def check(self, data_size):
print('=' * 50)
print(' data_size: %s' % str(data_size))
print('=' * 50)
for data_type in ['dev', 'eval1', 'eval2', 'eval3']:
if data_type == 'train':
label_paths = path.trans(data_type='train_' + data_size)
else:
label_paths = path.trans(data_type=data_type)
save_vocab_file = True if data_type == 'train'
is_test = True if 'eval' in data_type else False
print('---------- %s ----------' % data_type)
read_sdb(
label_paths=label_paths,
data_size=data_size,
vocab_file_save_path=mkdir_join('../config', 'vocab_files'),
is_test=is_test,
data_type=data_type)
def check(self, normalize, tool):
print('==================================================')
print(' normalize: %s' % normalize)
print(' tool: %s' % tool)
print('==================================================')
audio_paths = htk_paths if tool == 'htk' else wav_paths
print('---------- train ----------')
speaker_dict = read_sdb(label_paths=label_paths['train'],
data_size='subset',
vocab_file_save_path='../config/mapping_files',
is_training=True,
save_vocab_file=True)
global_mean_male, global_mean_female, global_std_male, global_std_female = read_audio(
audio_paths=audio_paths['train'],
speaker_dict=speaker_dict,
tool=tool,
config=CONFIG,
normalize=normalize,
is_training=True)
for data_type in ['dev', 'eval1', 'eval2', 'eval3']:
print('---------- %s ----------' % data_type)
speaker_dict = read_sdb(
label_paths=label_paths[data_type],
data_size='subset',
vocab_file_save_path='../config/mapping_files',
is_test=True)
read_audio(audio_paths=audio_paths[data_type],
speaker_dict=speaker_dict,
tool=tool,
config=CONFIG,
normalize=normalize,
is_training=False,
global_mean_male=global_mean_male,
global_mean_female=global_mean_female,
global_std_male=global_std_male,
global_std_female=global_std_female)
def main(data_size):
speaker_dict_dict = {} # dict of speaker_dict
for data_type in ['train', 'eval1', 'eval2', 'eval3']:
print('=' * 50)
print(' ' * 20 + data_type + ' (' + data_size + ')' + ' ' * 20)
print('=' * 50)
########################################
# labels
########################################
if data_type == 'train':
label_paths = path.trans(data_type='train_' + data_size)
else:
label_paths = path.trans(data_type=data_type)
save_vocab_file = True if data_type == 'train' else False
is_test = True if 'eval' in data_type else False
print('=> Processing transcripts...')
speaker_dict_dict[data_type] = read_sdb(
label_paths=label_paths,
data_size=data_size,
vocab_file_save_path=mkdir_join('./config', 'vocab_files'),
save_vocab_file=save_vocab_file,
is_test=is_test,
data_type=data_type)
########################################
# inputs
########################################
print('\n=> Processing input data...')
input_save_path = mkdir_join(args.feature_save_path, args.save_format,
data_size)
if isfile(join(input_save_path, data_type, 'complete.txt')):
print('Already exists.')
else:
if args.save_format == 'wav':
########################################
# Split WAV files per utterance
########################################
if data_type == 'train':
wav_paths = path.wav(corpus='train' + data_size)
else:
wav_paths = path.wav(corpus=data_type)
split_wav(wav_paths=wav_paths,
speaker_dict=speaker_dict_dict[data_type],
save_path=mkdir_join(input_save_path, data_type))
# NOTE: ex.) save_path:
# csj/feature/save_format/data_size/data_type/speaker/utt_name.npy
elif args.save_format in ['numpy', 'htk']:
if data_type == 'train':
if args.tool == 'htk':
audio_paths = path.htk(data_type='train_' + data_size)
else:
audio_paths = path.wav(data_type='train_' + data_size)
is_training = True
global_mean_male, global_std_male, global_mean_female, global_std_female = None, None, None, None
else:
if args.tool == 'htk':
audio_paths = path.htk(data_type=data_type)
else:
audio_paths = path.wav(data_type=data_type)
is_training = False
# Load statistics over train dataset
global_mean_male = np.load(
join(input_save_path, 'train/global_mean_male.npy'))
global_std_male = np.load(
join(input_save_path, 'train/global_std_male.npy'))
global_mean_female = np.load(
join(input_save_path, 'train/global_mean_female.npy'))
global_std_female = np.load(
join(input_save_path, 'train/global_std_female.npy'))
read_audio(audio_paths=audio_paths,
speaker_dict=speaker_dict_dict[data_type],
tool=args.tool,
config=CONFIG,
normalize=args.normalize,
is_training=is_training,
save_path=mkdir_join(input_save_path, data_type),
save_format=args.save_format,
global_mean_male=global_mean_male,
global_std_male=global_std_male,
global_mean_female=global_mean_female,
global_std_female=global_std_female)
# NOTE: ex.) save_path:
# csj/feature/save_format/data_size/data_type/speaker/*.npy
# Make a confirmation file to prove that dataset was saved
# correctly
with open(join(input_save_path, data_type, 'complete.txt'),
'w') as f:
f.write('')
########################################
# dataset (csv)
########################################
print('\n=> Saving dataset files...')
