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from featurizers.speech_featurizers import SpeechFeaturizer
from configs.config import Config
from random import shuffle
import numpy as np
from vocab.vocab import Vocab
import os
import math
import librosa
import tensorflow as tf
def wav_padding(wav_data_lst, wav_max_len, fbank_dim):
wav_lens = [len(data) for data in wav_data_lst]
# input wav from 1200 frames down sample 8 times to 150 frames
wav_lens = [math.ceil(x/8) for x in wav_lens]
wav_lens = np.array(wav_lens)
new_wav_data_lst = np.zeros((len(wav_data_lst), wav_max_len, fbank_dim))
for i in range(len(wav_data_lst)):
new_wav_data_lst[i, :wav_data_lst[i].shape[0], :] = wav_data_lst[i]
return new_wav_data_lst, wav_lens
class DatDataSet:
def __init__(self,
batch_size,
data_type,
vocab: Vocab,
speech_featurizer: SpeechFeaturizer,
config: Config):
self.batch_size = batch_size
self.data_type = data_type
self.vocab = vocab
self.data_path =config.dataset_config['data_path']
self.corpus_name = config.dataset_config['corpus_name']
self.fbank_dim = config.speech_config['num_feature_bins']
self.max_audio_length =config.dataset_config['max_audio_length']
self.mel_banks = config.speech_config['num_feature_bins']
self.file_nums = config.dataset_config['file_nums']
self.language_classes = config.running_config['language_classes']
self.suffix = config.dataset_config['suffix']
self.READ_BUFFER_SIZE = 2 * 1024 * 1024 * 1024
self.shuffle = True
self.blank = 0
self.source_init()
def source_init(self):
self.dat_file_list, self.txt_file_list = self.get_dat_txt_list(self.data_type)
print('>>', self.data_type, 'load dat files:', len(self.dat_file_list))
print('>>', self.data_type, 'load txt files:', len(self.txt_file_list))
max_binary_file_size = max([os.path.getsize(dat) for dat in self.dat_file_list])
print('>> max binary file size:', max_binary_file_size)
# alloc a huge memory block
self.feature_binary = np.zeros(max_binary_file_size // 4 + 1, np.float32)
def get_dat_txt_list(self, dir_name):
corpus_dir = self.data_path+'/'+self.corpus_name + '/'
print('!!', corpus_dir)
file_lst = os.listdir(corpus_dir)
txt_file_lst = []
dat_file_lst = []
for align_file in file_lst:
if align_file.endswith(self.suffix):
file_name = align_file[:-len(self.suffix)]
dat_file = file_name + '.dat'
if dir_name in file_name:
# if dir_name in ['dev', 'test']:
# dat_file = dat_file.replace(dir_name, 'train')
dat_file_lst.append(corpus_dir + dat_file)
txt_file_lst.append(corpus_dir + align_file)
print('*********',dir_name, txt_file_lst, dat_file_lst)
return dat_file_lst, txt_file_lst
def load_dat_file(self, dat_file_path):
f = open(dat_file_path, 'rb')
pos = 0
buf = f.read(self.READ_BUFFER_SIZE)
while len(buf) > 0:
nbuf = np.frombuffer(buf, np.float32)
self.feature_binary[pos: pos + len(nbuf)] = nbuf
pos += len(nbuf)
buf = f.read(self.READ_BUFFER_SIZE)
def get_batch(self):
while 1:
shuffle_did_list = [i for i in range(len(self.dat_file_list))]
if self.shuffle:
shuffle(shuffle_did_list)
for did in shuffle_did_list:
wav_lst = []
label_lst = []
self.load_dat_file(self.dat_file_list[did])
txt_file = open(self.txt_file_list[did], 'r', encoding='utf8')
utt_lines = txt_file.readlines()
txt_lines = utt_lines
if self.shuffle:
shuffle(txt_lines)
# sort lines by wav len
# txt_lines = sorted(
# txt_lines,
# key=lambda line: int(line.split('\t')[0].split(':')[2]) - int(line.split('\t')[0].split(':')[1]),
# reverse=False)
for line in txt_lines:
wav_file, label = line.split('\t')
wav_lst.append(wav_file)
label_lst.append(label.strip('\n'))
shuffle_list = [i for i in range(len(wav_lst) // self.batch_size)]
if self.shuffle:
shuffle(shuffle_list)
for i in shuffle_list:
begin = i * self.batch_size
end = begin + self.batch_size
sub_list = list(range(begin, end, 1))
# label batch
label_data_lst = [label_lst[index] for index in sub_list]
prediction = np.array(
[self.vocab.token_list.index(line) for
line in label_data_lst],
dtype=np.int32)
feature_lst = []
wav_path = []
get_next_batch = False
for index in sub_list:
