| import glob
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| from utils.display import *
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| from utils.dsp import *
|
| from utils import hparams as hp
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| from multiprocessing import Pool, cpu_count
|
| from utils.paths import Paths
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| import pickle
|
| import argparse
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| from utils.text.recipes import ljspeech
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| from utils.files import get_files
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| from pathlib import Path
|
|
|
|
|
|
|
| def valid_n_workers(num):
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| n = int(num)
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| if n < 1:
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| raise argparse.ArgumentTypeError('%r must be an integer greater than 0' % num)
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| return n
|
|
|
| parser = argparse.ArgumentParser(description='Preprocessing for WaveRNN and Tacotron')
|
| parser.add_argument('--path', '-p', help='directly point to dataset path (overrides hparams.wav_path')
|
| parser.add_argument('--extension', '-e', metavar='EXT', default='.wav', help='file extension to search for in dataset folder')
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| 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('--hp_file', metavar='FILE', default='hparams.py', help='The file to use for the hyperparameters')
|
|
|
| args = parser.parse_args()
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| hp.configure(args.hp_file)
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| if args.path is None:
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| args.path = hp.wav_path
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|
|
| extension = args.extension
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| path = args.path
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|
|
| wav_files = get_files(path, extension)
|
| paths = Paths(hp.data_path, hp.voc_model_id, hp.tts_model_id)
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|
|
| print(f'\n{len(wav_files)} {extension[1:]} files found in "{path}"\n')
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|
|
|
|
|
|
| def convert_file(path: Path):
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| y = load_wav(path)
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| peak = np.abs(y).max()
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| if hp.peak_norm or peak > 1.0:
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| y /= peak
|
| mel = melspectrogram(y)
|
| if hp.voc_mode == 'RAW':
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| quant = encode_mu_law(y, mu=2**hp.bits) if hp.mu_law else float_2_label(y, bits=hp.bits)
|
| elif hp.voc_mode == 'MOL':
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| quant = float_2_label(y, bits=16)
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|
|
| return mel.astype(np.float32), quant.astype(np.int64)
|
|
|
|
|
| def process_wav(path: Path):
|
| wav_id = path.stem
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| m, x = convert_file(path)
|
|
|
| np.save(paths.mel/f'{wav_id}.npy', m, allow_pickle=False)
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| np.save(paths.quant/f'{wav_id}.npy', x, allow_pickle=False)
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| return wav_id, m.shape[-1]
|
|
|
| if __name__ == '__main__':
|
|
|
|
|
| if len(wav_files) == 0:
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|
|
| 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)
|
|
|
| with open(paths.data/'text_dict.pkl', 'wb') as f:
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| 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 = []
|
| print("test22222")
|
| for i, (item_id, length) in enumerate(pool.imap_unordered(process_wav, wav_files), 1):
|
| dataset += [(item_id, length)]
|
| bar = progbar(i, len(wav_files))
|
| message = f'{bar} {i}/{len(wav_files)} '
|
| stream(message)
|
|
|
| with open(paths.data/'dataset.pkl', 'wb') as f:
|
| pickle.dump(dataset, f)
|
|
|
| print('\n\nCompleted. Ready to run "python train_tacotron.py" or "python train_wavernn.py". \n')
|
|
|