dataset_save_path = mkdir_join(args.dataset_save_path,
args.save_format, data_size, data_type)
df_columns = ['frame_num', 'input_path', 'transcript']
df_kanji = pd.DataFrame([], columns=df_columns)
df_kanji_divide = pd.DataFrame([], columns=df_columns)
df_kana = pd.DataFrame([], columns=df_columns)
df_kana_divide = pd.DataFrame([], columns=df_columns)
df_phone = pd.DataFrame([], columns=df_columns)
df_phone_divide = pd.DataFrame([], columns=df_columns)
df_word_freq1 = pd.DataFrame([], columns=df_columns)
df_word_freq5 = pd.DataFrame([], columns=df_columns)
df_word_freq10 = pd.DataFrame([], columns=df_columns)
df_word_freq15 = pd.DataFrame([], columns=df_columns)
with open(join(input_save_path, data_type, 'frame_num.pickle'),
'rb') as f:
frame_num_dict = pickle.load(f)
utt_count = 0
df_kanji_list, df_kanji_divide_list = [], []
df_kana_list, df_kana_divide_list = [], []
df_phone_list, df_phone_divide_list = [], []
df_word_freq1_list, df_word_freq5_list = [], []
df_word_freq10_list, df_word_freq15_list = [], []
speaker_dict = speaker_dict_dict[data_type]
for speaker, utt_dict in tqdm(speaker_dict.items()):
for utt_index, utt_info in utt_dict.items():
kanji_indices, kanji_divide_indices = utt_info[2:4]
kana_indices, kana_divide_indices = utt_info[4:6]
phone_indices, phone_divide_indices = utt_info[6:8]
word_freq1_indices, word_freq5_indices = utt_info[8:10]
word_freq10_indices, word_freq15_indices = utt_info[10:12]
if args.save_format == 'numpy':
input_utt_save_path = join(
input_save_path, data_type, speaker,
speaker + '_' + utt_index + '.npy')
elif args.save_format == 'htk':
input_utt_save_path = join(
input_save_path, data_type, speaker,
speaker + '_' + utt_index + '.htk')
elif args.save_format == 'wav':
input_utt_save_path = path.utt2wav(utt_index)
else:
raise ValueError('save_format is numpy or htk or wav.')
