故障

最近频繁遇到“Com Surrogate 已停止工作”

  应用程序名:    DllHost.exe
  应用程序版本:    6.1.7600.16385
  应用程序时间戳:    4a5bca54
  故障模块名称:    DL180pdfl.dll
  故障模块版本:    18.0.0.2
  故障模块时间戳:    5e4e9f10
  异常代码:    c0000005
  异常偏移:    000000000010c045
  OS 版本:    6.1.7601.2.1.0.256.1
  区域设置 ID:    2052
  其他信息 1:    2d77
  其他信息 2:    2d77fd4d485c292455d4611a06bf410b
  其他信息 3:    480d
  其他信息 4:    480dc49c94a08920f82ec978d8269db2

联机阅读隐私声明:
  http://go.microsoft.com/fwlink/?linkid=104288&clcid=0x0804

如果无法获取联机隐私声明，请脱机阅读我们的隐私声明:
  C:\Windows\system32\zh-CN\erofflps.txt

解决办法

第一种

找到 故障模块 文件，删除；

第二种

1. 点击“计算机” →“ 属性” → “高级系统设置” → “高级” → “性能 ”→ “设置”

2. 进入设置后，再点击进入“数据执行保护”

3. 选下面的“单选按钮为除下列选定程序之外的所有程序和服务启用DEP” 点添加，路径是C:\Windows\System32\dllhost.exe，其实默认就是 System32目录，直接输入 dllhost.exe 点打开，这样会弹出一个警告窗口，不要理，直接点确定

4. 点击确定后，然后重启计算机，这时你就会发现com surrogate已停止工作的故障已经解决了

注：经测试，Windows 7 SP1 上数据执行保护提示“您不能在64-位可执行文件上设置 DEP 属性”。

第三种

1.Win+R调出运行框，输入Eventvwr.msc【注意大小写】打开事件查看器。

2.展开Windows日志，应用程序，找到对应的Application Error日志，查看崩溃模块。

3.如果崩溃模块属于第三方软件，则考虑卸载重装对应的软件。

EventData 

   DllHost.exe 
   6.1.7600.16385 
   4a5bca54 
   DL180pdfl.dll 
   18.0.0.2 
   5e4e9f10 
   c0000005 
   000000000010c045 
   5254 
   01d958ce0149ceca 
   C:\Windows\system32\DllHost.exe 
   C:\Program Files (x86)\ABBYY FineReader 15\x64\DL180pdfl.dll 
   435714bb-c4c1-11ed-b98c-f4b7e25ae2ec 

PIP是通用的Python包管理工具，可以方便安装、列出，卸载python的模块/库/包等。

注意：在Python3.4（一说是3.6）及更新的版本中，PIP已经捆绑安装了，不需要再单独安装（应该需要更新）。

安装卸载

常见使用, 例如:
cmd下:
安装pycurl包

pip install pycurl

列出已经安装的python包

pip list

输出pycurl包的信息

pip show pycurl

卸载pycurl包

pip uninstall pycurl

导出包名到

pip freeze > package20210627.txt

pip国内位置

-i https://mirrors.aliyun.com/pypi/simple/

批量安装和卸载

pip install -r package.txt
pip uninstall -r package.txt
pip install -r “F:\Program Project\python\packages\packages.txt”

添加环境变量

path=%path%;C:\Python27

安装pip

python -m pip install pip
上代码提示：No module named pip
py2 -m ensurepip

升级pip

python37 -m pip install --upgrade pip

查看可更新包：

pip list --outdated --format=columns

安装批量更新命令

pip install pip-review

依次更新所有包

pip -review --local --interactive

pip list --outdated #列出所有过期的库
pip install --upgrade 库名

python pip 删除所有包

导出所有包
pip freeze > requirements.txt
删除所有包
pip uninstall -r requirements.txt
or
pip uninstall -r requirements.txt -y

命令

Commands:
  install                     Install packages.
  download                    Download packages.
  uninstall                   Uninstall packages.
  freeze                      Output installed packages in requirements format.
  inspect                     Inspect the python environment.
  list                        List installed packages.
  show                        Show information about installed packages.
  check                       Verify installed packages have compatible dependencies.
  config                      Manage local and global configuration.
  search                      Search PyPI for packages.
  cache                       Inspect and manage pip's wheel cache.
  index                       Inspect information available from package indexes.
  wheel                       Build wheels from your requirements.
  hash                        Compute hashes of package archives.
  completion                  A helper command used for command completion.
  debug                       Show information useful for debugging.
  help                        Show help for commands.

常用选项

General Options:
  -h, --help                  Show help.
  --debug                     Let unhandled exceptions propagate outside the
                              main subroutine, instead of logging them to
                              stderr.
  --isolated                  Run pip in an isolated mode, ignoring
                              environment variables and user configuration.
  --require-virtualenv        Allow pip to only run in a virtual environment;
                              exit with an error otherwise.
  --python <python>           Run pip with the specified Python interpreter.
  -v, --verbose               Give more output. Option is additive, and can be
                              used up to 3 times.
  -V, --version               Show version and exit.
  -q, --quiet                 Give less output. Option is additive, and can be
                              used up to 3 times (corresponding to WARNING,
                              ERROR, and CRITICAL logging levels).
  --log <path>                Path to a verbose appending log.
  --no-input                  Disable prompting for input.
  --proxy <proxy>             Specify a proxy in the form
                              scheme://[user:passwd@]proxy.server:port.
  --retries <retries>         Maximum number of retries each connection should
                              attempt (default 5 times).
  --timeout <sec>             Set the socket timeout (default 15 seconds).
  --exists-action <action>    Default action when a path already exists:
                              (s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.
  --trusted-host <hostname>   Mark this host or host:port pair as trusted,
                              even though it does not have valid or any HTTPS.
  --cert <path>               Path to PEM-encoded CA certificate bundle. If
                              provided, overrides the default. See 'SSL
                              Certificate Verification' in pip documentation
                              for more information.
  --client-cert <path>        Path to SSL client certificate, a single file
                              containing the private key and the certificate
                              in PEM format.
  --cache-dir <dir>           Store the cache data in <dir>.
  --no-cache-dir              Disable the cache.
  --disable-pip-version-check
                              Don't periodically check PyPI to determine
                              whether a new version of pip is available for
                              download. Implied with --no-index.
  --no-color                  Suppress colored output.
  --no-python-version-warning
                              Silence deprecation warnings for upcoming
                              unsupported Pythons.
  --use-feature <feature>     Enable new functionality, that may be backward
                              incompatible.
  --use-deprecated <feature>  Enable deprecated functionality, that will be
                              removed in the future.

常见问题

问题一

ERROR: pip’s dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.

pip 的依赖项解析器当前未考虑安装的所有包。此行为是以下依赖项冲突的根源。

Installing collected packages: mpmath, sympy, pillow, numpy, networkx, MarkupSafe, idna, filelock, charset-normalizer, certifi, requests, jinja2, torch, torchvision, torchaudio
  Attempting uninstall: torchvision
    Found existing installation: torchvision 0.14.1
    Uninstalling torchvision-0.14.1:
      Successfully uninstalled torchvision-0.14.1
  Attempting uninstall: torchaudio
    Found existing installation: torchaudio 0.13.1
    Uninstalling torchaudio-0.13.1:
      Successfully uninstalled torchaudio-0.13.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
transformers 4.26.1 requires huggingface-hub<1.0,>=0.11.0, which is not installed.
transformers 4.26.1 requires packaging>=20.0, which is not installed.
transformers 4.26.1 requires pyyaml>=5.1, which is not installed.
transformers 4.26.1 requires regex!=2019.12.17, which is not installed.
transformers 4.26.1 requires tokenizers!=0.11.3,<0.14,>=0.11.1, which is not installed.
Successfully installed MarkupSafe-2.1.2 certifi-2022.12.7 charset-normalizer-2.1.1 filelock-3.9.0 idna-3.4 jinja2-3.1.2 mpmath-1.2.1 networkx-3.0rc1 numpy-1.24.1 pillow-9.3.0 requests-2.28.1 sympy-1.11.1 torch-2.1.0.dev20230312+cpu torchaudio-2.0.0.dev20230312+cpu torchvision-0.15.0.dev20230312+cpu

重新安装这几个包

问题二

Read timed out 超时问题

一般windows系统出现这个问题，可以在命令后面加上–user参数，类似：

pip install pyinstaller 
# 上句报超时错误
pip install pyinstaller --user

使用–help去查看–user的作用

大意就是：把这个包换个地方安装

问题三

你想要安装一个第三方包，但是没有权限将它安装到系统Python库中去。 或者，你可能想要安装一个供自己使用的包，而不是系统上面所有用户。

Python有一个用户安装目录，通常类似”~/.local/lib/python3.3/site-packages”。 要强制在这个目录中安装包，可使用安装选项“–user”。例如：

python3 setup.py install --user

或者

pip install --user packagename

在sys.path中用户的“site-packages”目录位于系统的“site-packages”目录之前。 因此，你安装在里面的包就比系统已安装的包优先级高 （尽管并不总是这样，要取决于第三方包管理器，比如distribute或pip）。

讨论

通常包会被安装到系统的site-packages目录中去，路径类似“/usr/local/lib/python3.3/site-packages”。 不过，这样做需要有管理员权限并且使用sudo命令。 就算你有这样的权限去执行命令，使用sudo去安装一个新的，可能没有被验证过的包有时候也不安全。

安装包到用户目录中通常是一个有效的方案，它允许你创建一个自定义安装。

另外，你还可以创建一个虚拟环境。

问题四

Installing build dependencies error安装构建依赖错误

一般这种都是因为缺乏一些依赖包导致的，可以考虑直接使用conda安装，

conda install -c conda-forge pyinstaller

问题五

pip install 没反应怎么办

用python -m pip install便可

参考

10.13 安装私有的包
python用pip install时安装失败的一系列问题及解决方法

前期准备

准备过程

Visit our demo for audio samples.
We also provide the pretrained models.
** Update note: Thanks to Rishikesh (ऋषिकेश), our interactive TTS demo is now available on Colab Notebook.

预训练模型在Google Drive上，需要科学上网

1. 下载数据集
   i. 下载并解压缩 LJ Speech 数据集，然后重命名或创建指向数据集文件夹的链接： ln -s /path/to/LJSpeech-1.1/wavs DUMMY1
   ii. 对于 mult-speaker 设置，下载并提取VCTK数据集，并将wav文件降采样至22050 Hz。然后重命名或创建指向数据集文件夹的链接： ln -s /path/to/VCTK-Corpus/downsampled_wavs DUMMY2
   在Linux系统中存在两种链接文件方式
   • 软链接（类似windows下的快捷方式）

   ln -s 原文件名 链接文件名
   

   • 硬链接（类似复制文件）

   ln 原文件名 链接文件名
   

   如果文件被删除，则软链接文件失去指向，变为不可用
   如果文件被删除，由于硬链接文件直接指向内容，因此不受影响
   详解：深度剖析 Linux 的 3 种“拷贝”命令

Administrator@AUTOBVT-Q90417J MINGW64 /e/vits (main)
$ ln -s "E:\vits\LJSpeech-1.1\wavs" DUMMY1
/*请用上面的命令,生成的 DUMMY1 文件夹里是 wavs 文件夹中的文件，没有 wavs 文件夹*/
Administrator@AUTOBVT-Q90417J MINGW64 /e/vits (main)
$ ln -s "E:\vits\LJSpeech-1.1\wavs" DUMMY1
/*如果删掉 DUMMY1 文件夹中 wavs 文件，输入上面的命令 DUMMY1 文件夹中会出现 wavs 文件夹*/
Administrator@AUTOBVT-Q90417J MINGW64 /e/vits (main)
$ ln -s E:\vits\LJSpeech-1.1\wavs DUMMY1
/*如果删掉 DUMMY1 文件夹中 wavs 文件，输入上面的命令会出现*/
ln: failed to create symbolic link 'DUMMY1/vitsLJSpeech-1.1wavs': No such file or directory
/*创建一个 DUMMY1 空白文件夹，使用下面的命令*/
Administrator@AUTOBVT-Q90417J MINGW64 /e/vits (main)
$ ln -s E:\vits\LJSpeech-1.1\wavs DUMMY1
ln: failed to create symbolic link 'DUMMY1/vitsLJSpeech-1.1wavs': No such file or directory
/*不创建 DUMMY1 空白文件夹，使用下面的命令*/
Administrator@AUTOBVT-Q90417J MINGW64 /e/vits (main)
$ ln -s E:\vits\LJSpeech-1.1\wavs DUMMY1
ln: failed to create symbolic link 'DUMMY1': No such file or directory

4. 如果您使用自己的数据集，请构建单调对齐搜索并运行预处理。

# Cython-version Monotonoic Alignment Search
cd monotonic_align
python setup.py build_ext --inplace

# Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided.
# python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt 
# python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt

翻回头我们看看数据集

LJspeech数据集

描述：
这是一个公共领域的语音数据集，由13,100个简短的音频剪辑组成，这些音频剪辑是单个说话者阅读7本非小说类书籍中的段落。为每个剪辑提供转录。短片的长度从1秒到10秒不等，总长度约为24小时。

这些文本出版于1884年至1964年，属于公有领域。该音频于2016-17年由LibriVox项目录制，也属于公有领域。

Homepage: The LJ Speech Dataset

介绍：
ljspeech

在网上翻了翻——
LJspeech数据集 1.0版
链接：https://pan.baidu.com/s/1OGDXtmNtKn-5258HfabTGA
提取码：jkre
LJspeech数据集 1.1版
数据集：http://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2 （用迅雷下载很快）
百度网盘地址：链接：https://pan.baidu.com/s/197LRZLNBb5gyREpYsMpkCg 提取码：7o1a

现在我没下载官方提供的预训练模型，

VCTK数据集

描述：
CSTR VCTK语料库包括110名英语使用者使用不同口音发出的语音数据。每个演讲者朗读大约400个句子，这些句子选自一份报纸、rainbow文章和一段用于语音重音档案的启发段落。
文本是根据贪婪算法选择的，贪婪算法可以增加上下文和语音覆盖率。
所有语音数据均使用相同的录音设置进行录音：一个全向麦克风（DPA 4035）和一个小振膜电容麦克风，带宽非常宽（Sennheiser MKH 800），采样频率为96kHz，24位，位于爱丁堡大学的半消声室中。
所有记录均转换为16位，降采样至48 kHz
该语料库最初用于基于HMM的文本到语音合成系统，尤其是基于说话人自适应HMM的语音合成，该合成使用多个说话人的平均语音模型和说话人自适应技术。该语料库也适用于基于DNN的多说话人文语合成系统和波形建模。这里的思想和PCA提取人脸特征加上平均人脸来合成指定人脸的思想类似

Homepage: CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit (version 0.92)

介绍：
关于VCTK数据集
-MNIST dataset
vctk

训练示例

# LJ Speech
python train.py -c configs/ljs_base.json -m ljs_base

# VCTK
python train_ms.py -c configs/vctk_base.json -m vctk_base

训练测试

用 LJspeech 和 VCTK 数据集测试

已下载 LJspeech 数据集并创建指向数据集文件夹的链接，没有下载预训练模型，直接运行

# LJ Speech
python train.py -c configs/ljs_base.json -m ljs_base

cmd运行结果——

Administrator@AUTOBVT-Q90417J MINGW64 /e/vits (main)
$ python train.py -c configs/ljs_base.json -m ljs_base
DEBUG:numba.core.byteflow:bytecode dump:
>          0    NOP(arg=None, lineno=1054)
           2    LOAD_FAST(arg=0, lineno=1054)
           4    LOAD_CONST(arg=1, lineno=1054)
           6    BINARY_SUBSCR(arg=None, lineno=1054)
           8    LOAD_FAST(arg=0, lineno=1054)
          10    LOAD_CONST(arg=2, lineno=1054)
          12    BINARY_SUBSCR(arg=None, lineno=1054)
          14    COMPARE_OP(arg=4, lineno=1054)
          16    LOAD_FAST(arg=0, lineno=1054)
          18    LOAD_CONST(arg=1, lineno=1054)
          20    BINARY_SUBSCR(arg=None, lineno=1054)
          22    LOAD_FAST(arg=0, lineno=1054)
          24    LOAD_CONST(arg=3, lineno=1054)
          26    BINARY_SUBSCR(arg=None, lineno=1054)
          28    COMPARE_OP(arg=5, lineno=1054)
          30    BINARY_AND(arg=None, lineno=1054)
          32    RETURN_VALUE(arg=None, lineno=1054)
DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=0 nstack_initial=0)])
DEBUG:numba.core.byteflow:stack: []
DEBUG:numba.core.byteflow:dispatch pc=0, inst=NOP(arg=None, lineno=1054)
DEBUG:numba.core.byteflow:stack []
DEBUG:numba.core.byteflow:dispatch pc=2, inst=LOAD_FAST(arg=0, lineno=1054)
DEBUG:numba.core.byteflow:stack []
DEBUG:numba.core.byteflow:dispatch pc=4, inst=LOAD_CONST(arg=1, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$x2.0']
DEBUG:numba.core.byteflow:dispatch pc=6, inst=BINARY_SUBSCR(arg=None, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$x2.0', '$const4.1']
DEBUG:numba.core.byteflow:dispatch pc=8, inst=LOAD_FAST(arg=0, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$6binary_subscr.2']
DEBUG:numba.core.byteflow:dispatch pc=10, inst=LOAD_CONST(arg=2, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$6binary_subscr.2', '$x8.3']
DEBUG:numba.core.byteflow:dispatch pc=12, inst=BINARY_SUBSCR(arg=None, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$6binary_subscr.2', '$x8.3', '$const10.4']
DEBUG:numba.core.byteflow:dispatch pc=14, inst=COMPARE_OP(arg=4, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$6binary_subscr.2', '$12binary_subscr.5']
DEBUG:numba.core.byteflow:dispatch pc=16, inst=LOAD_FAST(arg=0, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6']
DEBUG:numba.core.byteflow:dispatch pc=18, inst=LOAD_CONST(arg=1, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$x16.7']
DEBUG:numba.core.byteflow:dispatch pc=20, inst=BINARY_SUBSCR(arg=None, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$x16.7', '$const18.8']
DEBUG:numba.core.byteflow:dispatch pc=22, inst=LOAD_FAST(arg=0, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$20binary_subscr.9']
DEBUG:numba.core.byteflow:dispatch pc=24, inst=LOAD_CONST(arg=3, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$20binary_subscr.9', '$x22.10']
DEBUG:numba.core.byteflow:dispatch pc=26, inst=BINARY_SUBSCR(arg=None, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$20binary_subscr.9', '$x22.10', '$const24.11']
DEBUG:numba.core.byteflow:dispatch pc=28, inst=COMPARE_OP(arg=5, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$20binary_subscr.9', '$26binary_subscr.12']
DEBUG:numba.core.byteflow:dispatch pc=30, inst=BINARY_AND(arg=None, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$28compare_op.13']
DEBUG:numba.core.byteflow:dispatch pc=32, inst=RETURN_VALUE(arg=None, lineno=1054)
DEBUG:numba.core.byteflow:stack ['$30binary_and.14']
DEBUG:numba.core.byteflow:end state. edges=[]
DEBUG:numba.core.byteflow:-------------------------Prune PHIs-------------------------
DEBUG:numba.core.byteflow:Used_phis: defaultdict(<class 'set'>, {State(pc_initial=0 nstack_initial=0): set()})
DEBUG:numba.core.byteflow:defmap: {}
DEBUG:numba.core.byteflow:phismap: defaultdict(<class 'set'>, {})
DEBUG:numba.core.byteflow:changing phismap: defaultdict(<class 'set'>, {})
DEBUG:numba.core.byteflow:keep phismap: {}
DEBUG:numba.core.byteflow:new_out: defaultdict(<class 'dict'>, {})
DEBUG:numba.core.byteflow:----------------------DONE Prune PHIs-----------------------
DEBUG:numba.core.byteflow:block_infos State(pc_initial=0 nstack_initial=0):
AdaptBlockInfo(insts=((0, {}), (2, {'res': '$x2.0'}), (4, {'res': '$const4.1'}), (6, {'index': '$const4.1', 'target': '$x2.0', 'res': '$6binary_subscr.2'}), (8, {'res': '$x8.3'}), (10, {'res': '$const10.4'}), (12, {'index': '$const10.4', 'target': '$x8.3', 'res': '$12binary_subscr.5'}), (14, {'lhs': '$6binary_subscr.2', 'rhs': '$12binary_subscr.5', 'res': '$14compare_op.6'}), (16, {'res': '$x16.7'}), (18, {'res': '$const18.8'}), (20, {'index': '$const18.8', 'target': '$x16.7', 'res': '$20binary_subscr.9'}), (22, {'res': '$x22.10'}), (24, {'res': '$const24.11'}), (26, {'index': '$const24.11', 'target': '$x22.10', 'res': '$26binary_subscr.12'}), (28, {'lhs': '$20binary_subscr.9', 'rhs': '$26binary_subscr.12', 'res': '$28compare_op.13'}), (30, {'lhs': '$14compare_op.6', 'rhs': '$28compare_op.13', 'res': '$30binary_and.14'}), (32, {'retval': '$30binary_and.14', 'castval': '$32return_value.15'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})
DEBUG:numba.core.interpreter:label 0:
    x = arg(0, name=x)                       ['x']
    $const4.1 = const(int, 0)                ['$const4.1']
    $6binary_subscr.2 = getitem(value=x, index=$const4.1, fn=<built-in function getitem>) ['$6binary_subscr.2', '$const4.1', 'x']
    $const10.4 = const(int, -1)              ['$const10.4']
    $12binary_subscr.5 = getitem(value=x, index=$const10.4, fn=<built-in function getitem>) ['$12binary_subscr.5', '$const10.4', 'x']
    $14compare_op.6 = $6binary_subscr.2 > $12binary_subscr.5 ['$12binary_subscr.5', '$14compare_op.6', '$6binary_subscr.2']
    $const18.8 = const(int, 0)               ['$const18.8']
    $20binary_subscr.9 = getitem(value=x, index=$const18.8, fn=<built-in function getitem>) ['$20binary_subscr.9', '$const18.8', 'x']
    $const24.11 = const(int, 1)              ['$const24.11']
    $26binary_subscr.12 = getitem(value=x, index=$const24.11, fn=<built-in function getitem>) ['$26binary_subscr.12', '$const24.11', 'x']
    $28compare_op.13 = $20binary_subscr.9 >= $26binary_subscr.12 ['$20binary_subscr.9', '$26binary_subscr.12', '$28compare_op.13']
    $30binary_and.14 = $14compare_op.6 & $28compare_op.13 ['$14compare_op.6', '$28compare_op.13', '$30binary_and.14']
    $32return_value.15 = cast(value=$30binary_and.14) ['$30binary_and.14', '$32return_value.15']
    return $32return_value.15                ['$32return_value.15']