# data_aishell/wav/test/S0764/BAC009S0764W0121.wav:0:33680 chinese
_, start, end = wav_lst[index].split(':')
feature = self.feature_binary[int(start): int(end)]
feature = np.reshape(feature, (-1, 80))
feature = feature[:self.max_audio_length, :]
feature_lst.append(feature)
wav_path.append(wav_lst[index])
if get_next_batch:
continue
features, input_length = wav_padding(feature_lst, self.max_audio_length, self.fbank_dim)
yield features, input_length, prediction
class TxtDataSet:
def __init__(self,
batch_size,
data_type,
vocab,
speech_featurizer: SpeechFeaturizer,
config: Config
):
self.batch_size = batch_size
self.data_type = data_type
self.vocab = vocab
self.feature_extracter = speech_featurizer
self.data_path = config.dataset_config['data_path']
self.corpus_name = config.dataset_config['corpus_name']
self.fbank_dim = config.speech_config['num_feature_bins']
self.max_audio_length =config.dataset_config['max_audio_length']
self.mel_banks = config.speech_config['num_feature_bins']
self.file_nums = config.dataset_config['file_nums']
self.data_length = config.dataset_config['data_length']
self.shuffle = True
self.sentence_list = []
self.wav_lst = []
self.label_lst = []
self.max_sentence_length = 0
self.source_init()
def source_init(self):
read_files = []
if self.data_type == 'train':
read_files.append(self.corpus_name + '_train.txt')
elif self.data_type == 'dev':
read_files.append(self.corpus_name + '_dev.txt')
elif self.data_type == 'test':
read_files.append(self.corpus_name + '_test.txt')
print('data type:{} \n files:{}'.format(self.data_type, read_files))
total_lines = 0
for sub_file in read_files:
with open(sub_file, 'r', encoding='utf8') as f:
for line in f:
wav_file, label = line.split(' ', 1)
label = label.strip('\n').split()
self.label_lst.append(label)
self.wav_lst.append(wav_file)
total_lines += 1
if self.data_length:
if total_lines == self.data_length:
break
if total_lines % 10000 == 0:
print('\rload', total_lines, end='', flush=True)
if not self.data_length:
self.wav_lst = self.wav_lst[:self.data_length]
self.label_lst = self.label_lst[:self.data_length]
print('number of', self.data_type, 'data:', len(self.wav_lst))
def get_batch(self):
shuffle_list = [i for i in range(len(self.wav_lst))]
while 1:
if self.shuffle:
shuffle(shuffle_list)
for i in range(len(self.wav_lst) // self.batch_size):
begin = i * self.batch_size
end = begin + self.batch_size
sub_list = shuffle_list[begin:end]
label_data_lst = [self.label_lst[index] for index in sub_list]
prediction = np.array(
[self.vocab.token_list.index(line[0]) for
line in label_data_lst],
dtype=np.int32)
feature_lst = []
wav_path = []
get_next_batch = False
for index in sub_list:
# start = time.time()
audio, _ = librosa.load(self.data_path + self.wav_lst[index], sr=16000)
if len(audio) == 0:
get_next_batch = True
break
feature = self.feature_extracter.extract(audio)
feature_lst.append(feature)
wav_path.append(self.wav_lst[index])
if get_next_batch:
continue # get next batch
features, input_length = wav_padding(feature_lst, self.max_audio_length, self.fbank_dim)
yield features,input_length, prediction
def create_dataset(batch_size, load_type, data_type, speech_featurizer, config, vocab):
"""
batch_size: global batch size
data_type: the type of lode data, supports type: txt, dat()
"""
if load_type == 'dat':
dataset = DatDataSet(batch_size, data_type, vocab, speech_featurizer, config)
dataset = tf.data.Dataset.from_generator(dataset.get_batch,
output_types=(tf.float32, tf.int32, tf.int32),
output_shapes=([None, None, config.speech_config['num_feature_bins']],
[None], [None]))
elif load_type == 'txt':
dataset = TxtDataSet(batch_size, data_type, vocab, speech_featurizer, config)
dataset = tf.data.Dataset.from_generator(dataset.get_batch,
output_types=(tf.float32, tf.int32, tf.int32),
output_shapes=([None, None, config.speech_config['num_feature_bins']],
[None], [None]))
else:
print('load_type must be dat or txt!!')
return
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA.DATA
dataset = dataset.with_options(options)
return dataset |