assert isfile(input_utt_save_path)
frame_num = frame_num_dict[speaker + '_' + utt_index]
df_kanji = add_element(
df_kanji, [frame_num, input_utt_save_path, kanji_indices])
df_kanji_divide = add_element(
df_kanji_divide,
[frame_num, input_utt_save_path, kanji_divide_indices])
df_kana = add_element(
df_kana, [frame_num, input_utt_save_path, kana_indices])
df_kana_divide = add_element(
df_kana_divide,
[frame_num, input_utt_save_path, kana_divide_indices])
df_phone = add_element(
df_phone, [frame_num, input_utt_save_path, phone_indices])
df_phone_divide = add_element(
df_phone_divide,
[frame_num, input_utt_save_path, phone_divide_indices])
df_word_freq1 = add_element(
df_word_freq1,
[frame_num, input_utt_save_path, word_freq1_indices])
df_word_freq5 = add_element(
df_word_freq5,
[frame_num, input_utt_save_path, word_freq5_indices])
df_word_freq10 = add_element(
df_word_freq10,
[frame_num, input_utt_save_path, word_freq10_indices])
df_word_freq15 = add_element(
df_word_freq15,
[frame_num, input_utt_save_path, word_freq15_indices])
utt_count += 1
# Reset
if utt_count == 10000:
df_kanji_list.append(df_kanji)
df_kanji_divide_list.append(df_kanji_divide)
df_kana_list.append(df_kana)
df_kana_divide_list.append(df_kana_divide)
df_phone_list.append(df_phone)
df_phone_divide_list.append(df_phone_divide)
df_word_freq1_list.append(df_word_freq1)
df_word_freq5_list.append(df_word_freq5)
df_word_freq10_list.append(df_word_freq10)
df_word_freq15_list.append(df_word_freq15)
df_kanji = pd.DataFrame([], columns=df_columns)
df_kanji_divide = pd.DataFrame([], columns=df_columns)
df_kana = pd.DataFrame([], columns=df_columns)
df_kana_divide = pd.DataFrame([], columns=df_columns)
df_phone = pd.DataFrame([], columns=df_columns)
df_phone_divide = pd.DataFrame([], columns=df_columns)
df_word_freq1 = pd.DataFrame([], columns=df_columns)
df_word_freq5 = pd.DataFrame([], columns=df_columns)
df_word_freq10 = pd.DataFrame([], columns=df_columns)
df_word_freq15 = pd.DataFrame([], columns=df_columns)
utt_count = 0
# Last dataframe
df_kanji_list.append(df_kanji)
df_kanji_divide_list.append(df_kanji_divide)
df_kana_list.append(df_kana)
df_kana_divide_list.append(df_kana_divide)
df_phone_list.append(df_phone)
df_phone_divide_list.append(df_phone_divide)
df_word_freq1_list.append(df_word_freq1)
df_word_freq5_list.append(df_word_freq5)
df_word_freq10_list.append(df_word_freq10)
df_word_freq15_list.append(df_word_freq15)
# Concatenate all dataframes
df_kanji = df_kanji_list[0]
df_kanji_divide = df_kanji_divide_list[0]
df_kana = df_kana_list[0]
df_kana_divide = df_kana_divide_list[0]
df_phone = df_phone_list[0]
df_phone_divide = df_phone_divide_list[0]
df_word_freq1 = df_word_freq1_list[0]
df_word_freq5 = df_word_freq5_list[0]
df_word_freq10 = df_word_freq10_list[0]
df_word_freq15 = df_word_freq15_list[0]
for df_i in df_kanji_list[1:]:
df_kanji = pd.concat([df_kanji, df_i], axis=0)
for df_i in df_kanji_divide_list[1:]:
df_kanji_divide = pd.concat([df_kanji_divide, df_i], axis=0)
for df_i in df_kana_list[1:]:
df_kana = pd.concat([df_kana, df_i], axis=0)
for df_i in df_kana_divide_list[1:]:
df_kana_divide = pd.concat([df_kana_divide, df_i], axis=0)
for df_i in df_phone_list[1:]:
df_phone = pd.concat([df_phone, df_i], axis=0)
for df_i in df_phone_divide_list[1:]:
df_phone_divide = pd.concat([df_phone_divide, df_i], axis=0)
for df_i in df_word_freq1_list[1:]:
df_word_freq1 = pd.concat([df_word_freq1, df_i], axis=0)
for df_i in df_word_freq5_list[1:]:
df_word_freq5 = pd.concat([df_word_freq5, df_i], axis=0)
for df_i in df_word_freq10_list[1:]:
df_word_freq10 = pd.concat([df_word_freq10, df_i], axis=0)
for df_i in df_word_freq15_list[1:]:
df_word_freq15 = pd.concat([df_word_freq15, df_i], axis=0)
df_kanji.to_csv(join(dataset_save_path, 'kanji.csv'))