DEBUG:numba.core.byteflow:bytecode dump:
>          0    NOP(arg=None, lineno=1060)
           2    LOAD_FAST(arg=0, lineno=1060)
           4    LOAD_CONST(arg=1, lineno=1060)
           6    BINARY_SUBSCR(arg=None, lineno=1060)
           8    LOAD_FAST(arg=0, lineno=1060)
          10    LOAD_CONST(arg=2, lineno=1060)
          12    BINARY_SUBSCR(arg=None, lineno=1060)
          14    COMPARE_OP(arg=0, lineno=1060)
          16    LOAD_FAST(arg=0, lineno=1060)
          18    LOAD_CONST(arg=1, lineno=1060)
          20    BINARY_SUBSCR(arg=None, lineno=1060)
          22    LOAD_FAST(arg=0, lineno=1060)
          24    LOAD_CONST(arg=3, lineno=1060)
          26    BINARY_SUBSCR(arg=None, lineno=1060)
          28    COMPARE_OP(arg=1, lineno=1060)
          30    BINARY_AND(arg=None, lineno=1060)
          32    RETURN_VALUE(arg=None, lineno=1060)
DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=0 nstack_initial=0)])
DEBUG:numba.core.byteflow:stack: []
DEBUG:numba.core.byteflow:dispatch pc=0, inst=NOP(arg=None, lineno=1060)
DEBUG:numba.core.byteflow:stack []
DEBUG:numba.core.byteflow:dispatch pc=2, inst=LOAD_FAST(arg=0, lineno=1060)
DEBUG:numba.core.byteflow:stack []
DEBUG:numba.core.byteflow:dispatch pc=4, inst=LOAD_CONST(arg=1, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$x2.0']
DEBUG:numba.core.byteflow:dispatch pc=6, inst=BINARY_SUBSCR(arg=None, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$x2.0', '$const4.1']
DEBUG:numba.core.byteflow:dispatch pc=8, inst=LOAD_FAST(arg=0, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$6binary_subscr.2']
DEBUG:numba.core.byteflow:dispatch pc=10, inst=LOAD_CONST(arg=2, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$6binary_subscr.2', '$x8.3']
DEBUG:numba.core.byteflow:dispatch pc=12, inst=BINARY_SUBSCR(arg=None, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$6binary_subscr.2', '$x8.3', '$const10.4']
DEBUG:numba.core.byteflow:dispatch pc=14, inst=COMPARE_OP(arg=0, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$6binary_subscr.2', '$12binary_subscr.5']
DEBUG:numba.core.byteflow:dispatch pc=16, inst=LOAD_FAST(arg=0, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6']
DEBUG:numba.core.byteflow:dispatch pc=18, inst=LOAD_CONST(arg=1, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$x16.7']
DEBUG:numba.core.byteflow:dispatch pc=20, inst=BINARY_SUBSCR(arg=None, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$x16.7', '$const18.8']
DEBUG:numba.core.byteflow:dispatch pc=22, inst=LOAD_FAST(arg=0, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$20binary_subscr.9']
DEBUG:numba.core.byteflow:dispatch pc=24, inst=LOAD_CONST(arg=3, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$20binary_subscr.9', '$x22.10']
DEBUG:numba.core.byteflow:dispatch pc=26, inst=BINARY_SUBSCR(arg=None, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$20binary_subscr.9', '$x22.10', '$const24.11']
DEBUG:numba.core.byteflow:dispatch pc=28, inst=COMPARE_OP(arg=1, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$20binary_subscr.9', '$26binary_subscr.12']
DEBUG:numba.core.byteflow:dispatch pc=30, inst=BINARY_AND(arg=None, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$14compare_op.6', '$28compare_op.13']
DEBUG:numba.core.byteflow:dispatch pc=32, inst=RETURN_VALUE(arg=None, lineno=1060)
DEBUG:numba.core.byteflow:stack ['$30binary_and.14']
DEBUG:numba.core.byteflow:end state. edges=[]
DEBUG:numba.core.byteflow:-------------------------Prune PHIs-------------------------
DEBUG:numba.core.byteflow:Used_phis: defaultdict(<class 'set'>, {State(pc_initial=0 nstack_initial=0): set()})
DEBUG:numba.core.byteflow:defmap: {}
DEBUG:numba.core.byteflow:phismap: defaultdict(<class 'set'>, {})
DEBUG:numba.core.byteflow:changing phismap: defaultdict(<class 'set'>, {})
DEBUG:numba.core.byteflow:keep phismap: {}
DEBUG:numba.core.byteflow:new_out: defaultdict(<class 'dict'>, {})
DEBUG:numba.core.byteflow:----------------------DONE Prune PHIs-----------------------
DEBUG:numba.core.byteflow:block_infos State(pc_initial=0 nstack_initial=0):
AdaptBlockInfo(insts=((0, {}), (2, {'res': '$x2.0'}), (4, {'res': '$const4.1'}), (6, {'index': '$const4.1', 'target': '$x2.0', 'res': '$6binary_subscr.2'}), (8, {'res': '$x8.3'}), (10, {'res': '$const10.4'}), (12, {'index': '$const10.4', 'target': '$x8.3', 'res': '$12binary_subscr.5'}), (14, {'lhs': '$6binary_subscr.2', 'rhs': '$12binary_subscr.5', 'res': '$14compare_op.6'}), (16, {'res': '$x16.7'}), (18, {'res': '$const18.8'}), (20, {'index': '$const18.8', 'target': '$x16.7', 'res': '$20binary_subscr.9'}), (22, {'res': '$x22.10'}), (24, {'res': '$const24.11'}), (26, {'index': '$const24.11', 'target': '$x22.10', 'res': '$26binary_subscr.12'}), (28, {'lhs': '$20binary_subscr.9', 'rhs': '$26binary_subscr.12', 'res': '$28compare_op.13'}), (30, {'lhs': '$14compare_op.6', 'rhs': '$28compare_op.13', 'res': '$30binary_and.14'}), (32, {'retval': '$30binary_and.14', 'castval': '$32return_value.15'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})
DEBUG:numba.core.interpreter:label 0:
    x = arg(0, name=x)                       ['x']
    $const4.1 = const(int, 0)                ['$const4.1']
    $6binary_subscr.2 = getitem(value=x, index=$const4.1, fn=<built-in function getitem>) ['$6binary_subscr.2', '$const4.1', 'x']
    $const10.4 = const(int, -1)              ['$const10.4']
    $12binary_subscr.5 = getitem(value=x, index=$const10.4, fn=<built-in function getitem>) ['$12binary_subscr.5', '$const10.4', 'x']
    $14compare_op.6 = $6binary_subscr.2 < $12binary_subscr.5 ['$12binary_subscr.5', '$14compare_op.6', '$6binary_subscr.2']
    $const18.8 = const(int, 0)               ['$const18.8']
    $20binary_subscr.9 = getitem(value=x, index=$const18.8, fn=<built-in function getitem>) ['$20binary_subscr.9', '$const18.8', 'x']
    $const24.11 = const(int, 1)              ['$const24.11']
    $26binary_subscr.12 = getitem(value=x, index=$const24.11, fn=<built-in function getitem>) ['$26binary_subscr.12', '$const24.11', 'x']
    $28compare_op.13 = $20binary_subscr.9 <= $26binary_subscr.12 ['$20binary_subscr.9', '$26binary_subscr.12', '$28compare_op.13']
    $30binary_and.14 = $14compare_op.6 & $28compare_op.13 ['$14compare_op.6', '$28compare_op.13', '$30binary_and.14']
    $32return_value.15 = cast(value=$30binary_and.14) ['$30binary_and.14', '$32return_value.15']
    return $32return_value.15                ['$32return_value.15']

Traceback (most recent call last):
  File "train.py", line 23, in <module>
    from models import (
  File "E:\vits\models.py", line 10, in <module>
    import monotonic_align
  File "E:\vits\monotonic_align\__init__.py", line 3, in <module>
    from .monotonic_align.core import maximum_path_c
ModuleNotFoundError: No module named 'monotonic_align.monotonic_align'

创建了E:\vits\monotonic_align\__pycache__和E:\vits\__pycache__。
ModuleNotFoundError：没有名为“monotonic_align.monotonic_align”的模块
构建单调对齐搜索并运行预处理

# Cython-version Monotonoic Alignment Search
cd monotonic_align
python setup.py build_ext --inplace

cmd运行结果——

Administrator@AUTOBVT-Q90417J MINGW64 /e/vits (main)
$ cd monotonic_align

Administrator@AUTOBVT-Q90417J MINGW64 /e/vits/monotonic_align (main)
$ python setup.py build_ext --inplace
Compiling core.pyx because it changed.
[1/1] Cythonizing core.pyx
running build_ext
building 'monotonic_align.core' extension
C:\Program Files\Python38\lib\site-packages\Cython\Compiler\Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: E:\vits\monotonic_align\core.pyx
  tree = Parsing.p_module(s, pxd, full_module_name)
error: Unable to find vcvarsall.bat

报告了两个错误
1、C:\Program Files\Python38\lib\site-packages\Cython\Compiler\Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release!

如果你期望编译的版本不是python2，那就指定自己要用哪个版本编译，或者在每个要编译的版本 .py 文件顶上添加一行指定cython版本，即# cython: language_level=3，但如果有成千上成个 .py 或 .pyx 文件，就不好处理了，在 setup.py 中添加：

cythonize(module_item,compiler_directives={'language_level': '3'})

此处摘自Cython directive ’language_level’ not set, using 2 for now (Py2)
Cython——[FutureWarning: Cython directive ‘language_level’ not set, using 2 for now (Py2)]解决方案
2、在运行带Cython模块的py文件时，有可能输出如下报错信息：

error: Unable to find vcvarsall.bat

原因是没有找到vcvarsall.bat指定的vc++编译器进行编译。大多数解决方案都要求安装Visual Studio。
当前主流Python版本与VC和VS的版本对应关系及各版本VS下载地址：

上表摘自Cython出现错误：Unable to find vcvarsall.bat
无需安装VS，一行命令解决"Unable to find vcvarsall.bat"提供了另一种解决方法

运行环境

• Windows 10 (64-bit)
• Python 3.7

1、安装anaconda。Anaconda强大的包管理和环境管理可以帮助我们节省大量时间与精力，让我们能更专注于代码，而不是把精力花在各种莫名其妙的环境或依赖问题上。
2、在anaconda的命令行中输入命令：conda install libpython

我用pip安装它：pip install libpython
cmd运行结果——

$ pip install libpython
Collecting libpython
  Downloading libpython-0.2.tar.gz (15 kB)
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'done'
Requirement already satisfied: requests in c:\program files\python38\lib\site-packages (from libpython) (2.28.2)
Requirement already satisfied: idna<4,>=2.5 in c:\program files\python38\lib\site-packages (from requests->libpython) (3.4)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\program files\python38\lib\site-packages (from requests->libpython) (1.26.14)
Requirement already satisfied: certifi>=2017.4.17 in c:\program files\python38\lib\site-packages (from requests->libpython) (2022.12.7)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\program files\python38\lib\site-packages (from requests->libpython) (3.0.1)
Building wheels for collected packages: libpython
  Building wheel for libpython (setup.py): started
  Building wheel for libpython (setup.py): finished with status 'done'
  Created wheel for libpython: filename=libpython-0.2-py3-none-any.whl size=14410 sha256=c8c0bf0dbd5502f14e73d0da51314ce2507c4e118dc866d6722720c3f5c8c743
  Stored in directory: c:\users\administrator\appdata\local\pip\cache\wheels\f8\0e\ae\9a8610c41be91787c7899e435d6bcb161fa8df32ea3d371ecf
Successfully built libpython
Installing collected packages: libpython
Successfully installed libpython-0.2

回到「构建单调对齐搜索并运行预处理」，看看会发生什么

Administrator@AUTOBVT-Q90417J MINGW64 /e/vits (main)
$ cd monotonic_align

Administrator@AUTOBVT-Q90417J MINGW64 /e/vits/monotonic_align (main)
$ python setup.py build_ext --inplace
running build_ext
building 'monotonic_align.core' extension
error: Unable to find vcvarsall.bat

即便卸载 libpython 也不再出现

C:\Program Files\Python38\lib\site-packages\Cython\Compiler\Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: E:\vits\monotonic_align\core.pyx
  tree = Parsing.p_module(s, pxd, full_module_name)

删掉本地仓库重来方重现报错
看来在Windows 7 上安装 libpython 可能解决不了问题……

# Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided.
# python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt 
# python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt

cmd运行结果——

$ python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt
START: filelists/ljs_audio_text_train_filelist.txt
Traceback (most recent call last):
  File "preprocess.py", line 20, in <module>
    cleaned_text = text._clean_text(original_text, args.text_cleaners)
  File "E:\vits\text\__init__.py", line 53, in _clean_text
    text = cleaner(text)
  File "E:\vits\text\cleaners.py", line 98, in english_cleaners2
    phonemes = phonemize(text, language='en-us', backend='espeak', strip=True, preserve_punctuation=True, with_stress=True)
  File "C:\Program Files\Python38\lib\site-packages\phonemizer\phonemize.py", line 206, in phonemize
    phonemizer = BACKENDS[backend](
  File "C:\Program Files\Python38\lib\site-packages\phonemizer\backend\espeak\espeak.py", line 45, in __init__
    super().__init__(
  File "C:\Program Files\Python38\lib\site-packages\phonemizer\backend\espeak\base.py", line 39, in __init__
    super().__init__(
  File "C:\Program Files\Python38\lib\site-packages\phonemizer\backend\base.py", line 77, in __init__
    raise RuntimeError(  # pragma: nocover
RuntimeError: espeak not installed on your system

创建了E:\vits\text\__pycache__和E:\vits\__pycache__。
解决方法：
RuntimeError: espeak not installed on your system【已解决】
RuntimeError: espeak not installed on your system #44

未完待续

jaywalnut310/vits坑就踩到这里，安装的依赖库严重影响Whisper正常使用。以后用Linux再试。
与jaywalnut310/vits相关的「端到端语音合成模型VITS，日语数据训练」Ikaros/vits-japanese

下一篇开始学习
CjangCjengh/vits
下江小春也能看懂的语音模型训练教程
【VITS/语音合成】使用『预训练模型』快速拟合你的语音模型

VITS（Variational Inference with adversarial learning for end-to-end Text-to-Speech）是一种结合变分推理（variational inference）、标准化流（normalizing flows）和对抗训练的高表现力语音合成模型。

论文地址：Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

细读论文：细读经典：VITS，用于语音合成带有对抗学习的条件变分自编码器

VITS Github 仓库：jaywalnut310/vits

前期准备

硬件要求：
内存最好在16G以上。
显存最好在12G以上，最低也得6G，而且必须是支持CUDA的N卡。A卡目前理论上也有办法跑torch，但是非常复杂且麻烦。
中文互联网上没找到小白能用的VITS本地训练教程（注：特指jaywalnut310/vits）。目前我的电脑无法完成本地训练，又无法长时间科学上网（学习jaywalnut310/vits仓库对应的云端训练），只能尝试一下是否可以完成模型训练前的所有操作。

VITS仓库README原文

1. Python >= 3.6

2. Clone this repository

3. Install python requirements. Please refer requirements.txt

   i. You may need to install espeak first: apt-get install espeak

4. Download datasets

   i. Download and extract the LJ Speech dataset, then rename or create a link to the dataset folder: ln -s /path/to/LJSpeech-1.1/wavs DUMMY1
   ii. For mult-speaker setting, download and extract the VCTK dataset, and downsample wav files to 22050 Hz. Then rename or create a link to the dataset folder: ln -s /path/to/VCTK-Corpus/downsampled_wavs DUMMY2

5. Build Monotonic Alignment Search and run preprocessing if you use your own datasets.

# Cython-version Monotonoic Alignment Search
cd monotonic_align
python setup.py build_ext --inplace

# Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided.
# python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt 
# python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt

准备过程

0. Windows 7 用户选择 Python 3.8
1. 克隆仓库

git clone git@github.com:jaywalnut310/vits.git

2. 安装 Python 环境，参阅 requirements.txt，如下：

Cython==0.29.21
librosa==0.8.0
matplotlib==3.3.1
numpy==1.18.5
phonemizer==2.2.1
scipy==1.5.2
tensorboard==2.3.0
torch==1.6.0
torchvision==0.7.0
Unidecode==1.1.1

您可能需要先安装espeak：apt-get install espeak

安装espeak

Windows下的 apt-get 官网地址如下:https://chocolatey.org/
在Win7中尝试装chocolatey时挺折腾人的，可以看这两篇文章——
Windows7下的包管理器Chocolatey的安装
如何在Win 7中安装chocolatey

不想用Chocolatey有没有办法安装呢？
去 Espeak 官网下载Windows安装包（.exe文件)

网页跳转打不开，搜索Github出来一个espeak-NG
安装espeak-NG看看

自动添加了系统环境变量

教程：eSpeak NG Windows 版中文发音简易教程

上面暂时下载不了，下载这个看看——

打开软件，提示——

这是一个编辑器，需要读取C:\Program Files\eSpeak\espeak-data。

重启电脑后可以 eSpeak 下载页面打开了。
参阅Espeak最详细安装过程！安装 eSpeak 。

i. 安装过程修改安装路径为 C:\Program Files\eSpeak。

ii. 选择要安装的语言包，添加en-us、it、fr。

各种语言的缩写请参见下表，更多信息请点我：

af is an Africaans voice.
bs is a Bosnian voice.
ca is a Catalan voice.
cs is a Czech voice.
cy is a Welsh voice.
da is a Danish voice.
de is a German voice.
el is a Greek voice.
en is an English voice.
en-us is an American English voice.
eo is a Esperanto voice.
es is a Spanish voice.
es-la is a Spanish Latin American voice.
fi is a Finnish voice.
fr is a French voice.
fr-be is a French Belgian voice.
hi is a Hindi voice.
hr is a Croatian voice.
hu is a Hungarian voice.
hy is an Armenian voice.
hy-west is an Armenian-west voice.
id is an Indonesian voice.
is is an experimental Icelandic voice.
it is an Italian voice.
ka is a Georgian voice.
kn is a Kannada voice.
ku is a Kurdish voice.
la is a Latin voice.
lv is a Lativian voice.
mk is an experimental Macedonian voice.
ml is a Malayam voice.
nl is an experimental Dutch voice.
no is a Norwegian Bokmal voice.
pl is a Polish voice.
pt is a Brazilian Portuguese voice.
pt-pt is Portuguese voice.
ro is a Romanian voice.
ru is an experimental Russian voice.
sk is a Slovak voice.
sq is an Albanian voice.
sr is a Serbian voice.
sv is a Swedish voice.
sw is an experimental Swahili voice.
ta is a Tamil voice.
tr is a Turkish voice.
vi is a Vietnam voice.
zh is a Mandarin Chinese voice.
zh-yue is a Cantonese voice.

iii. 输入jp，安装后无法正常朗读，查阅Can’t find Japanese language code.
反复提到

“Can you check that ja_dict is really missing from C:\Program Files\eSpeak NG/espeak-ng-data”

尝试把 C:\Program Files\eSpeak NG\espeak-ng-data 部分除 lang 以外移动到 C:\Program Files\eSpeak\espeak-data ，安装时添加语言ja。
很好，程序寄了。

eSpeak的其他数据：在该网站下载文件后，解压缩到eSpeak的dictsource目录中。 在dictsource目录中，执行

espeak --compile=zh
espeak --compile=zh-yue
espeak --compile=ru

iV. 将 espeak/command_line 加入环境变量

另外，不知安装 eSpeak 后是否需要安装 python-espeak 。如果需要，打开这个网站——Text-To-Speech with Python Espeak。

Requires espeak and its libraries to be installed espeak/speak_lib.h should be in your include path somewhere. Install with

`python setup.py install`

or python setup.py build to get the library in the build without installation.

