| 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() |