df_kanji_divide.to_csv(join(dataset_save_path, 'kanji_divide.csv'))
df_kana.to_csv(join(dataset_save_path, 'kana.csv'))
df_kana_divide.to_csv(join(dataset_save_path, 'kana_divide.csv'))
df_phone.to_csv(join(dataset_save_path, 'phone.csv'))
df_phone_divide.to_csv(join(dataset_save_path, 'phone_divide.csv'))
df_word_freq1.to_csv(join(dataset_save_path, 'word_freq1.csv'))
df_word_freq5.to_csv(join(dataset_save_path, 'word_freq5.csv'))
df_word_freq10.to_csv(join(dataset_save_path, 'word_freq10.csv'))
df_word_freq15.to_csv(join(dataset_save_path, 'word_freq15.csv'))
def main(data_size):
speaker_dict_dict = {} # dict of speaker_dict
for data_type in ['train', 'eval1', 'eval2', 'eval3']:
print('=' * 50)
print(' ' * 20 + data_type + ' (' + data_size + ')' + ' ' * 20)
print('=' * 50)
########################################
# labels
########################################
if data_type == 'train':
label_paths = path.trans(data_type='train_' + data_size)
else:
label_paths = path.trans(data_type=data_type)
save_vocab_file = True if data_type == 'train' else False
is_test = True if 'eval' in data_type else False
print('=> Processing transcripts...')
speaker_dict_dict[data_type] = read_sdb(
label_paths=label_paths,
data_size=data_size,
vocab_file_save_path=mkdir_join('./config', 'vocab_files'),
save_vocab_file=save_vocab_file,
is_test=is_test,
data_type=data_type)
########################################
# inputs
########################################
print('\n=> Processing input data...')
input_save_path = mkdir_join(args.feature_save_path, args.save_format,
data_size)
if isfile(join(input_save_path, data_type, 'complete.txt')):
print('Already exists.')
else:
if args.save_format == 'wav':
########################################
# Split WAV files per utterance
########################################
if data_type == 'train':
wav_paths = path.wav(corpus='train' + data_size)
else:
wav_paths = path.wav(corpus=data_type)
split_wav(wav_paths=wav_paths,
speaker_dict=speaker_dict_dict[data_type],
save_path=mkdir_join(input_save_path, data_type))
# NOTE: ex.) save_path:
# csj/feature/save_format/data_size/data_type/speaker/utt_name.npy
elif args.save_format in ['numpy', 'htk']:
if data_type == 'train':
if args.tool == 'htk':
audio_paths = path.htk(data_type='train_' + data_size)
else:
audio_paths = path.wav(data_type='train_' + data_size)
is_training = True
global_mean_male, global_std_male, global_mean_female, global_std_female = None, None, None, None
else:
if args.tool == 'htk':
audio_paths = path.htk(data_type=data_type)
else:
audio_paths = path.wav(data_type=data_type)
is_training = False
# Load statistics over train dataset
global_mean_male = np.load(
join(input_save_path, 'train/global_mean_male.npy'))
global_std_male = np.load(
join(input_save_path, 'train/global_std_male.npy'))
global_mean_female = np.load(
join(input_save_path, 'train/global_mean_female.npy'))
global_std_female = np.load(
join(input_save_path, 'train/global_std_female.npy'))
read_audio(audio_paths=audio_paths,
speaker_dict=speaker_dict_dict[data_type],
tool=args.tool,
config=CONFIG,
normalize=args.normalize,
is_training=is_training,
save_path=mkdir_join(input_save_path, data_type),
save_format=args.save_format,
global_mean_male=global_mean_male,
global_std_male=global_std_male,
global_mean_female=global_mean_female,
global_std_female=global_std_female)
# NOTE: ex.) save_path:
# csj/feature/save_format/data_size/data_type/speaker/*.npy
# Make a confirmation file to prove that dataset was saved
# correctly
with open(join(input_save_path, data_type, 'complete.txt'),
'w') as f:
f.write('')
########################################
# dataset (csv)
########################################
print('\n=> Saving dataset files...')