至此，eSpeak安装完成，没有按照官方建议 apt-get install espeak 可能会出错。

安装 requirements.txt 中相关包

首先命令行输入 pip list 查看已安装的库或第三方包信息。

C:\Users\Administrator>pip list
Package            Version
------------------ ----------
certifi            2022.12.7
charset-normalizer 3.0.1
colorama           0.4.6
ffmpeg-python      0.2.0
filelock           3.9.0
future             0.18.3
huggingface-hub    0.12.1
idna               3.4
more-itertools     9.0.0
numpy              1.24.2
openai-whisper     20230124
packaging          23.0
Pillow             9.4.0
pip                23.0.1
PyYAML             6.0
regex              2022.10.31
requests           2.28.2
setuptools         56.0.0
tokenizers         0.13.2
torch              1.13.1
torchaudio         0.13.1
torchvision        0.14.1
tqdm               4.64.1
transformers       4.26.1
typing_extensions  4.5.0
urllib3            1.26.14

看来现在需要安装

Cython==0.29.21
librosa==0.8.0
matplotlib==3.3.1
numpy==1.18.5
phonemizer==2.2.1
scipy==1.5.2
tensorboard==2.3.0
Unidecode==1.1.1

安装Cython

安装Cython的最简单方法是使用pip：

pip install Cython

最新的Cython版本始终可以从https://cython.org/下载 。
Cython的最新版本是3.0 beta 1（发布日期：2023年2月25日）。可以从PyPI包索引库中获得Cython。
解压缩tarball或zip文件，输入目录，然后运行：

python setup.py install

cmd安装过程——

Administrator@AUTOBVT-Q90417J MINGW64 ~
$ pip install Cython
Collecting Cython
  Downloading Cython-0.29.33-py2.py3-none-any.whl (987 kB)
     -------------------------------------- 987.3/987.3 kB 1.2 MB/s eta 0:00:00
Installing collected packages: Cython
Successfully installed Cython-0.29.33

Christoph Gohlke已经创建了Windows安装程序，可以在他的网站上下载。
下载whl文件，与手动安装 torch 类似的命令——

activate pytorch_env (就是用activate打开自己的环境）
cd Desktop (打开下载文件的地址）
pip install torch-1.4.0+cpu-cp36-cp36m-win_amd64.whl (直接安装）

（以上命令摘自python安装torch的详细步骤（亲测成功）
，activate pytorch_env不知道有没有起到作用，之前手动安装pytorch报了与文中类似的警告。）

对于一次性构建，例如用于CI /测试，在PyPI上提供的一个轮组件未涵盖的平台上，它比完全源构建快得多，以安装未编译（较慢）的Cython版本

pip install Cython --install-option="--no-cython-compile"

安装librosa

安装librosa的最简单方法是使用pip：

pip install librosa

如果安装了Anaconda，可以通过conda命令安装：

conda install -c conda-forge librosa

直接使用源码安装，需要提前下载源码（https://github.com/librosa/librosa/releases/），通过下面命令安装：

tar xzf librosa-VERSION.tar.gz
cd librosa-VERSION/
python setup.py install

cmd安装过程——

Administrator@AUTOBVT-Q90417J MINGW64 /
$ pip install librosa
Collecting librosa
  WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000000004B31070>, 'Connection to files.pythonhosted.org timed out. (connect timeout=15)')': /packages/bc/2e/80370da514096c6190f8913668198380ea09c2d252cfa4e85a9c096d3b40/librosa-0.10.0-py3-none-any.whl
  WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000000004B312E0>, 'Connection to files.pythonhosted.org timed out. (connect timeout=15)')': /packages/bc/2e/80370da514096c6190f8913668198380ea09c2d252cfa4e85a9c096d3b40/librosa-0.10.0-py3-none-any.whl
  WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000000004B31460>, 'Connection to files.pythonhosted.org timed out. (connect timeout=15)')': /packages/bc/2e/80370da514096c6190f8913668198380ea09c2d252cfa4e85a9c096d3b40/librosa-0.10.0-py3-none-any.whl
  WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000000004B31550>, 'Connection to files.pythonhosted.org timed out. (connect timeout=15)')': /packages/bc/2e/80370da514096c6190f8913668198380ea09c2d252cfa4e85a9c096d3b40/librosa-0.10.0-py3-none-any.whl
  WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000000004B316D0>, 'Connection to files.pythonhosted.org timed out. (connect timeout=15)')': /packages/bc/2e/80370da514096c6190f8913668198380ea09c2d252cfa4e85a9c096d3b40/librosa-0.10.0-py3-none-any.whl
ERROR: Could not install packages due to an OSError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Max retries exceeded with url: /packages/bc/2e/80370da514096c6190f8913668198380ea09c2d252cfa4e85a9c096d3b40/librosa-0.10.0-py3-none-any.whl (Caused by ConnectTimeoutError(<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000000004B31850>, 'Connection to files.pythonhosted.org timed out. (connect timeout=15)'))

连接超时了，所以我们只要用一些国内的pip源就可以完美的解决。

pip install [whatyouwant] -i url

[whatyouwant]替换成自己需要的包，url替换成pip源
几个国内的pip源

阿里云 http://mirrors.aliyun.com/pypi/simple/

中国科技大学 https://pypi.mirrors.ustc.edu.cn/simple/

豆瓣(douban) http://pypi.douban.com/simple/

清华大学 https://pypi.tuna.tsinghua.edu.cn/simple/

中国科学技术大学 http://pypi.mirrors.ustc.edu.cn/simple/

cmd输入 pip install librosa -i http://mirrors.aliyun.com/pypi/simple/ ，安装过程——

C:\Users\Administrator>pip install librosa -i http://mirrors.aliyun.com/pypi/simple/
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
WARNING: The repository located at mirrors.aliyun.com is not a trusted or secure
 host and is being ignored. If this repository is available via HTTPS we recomme
nd you use HTTPS instead, otherwise you may silence this warning and allow it an
yway with '--trusted-host mirrors.aliyun.com'.
ERROR: Could not find a version that satisfies the requirement librosa (from ver
sions: none)
ERROR: No matching distribution found for librosa

警告：存储库位于镜像。阿里云。com不是受信任或安全的主机，正在被忽略。如果此存储库可通过HTTPS访问，我们建议您改用HTTPS，否则您可能会使此waming静音，并允许使用’-受信任的主机镜像。阿里云。com”。
解决方法：在pip命令后面加上--trusted-host mirrors.aliyun.com

pip install librosa -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

cmd安装过程——

Administrator@AUTOBVT-Q90417J MINGW64 /
$ pip install librosa -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
Collecting librosa
  Downloading http://mirrors.aliyun.com/pypi/packages/bc/2e/80370da514096c6190f8913668198380ea09c2d252cfa4e85a9c096d3b40/librosa-0.10.0-py3-none-any.whl (252 kB)
     ------------------------------------ 252.9/252.9 kB 353.7 kB/s eta 0:00:00
Collecting audioread>=2.1.9
  Downloading http://mirrors.aliyun.com/pypi/packages/5d/cb/82a002441902dccbe427406785db07af10182245ee639ea9f4d92907c923/audioread-3.0.0.tar.gz (377 kB)
     ------------------------------------ 377.0/377.0 kB 346.0 kB/s eta 0:00:00
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'done'
Collecting scikit-learn>=0.20.0
  Downloading http://mirrors.aliyun.com/pypi/packages/5d/30/3af7a1073da6181208cdefe749f8243cd66e1036601bc870dfafb7fd3602/scikit_learn-1.2.1-cp38-cp38-win_amd64.whl (8.3 MB)
     ---------------------------------------- 8.3/8.3 MB 385.0 kB/s eta 0:00:00
Collecting numba>=0.51.0
  Downloading http://mirrors.aliyun.com/pypi/packages/22/6e/880d8ae26f26a3ecce71922797cc09b3b8a4e5274adecd0793f9b59d50b8/numba-0.56.4-cp38-cp38-win_amd64.whl (2.5 MB)
     ---------------------------------------- 2.5/2.5 MB 387.2 kB/s eta 0:00:00
Collecting joblib>=0.14
  Downloading http://mirrors.aliyun.com/pypi/packages/91/d4/3b4c8e5a30604df4c7518c562d4bf0502f2fa29221459226e140cf846512/joblib-1.2.0-py3-none-any.whl (297 kB)
     ------------------------------------ 298.0/298.0 kB 335.3 kB/s eta 0:00:00
Collecting soxr>=0.3.2
  Downloading http://mirrors.aliyun.com/pypi/packages/e8/f5/bfcf99a10250381ed76793d930da816836f2ac8a276de48522001271cc98/soxr-0.3.4-cp38-cp38-win_amd64.whl (184 kB)
     ------------------------------------ 184.8/184.8 kB 338.9 kB/s eta 0:00:00
Requirement already satisfied: typing-extensions>=4.1.1 in c:\program files\python38\lib\site-packages (from librosa) (4.5.0)
Collecting lazy-loader>=0.1
  Downloading http://mirrors.aliyun.com/pypi/packages/bc/bf/58dbe1f382ecac2c0571c43b6e95028b14e159d67d75e49a00c26ef63d8f/lazy_loader-0.1-py3-none-any.whl (8.6 kB)
Requirement already satisfied: numpy>=1.20.3 in c:\program files\python38\lib\site-packages (from librosa) (1.24.2)
Collecting soundfile>=0.12.1
  Downloading http://mirrors.aliyun.com/pypi/packages/50/ff/26a4ee48d0b66625a4e4028a055b9f25bc9d7c7b2d17d21a45137621a50d/soundfile-0.12.1-py2.py3-none-win_amd64.whl (1.0 MB)
     ---------------------------------------- 1.0/1.0 MB 363.8 kB/s eta 0:00:00
Collecting scipy>=1.2.0
  Downloading http://mirrors.aliyun.com/pypi/packages/32/8e/7f403535ddf826348c9b8417791e28712019962f7e90ff845896d6325d09/scipy-1.10.1-cp38-cp38-win_amd64.whl (42.2 MB)
     -------------------------------------- 42.2/42.2 MB 391.8 kB/s eta 0:00:00
Collecting pooch>=1.0
  Downloading http://mirrors.aliyun.com/pypi/packages/84/8c/4da580db7fb4cfce8f5ed78e7d2aa542e6f201edd69d3d8a96917a8ff63c/pooch-1.7.0-py3-none-any.whl (60 kB)
     -------------------------------------- 60.9/60.9 kB 269.3 kB/s eta 0:00:00
Collecting decorator>=4.3.0
  Downloading http://mirrors.aliyun.com/pypi/packages/d5/50/83c593b07763e1161326b3b8c6686f0f4b0f24d5526546bee538c89837d6/decorator-5.1.1-py3-none-any.whl (9.1 kB)
Collecting msgpack>=1.0
  Downloading http://mirrors.aliyun.com/pypi/packages/45/79/9d51bf36ab55059f8e96b13161d5867bb4bb359b03e82f240f64898d3ece/msgpack-1.0.4-cp38-cp38-win_amd64.whl (62 kB)
     -------------------------------------- 62.2/62.2 kB 368.8 kB/s eta 0:00:00
Collecting importlib-metadata
  Downloading http://mirrors.aliyun.com/pypi/packages/26/a7/9da7d5b23fc98ab3d424ac2c65613d63c1f401efb84ad50f2fa27b2caab4/importlib_metadata-6.0.0-py3-none-any.whl (21 kB)
Collecting numpy>=1.20.3
  Downloading http://mirrors.aliyun.com/pypi/packages/4c/42/6274f92514fbefcb1caa66d56d82ac7ac89f7652c0cef1e159a4b79e09f1/numpy-1.23.5-cp38-cp38-win_amd64.whl (14.7 MB)
     -------------------------------------- 14.7/14.7 MB 391.7 kB/s eta 0:00:00
Collecting llvmlite<0.40,>=0.39.0dev0
  Downloading http://mirrors.aliyun.com/pypi/packages/75/7f/9055977016e713a5c033c376a9ea9cb3d1092a02ee1421c41ccbcc5aa043/llvmlite-0.39.1-cp38-cp38-win_amd64.whl (23.2 MB)
     -------------------------------------- 23.2/23.2 MB 362.2 kB/s eta 0:00:00
Requirement already satisfied: setuptools in c:\program files\python38\lib\site-packages (from numba>=0.51.0->librosa) (56.0.0)
Collecting platformdirs>=2.5.0
  Downloading http://mirrors.aliyun.com/pypi/packages/ca/de/a33823fe54d52ea72fdae011115d737a2642d441c93b68ed17455a328e4c/platformdirs-3.1.0-py3-none-any.whl (14 kB)
Requirement already satisfied: requests>=2.19.0 in c:\program files\python38\lib\site-packages (from pooch>=1.0->librosa) (2.28.2)
Requirement already satisfied: packaging>=20.0 in c:\program files\python38\lib\site-packages (from pooch>=1.0->librosa) (23.0)
Collecting threadpoolctl>=2.0.0
  Downloading http://mirrors.aliyun.com/pypi/packages/61/cf/6e354304bcb9c6413c4e02a747b600061c21d38ba51e7e544ac7bc66aecc/threadpoolctl-3.1.0-py3-none-any.whl (14 kB)
Collecting cffi>=1.0
  Downloading http://mirrors.aliyun.com/pypi/packages/c9/e3/0a52838832408cfbbf3a59cb19bcd17e64eb33795c9710ca7d29ae10b5b7/cffi-1.15.1-cp38-cp38-win_amd64.whl (178 kB)
     ------------------------------------ 178.8/178.8 kB 327.4 kB/s eta 0:00:00
Collecting pycparser
  Downloading http://mirrors.aliyun.com/pypi/packages/62/d5/5f610ebe421e85889f2e55e33b7f9a6795bd982198517d912eb1c76e1a53/pycparser-2.21-py2.py3-none-any.whl (118 kB)
     ------------------------------------ 118.7/118.7 kB 330.7 kB/s eta 0:00:00
Requirement already satisfied: charset-normalizer<4,>=2 in c:\program files\python38\lib\site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.0.1)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\program files\python38\lib\site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (1.26.14)
Requirement already satisfied: certifi>=2017.4.17 in c:\program files\python38\lib\site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2022.12.7)
Requirement already satisfied: idna<4,>=2.5 in c:\program files\python38\lib\site-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.4)
Collecting zipp>=0.5
  Downloading http://mirrors.aliyun.com/pypi/packages/5b/fa/c9e82bbe1af6266adf08afb563905eb87cab83fde00a0a08963510621047/zipp-3.15.0-py3-none-any.whl (6.8 kB)
Installing collected packages: msgpack, zipp, threadpoolctl, pycparser, platformdirs, numpy, llvmlite, lazy-loader, joblib, decorator, audioread, soxr, scipy, pooch, importlib-metadata, cffi, soundfile, scikit-learn, numba, librosa
  Attempting uninstall: numpy
    Found existing installation: numpy 1.24.2
    Uninstalling numpy-1.24.2:
      Successfully uninstalled numpy-1.24.2
  DEPRECATION: audioread is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559
  Running setup.py install for audioread: started
  Running setup.py install for audioread: finished with status 'done'
Successfully installed audioread-3.0.0 cffi-1.15.1 decorator-5.1.1 importlib-metadata-6.0.0 joblib-1.2.0 lazy-loader-0.1 librosa-0.10.0 llvmlite-0.39.1 msgpack-1.0.4 numba-0.56.4 numpy-1.23.5 platformdirs-3.1.0 pooch-1.7.0 pycparser-2.21 scikit-learn-1.2.1 scipy-1.10.1 soundfile-0.12.1 soxr-0.3.4 threadpoolctl-3.1.0 zipp-3.15.0

librosa常用功能介绍

安装matplotlib

matplotlib用户指南——安装

0. 安装matplotlib的最简单方法是使用pip：

pip install matplotlib

用国内的pip源安装

pip install matplotlib -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

该过程不仅安装了 matplotlib ，还安装了依赖的 numpy、python-dateutil、kiwisolver 、 pillow 、pyparsing 、cycler 、six 库。

1. 如果安装了Anaconda，可以通过conda命令安装：

conda install matplotlib

2. 安装每晚构建
   Matplotlib 在 scipy-wheels-nightly Anaconda Cloud 组织上提供夜间开发构建轮。pip可以通过将 scipy-wheels-nightly 指定为要查询的包索引来安装这些轮子：

python -m pip install \
  --upgrade \
  --pre \
  --index-url https://pypi.anaconda.org/scipy-wheels-nightly/simple \
  --extra-index-url https://pypi.org/simple \
  matplotlib

3. 如果您想从 tarball 构建，请从PyPI 文件页面获取最新的tar.gz发布文件。（python安装合适版本的matplotlib中的截图略有不同）

cmd安装过程——

Administrator@AUTOBVT-Q90417J MINGW64 /
$ pip install matplotlib -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
Collecting matplotlib
  Downloading http://mirrors.aliyun.com/pypi/packages/92/01/2c04d328db6955d77f8f60c17068dde8aa66f153b2c599ca03c2cb0d5567/matplotlib-3.7.1-cp38-cp38-win_amd64.whl (7.6 MB)
     ---------------------------------------- 7.6/7.6 MB 389.8 kB/s eta 0:00:00
Requirement already satisfied: pillow>=6.2.0 in c:\program files\python38\lib\site-packages (from matplotlib) (9.4.0)
Collecting kiwisolver>=1.0.1
  Downloading http://mirrors.aliyun.com/pypi/packages/4f/05/59b34e788bf2b45c7157c3d898d567d28bc42986c1b6772fb1af329eea0d/kiwisolver-1.4.4-cp38-cp38-win_amd64.whl (55 kB)
     -------------------------------------- 55.4/55.4 kB 289.4 kB/s eta 0:00:00
Collecting contourpy>=1.0.1
  Downloading http://mirrors.aliyun.com/pypi/packages/08/ce/9bfe9f028cb5a8ee97898da52f4905e0e2d9ca8203ffdcdbe80e1769b549/contourpy-1.0.7-cp38-cp38-win_amd64.whl (162 kB)
     ------------------------------------ 163.0/163.0 kB 361.9 kB/s eta 0:00:00
Collecting pyparsing>=2.3.1
  Downloading http://mirrors.aliyun.com/pypi/packages/6c/10/a7d0fa5baea8fe7b50f448ab742f26f52b80bfca85ac2be9d35cdd9a3246/pyparsing-3.0.9-py3-none-any.whl (98 kB)
     -------------------------------------- 98.3/98.3 kB 297.6 kB/s eta 0:00:00
Requirement already satisfied: numpy>=1.20 in c:\program files\python38\lib\site-packages (from matplotlib) (1.23.5)
Collecting python-dateutil>=2.7
  Downloading http://mirrors.aliyun.com/pypi/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
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Collecting cycler>=0.10
  Downloading http://mirrors.aliyun.com/pypi/packages/5c/f9/695d6bedebd747e5eb0fe8fad57b72fdf25411273a39791cde838d5a8f51/cycler-0.11.0-py3-none-any.whl (6.4 kB)
Collecting importlib-resources>=3.2.0
  Downloading http://mirrors.aliyun.com/pypi/packages/38/71/c13ea695a4393639830bf96baea956538ba7a9d06fcce7cef10bfff20f72/importlib_resources-5.12.0-py3-none-any.whl (36 kB)
Requirement already satisfied: packaging>=20.0 in c:\program files\python38\lib\site-packages (from matplotlib) (23.0)
Collecting fonttools>=4.22.0
  Downloading http://mirrors.aliyun.com/pypi/packages/e3/d9/e9bae85e84737e76ebbcbea13607236da0c0699baed0ae4f1151b728a608/fonttools-4.38.0-py3-none-any.whl (965 kB)
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Requirement already satisfied: zipp>=3.1.0 in c:\program files\python38\lib\site-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)
Collecting six>=1.5
  Downloading http://mirrors.aliyun.com/pypi/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl (11 kB)
Installing collected packages: six, pyparsing, kiwisolver, importlib-resources, fonttools, cycler, contourpy, python-dateutil, matplotlib
Successfully installed contourpy-1.0.7 cycler-0.11.0 fonttools-4.38.0 importlib-resources-5.12.0 kiwisolver-1.4.4 matplotlib-3.7.1 pyparsing-3.0.9 python-dateutil-2.8.2 six-1.16.0

python安装matplotlib模块报错问题
看了几篇博文，read timed out error ：microsoft visual c+ is required error： command cl.exe failed with exit status 2 Check the logs for full command output. 报错经常串在一起。

1. Check the logs for full command output.
   输入pip install --upgrade pip指令 升级pip安装包。
   输入pip install wheel指令安装wheel后在安装matplotlib
   安装14.0以上版本的Microsoft C++ Build Tools，如Microsoft Visual C++ 14.0、Microsoft Visual C++ 2019。
   以上pass，下载编译包以后，cmd命令输入 pip install +编译包名称 指令安装就可以了！编译包在上文提到的PyPI 文件页面下载。
   （题外话）安装sklearn包的时候发现上面网站没有对应安装包，那只能上终极大招了：直接安装Microsoft Visual C++ 2015 BuildTools，不占用内存，而且完全满足python对C/C++源码编译的需求。安装包下载链接如下，下载以后联网默认安装步骤就可以了。
2. read timed out
   由于某原因导致访问pip仓库pypi时，安装时间超过默认值。

（1）将默认连接时长修改更长（不推荐）

pip3 --default-timeout=100 install [whatyouwant]

（2）进入pypi直接下载安装（不推荐）

• pypi地址：https://pypi.org/
• 安装方式：
  下载完成后，进入安装包

python setup.py install

（3）使用清华大学开源软件镜像站（强烈推荐）

• 传送门：https://mirrors.tuna.tsinghua.edu.cn/
• 安装方式

pip install [whatyouwant] -i https://mirrors.ustc.edu.cn/pypi/web/simple/

3. error： command cl.exe failed with exit status 2

python3.8下载matplotlib模块时，总是出现以下错误
ERROR: Command errored out with exit status 1: ‘c:\users\air\python\python38\python.exe’ -u -c ‘import sys, setuptools, tokenize; sys.argv[0] = ‘"’"‘C:\Users\air\AppData\Local\Temp\pip-install-5ntug3if\matplotlib\setup.py’"’"’; file=’"’"‘C:\Users\air\AppData\Local\Temp\pip-install-5ntug3if\matplotlib\setup.py’"’"’;f=getattr(tokenize, ‘"’"‘open’"’"’, open)(file);code=f.read().replace(’"’"’\r\n’"’"’, ‘"’"’\n’"’"’);f.close();exec(compile(code, file, ‘"’"’exec’"’"’))’ install –record ‘C:\Users\air\AppData\Local\Temp\pip-record-rp63qqyg\install-record.txt’ –single-version-externally-managed –compile Check the logs for full command output.

