forked from mrq/tortoise-tts
01b783fc02
- Adds a new script and API endpoints for doing this - Reworks autoregressive and diffusion models so that the conditioning is computed separately (which will actually provide a mild performance boost) - Updates README This is untested. Need to do the following manual tests (and someday write unit tests for this behemoth before it becomes a problem..) 1) Does get_conditioning_latents.py work? 2) Can I feed those latents back into the model by creating a new voice? 3) Can I still mix and match voices (both with conditioning latents and normal voices) with read.py?
75 lines
3.4 KiB
Python
75 lines
3.4 KiB
Python
import argparse
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import os
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import torch
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import torchaudio
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from api import TextToSpeech
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from tortoise.utils.audio import load_audio, get_voices, load_voices
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def split_and_recombine_text(texts, desired_length=200, max_len=300):
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# TODO: also split across '!' and '?'. Attempt to keep quotations together.
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texts = [s.strip() + "." for s in texts.split('.')]
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i = 0
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while i < len(texts):
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ltxt = texts[i]
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if len(ltxt) >= desired_length or i == len(texts)-1:
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i += 1
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continue
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if len(ltxt) + len(texts[i+1]) > max_len:
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i += 1
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continue
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texts[i] = f'{ltxt} {texts[i+1]}'
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texts.pop(i+1)
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return texts
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt")
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parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
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'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat')
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parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
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parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
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parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
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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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default=.5)
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args = parser.parse_args()
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outpath = args.output_path
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selected_voices = args.voice.split(',')
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regenerate = args.regenerate
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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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for selected_voice in selected_voices:
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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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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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texts = split_and_recombine_text(text)
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tts = TextToSpeech()
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if '&' in selected_voice:
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voice_sel = selected_voice.split('&')
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else:
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voice_sel = [selected_voice]
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voice_samples, conditioning_latents = load_voices(voice_sel)
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all_parts = []
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for j, text in enumerate(texts):
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if regenerate is not None and j not in regenerate:
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all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000))
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continue
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gen = tts.tts_with_preset(text, 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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gen = gen.squeeze(0).cpu()
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torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen, 24000)
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all_parts.append(gen)
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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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