 'what': 10, 'when': 10, 'all': 10, 'see': 10, 'if': 10, 'rabbit': 9, 'do': 9, 'into': 9, 'time': 9, 'how': 9, 'one': 9, 'll': 9, 'which': 9, 'thought': 8, 'could': 8, 'about': 8, 'were': 8, 'said': 8, 's': 8, 'get': 7, 'nothing': 7, 'well': 7, 'would': 7, 'went': 7, 'found': 7, 'eat': 7, 'door': 7, 'by': 6, 'is': 6, 'much': 6, 'say': 6, 'either': 6, 'wonder': 6, 'going': 6, 'they': 6, 'off': 6, 'through': 6, 'key': 6, 'use': 5, 'suddenly': 5, 'shall': 5, 'then': 5, 'never': 5, 'before': 5, 'after': 5, 'tried': 5, 'too': 5, 't': 5, 'things': 5, 'dinah': 5, 'my': 5, 'table': 5, 'hole': 4, 'once': 4, 'book': 4, 'oh': 4, 'quite': 4, 'looked': 4, 'moment': 4, 'again': 4, 'fell': 4, 'first': 4, 'here': 4, 'upon': 4, 'fall': 4, 'right': 4, 'got': 4, 'people': 4, 'soon': 4, 'might': 4, 'cats': 4, 'bats': 4, 'now': 4, 'ever': 4, 'hall': 4, 'any': 4, 'garden': 4, 'poor': 4, 'bottle': 4, 'marked': 4, 'having': 3, 'pictures': 3, 'hot': 3, 'getting': 3, 'did': 3, 'dear': 3, 'over': 3, 'seemed': 3, 'seen': 3, 'just': 3, 'large': 3, 'under': 3, 'another': 3, 'look': 3, 'dark': 3, 'from': 3, 'such': 3, 'even': 3, 'come': 3, 'sort': 3, 'good': 3, 'nice': 3, 'words': 3, 'began': 3, 'rather': 3, 'them': 3, 'know': 3, 'remember': 3, 'hand': 3, 'came': 3, 'long': 3, 'passage': 3, 'round': 3, 'trying': 3, 'glass': 3, 'golden': 3, 'small': 3, 'however': 3, 'head': 3, 'drink': 3, 'poison': 3, 'candle': 3, 'cake': 3, 'tired': 2, 'sister': 2, 'conversations': 2, 'without': 2, 'considering': 2, 'own': 2, 'mind': 2, 'made': 2, 'feel': 2, 'sleepy': 2, 'stupid': 2, 'whether': 2, 'white': 2, 'eyes': 2, 'ran': 2, 'close': 2, 'hear': 2, 'late': 2, 'have': 2, 'took': 2, 'watch': 2, 'waistcoat': 2, 'pocket': 2, 'feet': 2, 'across': 2, 'falling': 2, 'deep': 2, 'happen': 2, 'make': 2, 'anything': 2, 'noticed': 2, 'cupboards': 2, 'shelves': 2, 'saw': 2, 'jar': 2, 'great': 2, 'put': 2, 'why': 2, 'top': 2, 'an': 2, 'end': 2, 'many': 2, 'miles': 2, 've': 2, 'must': 2, 'somewhere': 2, 'earth': 2, 'several': 2, 'though': 2, 'still': 2, 'latitude': 2, 'longitude': 2, 'among': 2, 'their': 2, 'didn': 2, 'ask': 2, 'fancy': 2, 'air': 2, 'should': 2, 'wish': 2, 'bat': 2, 'saying': 2, 'sometimes': 2, 'felt': 2, 'begun': 2, 'thump': 2, 'bit': 2, 'turned': 2, 'corner': 2, 'ears': 2, 'behind': 2, 'low': 2, 'doors': 2, 'other': 2, 'alas': 2, 'rate': 2, 'inches': 2, 'high': 2, 'opened': 2, 'larger': 2, 'those': 2, 'go': 2, 'telescope': 2, 'only': 2, 'happened': 2, 'few': 2, 'indeed': 2, 'back': 2, 'find': 2, 'rules': 2, 'shutting': 2, 'beautifully': 2, 'forgotten': 2, 'finding': 2, 'finished': 2, 'curious': 2, 'size': 2, 'thing': 2, 'reach': 2, 'generally': 2, 'box': 2, 'two': 2, 'makes': 2, 'grow': 2, 'can': 2, 'happens': 2, 'beginning': 1, 'sitting': 1, 'bank': 1, 'twice': 1, 'peeped': 1, 'reading': 1, 'day': 1, 'pleasure': 1, 'making': 1, 'daisy': 1, 'chain': 1, 'worth': 1, 'trouble': 1, 'picking': 1, 'daisies': 1, 'pink': 1, 'remarkable': 1, 'nor': 1, 'itself': 1, 'afterwards': 1, 'occurred': 1, 'ought': 1, 'wondered': 1, 'natural': 1, 'actually': 1, 'its': 1, 'hurried': 1, 'started': 1, 'flashed': 1, 'take': 1, 'burning': 1, 'curiosity': 1, 'field': 1, 'fortunately': 1, 'pop': 1, 'hedge': 1, 'world': 1, 'straight': 1, 'tunnel': 1, 'some': 1, 'dipped': 1, 'stopping': 1, 'slowly': 1, 'plenty': 1, 'next': 1, 'coming': 1, 'sides': 1, 'filled': 1, 'maps': 1, 'hung': 1, 'pegs': 1, 'passed': 1, 'labelled': 1, 'orange': 1, 'marmalade': 1, 'disappointment': 1, 'empty': 1, 'drop': 1, 'fear': 1, 'killing': 1, 'somebody': 1, 'underneath': 1, 'managed': 1, 'past': 1, 'tumbling': 1, 'stairs': 1, 'brave': 1, 'home': 1, 'wouldn': 1, 'house': 1, 'likely': 1, 'true': 1, 'fallen': 1, 'aloud': 1, 'near': 1, 'centre': 1, 'let': 1, 'four': 1, 'thousand': 1, 'learnt': 1, 'lessons': 1, 'schoolroom': 1, 'opportunity': 1, 'showing': 1, 'knowledge': 1, 'listen': 1, 'practice': 1, 'yes': 1, 'distance': 1, 'idea': 1, 'grand': 1, 'presently': 1, 'funny': 1, 'seem': 1, 'walk': 1, 'heads': 1, 'downward': 1, 'antipathies': 1, 'glad': 1, 'listening': 1, 'sound': 1, 'word': 1, 'name': 1, 'country': 1, 'please': 1, 'ma': 1, 'am': 1, 'new': 1, 'zealand': 1, 'australia': 1, 'curtsey': 1, 'spoke': 1, 'curtseying': 1, 're': 1, 'manage': 1, 'ignorant': 1, 'girl': 1, 'asking': 1, 'perhaps': 1, 'written': 1, 'else': 1, 'talking': 1, 'miss': 1, 'night': 1, 'cat': 1, 'hope': 1, 'saucer': 1, 'milk': 1, 'tea': 1, 'are': 1, 'mice': 1, 'm': 1, 'afraid': 1, 'catch': 1, 'mouse': 1, 'dreamy': 1, 'couldn': 1, 'answer': 1, 'question': 1, 'matter': 1, 'dozing': 1, 'dream': 1, 'walking': 1, 'earnestly': 1, 'tell': 1, 'truth': 1, 'heap': 1, 'sticks': 1, 'dry': 1, 'leaves': 1, 'hurt': 1, 'jumped': 1, 'overhead': 1, 'sight': 1, 'hurrying': 1, 'lost': 1, 'away': 1, 'wind': 1, 'whiskers': 1, 'longer': 1, 'lit': 1, 'row': 1, 'lamps': 1, 'hanging': 1, 'roof': 1, 'locked': 1, 'been': 1, 'side': 1, 'every': 1, 'walked': 1, 'sadly': 1, 'middle': 1, 'wondering': 1, 'three': 1, 'legged': 1, 'solid': 1, 'except': 1, 'tiny': 1, 'belong': 1, 'locks': 1, 'open': 1, 'second': 1, 'curtain': 1, 'fifteen': 1, 'lock': 1, 'delight': 1, 'fitted': 1, 'led': 1, 'than': 1, 'rat': 1, 'knelt': 1, 'along': 1, 'loveliest': 1, 'longed': 1, 'wander': 1, 'beds': 1, 'bright': 1, 'flowers': 1, 'cool': 1, 'fountains': 1, 'doorway': 1, 'shoulders': 1, 'shut': 1, 'knew': 1, 'begin': 1, 'lately': 1, 'really': 1, 'impossible': 1, 'waiting': 1, 'half': 1, 'hoping': 1, 'telescopes': 1, 'certainly': 1, 'neck': 1, 'paper': 1, 'label': 1, 'printed': 1, 'letters': 1, 'wise': 1, 'hurry': 1, 'read': 1, 'histories': 1, 'children': 1, 'who': 1, 'burnt': 1, 'eaten': 1, 'wild': 1, 'beasts': 1, 'unpleasant': 1, 'because': 1, 'simple': 1, 'friends': 1, 'taught': 1, 'red': 1, 'poker': 1, 'will': 1, 'burn': 1, 'hold': 1, 'cut': 1, 'your': 1, 'finger': 1, 'deeply': 1, 'knife': 1, 'usually': 1, 'bleeds': 1, 'almost': 1, 'certain': 1, 'disagree': 1, 'sooner': 1, 'later': 1, 'ventured': 1, 'taste': 1, 'fact': 1, 'mixed': 1, 'flavour': 1, 'cherry': 1, 'tart': 1, 'custard': 1, 'pine': 1, 'apple': 1, 'roast': 1, 'turkey': 1, 'toffee': 1, 'buttered': 1, 'toast': 1, 'feeling': 1, 'ten': 1, 'face': 1, 'brightened': 1, 'lovely': 1, 'waited': 1, 'minutes': 1, 'shrink': 1, 'further': 1, 'nervous': 1, 'altogether': 1, 'flame': 1, 'blown': 1, 'while': 1, 'more': 1, 'decided': 1, 'possibly': 1, 'plainly': 1, 'best': 1, 'climb': 1, 'legs': 1, 'slippery': 1, 'sat': 1, 'cried': 1, 'crying': 1, 'sharply': 1, 'advise': 1, 'leave': 1, 'minute': 1, 'gave': 1, 'advice': 1, 'seldom': 1, 'followed': 1, 'scolded': 1, 'severely': 1, 'bring': 1, 'tears': 1, 'remembered': 1, 'cheated': 1, 'game': 1, 'croquet': 1, 'playing': 1, 'against': 1, 'child': 1, 'fond': 1, 'pretending': 1, 'pretend': 1, 'hardly': 1, 'enough': 1, 'left': 1, 'respectable': 1, 'person': 1, 'eye': 1, 'lying': 1, 'currants': 1, 'smaller': 1, 'creep': 1, 'don': 1, 'care': 1, 'ate': 1, 'anxiously': 1, 'holding': 1, 'growing': 1, 'surprised': 1, 'remained': 1, 'same': 1, 'sure': 1, 'eats': 1, 'expecting': 1, 'dull': 1, 'life': 1, 'common': 1, 'set': 1, 'work': 1}
|
Kisan_Mitra.ipynb
|
###Markdown
Model Architecture
###Code
import tensorflow as tf
import numpy as np
EPOCHS = 20
BATCH_SIZE = 128
from tensorflow.contrib.layers import flatten
def LeNet(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
#keep_prob=0.6
# Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
conv1_W=tf.Variable(tf.truncated_normal([5,5,1,6],mean=mu,stddev=sigma),name="Pholder3")
conv1_B=tf.Variable(tf.zeros([6]),name="Pholder4")
conv1=tf.nn.conv2d(x,conv1_W,[1,1,1,1],'VALID',name='tagget_conv')+conv1_B
conv1=tf.nn.relu(conv1,name="Pholder7")
# Pooling. Input = 28x28x6. Output = 14x14x6.
conv1=tf.nn.max_pool(conv1,[1,2,2,1],[1,2,2,1],'VALID',name="Pholder6")
