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- from argparse import ArgumentParser
- from pathlib import Path
- import numpy as np
- from model.factory import tts_ljspeech
- from data.audio import Audio
- from model.models import ForwardTransformer
- if __name__ == '__main__':
- parser = ArgumentParser()
- parser.add_argument('--path', '-p', dest='path', default=None, type=str)
- parser.add_argument('--step', dest='step', default='90000', type=str)
- parser.add_argument('--text', '-t', dest='text', default=None, type=str)
- parser.add_argument('--file', '-f', dest='file', default=None, type=str)
- parser.add_argument('--outdir', '-o', dest='outdir', default=None, type=str)
- parser.add_argument('--store_mel', '-m', dest='store_mel', action='store_true')
- parser.add_argument('--verbose', '-v', dest='verbose', action='store_true')
- parser.add_argument('--single', '-s', dest='single', action='store_true')
- args = parser.parse_args()
-
- if args.file is not None:
- with open(args.file, 'r') as file:
- text = file.readlines()
- fname = Path(args.file).stem
- elif args.text is not None:
- text = [args.text]
- fname = 'custom_text'
- else:
- fname = None
- text = None
- print(f'Specify either an input text (-t "some text") or a text input file (-f /path/to/file.txt)')
- exit()
- # load the appropriate model
- outdir = Path(args.outdir) if args.outdir is not None else Path('.')
- if args.path is not None:
- print(f'Loading model from {args.path}')
- model = ForwardTransformer.load_model(args.path)
- else:
- model = tts_ljspeech(args.step)
- file_name = f"{fname}_{model.config['data_name']}_{model.config['git_hash']}_{model.config['step']}"
- outdir = outdir / 'outputs' / f'{fname}'
- outdir.mkdir(exist_ok=True, parents=True)
- output_path = (outdir / file_name).with_suffix('.wav')
- audio = Audio.from_config(model.config)
- print(f'Output wav under {output_path.parent}')
- wavs = []
- for i, text_line in enumerate(text):
- phons = model.text_pipeline.phonemizer(text_line)
- tokens = model.text_pipeline.tokenizer(phons)
- if args.verbose:
- print(f'Predicting {text_line}')
- print(f'Phonemes: "{phons}"')
- print(f'Tokens: "{tokens}"')
- out = model.predict(tokens, encode=False, phoneme_max_duration=None)
- mel = out['mel'].numpy().T
- wav = audio.reconstruct_waveform(mel)
- wavs.append(wav)
- if args.store_mel:
- np.save((outdir / (file_name + f'_{i}')).with_suffix('.mel'), out['mel'].numpy())
- if args.single:
- audio.save_wav(wav, (outdir / (file_name + f'_{i}')).with_suffix('.wav'))
- audio.save_wav(np.concatenate(wavs), output_path)
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