DL-Art-School/codes/scripts/audio/preparation/save_mels_to_disk.py

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