21 lines
768 B
Python
21 lines
768 B
Python
import os
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import torch
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from data.util import find_files_of_type, is_audio_file
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from trainer.injectors.audio_injectors import MelSpectrogramInjector
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from utils.util import load_audio
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if __name__ == '__main__':
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path = 'C:\\Users\\jbetk\\Documents\\tmp\\some_audio'
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inj = MelSpectrogramInjector({'in': 'wav', 'out': 'mel',
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'mel_fmax': 12000, 'sampling_rate': 22050, 'n_mel_channels': 100
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},{})
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audio = find_files_of_type('img', path, qualifier=is_audio_file)[0]
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for clip in audio:
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if not clip.endswith('.wav'):
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continue
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wav = load_audio(clip, 24000)
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mel = inj({'wav': wav.unsqueeze(0)})['mel']
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torch.save(mel, clip.replace('.wav', '.mel')) |