forked from mrq/tortoise-tts
read.py combines all candidates
If candidates where greater than 1 on in read.py, only the fist candidate clips would be combined. This adds a bit of code to make a combined file for every candidate.
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@ -72,11 +72,22 @@ if __name__ == '__main__':
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gen = gen[0].squeeze(0).cpu()
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gen = gen[0].squeeze(0).cpu()
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all_parts.append(gen)
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all_parts.append(gen)
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full_audio = torch.cat(all_parts, dim=-1)
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if args.candidates == 1:
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torchaudio.save(os.path.join(voice_outpath, 'combined.wav'), full_audio, 24000)
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full_audio = torch.cat(all_parts, dim=-1)
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torchaudio.save(os.path.join(voice_outpath, 'combined.wav'), full_audio, 24000)
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if args.produce_debug_state:
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if args.produce_debug_state:
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os.makedirs('debug_states', exist_ok=True)
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os.makedirs('debug_states', exist_ok=True)
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dbg_state = (seed, texts, voice_samples, conditioning_latents)
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dbg_state = (seed, texts, voice_samples, conditioning_latents)
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torch.save(dbg_state, f'debug_states/read_debug_{selected_voice}.pth')
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torch.save(dbg_state, f'debug_states/read_debug_{selected_voice}.pth')
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# Combine each candidate's audio clips.
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if args.candidates > 1:
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audio_clips = []
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for candidate in range(args.candidates):
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for line in range(len(texts)):
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wav_file = os.path.join(voice_outpath, str(line), f"{candidate}.wav")
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audio_clips.append(load_audio(wav_file, 24000))
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audio_clips = torch.cat(audio_clips, dim=-1)
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torchaudio.save(os.path.join(voice_outpath, f"combined_{candidate:02d}.wav"), audio_clips, 24000)
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audio_clips = []
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