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import torch
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from models.fatchord_version import WaveRNN
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from utils import hparams as hp
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from utils.text.symbols import symbols
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from models.tacotron import Tacotron
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import argparse
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from utils.text import text_to_sequence
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from utils.display import save_attention, simple_table
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import zipfile, os
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os.makedirs('quick_start/tts_weights/', exist_ok=True)
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os.makedirs('quick_start/voc_weights/', exist_ok=True)
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zip_ref = zipfile.ZipFile('pretrained/ljspeech.wavernn.mol.800k.zip', 'r')
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zip_ref.extractall('quick_start/voc_weights/')
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zip_ref.close()
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zip_ref = zipfile.ZipFile('pretrained/ljspeech.tacotron.r2.180k.zip', 'r')
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zip_ref.extractall('quick_start/tts_weights/')
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zip_ref.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='TTS Generator')
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parser.add_argument('--input_text', '-i', type=str, help='[string] Type in something here and TTS will generate it!')
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parser.add_argument('--batched', '-b', dest='batched', action='store_true', help='Fast Batched Generation (lower quality)')
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parser.add_argument('--unbatched', '-u', dest='batched', action='store_false', help='Slower Unbatched Generation (better quality)')
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parser.add_argument('--force_cpu', '-c', action='store_true', help='Forces CPU-only training, even when in CUDA capable environment')
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parser.add_argument('--hp_file', metavar='FILE', default='hparams.py',
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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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parser.set_defaults(batched=True)
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parser.set_defaults(input_text=None)
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batched = args.batched
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input_text = args.input_text
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if not args.force_cpu and torch.cuda.is_available():
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device = torch.device('cuda')
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else:
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device = torch.device('cpu')
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print('Using device:', device)
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print('\nInitialising WaveRNN Model...\n')
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voc_model = WaveRNN(rnn_dims=hp.voc_rnn_dims,
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fc_dims=hp.voc_fc_dims,
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bits=hp.bits,
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pad=hp.voc_pad,
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upsample_factors=hp.voc_upsample_factors,
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feat_dims=hp.num_mels,
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compute_dims=hp.voc_compute_dims,
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res_out_dims=hp.voc_res_out_dims,
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res_blocks=hp.voc_res_blocks,
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hop_length=hp.hop_length,
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sample_rate=hp.sample_rate,
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mode='MOL').to(device)
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voc_model.load('quick_start/voc_weights/latest_weights.pyt')
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print('\nInitialising Tacotron Model...\n')
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tts_model = Tacotron(embed_dims=hp.tts_embed_dims,
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num_chars=len(symbols),
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encoder_dims=hp.tts_encoder_dims,
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decoder_dims=hp.tts_decoder_dims,
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n_mels=hp.num_mels,
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fft_bins=hp.num_mels,
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postnet_dims=hp.tts_postnet_dims,
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encoder_K=hp.tts_encoder_K,
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lstm_dims=hp.tts_lstm_dims,
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postnet_K=hp.tts_postnet_K,
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num_highways=hp.tts_num_highways,
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dropout=hp.tts_dropout,
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stop_threshold=hp.tts_stop_threshold).to(device)
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tts_model.load('quick_start/tts_weights/latest_weights.pyt')
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if input_text:
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inputs = [text_to_sequence(input_text.strip(), hp.tts_cleaner_names)]
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else:
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with open('sentences.txt') as f:
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inputs = [text_to_sequence(l.strip(), hp.tts_cleaner_names) for l in f]
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voc_k = voc_model.get_step() // 1000
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tts_k = tts_model.get_step() // 1000
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r = tts_model.r
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simple_table([('WaveRNN', str(voc_k) + 'k'),
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(f'Tacotron(r={r})', str(tts_k) + 'k'),
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('Generation Mode', 'Batched' if batched else 'Unbatched'),
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('Target Samples', 11_000 if batched else 'N/A'),
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('Overlap Samples', 550 if batched else 'N/A')])
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for i, x in enumerate(inputs, 1):
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print(f'\n| Generating {i}/{len(inputs)}')
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_, m, attention = tts_model.generate(x)
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if input_text:
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save_path = f'quick_start/__input_{input_text[:10]}_{tts_k}k.wav'
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else:
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save_path = f'quick_start/{i}_batched{str(batched)}_{tts_k}k.wav'
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m = torch.tensor(m).unsqueeze(0)
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m = (m + 4) / 8
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voc_model.generate(m, save_path, batched, 11_000, 550, hp.mu_law)
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print('\n\nDone.\n')
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