forked from mrq/tortoise-tts
add support for specifying the model_dir
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01b783fc02
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66805da4bd
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@ -16,10 +16,12 @@ if __name__ == '__main__':
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help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
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help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
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default=.5)
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default=.5)
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parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
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parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
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parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
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'should only be specified if you have custom checkpoints.', default='.models')
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args = parser.parse_args()
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args = parser.parse_args()
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os.makedirs(args.output_path, exist_ok=True)
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os.makedirs(args.output_path, exist_ok=True)
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tts = TextToSpeech()
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tts = TextToSpeech(models_dir=args.model_dir)
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selected_voices = args.voice.split(',')
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selected_voices = args.voice.split(',')
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for voice in selected_voices:
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for voice in selected_voices:
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@ -37,13 +37,17 @@ if __name__ == '__main__':
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parser.add_argument('--voice_diversity_intelligibility_slider', type=float,
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parser.add_argument('--voice_diversity_intelligibility_slider', type=float,
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help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
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help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
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default=.5)
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default=.5)
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parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
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'should only be specified if you have custom checkpoints.', default='.models')
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args = parser.parse_args()
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args = parser.parse_args()
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tts = TextToSpeech(models_dir=args.model_dir)
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outpath = args.output_path
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outpath = args.output_path
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selected_voices = args.voice.split(',')
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selected_voices = args.voice.split(',')
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regenerate = args.regenerate
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regenerate = args.regenerate
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if regenerate is not None:
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if regenerate is not None:
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regenerate = [int(e) for e in regenerate.split(',')]
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regenerate = [int(e) for e in regenerate.split(',')]
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for selected_voice in selected_voices:
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for selected_voice in selected_voices:
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voice_outpath = os.path.join(outpath, selected_voice)
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voice_outpath = os.path.join(outpath, selected_voice)
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os.makedirs(voice_outpath, exist_ok=True)
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os.makedirs(voice_outpath, exist_ok=True)
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@ -51,7 +55,6 @@ if __name__ == '__main__':
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with open(args.textfile, 'r', encoding='utf-8') as f:
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with open(args.textfile, 'r', encoding='utf-8') as f:
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text = ''.join([l for l in f.readlines()])
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text = ''.join([l for l in f.readlines()])
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texts = split_and_recombine_text(text)
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texts = split_and_recombine_text(text)
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tts = TextToSpeech()
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if '&' in selected_voice:
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if '&' in selected_voice:
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voice_sel = selected_voice.split('&')
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voice_sel = selected_voice.split('&')
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