# Layer 2: Convolutional. Output = 10x10x16.
conv2_W=tf.Variable(tf.truncated_normal([5,5,6,16],mean=mu,stddev=sigma,name="Pholder8"),name="Pholder5")
conv2_B=tf.Variable(tf.zeros([16]),name="Pholder9")
conv2=tf.nn.conv2d(conv1,conv2_W,[1,1,1,1],'VALID',name="Pholder10")+conv2_B
conv2=tf.nn.relu(conv2)
conv2=tf.nn.max_pool(conv2,[1,2,2,1],[1,2,2,1],'VALID')
fc0=flatten(conv2)
fc1_W=tf.Variable(tf.truncated_normal(shape=(2704,120),mean=mu,stddev=sigma))
fc1_B=tf.Variable(tf.zeros(120))
fc1=tf.matmul(fc0,fc1_W)+fc1_B
fc1=tf.nn.relu(fc1)
fc1=tf.nn.dropout(fc1,keep_prob)
fc2_W=tf.Variable(tf.truncated_normal(shape=(120,84),mean=mu,stddev=sigma))
fc2_B=tf.Variable(tf.zeros(84))
fc2=tf.matmul(fc1,fc2_W)+fc2_B
fc2=tf.nn.relu(fc2)
fc2=tf.nn.dropout(fc2,keep_prob)
fc3_W=tf.Variable(tf.truncated_normal(shape=(84,38),mean=mu,stddev=sigma))
fc3_B=tf.Variable(tf.zeros(38))
logits=tf.matmul(fc2,fc3_W,name="logit")+fc3_B
print(logits)
return logits
###Output
_____no_output_____
###Markdown
Train, Validate and Test the Model
###Code
x = tf.placeholder(tf.float32, (None, 64, 64, 1), name="Pholder0")
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 38)
keep_prob=tf.placeholder(tf.float32,name="Pholder11")
rate = 0.00098
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits,name="softmax")
loss_operation = tf.reduce_mean(cross_entropy,name="target_conv")
optimizer = tf.train.AdamOptimizer(learning_rate = rate,name="adam_optimized")
training_operation = optimizer.minimize(loss_operation)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
#loss_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y,keep_prob:1})
#loss=sess.run(loss_operation, feed_dict={x: batch_x, y: batch_y})
total_accuracy += (accuracy * len(batch_x))
#loss_accuracy += (loss * len(batch_x))
return total_accuracy / num_examples
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
print("Training...")
print()
for i in range(EPOCHS):
X_train, y_train = shuffle(X_train, y_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train[offset:end], y_train[offset:end]
#print(batch_y.shape)
sess.run(training_operation, feed_dict={x: batch_x, y: batch_y,keep_prob:0.6})
training_accuracy = evaluate(X_train, y_train)
validation_accuracy = evaluate(X_valid, y_valid)
print("EPOCH {} ...".format(i+1))
print("Train Accuracy = {:.3f}".format(training_accuracy))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print()
saver.save(sess, './lenet')
print("Model saved")
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
test_accuracy = evaluate(X_test, y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy))
import tensorflow as tf
meta_path = './lenet.meta' # .meta file\n",
output_node_names = ['logit'] # Output nodes\n",
with tf.Session() as sess:
# Restore the graph\n",
saver = tf.train.import_meta_graph(meta_path)
# Load weights\n",
saver.restore(sess,tf.train.latest_checkpoint('.'))
# Freeze the graph\n",
frozen_graph_def = tf.graph_util.convert_variables_to_constants(sess,sess.graph_def,output_node_names)
# Save the frozen graph\n",
with open('output_graph.pb', 'wb') as f:
f.write(frozen_graph_def.SerializeToString())
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
test_images=[]
image_labels=[0,1,2,3,4,5,6,7,8,9]
path='./test_data/'
for image in os.listdir(path):
print(image)
image_path=cv2.imread(path+image)
image=cv2.resize(image_path,(64,64))
# image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
test_images.append(image)
test_image_array=np.array(test_images)
import cv2
import glob
import matplotlib.pyplot as plt
filelist = glob.glob('crowdai/c_36/*.jpg')
# load the image with imread()
for i in filelist:
imageSource = i
img = cv2.imread(imageSource)
horizontal_img = img.copy()
vertical_img = img.copy()
both_img = img.copy()
horizontal_img = cv2.flip( img, 0 )
vertical_img = cv2.flip( img, 1 )
both_img = cv2.flip( img, -1 )
path=i.split('.')
path=''.join(path[:len(path)-1])
cv2.imwrite( path+"H.jpg", horizontal_img )
cv2.imwrite(path+"V.jpg" , vertical_img )
cv2.imwrite(path+"B.jpg", both_img )
import cv2
import numpy as np
import glob
import matplotlib.pyplot as plt
filelist = glob.glob('crowdai/c_09/*.jpg')
# load the image with imread()
for i in filelist:
img = cv2.imread(i)
num_rows, num_cols = img.shape[:2]
rotation_matrix = cv2.getRotationMatrix2D((num_cols/2, num_rows/2), 30, 1)
img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
path=i.split('.')
path=''.join(path[:len(path)-1])
cv2.imwrite(path+"Rotate.jpg", img_rotation)
test_image_array=np.average(test_image_array,axis=3,weights=[0.299,0.587,0.114])
test_image_array=(test_image_array-128)/128
test_image_array=np.reshape(test_image_array,(10,64,64,1))
import tensorflow as tf
from tensorflow.contrib.lite.toco.python.toco_wrapper import main
print(tf.__version__)
###Output
_____no_output_____
###Markdown
Analyze Performance
###Code
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver_test=tf.train.import_meta_graph('lenet.meta')
saver_test.restore(sess,tf.train.latest_checkpoint('.'))
test_accuracy=evaluate(test_image_array,image_labels)
print("Test Accuracy={:.3f}".format(test_accuracy))
def load_graph(frozen_graph_filename):
# We load the protobuf file from the disk and parse it to retrieve the
# unserialized graph_def
with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Then, we can use again a convenient built-in function to import a graph_def into the
# current default Graph
with tf.Graph().as_default() as graph:
tf.import_graph_def(
graph_def,
input_map=None,
return_elements=None,
name="prefix",
op_dict=None,
producer_op_list=None
)
return graph
graph = load_graph("./optimized_graph.pb")
for op in graph.get_operations():
print(op.name)
print(op.values())
x1 = graph.get_tensor_by_name('prefix/Pholder0:0')
y1 = graph.get_tensor_by_name('prefix/logit:0')
keep_probab=graph.get_tensor_by_name('prefix/Pholder11:0')
with tf.Session(graph=graph) as sess:
test_features = test_image_array
# compute the predicted output for test_x
pred_y = sess.run(tf.nn.softmax(y1), feed_dict={x1: test_features,keep_probab:1.0} )
# max=max(pred_y[0])
top_five=sess.run(tf.nn.top_k(pred_y,k=1))
print(top_five)
###Output
_____no_output_____
###Markdown
Output Top 5 Softmax Probabilities For Each Image Found on the Web
###Code
softmax=tf.nn.softmax(logits)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver=tf.train.import_meta_graph('lenet.meta')
saver.restore(sess, tf.train.latest_checkpoint('.'))
probability=sess.run(tf.nn.softmax(logits),{x:test_image_array,y:image_labels, keep_prob:1.0})
top_five=sess.run(tf.nn.top_k(probability,k=1))
print(top_five)
def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):
activation = tf_activation.eval(session=sess,feed_dict={x : image_input})
featuremaps = activation.shape[3]
plt.figure(plt_num, figsize=(15,15))
for featuremap in range(featuremaps):
plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column
plt.title('FeatureMap ' + str(featuremap))
if activation_min != -1 & activation_max != -1:
plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin =activation_min, vmax=activation_max, cmap="gray")
elif activation_max != -1:
plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmax=activation_max, cmap="gray")
elif activation_min !=-1:
plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin=activation_min, cmap="gray")
else:
plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", cmap="gray")
###Output
_____no_output_____
|
actor-critic-agent-with-tensorflow2.ipynb
|
###Markdown
Advantage Actor-Critic with TensorFlow 2.1 Setup
###Code
import gym
import logging
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import tensorflow.keras.layers as kl
import tensorflow.keras.losses as kls
import tensorflow.keras.optimizers as ko
%matplotlib inline
print("TensorFlow Ver: ", tf.__version__)
# Eager by default!
print("Eager Execution:", tf.executing_eagerly())
print("1 + 2 + 3 + 4 + 5 =", tf.reduce_sum([1, 2, 3, 4, 5]))
###Output
Eager Execution: True
1 + 2 + 3 + 4 + 5 = tf.Tensor(15, shape=(), dtype=int32)
###Markdown
Policy & Value Model Class
###Code
class ProbabilityDistribution(tf.keras.Model):
def call(self, logits, **kwargs):
# Sample a random categorical action from the given logits.
return tf.squeeze(tf.random.categorical(logits, 1), axis=-1)
class Model(tf.keras.Model):
def __init__(self, num_actions):
super().__init__('mlp_policy')
# Note: no tf.get_variable(), just simple Keras API!
self.hidden1 = kl.Dense(128, activation='relu')
self.hidden2 = kl.Dense(128, activation='relu')
self.value = kl.Dense(1, name='value')
# Logits are unnormalized log probabilities.
self.logits = kl.Dense(num_actions, name='policy_logits')
self.dist = ProbabilityDistribution()
def call(self, inputs, **kwargs):
# Inputs is a numpy array, convert to a tensor.
x = tf.convert_to_tensor(inputs)
# Separate hidden layers from the same input tensor.
hidden_logs = self.hidden1(x)
hidden_vals = self.hidden2(x)
return self.logits(hidden_logs), self.value(hidden_vals)
def action_value(self, obs):
# Executes `call()` under the hood.
logits, value = self.predict_on_batch(obs)
action = self.dist.predict_on_batch(logits)
# Another way to sample actions:
# action = tf.random.categorical(logits, 1)
# Will become clearer later why we don't use it.
return np.squeeze(action, axis=-1), np.squeeze(value, axis=-1)
# Verify everything works by sampling a single action.
env = gym.make('CartPole-v0')
model = Model(num_actions=env.action_space.n)
model.action_value(env.reset()[None, :])
###Output
_____no_output_____
###Markdown
Advantage Actor-Critic Agent Class
###Code
class A2CAgent:
def __init__(self, model, lr=7e-3, gamma=0.99, value_c=0.5, entropy_c=1e-4):
# `gamma` is the discount factor; coefficients are used for the loss terms.
self.gamma = gamma
self.value_c = value_c
self.entropy_c = entropy_c
self.model = model
self.model.compile(
optimizer=ko.RMSprop(lr=lr),
# Define separate losses for policy logits and value estimate.
loss=[self._logits_loss, self._value_loss])
def train(self, env, batch_sz=64, updates=250):
# Storage helpers for a single batch of data.
actions = np.empty((batch_sz,), dtype=np.int32)
rewards, dones, values = np.empty((3, batch_sz))
observations = np.empty((batch_sz,) + env.observation_space.shape)
# Training loop: collect samples, send to optimizer, repeat updates times.
ep_rewards = [0.0]
next_obs = env.reset()
for update in range(updates):
for step in range(batch_sz):
observations[step] = next_obs.copy()
actions[step], values[step] = self.model.action_value(next_obs[None, :])
next_obs, rewards[step], dones[step], _ = env.step(actions[step])
ep_rewards[-1] += rewards[step]
if dones[step]:
ep_rewards.append(0.0)
next_obs = env.reset()
logging.info("Episode: %03d, Reward: %03d" % (len(ep_rewards) - 1, ep_rewards[-2]))
_, next_value = self.model.action_value(next_obs[None, :])
returns, advs = self._returns_advantages(rewards, dones, values, next_value)
# A trick to input actions and advantages through same API.
acts_and_advs = np.concatenate([actions[:, None], advs[:, None]], axis=-1)
