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\\eval_new_autoregressive' 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 # We need to support this, but cannot yet. 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=200) 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()