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| import numpy as np | |
| # This function is obtained from librosa. | |
| def get_rms( | |
| y, | |
| *, | |
| frame_length=2048, | |
| hop_length=512, | |
| pad_mode="constant", | |
| ): | |
| padding = (int(frame_length // 2), int(frame_length // 2)) | |
| y = np.pad(y, padding, mode=pad_mode) | |
| axis = -1 | |
| # put our new within-frame axis at the end for now | |
| out_strides = y.strides + tuple([y.strides[axis]]) | |
| # Reduce the shape on the framing axis | |
| x_shape_trimmed = list(y.shape) | |
| x_shape_trimmed[axis] -= frame_length - 1 | |
| out_shape = tuple(x_shape_trimmed) + tuple([frame_length]) | |
| xw = np.lib.stride_tricks.as_strided( | |
| y, shape=out_shape, strides=out_strides | |
| ) | |
| if axis < 0: | |
| target_axis = axis - 1 | |
| else: | |
| target_axis = axis + 1 | |
| xw = np.moveaxis(xw, -1, target_axis) | |
| # Downsample along the target axis | |
| slices = [slice(None)] * xw.ndim | |
| slices[axis] = slice(0, None, hop_length) | |
| x = xw[tuple(slices)] | |
| # Calculate power | |
| power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True) | |
| return np.sqrt(power) | |
| class Slicer: | |
| def __init__(self, | |
| sr: int, | |
| threshold: float = -40., | |
| min_length: int = 5000, | |
| min_interval: int = 300, | |
| hop_size: int = 20, | |
| max_sil_kept: int = 5000): | |
| if not min_length >= min_interval >= hop_size: | |
| raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size') | |
| if not max_sil_kept >= hop_size: | |
| raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size') | |
| min_interval = sr * min_interval / 1000 | |
| self.threshold = 10 ** (threshold / 20.) | |
| self.hop_size = round(sr * hop_size / 1000) | |
| self.win_size = min(round(min_interval), 4 * self.hop_size) | |
| self.min_length = round(sr * min_length / 1000 / self.hop_size) | |
| self.min_interval = round(min_interval / self.hop_size) | |
| self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) | |
| def _apply_slice(self, waveform, begin, end): | |
| if len(waveform.shape) > 1: | |
| return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)] | |
| else: | |
| return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)] | |
| # @timeit | |
| def slice(self, waveform): | |
| if len(waveform.shape) > 1: | |
| samples = waveform.mean(axis=0) | |
| else: | |
| samples = waveform | |
| if (samples.shape[0] + self.hop_size - 1) // self.hop_size <= self.min_length: | |
| return [waveform] | |
| rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0) | |
| sil_tags = [] | |
| silence_start = None | |
| clip_start = 0 | |
| for i, rms in enumerate(rms_list): | |
| # Keep looping while frame is silent. | |
| if rms < self.threshold: | |
| # Record start of silent frames. | |
| if silence_start is None: | |
| silence_start = i | |
| continue | |
| # Keep looping while frame is not silent and silence start has not been recorded. | |
| if silence_start is None: | |
| continue | |
| # Clear recorded silence start if interval is not enough or clip is too short | |
| is_leading_silence = silence_start == 0 and i > self.max_sil_kept | |
| need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length | |
| if not is_leading_silence and not need_slice_middle: | |
| silence_start = None | |
| continue | |
| # Need slicing. Record the range of silent frames to be removed. | |
| if i - silence_start <= self.max_sil_kept: | |
| pos = rms_list[silence_start: i + 1].argmin() + silence_start | |
| if silence_start == 0: | |
| sil_tags.append((0, pos)) | |
| else: | |
| sil_tags.append((pos, pos)) | |
| clip_start = pos | |
| elif i - silence_start <= self.max_sil_kept * 2: | |
| pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin() | |
| pos += i - self.max_sil_kept | |
| pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start | |
| pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept | |
| if silence_start == 0: | |
| sil_tags.append((0, pos_r)) | |
| clip_start = pos_r | |
| else: | |
| sil_tags.append((min(pos_l, pos), max(pos_r, pos))) | |
| clip_start = max(pos_r, pos) | |
| else: | |
| pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start | |
| pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept | |
| if silence_start == 0: | |
| sil_tags.append((0, pos_r)) | |
| else: | |
| sil_tags.append((pos_l, pos_r)) | |
| clip_start = pos_r | |
| silence_start = None | |
| # Deal with trailing silence. | |
| total_frames = rms_list.shape[0] | |
| if silence_start is not None and total_frames - silence_start >= self.min_interval: | |
| silence_end = min(total_frames, silence_start + self.max_sil_kept) | |
| pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start | |
| sil_tags.append((pos, total_frames + 1)) | |
| # Apply and return slices. | |
| if len(sil_tags) == 0: | |
| return [waveform] | |
| else: | |
| chunks = [] | |
| if sil_tags[0][0] > 0: | |
| chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0])) | |
| for i in range(len(sil_tags) - 1): | |
| chunks.append(self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0])) | |
| if sil_tags[-1][1] < total_frames: | |
| chunks.append(self._apply_slice(waveform, sil_tags[-1][1], total_frames)) | |
| return chunks | |
| def main(): | |
| import os.path | |
| from argparse import ArgumentParser | |
| import librosa | |
| import soundfile | |
| 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_length', type=int, required=False, default=5000, | |
| help='The minimum milliseconds required for each sliced audio clip') | |
| parser.add_argument('--min_interval', type=int, required=False, default=300, | |
| help='The minimum milliseconds for a silence part to be sliced') | |
| parser.add_argument('--hop_size', type=int, required=False, default=10, | |
| help='Frame length in milliseconds') | |
| parser.add_argument('--max_sil_kept', type=int, required=False, default=500, | |
| help='The maximum silence length kept around the sliced clip, 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, mono=False) | |
| slicer = Slicer( | |
| sr=sr, | |
| threshold=args.db_thresh, | |
| min_length=args.min_length, | |
| min_interval=args.min_interval, | |
| hop_size=args.hop_size, | |
| max_sil_kept=args.max_sil_kept | |
| ) | |
| chunks = slicer.slice(audio) | |
| if not os.path.exists(out): | |
| os.makedirs(out) | |
| for i, chunk in enumerate(chunks): | |
| if len(chunk.shape) > 1: | |
| chunk = chunk.T | |
| 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() |