# Performs a full training step on the collected batch.
# Note: no need to mess around with gradients, Keras API handles it.
losses = self.model.train_on_batch(observations, [acts_and_advs, returns])
logging.debug("[%d/%d] Losses: %s" % (update + 1, updates, losses))
return ep_rewards
def test(self, env, render=False):
obs, done, ep_reward = env.reset(), False, 0
while not done:
action, _ = self.model.action_value(obs[None, :])
obs, reward, done, _ = env.step(action)
ep_reward += reward
if render:
env.render()
return ep_reward
def _returns_advantages(self, rewards, dones, values, next_value):
# `next_value` is the bootstrap value estimate of the future state (critic).
returns = np.append(np.zeros_like(rewards), next_value, axis=-1)
# Returns are calculated as discounted sum of future rewards.
for t in reversed(range(rewards.shape[0])):
returns[t] = rewards[t] + self.gamma * returns[t + 1] * (1 - dones[t])
returns = returns[:-1]
# Advantages are equal to returns - baseline (value estimates in our case).
advantages = returns - values
return returns, advantages
def _value_loss(self, returns, value):
# Value loss is typically MSE between value estimates and returns.
return self.value_c * kls.mean_squared_error(returns, value)
def _logits_loss(self, actions_and_advantages, logits):
# A trick to input actions and advantages through the same API.
actions, advantages = tf.split(actions_and_advantages, 2, axis=-1)
# Sparse categorical CE loss obj that supports sample_weight arg on `call()`.
# `from_logits` argument ensures transformation into normalized probabilities.
weighted_sparse_ce = kls.SparseCategoricalCrossentropy(from_logits=True)
# Policy loss is defined by policy gradients, weighted by advantages.
# Note: we only calculate the loss on the actions we've actually taken.
actions = tf.cast(actions, tf.int32)
policy_loss = weighted_sparse_ce(actions, logits, sample_weight=advantages)
# Entropy loss can be calculated as cross-entropy over itself.
probs = tf.nn.softmax(logits)
entropy_loss = kls.categorical_crossentropy(probs, probs)
# We want to minimize policy and maximize entropy losses.
# Here signs are flipped because the optimizer minimizes.
return policy_loss - self.entropy_c * entropy_loss
# Verify everything works with random weights.
agent = A2CAgent(model)
rewards_sum = agent.test(env)
print("Total Episode Reward: %d out of 200" % agent.test(env))
###Output
Total Episode Reward: 18 out of 200
###Markdown
Training A2C Agent & Results
###Code
# set to logging.WARNING to disable logs or logging.DEBUG to see losses as well
logging.getLogger().setLevel(logging.INFO)
model = Model(num_actions=env.action_space.n)
agent = A2CAgent(model)
rewards_history = agent.train(env)
print("Finished training! Testing...")
print("Total Episode Reward: %d out of 200" % agent.test(env))
plt.style.use('seaborn')
plt.plot(np.arange(0, len(rewards_history), 5), rewards_history[::5])
plt.xlabel('Episode')
plt.ylabel('Total Reward')
plt.show()
###Output
INFO:root:Episode: 001, Reward: 013
INFO:root:Episode: 002, Reward: 024
INFO:root:Episode: 003, Reward: 015
INFO:root:Episode: 004, Reward: 012
INFO:root:Episode: 005, Reward: 012
INFO:root:Episode: 006, Reward: 023
INFO:root:Episode: 007, Reward: 023
INFO:root:Episode: 008, Reward: 021
INFO:root:Episode: 009, Reward: 021
INFO:root:Episode: 010, Reward: 022
INFO:root:Episode: 011, Reward: 023
INFO:root:Episode: 012, Reward: 036
INFO:root:Episode: 013, Reward: 024
INFO:root:Episode: 014, Reward: 023
INFO:root:Episode: 015, Reward: 009
INFO:root:Episode: 016, Reward: 031
INFO:root:Episode: 017, Reward: 020
INFO:root:Episode: 018, Reward: 043
INFO:root:Episode: 019, Reward: 028
INFO:root:Episode: 020, Reward: 089
INFO:root:Episode: 021, Reward: 021
INFO:root:Episode: 022, Reward: 016
INFO:root:Episode: 023, Reward: 047
INFO:root:Episode: 024, Reward: 033
INFO:root:Episode: 025, Reward: 026
INFO:root:Episode: 026, Reward: 038
INFO:root:Episode: 027, Reward: 052
INFO:root:Episode: 028, Reward: 026
INFO:root:Episode: 029, Reward: 021
INFO:root:Episode: 030, Reward: 030
INFO:root:Episode: 031, Reward: 035
INFO:root:Episode: 032, Reward: 083
INFO:root:Episode: 033, Reward: 043
INFO:root:Episode: 034, Reward: 028
INFO:root:Episode: 035, Reward: 031
INFO:root:Episode: 036, Reward: 070
INFO:root:Episode: 037, Reward: 080
INFO:root:Episode: 038, Reward: 077
INFO:root:Episode: 039, Reward: 027
INFO:root:Episode: 040, Reward: 155
INFO:root:Episode: 041, Reward: 043
INFO:root:Episode: 042, Reward: 042
INFO:root:Episode: 043, Reward: 048
INFO:root:Episode: 044, Reward: 026
INFO:root:Episode: 045, Reward: 036
INFO:root:Episode: 046, Reward: 035
INFO:root:Episode: 047, Reward: 041
INFO:root:Episode: 048, Reward: 089
INFO:root:Episode: 049, Reward: 137
INFO:root:Episode: 050, Reward: 186
INFO:root:Episode: 051, Reward: 115
INFO:root:Episode: 052, Reward: 096
INFO:root:Episode: 053, Reward: 089
INFO:root:Episode: 054, Reward: 067
INFO:root:Episode: 055, Reward: 092
INFO:root:Episode: 056, Reward: 104
INFO:root:Episode: 057, Reward: 177
INFO:root:Episode: 058, Reward: 183
INFO:root:Episode: 059, Reward: 131
INFO:root:Episode: 060, Reward: 088
INFO:root:Episode: 061, Reward: 078
INFO:root:Episode: 062, Reward: 089
INFO:root:Episode: 063, Reward: 096
INFO:root:Episode: 064, Reward: 112
INFO:root:Episode: 065, Reward: 073
INFO:root:Episode: 066, Reward: 114
INFO:root:Episode: 067, Reward: 092
INFO:root:Episode: 068, Reward: 117
INFO:root:Episode: 069, Reward: 150
INFO:root:Episode: 070, Reward: 200
INFO:root:Episode: 071, Reward: 200
INFO:root:Episode: 072, Reward: 015
INFO:root:Episode: 073, Reward: 112
INFO:root:Episode: 074, Reward: 199
INFO:root:Episode: 075, Reward: 200
INFO:root:Episode: 076, Reward: 181
INFO:root:Episode: 077, Reward: 127
INFO:root:Episode: 078, Reward: 083
INFO:root:Episode: 079, Reward: 148
INFO:root:Episode: 080, Reward: 200
INFO:root:Episode: 081, Reward: 200
INFO:root:Episode: 082, Reward: 145
INFO:root:Episode: 083, Reward: 184
INFO:root:Episode: 084, Reward: 200
INFO:root:Episode: 085, Reward: 200
INFO:root:Episode: 086, Reward: 190
INFO:root:Episode: 087, Reward: 133
INFO:root:Episode: 088, Reward: 200
INFO:root:Episode: 089, Reward: 200
INFO:root:Episode: 090, Reward: 200
INFO:root:Episode: 091, Reward: 110
INFO:root:Episode: 092, Reward: 154
INFO:root:Episode: 093, Reward: 200
INFO:root:Episode: 094, Reward: 200
INFO:root:Episode: 095, Reward: 200
INFO:root:Episode: 096, Reward: 200
INFO:root:Episode: 097, Reward: 200
INFO:root:Episode: 098, Reward: 136
INFO:root:Episode: 099, Reward: 200
INFO:root:Episode: 100, Reward: 200
INFO:root:Episode: 101, Reward: 200
INFO:root:Episode: 102, Reward: 200
INFO:root:Episode: 103, Reward: 200
INFO:root:Episode: 104, Reward: 181
INFO:root:Episode: 105, Reward: 200
INFO:root:Episode: 106, Reward: 153
INFO:root:Episode: 107, Reward: 200
INFO:root:Episode: 108, Reward: 200
INFO:root:Episode: 109, Reward: 134
INFO:root:Episode: 110, Reward: 169
INFO:root:Episode: 111, Reward: 083
INFO:root:Episode: 112, Reward: 200
INFO:root:Episode: 113, Reward: 200
INFO:root:Episode: 114, Reward: 200
INFO:root:Episode: 115, Reward: 200
INFO:root:Episode: 116, Reward: 200
INFO:root:Episode: 117, Reward: 200
INFO:root:Episode: 118, Reward: 072
INFO:root:Episode: 119, Reward: 183
INFO:root:Episode: 120, Reward: 191
INFO:root:Episode: 121, Reward: 200
INFO:root:Episode: 122, Reward: 184
INFO:root:Episode: 123, Reward: 123
INFO:root:Episode: 124, Reward: 102
INFO:root:Episode: 125, Reward: 162
INFO:root:Episode: 126, Reward: 176
INFO:root:Episode: 127, Reward: 162
INFO:root:Episode: 128, Reward: 200
INFO:root:Episode: 129, Reward: 200
INFO:root:Episode: 130, Reward: 200
INFO:root:Episode: 131, Reward: 200
INFO:root:Episode: 132, Reward: 200
INFO:root:Episode: 133, Reward: 200
INFO:root:Episode: 134, Reward: 200
INFO:root:Episode: 135, Reward: 200
INFO:root:Episode: 136, Reward: 200
###Markdown
Static Computational Graph
###Code
with tf.Graph().as_default():
print("Eager Execution:", tf.executing_eagerly()) # False
model = Model(num_actions=env.action_space.n)
agent = A2CAgent(model)
rewards_history = agent.train(env)
print("Finished training! Testing...")
print("Total Episode Reward: %d out of 200" % agent.test(env))
###Output
Eager Execution: False
Finished training! Testing...
Total Episode Reward: 200 out of 200
###Markdown
BenchmarksNote: wall time doesn't show the whole picture, it's better to compare CPU time.
