forked from mrq/DL-Art-School
67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
from scipy.io import wavfile
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from spleeter.separator import Separator
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from tqdm import tqdm
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from data.util import find_audio_files
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import os
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import os.path as osp
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from spleeter.audio.adapter import AudioAdapter
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import numpy as np
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# Uses spleeter to divide audio clips into one of two bins:
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# 1. Audio has little to no background noise, saved to "output_dir"
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# 2. Audio has a lot of background noise, bg noise split off and saved to "output_dir_bg"
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if __name__ == '__main__':
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src_dir = 'F:\\split\\books1'
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output_dir = 'F:\\split\\cleaned\\books1'
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output_dir_bg = 'F:\\split\\background-noise\\books1'
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output_sample_rate=22050
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os.makedirs(output_dir_bg, exist_ok=True)
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os.makedirs(output_dir, exist_ok=True)
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audio_loader = AudioAdapter.default()
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separator = Separator('spleeter:2stems')
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files = find_audio_files(src_dir, include_nonwav=True)
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for e, file in enumerate(tqdm(files)):
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if e < 406500:
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continue
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file_basis = osp.relpath(file, src_dir)\
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.replace('/', '_')\
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.replace('\\', '_')\
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.replace('.', '_')\
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.replace(' ', '_')\
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.replace('!', '_')\
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.replace(',', '_')
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if len(file_basis) > 100:
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file_basis = file_basis[:100]
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try:
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wave, sample_rate = audio_loader.load(file, sample_rate=output_sample_rate)
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except:
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print(f"Error with {file}")
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continue
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sep = separator.separate(wave)
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vocals = sep['vocals']
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bg = sep['accompaniment']
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vmax = np.abs(vocals).mean()
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bmax = np.abs(bg).mean()
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# Only output to the "good" sample dir if the ratio of background noise to vocal noise is high enough.
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ratio = vmax / (bmax+.0000001)
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if ratio >= 25: # These values were derived empirically
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od = output_dir
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os = wave
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elif ratio <= 1:
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od = output_dir_bg
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os = bg
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else:
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continue
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# Strip out channels.
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if len(os.shape) > 1:
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os = os[:, 0] # Just use the first channel.
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wavfile.write(osp.join(od, f'{e}_{file_basis}.wav'), output_sample_rate, os)
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