Upload folder using huggingface_hub
Browse files- app/content.py +1 -3
- app/draw_diagram.py +58 -51
- app/pages.py +6 -18
app/content.py
CHANGED
@@ -145,9 +145,7 @@ dataset_diaplay_information = {
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'YTB-SQA-Batch1': 'Under Development',
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'YTB-SDS-Batch1': 'Under Development',
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'YTB-PQA-Batch1': 'Under Development',
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}
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'YTB-SQA-Batch1': 'Under Development',
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'YTB-SDS-Batch1': 'Under Development',
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'YTB-PQA-Batch1': 'Under Development',
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}
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app/draw_diagram.py
CHANGED
@@ -1,6 +1,8 @@
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import streamlit as st
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import pandas as pd
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import numpy as np
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from streamlit_echarts import st_echarts
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from app.show_examples import *
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from app.content import *
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@@ -11,47 +13,56 @@ from model_information import get_dataframe
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info_df = get_dataframe()
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def
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folder = f"./results_organized/{metrics}/"
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# Load the results from CSV
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data_path = f'{folder}/{category_name.lower()}.csv'
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chart_data = pd.read_csv(data_path).round(3)
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dataset_name = displayname2datasetname[displayname]
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chart_data = chart_data[['Model', dataset_name]]
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# Rename to proper display name
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chart_data = chart_data.rename(columns=datasetname2diaplayname)
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""", unsafe_allow_html=True)
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# remap model names
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display_model_names = {key.strip() :val.strip() for key, val in zip(info_df['Original Name'], info_df['Proper Display Name'])}
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chart_data['model_show'] = chart_data['Model'].map(lambda x: display_model_names.get(x, x))
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models = st.multiselect("Please choose the model",
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chart_data = chart_data[chart_data['model_show'].isin(models)]
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chart_data = chart_data.sort_values(by=[displayname], ascending=cus_sort).dropna(axis=0)
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if len(chart_data) == 0: return
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@@ -62,28 +73,27 @@ def draw(folder_name, category_name, displayname, metrics, cus_sort=True):
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with st.container():
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st.markdown('##### TABLE')
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model_link = {key.strip(): val for key, val in zip(info_df['Proper Display Name'], info_df['Link'])}
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-
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-
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# Format numeric columns to 2 decimal places
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#chart_data_table[chart_data_table.columns[1]] = chart_data_table[chart_data_table.columns[1]].apply(lambda x: round(float(x), 3) if isinstance(float(x), (int, float)) else float(x))
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-
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def highlight_first_element(x):
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# Create a DataFrame with the same shape as the input
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df_style = pd.DataFrame('', index=x.index, columns=x.columns)
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# Apply background color to the first element in row 0 (df[0][0])
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# df_style.iloc[0, 1] = 'background-color: #b0c1d7; color: white'
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df_style.iloc[0, 1] = 'background-color: #b0c1d7'
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return df_style
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if
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'LibriSpeech-Clean',
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'LibriSpeech-Other',
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'CommonVoice-15-EN',
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st.dataframe(
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styled_df,
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column_config={
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'model_show': 'Model',
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chart_data_table.columns[1]: {'alignment': 'left'},
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"model_link": st.column_config.LinkColumn(
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"Model Link",
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),
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},
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hide_index=True,
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use_container_width=True
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st.markdown('##### CHART')
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# Get Values
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data_values =
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# Calculate Q1 and Q3
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q1 = data_values.quantile(0.25)
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"type": "category",
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"boundaryGap": True,
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"triggerEvent": True,
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"data":
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}
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],
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"yAxis": [{"type": "value",
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# "splitNumber": 10
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}],
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"series": [{
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"name": f"{
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"type": "bar",
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"data":
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}],
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}
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@@ -242,7 +250,6 @@ def draw(folder_name, category_name, displayname, metrics, cus_sort=True):
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st.session_state.show_examples = not st.session_state.show_examples
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if st.session_state.show_examples:
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st.markdown('To be implemented')
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# # if dataset_name in ['Earnings21-Test', 'Earnings22-Test', 'Tedlium3-Test', 'Tedlium3-Long-form-Test']:
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import streamlit as st
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import pandas as pd
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import numpy as np
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import json
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from streamlit_echarts import st_echarts
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from app.show_examples import *
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from app.content import *
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info_df = get_dataframe()
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def draw_table(dataset_displayname, metrics):
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dataset_nickname = displayname2datasetname[dataset_displayname]
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with open('organize_model_results.json', 'r') as f:
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organize_model_results = json.load(f)
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model_results = organize_model_results[dataset_nickname][metrics]
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model_name_mapping = {key.strip(): val for key, val in zip(info_df['Original Name'], info_df['Proper Display Name'])}
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model_results = {model_name_mapping.get(key, key): val for key, val in model_results.items()}
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# folder = f"./results_organized/{metrics}/"
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# # Load the results from CSV
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# data_path = f'{folder}/{category_name.lower()}.csv'
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# chart_data = pd.read_csv(data_path).round(3)
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# dataset_name = displayname2datasetname[displayname]
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# chart_data = chart_data[['Model', dataset_name]]
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# # Rename to proper display name
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# chart_data = chart_data.rename(columns=datasetname2diaplayname)
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# st.markdown("""
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# <style>
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# .stMultiSelect [data-baseweb=select] span {
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# max-width: 800px;
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# font-size: 0.9rem;
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# background-color: #3C6478 !important; /* Background color for selected items */
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# color: white; /* Change text color */
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# back
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# }
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# </style>
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# """, unsafe_allow_html=True)
