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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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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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