forked from mrq/tortoise-tts
40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
import os
|
|
|
|
import torchaudio
|
|
|
|
from api import TextToSpeech
|
|
from utils.audio import load_audio
|
|
|
|
if __name__ == '__main__':
|
|
fname = 'Y:\\clips\\books2\\subset512-oco.tsv'
|
|
stop_after = 128
|
|
outpath_base = 'D:\\tmp\\tortoise-tts-eval\\audiobooks'
|
|
outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
|
|
|
|
os.makedirs(outpath_real, exist_ok=True)
|
|
with open(fname, 'r', encoding='utf-8') as f:
|
|
lines = [l.strip().split('\t') for l in f.readlines()]
|
|
|
|
tts = TextToSpeech()
|
|
for k in range(4):
|
|
outpath = f'{outpath_base}_{k}'
|
|
os.makedirs(outpath, exist_ok=True)
|
|
recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8')
|
|
for e, line in enumerate(lines):
|
|
if e >= stop_after:
|
|
break
|
|
transcript = line[0]
|
|
path = os.path.join(os.path.dirname(fname), line[1])
|
|
cond_audio = load_audio(path, 22050)
|
|
torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
|
|
sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=128, k=1,
|
|
repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5,
|
|
diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=70)
|
|
|
|
down = torchaudio.functional.resample(sample, 24000, 22050)
|
|
fout_path = os.path.join(outpath, os.path.basename(line[1]))
|
|
torchaudio.save(fout_path, down.squeeze(0), 22050)
|
|
|
|
recorder.write(f'{transcript}\t{fout_path}\n')
|
|
recorder.flush()
|
|
recorder.close() |