143 lines
6.5 KiB
Python
143 lines
6.5 KiB
Python
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
|