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import librosa |
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import torch |
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import torchaudio |
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class Slicer: |
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def __init__(self, |
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sr: int, |
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threshold: float = -40, |
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min_length: int = 5000, |
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min_interval: int = 300, |
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hop_size: int = 20, |
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max_sil_kept: int = 5000): |
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if not min_length >= min_interval >= hop_size: |
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raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size') |
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if not max_sil_kept >= hop_size: |
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raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size') |
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min_interval = sr * min_interval / 1000 |
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self.threshold = 10 ** (threshold / 20.) |
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self.hop_size = round(sr * hop_size / 1000) |
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self.win_size = min(round(min_interval), 4 * self.hop_size) |
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self.min_length = round(sr * min_length / 1000 / self.hop_size) |
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self.min_interval = round(min_interval / self.hop_size) |
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self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) |
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def _apply_slice(self, waveform, begin, end): |
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if len(waveform.shape) > 1: |
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return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)] |
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else: |
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return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)] |
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def slice(self, waveform): |
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if len(waveform.shape) > 1: |
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samples = librosa.to_mono(waveform) |
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else: |
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samples = waveform |
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if samples.shape[0] <= self.min_length: |
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return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}} |
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rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0) |
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sil_tags = [] |
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silence_start = None |
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clip_start = 0 |
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for i, rms in enumerate(rms_list): |
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if rms < self.threshold: |
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if silence_start is None: |
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silence_start = i |
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continue |
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if silence_start is None: |
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continue |
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is_leading_silence = silence_start == 0 and i > self.max_sil_kept |
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need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length |
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if not is_leading_silence and not need_slice_middle: |
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silence_start = None |
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continue |
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if i - silence_start <= self.max_sil_kept: |
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pos = rms_list[silence_start: i + 1].argmin() + silence_start |
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if silence_start == 0: |
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sil_tags.append((0, pos)) |
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else: |
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sil_tags.append((pos, pos)) |
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clip_start = pos |
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elif i - silence_start <= self.max_sil_kept * 2: |
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pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin() |
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pos += i - self.max_sil_kept |
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pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start |
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pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept |
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if silence_start == 0: |
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sil_tags.append((0, pos_r)) |
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clip_start = pos_r |
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else: |
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sil_tags.append((min(pos_l, pos), max(pos_r, pos))) |
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clip_start = max(pos_r, pos) |
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else: |
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pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start |
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pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept |
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if silence_start == 0: |
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sil_tags.append((0, pos_r)) |
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else: |
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sil_tags.append((pos_l, pos_r)) |
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clip_start = pos_r |
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silence_start = None |
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total_frames = rms_list.shape[0] |
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if silence_start is not None and total_frames - silence_start >= self.min_interval: |
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silence_end = min(total_frames, silence_start + self.max_sil_kept) |
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pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start |
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sil_tags.append((pos, total_frames + 1)) |
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if len(sil_tags) == 0: |
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return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}} |
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else: |
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chunks = [] |
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if sil_tags[0][0]: |
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chunks.append( |
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{"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"}) |
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for i in range(0, len(sil_tags)): |
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if i: |
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chunks.append({"slice": False, |
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"split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"}) |
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chunks.append({"slice": True, |
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"split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"}) |
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if sil_tags[-1][1] * self.hop_size < len(waveform): |
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chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"}) |
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chunk_dict = {} |
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for i in range(len(chunks)): |
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chunk_dict[str(i)] = chunks[i] |
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return chunk_dict |
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def cut(audio_path, db_thresh=-30, min_len=5000): |
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audio, sr = librosa.load(audio_path, sr=None) |
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slicer = Slicer( |
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sr=sr, |
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threshold=db_thresh, |
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min_length=min_len |
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) |
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chunks = slicer.slice(audio) |
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return chunks |
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def chunks2audio(audio_path, chunks): |
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chunks = dict(chunks) |
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audio, sr = torchaudio.load(audio_path) |
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if len(audio.shape) == 2 and audio.shape[1] >= 2: |
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audio = torch.mean(audio, dim=0).unsqueeze(0) |
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audio = audio.cpu().numpy()[0] |
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result = [] |
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for k, v in chunks.items(): |
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tag = v["split_time"].split(",") |
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if tag[0] != tag[1]: |
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result.append((v["slice"], audio[int(tag[0]):int(tag[1])])) |
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return result, sr |
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