在cmd里输入下面的语句试试：

python -m pip install matplotlib

如果还是不行，再试试这个：

python -m pip install matplotlib --user air

安装 matplotlib 参阅资料：
【Python】matplotlib库的安装和简单使用
python安装matplotlib库三种失败情况
Python安装matplotlib库失败解决方法【Command errored out with exit status 1】
解决 python安装matplotlib模块报错问题详细步骤
pip安装python模块时报错443超时

安装phonemizer

Phonemizer 是一个精确寻址的 Python 包, 它将文本从其拼写表示转录为语音表示。该包设计用户友好的，并公开了一个高级音素化函数， 支持大约100种不同的语言。phonemizer 使用的默认后端是 eSpeak （Dunn & Vitolins，2019 年），一种基于语言专业知识和手写转录规则的文本转语音软件。它将文本转录成国际音标，并支持一百多种语言。使用 MBROLA 声音（Tits & Vitolins，2019），eSpeak 后端可用于大约 35 种语言，以 SAMPA 计算机可读语音字母表转录文本。
安装phonemizer前需要配置espeak。Phonemizer 文档

0. 安装phonemizer的最简单方法是使用pip：

pip install phonemizer

用国内的pip源安装

pip install phonemizer -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

该过程不仅安装了 phonemizer ，还安装了依赖的 attrs、babel、clldutils、colorlog、csvw、dlinfo、isodate、jsonschema、language-tags、lxml、markdown、markupsafe、pkgutil-resolve-name、pylatexenc、pyrsistent、pytz、rdflib、rfc3986、segments、tabulate、uritemplate 库。

1. 或者从以下来源安装：：

git clone https://github.com/bootphon/phonemizer
cd phonemizer
python setup.py install

如果在安装期间遇到错误，例如ImportError: No module named setuptools，请参阅问题#11。

2. 如果您想从 tarball 构建，请从PyPI 文件页面获取最新的tar.gz发布文件。
   当然你也可以选择下载whl文件

pip install phonemizer-3.2.1-py3-none-any.whl

cmd安装过程——

Administrator@AUTOBVT-Q90417J MINGW64 /
$ pip install phonemizer -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
Collecting phonemizer
  Downloading http://mirrors.aliyun.com/pypi/packages/cb/5a/b699d5c74959c69728b44692cbacaf1035838ba5dc6aee9b8e80e60637f3/phonemizer-3.2.1-py3-none-any.whl (90 kB)
     -------------------------------------- 90.6/90.6 kB 270.6 kB/s eta 0:00:00
Requirement already satisfied: joblib in c:\program files\python38\lib\site-packages (from phonemizer) (1.2.0)
Collecting attrs>=18.1
  Downloading http://mirrors.aliyun.com/pypi/packages/fb/6e/6f83bf616d2becdf333a1640f1d463fef3150e2e926b7010cb0f81c95e88/attrs-22.2.0-py3-none-any.whl (60 kB)
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Collecting segments
  Downloading http://mirrors.aliyun.com/pypi/packages/93/d4/74dba5011533e66becf35aae5cf1d726e760f445db052592bad70e75305c/segments-2.2.1-py2.py3-none-any.whl (15 kB)
Requirement already satisfied: typing-extensions in c:\program files\python38\lib\site-packages (from phonemizer) (4.5.0)
Collecting dlinfo
  Downloading http://mirrors.aliyun.com/pypi/packages/a7/f9/e014eb5740dfc6ebe6105f4c38890f361e5b0e1537a9f04bb4f34432efb9/dlinfo-1.2.1-py3-none-any.whl (3.6 kB)
Requirement already satisfied: regex in c:\program files\python38\lib\site-packages (from segments->phonemizer) (2022.10.31)
Collecting csvw>=1.5.6
  Downloading http://mirrors.aliyun.com/pypi/packages/93/0c/fbada6f0f50a57408b9f6699fecdc39c6ddbf46175d975a7de18edf605ae/csvw-3.1.3-py2.py3-none-any.whl (56 kB)
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Collecting clldutils>=1.7.3
  Downloading http://mirrors.aliyun.com/pypi/packages/5c/75/18cfdd83c1176bf373c9bcfc60eb8e8c3358c56b24963dce9faaeb2c68ba/clldutils-3.19.0-py2.py3-none-any.whl (1.7 MB)
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Collecting pylatexenc
  Downloading http://mirrors.aliyun.com/pypi/packages/5d/ab/34ec41718af73c00119d0351b7a2531d2ebddb51833a36448fc7b862be60/pylatexenc-2.10.tar.gz (162 kB)
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  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'done'
Collecting markupsafe
  Downloading http://mirrors.aliyun.com/pypi/packages/93/fa/d72f68f84f8537ee8aa3e0764d1eb11e5e025a5ca90c16e94a40f894c2fc/MarkupSafe-2.1.2-cp38-cp38-win_amd64.whl (16 kB)
Collecting tabulate>=0.7.7
  Downloading http://mirrors.aliyun.com/pypi/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl (35 kB)
Collecting lxml
  Downloading http://mirrors.aliyun.com/pypi/packages/95/2c/b6326b95954fcd2d1133ff60e7c10af8d7dd17b52d09eaa6db828fd13afb/lxml-4.9.2-cp38-cp38-win_amd64.whl (3.9 MB)
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Requirement already satisfied: python-dateutil in c:\program files\python38\lib\site-packages (from clldutils>=1.7.3->segments->phonemizer) (2.8.2)
Collecting markdown
  Downloading http://mirrors.aliyun.com/pypi/packages/86/be/ad281f7a3686b38dd8a307fa33210cdf2130404dfef668a37a4166d737ca/Markdown-3.4.1-py3-none-any.whl (93 kB)
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Collecting colorlog
  Downloading http://mirrors.aliyun.com/pypi/packages/58/43/a363c213224448f9e194d626221123ce00e3fb3d87c0c22aed52b620bdd1/colorlog-6.7.0-py2.py3-none-any.whl (11 kB)
Requirement already satisfied: colorama in c:\program files\python38\lib\site-packages (from csvw>=1.5.6->segments->phonemizer) (0.4.6)
Requirement already satisfied: requests in c:\program files\python38\lib\site-packages (from csvw>=1.5.6->segments->phonemizer) (2.28.2)
Collecting uritemplate>=3.0.0
  Downloading http://mirrors.aliyun.com/pypi/packages/81/c0/7461b49cd25aeece13766f02ee576d1db528f1c37ce69aee300e075b485b/uritemplate-4.1.1-py2.py3-none-any.whl (10 kB)
Collecting rdflib
  Downloading http://mirrors.aliyun.com/pypi/packages/50/fb/a0f8b6ab6598b49871a48a189dc1942fb0b0543ab4c84f689486233ef1ec/rdflib-6.2.0-py3-none-any.whl (500 kB)
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Collecting jsonschema
  Downloading http://mirrors.aliyun.com/pypi/packages/c1/97/c698bd9350f307daad79dd740806e1a59becd693bd11443a0f531e3229b3/jsonschema-4.17.3-py3-none-any.whl (90 kB)
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Collecting rfc3986<2
  Downloading http://mirrors.aliyun.com/pypi/packages/c4/e5/63ca2c4edf4e00657584608bee1001302bbf8c5f569340b78304f2f446cb/rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)
Collecting language-tags
  Downloading http://mirrors.aliyun.com/pypi/packages/b0/42/327554649ed2dd5ce59d3f5da176c7be20f9352c7c6c51597293660b7b08/language_tags-1.2.0-py3-none-any.whl (213 kB)
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Collecting babel
  Downloading http://mirrors.aliyun.com/pypi/packages/df/c4/1088865e0246d7ecf56d819a233ab2b72f7d6ab043965ef327d0731b5434/Babel-2.12.1-py3-none-any.whl (10.1 MB)
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Collecting isodate
  Downloading http://mirrors.aliyun.com/pypi/packages/b6/85/7882d311924cbcfc70b1890780763e36ff0b140c7e51c110fc59a532f087/isodate-0.6.1-py2.py3-none-any.whl (41 kB)
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Collecting pytz>=2015.7
  Downloading http://mirrors.aliyun.com/pypi/packages/2e/09/fbd3c46dce130958ee8e0090f910f1fe39e502cc5ba0aadca1e8a2b932e5/pytz-2022.7.1-py2.py3-none-any.whl (499 kB)
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Requirement already satisfied: six in c:\program files\python38\lib\site-packages (from isodate->csvw>=1.5.6->segments->phonemizer) (1.16.0)
Collecting pkgutil-resolve-name>=1.3.10
  Downloading http://mirrors.aliyun.com/pypi/packages/c9/5c/3d4882ba113fd55bdba9326c1e4c62a15e674a2501de4869e6bd6301f87e/pkgutil_resolve_name-1.3.10-py3-none-any.whl (4.7 kB)
Collecting pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0
  Downloading http://mirrors.aliyun.com/pypi/packages/b1/8d/bbce2d857ecdefb7170a8a37ade1de0f060052236c07693856ac23f3b1ee/pyrsistent-0.19.3-cp38-cp38-win_amd64.whl (62 kB)
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Requirement already satisfied: importlib-resources>=1.4.0 in c:\program files\python38\lib\site-packages (from jsonschema->csvw>=1.5.6->segments->phonemizer) (5.12.0)
Requirement already satisfied: importlib-metadata>=4.4 in c:\program files\python38\lib\site-packages (from markdown->clldutils>=1.7.3->segments->phonemizer) (6.0.0)
Requirement already satisfied: setuptools in c:\program files\python38\lib\site-packages (from rdflib->csvw>=1.5.6->segments->phonemizer) (56.0.0)
Requirement already satisfied: pyparsing in c:\program files\python38\lib\site-packages (from rdflib->csvw>=1.5.6->segments->phonemizer) (3.0.9)
Requirement already satisfied: idna<4,>=2.5 in c:\program files\python38\lib\site-packages (from requests->csvw>=1.5.6->segments->phonemizer) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in c:\program files\python38\lib\site-packages (from requests->csvw>=1.5.6->segments->phonemizer) (2022.12.7)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\program files\python38\lib\site-packages (from requests->csvw>=1.5.6->segments->phonemizer) (1.26.14)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\program files\python38\lib\site-packages (from requests->csvw>=1.5.6->segments->phonemizer) (3.0.1)
Requirement already satisfied: zipp>=0.5 in c:\program files\python38\lib\site-packages (from importlib-metadata>=4.4->markdown->clldutils>=1.7.3->segments->phonemizer) (3.15.0)
Installing collected packages: rfc3986, pytz, pylatexenc, language-tags, dlinfo, uritemplate, tabulate, pyrsistent, pkgutil-resolve-name, markupsafe, lxml, isodate, colorlog, babel, attrs, rdflib, markdown, jsonschema, csvw, clldutils, segments, phonemizer
  DEPRECATION: pylatexenc is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559
  Running setup.py install for pylatexenc: started
  Running setup.py install for pylatexenc: finished with status 'done'
Successfully installed attrs-22.2.0 babel-2.12.1 clldutils-3.19.0 colorlog-6.7.0 csvw-3.1.3 dlinfo-1.2.1 isodate-0.6.1 jsonschema-4.17.3 language-tags-1.2.0 lxml-4.9.2 markdown-3.4.1 markupsafe-2.1.2 phonemizer-3.2.1 pkgutil-resolve-name-1.3.10 pylatexenc-2.10 pyrsistent-0.19.3 pytz-2022.7.1 rdflib-6.2.0 rfc3986-1.5.0 segments-2.2.1 tabulate-0.9.0 uritemplate-4.1.1

安装tensorboard

tensorboard是tensorflow开发的一款绘图插件，它可以绘制网络的图像，可以绘制训练时的 Loss ,Accuracy等参数指标，tensorboard现在已经支持在pytorch中使用，使用方法参考pytorch文档，链接如下：torch.utils.tensorboard

TensorBoard最全使用教程：看这篇就够了

0. 安装tensorboard的最简单方法是使用pip：

pip install tensorboard

用国内的pip源安装

pip install tensorboard -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

该过程不仅安装了 tensorboard ，还安装了依赖的absl-py、cachetools、google-auth、google-
auth-oauthlib、grpcio、oauthlib、protobuf 、pyasn1、pyasn1-modules、requests-oauthlib、rsa、tensorboard-data-serve、tensorboard-plugin-wit、werkzeug、wheel库。

1. 你也可以选择下载 whl 文件离线安装
   进入.whl文件所在的网站，下载对应版本到本地。注意，要对应本地环境的python版本。

2. 如果您想从 tarball 构建，请从PyPI 文件页面获取最新的tar.gz发布文件。
   cmd安装过程——

C:\Users\Administrator>pip install tensorboard -i http://mirrors.aliyun.com/pypi
/simple/ --trusted-host mirrors.aliyun.com
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
Collecting tensorboard
  Downloading http://mirrors.aliyun.com/pypi/packages/8d/71/75fcfab1ff98e3fad240
f760d3a6b5ca6bdbcc5ed141fb7abd35cf63134c/tensorboard-2.12.0-py3-none-any.whl (5.
6 MB)
     ---------------------------------------- 5.6/5.6 MB 453.2 kB/s eta 0:00:00
Collecting grpcio>=1.48.2
  Downloading http://mirrors.aliyun.com/pypi/packages/8d/0b/6b75908dac1028c0e7d0
70088e10951a3fe8f5ecc189ed12175526568a89/grpcio-1.51.3-cp38-cp38-win_amd64.whl (
3.7 MB)
     ---------------------------------------- 3.7/3.7 MB 185.7 kB/s eta 0:00:00
Collecting absl-py>=0.4
  Downloading http://mirrors.aliyun.com/pypi/packages/dd/87/de5c32fa1b1c6c3305d5
76e299801d8655c175ca9557019906247b994331/absl_py-1.4.0-py3-none-any.whl (126 kB)

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Requirement already satisfied: markdown>=2.6.8 in c:\program files\python38\lib\
site-packages (from tensorboard) (3.4.1)
Collecting protobuf>=3.19.6
  Downloading http://mirrors.aliyun.com/pypi/packages/7e/76/df06bc132557a83e8a34
77e50c3ccf06c489a90cdbc78083aa2eaeb60a4c/protobuf-4.22.0-cp38-cp38-win_amd64.whl
 (420 kB)
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Requirement already satisfied: setuptools>=41.0.0 in c:\program files\python38\l
ib\site-packages (from tensorboard) (56.0.0)
Collecting wheel>=0.26
  Downloading http://mirrors.aliyun.com/pypi/packages/bd/7c/d38a0b30ce22fc26ed7d
bc087c6d00851fb3395e9d0dac40bec1f905030c/wheel-0.38.4-py3-none-any.whl (36 kB)
Collecting tensorboard-plugin-wit>=1.6.0
  Downloading http://mirrors.aliyun.com/pypi/packages/e0/68/e8ecfac5dd594b676c23
a7f07ea34c197d7d69b3313afdf8ac1b0a9905a2/tensorboard_plugin_wit-1.8.1-py3-none-a
ny.whl (781 kB)
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Collecting werkzeug>=1.0.1
  Downloading http://mirrors.aliyun.com/pypi/packages/f6/f8/9da63c1617ae2a1dec2f
bf6412f3a0cfe9d4ce029eccbda6e1e4258ca45f/Werkzeug-2.2.3-py3-none-any.whl (233 kB
)
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Requirement already satisfied: numpy>=1.12.0 in c:\program files\python38\lib\si
te-packages (from tensorboard) (1.23.5)
Collecting tensorboard-data-server<0.8.0,>=0.7.0
  Downloading http://mirrors.aliyun.com/pypi/packages/9d/cc/6f07c0043b44b3c3879e
cfec1b8a450b6f5e3f8dccfedc9f5f1bc2c650e6/tensorboard_data_server-0.7.0-py3-none-
any.whl (2.4 kB)
Requirement already satisfied: requests<3,>=2.21.0 in c:\program files\python38\
lib\site-packages (from tensorboard) (2.28.2)
Collecting google-auth<3,>=1.6.3
  Downloading http://mirrors.aliyun.com/pypi/packages/93/c4/16f8ad44ed7544244a98
83f35cc99dc96378652a0ec7cc39028b1c697a1e/google_auth-2.16.2-py2.py3-none-any.whl
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Collecting google-auth-oauthlib<0.5,>=0.4.1
  Downloading http://mirrors.aliyun.com/pypi/packages/b1/0e/0636cc1448a7abc444fb
1b3a63655e294e0d2d49092dc3de05241be6d43c/google_auth_oauthlib-0.4.6-py2.py3-none
-any.whl (18 kB)
Collecting rsa<5,>=3.1.4
  Downloading http://mirrors.aliyun.com/pypi/packages/49/97/fa78e3d2f65c02c8e126
8b9aba606569fe97f6c8f7c2d74394553347c145/rsa-4.9-py3-none-any.whl (34 kB)
Collecting pyasn1-modules>=0.2.1
  Downloading http://mirrors.aliyun.com/pypi/packages/95/de/214830a981892a3e286c
3794f41ae67a4495df1108c3da8a9f62159b9a9d/pyasn1_modules-0.2.8-py2.py3-none-any.w
hl (155 kB)
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Collecting cachetools<6.0,>=2.0.0
  Downloading http://mirrors.aliyun.com/pypi/packages/db/14/2b48a834d349eee94677
e8702ea2ef98b7c674b090153ea8d3f6a788584e/cachetools-5.3.0-py3-none-any.whl (9.3
kB)
Requirement already satisfied: six>=1.9.0 in c:\program files\python38\lib\site-
packages (from google-auth<3,>=1.6.3->tensorboard) (1.16.0)
Collecting requests-oauthlib>=0.7.0
  Downloading http://mirrors.aliyun.com/pypi/packages/6f/bb/5deac77a9af870143c68
4ab46a7934038a53eb4aa975bc0687ed6ca2c610/requests_oauthlib-1.3.1-py2.py3-none-an
y.whl (23 kB)
Requirement already satisfied: importlib-metadata>=4.4 in c:\program files\pytho
n38\lib\site-packages (from markdown>=2.6.8->tensorboard) (6.0.0)
Requirement already satisfied: idna<4,>=2.5 in c:\program files\python38\lib\sit
e-packages (from requests<3,>=2.21.0->tensorboard) (3.4)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\program files\pyth
on38\lib\site-packages (from requests<3,>=2.21.0->tensorboard) (3.0.1)
Requirement already satisfied: certifi>=2017.4.17 in c:\program files\python38\l
ib\site-packages (from requests<3,>=2.21.0->tensorboard) (2022.12.7)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\program files\python3
8\lib\site-packages (from requests<3,>=2.21.0->tensorboard) (1.26.14)
Requirement already satisfied: MarkupSafe>=2.1.1 in c:\program files\python38\li
b\site-packages (from werkzeug>=1.0.1->tensorboard) (2.1.2)
Requirement already satisfied: zipp>=0.5 in c:\program files\python38\lib\site-p
ackages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard) (3.15.0)
Collecting pyasn1<0.5.0,>=0.4.6
  Downloading http://mirrors.aliyun.com/pypi/packages/62/1e/a94a8d635fa3ce4cfc7f
506003548d0a2447ae76fd5ca53932970fe3053f/pyasn1-0.4.8-py2.py3-none-any.whl (77 k
B)
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Collecting oauthlib>=3.0.0
  Downloading http://mirrors.aliyun.com/pypi/packages/7e/80/cab10959dc1faead58dc
8384a781dfbf93cb4d33d50988f7a69f1b7c9bbe/oauthlib-3.2.2-py3-none-any.whl (151 kB
)
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Installing collected packages: tensorboard-plugin-wit, pyasn1, wheel, werkzeug,
tensorboard-data-server, rsa, pyasn1-modules, protobuf, oauthlib, grpcio, cachet
ools, absl-py, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard

Successfully installed absl-py-1.4.0 cachetools-5.3.0 google-auth-2.16.2 google-
auth-oauthlib-0.4.6 grpcio-1.51.3 oauthlib-3.2.2 protobuf-4.22.0 pyasn1-0.4.8 py
asn1-modules-0.2.8 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.12.0 tensorboar
d-data-server-0.7.0 tensorboard-plugin-wit-1.8.1 werkzeug-2.2.3 wheel-0.38.4

安装 tensorboard 参阅资料：
Pytorch下tensorboard的安装与配置
TensorBoard最全使用教程：看这篇就够了

安装Unidecode

安装Unidecode的最简单方法是使用pip：

pip install Unidecode

要从源发行版安装Unidecode并运行单元测试，请用：

python setup.py install
python setup.py test

您可以通过以下方式获得Unidecode的最新开发版本：

$ git clone https://www.tablix.org/~avian/git/unidecode.git

在 GitHub 上 有 一 个 官方 镜像 。 https://github.com/avian2/unidecode
cmd安装过程——

C:\Users\Administrator>pip install Unidecode
Collecting Unidecode
  Downloading Unidecode-1.3.6-py3-none-any.whl (235 kB)
     ------------------------------------ 235.9/235.9 kB 268.0 kB/s eta 0:00:00
Installing collected packages: Unidecode
Successfully installed Unidecode-1.3.6

近日为了制作 VITS 语音，需要获取某视频语音的日文字幕。我翻找了国内外多个网站，发现仅青翼字幕组、海月字幕组发过双语字幕，アニメ发过粤日雙語，极影字幕社&天使字幕组发的可能是双语字幕，但都已经死种。本人日语不好，于是打算借助语音识别生成日语字幕。
考虑再三，网易见外只识别中英文，别的大多需要阿里、腾讯的付费服务，部分Windows 7 上无法使用，遂尝试OpenAI Whisper。

安装OpenAI Whisper

Github上的安装说明——

We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.10 and recent PyTorch versions. The codebase also depends on a few Python packages, most notably HuggingFace Transformers for their fast tokenizer implementation and ffmpeg-python for reading audio files. You can download and install (or update to) the latest release of Whisper with the following command:

pip install -U openai-whisper

Alternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:

pip install git+https://github.com/openai/whisper.git 

To update the package to the latest version of this repository, please run:

pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git

It also requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers:

