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import random
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from multiprocessing import Pool
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from pathlib import Path
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import click
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import librosa
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import torch.nn.functional as F
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import torchaudio
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from tqdm import tqdm
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from tools.file import AUDIO_EXTENSIONS, list_files
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threshold = 10 ** (-50 / 20.0)
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def process(file):
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waveform, sample_rate = torchaudio.load(str(file), backend="sox")
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if waveform.size(0) > 1:
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waveform = waveform.mean(dim=0, keepdim=True)
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loudness = librosa.feature.rms(
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y=waveform.numpy().squeeze(), frame_length=2048, hop_length=512, center=True
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)[0]
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for i in range(len(loudness) - 1, 0, -1):
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if loudness[i] > threshold:
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break
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end_silent_time = (len(loudness) - i) * 512 / sample_rate
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if end_silent_time <= 0.3:
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random_time = random.uniform(0.3, 0.7) - end_silent_time
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waveform = F.pad(
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waveform, (0, int(random_time * sample_rate)), mode="constant", value=0
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)
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for i in range(len(loudness)):
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if loudness[i] > threshold:
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break
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start_silent_time = i * 512 / sample_rate
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if start_silent_time > 0.02:
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waveform = waveform[:, int((start_silent_time - 0.02) * sample_rate) :]
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torchaudio.save(uri=str(file), src=waveform, sample_rate=sample_rate)
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@click.command()
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@click.argument("source", type=Path)
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@click.option("--num-workers", type=int, default=12)
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def main(source, num_workers):
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files = list(list_files(source, AUDIO_EXTENSIONS, recursive=True))
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with Pool(num_workers) as p:
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list(tqdm(p.imap_unordered(process, files), total=len(files)))
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if __name__ == "__main__":
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main()
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