forked from mrq/DL-Art-School
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import audio2numpy
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from scipy.io import wavfile
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from tqdm import tqdm
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from data.util import find_audio_files
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import numpy as np
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import torch
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import torch.nn.functional as F
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import os.path as osp
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if __name__ == '__main__':
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src_dir = 'O:\\podcast_dumps'
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#src_dir = 'E:\\audio\\books'
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output_dir = 'D:\\data\\audio\\podcasts-split'
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#output_dir = 'E:\\audio\\books-clips'
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clip_length = 5 # In seconds
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sparsity = .05 # Only this proportion of the total clips are extracted as wavs.
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output_sample_rate=22050
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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 < 1486:
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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 = audio2numpy.open_audio(file)
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except:
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print(f"Error with {file}")
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continue
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wave = torch.tensor(wave)
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# Strip out channels.
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if len(wave.shape) > 1:
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wave = wave[:, 1] # Just use the first channel.
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# Calculate how much data we need to extract for each clip.
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clip_sz = sample_rate * clip_length
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interval = int(sample_rate * (clip_length / sparsity))
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i = 0
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if wave.shape[-1] == 0:
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print("Something went wrong: wave shape is 0.")
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while (i+clip_sz) < wave.shape[-1]:
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clip = wave[i:i+clip_sz]
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clip = F.interpolate(clip.view(1,1,clip_sz), scale_factor=output_sample_rate/sample_rate).squeeze()
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wavfile.write(osp.join(output_dir, f'{e}_{file_basis}_{i}.wav'), output_sample_rate, clip.numpy())
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i = i + interval
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