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| import librosa | |
| import torch | |
| import torchaudio | |
| 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 = librosa.to_mono(waveform) | |
| else: | |
| samples = waveform | |
| if samples.shape[0] <= self.min_length: | |
| return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}} | |
| rms_list = librosa.feature.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 {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}} | |
| else: | |
| chunks = [] | |
| # 第一段静音并非从头开始，补上有声片段 | |
| if sil_tags[0][0]: | |
| chunks.append( | |
| {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"}) | |
| for i in range(0, len(sil_tags)): | |
| # 标识有声片段（跳过第一段） | |
| if i: | |
| chunks.append({"slice": False, | |
| "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"}) | |
| # 标识所有静音片段 | |
| chunks.append({"slice": True, | |
| "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"}) | |
| # 最后一段静音并非结尾，补上结尾片段 | |
| if sil_tags[-1][1] * self.hop_size < len(waveform): | |
| chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"}) | |
| chunk_dict = {} | |
| for i in range(len(chunks)): | |
| chunk_dict[str(i)] = chunks[i] | |
| return chunk_dict | |
| def cut(audio_path, db_thresh=-30, min_len=5000): | |
| audio, sr = librosa.load(audio_path, sr=None) | |
| slicer = Slicer( | |
| sr=sr, | |
| threshold=db_thresh, | |
| min_length=min_len | |
| ) | |
| chunks = slicer.slice(audio) | |
| return chunks | |
| def chunks2audio(audio_path, chunks): | |
| chunks = dict(chunks) | |
| audio, sr = torchaudio.load(audio_path) | |
| if len(audio.shape) == 2 and audio.shape[1] >= 2: | |
| audio = torch.mean(audio, dim=0).unsqueeze(0) | |
| audio = audio.cpu().numpy()[0] | |
| result = [] | |
| for k, v in chunks.items(): | |
| tag = v["split_time"].split(",") | |
| if tag[0] != tag[1]: | |
| result.append((v["slice"], audio[int(tag[0]):int(tag[1])])) | |
| return result, sr | |