44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
import argparse
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import os
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import numpy
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import torch
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from spleeter.audio.adapter import AudioAdapter
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from tqdm import tqdm
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from data.util import find_audio_files
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# Uses pydub to process a directory of audio files, splitting them into clips at points where it detects a small amount
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# of silence.
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from trainer.injectors.base_injectors import MelSpectrogramInjector
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--path')
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args = parser.parse_args()
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files = find_audio_files(args.path, include_nonwav=True)
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mel_inj = MelSpectrogramInjector({'in':'in', 'out':'out'}, {})
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audio_loader = AudioAdapter.default()
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for e, wav_file in enumerate(tqdm(files)):
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if e < 0:
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continue
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print(f"Processing {wav_file}..")
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outfile = f'{wav_file}.npz'
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if os.path.exists(outfile):
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continue
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try:
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wave, sample_rate = audio_loader.load(wav_file, sample_rate=22050)
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wave = torch.tensor(wave)[:,0].unsqueeze(0)
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wave = wave / wave.abs().max()
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except:
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print(f"Error with {wav_file}")
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continue
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inj = mel_inj({'in': wave})
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numpy.savez_compressed(outfile, inj['out'].numpy())
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if __name__ == '__main__':
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main()
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