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import os.path |
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from argparse import ArgumentParser |
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import time |
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import librosa |
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import numpy as np |
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import soundfile |
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from scipy.ndimage import maximum_filter1d, uniform_filter1d |
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def timeit(func): |
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def run(*args, **kwargs): |
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t = time.time() |
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res = func(*args, **kwargs) |
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print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t)) |
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return res |
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return run |
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def _window_maximum(arr, win_sz): |
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return maximum_filter1d(arr, size=win_sz)[win_sz // 2: win_sz // 2 + arr.shape[0] - win_sz + 1] |
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def _window_rms(arr, win_sz): |
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filtered = np.sqrt(uniform_filter1d(np.power(arr, 2), win_sz) - np.power(uniform_filter1d(arr, win_sz), 2)) |
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return filtered[win_sz // 2: win_sz // 2 + arr.shape[0] - win_sz + 1] |
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def level2db(levels, eps=1e-12): |
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return 20 * np.log10(np.clip(levels, a_min=eps, a_max=1)) |
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def _apply_slice(audio, begin, end): |
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if len(audio.shape) > 1: |
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return audio[:, begin: end] |
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else: |
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return audio[begin: end] |
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class Slicer: |
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def __init__(self, |
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sr: int, |
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db_threshold: float = -40, |
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min_length: int = 5000, |
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win_l: int = 300, |
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win_s: int = 20, |
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max_silence_kept: int = 500): |
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self.db_threshold = db_threshold |
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self.min_samples = round(sr * min_length / 1000) |
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self.win_ln = round(sr * win_l / 1000) |
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self.win_sn = round(sr * win_s / 1000) |
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self.max_silence = round(sr * max_silence_kept / 1000) |
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if not self.min_samples >= self.win_ln >= self.win_sn: |
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raise ValueError('The following condition must be satisfied: min_length >= win_l >= win_s') |
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if not self.max_silence >= self.win_sn: |
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raise ValueError('The following condition must be satisfied: max_silence_kept >= win_s') |
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@timeit |
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def slice(self, audio): |
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if len(audio.shape) > 1: |
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samples = librosa.to_mono(audio) |
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else: |
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samples = audio |
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if samples.shape[0] <= self.min_samples: |
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return [audio] |
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abs_amp = np.abs(samples - np.mean(samples)) |
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win_max_db = level2db(_window_maximum(abs_amp, win_sz=self.win_ln)) |
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sil_tags = [] |
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left = right = 0 |
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while right < win_max_db.shape[0]: |
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if win_max_db[right] < self.db_threshold: |
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right += 1 |
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elif left == right: |
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left += 1 |
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right += 1 |
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else: |
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if left == 0: |
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split_loc_l = left |
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else: |
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sil_left_n = min(self.max_silence, (right + self.win_ln - left) // 2) |
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rms_db_left = level2db(_window_rms(samples[left: left + sil_left_n], win_sz=self.win_sn)) |
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split_win_l = left + np.argmin(rms_db_left) |
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split_loc_l = split_win_l + np.argmin(abs_amp[split_win_l: split_win_l + self.win_sn]) |
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if len(sil_tags) != 0 and split_loc_l - sil_tags[-1][1] < self.min_samples and right < win_max_db.shape[0] - 1: |
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right += 1 |
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left = right |
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continue |
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if right == win_max_db.shape[0] - 1: |
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split_loc_r = right + self.win_ln |
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else: |
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sil_right_n = min(self.max_silence, (right + self.win_ln - left) // 2) |
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rms_db_right = level2db(_window_rms(samples[right + self.win_ln - sil_right_n: right + self.win_ln], win_sz=self.win_sn)) |
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split_win_r = right + self.win_ln - sil_right_n + np.argmin(rms_db_right) |
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split_loc_r = split_win_r + np.argmin(abs_amp[split_win_r: split_win_r + self.win_sn]) |
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sil_tags.append((split_loc_l, split_loc_r)) |
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right += 1 |
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left = right |
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if left != right: |
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sil_left_n = min(self.max_silence, (right + self.win_ln - left) // 2) |
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rms_db_left = level2db(_window_rms(samples[left: left + sil_left_n], win_sz=self.win_sn)) |
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split_win_l = left + np.argmin(rms_db_left) |
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split_loc_l = split_win_l + np.argmin(abs_amp[split_win_l: split_win_l + self.win_sn]) |
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sil_tags.append((split_loc_l, samples.shape[0])) |
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if len(sil_tags) == 0: |
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return [audio] |
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else: |
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chunks = [] |
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if sil_tags[0][0] > 0: |
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chunks.append(_apply_slice(audio, 0, sil_tags[0][0])) |
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for i in range(0, len(sil_tags) - 1): |
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chunks.append(_apply_slice(audio, sil_tags[i][1], sil_tags[i + 1][0])) |
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if sil_tags[-1][1] < samples.shape[0] - 1: |
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chunks.append(_apply_slice(audio, sil_tags[-1][1], samples.shape[0])) |
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return chunks |
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def main(): |
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parser = ArgumentParser() |
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parser.add_argument('audio', type=str, help='The audio to be sliced') |
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parser.add_argument('--out', type=str, help='Output directory of the sliced audio clips') |
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parser.add_argument('--db_thresh', type=float, required=False, default=-40, help='The dB threshold for silence detection') |
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parser.add_argument('--min_len', type=int, required=False, default=5000, help='The minimum milliseconds required for each sliced audio clip') |
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parser.add_argument('--win_l', type=int, required=False, default=300, help='Size of the large sliding window, presented in milliseconds') |
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parser.add_argument('--win_s', type=int, required=False, default=20, help='Size of the small sliding window, presented in milliseconds') |
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parser.add_argument('--max_sil_kept', type=int, required=False, default=500, help='The maximum silence length kept around the sliced audio, presented in milliseconds') |
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args = parser.parse_args() |
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out = args.out |
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if out is None: |
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out = os.path.dirname(os.path.abspath(args.audio)) |
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audio, sr = librosa.load(args.audio, sr=None) |
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slicer = Slicer( |
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sr=sr, |
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db_threshold=args.db_thresh, |
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min_length=args.min_len, |
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win_l=args.win_l, |
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win_s=args.win_s, |
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max_silence_kept=args.max_sil_kept |
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) |
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chunks = slicer.slice(audio) |
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if not os.path.exists(args.out): |
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os.makedirs(args.out) |
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for i, chunk in enumerate(chunks): |
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soundfile.write(os.path.join(out, f'%s_%d.wav' % (os.path.basename(args.audio).rsplit('.', maxsplit=1)[0], i)), chunk, sr) |
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if __name__ == '__main__': |
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main() |