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# # remap model names
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# display_model_names = {key.strip() :val.strip() for key, val in zip(info_df['Original Name'], info_df['Proper Display Name'])}
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# chart_data['model_show'] = chart_data['Model'].map(lambda x: display_model_names.get(x, x))
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# models = st.multiselect("Please choose the model",
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# sorted(chart_data['model_show'].tolist()),
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# default = sorted(chart_data['model_show'].tolist()),
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# )
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# chart_data = chart_data[chart_data['model_show'].isin(models)]
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# chart_data = chart_data.sort_values(by=[displayname], ascending=cus_sort).dropna(axis=0)
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# if len(chart_data) == 0: return
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with st.container():
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st.markdown('##### TABLE')
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model_link_mapping = {key.strip(): val for key, val in zip(info_df['Proper Display Name'], info_df['Link'])}
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chart_data_table = pd.DataFrame(list(model_results.items()), columns=["model_show", dataset_displayname])
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chart_data_table["model_link"] = chart_data_table["model_show"].map(model_link_mapping)
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# chart_data['model_link'] = chart_data['model_show'].map(model_link)
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# chart_data_table = chart_data[['model_show', chart_data.columns[1], chart_data.columns[3]]]
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# Format numeric columns to 2 decimal places
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#chart_data_table[chart_data_table.columns[1]] = chart_data_table[chart_data_table.columns[1]].apply(lambda x: round(float(x), 3) if isinstance(float(x), (int, float)) else float(x))
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# dataset_name = chart_data_table.columns[1]
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def highlight_first_element(x):
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# Create a DataFrame with the same shape as the input
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df_style = pd.DataFrame('', index=x.index, columns=x.columns)
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df_style.iloc[0, 1] = 'background-color: #b0c1d7'
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return df_style
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if dataset_displayname in [
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'LibriSpeech-Clean',
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'LibriSpeech-Other',
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'CommonVoice-15-EN',
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st.dataframe(
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styled_df,
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column_config={
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'model_show' : 'Model',
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chart_data_table.columns[1]: {'alignment': 'left'},
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"model_link" : st.column_config.LinkColumn("Model Link"),
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},
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hide_index=True,
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use_container_width=True
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st.markdown('##### CHART')
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# Get Values
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data_values = chart_data_table.iloc[:, 1]
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# Calculate Q1 and Q3
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q1 = data_values.quantile(0.25)
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"type": "category",
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"boundaryGap": True,
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"triggerEvent": True,
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"data": chart_data_table['model_show'].tolist(),
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}
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],
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"yAxis": [{"type": "value",
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# "splitNumber": 10
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}],
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"series": [{
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"name": f"{dataset_nickname}",
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"type": "bar",
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"data": chart_data_table[f'{dataset_displayname}'].tolist(),
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}],
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}
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st.session_state.show_examples = not st.session_state.show_examples
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if st.session_state.show_examples:
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st.markdown('To be implemented')
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# # if dataset_name in ['Earnings21-Test', 'Earnings22-Test', 'Tedlium3-Test', 'Tedlium3-Long-form-Test']:
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app/pages.py
CHANGED
@@ -4,7 +4,6 @@ from app.content import *
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from app.summarization import *
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def dataset_contents(dataset, metrics):
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custom_css = """
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<style>
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.my-dataset-info {
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@@ -39,7 +38,6 @@ def dashboard():
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**Resource for AudioLLMs:** [][gh2]
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""")
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st.markdown("""
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#### Recent updates
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- **Jan. 2025**: AudioBench is officially accepted to NAACL 2025!
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""")
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st.divider()
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st.markdown("""
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#### Evaluating Audio-based Large Language Models
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"""
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)
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with st.container():
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st.markdown('''
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''')
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year={2024}
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}
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```
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-
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""")
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def asr_english():
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st.title("Task: Automatic Speech Recognition - English")
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left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
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with left:
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-
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if
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if
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sum_table_mulit_metrix('asr_english', ['wer'])
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else:
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dataset_contents(dataset_diaplay_information[
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-
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from app.summarization import *
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def dataset_contents(dataset, metrics):
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custom_css = """
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<style>
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.my-dataset-info {
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**Resource for AudioLLMs:** [][gh2]
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""")
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st.markdown("""
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#### Recent updates
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- **Jan. 2025**: AudioBench is officially accepted to NAACL 2025!
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""")
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st.divider()
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st.markdown("""
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#### Evaluating Audio-based Large Language Models
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"""
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)
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with st.container():
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st.markdown('''
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''')
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year={2024}
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}
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```
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""")
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def asr_english():
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st.title("Task: Automatic Speech Recognition - English")
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left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
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with left:
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tab_section = st.selectbox('Dataset', filters_levelone)
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if tab_section:
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if tab_section in sum:
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sum_table_mulit_metrix('asr_english', ['wer'])
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else:
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dataset_contents(dataset_diaplay_information[tab_section], metrics_info['wer'])
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draw_table(tab_section, 'wer')
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