###Code
# Generate 100k observations to run benchmarks on.
env = gym.make('CartPole-v0')
obs = np.repeat(env.reset()[None, :], 100000, axis=0)
###Output
_____no_output_____
###Markdown
Eager Benchmark
###Code
%%time
model = Model(env.action_space.n)
model.run_eagerly = True
print("Eager Execution: ", tf.executing_eagerly())
print("Eager Keras Model:", model.run_eagerly)
_ = model.predict_on_batch(obs)
###Output
Eager Execution: True
Eager Keras Model: True
CPU times: user 24 ms, sys: 12.7 ms, total: 36.7 ms
Wall time: 35.1 ms
###Markdown
Static Benchmark
###Code
%%time
with tf.Graph().as_default():
model = Model(env.action_space.n)
print("Eager Execution: ", tf.executing_eagerly())
print("Eager Keras Model:", model.run_eagerly)
_ = model.predict_on_batch(obs)
###Output
Eager Execution: False
Eager Keras Model: False
CPU times: user 81.6 ms, sys: 21 ms, total: 103 ms
Wall time: 99.6 ms
###Markdown
Default Benchmark
###Code
%%time
model = Model(env.action_space.n)
print("Eager Execution: ", tf.executing_eagerly())
print("Eager Keras Model:", model.run_eagerly)
_ = model.predict_on_batch(obs)
###Output
Eager Execution: True
Eager Keras Model: False
CPU times: user 54.2 ms, sys: 4.58 ms, total: 58.7 ms
Wall time: 56 ms
|
Notebooks/Proposal_SF_PongNFS_0_20191018.ipynb
|
###Markdown
DQN implementation with PyTorch using PongNoFrameskip-v4 benchmark.In this notebook, we implement Deep Q-Network (DQN), one of the rainforcement learning algorithm, using `PyTorch`. This code refers to [jmichaux/dqn-pytorch](https://github.com/jmichaux/dqn-pytorch). In this code, we propose the new method of improve the escape from local-minimum of rainforcement learning. Our method takeing multi-agent approach. In addition, we also define the agent durabiity inspired by Evolutionary computation. Setup
###Code
!apt-get install -y cmake zlib1g-dev libjpeg-dev xvfb ffmpeg xorg-dev python-opengl libboost-all-dev libsdl2-dev swig freeglut3-dev
!pip install -U gym imageio PILLOW pyvirtualdisplay 'gym[atari]' 'pyglet==1.3.2' pyopengl scipy JSAnimation opencv-python pillow h5py pyyaml hyperdash pyvirtualdisplay hyperdash
!apt-get install xvfb
from google.colab import drive
drive.mount('/content/drive')
!cp /content/drive/My\ Drive/Colab\ Notebooks/MT/Utils/xdpyinfo /usr/bin/
!cp /content/drive/My\ Drive/Colab\ Notebooks/MT/Utils/libXxf86dga.* /usr/lib/x86_64-linux-gnu/
!chmod +x /usr/bin/xdpyinfo
!hyperdash signup --github
###Output
_____no_output_____
###Markdown
Package Import
###Code
import copy
from collections import namedtuple
from itertools import count
import math
import random
import numpy as np
import os
import time
import json
import gym
from collections import deque
from hyperdash import Experiment
import cv2
import pyvirtualdisplay
import base64
import IPython
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as T
###Output
_____no_output_____
###Markdown
Hyper parameters
###Code
# Runtime settings
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
Transition = namedtuple('Transion', ('state', 'action', 'next_state', 'reward'))
cv2.ocl.setUseOpenCL(False)
time_stamp = str(int(time.time()))
random.seed(0)
np.random.seed(0)
# Hyper parameters
BATCH_SIZE = 32 # @param
GAMMA = 0.99 # @param
EPS_START = 1 # @param
EPS_END = 0.02 # @param
EPS_DECAY = 1000000 # @param
TARGET_UPDATE = 1000 # @param
DEFAULT_DURABILITY = 1000 # @param
LEARNING_RATE = 1e-4 # @param
INITIAL_MEMORY = 10000 # @param
MEMORY_SIZE = 10 * INITIAL_MEMORY # @param
DEFAULT_DURABILITY_DECREASED_LEVEL = 1 # @param
DEFAULT_DURABILITY_INCREASED_LEVEL = 1 # @param
DURABILITY_CHECK_FREQUENCY = 80 # @param
# Some settings
ENV_NAME = "PongNoFrameskip-v4" # @param
EXP_NAME = "PongNoFrameskip-v4_" + time_stamp # @param
RENDER = False # @param
RUN_NAME = "videos_proposal" # @param
output_directory = os.path.abspath(
os.path.join(os.path.curdir, "/content/drive/My Drive/Colab Notebooks/MT/Runs", ENV_NAME + "_" + RUN_NAME + "_" + time_stamp))
TRAIN_LOG_FILE_PATH = output_directory + "/" + ENV_NAME + "_train_" + time_stamp + ".log" # @param
TEST_LOG_FILE_PATH = output_directory + "/" + ENV_NAME + "_test_" + time_stamp + ".log" # @param
PARAMETER_LOG_FILE_PATH = output_directory + "/" + ENV_NAME + "_params_" + time_stamp + ".json" # @param
if not os.path.exists(output_directory):
os.makedirs(output_directory)
hyper_params = {"BATCH_SIZE": BATCH_SIZE, "GAMMA": GAMMA, "EPS_START": EPS_START,
"EPS_END": EPS_END, "EPS_DECAY": EPS_DECAY,
"TARGET_UPDATE": TARGET_UPDATE,
"DEFAULT_DURABILITY": DEFAULT_DURABILITY,
"LEARNING_RATE": LEARNING_RATE,
"INITIAL_MEMORY": INITIAL_MEMORY, "MEMORY_SIZE": MEMORY_SIZE,
"DEFAULT_DURABILITY_DECREASED_LEVEL": DEFAULT_DURABILITY_DECREASED_LEVEL,
"DURABILITY_CHECK_FREQUENCY": DURABILITY_CHECK_FREQUENCY,
"ENV_NAME" : ENV_NAME, "EXP_NAME": EXP_NAME,
"TRAIN_LOG_FILE_PATH": TRAIN_LOG_FILE_PATH,
"TEST_LOG_FILE_PATH": TEST_LOG_FILE_PATH,
"PARAMETER_LOG_FILE_PATH": PARAMETER_LOG_FILE_PATH,
"RENDER": str(RENDER)}
###Output
_____no_output_____
###Markdown
Define the Replay memory
###Code
class ReplayMemory(object):
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
self.position = 0
def push(self, *args):
if len(self.memory) < self.capacity:
self.memory.append(None)
self.memory[self.position] = Transition(*args)
self.position = (self.position + 1) % self.capacity
def sample(self, batch_size):
return random.sample(self.memory, batch_size)
def __len__(self):
return len(self.memory)
class PrioritizedReplay(object):
def __init__(self, capacity):
pass
###Output
_____no_output_____
###Markdown
Define the DQNsNow we define the two types of DQN. One is simple q-network using 3 layers CNN. On the other one is batch normalaized 4 layers CNN.
###Code
SQRT2 = math.sqrt(2.0)
ACT = nn.ReLU
class DQN(torch.jit.ScriptModule):
def __init__(self, in_channels=4, n_actions=14):
super(DQN, self).__init__()
self.convs = nn.Sequential(
nn.Conv2d(in_channels, 32, kernel_size=8, stride=4),
ACT(),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
ACT(),
nn.Conv2d(64, 64, kernel_size=3, stride=1),
ACT()
)
self.fc = nn.Sequential(
nn.Linear(7 * 7 * 64, 512),
ACT(),
nn.Linear(512, n_actions)
)
@torch.jit.script_method
def forward(self, x):
x = x.float() / 255
x = self.convs(x)
x = x.view(x.size(0), -1)
return self.fc(x)
class DDQN(torch.jit.ScriptModule):
def __init__(self, in_channels=4, n_actions=14):
__constants__ = ['n_actions']
super(DDQN, self).__init__()
self.n_actions = n_actions
self.convs = nn.Sequential(
nn.Conv2d(in_channels, 32, kernel_size=8, stride=4),
ACT(),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
ACT(),
nn.Conv2d(64, 64, kernel_size=3, stride=1),
ACT()
)
self.fc_adv = nn.Sequential(
nn.Linear(7 * 7 * 64, 512),
ACT(),
nn.Linear(512, n_actions)
)
self.fc_val = nn.Sequential(
nn.Linear(7 * 7 * 64, 512),
ACT(),
nn.Linear(512, 1)
)
def scale_grads_hook(module, grad_out, grad_in):
"""scale gradient by 1/sqrt(2) as in the original paper"""
grad_out = tuple(map(lambda g: g / SQRT2, grad_out))
return grad_out
self.fc_adv.register_backward_hook(scale_grads_hook)
self.fc_val.register_backward_hook(scale_grads_hook)
@torch.jit.script_method
def forward(self, x):
x = x.float() / 255
x = self.convs(x)
x = x.view(x.size(0), -1)
adv = self.fc_adv(x)
val = self.fc_val(x)
return val + adv - adv.mean(1).unsqueeze(1)
@torch.jit.script_method
def value(self, x):
x = x.float() / 255
x = self.convs(x)
x = x.view(x.size(0), -1)
return self.fc_val(x)
class LanderDQN(torch.jit.ScriptModule):
def __init__(self, n_state, n_actions, nhid=64):
super(LanderDQN, self).__init__()
self.layers = nn.Sequential(
nn.Linear(n_state, nhid),
ACT(),
nn.Linear(nhid, nhid),
ACT(),
nn.Linear(nhid, n_actions)
)
@torch.jit.script_method
def forward(self, x):
x = self.layers(x)
return x
class RamDQN(torch.jit.ScriptModule):
def __init__(self, n_state, n_actions):
super(RamDQN, self).__init__()
self.layers = nn.Sequential(
nn.Linear(n_state, 256),
ACT(),
nn.Linear(256, 128),
ACT(),
nn.Linear(128, 64),
ACT(),
nn.Linear(64, n_actions)
)
@torch.jit.script_method
def forward(self, x):
return self.layers(x)
class DQNbn(nn.Module):
def __init__(self, in_channels=4, n_actions=14):
"""
Initialize Deep Q Network
Args:
in_channels (int): number of input channels
n_actions (int): number of outputs
"""
super(DQNbn, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)
self.bn1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
self.bn2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
self.bn3 = nn.BatchNorm2d(64)
self.fc4 = nn.Linear(7 * 7 * 64, 512)
self.head = nn.Linear(512, n_actions)
def forward(self, x):
x = x.float() / 255
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
x = F.relu(self.fc4(x.view(x.size(0), -1)))
return self.head(x)
# class DQN(nn.Module):
# def __init__(self, in_channels=4, n_actions=14):
# """
# Initialize Deep Q Network
# Args:
# in_channels (int): number of input channels
# n_actions (int): number of outputs
# """
# super(DQN, self).__init__()
# self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)
# # self.bn1 = nn.BatchNorm2d(32)
# self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
# # self.bn2 = nn.BatchNorm2d(64)
# self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
# # self.bn3 = nn.BatchNorm2d(64)
# self.fc4 = nn.Linear(7 * 7 * 64, 512)
# self.head = nn.Linear(512, n_actions)
# def forward(self, x):
# x = x.float() / 255
# x = F.relu(self.conv1(x))
# x = F.relu(self.conv2(x))
# x = F.relu(self.conv3(x))
# x = F.relu(self.fc4(x.view(x.size(0), -1)))
# return self.head(x)
###Output
_____no_output_____
###Markdown
Define the Agent
###Code
class Agent:
def __init__(self, policy_net, target_net, durability, optimizer, name):
self.policy_net = policy_net
self.target_net = target_net
self.target_net.load_state_dict(policy_net.state_dict())
self.durability = durability
self.optimizer = optimizer
self.name = name
self.memory = ReplayMemory(MEMORY_SIZE)
self.steps_done = 0
self.total_reward = 0.0
def select_action(self, state):
sample = random.random()
eps_threshold = EPS_END + (EPS_START - EPS_END) * math.exp(-1. * self.steps_done / EPS_DECAY)
self.steps_done += 1
if sample > eps_threshold:
with torch.no_grad():
return self.policy_net(state.to('cuda')).max(1)[1].view(1,1)
else:
return torch.tensor([[random.randrange(4)]], device=device, dtype=torch.long)
def optimize_model(self):
if len(self.memory) < BATCH_SIZE:
return
transitions = self.memory.sample(BATCH_SIZE)
"""
zip(*transitions) unzips the transitions into
Transition(*) creates new named tuple
batch.state - tuple of all the states (each state is a tensor)
batch.next_state - tuple of all the next states (each state is a tensor)
batch.reward - tuple of all the rewards (each reward is a float)
batch.action - tuple of all the actions (each action is an int)
"""
batch = Transition(*zip(*transitions))
actions = tuple((map(lambda a: torch.tensor([[a]], device='cuda'), batch.action)))
rewards = tuple((map(lambda r: torch.tensor([r], device='cuda'), batch.reward)))
non_final_mask = torch.tensor(
tuple(map(lambda s: s is not None, batch.next_state)),
device=device, dtype=torch.bool)
non_final_next_states = torch.cat([s for s in batch.next_state
if s is not None]).to('cuda')
state_batch = torch.cat(batch.state).to('cuda')
action_batch = torch.cat(actions)
reward_batch = torch.cat(rewards)
state_action_values = self.policy_net(state_batch).gather(1, action_batch)
next_state_values = torch.zeros(BATCH_SIZE, device=device)
next_state_values[non_final_mask] = self.target_net(non_final_next_states).max(1)[0].detach()
expected_state_action_values = (next_state_values * GAMMA) + reward_batch
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
def get_state(self):
return self.state
def set_state(self, state):
self.state = state
def set_env(self, env):
self.env = env
def get_env(self):
return self.env
def set_action(self, action):
self.action = action
def get_action(self):
return self.action
def get_durability(self):
return self.durability
def get_policy_net(self):
return self.policy_net
def reduce_durability(self, value):
self.durability = self.durability - value
def heal_durability(self, value):
self.durability = self.durability + value
def set_done_state(self, done):
self.done = done
def set_total_reward(self, reward):
self.reward = self.reward + reward
def get_total_reward(self):
return self.total_reward
def set_step_retrun_value(self, obs, reward, done, info):
self.obs = obs
self.reward = reward
self.done = done
self.info = info
def is_done(self):
return self.done
###Output
_____no_output_____
###Markdown
Define the Environment**TODO: Make sure to create environment class**
###Code
# def make_env(env, stack_frames=True, episodic_life=True, clip_rewards=False, scale=False):
# if episodic_life:
# env = EpisodicLifeEnv(env)
#
# env = NoopResetEnv(env, noop_max=30)
# env = MaxAndSkipEnv(env, skip=4)
# if 'FIRE' in env.unwrapped.get_action_meanings():
# env = FireResetEnv(env)
#
# env = WarpFrame(env)
# if stack_frames:
# env = FrameStack(env, 4)
# if clip_rewards:
# env = ClipRewardEnv(env)
# return env
def get_state(obs):
state = np.array(obs)
state = state.transpose((2, 0, 1))
state = torch.from_numpy(state)
return state.unsqueeze(0)
class Environment:
def __init__(self):
self.env = gym.make(ENV_NAME)
self.env = self.make_env(self.env)
def get_env(self):
return self.env
def make_env(self, env, stack_frames=True, episodic_life=True, clip_rewards=False, scale=False):
if episodic_life:
env = EpisodicLifeEnv(env)
env = NoopResetEnv(self.env, noop_max=30)
env = MaxAndSkipEnv(self.env, skip=4)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(self.env)
env = WarpFrame(env)
if stack_frames:
env = FrameStack(env, 4)
if clip_rewards:
env = ClipRewardEnv(env)
return env
def get_state(obs):
state = np.array(obs)
state = state.transpose((2, 0, 1))
state = torch.from_numpy(state)
return state.unsqueeze(0)
class RewardScaler(gym.RewardWrapper):
def reward(self, reward):
return reward * 0.1
class ClipRewardEnv(gym.RewardWrapper):
def __init__(self, env):
gym.RewardWrapper.__init__(self, env)
def reward(self, reward):
"""Bin reward to {+1, 0, -1} by its sign."""
