广州公司注册提供地址搜索引擎优化指的是什么

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 nowout_strides = y.strides + tuple([y.strides[axis]])# Reduce the shape on the framing axisx_shape_trimmed = list(y.shape)x_shape_trimmed[axis] -= frame_length - 1out_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 - 1else:target_axis = axis + 1xw = np.moveaxis(xw, -1, target_axis)# Downsample along the target axisslices = [slice(None)] * xw.ndimslices[axis] = slice(0, None, hop_length)x = xw[tuple(slices)]# Calculate powerpower = 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 / 1000self.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)]# @timeitdef slice(self, waveform):if len(waveform.shape) > 1:samples = waveform.mean(axis=0)else:samples = waveformif 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 = Noneclip_start = 0for 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 = icontinue# 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 shortis_leading_silence = silence_start == 0 and i > self.max_sil_keptneed_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_lengthif not is_leading_silence and not need_slice_middle:silence_start = Nonecontinue# 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_startif silence_start == 0:sil_tags.append((0, pos))else:sil_tags.append((pos, pos))clip_start = poselif 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_keptpos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_startpos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_keptif silence_start == 0:sil_tags.append((0, pos_r))clip_start = pos_relse: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_startpos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_keptif silence_start == 0:sil_tags.append((0, pos_r))else:sil_tags.append((pos_l, pos_r))clip_start = pos_rsilence_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_startsil_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 chunksdef main():import os.pathfrom argparse import ArgumentParserimport librosaimport soundfileparser = 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.outif 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.Tsoundfile.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 Slicerclass 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.Toutput_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:breakself.slicer_params["min_interval"] = self.slicer_params["min_interval"] // 2if 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.Tnew_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:breakself.slicer_params["min_interval"] = self.original_min_intervalfor 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 = 0for filepath, audio, duration in short_files:if current_duration + duration <= max_sec:merged_audio.append(audio)current_duration += durationos.remove(filepath)else:if merged_audio:output_audio = np.concatenate(merged_audio, axis=-1)if len(output_audio.shape) > 1:output_audio = output_audio.Toutput_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 = durationos.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.Toutput_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 = 0for 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 = -1min_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 0rate_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