Add support for multiple output candidates in do_tts.
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@ -18,6 +18,7 @@ if __name__ == '__main__':
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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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parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice.', default=3)
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args = parser.parse_args()
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os.makedirs(args.output_path, exist_ok=True)
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@ -26,7 +27,11 @@ if __name__ == '__main__':
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selected_voices = args.voice.split(',')
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for k, voice in enumerate(selected_voices):
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voice_samples, conditioning_latents = load_voice(voice)
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gen = tts.tts_with_preset(args.text, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
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gen = tts.tts_with_preset(args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
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preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider)
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torchaudio.save(os.path.join(args.output_path, f'{voice}_{k}.wav'), gen.squeeze(0).cpu(), 24000)
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if isinstance(gen, list):
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for j, g in enumerate(gen):
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torchaudio.save(os.path.join(args.output_path, f'{voice}_{k}_{j}.wav'), g.squeeze(0).cpu(), 24000)
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else:
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torchaudio.save(os.path.join(args.output_path, f'{voice}_{k}.wav'), gen.squeeze(0).cpu(), 24000)
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