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Zero
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import numpy as np
class Slicer:
"""
A class for slicing audio waveforms into segments based on silence detection.
Attributes:
sr (int): Sampling rate of the audio waveform.
threshold (float): RMS threshold for silence detection, in dB.
min_length (int): Minimum length of a segment, in milliseconds.
min_interval (int): Minimum interval between segments, in milliseconds.
hop_size (int): Hop size for RMS calculation, in milliseconds.
max_sil_kept (int): Maximum length of silence to keep at the beginning or end of a segment, in milliseconds.
Methods:
slice(waveform): Slices the given waveform into segments.
"""
def __init__(
self,
sr: int,
threshold: float = -40.0,
min_length: int = 5000,
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 5000,
):
"""
Initializes a Slicer object.
Args:
sr (int): Sampling rate of the audio waveform.
threshold (float, optional): RMS threshold for silence detection, in dB. Defaults to -40.0.
min_length (int, optional): Minimum length of a segment, in milliseconds. Defaults to 5000.
min_interval (int, optional): Minimum interval between segments, in milliseconds. Defaults to 300.
hop_size (int, optional): Hop size for RMS calculation, in milliseconds. Defaults to 20.
max_sil_kept (int, optional): Maximum length of silence to keep at the beginning or end of a segment, in milliseconds. Defaults to 5000.
Raises:
ValueError: If the input parameters are not valid.
"""
if not min_length >= min_interval >= hop_size:
raise ValueError("min_length >= min_interval >= hop_size is required")
if not max_sil_kept >= hop_size:
raise ValueError("max_sil_kept >= hop_size is required")
# Convert time-based parameters to sample-based parameters
min_interval = sr * min_interval / 1000
self.threshold = 10 ** (threshold / 20.0)
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):
"""
Applies a slice to the waveform.
Args:
waveform (numpy.ndarray): The waveform to slice.
begin (int): Start frame index.
end (int): End frame index.
"""
start_idx = begin * self.hop_size
if len(waveform.shape) > 1:
end_idx = min(waveform.shape[1], end * self.hop_size)
return waveform[:, start_idx:end_idx]
else:
end_idx = min(waveform.shape[0], end * self.hop_size)
return waveform[start_idx:end_idx]
def slice(self, waveform):
"""
Slices the given waveform into segments.
Args:
waveform (numpy.ndarray): The waveform to slice.
"""
# Calculate RMS for each frame
samples = waveform.mean(axis=0) if len(waveform.shape) > 1 else waveform
if samples.shape[0] <= self.min_length:
return [waveform]
rms_list = get_rms(
y=samples, frame_length=self.win_size, hop_length=self.hop_size
).squeeze(0)
# Detect silence segments and mark them
sil_tags = []
silence_start, clip_start = None, 0
for i, rms in enumerate(rms_list):
# If current frame is silent
if rms < self.threshold:
if silence_start is None:
silence_start = i
continue
# If current frame is not silent
if silence_start is None:
continue
# Check if current silence segment is leading silence or need to slice
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 leading silence and not need to slice middle
if not is_leading_silence and not need_slice_middle:
silence_start = None
continue
# Handle different cases of silence segments
if i - silence_start <= self.max_sil_kept:
# Short silence
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:
# Medium silence
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:
# Long silence
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
# Handle 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))
# Extract segments based on silence tags
if not sil_tags:
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 get_rms(
y,
frame_length=2048,
hop_length=512,
pad_mode="constant",
):
"""
Calculates the root mean square (RMS) of a waveform.
Args:
y (numpy.ndarray): The waveform.
frame_length (int, optional): The length of the frame in samples. Defaults to 2048.
hop_length (int, optional): The hop length between frames in samples. Defaults to 512.
pad_mode (str, optional): The padding mode used for the waveform. Defaults to "constant".
"""
padding = (int(frame_length // 2), int(frame_length // 2))
y = np.pad(y, padding, mode=pad_mode)
axis = -1
out_strides = y.strides + tuple([y.strides[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)
slices = [slice(None)] * xw.ndim
slices[axis] = slice(0, None, hop_length)
x = xw[tuple(slices)]
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
return np.sqrt(power)
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