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="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) args = parser.parse_args() 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) tts = TextToSpeech() 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)