我是把so-vits中小工具,分析源码然后提取出来了。以后可以写在自己的程序里。
从开源代码中快速获取自己需要的东西
如果有界面f12看他里面的接口,然后在源码中全局搜索,没有接口比如socket,看他的消息字段,然后推测。然后提取补齐代码就行了
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提取出来有3个类
run.py是我自己写的
其他是我提取的源码,首先你得install一些包
numpy,librosa,soundfile
- import numpy as np
-
-
- # This function is obtained from librosa.
- def get_rms(
- y,
- *,
- frame_length=2048,
- hop_length=512,
- pad_mode="constant",
- ):
- padding = (int(frame_length // 2), int(frame_length // 2))
- y = np.pad(y, padding, mode=pad_mode)
-
- axis = -1
- # put our new within-frame axis at the end for now
- out_strides = y.strides + tuple([y.strides[axis]])
- # Reduce the shape on the framing 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)
- # Downsample along the target axis
- slices = [slice(None)] * xw.ndim
- slices[axis] = slice(0, None, hop_length)
- x = xw[tuple(slices)]
-
- # Calculate power
- power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
-
- return np.sqrt(power)
-
-
- 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 = waveform.mean(axis=0)
- else:
- samples = 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)
- 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 [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 main():
- import os.path
- from argparse import ArgumentParser
-
- import librosa
- import soundfile
-
- parser = ArgumentParser()
- parser.add_argument('audio', type=str, help='The audio to be sliced')
- parser.add_argument('--out', type=str, help='Output directory of the sliced audio clips')
- parser.add_argument('--db_thresh', type=float, required=False, default=-40,
- help='The dB threshold for silence detection')
- parser.add_argument('--min_length', type=int, required=False, default=5000,
- help='The minimum milliseconds required for each sliced audio clip')
- parser.add_argument('--min_interval', type=int, required=False, default=300,
- help='The minimum milliseconds for a silence part to be sliced')
- parser.add_argument('--hop_size', type=int, required=False, default=10,
- help='Frame length in milliseconds')
- parser.add_argument('--max_sil_kept', type=int, required=False, default=500,
- help='The maximum silence length kept around the sliced clip, presented in milliseconds')
- args = parser.parse_args()
- out = args.out
- if out is None:
- out = os.path.dirname(os.path.abspath(args.audio))
- audio, sr = librosa.load(args.audio, sr=None, mono=False)
- slicer = Slicer(
- sr=sr,
- threshold=args.db_thresh,
- min_length=args.min_length,
- min_interval=args.min_interval,
- hop_size=args.hop_size,
- max_sil_kept=args.max_sil_kept
- )
- chunks = slicer.slice(audio)
- if not os.path.exists(out):
- os.makedirs(out)
- for i, chunk in enumerate(chunks):
- if len(chunk.shape) > 1:
- chunk = chunk.T
- soundfile.write(os.path.join(out, f'%s_%d.wav' % (os.path.basename(args.audio).rsplit('.', maxsplit=1)[0], i)), chunk, sr)
-
-
- if __name__ == '__main__':
- main()
- import os
- import numpy as np
- import librosa
- import soundfile as sf
- from slicer2 import Slicer
-
- class AutoSlicer:
- def __init__(self):
- self.slicer_params = {
- "threshold": -40,
- "min_length": 5000,
- "min_interval": 300,
- "hop_size": 10,
- "max_sil_kept": 500,
- }
- self.original_min_interval = self.slicer_params["min_interval"]
-
- def auto_slice(self, filename, input_dir, output_dir, max_sec):
- audio, sr = librosa.load(os.path.join(input_dir, filename), sr=None, mono=False)
- slicer = Slicer(sr=sr, **self.slicer_params)
- chunks = slicer.slice(audio)
- files_to_delete = []
- for i, chunk in enumerate(chunks):
- if len(chunk.shape) > 1:
- chunk = chunk.T
- output_filename = f"{os.path.splitext(filename)[0]}_{i}"
- output_filename = "".join(c for c in output_filename if c.isascii() or c == "_") + ".wav"
- output_filepath = os.path.join(output_dir, output_filename)
- sf.write(output_filepath, chunk, sr)
- #Check and re-slice audio that more than max_sec.