return np.sign(reward)
class LazyFrames(object):
def __init__(self, frames):
"""This object ensures that common frames between the observations are only stored once.
It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
buffers.
This object should only be converted to numpy array before being passed to the model.
You'd not believe how complex the previous solution was."""
self._frames = frames
self._out = None
def _force(self):
if self._out is None:
self._out = np.concatenate(self._frames, axis=2)
self._frames = None
return self._out
def __array__(self, dtype=None):
out = self._force()
if dtype is not None:
out = out.astype(dtype)
return out
def __len__(self):
return len(self._force())
def __getitem__(self, i):
return self._force()[i]
class FrameStack(gym.Wrapper):
def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
See Also
--------
baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
self.observation_space = gym.spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k), dtype=env.observation_space.dtype)
def reset(self):
ob = self.env.reset()
for _ in range(self.k):
self.frames.append(ob)
return self._get_ob()
def step(self, action):
ob, reward, done, info = self.env.step(action)
self.frames.append(ob)
return self._get_ob(), reward, done, info
def _get_ob(self):
assert len(self.frames) == self.k
return LazyFrames(list(self.frames))
class WarpFrame(gym.ObservationWrapper):
def __init__(self, env):
"""Warp frames to 84x84 as done in the Nature paper and later work."""
gym.ObservationWrapper.__init__(self, env)
self.width = 84
self.height = 84
self.observation_space = gym.spaces.Box(low=0, high=255,
shape=(self.height, self.width, 1), dtype=np.uint8)
def observation(self, frame):
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
return frame[:, :, None]
class FireResetEnv(gym.Wrapper):
def __init__(self, env=None):
"""For environments where the user need to press FIRE for the game to start."""
super(FireResetEnv, self).__init__(env)
assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
assert len(env.unwrapped.get_action_meanings()) >= 3
def step(self, action):
return self.env.step(action)
def reset(self):
self.env.reset()
obs, _, done, _ = self.env.step(1)
if done:
self.env.reset()
obs, _, done, _ = self.env.step(2)
if done:
self.env.reset()
return obs
class EpisodicLifeEnv(gym.Wrapper):
def __init__(self, env=None):
"""Make end-of-life == end-of-episode, but only reset on true game over.
Done by DeepMind for the DQN and co. since it helps value estimation.
"""
super(EpisodicLifeEnv, self).__init__(env)
self.lives = 0
self.was_real_done = True
self.was_real_reset = False
def step(self, action):
obs, reward, done, info = self.env.step(action)
self.was_real_done = done
# check current lives, make loss of life terminal,
# then update lives to handle bonus lives
lives = self.env.unwrapped.ale.lives()
if lives < self.lives and lives > 0:
# for Qbert somtimes we stay in lives == 0 condtion for a few frames
# so its important to keep lives > 0, so that we only reset once
# the environment advertises done.
done = True
self.lives = lives
return obs, reward, done, info
def reset(self):
"""Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
"""
if self.was_real_done:
obs = self.env.reset()
self.was_real_reset = True
else:
# no-op step to advance from terminal/lost life state
obs, _, _, _ = self.env.step(0)
self.was_real_reset = False
self.lives = self.env.unwrapped.ale.lives()
return obs
class MaxAndSkipEnv(gym.Wrapper):
def __init__(self, env=None, skip=4):
"""Return only every `skip`-th frame"""
super(MaxAndSkipEnv, self).__init__(env)
# most recent raw observations (for max pooling across time steps)
self._obs_buffer = deque(maxlen=2)
self._skip = skip
def step(self, action):
total_reward = 0.0
done = None
for _ in range(self._skip):
obs, reward, done, info = self.env.step(action)
self._obs_buffer.append(obs)
total_reward += reward
if done:
break
max_frame = np.max(np.stack(self._obs_buffer), axis=0)
return max_frame, total_reward, done, info
def reset(self):
"""Clear past frame buffer and init. to first obs. from inner env."""
self._obs_buffer.clear()
obs = self.env.reset()
self._obs_buffer.append(obs)
return obs
class NoopResetEnv(gym.Wrapper):
def __init__(self, env=None, noop_max=30):
"""Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
"""
super(NoopResetEnv, self).__init__(env)
self.noop_max = noop_max
self.override_num_noops = None
assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
def step(self, action):
return self.env.step(action)
def reset(self):
""" Do no-op action for a number of steps in [1, noop_max]."""
self.env.reset()
if self.override_num_noops is not None:
noops = self.override_num_noops
else:
noops = np.random.randint(1, self.noop_max + 1)
assert noops > 0
obs = None
for _ in range(noops):
obs, _, done, _ = self.env.step(0)
if done:
obs = self.env.reset()
return obs
###Output
_____no_output_____
###Markdown
Deprecated code**Thease code move to agent class.** @deprecateddef select_action(state): global steps_done sample = random.random() eps_threshold = EPS_END + (EPS_START - EPS_END)* \ math.exp(-1. * steps_done / EPS_DECAY) steps_done += 1 if sample > eps_threshold: with torch.no_grad(): return policy_net(state.to('cuda')).max(1)[1].view(1,1) else: return torch.tensor([[random.randrange(4)]], device=device, dtype=torch.long)@deprecateddef optimize_model(): if len(memory) < BATCH_SIZE: return transitions = memory.sample(BATCH_SIZE) """ zip(*transitions) unzips the transitions into Transition(*) creates new named tuple batch.state - tuple of all the states (each state is a tensor) batch.next_state - tuple of all the next states (each state is a tensor) batch.reward - tuple of all the rewards (each reward is a float) batch.action - tuple of all the actions (each action is an int) """ batch = Transition(*zip(*transitions)) actions = tuple((map(lambda a: torch.tensor([[a]], device='cuda'), batch.action))) rewards = tuple((map(lambda r: torch.tensor([r], device='cuda'), batch.reward))) non_final_mask = torch.tensor( tuple(map(lambda s: s is not None, batch.next_state)), device=device, dtype=torch.uint8) non_final_next_states = torch.cat([s for s in batch.next_state if s is not None]).to('cuda') state_batch = torch.cat(batch.state).to('cuda') action_batch = torch.cat(actions) reward_batch = torch.cat(rewards) state_action_values = policy_net(state_batch).gather(1, action_batch) next_state_values = torch.zeros(BATCH_SIZE, device=device) next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach() expected_state_action_values = (next_state_values * GAMMA) + reward_batch loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1)) optimizer.zero_grad() loss.backward() for param in policy_net.parameters(): param.grad.data.clamp_(-1, 1) optimizer.step() Degine the train stepsIn my research, make this code multi-agent (**Note**: Multi-agent here means multiple independent agents sharing a task environment)
###Code
# TODO : To change the deprecated function to Agent clsss fuction
def train(envs, agents, core_env, core_agent, n_episodes, agent_n, exp, render=False):
"""
Training step.
In this code, we use the multi-agents to create candidate for core agent.
The core agent and environment is main RL set. In addition, each agent has
own environment and durabiliry. Each agent's reward is checked for the
specified number of episodes, and if an agent is not selected as the
best-agent, that agent's durability is reduced.