# on Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg

# on Arch Linux
sudo pacman -S ffmpeg

# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg

# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg

# on Windows using Scoop (https://scoop.sh/)
scoop install ffmpeg

You may need rust installed as well, in case tokenizers does not provide a pre-built wheel for your platform. If you see installation errors during the pip install command above, please follow the Getting started page to install Rust development environment. Additionally, you may need to configure the PATH environment variable, e.g. export PATH="$HOME/.cargo/bin:$PATH". If the installation fails with No module named ‘setuptools_rust’, you need to install setuptools_rust, e.g. by running:

pip install setuptools-rust

第一步，安装FFmpeg和Git

第二步，安装Python

Windows 7 建议安装Python 3.8，不要捣鼓网上那些Windows 10 安装Python 3.10的招了，试过了，一样的操作现在不管用了，学人家那样费时费力换各个小版本，下载既不方便，大多也无法实现。

安装过程按这篇文章来就行——Python3.8 最新详细安装步骤

第三步，安装 PyTorch

使用pip安装。

打开pytorch.org,下拉页面。

按照下图选择要安装的版本。我选择的是稳定版，windows系统，pip安装方式，python语言、cpu版本的软件。

CUDA 11.6和CUDA 11.7都是gpu版本的软件，我一开始下载的也是gpu版本的，但是因为我的电脑显卡的显存比较低，运行whisper模型的时候大模型运行不了。为了能运行更大的模型以保证语音识别较高的准确率，我最终只能选择安装cpu版本。

cmd安装过程——

C:\Users\Administrator>pip3 install torch torchvision torchaudio
Collecting torch
  Using cached torch-1.13.1-cp38-cp38-win_amd64.whl (162.6 MB)
Collecting torchvision
  Using cached torchvision-0.14.1-cp38-cp38-win_amd64.whl (1.1 MB)
Collecting torchaudio
  Using cached torchaudio-0.13.1-cp38-cp38-win_amd64.whl (2.0 MB)
Requirement already satisfied: typing-extensions in c:\program files\python38\li
b\site-packages (from torch) (4.5.0)
Requirement already satisfied: requests in c:\program files\python38\lib\site-pa
ckages (from torchvision) (2.28.2)
Requirement already satisfied: numpy in c:\program files\python38\lib\site-packa
ges (from torchvision) (1.24.2)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\program files\python3
8\lib\site-packages (from torchvision) (9.4.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\program files\python38\l
ib\site-packages (from requests->torchvision) (2022.12.7)
Requirement already satisfied: idna<4,>=2.5 in c:\program files\python38\lib\sit
e-packages (from requests->torchvision) (3.4)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\program files\python3
8\lib\site-packages (from requests->torchvision) (1.26.14)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\program files\pyth
on38\lib\site-packages (from requests->torchvision) (3.0.1)
Installing collected packages: torch, torchvision, torchaudio
Successfully installed torch-1.13.1 torchaudio-0.13.1 torchvision-0.14.1

安装PyTorch一般不会出错。

第四步，安装Whisper

在终端命令行中执行

pip install git+https://github.com/openai/whisper.git 

cmd报错——

C:\Users\Administrator>pip install git+https://github.com/openai/whisper.git
Collecting git+https://github.com/openai/whisper.git
  Cloning https://github.com/openai/whisper.git to c:\users\administrator\appdat
a\local\temp\pip-req-build-zed49a2d
  Running command git clone --filter=blob:none --quiet https://github.com/openai
/whisper.git 'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-zed49a2d'
  error: RPC failed; curl 92 HTTP/2 stream 3 was not closed cleanly before end o
f the underlying stream
  error: 6240 bytes of body are still expected
  fetch-pack: unexpected disconnect while reading sideband packet
  fatal: early EOF
  fatal: index-pack failed
  fatal: could not fetch 116c859375fdf68107b80ceb7eb3678780eef5b8 from promisor
remote
  warning: Clone succeeded, but checkout failed.
  You can inspect what was checked out with 'git status'
  and retry with 'git restore --source=HEAD :/'

  error: subprocess-exited-with-error

  × git clone --filter=blob:none --quiet https://github.com/openai/whisper.git
'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-zed49a2d' did not run s
uccessfully.
  │ exit code: 128
  ╰─> See above for output.

  note: This error originates from a subprocess, and is likely not a problem wit
h pip.
error: subprocess-exited-with-error

× git clone --filter=blob:none --quiet https://github.com/openai/whisper.git 'C
:\Users\Administrator\AppData\Local\Temp\pip-req-build-zed49a2d' did not run suc
cessfully.
│ exit code: 128
╰─> See above for output.

note: This error originates from a subprocess, and is likely not a problem with
pip.

分析报错提示可知，上述报错主要是由于git 使用 https 协议时报错。
解决方案

git config --global --unset http.proxy

接着，我重新输入pip install git+https://github.com/openai/whisper.git
cmd安装过程——

C:\Users\Administrator>pip install git+https://github.com/openai/whisper.git
Collecting git+https://github.com/openai/whisper.git
  Cloning https://github.com/openai/whisper.git to c:\users\administrator\appdat
a\local\temp\pip-req-build-zfi9jb7o
  Running command git clone --filter=blob:none --quiet https://github.com/openai
/whisper.git 'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-zfi9jb7o'
  Resolved https://github.com/openai/whisper.git to commit 7858aa9c08d98f7557503
5ecd6481f462d66ca27
  Preparing metadata (setup.py) ... done
Requirement already satisfied: numpy in c:\program files\python38\lib\site-packa
ges (from openai-whisper==20230124) (1.24.2)
Requirement already satisfied: torch in c:\program files\python38\lib\site-packa
ges (from openai-whisper==20230124) (1.13.1)
Requirement already satisfied: tqdm in c:\program files\python38\lib\site-packag
es (from openai-whisper==20230124) (4.64.1)
Requirement already satisfied: more-itertools in c:\program files\python38\lib\s
ite-packages (from openai-whisper==20230124) (9.0.0)
Requirement already satisfied: transformers>=4.19.0 in c:\program files\python38
\lib\site-packages (from openai-whisper==20230124) (4.26.1)
Collecting ffmpeg-python==0.2.0
  Using cached ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)
Requirement already satisfied: future in c:\program files\python38\lib\site-pack
ages (from ffmpeg-python==0.2.0->openai-whisper==20230124) (0.18.3)
Requirement already satisfied: regex!=2019.12.17 in c:\program files\python38\li
b\site-packages (from transformers>=4.19.0->openai-whisper==20230124) (2022.10.3
1)
Requirement already satisfied: filelock in c:\program files\python38\lib\site-pa
ckages (from transformers>=4.19.0->openai-whisper==20230124) (3.9.0)
Requirement already satisfied: packaging>=20.0 in c:\program files\python38\lib\
site-packages (from transformers>=4.19.0->openai-whisper==20230124) (23.0)
Requirement already satisfied: pyyaml>=5.1 in c:\program files\python38\lib\site
-packages (from transformers>=4.19.0->openai-whisper==20230124) (6.0)
Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in c:\program f
iles\python38\lib\site-packages (from transformers>=4.19.0->openai-whisper==2023
0124) (0.13.2)
Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in c:\program files\
python38\lib\site-packages (from transformers>=4.19.0->openai-whisper==20230124)
 (0.12.1)
Requirement already satisfied: requests in c:\program files\python38\lib\site-pa
ckages (from transformers>=4.19.0->openai-whisper==20230124) (2.28.2)
Requirement already satisfied: colorama in c:\program files\python38\lib\site-pa
ckages (from tqdm->openai-whisper==20230124) (0.4.6)
Requirement already satisfied: typing-extensions in c:\program files\python38\li
b\site-packages (from torch->openai-whisper==20230124) (4.5.0)
Requirement already satisfied: certifi>=2017.4.17 in c:\program files\python38\l
ib\site-packages (from requests->transformers>=4.19.0->openai-whisper==20230124)
 (2022.12.7)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\program files\pyth
on38\lib\site-packages (from requests->transformers>=4.19.0->openai-whisper==202
30124) (3.0.1)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\program files\python3
8\lib\site-packages (from requests->transformers>=4.19.0->openai-whisper==202301
24) (1.26.14)
Requirement already satisfied: idna<4,>=2.5 in c:\program files\python38\lib\sit
e-packages (from requests->transformers>=4.19.0->openai-whisper==20230124) (3.4)

Installing collected packages: ffmpeg-python, openai-whisper
  DEPRECATION: openai-whisper is being installed using the legacy 'setup.py inst
all' method, because it does not have a 'pyproject.toml' and the 'wheel' package
 is not installed. pip 23.1 will enforce this behaviour change. A possible repla
cement is to enable the '--use-pep517' option. Discussion can be found at https:
//github.com/pypa/pip/issues/8559
  Running setup.py install for openai-whisper ... done
Successfully installed ffmpeg-python-0.2.0 openai-whisper-20230124

C:\Users\Administrator>

注意上文这一段
To update the package to the latest version of this repository, please run:

pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git

输入上面的代码将软件包更新到此存储库的最新版本
我跳过这步直接输入whisper audio.mp3，开始语音识别
cmd运行——

Administrator@AUTOBVT-Q90417J MINGW64 ~/Desktop/新建文件夹
$ whisper audio.mp3
c:\program files\python38\lib\site-packages\whisper\__init__.py:48: UserWarning: C:\Users\Administrator\.cache\whisper\small.pt exists, but the SHA256 checksum does not match; re-downloading the file
  warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
  0%|▒▒                                     | 1.55M/461M [00:01<07:29, 1.07MiB/s  0%|▒▒                                     | 1.66M/461M [00:01<07:38, 1.05MiB/s  0%|▒▒                                     | 1.77M/461M [00:01<07:34, 1.06MiB/s  0%|▒▒                                     | 1.89M/461M [00:02<07:17, 1.10MiB/s  0%|▒▒                                     | 2.02M/461M [00:02<06:59, 1.15MiB/s  0%|▒▒                                     | 2.13M/461M [00:02<07:04, 1.13MiB/s  0%|▒▒                                     | 2.25M/461M [00:02<06:54, 1.16MiB/s  1%|▒▒                                     | 2.38M/461M [00:02<06:41, 1.20MiB/s  1%|▒▒                                     | 2.49M/461M [00:02<06:43, 1.19MiB/s  1%|▒▒                                     | 2.61M/461M [00:02<06:39, 1.20MiB/s  1%|▒▒                                     | 2.73M/461M [00:02<06:38, 1.21MiB/s  1%|▒▒                                     | 2.84M/461M [00:02<06:38, 1.21MiB/s  1%|▒▒                                     | 2.96M/461M [00:02<06:52, 1.16MiB/s  1%|▒▒                                     | 3.11M/461M [00:03<06:56, 1.15MiB/s  1%|▒▒                                     | 3.25M/461M [00:03<06:29, 1.23MiB/s  1%|▒▒                                     | 3.38M/461M [00:03<06:36, 1.21MiB/s  1%|▒▒                                     | 3.50M/461M [00:03<06:29, 1.23MiB/s  1%|▒▒                                     | 3.66M/461M [00:03<06:11, 1.29MiB/s  1%|▒▒                                     | 3.80M/461M [00:03<06:31, 1.23MiB/s  1%|▒▒                                     | 3.95M/461M [00:03<06:02, 1.32MiB/s  1%|▒▒                                     | 4.09M/461M [00:03<05:55, 1.35MiB/s  1%|▒▒                                     | 4.23M/461M [00:04<05:56, 1.34MiB/s  1%|▒▒                                     | 4.36M/461M [00:04<05:53, 1.36MiB/s  1%|▒▒                                     | 4.52M/461M [00:04<05:56, 1.34MiB/s  1%|▒▒                                     | 4.65M/461M [00:04<06:21, 1.25MiB/s  1%|▒▒                                     | 4.77M/461M [00:04<07:00, 1.14MiB/s  1%|▒▒                                      | 4.89M/461M [00:04<08:00, 996kiB/s  1%|▒▒                                      | 4.99M/461M [00:04<08:23, 951kiB/s  1%|▒▒                                      | 5.09M/461M [00:04<08:50, 902kiB/s  1%|▒▒                                      | 5.18M/461M [00:05<09:48, 812kiB/s  1%|▒▒                                      | 5.27M/461M [00:05<09:37, 827kiB/s  1%|▒▒                                      | 5.36M/461M [00:05<09:14, 862kiB/s  1%|▒▒                                      | 5.45M/461M [00:05<09:10, 868kiB/s  1%|▒▒                                      | 5.53M/461M [00:05<09:24, 847kiB/s  1%|▒▒                                      | 5.62M/461M [00:05<10:13, 779kiB/s  1%|▒▒                                      | 5.70M/461M [00:05<10:09, 784kiB/s  1%|▒▒                                      | 5.81M/461M [00:05<09:29, 838kiB/s  1%|▒▒                                      | 5.90M/461M [00:05<09:48, 812kiB/s  1%|▒▒                                      | 5.98M/461M [00:06<09:35, 829kiB/s  1%|▒▒                                      | 6.08M/461M [00:06<09:20, 852kiB/s  1%|▒▒                                      | 6.17M/461M [00:06<09:06, 874kiB/s  1%|▒▒                                      | 6.30M/461M [00:06<08:05, 982kiB/s  1%|▒▒                                      | 6.41M/461M [00:06<08:08, 976kiB/s  1%|▒▒                                     | 6.53M/461M [00:06<07:37, 1.04MiB/s  1%|▒▒                                     | 6.66M/461M [00:06<07:16, 1.09MiB/s  1%|▒▒                                     | 6.77M/461M [00:06<07:22, 1.08MiB/s  1%|▒▒                                     | 6.88M/461M [00:06<07:38, 1.04MiB/s  2%|▒▒                                     | 7.02M/461M [00:07<07:02, 1.13MiB/s  2%|▒▒                                     | 7.16M/461M [00:07<06:30, 1.22MiB/s  2%|▒▒                                     | 7.28M/461M [00:07<07:04, 1.12MiB/s  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6%|▒▒▒▒▒▒                                   | 26.9M/461M [00:29<07:25, 1.02MiB  6%|▒▒▒▒▒▒                                   | 27.0M/461M [00:29<07:33, 1.00MiB  6%|▒▒▒▒▒▒                                   | 27.1M/461M [00:29<07:32, 1.01MiB  6%|▒▒▒▒▒▒                                    | 27.2M/461M [00:29<07:56, 954kiB  6%|▒▒▒▒▒▒                                   | 27.3M/461M [00:29<07:27, 1.02MiB  6%|▒▒▒▒▒▒                                   | 27.4M/461M [00:29<07:25, 1.02MiB  6%|▒▒▒▒▒▒                                   | 27.5M/461M [00:29<07:29, 1.01MiB  6%|▒▒▒▒▒▒                                   | 27.6M/461M [00:29<07:16, 1.04MiB  6%|▒▒▒▒▒▒                                    | 27.7M/461M [00:29<08:56, 847kiB  6%|▒▒▒▒▒▒                                    | 27.9M/461M [00:30<07:48, 969kiB  6%|▒▒▒▒▒▒                                    | 28.0M/461M [00:30<08:00, 946kiB  6%|▒▒▒▒▒▒                                    | 28.1M/461M [00:30<08:19, 909kiB  6%|▒▒▒▒▒▒                                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6%|▒▒▒▒▒▒                                   | 29.5M/461M [00:31<07:04, 1.07MiB  6%|▒▒▒▒▒▒                                    | 29.6M/461M [00:32<08:41, 868kiB  6%|▒▒▒▒▒▒                                    | 29.8M/461M [00:32<07:38, 986kiB  6%|▒▒▒▒▒▒                                    | 29.9M/461M [00:32<07:47, 967kiB  7%|▒▒▒▒▒▒                                    | 30.0M/461M [00:32<08:04, 934kiB  7%|▒▒▒▒▒▒                                    | 30.1M/461M [00:32<08:02, 937kiB  7%|▒▒▒▒▒▒                                    | 30.2M/461M [00:32<08:01, 939kiB  7%|▒▒▒▒▒▒                                    | 30.3M/461M [00:32<07:50, 959kiB  7%|▒▒▒▒▒▒                                    | 30.4M/461M [00:32<07:42, 976kiB  7%|▒▒▒▒▒▒                                   | 30.5M/461M [00:32<07:22, 1.02MiB  7%|▒▒▒▒▒▒                                   | 30.6M/461M [00:33<07:16, 1.04MiB  7%|▒▒▒▒▒▒                                   | 30.7M/461M [00:33<07:18, 1.03MiB  7%|▒▒▒▒▒▒                                   | 30.9M/461M [00:33<06:56, 1.08MiB  7%|▒▒▒▒▒▒                                    | 31.0M/461M [00:33<08:06, 927kiB  7%|▒▒▒▒▒▒                                   | 31.1M/461M [00:33<07:26, 1.01MiB  7%|▒▒▒▒▒▒                                   | 31.2M/461M [00:33<07:30, 1.00MiB  7%|▒▒▒▒▒▒                                    | 31.3M/461M [00:33<08:08, 922kiB  7%|▒▒▒▒▒▒                                    | 31.4M/461M [00:33<08:00, 938kiB  7%|▒▒▒▒▒▒                                    | 31.5M/461M [00:33<08:03, 932kiB  7%|▒▒▒▒▒▒                                    | 31.6M/461M [00:34<08:00, 938kiB  7%|▒▒▒▒▒▒                                    | 31.7M/461M [00:34<07:35, 988kiB  7%|▒▒▒▒▒▒                                    | 31.8M/461M [00:34<07:52, 953kiB  7%|▒▒▒▒▒▒                                    | 31.9M/461M [00:34<07:54, 948kiB  7%|▒▒▒▒▒▒                                    | 32.0M/461M [00:34<08:10, 917kiB  7%|▒▒▒▒▒▒                                   | 32.1M/461M [00:34<07:24, 1.01MiB  7%|▒▒▒▒▒▒                                   | 32.2M/461M [00:34<07:09, 1.05MiB  7%|▒▒▒▒▒▒                                   | 32.3M/461M [00:34<07:08, 1.05MiB  7%|▒▒▒▒▒▒                                    | 32.4M/461M [00:35<08:21, 896kiB  7%|▒▒▒▒▒▒                                   | 32.6M/461M [00:35<07:16, 1.03MiB  7%|▒▒▒▒▒▒                                    | 32.7M/461M [00:35<07:52, 952kiB  7%|▒▒▒▒▒▒                                    | 32.8M/461M [00:35<07:47, 961kiB  7%|▒▒▒▒▒▒                                    | 32.9M/461M [00:35<08:11, 914kiB  7%|▒▒▒▒▒▒                                    | 33.0M/461M [00:35<07:45, 964kiB  7%|▒▒▒▒▒▒                                    | 33.1M/461M [00:35<07:40, 975kiB  7%|▒▒▒▒▒▒                                    | 33.2M/461M [00:35<07:33, 989kiB  7%|▒▒▒▒▒▒                                   | 33.3M/461M [00:35<07:18, 1.02MiB  7%|▒▒▒▒▒▒                                   | 33.4M/461M [00:36<07:16, 1.03MiB  7%|▒▒▒▒▒▒                                   | 33.5M/461M [00:36<07:28, 1.00MiB  7%|▒▒▒▒▒▒                                   | 33.6M/461M [00:36<07:20, 1.02MiB  7%|▒▒▒▒▒▒                                   | 33.7M/461M [00:36<07:07, 1.05MiB  7%|▒▒▒▒▒▒                                   | 33.8M/461M [00:36<06:57, 1.07MiB  7%|▒▒▒▒▒▒                                    | 33.9M/461M [00:36<07:53, 947kiB  7%|▒▒▒▒▒▒                                    | 34.0M/461M [00:36<08:10, 913kiB  7%|▒▒▒▒▒▒                                    | 34.1M/461M [00:36<08:15, 903kiB  7%|▒▒▒▒▒▒                                    | 34.2M/461M [00:36<08:22, 891kiB  7%|▒▒▒▒▒▒                                    | 34.3M/461M [00:37<08:26, 884kiB  7%|▒▒▒▒▒▒                                    | 34.4M/461M [00:37<08:52, 840kiB  7%|▒▒▒▒▒▒                                    | 34.5M/461M [00:37<08:52, 841kiB  7%|▒▒▒▒▒▒                                    | 34.6M/461M [00:37<09:56, 750kiB  8%|▒▒▒▒▒▒                                    | 34.6M/461M [00:37<09:48, 760kiB  8%|▒▒▒▒▒▒                                    | 34.7M/461M [00:37<09:47, 761kiB  8%|▒▒▒▒▒▒                                    | 34.8M/461M [00:37<09:54, 753kiB  8%|▒▒▒▒▒▒                                    | 34.9M/461M [00:37<09:30, 783kiB  8%|▒▒▒▒▒▒                                    | 35.0M/461M [00:38<09:37, 774kiB  8%|▒▒▒▒▒▒                                    | 35.1M/461M [00:38<09:48, 759kiB  8%|▒▒▒▒▒▒                                    | 35.1M/461M [00:38<09:40, 769kiB  8%|▒▒▒▒▒▒                                    | 35.2M/461M [00:38<09:38, 773kiB  8%|▒▒▒▒▒▒                                    | 35.3M/461M [00:38<09:11, 809kiB  8%|▒▒▒▒▒▒                                    | 35.4M/461M [00:38<08:46, 849kiB  8%|▒▒▒▒▒▒                                    | 35.5M/461M [00:38<08:37, 862kiB  8%|▒▒▒▒▒▒                                    | 35.6M/461M [00:38<08:05, 920kiB  8%|▒▒▒▒▒▒                                    | 35.7M/461M [00:38<07:59, 930kiB  8%|▒▒▒▒▒▒                                    | 35.8M/461M [00:38<07:32, 986kiB  8%|▒▒▒▒▒▒                                    | 35.9M/461M [00:39<07:34, 982kiB  8%|▒▒▒▒▒▒                                   | 36.0M/461M [00:39<06:58, 1.07MiB  8%|▒▒▒▒▒▒                                   | 36.1M/461M [00:39<06:49, 1.09MiB  8%|▒▒▒▒▒▒                                   | 36.3M/461M [00:39<06:46, 1.10MiB  8%|▒▒▒▒▒▒                                   | 36.4M/461M [00:39<06:25, 1.16MiB  8%|▒▒▒▒▒▒                                   | 36.5M/461M [00:39<06:21, 1.17MiB  8%|▒▒▒▒▒▒                                   | 36.6M/461M [00:39<06:15, 1.18MiB  8%|▒▒▒▒▒▒                                   | 36.8M/461M [00:39<06:04, 1.22MiB  8%|▒▒▒▒▒▒                                   | 36.9M/461M [00:39<06:30, 1.14MiB  8%|▒▒▒▒▒▒                                   | 37.0M/461M [00:40<06:25, 1.16MiB  8%|▒▒▒▒▒▒                                   | 37.1M/461M [00:40<06:10, 1.20MiB  8%|▒▒▒▒▒▒                                   | 37.3M/461M [00:40<05:54, 1.25MiB  8%|▒▒▒▒▒▒                                   | 37.4M/461M [00:40<05:39, 1.31MiB  8%|▒▒▒▒▒▒                                   | 37.5M/461M [00:40<06:38, 1.12MiB  8%|▒▒▒▒▒▒                                   | 37.7M/461M [00:40<06:16, 1.18MiB  8%|▒▒▒▒▒▒                                   | 37.8M/461M [00:40<07:07, 1.04MiB  8%|▒▒▒▒▒▒▒▒                                   | 37.9M/461M [00:40<08:22, 883ki  8%|▒▒▒▒▒▒▒▒                                   | 38.0M/461M [00:41<09:30, 778ki  8%|▒▒▒▒▒▒▒▒                                   | 38.1M/461M [00:41<10:35, 698ki  8%|▒▒▒▒▒▒▒▒                                   | 38.2M/461M [00:41<10:53, 679ki  8%|▒▒▒▒▒▒▒▒                                   | 38.2M/461M [00:41<10:47, 685ki  8%|▒▒▒▒▒▒▒▒                                   | 38.3M/461M [00:41<10:52, 679ki  8%|▒▒▒▒▒▒▒▒                                   | 38.4M/461M [00:41<10:03, 735ki  8%|▒▒▒▒▒▒▒▒                                   | 38.5M/461M [00:41<10:54, 677ki  8%|▒▒▒▒▒▒▒▒                                   | 38.5M/461M [00:42<10:45, 687ki  8%|▒▒▒▒▒▒▒▒                                   | 38.6M/461M [00:42<09:47, 755ki  8%|▒▒▒▒▒▒▒▒                                   | 38.7M/461M [00:42<09:33, 772ki  8%|▒▒▒▒▒▒▒▒                                   | 38.8M/461M [00:42<09:25, 783ki  8%|▒▒▒▒▒▒▒▒                                   | 38.9M/461M [00:42<08:51, 833ki  8%|▒▒▒▒▒▒▒▒                                   | 39.0M/461M [00:42<08:48, 837ki  8%|▒▒▒▒▒▒▒▒                                   | 39.1M/461M [00:42<08:23, 878ki  8%|▒▒▒▒▒▒▒▒                                   | 39.2M/461M [00:42<08:09, 903ki  9%|▒▒▒▒▒▒▒▒                                   | 39.3M/461M [00:42<07:59, 924ki  9%|▒▒▒▒▒▒▒▒                                   | 39.4M/461M [00:42<07:31, 980ki  9%|▒▒▒▒▒▒▒▒                                   | 39.5M/461M [00:43<07:22, 999ki  9%|▒▒▒▒▒▒▒▒                                  | 39.6M/461M [00:43<07:09, 1.03Mi  9%|▒▒▒▒▒▒▒▒                                  | 39.7M/461M [00:43<06:48, 1.08Mi  9%|▒▒▒▒▒▒▒▒                                  | 39.8M/461M [00:43<06:42, 1.10Mi  9%|▒▒▒▒▒▒▒▒                                  | 39.9M/461M [00:43<06:38, 1.11Mi  9%|▒▒▒▒▒▒▒▒                                  | 40.1M/461M [00:43<06:23, 1.15Mi  9%|▒▒▒▒▒▒▒▒                                  | 40.2M/461M [00:43<06:20, 1.16Mi  9%|▒▒▒▒▒▒▒▒                                  | 40.3M/461M [00:43<06:19, 1.16Mi  9%|▒▒▒▒▒▒▒▒                                  | 40.4M/461M [00:43<06:09, 1.20Mi  9%|▒▒▒▒▒▒▒▒                                  | 40.6M/461M [00:43<05:51, 1.25Mi  9%|▒▒▒▒▒▒▒▒                                  | 40.7M/461M [00:44<05:47, 1.27Mi  9%|▒▒▒▒▒▒▒▒                                  | 40.8M/461M [00:44<05:32, 1.33Mi  9%|▒▒▒▒▒▒▒▒                                  | 41.0M/461M [00:44<05:23, 1.36Mi  9%|▒▒▒▒▒▒▒▒                                  | 41.1M/461M [00:44<05:15, 1.39Mi  9%|▒▒▒▒▒▒▒▒                                  | 41.3M/461M [00:44<05:14, 1.40Mi  9%|▒▒▒▒▒▒▒▒                              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9%|▒▒▒▒▒▒▒▒                                  | 42.8M/461M [00:46<07:04, 1.03Mi  9%|▒▒▒▒▒▒▒▒                                  | 42.9M/461M [00:46<06:51, 1.07Mi  9%|▒▒▒▒▒▒▒▒                                  | 43.0M/461M [00:46<06:47, 1.08Mi  9%|▒▒▒▒▒▒▒▒                                  | 43.1M/461M [00:46<06:25, 1.14Mi  9%|▒▒▒▒▒▒▒▒                                   | 43.2M/461M [00:46<07:41, 949ki  9%|▒▒▒▒▒▒▒▒                                  | 43.4M/461M [00:46<07:02, 1.04Mi  9%|▒▒▒▒▒▒▒▒                                  | 43.5M/461M [00:46<07:17, 1.00Mi  9%|▒▒▒▒▒▒▒▒                                   | 43.6M/461M [00:47<07:28, 977ki  9%|▒▒▒▒▒▒▒▒                                   | 43.7M/461M [00:47<07:20, 995ki  9%|▒▒▒▒▒▒▒▒                                  | 43.8M/461M [00:47<07:17, 1.00Mi 10%|▒▒▒▒▒▒▒▒                                   | 43.9M/461M [00:47<08:12, 889ki 10%|▒▒▒▒▒▒▒▒                                   | 44.0M/461M [00:47<08:01, 908ki 10%|▒▒▒▒▒▒▒▒                                   | 44.1M/461M [00:47<08:00, 911ki 10%|▒▒▒▒▒▒▒▒                                   | 44.2M/461M [00:47<08:55, 817ki 10%|▒▒▒▒▒▒▒▒                                   | 44.3M/461M [00:47<08:44, 834ki 10%|▒▒▒▒▒▒▒▒                                   | 44.3M/461M [00:47<08:43, 834ki 10%|▒▒▒▒▒▒▒▒                                   | 44.4M/461M [00:48<08:52, 821ki 10%|▒▒▒▒▒▒▒▒                                   | 44.5M/461M [00:48<08:38, 843ki 10%|▒▒▒▒▒▒▒▒                                   | 44.6M/461M [00:48<08:38, 843ki 10%|▒▒▒▒▒▒▒▒                                   | 44.7M/461M [00:48<08:29, 857ki 10%|▒▒▒▒▒▒▒▒                                   | 44.8M/461M [00:48<08:30, 855ki 10%|▒▒▒▒▒▒▒▒                                   | 44.9M/461M [00:48<08:11, 889ki 10%|▒▒▒▒▒▒▒▒                                   | 45.0M/461M [00:48<08:06, 898ki 10%|▒▒▒▒▒▒▒▒                                   | 45.1M/461M [00:48<07:46, 935ki 10%|▒▒▒▒▒▒▒▒                                   | 45.2M/461M [00:48<07:22, 987ki 10%|▒▒▒▒▒▒▒▒                                  | 45.3M/461M [00:49<07:03, 1.03Mi 10%|▒▒▒▒▒▒▒▒                                  | 45.4M/461M [00:49<06:52, 1.06Mi 10%|▒▒▒▒▒▒▒▒                                  | 45.5M/461M [00:49<06:42, 1.08Mi 10%|▒▒▒▒▒▒▒▒                                  | 45.6M/461M [00:49<06:38, 1.09Mi 10%|▒▒▒▒▒▒▒▒                                  | 45.7M/461M [00:49<06:19, 1.15Mi 10%|▒▒▒▒▒▒▒▒                                  | 45.9M/461M [00:49<06:06, 1.19Mi 10%|▒▒▒▒▒▒▒▒                                   | 46.0M/461M [00:49<07:19, 990ki 10%|▒▒▒▒▒▒▒▒                                  | 46.1M/461M [00:49<06:37, 1.09Mi 10%|▒▒▒▒▒▒▒▒                                  | 46.2M/461M [00:49<07:05, 1.02Mi 10%|▒▒▒▒▒▒▒▒                                  | 46.3M/461M [00:50<06:55, 1.05Mi 10%|▒▒▒▒▒▒▒▒                                  | 46.5M/461M [00:50<06:55, 1.05Mi 10%|▒▒▒▒▒▒▒▒                                  | 46.6M/461M [00:50<06:55, 1.05Mi 10%|▒▒▒▒▒▒▒▒                                  | 46.7M/461M [00:50<06:48, 1.06Mi 10%|▒▒▒▒▒▒▒▒                                   | 46.8M/461M [00:50<08:21, 866ki 10%|▒▒▒▒▒▒▒▒                                   | 46.9M/461M [00:50<08:11, 884ki 10%|▒▒▒▒▒▒▒▒                                   | 47.0M/461M [00:50<08:12, 882ki 10%|▒▒▒▒▒▒▒▒                                   | 47.1M/461M [00:50<08:55, 811ki 10%|▒▒▒▒▒▒▒▒                                   | 47.1M/461M [00:51<08:43, 829ki 10%|▒▒▒▒▒▒▒▒                                   | 47.2M/461M [00:51<08:39, 835ki 10%|▒▒▒▒▒▒▒▒                                   | 47.3M/461M [00:51<08:46, 824ki 10%|▒▒▒▒▒▒▒▒                                   | 47.4M/461M [00:51<08:33, 845ki 10%|▒▒▒▒▒▒▒▒                                   | 47.5M/461M [00:51<08:25, 858ki 10%|▒▒▒▒▒▒▒▒                                   | 47.6M/461M [00:51<08:06, 891ki 10%|▒▒▒▒▒▒▒▒                                   | 47.7M/461M [00:51<07:37, 948ki 10%|▒▒▒▒▒▒▒▒                                   | 47.8M/461M [00:51<07:34, 953ki 10%|▒▒▒▒▒▒▒▒                                   | 47.9M/461M [00:51<07:30, 962ki 10%|▒▒▒▒▒▒▒▒                                   | 48.0M/461M [00:52<07:25, 972ki 10%|▒▒▒▒▒▒▒▒                                  | 48.1M/461M [00:52<07:10, 1.01Mi 10%|▒▒▒▒▒▒▒▒                                  | 48.3M/461M [00:52<06:44, 1.07Mi 10%|▒▒▒▒▒▒▒▒                                  | 48.4M/461M [00:52<06:36, 1.09Mi 11%|▒▒▒▒▒▒▒▒                                  | 48.5M/461M [00:52<06:17, 1.15Mi 11%|▒▒▒▒▒▒▒▒                                  | 48.6M/461M [00:52<06:04, 1.19Mi 11%|▒▒▒▒▒▒▒▒                                  | 48.7M/461M [00:52<06:39, 1.08Mi 11%|▒▒▒▒▒▒▒▒▒▒                                  | 48.8M/461M [00:52<07:22, 978k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 48.9M/461M [00:52<07:23, 975k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 49.0M/461M [00:53<07:16, 991k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 49.1M/461M [00:53<07:13, 998k 11%|▒▒▒▒▒▒▒▒                                  | 49.2M/461M [00:53<07:10, 1.00Mi 11%|▒▒▒▒▒▒▒▒                                  | 49.4M/461M [00:53<07:10, 1.00Mi 11%|▒▒▒▒▒▒▒▒▒▒                                  | 49.5M/461M [00:53<07:58, 903k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 49.5M/461M [00:53<07:50, 917k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 49.6M/461M [00:53<07:59, 901k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 49.7M/461M [00:53<08:48, 816k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 49.8M/461M [00:53<08:47, 817k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 49.9M/461M [00:54<08:38, 833k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 50.0M/461M [00:54<08:32, 842k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 50.1M/461M [00:54<08:40, 829k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 50.2M/461M [00:54<08:29, 846k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 50.3M/461M [00:54<08:08, 882k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 50.4M/461M [00:54<07:59, 898k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 50.5M/461M [00:54<07:33, 951k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 50.6M/461M [00:54<07:17, 984k 11%|▒▒▒▒▒▒▒▒▒▒                                 | 50.7M/461M [00:54<07:08, 1.00M 11%|▒▒▒▒▒▒▒▒▒▒                                 | 50.8M/461M [00:55<06:51, 1.05M 11%|▒▒▒▒▒▒▒▒▒▒                                 | 50.9M/461M [00:55<06:42, 1.07M 11%|▒▒▒▒▒▒▒▒▒▒                                 | 51.0M/461M [00:55<06:22, 1.13M 11%|▒▒▒▒▒▒▒▒▒▒                                 | 51.1M/461M [00:55<06:24, 1.12M 11%|▒▒▒▒▒▒▒▒▒▒                                 | 51.2M/461M [00:55<06:24, 1.12M 11%|▒▒▒▒▒▒▒▒▒▒                                  | 51.4M/461M [00:55<07:26, 963k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 51.5M/461M [00:55<07:21, 974k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 51.6M/461M [00:55<07:19, 978k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 51.7M/461M [00:55<07:19, 977k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 51.8M/461M [00:56<08:44, 818k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 51.9M/461M [00:56<07:46, 920k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.0M/461M [00:56<07:52, 909k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.1M/461M [00:56<08:32, 838k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.2M/461M [00:56<08:22, 854k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.3M/461M [00:56<08:26, 846k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.3M/461M [00:56<08:18, 861k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.4M/461M [00:56<08:15, 865k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.5M/461M [00:56<08:42, 820k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.6M/461M [00:57<08:33, 835k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.7M/461M [00:57<08:11, 871k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.8M/461M [00:57<07:56, 898k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 52.9M/461M [00:57<07:46, 917k 11%|▒▒▒▒▒▒▒▒▒▒                                  | 53.0M/461M [00:57<07:27, 956k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 53.1M/461M [00:57<07:11, 991k 12%|▒▒▒▒▒▒▒▒▒▒                                 | 53.2M/461M [00:57<07:07, 1.00M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 53.3M/461M [00:57<07:05, 1.01M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 53.4M/461M [00:57<06:50, 1.04M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 53.5M/461M [00:58<06:42, 1.06M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 53.7M/461M [00:58<06:23, 1.12M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 53.8M/461M [00:58<06:07, 1.16M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 53.9M/461M [00:58<06:02, 1.18M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 54.1M/461M [00:58<05:41, 1.25M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 54.2M/461M [00:58<05:28, 1.30M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 54.3M/461M [00:58<06:15, 1.14M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 54.4M/461M [00:58<06:50, 1.04M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 54.5M/461M [00:58<06:49, 1.04M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 54.7M/461M [00:59<06:43, 1.06M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 54.8M/461M [00:59<06:41, 1.06M 12%|▒▒▒▒▒▒▒▒▒▒                                  | 54.9M/461M [00:59<08:09, 870k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.0M/461M [00:59<07:28, 949k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.1M/461M [00:59<07:27, 952k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.2M/461M [00:59<08:04, 878k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.3M/461M [00:59<07:57, 891k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.4M/461M [00:59<07:45, 915k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.5M/461M [01:00<07:40, 923k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.6M/461M [01:00<08:11, 866k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.7M/461M [01:00<07:52, 900k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.8M/461M [01:00<07:50, 903k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 55.9M/461M [01:00<07:43, 917k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 56.0M/461M [01:00<07:25, 954k 12%|▒▒▒▒▒▒▒▒▒▒                                 | 56.1M/461M [01:00<07:02, 1.01M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 56.2M/461M [01:00<06:37, 1.07M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 56.3M/461M [01:00<06:32, 1.08M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 56.4M/461M [01:01<06:26, 1.10M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 56.5M/461M [01:01<06:25, 1.10M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 56.6M/461M [01:01<07:03, 1.00M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 56.8M/461M [01:01<06:49, 1.04M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 56.9M/461M [01:01<06:49, 1.03M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 57.0M/461M [01:01<07:00, 1.01M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 57.1M/461M [01:01<06:57, 1.02M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 57.2M/461M [01:01<06:53, 1.02M 12%|▒▒▒▒▒▒▒▒▒▒                                 | 57.3M/461M [01:01<06:52, 1.03M 12%|▒▒▒▒▒▒▒▒▒▒                                  | 57.4M/461M [01:02<07:35, 930k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 57.5M/461M [01:02<07:35, 929k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 57.6M/461M [01:02<08:31, 828k 12%|▒▒▒▒▒▒▒▒▒▒                                  | 57.6M/461M [01:02<08:26, 836k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 57.7M/461M [01:02<08:18, 849k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 57.8M/461M [01:02<08:11, 861k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 57.9M/461M [01:02<08:11, 860k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 58.0M/461M [01:02<07:52, 895k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 58.1M/461M [01:02<07:47, 905k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 58.2M/461M [01:03<07:38, 922k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 58.3M/461M [01:03<07:30, 937k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 58.4M/461M [01:03<07:06, 990k 13%|▒▒▒▒▒▒▒▒▒▒                                 | 58.5M/461M [01:03<06:57, 1.01M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 58.6M/461M [01:03<06:55, 1.02M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 58.7M/461M [01:03<06:58, 1.01M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 58.8M/461M [01:03<06:48, 1.03M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 58.9M/461M [01:03<06:30, 1.08M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.0M/461M [01:03<06:23, 1.10M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.2M/461M [01:03<06:08, 1.14M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.3M/461M [01:04<06:56, 1.01M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.4M/461M [01:04<06:33, 1.07M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.5M/461M [01:04<06:55, 1.01M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.6M/461M [01:04<06:47, 1.03M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.7M/461M [01:04<06:46, 1.04M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.8M/461M [01:04<06:54, 1.02M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 59.9M/461M [01:04<06:59, 1.00M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 60.0M/461M [01:04<06:53, 1.02M 13%|▒▒▒▒▒▒▒▒▒▒                                  | 60.1M/461M [01:05<07:31, 931k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 60.2M/461M [01:05<07:25, 945k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 60.3M/461M [01:05<07:37, 920k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 60.4M/461M [01:05<08:04, 868k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 60.5M/461M [01:05<08:05, 865k 13%|▒▒▒▒▒▒▒▒▒▒                                  | 60.6M/461M [01:05<08:06, 864k 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 60.7M/461M [01:05<07:57, 880 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 60.8M/461M [01:05<07:46, 901 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 60.9M/461M [01:05<07:39, 914 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 61.0M/461M [01:05<07:17, 958 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 61.1M/461M [01:06<07:09, 976 13%|▒▒▒▒▒▒▒▒▒▒                                 | 61.2M/461M [01:06<06:51, 1.02M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 61.3M/461M [01:06<06:43, 1.04M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 61.4M/461M [01:06<06:31, 1.07M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 61.5M/461M [01:06<06:44, 1.04M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 61.7M/461M [01:06<06:23, 1.09M 13%|▒▒▒▒▒▒▒▒▒▒                                 | 61.8M/461M [01:06<06:45, 1.03M 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 61.9M/461M [01:06<07:03, 988 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 62.0M/461M [01:07<07:36, 918 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 62.1M/461M [01:07<07:31, 927 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 62.2M/461M [01:07<07:26, 937 13%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 62.3M/461M [01:07<07:24, 942 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 62.4M/461M [01:07<07:01, 991 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 62.5M/461M [01:07<06:54, 1.01 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 62.6M/461M [01:07<06:53, 1.01 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 62.7M/461M [01:07<06:48, 1.02 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 62.8M/461M [01:07<06:51, 1.01 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 62.9M/461M [01:07<06:42, 1.04 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 63.0M/461M [01:08<06:56, 1.00 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.1M/461M [01:08<07:36, 914 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.2M/461M [01:08<07:37, 913 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.3M/461M [01:08<07:58, 873 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.4M/461M [01:08<08:03, 863 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.5M/461M [01:08<08:00, 868 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.5M/461M [01:08<07:55, 877 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.6M/461M [01:08<07:47, 892 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.7M/461M [01:08<07:35, 915 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.8M/461M [01:09<07:29, 927 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 63.9M/461M [01:09<07:24, 938 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 64.0M/461M [01:09<07:09, 970 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 64.1M/461M [01:09<06:48, 1.02 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 64.2M/461M [01:09<06:40, 1.04 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 64.4M/461M [01:09<06:31, 1.06 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 64.5M/461M [01:09<06:22, 1.09 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 64.6M/461M [01:09<06:25, 1.08 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 64.7M/461M [01:09<07:22, 939 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 64.8M/461M [01:10<06:42, 1.03 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 64.9M/461M [01:10<07:24, 934 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 65.0M/461M [01:10<07:24, 935 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 65.1M/461M [01:10<07:00, 987 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 65.2M/461M [01:10<06:56, 998 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 65.3M/461M [01:10<06:56, 997 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 65.4M/461M [01:10<06:53, 1.00 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 65.6M/461M [01:10<06:44, 1.03 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 65.7M/461M [01:10<06:32, 1.06 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 65.8M/461M [01:11<07:12, 958 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 65.9M/461M [01:11<07:04, 976 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.0M/461M [01:11<06:55, 997 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.1M/461M [01:11<07:45, 890 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.2M/461M [01:11<07:43, 893 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.3M/461M [01:11<07:38, 903 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.4M/461M [01:11<07:43, 893 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.5M/461M [01:11<07:34, 910 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.6M/461M [01:12<07:07, 968 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.7M/461M [01:12<07:24, 932 14%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 66.8M/461M [01:12<07:12, 956 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 66.9M/461M [01:12<06:51, 1.01 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 67.0M/461M [01:12<06:35, 1.04 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 67.1M/461M [01:12<06:28, 1.06 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 67.2M/461M [01:12<06:22, 1.08 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 67.3M/461M [01:12<06:46, 1.02 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 67.4M/461M [01:12<07:09, 961 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 67.5M/461M [01:13<07:36, 903 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 67.6M/461M [01:13<07:33, 910 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 67.7M/461M [01:13<07:15, 947 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 67.8M/461M [01:13<07:12, 954 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 67.9M/461M [01:13<06:52, 1.00 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 68.0M/461M [01:13<06:40, 1.03 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 68.1M/461M [01:13<06:59, 982 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 68.3M/461M [01:13<06:56, 989 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 68.4M/461M [01:13<06:47, 1.01 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 68.5M/461M [01:14<06:32, 1.05 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 68.6M/461M [01:14<06:16, 1.09 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 68.7M/461M [01:14<06:50, 1.00 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 68.8M/461M [01:14<06:55, 989 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 68.9M/461M [01:14<07:33, 907 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 69.0M/461M [01:14<07:39, 894 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 69.1M/461M [01:14<07:41, 892 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 69.2M/461M [01:14<07:38, 897 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 69.3M/461M [01:14<07:43, 887 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 69.4M/461M [01:15<07:17, 940 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 69.5M/461M [01:15<07:18, 937 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 69.6M/461M [01:15<06:57, 983 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 69.7M/461M [01:15<06:48, 1.01 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 69.8M/461M [01:15<06:43, 1.02 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 69.9M/461M [01:15<06:33, 1.04 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 70.1M/461M [01:15<06:09, 1.11 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 70.2M/461M [01:15<06:08, 1.11 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 70.3M/461M [01:15<06:36, 1.03 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 70.4M/461M [01:16<06:57, 982 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 70.5M/461M [01:16<07:19, 931 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 70.6M/461M [01:16<07:16, 938 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 70.7M/461M [01:16<07:15, 940 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 70.8M/461M [01:16<07:10, 952 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 70.9M/461M [01:16<06:55, 985 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 71.0M/461M [01:16<06:38, 1.03 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 71.1M/461M [01:16<06:31, 1.04 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.2M/461M [01:16<07:07, 956 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.3M/461M [01:17<07:18, 933 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.4M/461M [01:17<07:42, 884 15%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.5M/461M [01:17<07:42, 884 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.6M/461M [01:17<07:43, 881 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.6M/461M [01:17<07:43, 882 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.7M/461M [01:17<07:53, 863 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.8M/461M [01:17<07:48, 871 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 71.9M/461M [01:17<07:47, 872 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 72.0M/461M [01:17<07:50, 868 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 72.1M/461M [01:18<07:24, 917 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 72.2M/461M [01:18<07:18, 931 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                 | 72.3M/461M [01:18<06:53, 985 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 72.4M/461M [01:18<06:39, 1.02 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 72.5M/461M [01:18<06:35, 1.03 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 72.6M/461M [01:18<06:19, 1.07 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 72.7M/461M [01:18<06:13, 1.09 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 72.8M/461M [01:18<06:07, 1.11 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.0M/461M [01:18<05:53, 1.15 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.1M/461M [01:19<06:38, 1.02 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.2M/461M [01:19<06:19, 1.07 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.3M/461M [01:19<06:55, 98 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.4M/461M [01:19<06:47, 99 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.5M/461M [01:19<06:46, 99 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.6M/461M [01:19<06:39, 1.02 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.7M/461M [01:19<06:40, 1.01 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.8M/461M [01:19<06:37, 1.02 16%|▒▒▒▒▒▒▒▒▒▒▒▒                                | 73.9M/461M [01:19<06:37, 1.02 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.0M/461M [01:19<06:56, 97 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.1M/461M [01:20<08:06, 83 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.2M/461M [01:20<08:01, 84 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.3M/461M [01:20<08:16, 81 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.4M/461M [01:20<08:08, 83 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.5M/461M [01:20<08:07, 83 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.5M/461M [01:20<07:59, 84 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.6M/461M [01:20<07:54, 85 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.7M/461M [01:20<07:47, 86 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.8M/461M [01:20<07:44, 87 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 74.9M/461M [01:21<07:22, 91 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 75.0M/461M [01:21<07:08, 94 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 75.1M/461M [01:21<06:49, 98 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 75.2M/461M [01:21<06:34, 1.0 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 75.4M/461M [01:21<06:49, 98 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 75.5M/461M [01:21<06:33, 1.0 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 75.6M/461M [01:21<06:11, 1.0 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 75.7M/461M [01:21<06:05, 1.1 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 75.8M/461M [01:21<05:51, 1.1 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 75.9M/461M [01:22<05:39, 1.1 16%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 76.1M/461M [01:22<06:26, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 76.2M/461M [01:22<06:29, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 76.3M/461M [01:22<06:43, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 76.4M/461M [01:22<06:45, 99 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 76.5M/461M [01:22<06:47, 98 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 76.6M/461M [01:22<06:33, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 76.7M/461M [01:22<06:39, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 76.8M/461M [01:22<06:39, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 76.9M/461M [01:23<06:28, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.0M/461M [01:23<07:20, 91 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.1M/461M [01:23<07:04, 94 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.2M/461M [01:23<07:08, 94 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.3M/461M [01:23<07:11, 93 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.4M/461M [01:23<07:37, 87 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.5M/461M [01:23<07:34, 88 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.6M/461M [01:23<07:29, 89 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.7M/461M [01:24<07:35, 88 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.8M/461M [01:24<07:27, 89 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 77.9M/461M [01:24<07:09, 93 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 78.0M/461M [01:24<06:54, 97 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 78.1M/461M [01:24<06:57, 96 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 78.2M/461M [01:24<06:42, 99 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 78.3M/461M [01:24<06:30, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 78.4M/461M [01:24<06:27, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 78.5M/461M [01:24<06:14, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 78.6M/461M [01:24<06:11, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 78.8M/461M [01:25<05:54, 1.1 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 78.9M/461M [01:25<06:30, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 79.0M/461M [01:25<06:21, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 79.1M/461M [01:25<06:36, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 79.2M/461M [01:25<06:51, 97 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 79.3M/461M [01:25<06:48, 98 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 79.4M/461M [01:25<06:50, 97 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 79.5M/461M [01:25<06:54, 96 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 79.6M/461M [01:26<07:02, 94 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 79.7M/461M [01:26<07:04, 94 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 79.8M/461M [01:26<06:43, 99 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 79.9M/461M [01:26<06:41, 99 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 80.0M/461M [01:26<06:35, 1.0 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.1M/461M [01:26<07:28, 89 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.2M/461M [01:26<06:50, 97 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.3M/461M [01:26<06:49, 97 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.4M/461M [01:26<07:36, 87 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.5M/461M [01:27<07:24, 89 17%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.6M/461M [01:27<07:17, 91 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.7M/461M [01:27<07:10, 92 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.8M/461M [01:27<07:05, 93 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 80.9M/461M [01:27<06:49, 97 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 81.0M/461M [01:27<06:55, 96 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 81.1M/461M [01:27<06:33, 1.0 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 81.2M/461M [01:27<06:24, 1.0 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 81.3M/461M [01:27<06:42, 99 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 81.4M/461M [01:28<06:59, 95 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                                | 81.6M/461M [01:28<06:47, 97 18%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒                               | 81.7M/461M [01:28<06:23, 1.0100%|▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒| 461M/461M [08:36<00:00, 937kiB/s]
c:\program files\python38\lib\site-packages\whisper\transcribe.py:79: UserWarning: FP16 is not supported on CPU; using FP32 instead
  warnings.warn("FP16 is not supported on CPU; using FP32 instead")
Detecting language using up to the first 30 seconds. Use `--language` to specify the language
Detected language: Chinese
[00:00.000 --> 00:03.000] ▒▒λ▒^▒▒ ▒▒▒Ϻ▒
[00:03.000 --> 00:07.000] ▒▒▒▒▒▒12▒▒29̖ ▒▒▒▒▒▒ ▒r▒▒12▒³▒▒▒
[00:07.000 --> 00:09.000] ▒gӭ▒տ▒▒▒ī朲▒▒▒Ŀ
[00:09.000 --> 00:29.000] ▒▒▒Ȟ▒▒▒▒▒B▒▒▒칝Ŀ▒▒▒▒Ҫ▒▒▒▒

