File size: 3,750 Bytes
8215af8 f7c8dec 0ffc191 8215af8 da31baa 8215af8 da31baa 8215af8 8696bb4 c52cc78 b9a534a 39ec1b0 8696bb4 2a5166d 8696bb4 b1fc2b1 8215af8 f499d66 8215af8 c52cc78 2a5166d b1fc2b1 c52cc78 8215af8 c52cc78 8215af8 0ffc191 a05af09 c52cc78 2a5166d 0ffc191 a05af09 8215af8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
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)
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)
|