Parameters
----------
envs: list of Environment
List of environment for multi-agent
agents: list of Agent
List of multi-agents to create candidates for core_agent
core_env: Environment
Main environment of this train step
core_agent: Agent
Main agent of this train step
n_episodes: int
The number of episodes
agent_n : int
The number of agent
exp: Experiment
The Experiment object used by hyperdash
render: boolean, default False
Flag for whether to render the environment
"""
for episode in range(n_episodes):
print("episode: {}".format(episode));
# 0. Initalize the environment, state and agent params
obs = core_env.reset()
core_state = get_state(obs)
core_agent.total_reward = 0.0
core_agent.set_state(core_state)
for agent in agents:
obs = agent.get_env().reset()
state = get_state(obs)
agent.set_state(state)
agent.total_reward = 0.0
# agent.durability = DEFAULT_DURABILITY
for t in count():
#if t % 20 != 0:
# print(str(t) + " ", end='')
#else:
# print("\n")
# print([agent.get_durability() for agent in agents])
# print(str(t) + " ", end='') """"
# 1. Select action from environment of each agent
for agent in agents:
agent.set_env(core_agent.get_env())
action = agent.select_action(agent.get_state())
agent.set_action(action)
# 2. Proceed step of each agent
for agent in agents:
obs, reward, done, info = agent.get_env().step(agent.get_action())
agent.set_step_retrun_value(obs, reward, done, info)
agent.set_total_reward(reward)
if not done:
next_state = get_state(obs)
else:
next_state = None
reward = torch.tensor([reward], device=device)
agent.memory.push(agent.get_state(), agent.get_action().to('cpu'),
next_state, reward.to('cpu'))
agent.set_state(next_state)
if agent.steps_done > INITIAL_MEMORY:
agent.optimize_model()
if agent.steps_done % TARGET_UPDATE == 0:
agent.target_net.load_state_dict(agent.policy_net.state_dict())
# ---------------
# Proposal method
# ---------------
# 3. Select best agent in this step
reward_list = [agent.get_total_reward() for agent in agents]
best_agents = [i for i, v in enumerate(reward_list) if v == max(reward_list)]
best_agent_index = random.choice(best_agents)
best_agent = agents[best_agent_index]
best_agent.heal_durability(DEFAULT_DURABILITY_INCREASED_LEVEL)
# Best_agent infomation
# exp.log("Current best agent: {}".format(best_agent.name))
# 4. Check the agent durability in specified step
if t % DURABILITY_CHECK_FREQUENCY == 0:
if len(agents) > 1:
index = [i for i in range(len(agents)) if i not in best_agents]
for i in index:
agents[i].reduce_durability(DEFAULT_DURABILITY_DECREASED_LEVEL)
# 5. Main step of core agent
core_agent_action = best_agent.get_action()
core_agent.set_action(core_agent_action)
core_obs, core_reward, core_done, core_info = core_agent.get_env().step(
core_agent.get_action())
core_agent.set_step_retrun_value(core_obs, core_reward, core_done, core_info)
core_agent.set_done_state(core_done)
core_agent.set_total_reward(core_reward)
if not core_done:
core_next_state = get_state(core_obs)
else:
core_next_state = None
core_reward = torch.tensor([core_reward], device=device)
core_agent.memory.push(core_agent.get_state(),
core_agent.get_action().to('cpu'),
core_next_state, core_reward.to('cpu'))
core_agent.set_state(core_next_state)
if core_agent.steps_done > INITIAL_MEMORY:
core_agent.optimize_model()
if core_agent.steps_done % TARGET_UPDATE == 0:
core_agent.target_net.load_state_dict(core_agent.policy_net.state_dict())
if core_agent.is_done():
print("\n")
break
# 6. Swap agent
if len(agents) > 1 and episode % DURABILITY_CHECK_FREQUENCY == 0:
for agent, i in zip(agents, range(len(agents))):
if agent.durability <= 0:
del agents[i]
# ----------------------
# End of proposal method
# ----------------------
exp.metric("total_reward", core_agent.get_total_reward())
out_str = 'Total steps: {} \t Episode: {}/{} \t Total reward: {}'.format(
core_agent.steps_done, episode, t, core_agent.total_reward)
if episode % 20 == 0:
print(out_str)
out_str = str("\n" + out_str + "\n")
exp.log(out_str)
else:
print(out_str)
exp.log(out_str)
#with open(TRAIN_LOG_FILE_PATH, 'wt') as f:
# f.write(out_str)
env.close()
###Output
_____no_output_____
###Markdown
Define the test steps
###Code
# TODO : To change the deprecated function to Agent clsss fuction
def test(env, n_episodes, policy, exp, render=True):
# Save video as mp4 on specified directory
env = gym.wrappers.Monitor(env, './videos/' + 'dqn_pong_video')
for episode in range(n_episodes):
obs = env.reset()
state = env.get_state(obs)
total_reward = 0.0
for t in count():
action = policy(state.to('cuda')).max(1)[1].view(1,1)
if render:
env.render()
time.sleep(0.02)
obs, reward, done, info = env.step(action)
total_reward += reward
if not done:
next_state = env.get_state(obs)
else:
next_state = None
state = next_state
if done:
out_str = "Finished Episode {} with reward {}".format(
episode, total_reward)
print(out_str)
exp.log(out_str)
with open(TEST_LOG_FILE_NAME, 'wt') as f:
f.write(out_str)
break
env.close()
###Output
_____no_output_____
###Markdown
Main steps
###Code
# Create Agent
agents = []
policy_net_0 = DQN(n_actions=4).to(device)
target_net_0 = DQN(n_actions=4).to(device)
optimizer_0 = optim.Adam(policy_net_0.parameters(), lr=LEARNING_RATE)
agents.append(Agent(policy_net_0, target_net_0, DEFAULT_DURABILITY,
optimizer_0, "cnn-dqn0"))
agents.append(Agent(policy_net_0, target_net_0, DEFAULT_DURABILITY,
optimizer_0, "cnn-dqn1"))
policy_net_1 = DDQN(n_actions=4).to(device)
target_net_1 = DDQN(n_actions=4).to(device)
optimizer_1 = optim.Adam(policy_net_1.parameters(), lr=LEARNING_RATE)
agents.append(Agent(policy_net_1, target_net_1, DEFAULT_DURABILITY,
optimizer_1, "cnn-ddqn0"))
agents.append(Agent(policy_net_1, target_net_1, DEFAULT_DURABILITY,
optimizer_1, "cnn-ddqn1"))
core_agent = Agent(policy_net_0, target_net_0, DEFAULT_DURABILITY, optimizer_0,
"core")
AGENT_N = len(agents)
# time_stamp = str(int(time.time()))
hyper_params["AGENT_N"] = AGENT_N
json_params = json.dumps(hyper_params)
if not os.path.exists(output_directory):
os.makedirs(output_directory)
with open(PARAMETER_LOG_FILE_PATH, 'wt') as f:
f.write(json_params)
# Deprecated code
# create networks
# policy_net = DQN(n_actions=4).to(device)
# target_net = DQN(n_actions=4).to(device)
# target_net.load_state_dict(policy_net.state_dict())
# create environment
# TODO: Create Environment class
# env = gym.make(ENV_NAME)
# env = make_env(env)
envs = []
for i in range(AGENT_N):
env = Environment()
env = env.get_env()
envs.append(env)
core_env = Environment()
core_env = core_env.get_env()
for agent, env in zip(agents, envs):
agent.set_env(env)
core_agent.set_env(core_env)
# setup optimizer
# optimizer = optim.Adam(policy_net.parameters(), lr=LEARNING_RATE)
# steps_done = 0
# Deprecated
# initialize replay memory
# memory = ReplayMemory(MEMORY_SIZE)
# Hyperdash experiment
exp = Experiment(EXP_NAME, capture_io=False)
print("Learning rate:{}".format(LEARNING_RATE))
exp.param("Learning rate", LEARNING_RATE)
exp.param("Environment", ENV_NAME)
exp.param("Batch size", BATCH_SIZE)
exp.param("Gamma", GAMMA)
exp.param("Episode start", EPS_START)
exp.param("Episode end", EPS_END)
exp.param("Episode decay", EPS_DECAY)
exp.param("Target update", TARGET_UPDATE)
exp.param("Render", str(RENDER))
exp.param("Initial memory", INITIAL_MEMORY)
exp.param("Memory size", MEMORY_SIZE)
# train model
train(envs, agents, core_env, core_agent, 400, AGENT_N, exp)
exp.end()
torch.save(policy_net, output_directory + "/dqn_pong_model")
# EB
exp.end()
# test model
test_env = Environment()
test_env = env.get_env()
policy_net = torch.load(output_directory + "/dqn_pong_model")
exp_test = Experiment(str(EXP_NAME + "_test_step"), capture_io=False)
test(test_env, 1, policy_net, exp_test, render=False)
exp_test.end()
###Output
_____no_output_____
###Markdown
Video vidualization
###Code
display = pyvirtualdisplay.Display(visible=0, size=(1400, 900)).start()
os.environ["DISPLAY"] = ":" + str(display.display) + "." + str(display.screen)
def embed_mp4(filename):
"""Embeds an mp4 file in the notebook."""
video = open(filename,'rb').read()
b64 = base64.b64encode(video)
tag = '''
<video width="640" height="480" controls>
<source src="data:video/mp4;base64,{0}" type="video/mp4">
Your browser does not support the video tag.
</video>'''.format(b64.decode())
return IPython.display.HTML(tag)
embed_mp4("/content/videos/dqn_pong_video/openaigym.video.0.122.video000000.mp4")
# !mv /content/drive/My\ Drive/Colab\ Notebooks/MT/pong_videos /content/drive/My\ Drive/Colab\ Notebooks/MT/pong_videos_1567682751
# !mv /content/dqn_pong_model /content/drive/My\ Drive/Colab\ Notebooks/MT/pong_videos_1567682751/
!mkdir /content/drive/My\ Drive/Colab\ Notebooks/MT/pong_videos_1568005544
!mv ./PongNoFrameskip-v4_*.log /content/drive/My\ Drive/Colab\ Notebooks/MT/pong_videos_1568005544/
!mv ./dqn_pong_model /content/drive/My\ Drive/Colab\ Notebooks/MT/pong_videos_1568005544/
!mv ./videos /content/drive/My\ Drive/Colab\ Notebooks/MT/pong_videos_1568005544/
###Output
_____no_output_____
|
Starter Notebooks/MLOps and Hosting/Hosting Models on SageMaker.ipynb
|
###Markdown
Hosting Models on SageMaker and Automate the WorkflowIn this module you will:- Host a pretrained SKLearn model on SageMaker- Enable autoscaling on your endpoint - Monitor your model- Perform hyperparameter tuning- Redploy a new model to the endpoint- Automate the pipeline using the notebook runner toolkitLet's get started! --- 1. Access your model artifactFirst, you should see a `model.tar.gz` file in this repository. Let's get that in your S3 bucket.
###Code
import sagemaker
import os
sess = sagemaker.Session()
# sagemaker will check to make sure this is a valid tar.gz object
local_model_file = 'model.tar.gz'
bucket = sess.default_bucket()
prefix = 'model-hosting'
s3_path = 's3://{}/{}/'.format(bucket, prefix)
msg = 'aws s3 cp {} {}'.format(local_model_file, s3_path)
os.system(msg)
###Output
_____no_output_____
###Markdown
2. Load your pretrained model artifact into SageMakerNow, we know that this model was trained using the SKLearn container within SageMaker. All we need to do get this into a SageMaker-managed endpoint is set it up as a model. Let's do that here!
###Code
model_data = '{}{}'.format(s3_path, local_model_file)
print (model_data)
%%writefile train.py
import argparse
import pandas as pd
import numpy as np
import os
from sklearn.metrics import confusion_matrix
from sklearn.neural_network import MLPClassifier
from sklearn.externals import joblib
def model_fn(model_dir):
"""Deserialized and return fitted model
Note that this should have the same name as the serialized model in the main method
"""
regr = joblib.load(os.path.join(model_dir, "model.joblib"))
return regr
def predict_fn(input_data, model):
'''return the class and the probability of the class'''
prediction = model.predict(input_data)
pred_prob = model.predict_proba(input_data) #a numpy array
return np.array(pred_prob)
def parse_args():
# Hyperparameters are described here. In this simple example we are just including one hyperparameter.
parser = argparse.ArgumentParser()
parser.add_argument('--max_leaf_nodes', type=int, default=-1)
# Sagemaker specific arguments. Defaults are set in the environment variables.
parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
parser.add_argument('--test', type=str, default = os.environ['SM_CHANNEL_TEST'])
# hyperparameters for tuning
parser.add_argument('--batch-size', type=int, default=256)
parser.add_argument('--lr', type=float, default = 0.001)
args = parser.parse_args()
return args
def train(args):
# Take the set of files and read them all into a single pandas dataframe
train_data=pd.read_csv(os.path.join(args.train, 'train_set.csv'), engine='python')
# labels are in the first column
train_y = train_data['truth']
train_X = train_data[train_data.columns[1:len(train_data)]]
# Now use scikit-learn's MLP Classifier to train the model.
regr = MLPClassifier(random_state=1, max_iter=500, batch_size = args.batch_size, learning_rate_init = args.lr, solver='lbfgs').fit(train_X, train_y)
regr.get_params()
# Print the coefficients of the trained classifier, and save the coefficients
joblib.dump(regr, os.path.join(args.model_dir, "model.joblib"))
return regr
def accuracy(y_pred, y_true):
cm = confusion_matrix(y_pred, y_true)
diagonal_sum = cm.trace()
sum_of_all_elements = cm.sum()
rt = diagonal_sum / sum_of_all_elements
print ('Accuracy: {}'.format(rt))
return rt
def test(regr, args):
test_data=pd.read_csv(os.path.join(args.test, 'test_set.csv'), engine='python')
# labels are in the first column
y_true = test_data['truth']
test_x = test_data[test_data.columns[1:len(test_data)]]
y_pred = regr.predict(test_x)
accuracy(y_pred, y_true)
if __name__ == '__main__':
args = parse_args()
regr = train(args)
test(regr, args)
from sagemaker.sklearn.model import SKLearnModel
role = sagemaker.get_execution_role()
model = SKLearnModel(model_data = model_data,
role = role,
framework_version = '0.20.0',
py_version='py3',
entry_point = 'train.py')
###Output
_____no_output_____
###Markdown
3. Create an Endpoint on SageMakerNow, here comes the complex maneuver. Kidding, it's dirt simple. Let's turn your model into a RESTful API!
###Code
predictor = model.deploy(1, 'ml.m4.2xlarge')
import sagemaker
from sagemaker.sklearn.model import SKLearnPredictor
sess = sagemaker.Session()
# optional. If your kernel times out, or your need to refresh, here's how you can easily point to an existing endpoint
endpoint_name = 'sagemaker-scikit-learn-2020-10-14-15-12-50-644'
predictor = SKLearnPredictor(endpoint_name = endpoint_name, sagemaker_session = sess)
###Output
_____no_output_____
###Markdown
Now let's get some predictions from that endpoint.
