np.save(paths.quant / f'{wav_id}.npy', x, allow_pickle=False)
return wav_id, m.shape[-1]
wav_files = get_files(path, extension)
paths = Paths(hp.data_path, hp.voc_model_id, hp.tts_model_id)
print(f'\n{len(wav_files)} {extension[1:]} files found in "{path}"\n')
if len(wav_files) == 0:
print('Please point wav_path in hparams.py to your dataset,')
print('or use the --path option.\n')
else:
text_dict = ljspeech(path)
with open(paths.data / 'text_dict.pkl', 'wb') as f:
pickle.dump(text_dict, f)
n_workers = max(1, args.num_workers)
simple_table([('Sample Rate', hp.sample_rate), ('Bit Depth', hp.bits),
('Mu Law', hp.mu_law), ('Hop Length', hp.hop_length),
('CPU Usage', f'{n_workers}/{cpu_count()}')])
pool = Pool(processes=n_workers)
dataset = []
for i, (item_id,
length) in enumerate(pool.imap_unordered(process_wav, wav_files),
1):
extension = args.extension
path = args.path
if __name__ == '__main__':
wav_files = get_files(path, extension)
# print(path, extension)
wav_ids = {w.stem for w in wav_files}
paths = Paths(hp.data_path, hp.voc_model_id, hp.tts_model_id)
# print(f'\n{len(wav_files)} {extension[1:]} files found in "{path}"')
assert len(wav_files) > 0, f'Found no wav files in {path}, exiting.'
# print(wav_files)
# exit()
if args.dt:
print('running preproccesing for dt...')
text_dict = ljspeech(path, dt=True)
else:
text_dict = ljspeech(path)
# print(len(text_dict))
# print(len(wav_ids))
# for item_id, text in text_dict.items():
# # print(item_id)
# if item_id in wav_ids:
# # print(item_id)
text_dict = {
item_id: text
for item_id, text in text_dict.items() if item_id in wav_ids
}
# exit()
wav_files = get_files(path, extension)
paths = Paths(hp.data_path, hp.voc_model_id, hp.tts_model_id)
print(f'\n{len(wav_files)} {extension[1:]} files found in "{path}"\n')
if len(wav_files) == 0:
print('Please point wav_path in hparams.py to your dataset,')
print('or use the --path option.\n')
else:
if not hp.ignore_tts:
# text_dict = ljspeech(path)
text_dict = ljspeech(Path(path).parent)
with open(paths.data / 'text_dict.pkl', 'wb') as f:
pickle.dump(text_dict, f)
n_workers = max(1, args.num_workers)
simple_table([('Sample Rate', hp.sample_rate), ('Bit Depth', hp.bits),
('Mu Law', hp.mu_law), ('Hop Length', hp.hop_length),
('CPU Usage', f'{n_workers}/{cpu_count()}')])
pool = Pool(processes=n_workers)
dataset = []
for i, (item_id,
length) in enumerate(pool.imap_unordered(process_wav, wav_files),
parser.add_argument('--path', '-p', help='directly point to dataset path')
parser.add_argument('--num_workers', '-w', metavar='N', type=valid_n_workers, default=cpu_count()-1, help='The number of worker threads to use for preprocessing')
parser.add_argument('--config', metavar='FILE', default='config.yaml', help='The config containing all hyperparams.')
args = parser.parse_args()
if __name__ == '__main__':
config = read_config(args.config)
wav_files = get_files(args.path, '.wav')
wav_ids = {w.stem for w in wav_files}
paths = Paths(config['data_path'], config['voc_model_id'], config['tts_model_id'])
print(f'\n{len(wav_files)} .wav files found in "{args.path}"')
assert len(wav_files) > 0, f'Found no wav files in {args.path}, exiting.'
text_dict = ljspeech(args.path)
text_dict = {item_id: text for item_id, text in text_dict.items()
if item_id in wav_ids and len(text) > config['preprocessing']['min_text_len']}
wav_files = [w for w in wav_files if w.stem in text_dict]
print(f'Using {len(wav_files)} wav files that are indexed in metafile.\n')
n_workers = max(1, args.num_workers)
dsp = DSP.from_config(config)
simple_table([
('Sample Rate', dsp.sample_rate),
('Bit Depth', dsp.bits),
('Mu Law', dsp.mu_law),
('Hop Length', dsp.hop_length),
('CPU Usage', f'{n_workers}/{cpu_count()}'),