151 lines
6.4 KiB
Python
151 lines
6.4 KiB
Python
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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# @timeit
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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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# @timeit
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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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# get absolute amplitudes
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abs_amp = np.abs(samples - np.mean(samples))
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# calculate local maximum with large window
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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() |