###Code
test_set = pd.read_csv('test_set.csv')
y_true = test_set['truth']
test_set.drop('truth', inplace=True, axis=1)
import pandas as pd
y_pred = pd.DataFrame(predictor.predict(test_set))
assert len(y_pred) == test_set.shape[0]
###Output
_____no_output_____
###Markdown
4. Enable Autoscaling on your EndpointFor the sake of argument, let's say we're happy with this model and want to continue supporting it in prod. Our next step might be to enable autoscaling. Let's do that right here.
###Code
import boto3
def get_resource_id(endpoint_name):
client = boto3.client('sagemaker')
response = client.describe_endpoint(
EndpointName=endpoint_name)
variant_name = response['ProductionVariants'][0]['VariantName']
resource_id = 'endpoint/{}/variant/{}'.format(endpoint_name, variant_name)
return resource_id
resource_id = get_resource_id(endpoint_name)
import boto3
role = sagemaker.get_execution_role()
def set_scaling_policy(resource_id, min_capacity = 1, max_capacity = 8, role = role):
scaling_client = boto3.client('application-autoscaling')
response = scaling_client.register_scalable_target(
ServiceNamespace='sagemaker',
ResourceId=resource_id,
ScalableDimension='sagemaker:variant:DesiredInstanceCount',
MinCapacity=min_capacity,
MaxCapacity=max_capacity,
RoleARN=role)
return response
res = set_scaling_policy(resource_id)
###Output
_____no_output_____
###Markdown
5. Enable Model Monitor on your EndpointNow that you have a model up and running, with autoscaling enabled, let's set up model monitor on that endpoint.
###Code
import sagemaker
import os
sess = sagemaker.Session()
bucket = sess.default_bucket()
prefix = 'model-hosting'
s3_capture_upload_path = 's3://{}/{}/model-monitor'.format(bucket, prefix)
print ('about to set up monitoring for endpoint named {}'.format(endpoint_name))
###Output
_____no_output_____
###Markdown
Now, let's set up a data capture config.
###Code
from sagemaker.model_monitor import DataCaptureConfig
data_capture_config = DataCaptureConfig(
enable_capture = True,
sampling_percentage=50,
destination_s3_uri=s3_capture_upload_path,
capture_options=["REQUEST", "RESPONSE"],
csv_content_types=["text/csv"],
json_content_types=["application/json"])
# Now it is time to apply the new configuration and wait for it to be applied
predictor.update_data_capture_config(data_capture_config=data_capture_config)
sess.wait_for_endpoint(endpoint=endpoint_name)
###Output
_____no_output_____
###Markdown
Next step here is to pass in our training data, and ask SageMaker to learn baseline thresholds for all of our features. First, let's make sure the data we used to train our model is stored in S3.
###Code
msg = 'aws s3 cp train_set.csv s3://{}/{}/train/'.format(bucket, prefix)
os.system(msg)
# todo - show them how to get access to this training data
s3_training_data_path = 's3://{}/{}/train/train_set.csv'.format(bucket, prefix)
s3_baseline_results = 's3://{}/{}/model-monitor/baseline-results'.format(bucket, prefix)
from sagemaker.model_monitor import DefaultModelMonitor
from sagemaker.model_monitor.dataset_format import DatasetFormat
my_default_monitor = DefaultModelMonitor(
role=role,
instance_count=1,
instance_type='ml.m5.xlarge',
volume_size_in_gb=20,
max_runtime_in_seconds=3600,
)
my_default_monitor.suggest_baseline(
baseline_dataset=s3_training_data_path,
# change header to false if not included
dataset_format=DatasetFormat.csv(header=False),
output_s3_uri=s3_baseline_results,
wait=True
)
###Output
_____no_output_____
###Markdown
If you like, you can download the results from S3 and analyze. In the interest of time, we'll move on to setting up the monitoring schedule.
###Code
from sagemaker.model_monitor import CronExpressionGenerator
from time import gmtime, strftime
mon_schedule_name = 'bi-hourly'
s3_report_path = 's3://{}/{}/model-monitor/monitoring-job-results'.format(bucket, prefix)
my_default_monitor.create_monitoring_schedule(
monitor_schedule_name=mon_schedule_name,
endpoint_input=endpoint_name,
output_s3_uri=s3_report_path,
statistics=my_default_monitor.baseline_statistics(),
constraints=my_default_monitor.suggested_constraints(),
schedule_cron_expression=CronExpressionGenerator.daily(),
enable_cloudwatch_metrics=True)
###Output
_____no_output_____
###Markdown
--- Tune your model and re-deploy onto the SageMaker EndpointAlright, we made it pretty far already! Now that we have monitoring enabled on this endpoint, let's imagine that something goes awry. We realize that we need a new model hosted on this RESTful API. How are we going to do that? First, let's go about getting a new model. Given that the dataset here is pretty small, less than even 500 rows on the training set, why not try out AutoGluon? AutoGluon is a competitive choice here because it will actually augment our data for us. Said another way, Autogluon will make our original dataset larger by using Transformers and masking columns. Pretty cool!
###Code
!mkdir src
%%writefile src/requirements.txt
autogluon
sagemaker
awscli
boto3
PrettyTable
bokeh
numpy==1.16.1
matplotlib
sagemaker-experiments
%%writefile src/train.py
import ast
import argparse
import logging
import warnings
import os
import json
import glob
import subprocess
import sys
import boto3
import pickle
import pandas as pd
from collections import Counter
from timeit import default_timer as timer
import time
from smexperiments.experiment import Experiment
from smexperiments.trial import Trial
from smexperiments.trial_component import TrialComponent
from smexperiments.tracker import Tracker
sys.path.insert(0, 'package')
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=DeprecationWarning)
from prettytable import PrettyTable
import autogluon as ag
from autogluon import TabularPrediction as task
from autogluon.task.tabular_prediction import TabularDataset
# ------------------------------------------------------------ #
# Training methods #
# ------------------------------------------------------------ #
def du(path):
"""disk usage in human readable format (e.g. '2,1GB')"""
return subprocess.check_output(['du','-sh', path]).split()[0].decode('utf-8')
def __load_input_data(path: str) -> TabularDataset:
"""
Load training data as dataframe
:param path:
:return: DataFrame
"""
input_data_files = os.listdir(path)
try:
input_dfs = [pd.read_csv(f'{path}/{data_file}') for data_file in input_data_files]
return task.Dataset(df=pd.concat(input_dfs))
except:
print(f'No csv data in {path}!')
return None
def train(args):
is_distributed = len(args.hosts) > 1
host_rank = args.hosts.index(args.current_host)
dist_ip_addrs = args.hosts
dist_ip_addrs.pop(host_rank)
ngpus_per_trial = 1 if args.num_gpus > 0 else 0
# load training and validation data
print(f'Train files: {os.listdir(args.train)}')
train_data = __load_input_data(args.train)
print(f'Label counts: {dict(Counter(train_data[args.label]))}')
predictor = task.fit(
train_data=train_data,
label=args.label,
output_directory=args.model_dir,
problem_type=args.problem_type,
eval_metric=args.eval_metric,
stopping_metric=args.stopping_metric,
auto_stack=args.auto_stack, # default: False
hyperparameter_tune=args.hyperparameter_tune, # default: False
feature_prune=args.feature_prune, # default: False
holdout_frac=args.holdout_frac, # default: None
num_bagging_folds=args.num_bagging_folds, # default: 0
num_bagging_sets=args.num_bagging_sets, # default: None
stack_ensemble_levels=args.stack_ensemble_levels, # default: 0
cache_data=args.cache_data,
time_limits=args.time_limits,
num_trials=args.num_trials, # default: None
search_strategy=args.search_strategy, # default: 'random'
search_options=args.search_options,
visualizer=args.visualizer,
verbosity=args.verbosity
)
# Results summary
predictor.fit_summary(verbosity=1)
# Leaderboard on optional test data
if args.test:
print(f'Test files: {os.listdir(args.test)}')
test_data = __load_input_data(args.test)
print('Running model on test data and getting Leaderboard...')
leaderboard = predictor.leaderboard(dataset=test_data, silent=True)
def format_for_print(df):
table = PrettyTable(list(df.columns))
for row in df.itertuples():
table.add_row(row[1:])
return str(table)
print(format_for_print(leaderboard), end='\n\n')
# Files summary
print(f'Model export summary:')
print(f"/opt/ml/model/: {os.listdir('/opt/ml/model/')}")
models_contents = os.listdir('/opt/ml/model/models')
print(f"/opt/ml/model/models: {models_contents}")
print(f"/opt/ml/model directory size: {du('/opt/ml/model/')}\n")
# ------------------------------------------------------------ #
# Training execution #
# ------------------------------------------------------------ #
def str2bool(v):
return v.lower() in ('yes', 'true', 't', '1')
def parse_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.register('type','bool',str2bool) # add type keyword to registries
parser.add_argument('--hosts', type=list, default=json.loads(os.environ['SM_HOSTS']))
parser.add_argument('--current-host', type=str, default=os.environ['SM_CURRENT_HOST'])
parser.add_argument('--num-gpus', type=int, default=os.environ['SM_NUM_GPUS'])
parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR']) # /opt/ml/model
parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAINING'])
parser.add_argument('--test', type=str, default='') # /opt/ml/input/data/test
parser.add_argument('--label', type=str, default='truth',
help="Name of the column that contains the target variable to predict.")
parser.add_argument('--problem_type', type=str, default=None,
help=("Type of prediction problem, i.e. is this a binary/multiclass classification or "
"regression problem options: 'binary', 'multiclass', 'regression'). "
"If `problem_type = None`, the prediction problem type is inferred based "
"on the label-values in provided dataset."))
parser.add_argument('--eval_metric', type=str, default=None,
help=("Metric by which predictions will be ultimately evaluated on test data."
"AutoGluon tunes factors such as hyperparameters, early-stopping, ensemble-weights, etc. "
"in order to improve this metric on validation data. "
"If `eval_metric = None`, it is automatically chosen based on `problem_type`. "
"Defaults to 'accuracy' for binary and multiclass classification and "
"'root_mean_squared_error' for regression. "
"Otherwise, options for classification: [ "
" 'accuracy', 'balanced_accuracy', 'f1', 'f1_macro', 'f1_micro', 'f1_weighted', "
" 'roc_auc', 'average_precision', 'precision', 'precision_macro', 'precision_micro', 'precision_weighted', "
" 'recall', 'recall_macro', 'recall_micro', 'recall_weighted', 'log_loss', 'pac_score']. "
"Options for regression: ['root_mean_squared_error', 'mean_squared_error', "
"'mean_absolute_error', 'median_absolute_error', 'r2']. "
"For more information on these options, see `sklearn.metrics`: "
"https://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics "
"You can also pass your own evaluation function here as long as it follows formatting of the functions "
"defined in `autogluon/utils/tabular/metrics/`. "))
parser.add_argument('--stopping_metric', type=str, default=None,
help=("Metric which models use to early stop to avoid overfitting. "
"`stopping_metric` is not used by weighted ensembles, instead weighted ensembles maximize `eval_metric`. "
"Defaults to `eval_metric` value except when `eval_metric='roc_auc'`, where it defaults to `log_loss`."))
parser.add_argument('--auto_stack', type='bool', default=False,
help=("Whether to have AutoGluon automatically attempt to select optimal "
"num_bagging_folds and stack_ensemble_levels based on data properties. "
"Note: Overrides num_bagging_folds and stack_ensemble_levels values. "
"Note: This can increase training time by up to 20x, but can produce much better results. "
"Note: This can increase inference time by up to 20x."))
parser.add_argument('--hyperparameter_tune', type='bool', default=False,
help=("Whether to tune hyperparameters or just use fixed hyperparameter values "
"for each model. Setting as True will increase `fit()` runtimes."))
parser.add_argument('--feature_prune', type='bool', default=False,
help="Whether or not to perform feature selection.")
