对音频切分成小音频（机器学习用）
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。
-------流程（这是我做的流程，你可以不用看）
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
-------
你需要看的
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
slicer2.py
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()
auto_slicer.py
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
run.py
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
