| import os.path |
| from argparse import ArgumentParser |
| import time |
|
|
| 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 |
|
|
|
|
| |
| 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] |
|
|
|
|
| |
| 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] |
| |
| abs_amp = np.abs(samples - np.mean(samples)) |
| |
| 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 = [] |
| if sil_tags[0][0] > 0: |
| chunks.append(_apply_slice(audio, 0, sil_tags[0][0])) |
| for i in range(0, len(sil_tags) - 1): |
| chunks.append(_apply_slice(audio, sil_tags[i][1], sil_tags[i + 1][0])) |
| if sil_tags[-1][1] < samples.shape[0] - 1: |
| chunks.append(_apply_slice(audio, sil_tags[-1][1], samples.shape[0])) |
| return chunks |
|
|
|
|
| def main(): |
| parser = ArgumentParser() |
| parser.add_argument('audio', type=str, help='The audio to be sliced') |
| 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) |
| for i, chunk in enumerate(chunks): |
| soundfile.write(os.path.join(out, f'%s_%d.wav' % (os.path.basename(args.audio).rsplit('.', maxsplit=1)[0], i)), chunk, sr) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