for data_type in ['train', 'eval1', 'eval2', 'eval3']:
dataset_save_path = mkdir_join(args.dataset_save_path,
args.save_format, data_size, data_type)
print('---------- %s ----------' % data_type)
df_kanji = pd.DataFrame(
[], columns=['frame_num', 'input_path', 'transcript'])
df_kana = pd.DataFrame(
[], columns=['frame_num', 'input_path', 'transcript'])
df_phone = pd.DataFrame(
[], columns=['frame_num', 'input_path', 'transcript'])
utt_count = 0
df_kanji_list, df_kana_list, df_phone_list = [], [], []
for speaker, utt_dict in tqdm(speaker_dict_dict[data_type].items()):
for utt_index, utt_info in utt_dict.items():
trans_kanji, trans_kana, trans_phone = utt_info[2:]
if args.save_format == 'numpy':
input_utt_save_path = join(
input_save_path, data_type, speaker,
speaker + '_' + utt_index + '.npy')
assert isfile(input_utt_save_path)
input_utt = np.load(input_utt_save_path)
elif args.save_format == 'htk':
input_utt_save_path = join(
input_save_path, data_type, speaker,
speaker + '_' + utt_index + '.htk')
assert isfile(input_utt_save_path)
input_utt, _, _ = read(input_utt_save_path)
elif args.save_format == 'wav':
input_utt_save_path = path.utt2wav(utt_index)
assert isfile(input_utt_save_path)
input_utt = w2f_psf(input_utt_save_path,
feature_type=CONFIG['feature_type'],
feature_dim=CONFIG['channels'],
use_energy=CONFIG['energy'],
use_delta1=CONFIG['delta'],
use_delta2=CONFIG['deltadelta'],
window=CONFIG['window'],
slide=CONFIG['slide'])
else:
raise ValueError('save_format is numpy or htk or wav.')
frame_num = input_utt.shape[0]
del input_utt
series_kanji = pd.Series(
[frame_num, input_utt_save_path, trans_kanji],
index=df_kanji.columns)
series_kana = pd.Series(
[frame_num, input_utt_save_path, trans_kana],
index=df_kana.columns)
series_phone = pd.Series(
[frame_num, input_utt_save_path, trans_phone],
index=df_phone.columns)
df_kanji = df_kanji.append(series_kanji, ignore_index=True)
df_kana = df_kana.append(series_kana, ignore_index=True)
df_phone = df_phone.append(series_phone, ignore_index=True)
utt_count += 1
# Reset
if utt_count == 50000:
df_kanji_list.append(df_kanji)
df_kana_list.append(df_kana)
df_phone_list.append(df_phone)
df_kanji = pd.DataFrame(
[], columns=['frame_num', 'input_path', 'transcript'])
df_kana = pd.DataFrame(
[], columns=['frame_num', 'input_path', 'transcript'])
df_phone = pd.DataFrame(
[], columns=['frame_num', 'input_path', 'transcript'])
utt_count = 0
# Last dataframe
df_kanji_list.append(df_kanji)
df_kana_list.append(df_kana)
df_phone_list.append(df_phone)
# Concatenate all dataframes
df_kanji = df_kanji_list[0]
df_kana = df_kana_list[0]
df_phone = df_phone_list[0]
for df_i in df_kanji_list[1:]:
df_kanji = pd.concat([df_kanji, df_i], axis=0)
for df_i in df_kana_list[1:]:
df_kana = pd.concat([df_kana, df_i], axis=0)
for df_i in df_phone_list[1:]:
df_phone = pd.concat([df_phone, df_i], axis=0)
df_kanji.to_csv(join(dataset_save_path, 'dataset_kanji.csv'))
df_kana.to_csv(join(dataset_save_path, 'dataset_kana.csv'))
df_phone.to_csv(join(dataset_save_path, 'dataset_phone.csv'))
# Use the first 4000 utterances as the dev set
if data_type == 'train':
df_kanji[:4000].to_csv(
mkdir_join(args.dataset_save_path, args.save_format, data_size,
'dev', 'dataset_kanji.csv'))
df_kana[:4000].to_csv(
mkdir_join(args.dataset_save_path, args.save_format, data_size,
'dev', 'dataset_kana.csv'))
df_phone[:4000].to_csv(
mkdir_join(args.dataset_save_path, args.save_format, data_size,
'dev', 'dataset_phone.csv'))