|
import os |
|
|
|
import torchaudio |
|
|
|
from api import TextToSpeech |
|
from utils.audio import load_audio |
|
|
|
if __name__ == '__main__': |
|
fname = 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv' |
|
outpath = 'D:\\tmp\\tortoise-tts-eval\\attempt_best' |
|
outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real' |
|
|
|
os.makedirs(outpath, exist_ok=True) |
|
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()] |
|
|
|
recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8') |
|
tts = TextToSpeech() |
|
for e, line in enumerate(lines): |
|
transcript = line[0] |
|
if len(transcript) > 120: |
|
continue |
|
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=512, k=1, |
|
repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5, |
|
diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=400) |
|
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() |
