| import argparse |
| import os |
|
|
| import torch |
| import torch.nn.functional as F |
| import torchaudio |
|
|
| from api import TextToSpeech, format_conditioning |
| from utils.audio import load_audio, get_voices |
| from utils.tokenizer import VoiceBpeTokenizer |
|
|
|
|
| def split_and_recombine_text(texts, desired_length=200, max_len=300): |
| |
| 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 |
| voices = get_voices() |
| 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] |
| cond_paths = [] |
| for vsel in voice_sel: |
| if vsel not in voices.keys(): |
| print(f'Error: voice {vsel} not available. Skipping.') |
| continue |
| cond_paths.extend(voices[vsel]) |
| if not cond_paths: |
| print('Error: no valid voices specified. Try again.') |
|
|
| conds = [] |
| for cond_path in cond_paths: |
| c = load_audio(cond_path, 22050) |
| conds.append(c) |
| 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, conds, 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) |
|
|
|
|