Administrator@AUTOBVT-Q90417J MINGW64 ~/Desktop/新建文件夹
$ 

因为没指定语言，所以出现了一堆▒▒，生成了以下文件

"C:\Users\Administrator\Desktop\新建文件夹\audio.mp3.vtt"
"C:\Users\Administrator\Desktop\新建文件夹\audio.mp3.json"
"C:\Users\Administrator\Desktop\新建文件夹\audio.mp3.srt"
"C:\Users\Administrator\Desktop\新建文件夹\audio.mp3.tsv"
"C:\Users\Administrator\Desktop\新建文件夹\audio.mp3.txt"

语音识别成功

1
00:00:00,000 --> 00:00:03,000
各位觀眾 晚上好

2
00:00:03,000 --> 00:00:07,000
今天是12月29號 星期四 農曆12月初期

3
00:00:07,000 --> 00:00:09,000
歡迎收看新墨鏈播節目

4
00:00:09,000 --> 00:00:29,000
我先為您介紹今天節目的主要內容

我们回过头来运行pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git
cmd运行——

Administrator@AUTOBVT-Q90417J MINGW64 ~/Desktop/新建文件夹
$ pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git
Collecting git+https://github.com/openai/whisper.git
  Cloning https://github.com/openai/whisper.git to c:\users\administrator\appdata\local\temp\pip-req-build-9lcjywga
  Running command git clone --filter=blob:none --quiet https://github.com/openai/whisper.git 'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-9lcjywga'
  error: RPC failed; curl 28 Recv failure: Connection was reset
  fatal: expected flush after ref listing
  error: subprocess-exited-with-error

  git clone --filter=blob:none --quiet https://github.com/openai/whisper.git 'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-9lcjywga' did not run successfully.
  exit code: 128

  See above for output.

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

git clone --filter=blob:none --quiet https://github.com/openai/whisper.git 'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-9lcjywga' did not run successfully.
exit code: 128

See above for output.

note: This error originates from a subprocess, and is likely not a problem with pip.

与上文相同的报错，如法炮制

Administrator@AUTOBVT-Q90417J MINGW64 ~/Desktop/新建文件夹
$ git config --global --unset http.proxy

这一次仍然报错

Administrator@AUTOBVT-Q90417J MINGW64 ~/Desktop/新建文件夹
$ pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git
Collecting git+https://github.com/openai/whisper.git
  Cloning https://github.com/openai/whisper.git to c:\users\administrator\appdata\local\temp\pip-req-build-01i8kn9k
  Running command git clone --filter=blob:none --quiet https://github.com/openai/whisper.git 'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-01i8kn9k'
  error: RPC failed; curl 28 Recv failure: Connection was reset
  fatal: expected 'packfile'
  error: subprocess-exited-with-error

  git clone --filter=blob:none --quiet https://github.com/openai/whisper.git 'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-01i8kn9k' did not run successfully.
  exit code: 128

  See above for output.

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

git clone --filter=blob:none --quiet https://github.com/openai/whisper.git 'C:\Users\Administrator\AppData\Local\Temp\pip-req-build-01i8kn9k' did not run successfully.
exit code: 128

See above for output.

note: This error originates from a subprocess, and is likely not a problem with pip.

Administrator@AUTOBVT-Q90417J MINGW64 ~/Desktop/新建文件夹
$ 

pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git是将软件包更新到此存储库的最新版本，既然 Whisper 能用，就不必纠结报错了。

使用Whisper

直接在命令行中执行whisper /Users/bmob/Downloads/8.m4a --model base --language Chinese
其中，/Users/bmob/Downloads/8.m4a 是你的语音文件路径，base是模型名称。

 [--model {tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large}]

越往后的模型，对硬件的要求越高，识别精度越高，当然了，速度也越慢。
问题不大，一个medium模型也就1.42GB，哪个模型不想用了去默认路径C:\Users\Administrator\.cache\whisper删掉 .pt 文件就行。
命令行运行whisper --help查看帮助。

Whisper 默认识别是英文，无法准确识别语言会自动Detecting language: English。
如果第一次命令设定了--language Chinese，自动下载的模型将能识别中文，往后命令加不加--language Chinese都能识别中文。这时命令如果设定--language Japanese会出现「第一次命令未设定--language时识别中文的情况」，即「cmd识别内容中有『▒』，识别结果正常」，同时cmd不会自动下载新的模型文件。

Administrator@AUTOBVT-Q90417J MINGW64 ~/Desktop/新建文件夹
$ whisper out.wav --language Japanese
c:\program files\python38\lib\site-packages\whisper\transcribe.py:79: UserWarning: FP16 is not supported on CPU; using FP32 instead
  warnings.warn("FP16 is not supported on CPU; using FP32 instead")
[00:00.000 --> 00:04.000] һ▒ˤ▒▒▒▒꤬▒⤦▒▒▒▒▒▒▒̤▒▒▒
[00:04.000 --> 00:07.500] ▒▒▒▒▒▒▒▒ȫ▒Ƥ▒ʼ▒ޤ▒
[00:07.500 --> 00:10.000] ▒▒▒▒▒ʯ▒Ȥʤ▒
[00:10.000 --> 00:12.000] ܞ▒▒▒▒ʼ▒▒▒
[00:13.000 --> 00:14.500] ▒ҡ▒▒Ϥɤ▒▒▒▒▒▒
[00:14.500 --> 00:34.500] ▒̤▒▒Ƥ▒▒▒▒▒▒▒!▒▒å▒▒▒▒`▒Θ▒!

因此我们还需研究设定--language基础上添加--model会不会下载一个新的模型，或是覆盖现有模型，还是需要我们删掉原模型文件重新下载新模型?

记录
原模型small.pf 修改日期2023.2.26 18:15 创建日期2023.2.26 17:50
输入命令whisper out.wav --model small --language Japanese

C:\Users\Administrator\Desktop\新建文件夹>whisper out.wav --model small --language Japanese
c:\program files\python38\lib\site-packages\whisper\transcribe.py:79: UserWarning: FP16 is not supported on CPU; using FP32 instead
  warnings.warn("FP16 is not supported on CPU; using FP32 instead")
[00:00.000 --> 00:04.000] 一人の青年がもうすぐ死ぬだろう
[00:04.000 --> 00:07.500] その死が全ての始まり
[00:07.500 --> 00:10.000] 世界は石となり
[00:10.000 --> 00:12.000] 転がり始める
[00:13.000 --> 00:14.500] 我々はどうすれば
[00:14.500 --> 00:34.500] 教えてください!ロックサーノ様!

模型small.pf 修改日期2023.2.26 18:15 创建日期2023.2.26 17:50
没有自动下载新模型，没有被覆盖，cmd识别内容显示正常。

现在我们需要研究删掉--model∪--language Japanese是否继续出现「▒」。
1、whisper out.wav
cmd运行——

C:\Users\Administrator\Desktop\新建文件夹>whisper out.wav
c:\program files\python38\lib\site-packages\whisper\transcribe.py:79: UserWarnin
g: FP16 is not supported on CPU; using FP32 instead
  warnings.warn("FP16 is not supported on CPU; using FP32 instead")
Detecting language using up to the first 30 seconds. Use `--language` to specify
 the language
Detected language: Japanese
[00:00.000 --> 00:04.000] 一人の青年がもうすぐ死ぬだろう
[00:04.000 --> 00:07.500] その死が全ての始まり
[00:07.500 --> 00:10.000] 世界は石となり
[00:10.000 --> 00:12.000] 転がり始める
[00:13.000 --> 00:14.500] 我々はどうすれば
[00:14.500 --> 00:34.500] 教えてください!ロックサーノ様!

2、whisper out.wav --model small
cmd运行——

C:\Users\Administrator\Desktop\新建文件夹>whisper out.wav --model small
c:\program files\python38\lib\site-packages\whisper\transcribe.py:79: UserWarnin
g: FP16 is not supported on CPU; using FP32 instead
  warnings.warn("FP16 is not supported on CPU; using FP32 instead")
Detecting language using up to the first 30 seconds. Use `--language` to specify
 the language
Detected language: Japanese
[00:00.000 --> 00:04.000] 一人の青年がもうすぐ死ぬだろう
[00:04.000 --> 00:07.500] その死が全ての始まり
[00:07.500 --> 00:10.000] 世界は石となり
[00:10.000 --> 00:12.000] 転がり始める
[00:13.000 --> 00:14.500] 我々はどうすれば
[00:14.500 --> 00:34.500] 教えてください!ロックサーノ様!

3、whisper out.wav --language Japanese
回过头来看是否继续乱码
cmd运行——

C:\Users\Administrator\Desktop\新建文件夹>whisper out.wav --language Japanese
c:\program files\python38\lib\site-packages\whisper\transcribe.py:79: UserWarnin
g: FP16 is not supported on CPU; using FP32 instead
  warnings.warn("FP16 is not supported on CPU; using FP32 instead")
[00:00.000 --> 00:04.000] 一人の青年がもうすぐ死ぬだろう
[00:04.000 --> 00:07.500] その死が全ての始まり
[00:07.500 --> 00:10.000] 世界は石となり
[00:10.000 --> 00:12.000] 転がり始める
[00:13.000 --> 00:14.500] 我々はどうすれば
[00:14.500 --> 00:34.500] 教えてください!ロックサーノ様!

速度：--model --language Japanese＞--language Japanese＞--model＞
（大概是这样）
现在whisper audio.mp3 --language Chinese切回去识别中文语音
cmd运行——

C:\Users\Administrator\Desktop\新建文件夹>whisper audio.mp3 --language Chinese
c:\program files\python38\lib\site-packages\whisper\transcribe.py:79: UserWarnin
g: FP16 is not supported on CPU; using FP32 instead
  warnings.warn("FP16 is not supported on CPU; using FP32 instead")
[00:00.000 --> 00:03.000] 各位觀眾 晚上好
[00:03.000 --> 00:07.000] 今天是12月29號 星期四 農曆12月初期
[00:07.000 --> 00:09.000] 歡迎收看新墨鏈播節目
[00:09.000 --> 00:29.000] 我先為您介紹今天節目的主要內容

正常

总结一下：
第一次输入whisper audio.mp3（自动下载模型）默认识别English，可以识别其他语言但cmd界面识别内容会有乱码，设定--language Chinese后可识别中英文，识别其他语言cmd仍有乱码。需要完整输入--model [xxx] --language [xxx]，cmd方可正常显示，往后识别该语言删掉model∪language，cmd均能正常显示。
还是不要偷懒为好。
建议将长音频拆分，批量识别。
目前还没读过 whisper-vits-japanese的代码，不知道作者是如何做到「将Whisper只能读取少数音频文件的限制，放宽到可以遍历文件夹下的所有音频文件。」的？
不过我目前缺少whisper-vits的条件，想办法把第一步路铺好是我当下能做到的，另外显然我需要校对文本。

演示

用CPU跑 Whisper ，第一次CPU使用率会高达100%，往后偶尔100%，正常使用一般保持在50%-70%；物理内存使用记录一个波形对应一次识别输出（四五段字幕），我的机型比较落后，识别1分钟语音需要等10~15分钟。

烧CPU跟Torch版本关系不大，如果电脑嗡嗡作响且CPU使用率保持100%，请关掉程序，命令中删除–language选项用短音频测试几次，确认正常后重启电脑。

一份24:22的音频，我使用 medium 模型，耗时8小时。选用 large 模型也能跑，跳出下面内容开始识别花的时间较长，加内存条速度会快些。

c:\program files\python38\lib\site-packages\whisper\transcribe.py:79: UserWarnin
g: FP16 is not supported on CPU; using FP32 instead
  warnings.warn("FP16 is not supported on CPU; using FP32 instead")

这句话的意思是FP16不支持CPU，现在用FP32代替。无论你的电脑没有显卡或者显卡不支持CUDA/CUDA版本低，还是在用CPU跑，都会蹦出这个提示。如果是前一种，安装支持CUDA新版本的Torch。如果是第二种，请无视。
另外，提示RuntimeError: CUDA out of memory意味着你的显卡适用不了当前模型，请换小点的模型；提示No module named 'setuptools_rust'意味着你要安装Rust——pip install setuptools_rust。
配置VITS环境后会严重拖慢Whisper，Unidecode库导致Whisper找不到正确编码，文件生成失败（未截图运行过程）。用较短音频再试一次即可恢复识别，cmd会出现『▒』乱码，进程加载变慢。

安装 whisper-webui

在你想要存放whisper-webui的地方打开git bash，克隆仓库。

git clone https://huggingface.co/spaces/aadnk/whisper-webui

进入本地仓库，安装requirements.txt中的包：

git+https://github.com/openai/whisper.git
transformers
ffmpeg-python==0.2.0
gradio==3.13.0
yt-dlp
torchaudio
altair

将 whisper-webui 中requirements.txt的第一行删去

pip install -r requirements.txt

cmd安装过程——

Administrator@AUTOBVT-Q90417J MINGW64 /e/whisper-webui (main)
$ pip install -r requirements.txt  -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
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使用 whisper-webui

