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