forked from mrq/tortoise-tts
33 lines
1.4 KiB
Python
33 lines
1.4 KiB
Python
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\\baseline'
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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 # We need to support this, but cannot yet.
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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, diffusion_iterations=200, cond_free=True)
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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() |