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import os |
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import torchaudio |
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from api import TextToSpeech |
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from utils.audio import load_audio |
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if __name__ == '__main__': |
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fname = 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv' |
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outpath = 'D:\\tmp\\tortoise-tts-eval\\eval_new_autoregressive' |
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outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real' |
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os.makedirs(outpath, exist_ok=True) |
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os.makedirs(outpath_real, exist_ok=True) |
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with open(fname, 'r', encoding='utf-8') as f: |
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lines = [l.strip().split('\t') for l in f.readlines()] |
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recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8') |
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tts = TextToSpeech() |
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for e, line in enumerate(lines): |
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transcript = line[0] |
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if len(transcript) > 120: |
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continue |
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path = os.path.join(os.path.dirname(fname), line[1]) |
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cond_audio = load_audio(path, 22050) |
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torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050) |
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sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=512, k=1, |
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repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5, |
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diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=200) |
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down = torchaudio.functional.resample(sample, 24000, 22050) |
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fout_path = os.path.join(outpath, os.path.basename(line[1])) |
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torchaudio.save(fout_path, down.squeeze(0), 22050) |
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recorder.write(f'{transcript}\t{fout_path}\n') |
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recorder.flush() |
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recorder.close() |
