mirror of https://github.com/coqui-ai/TTS.git
formatting
This commit is contained in:
Eren Golge
parent
23f6743ac9
commit
53d658fb74
102
synthesize.py
102
synthesize.py
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@ -20,12 +20,13 @@ def tts(model,
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ap_vocoder,
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use_cuda,
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batched_vocoder,
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speaker_id=None,
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speaker_id=None,
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figures=False):
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t_1 = time.time()
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use_vocoder_model = vocoder_model is not None
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waveform, alignment, decoder_outputs, postnet_output, stop_tokens = synthesis(
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model, text, C, use_cuda, ap, speaker_id, False, C.enable_eos_bos_chars)
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waveform, alignment, _, postnet_output, stop_tokens = synthesis(
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model, text, C, use_cuda, ap, speaker_id, False,
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C.enable_eos_bos_chars)
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if C.model == "Tacotron" and use_vocoder_model:
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postnet_output = ap.out_linear_to_mel(postnet_output.T).T
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# correct if there is a scale difference b/w two models
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@ -45,13 +46,10 @@ def tts(model,
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'text', type=str, help='Text to generate speech.')
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parser.add_argument(
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'config_path',
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type=str,
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help='Path to model config file.'
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)
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parser.add_argument('text', type=str, help='Text to generate speech.')
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parser.add_argument('config_path',
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type=str,
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help='Path to model config file.')
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parser.add_argument(
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'model_path',
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type=str,
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@ -62,8 +60,10 @@ if __name__ == "__main__":
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type=str,
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help='Path to save final wav file.',
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)
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parser.add_argument(
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'--use_cuda', type=bool, help='Run model on CUDA.', default=False)
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parser.add_argument('--use_cuda',
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type=bool,
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help='Run model on CUDA.',
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default=False)
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parser.add_argument(
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'--vocoder_path',
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type=str,
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@ -71,28 +71,24 @@ if __name__ == "__main__":
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'Path to vocoder model file. If it is not defined, model uses GL as vocoder. Please make sure that you installed vocoder library before (WaveRNN).',
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default="",
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)
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parser.add_argument(
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'--vocoder_config_path',
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type=str,
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help='Path to vocoder model config file.',
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default="")
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parser.add_argument('--vocoder_config_path',
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type=str,
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help='Path to vocoder model config file.',
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default="")
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parser.add_argument(
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'--batched_vocoder',
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type=bool,
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help="If True, vocoder model uses faster batch processing.",
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default=True)
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parser.add_argument(
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'--speakers_json',
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type=str,
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help="JSON file for multi-speaker model.",
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default=""
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)
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parser.add_argument('--speakers_json',
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type=str,
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help="JSON file for multi-speaker model.",
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default="")
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parser.add_argument(
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'--speaker_id',
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type=int,
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help="target speaker_id if the model is multi-speaker.",
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default=None
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)
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default=None)
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args = parser.parse_args()
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if args.vocoder_path != "":
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@ -128,22 +124,20 @@ if __name__ == "__main__":
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VC = load_config(args.vocoder_config_path)
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ap_vocoder = AudioProcessor(**VC.audio)
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bits = 10
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vocoder_model = VocoderModel(
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rnn_dims=512,
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fc_dims=512,
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mode=VC.mode,
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mulaw=VC.mulaw,
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pad=VC.pad,
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upsample_factors=VC.upsample_factors,
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feat_dims=VC.audio["num_mels"],
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compute_dims=128,
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res_out_dims=128,
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res_blocks=10,
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hop_length=ap.hop_length,
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sample_rate=ap.sample_rate,
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use_aux_net=True,
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use_upsample_net=True
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)
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vocoder_model = VocoderModel(rnn_dims=512,
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fc_dims=512,
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mode=VC.mode,
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mulaw=VC.mulaw,
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pad=VC.pad,
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upsample_factors=VC.upsample_factors,
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feat_dims=VC.audio["num_mels"],
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compute_dims=128,
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res_out_dims=128,
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res_blocks=10,
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hop_length=ap.hop_length,
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sample_rate=ap.sample_rate,
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use_aux_net=True,
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use_upsample_net=True)
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check = torch.load(args.vocoder_path)
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vocoder_model.load_state_dict(check['model'])
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@ -157,22 +151,22 @@ if __name__ == "__main__":
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# synthesize voice
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print(" > Text: {}".format(args.text))
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_, _, _, wav = tts(
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model,
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vocoder_model,
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C,
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VC,
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args.text,
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ap,
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ap_vocoder,
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args.use_cuda,
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args.batched_vocoder,
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speaker_id=args.speaker_id,
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figures=False)
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_, _, _, wav = tts(model,
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vocoder_model,
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C,
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VC,
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args.text,
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ap,
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ap_vocoder,
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args.use_cuda,
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args.batched_vocoder,
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speaker_id=args.speaker_id,
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figures=False)
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# save the results
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file_name = args.text.replace(" ", "_")
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file_name = file_name.translate(str.maketrans('', '', string.punctuation.replace('_', '')))+'.wav'
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file_name = file_name.translate(
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str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
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out_path = os.path.join(args.out_path, file_name)
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print(" > Saving output to {}".format(out_path))
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ap.save_wav(wav, out_path)
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