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import glob
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from utils.display import *
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from utils.dsp import *
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from utils import hparams as hp
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from multiprocessing import Pool, cpu_count
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from utils.paths import Paths
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import pickle
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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
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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
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parser = argparse.ArgumentParser(description='Preprocessing for WaveRNN and Tacotron')
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parser.add_argument('--path', '-p', help='directly point to dataset path (overrides hparams.wav_path')
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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')
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parser.add_argument('--hp_file', metavar='FILE', default='hparams.py', help='The file to use for the hyperparameters')
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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)
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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
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mel = melspectrogram(y)
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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)
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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)
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def process_wav(path: Path):
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wav_id = path.stem
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m, x = convert_file(path)
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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]
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if __name__ == '__main__':
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if len(wav_files) == 0:
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print('Please point wav_path in hparams.py to your dataset,')
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print('or use the --path option.\n')
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else:
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if not hp.ignore_tts:
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text_dict = ljspeech(path)
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with open(paths.data/'text_dict.pkl', 'wb') as f:
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pickle.dump(text_dict, f)
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n_workers = max(1, args.num_workers)
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simple_table([
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('Sample Rate', hp.sample_rate),
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('Bit Depth', hp.bits),
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('Mu Law', hp.mu_law),
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('Hop Length', hp.hop_length),
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('CPU Usage', f'{n_workers}/{cpu_count()}')
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])
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pool = Pool(processes=n_workers)
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dataset = []
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print("test22222")
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for i, (item_id, length) in enumerate(pool.imap_unordered(process_wav, wav_files), 1):
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dataset += [(item_id, length)]
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bar = progbar(i, len(wav_files))
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message = f'{bar} {i}/{len(wav_files)} '
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stream(message)
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with open(paths.data/'dataset.pkl', 'wb') as f:
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pickle.dump(dataset, f)
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print('\n\nCompleted. Ready to run "python train_tacotron.py" or "python train_wavernn.py". \n')
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