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| import argparse | |
| import os | |
| import torch | |
| import torchaudio | |
| from api import TextToSpeech | |
| from tortoise.utils.audio import load_audio, get_voices, load_voices | |
| def split_and_recombine_text(texts, desired_length=200, max_len=300): | |
| # TODO: also split across '!' and '?'. Attempt to keep quotations together. | |
| texts = [s.strip() + "." for s in texts.split('.')] | |
| i = 0 | |
| while i < len(texts): | |
| ltxt = texts[i] | |
| if len(ltxt) >= desired_length or i == len(texts)-1: | |
| i += 1 | |
| continue | |
| if len(ltxt) + len(texts[i+1]) > max_len: | |
| i += 1 | |
| continue | |
| texts[i] = f'{ltxt} {texts[i+1]}' | |
| texts.pop(i+1) | |
| return texts | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="tortoise/data/riding_hood.txt") | |
| parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) ' | |
| 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat') | |
| parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/') | |
| parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard') | |
| parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None) | |
| parser.add_argument('--voice_diversity_intelligibility_slider', type=float, | |
| 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', | |
| default=.5) | |
| parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this' | |
| 'should only be specified if you have custom checkpoints.', default='.models') | |
| args = parser.parse_args() | |
| tts = TextToSpeech(models_dir=args.model_dir) | |
| outpath = args.output_path | |
| selected_voices = args.voice.split(',') | |
| regenerate = args.regenerate | |
| if regenerate is not None: | |
| regenerate = [int(e) for e in regenerate.split(',')] | |
| for selected_voice in selected_voices: | |
| voice_outpath = os.path.join(outpath, selected_voice) | |
| os.makedirs(voice_outpath, exist_ok=True) | |
| with open(args.textfile, 'r', encoding='utf-8') as f: | |
| text = ''.join([l for l in f.readlines()]) | |
| texts = split_and_recombine_text(text) | |
| if '&' in selected_voice: | |
| voice_sel = selected_voice.split('&') | |
| else: | |
| voice_sel = [selected_voice] | |
| voice_samples, conditioning_latents = load_voices(voice_sel) | |
| all_parts = [] | |
| for j, text in enumerate(texts): | |
| if regenerate is not None and j not in regenerate: | |
| all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000)) | |
| continue | |
| gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents, | |
| preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider) | |
| gen = gen.squeeze(0).cpu() | |
| torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen, 24000) | |
| all_parts.append(gen) | |
| full_audio = torch.cat(all_parts, dim=-1) | |
| torchaudio.save(os.path.join(voice_outpath, 'combined.wav'), full_audio, 24000) | |