parser.add_argument('--holdout_frac', type=float, default=None,
help=("Fraction of train_data to holdout as tuning data for optimizing hyperparameters "
"(ignored unless `tuning_data = None`, ignored if `num_bagging_folds != 0`). "
"Default value is selected based on the number of rows in the training data. "
"Default values range from 0.2 at 2,500 rows to 0.01 at 250,000 rows. "
"Default value is doubled if `hyperparameter_tune = True`, up to a maximum of 0.2. "
"Disabled if `num_bagging_folds >= 2`."))
parser.add_argument('--num_bagging_folds', type=int, default=0,
help=("Number of folds used for bagging of models. When `num_bagging_folds = k`, "
"training time is roughly increased by a factor of `k` (set = 0 to disable bagging). "
"Disabled by default, but we recommend values between 5-10 to maximize predictive performance. "
"Increasing num_bagging_folds will result in models with lower bias but that are more prone to overfitting. "
"Values > 10 may produce diminishing returns, and can even harm overall results due to overfitting. "
"To further improve predictions, avoid increasing num_bagging_folds much beyond 10 "
"and instead increase num_bagging_sets. "))
parser.add_argument('--num_bagging_sets', type=int, default=None,
help=("Number of repeats of kfold bagging to perform (values must be >= 1). "
"Total number of models trained during bagging = num_bagging_folds * num_bagging_sets. "
"Defaults to 1 if time_limits is not specified, otherwise 20 "
"(always disabled if num_bagging_folds is not specified). "
"Values greater than 1 will result in superior predictive performance, "
"especially on smaller problems and with stacking enabled. "
"Increasing num_bagged_sets reduces the bagged aggregated variance without "
"increasing the amount each model is overfit."))
parser.add_argument('--stack_ensemble_levels', type=int, default=0,
help=("Number of stacking levels to use in stack ensemble. "
"Roughly increases model training time by factor of `stack_ensemble_levels+1` "
"(set = 0 to disable stack ensembling). "
"Disabled by default, but we recommend values between 1-3 to maximize predictive performance. "
"To prevent overfitting, this argument is ignored unless you have also set `num_bagging_folds >= 2`."))
parser.add_argument('--hyperparameters', type=lambda s: ast.literal_eval(s), default=None,
help="Refer to docs: https://autogluon.mxnet.io/api/autogluon.task.html")
parser.add_argument('--cache_data', type='bool', default=True,
help=("Whether the predictor returned by this `fit()` call should be able to be further trained "
"via another future `fit()` call. "
"When enabled, the training and validation data are saved to disk for future reuse."))
parser.add_argument('--time_limits', type=int, default=None,
help=("Approximately how long `fit()` should run for (wallclock time in seconds)."
"If not specified, `fit()` will run until all models have completed training, "
"but will not repeatedly bag models unless `num_bagging_sets` is specified."))
parser.add_argument('--num_trials', type=int, default=None,
help=("Maximal number of different hyperparameter settings of each "
"model type to evaluate during HPO. (only matters if "
"hyperparameter_tune = True). If both `time_limits` and "
"`num_trials` are specified, `time_limits` takes precedent."))
parser.add_argument('--search_strategy', type=str, default='random',
help=("Which hyperparameter search algorithm to use. "
"Options include: 'random' (random search), 'skopt' "
"(SKopt Bayesian optimization), 'grid' (grid search), "
"'hyperband' (Hyperband), 'rl' (reinforcement learner)"))
parser.add_argument('--search_options', type=lambda s: ast.literal_eval(s), default=None,
help="Auxiliary keyword arguments to pass to the searcher that performs hyperparameter optimization.")
parser.add_argument('--nthreads_per_trial', type=int, default=None,
help="How many CPUs to use in each training run of an individual model. This is automatically determined by AutoGluon when left as None (based on available compute).")
parser.add_argument('--ngpus_per_trial', type=int, default=None,
help="How many GPUs to use in each trial (ie. single training run of a model). This is automatically determined by AutoGluon when left as None.")
parser.add_argument('--dist_ip_addrs', type=list, default=None,
help="List of IP addresses corresponding to remote workers, in order to leverage distributed computation.")
parser.add_argument('--visualizer', type=str, default='none',
help=("How to visualize the neural network training progress during `fit()`. "
"Options: ['mxboard', 'tensorboard', 'none']."))
parser.add_argument('--verbosity', type=int, default=2,
help=("Verbosity levels range from 0 to 4 and control how much information is printed during fit(). "
"Higher levels correspond to more detailed print statements (you can set verbosity = 0 to suppress warnings). "
"If using logging, you can alternatively control amount of information printed via `logger.setLevel(L)`, "
"where `L` ranges from 0 to 50 (Note: higher values of `L` correspond to fewer print statements, "
"opposite of verbosity levels"))
parser.add_argument('--debug', type='bool', default=False,
help=("Whether to set logging level to DEBUG"))
parser.add_argument('--feature_importance', type='bool', default=True)
return parser.parse_args()
def set_experiment_config(experiment_basename = None):
'''
Optionally takes an base name for the experiment. Has a hard dependency on boto3 installation.
Creates a new experiment using the basename, otherwise simply uses autogluon as basename.
May run into issues on Experiments' requirements for basename config downstream.
'''
now = int(time.time())
if experiment_basename:
experiment_name = '{}-autogluon-{}'.format(experiment_basename, now)
else:
experiment_name = 'autogluon-{}'.format(now)
try:
client = boto3.Session().client('sagemaker')
except:
print ('You need to install boto3 to create an experiment. Try pip install --upgrade boto3')
return ''
try:
Experiment.create(experiment_name=experiment_name,
description="Running AutoGluon Tabular with SageMaker Experiments",
sagemaker_boto_client=client)
print ('Created an experiment named {}, you should be able to see this in SageMaker Studio right now.'.format(experiment_name))
except:
print ('Could not create the experiment. Is your basename properly configured? Also try installing the sagemaker experiments SDK with pip install sagemaker-experiments.')
return ''
return experiment_name
if __name__ == "__main__":
start = timer()
args = parse_args()
# Print SageMaker args
print('\n====== args ======')
for k,v in vars(args).items():
print(f'{k}, type: {type(v)}, value: {v}')
print()
train()
# Package inference code with model export
subprocess.call('mkdir /opt/ml/model/code'.split())
subprocess.call('cp /opt/ml/code/inference.py /opt/ml/model/code/'.split())
elapsed_time = round(timer()-start,3)
print(f'Elapsed time: {elapsed_time} seconds')
print('===== Training Completed =====')
from sagemaker.mxnet.estimator import MXNet
from sagemaker import get_execution_role
role = get_execution_role()
estimator = MXNet(source_dir = 'src',
entry_point = 'train.py',
role=role,
framework_version = '1.7.0',
py_version = 'py3',
instance_count=1,
instance_type='ml.m5.2xlarge',
volume_size=100)
s3_path = 's3://sagemaker-us-east-1-181880743555/model-hosting/test_set.csv'
estimator.fit(s3_path, wait=False)
# from sagemaker.sklearn.estimator import SKLearn
# from sagemaker import get_execution_role
# script_path = 'train.py'
# # first, let's get the estimator defined
# est = SKLearn(entry_point=script_path,
# instance_type="ml.c4.xlarge",
# instance_count = 1,
# role=role,
# sagemaker_session=sess,
# py_version = 'py3',
# framework_version = '0.20.0')
# # then, let's set up the tuning framework
# from sagemaker.tuner import IntegerParameter, CategoricalParameter, ContinuousParameter, HyperparameterTuner
# hyperparameter_ranges = {'lr': ContinuousParameter(0.00001, 0.001),
# 'batch-size': IntegerParameter(25, 300)}
# objective_metric_name = 'Accuracy'
# objective_type = 'Maximize'
# metric_definitions = [{'Name': 'Accuracy',
# 'Regex': 'Accuracy: ([0-9\\.]+)'}]
# tuner = HyperparameterTuner(est,
# objective_metric_name,
# hyperparameter_ranges,
# metric_definitions,
# max_jobs=20,
# max_parallel_jobs=3,
# objective_type=objective_type)
# msg = 'aws s3 cp test_set.csv s3://{}/{}/ && aws s3 cp train_set.csv s3://{}/{}/'.format(bucket, prefix, bucket, prefix)
# os.system(msg)
# # may complain about not wanting headers
# inputs = {'train': 's3://{}/{}/train_set.csv'.format(bucket, prefix),
# 'test': 's3://{}/{}/test_set.csv'.format(bucket, prefix)}
# tuner.fit(inputs)
###Output
_____no_output_____
###Markdown
Redeploy to existing SageMaker Endpoint
###Code
from sagemaker.tuner import HyperparameterTuner
job_name = 'sagemaker-scikit-lea-201014-1830'
tuner = HyperparameterTuner.attach(job_name)
best_estimator = tuner.best_estimator()
model = best_estimator.create_model()
model_name = model.name
import boto3
import random
import time
import datetime
def create_model(model, now):
sm_client = boto3.client('sagemaker')
x = random.randint(1, 100)
model_name = '{}-{}'.format(model.name, now)
response = sm_client.create_model(ModelName=model_name,
PrimaryContainer={'ContainerHostname': 'string','Image': model.image_uri, 'ModelDataUrl': model.model_data},
ExecutionRoleArn= 'arn:aws:iam::181880743555:role/service-role/AmazonSageMaker-ExecutionRole-20200929T125134')
return response
def get_endpoint_config(model_name, now):
sm_client = boto3.client('sagemaker')
endpoint_config_name = 'ec-{}-{}'.format(model_name, now)
response = sm_client.create_endpoint_config(EndpointConfigName= endpoint_config_name,
ProductionVariants=[{'VariantName': 'v-{}'.format(model_name),
'ModelName': model_name,
'InitialInstanceCount': 1,
'InstanceType':'ml.m5.large'}])
return endpoint_config_name
def update_endpoint(model_name, endpoint_name, now):
sm_client = boto3.client('sagemaker')
endpoint_config = get_endpoint_config(model_name, now)
# deregister a scaling policy
resource_id = get_resource_id(endpoint_name)
client = boto3.client('application-autoscaling')
try:
response = client.deregister_scalable_target(ServiceNamespace='sagemaker',
ResourceId=resource_id,
ScalableDimension='sagemaker:variant:DesiredInstanceCount')
except:
print ('no autoscaling policy to deregister, continuing')
# get monitoring schedules
try:
response = sm_client.list_monitoring_schedules(EndpointName=endpoint_name,
MaxResults=10,
StatusEquals='Scheduled')
# delete monitoring schedules
for each in response['MonitoringScheduleSummaries']:
name = each['MonitoringScheduleName']
response = sm_client.delete_monitoring_schedule(MonitoringScheduleName=name)
except:
print ('already deleted the monitoring schedules')
response = sm_client.update_endpoint(EndpointName=endpoint_name,
EndpointConfigName=endpoint_config)
return response
now = str(datetime.datetime.now()).split('.')[-1]
endpoint_name = 'sagemaker-scikit-learn-2020-10-14-15-12-50-644'
create_model(model, now)
update_endpoint(model_name, endpoint_name, now)
###Output
_____no_output_____
###Markdown
--- Automate with Notebook RunnerNow we're able to monitor new endpoints, we want the ability to automate this whole flow so that we can do it rapidly. As it so happens, a simple and fast way of doing that is using SageMaker processing jobs, CloudWatch, and Lambda. Luckily we can import all of the infrastructure we need using a simple toolkit, which we'll step through here.GitHub notes are right here: https://github.com/aws-samples/sagemaker-run-notebook
###Code
# todo - make sure they have the right execution role here, add cfn all access, then a trust relationship, then inlines to allow create stack, plus codebuild create project nad start build
# !wget https://github.com/aws-samples/sagemaker-run-notebook/releases/download/v0.15.0/sagemaker_run_notebook-0.15.0.tar.gz
# !pip install sagemaker_run_notebook-0.15.0.tar.gz
# !run-notebook create-infrastructure --update
%%writefile requirements.txt
awscli
boto3
sagemaker
pandas
sklearn
# !run-notebook create-container --requirements requirements.txt
# !wget https://github.com/aws-samples/sagemaker-run-notebook/releases/download/v0.15.0/install-run-notebook.sh
###Output
_____no_output_____
|
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