- while True:
- new_audio, sr = librosa.load(output_filepath, sr=None, mono=False)
- if librosa.get_duration(y=new_audio, sr=sr) <= max_sec:
- break
- self.slicer_params["min_interval"] = self.slicer_params["min_interval"] // 2
- if self.slicer_params["min_interval"] >= self.slicer_params["hop_size"]:
- new_chunks = Slicer(sr=sr, **self.slicer_params).slice(new_audio)
- for j, new_chunk in enumerate(new_chunks):
- if len(new_chunk.shape) > 1:
- new_chunk = new_chunk.T
- new_output_filename = f"{os.path.splitext(output_filename)[0]}_{j}.wav"
- sf.write(os.path.join(output_dir, new_output_filename), new_chunk, sr)
- files_to_delete.append(output_filepath)
- else:
- break
- self.slicer_params["min_interval"] = self.original_min_interval
- for file_path in files_to_delete:
- if os.path.exists(file_path):
- os.remove(file_path)
-
- def merge_short(self, output_dir, max_sec, min_sec):
- short_files = []
- for filename in os.listdir(output_dir):
- filepath = os.path.join(output_dir, filename)
- if filename.endswith(".wav"):
- audio, sr = librosa.load(filepath, sr=None, mono=False)
- duration = librosa.get_duration(y=audio, sr=sr)
- if duration < min_sec:
- short_files.append((filepath, audio, duration))
- short_files.sort(key=lambda x: x[2], reverse=True)
- merged_audio = []
- current_duration = 0
- for filepath, audio, duration in short_files:
- if current_duration + duration <= max_sec:
- merged_audio.append(audio)
- current_duration += duration
- os.remove(filepath)
- else:
- if merged_audio:
- output_audio = np.concatenate(merged_audio, axis=-1)
- if len(output_audio.shape) > 1:
- output_audio = output_audio.T
- output_filename = f"merged_{len(os.listdir(output_dir))}.wav"
- sf.write(os.path.join(output_dir, output_filename), output_audio, sr)
- merged_audio = [audio]
- current_duration = duration
- os.remove(filepath)
- if merged_audio and current_duration >= min_sec:
- output_audio = np.concatenate(merged_audio, axis=-1)
- if len(output_audio.shape) > 1:
- output_audio = output_audio.T
- output_filename = f"merged_{len(os.listdir(output_dir))}.wav"
- sf.write(os.path.join(output_dir, output_filename), output_audio, sr)
-
- def slice_count(self, input_dir, output_dir):
- orig_duration = final_duration = 0
- for file in os.listdir(input_dir):
- if file.endswith(".wav"):
- _audio, _sr = librosa.load(os.path.join(input_dir, file), sr=None, mono=False)
- orig_duration += librosa.get_duration(y=_audio, sr=_sr)
- wav_files = [file for file in os.listdir(output_dir) if file.endswith(".wav")]
- num_files = len(wav_files)
- max_duration = -1
- min_duration = float("inf")
- for file in wav_files:
- file_path = os.path.join(output_dir, file)
- audio, sr = librosa.load(file_path, sr=None, mono=False)
- duration = librosa.get_duration(y=audio, sr=sr)
- final_duration += float(duration)
- if duration > max_duration:
- max_duration = float(duration)
- if duration < min_duration:
- min_duration = float(duration)
- return num_files, max_duration, min_duration, orig_duration, final_duration
-
-
- import os
- from auto_slicer import AutoSlicer
- import librosa
- def slicer_fn(input_dir, output_dir, process_method, max_sec, min_sec):
- if output_dir == "":
- return "请先选择输出的文件夹"
- if output_dir == input_dir:
- return "输出目录不能和输入目录相同"
- slicer = AutoSlicer()
- if os.path.exists(output_dir) is not True:
- os.makedirs(output_dir)
- for filename in os.listdir(input_dir):
- if filename.lower().endswith(".wav"):
- slicer.auto_slice(filename, input_dir, output_dir, max_sec)
- if process_method == "丢弃":
- for filename in os.listdir(output_dir):
- if filename.endswith(".wav"):
- filepath = os.path.join(output_dir, filename)
- audio, sr = librosa.load(filepath, sr=None, mono=False)
- if librosa.get_duration(y=audio, sr=sr) < min_sec:
- os.remove(filepath)
- elif process_method == "将过短音频整合为长音频":
- slicer.merge_short(output_dir, max_sec, min_sec)
- file_count, max_duration, min_duration, orig_duration, final_duration = slicer.slice_count(input_dir, output_dir)
- hrs = int(final_duration / 3600)
- mins = int((final_duration % 3600) / 60)
- sec = format(float(final_duration % 60), '.2f')
- rate = format(100 * (final_duration / orig_duration), '.2f') if orig_duration != 0 else 0
- rate_msg = f"为原始音频时长的{rate}%" if rate != 0 else "因未知问题,无法计算切片时长的占比"
- return f"成功将音频切分为{file_count}条片段,其中最长{max_duration}秒,最短{min_duration}秒,切片后的音频总时长{hrs:02d}小时{mins:02d}分{sec}秒,{rate_msg}"
-
- input_dir="F:\sliper\input"#输入文件夹(这里面可以放多个wav)
- output_dir="F:\sliper\output"#输出文件夹
- process_method="丢弃"#如果音频小于3就丢弃
- max_sec=15#音频最长为15
- min_sec=3#音频最小为3
- slicer_fn(input_dir,output_dir,process_method,max_sec,min_sec)
测试输入
得到