mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-29 02:55:05 +08:00
c8261b2ccc
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
---------
Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
261 lines
9.0 KiB
Python
261 lines
9.0 KiB
Python
import numpy as np
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# This function is obtained from librosa.
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def get_rms(
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y,
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frame_length=2048,
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hop_length=512,
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pad_mode="constant",
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):
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padding = (int(frame_length // 2), int(frame_length // 2))
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y = np.pad(y, padding, mode=pad_mode)
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axis = -1
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# put our new within-frame axis at the end for now
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out_strides = y.strides + tuple([y.strides[axis]])
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# Reduce the shape on the framing axis
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x_shape_trimmed = list(y.shape)
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x_shape_trimmed[axis] -= frame_length - 1
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out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
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xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
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if axis < 0:
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target_axis = axis - 1
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else:
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target_axis = axis + 1
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xw = np.moveaxis(xw, -1, target_axis)
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# Downsample along the target axis
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slices = [slice(None)] * xw.ndim
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slices[axis] = slice(0, None, hop_length)
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x = xw[tuple(slices)]
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# Calculate power
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power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
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return np.sqrt(power)
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class Slicer:
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def __init__(
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self,
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sr: int,
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threshold: float = -40.0,
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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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):
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if not min_length >= min_interval >= hop_size:
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raise ValueError(
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"The following condition must be satisfied: min_length >= min_interval >= hop_size"
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)
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if not max_sil_kept >= hop_size:
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raise ValueError(
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"The following condition must be satisfied: max_sil_kept >= hop_size"
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)
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min_interval = sr * min_interval / 1000
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self.threshold = 10 ** (threshold / 20.0)
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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[
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:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)
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]
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else:
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return waveform[
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begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)
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]
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# @timeit
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def slice(self, waveform):
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if len(waveform.shape) > 1:
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samples = waveform.mean(axis=0)
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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 [waveform]
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rms_list = get_rms(
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y=samples, frame_length=self.win_size, hop_length=self.hop_size
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).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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# Keep looping while frame is silent.
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if rms < self.threshold:
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# Record start of silent frames.
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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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# Keep looping while frame is not silent and silence start has not been recorded.
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if silence_start is None:
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continue
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# Clear recorded silence start if interval is not enough or clip is too short
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is_leading_silence = silence_start == 0 and i > self.max_sil_kept
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need_slice_middle = (
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i - silence_start >= self.min_interval
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and i - clip_start >= self.min_length
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)
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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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# Need slicing. Record the range of silent frames to be removed.
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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[
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i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
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].argmin()
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pos += i - self.max_sil_kept
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pos_l = (
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rms_list[
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silence_start : silence_start + self.max_sil_kept + 1
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].argmin()
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+ silence_start
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)
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pos_r = (
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rms_list[i - self.max_sil_kept : i + 1].argmin()
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+ i
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- self.max_sil_kept
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)
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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 = (
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rms_list[
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silence_start : silence_start + self.max_sil_kept + 1
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].argmin()
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+ silence_start
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)
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pos_r = (
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rms_list[i - self.max_sil_kept : i + 1].argmin()
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+ i
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- self.max_sil_kept
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)
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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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# Deal with trailing silence.
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total_frames = rms_list.shape[0]
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if (
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silence_start is not None
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and total_frames - silence_start >= self.min_interval
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):
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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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# Apply and return slices.
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if len(sil_tags) == 0:
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return [waveform]
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else:
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chunks = []
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if sil_tags[0][0] > 0:
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chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0]))
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for i in range(len(sil_tags) - 1):
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chunks.append(
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self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0])
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)
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if sil_tags[-1][1] < total_frames:
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chunks.append(
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self._apply_slice(waveform, sil_tags[-1][1], total_frames)
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)
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return chunks
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def main():
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import os.path
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from argparse import ArgumentParser
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import librosa
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import soundfile
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parser = ArgumentParser()
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parser.add_argument("audio", type=str, help="The audio to be sliced")
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parser.add_argument(
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"--out", type=str, help="Output directory of the sliced audio clips"
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)
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parser.add_argument(
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"--db_thresh",
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type=float,
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required=False,
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default=-40,
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help="The dB threshold for silence detection",
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)
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parser.add_argument(
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"--min_length",
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type=int,
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required=False,
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default=5000,
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help="The minimum milliseconds required for each sliced audio clip",
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)
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parser.add_argument(
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"--min_interval",
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type=int,
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required=False,
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default=300,
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help="The minimum milliseconds for a silence part to be sliced",
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)
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parser.add_argument(
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"--hop_size",
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type=int,
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required=False,
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default=10,
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help="Frame length in milliseconds",
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)
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parser.add_argument(
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"--max_sil_kept",
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type=int,
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required=False,
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default=500,
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help="The maximum silence length kept around the sliced clip, presented in milliseconds",
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)
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args = parser.parse_args()
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out = args.out
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if out is None:
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out = os.path.dirname(os.path.abspath(args.audio))
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audio, sr = librosa.load(args.audio, sr=None, mono=False)
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slicer = Slicer(
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sr=sr,
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threshold=args.db_thresh,
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min_length=args.min_length,
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min_interval=args.min_interval,
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hop_size=args.hop_size,
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max_sil_kept=args.max_sil_kept,
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)
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chunks = slicer.slice(audio)
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if not os.path.exists(out):
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os.makedirs(out)
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for i, chunk in enumerate(chunks):
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if len(chunk.shape) > 1:
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chunk = chunk.T
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soundfile.write(
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os.path.join(
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out,
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f"%s_%d.wav"
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% (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i),
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),
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chunk,
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sr,
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)
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if __name__ == "__main__":
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main()
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