在whisper-webui的目录下，确保whisper环境激活：

python app.py --input_audio_max_duration -1

知乎Windows本地配置OpenAI Whisper+WebUI给出的提示是这样的——

我一直卡在 warnings.warn(（如下图）。

不过浏览器打开http://127.0.0.1:7860也能用。

对于较长的非英语音频文件（〉10分钟），建议您在VAD选项中选择Silero VAD（语音活动检测器）。

在完整界面中更改某些选项时要小心-这可能会导致模型崩溃。

建议使用Full界面，以调整更多参数。

Vad 选项：

• none：在整个音频输入上运行Whisper
• silero-vad：使用 Silero VAD 检测包含语音的部分，并在每个部分上独立运行Whisper。Whisper 也在运行关于每个语音部分之间的间隙。
• silero-vad-skip-gaps：同上，但不包含根据 Silero 的语音的部分将被跳过。这会稍微快一些，但是可能会导致对话被跳过。
• periodic-vad：“VAD - 最大合并大小”创建语音部分。这是非常快速和简单的，但可能会中断一个句子或单词一分为二。

VAD - Merge Window 选项：如果设置，任何相隔最多此秒数的相邻语音部分将被自动合并。

VAD - Max Merge Size (s) 选项：如果相邻语音部分的长度达到此秒数，则禁用它们的合并。

VAD - Padding (s) 选项：
添加到每个语音部分的开头和结尾的秒数（浮点数）。将此设置为一个数字，大于零确保 Whisper 更有可能正确转录句子开头的句子演讲部分。但是，这也增加了 Whisper 分配错误时间戳的概率到每个转录的行。默认值为 1 秒。

使用 Buzz

Buzz是whisper的GUI版。Buzz可在Mac、Windows和Linux上使用。
去Buzz开源地址下载安装包，下载并安装

打开Buzz的界面非常简单，话筒图标是直接调用录音软件录音转换字幕。+按钮则是选择语音或视频文件进行转换。
软件选项也很简单选择对应的语言和选择的模型进行转换即可，初次使用需要从网上下载模型到本地。
Buzz适合用于“实时录制”和“录制计算机播放的音频”场景。

使用 colab Google云端识别

Google相关服务需要科学上网。

上传音频文件

1. 上传音频文件到Google Drive，建议一个项目一个文件夹。
2. 点击左上角云端硬盘下方的+新建 ->更多 ->关联更多应用->搜索Google Colaboratory添加以使用 Colab。

安装 Whisper

点击代码执行程序->更改运行时类型->硬件加速器选择 GPU
编辑区域+代码

!pip install git+https://github.com/openai/whisper.git

装载云端硬盘

编辑区域+代码

from google.colab import drive
drive.mount('/content/drive')

运行whisper

编辑区域+文本
audio.mp3是你准备识别的语音文件，–model选择语音识别模型，large是模型名称，有 tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large 多个模型可供选择，–language选择识别语言。示例：
编辑区域+代码

!whisper audio.mp3 --model large --language Chinese

右键文件区域要识别的文件->复制路径

!whisper /content/drive/MyDrive/GOSICK-audio/output01.wav --model large --language Japanese

保存工程

点击左上角修改.ipynb名称，点击文件->保存。下一个运行时系统会自动删除sample_date同级目录下上传和生成的文件。

详细问题

移动硬盘插入后，任务栏「安全删除硬件并弹出媒体」不可安全弹出，强行拔掉USB提示「是否要将其格式化」；

「控制面板-设备和打印机-设备属性」硬件显示正常，

常规——制造商、型号编码、描述『不可用』；


「计算机管理-磁盘管理」虚拟磁盘服务长时间加载，

cmd输入chkdsk检查磁盘

不久后显示磁盘管理界面，「我的电脑」未显示的移动硬盘不显示『文件系统』

右击磁盘管理界面「未显示磁盘」- 属性发现「常规」0字节，无法执行磁盘检查

解决方法：

我们把移动硬盘插入后提示“格式化”，该怎么办？和移动硬盘插入无显示，不用格式化-修复方法两者结合一下。

cmd输入chkdsk检查磁盘，得到以上现象，先不动磁盘管理，等磁盘检查完（容量大磁盘检查时间太长）
此方法在诸多教程中往往伴随着备份-格式化磁盘-恢复。

不等了，关掉cmd，尝试指定盘符，更新驱动器符号和路径，回头再检查磁盘。（注：运行输入「diskmgmt.msc」可打开磁盘管理界面）
我也不知道能不能成，试试看。

很好，问题不大，「某些依赖启动器号的程序可能无法正确运行。您想继续吗？」。以前系统自动把盘符 H 变成 I 的时候也没见磁盘中的程序出乱子，且磁盘中的程序及路径设置不存储在控制面板和系统环境变量，这个错误应该可以无视。

加载完

大功告成

故障现象:
电脑一向使用正常，但不知什么时候网络图标变成未连接的状态，而实际上网正常。
工具原料；电脑　
解决方案:
1、同时按住Win+R，调开“运行”窗口，输入services.msc；

2、找到名为“Routing and remote access”服务项，选择“停止”;

3.同时按住alt+cltr+delete调出任务管理器找到进程在进程里面Explorer然后结束

4.再点击任务管理器上面的文件然后新建任务输入Explorer。
5.这时候网络图标已经恢复正常
6.如果重启电脑后依然出现故障，禁用“Routing and remote access”服务项即可。

Routing and Remote Access服务简称 RRA服务，Routing and Remote Access 是全功能的软件路由器，也是用于路由和互连网络工作的开放平台。它为局域网 (LAN) 和广域网 (WAN) 环境中的商务活动，或使用安全虚拟专用网 (VPN) 连接的 Internet 上的商务活动提供路由选择服务。

由上面我们可以看到，这个服务主要是为了VPN而服务的。VPN是专用虚拟拨号，在办公环境、游戏加速领域有所使用。

默认情况下Routing and Remote Access是禁用的。

一、Pandoc 简介

Pandoc是用于从一种标记格式转换为另一种标记格式的Haskell库，也是使用该库的命令行工具。它涵盖格式广泛且转换效果好，常用的 docx、md、tex、html 等格式都可以利用其进行相互转换。

二、Pandoc 安装

Pandoc 官网下载安装 ：https://pandoc.org/
Github release 下载安装：jgm/pandoc
Pandoc 为 Windows 系统提供了编译后的 msi 安装包，可以直接运行安装；或者直接下载免安装的 zip 文件解压。
首先进入安装目录，cmd输入pandoc --version/pandoc.exe --version验证安装：
（Windows 7）下载msi安装包安装使用，验证可能发生如下错误

C:\Users\Administrator\Downloads\Compressed\pandoc-3.0.1>pandoc.exe --version
pandoc: osReserveHeapMemory: VirtualAlloc MEM_RESERV at address 0000000000000000 bytes failed:

（Windows 7）下载zip安装使用，验证也可能发生上面的错误，一般再试一次就正常了（如下）

C:\Users\Administrator\Downloads\Compressed\pandoc-3.0.1>pandoc.exe --version
pandoc.exe 3.0.1
Features: +server +lua
Scripting engine: Lua 5.4
User data directory: C:\Users\Administrator\AppData\Roaming\pandoc
Copyright (C) 2006-2023 John MacFarlane. Web: https://pandoc.org
This is free software; see the source for copying conditions. There is no
warranty, not even for merchantability or fitness for a particular purpose.

以上命令返回了 Pandoc 的版本信息。
为了更好地运行进程，建议添加环境变量。计算机→属性→高级系统设置→环境变量，选中变量Path→编辑，添加到 pandoc.exe 所在目录，两个Path之间要加英文分号『;』。

三、Pandoc 使用

转换主体命令

pandoc -i test.xx -o test.yy

-i 为 input （输入）， -o 为 output （输出）

markdown转换docx

直接转换

pandoc test.md -o test.docx

自定义样式转换

pandoc test.md -o test.docx --reference-docx=reference.docx

转换后的docx将与参考文档 “reference.docx” 文档样式相同。

docx转换markdown

pandoc test.docx --extract-media=. -o test.md

直接输入pandoc test.docx -o test.md会丢失word文档中的图片。输入pandoc test.docx --extract-media=. -o test.md则不会丢失图片。这里用到Pandoc一个 选项 extract-media=DIR，用于处理文件转换过程中的图片等媒体文件。
输出结果包括一个 Markdown 文件 test.md 以及一个文件夹 media，其中包括 Word 文件 test.docx 中的所有图片，--extract-media=.的作用是把图片保存在 media 中，而不是其子文件夹中。

--extract-media=DIR这个选项来将 Markdown 中的图床链接替换为本地图片链接
执行下面的命令：

pandoc --wrap=preserve -f markdown input.md --extract-media=media -t markdown -o output.md

这行命令中，由于开启了 –extract-media=DIR 选项，Pandoc 会新建一个文件夹 media，将图床中的远程图片下载到本地并保存在该文件夹中，然后再输出为一个新的 Markdown 文件 output.md，并自动把原本的图床链接替换为本地图片链接，而不再需要手动编写复杂的正则表达式来查找替换。

markdown转换pdf

Pandoc使用Latex生成pdf，所以需要安装Latex引擎
直接使用命令 pandoc test.md -o test.pdf 会报错，应当输入这样的命令（如下）：

pandoc test.md -o test.pdf --pdf-engine=xelatex -V CJKmainfont="字体"

xelatex为排版引擎，字体应该填写相应英文标识，如：KaiTi（楷体）、SimSun（宋体）、FangSong（仿宋）、SimHei（黑体）等。

markdown转换HTML

pandoc xxx.md -f markdown -t html -s -o xxx.html

s选项表示创建一个"独立的"文件，有 header 和 footer ，而不仅仅是一个片段。
o xxx.html命令将输出放入xxx.html文件中。
注意，我们可以省略-f markdown和-t html，因为默认情况下是将markdown转换为HTML，但是包含它们也无妨。

markdown转换LaTeX

pandoc xxx.md -f markdown -t latex -s -o xxx.tex 或pandoc xxx.md -s -o xxx.tex
Wandmalfarbe/pandoc-latex-template ，一个pandoc LaTeX模板，用于将markdown文件转换为PDF或LaTeX，使用前请仔细阅读说明文件。

LaTeX转换包含数学公式的网站

pandoc math.tex -s --mathml  -o mathMathML.html

-mathml 参数强制 Pandoc 将 LaTeX 中的数学公式转换成 MathML，从而可以由现代浏览器进行渲染。

四、参考

Pandoc入門：MarkdownからHTML・PDF・Writer/Word文書・スライドを生成する
Pandoc官方使用手册（本地安装目录下有）
Pandoc 从入门到精通，你也可以学会这一个文本转换利器
在命令行使用 Pandoc 进行文件转换
在命令行使用 Pandoc 进行文件转换​
Windows下Pandoc使用指南

LaTeX介绍

LaTeX（LATEX，音译“拉泰赫”）是一种基于ΤΕΧ的排版系统，由美国计算机学家莱斯利·兰伯特（Leslie Lamport）在20世纪80年代初期开发，利用这种格式，即使使用者没有排版和程序设计的知识也可以充分发挥由TeX所提供的强大功能，能在几天、甚至几小时内生成很多具有书籍质量的印刷品。对于生成复杂表格和数学公式，这一点表现得尤为突出。因此它非常适用于生成高印刷质量的科技和数学类文档。这个系统同样适用于生成从简单的信件到完整书籍的所有其他种类的文档。

LaTeX是一种“非所见即所得”的排版系统，用户需要输入特定的代码，保存在后缀为.tex的文件中，通过编译得到所需的pdf文件，例如以下代码：

\documentclass{article}
\begin{document}
Hello, world!
\end{document}

最后输出的结果是一个pdf文件，内容是”Hello, world!“。

安装LaTeX与配置环境

LaTeX的使用主要需要安装两个东西： 1. 根据平台选择一个TeX发行版进行安装，建议选择最全功能最多的版本。TeX发行版的概念相当于Linux及其发行版，Linux内核虽然只有一个，但是有很多基于内核的不同特色的Linux发行版，比如Ubuntu，Fedora等等不胜枚举。这里我们选择TeXlive。 2. 选择一个合适的LaTex编辑器。在安装好LaTeX环境以后，通常都会有一个自带的编辑器，比如CTex的WinEdt，MacTeX的TeXShop，不过功能并不强大，好比 Windows 记事本，只有一些基本的文本编辑功能。这里我们选择比较强大的TeXstudio。

一、安装TeX Live

1. 下载

每个平台的简明操作指南：
在Unix/GNU/Linux上安装
在Windows上安装
在MacOS上安装：MacTeX发行版
您可以阅读完整手册了解所有可能性，包括自动安装和使用自定义存储库。
推荐下载离线安装包，然后安装的方式。可以使用官方的镜像下载，也可以利用国内的镜像下载。




官网下载比较慢，我们可以利用国内的镜像网站来下载。
清华大学：https://mirrors.tuna.tsinghua.edu.cn/CTAN/systems/texlive/Images/texlive2022.iso；
北京交通大学：https://mirror.bjtu.edu.cn/ctan/systems/texlive/Images/
选择 texlive2022.iso（以此类推）；
上海交通大学：https://mirrors.sjtug.sjtu.edu.cn/ctan/systems/texlive/Images/
选择 texlive2022.iso（以此类推）；
中国科技大学：https://mirrors.ustc.edu.cn/CTAN/systems/texlive/Images/
选择 texlive2022.iso（以此类推）；
重庆大学：https://mirrors.cqu.edu.cn/CTAN/systems/texlive/Images/
选择 texlive2022.iso（以此类推）；
腾讯云：https://mirrors.cloud.tencent.com/CTAN/systems/texlive/Images/
选择 texlive2022.iso（以此类推）。
下载Torrent文件使用BT下载也是个不错的选择，试了一下下载速度还行，有公网的可以用BT下载。

2. 安装

将texlive2022.iso 文件解压，然后右键“以管理员身份运行”打开其中的install-tl-advanced.bat文件。
点击Advanced。
修改Directories TEXDIR: Installation root 为C://texlive/2022，安装。
安装完成，用时56分钟。

验证是否安装成功，打开cmd，输入tex -version，如果出现版本号即说明安装成功。

Microsoft Windows [版本 6.1.7601]
版权所有 (c) 2009 Microsoft Corporation。保留所有权利。

C:\Users\Administrator>tex -version
TeX 3.141592653 (TeX Live 2022)
kpathsea version 6.3.4
Copyright 2022 D.E. Knuth.
There is NO warranty.  Redistribution of this software is
covered by the terms of both the TeX copyright and
the Lesser GNU General Public License.
For more information about these matters, see the file
named COPYING and the TeX source.
Primary author of TeX: D.E. Knuth.

latex2021安装失败

1、运行install-tl-windows.bat批处理程序之后出现了No such file or directory….

是操作系统用户名是中文的原因，使用win + R打开命令行即可看见自己的操作系统的用户名，这个时候可以使用下面的方法解决：
右键桌面我的电脑（或者是计算机）图标，找到属性–>高级系统设置–>环境变量，在最上面一栏可以看到用户变量这个时候找到TEMP，TMP的属性值修改为：%SystemRoot%\TEMP，对于下面一栏中的系统变量也是一样修改为%SystemRoot%\TEMP，修改完成之后运行之后就可以正常安装latex了，可以修改latex安装到任意一个盘
————————————————
版权声明：本文为CSDN博主「yuzhang_zy」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_39445165/article/details/116561117

2、texlive2021安装不成功（windows10），关于报错“Cannot open c"\Users...”

系统先前安装了ctex在C盘，根据官网（https://ctan.org/pkg/install-latex-guide-zh-cn ） 的安装介绍，已排查环境变量中【存在mingw，jdk的相关内容】或【缺失C:WIndowssystem32】的问题，但安装texlive2021时会出现如下报错

（...省略正常安装代码）
Installing [0092/4151, time/total: 03:01/02:18:44]: alkalami [125k]
Installing [0093/4151, time/total: 03:03/02:20:03]: allrunes [8078k]
tar: Cannot open C:UsersSONGSH~1LOCALS~1TempaSp7tkKDBEqgXEYiSL2c/allrunes.r42221.tar: Permission denied
TLUtils::untar: F:tlpkginstallertar.exe xmf C:UsersSONGSH~1LOCALS~1TempaSp7tkKDBEqgXEYiSL2c/allrunes.r42221.tar failed (in D:/texlive/2021/texmf-dist)
TLPDB::_install_data: untar failed for F:/archive/allrunes.r42221.tar.xz
TLUtils::install_packages: Failed to install allrunes
Installation failed.
Rerunning the installer will try to restart the installation.
Or you can restart by running the installer with:
install-tl-windows.bat --profile installation.profile [YOUR-EXTRA-ARGS]
or
install-tl-advanced.bat --profile installation.profile [YOUR-EXTRA-ARGS]
F:install-tl: Writing log in current directory: C:/WINDOWS/system32/install-tl.log
Wide character in print at F:install-tl line 2486, <STDIN> line 6.
Wide character in print at F:install-tl line 2486, <STDIN> line 6.
Wide character in print at F:install-tl line 2486, <STDIN> line 6.
Wide character in print at F:install-tl line 2486, <STDIN> line 6.

install-latex 最新版写了『如何在中文用户名下安装』
————————————————
原文链接：https://ask.latexstudio.net/ask/question/3809.html

3. 卸载

Windows下Tex Live的安装目录，如C:\texlive\2022\tlpkg\installer，然后运行里边的uninst.bat文件。

二、安装TeXstudio

1. 下载及安装

直接去官网下载，有时会上不去，可以到Github搜索texstudio-org/texstudio下载，也可以去这里下载。下载后直接点击安装，一样选择安装路径，然后进行一些简单的设置。这里我选择“texstudio-4.4.1-win-qt6.exe”。

2. 配置

依次找到Options→Configure TeXstudio→Commands，指定Commands命令的执行程序，将BibTeX、BibTeX 8-Bit、Biber的路径设置为TeXlive安装路径下对应EXE文件。

设置中文界面：安装结束后，一开始的打开界面是英文的，这里我们可以切换成中文。 依次点击：Options→Configure Texstudio→General→Language→zh_CN

添加行号：添加段落行号，这样可以很方便查看段落的某句话所在的位置，尤其是在运行报错时，有行号就非常方便查看错误的位置了。 依次点击：选项→设置Texstudio→显示高级选项→编辑器→显示行号→所有行号

设置编译器与编码：为了正常的输出中文，我们需要把默认编译器改成xelatex，utf-8编码。如果是为了编写英文论文，要用“pdflatex”。

3.测试

输入下述代码，运行，得到下述结果即代表安装配置成功。

\documentclass[11pt]{ctexart}  
\usepackage[top=2cm, bottom=2cm, left=2cm, right=2cm]{geometry}  
\usepackage{algorithm}  
\usepackage{algorithmicx}  
\usepackage{algpseudocode}  
\usepackage{amsmath}  

\floatname{algorithm}{算法}  
\renewcommand{\algorithmicrequire}{\textbf{输入:}}  
\renewcommand{\algorithmicensure}{\textbf{输出:}}  

\begin{document}  
    \begin{algorithm}  
        \caption{用归并排序求逆序数}  
        \begin{algorithmic}[1] %每行显示行号  
            \Require $Array$数组，$n$数组大小  
            \Ensure 逆序数  
            \Function {MergerSort}{$Array, left, right$}  
            \State $result \gets 0$  
            \If {$left < right$}  
            \State $middle \gets (left + right) / 2$  
            \State $result \gets result +$ \Call{MergerSort}{$Array, left, middle$}  
            \State $result \gets result +$ \Call{MergerSort}{$Array, middle, right$}  
            \State $result \gets result +$ \Call{Merger}{$Array,left,middle,right$}  
            \EndIf  
            \State \Return{$result$}  
            \EndFunction  
            \State  
            \Function{Merger}{$Array, left, middle, right$}  
            \State $i\gets left$  
            \State $j\gets middle$  
            \State $k\gets 0$  
            \State $result \gets 0$  
            \While{$i<middle$ \textbf{and} $j<right$}  
            \If{$Array[i]<Array[j]$}  
            \State $B[k++]\gets Array[i++]$  
            \Else  
            \State $B[k++] \gets Array[j++]$  
            \State $result \gets result + (middle - i)$  
            \EndIf  
            \EndWhile  
            \While{$i<middle$}  
            \State $B[k++] \gets Array[i++]$  
            \EndWhile  
            \While{$j<right$}  
            \State $B[k++] \gets Array[j++]$  
            \EndWhile  
            \For{$i = 0 \to k-1$}  
            \State $Array[left + i] \gets B[i]$  
            \EndFor  
            \State \Return{$result$}  
            \EndFunction  
        \end{algorithmic}  
    \end{algorithm}  
\end{document}

————————————————
版权声明：本文为知乎博主「一个努力吃胖的小瘦子」「Dylaaan」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：
https://zhuanlan.zhihu.com/p/138586028
https://zhuanlan.zhihu.com/p/456055339

Texstudio无法运行

双击启动提示
“无法定位程序输入点CreateEventW于动态链接库api-ms-win-core-synch-l1-2-0.dll”

windows 7 不支持Qt6，使用Qt5的平台能解决问题。也就是说，Windows 7 用户要么选择Qt5平台旧版本，要么弃用选择其他编辑器。

为什么检测远程链接失败？

远程连接失败说明你填错了，仔细检查各个配置内容

提示同步成功，但是网站内容没有变化

域名配置要输入正确，ｇｉｔｈｈｕｂ博客格式为（用户名）．ｇｉｔｕｂ．ｉｏ

ＣＮＡＭＥ是什么？

博客绑定的域名，没有就不填，填（用户名）．ｇｉｔｕｂ．ｉｏ没用

每次同步不了是真难受

挂个梯子设置一下代理就可以了,HTTP代理选择Ｐｒｏｘｙ

主题无法渲染

表现为ｃｓｓ样式无法加载
这并不是Ｇｒｉｄｅａ同步的问题，实际上即便选择本地output（C:\Users\Administrator.gridea\output） + Github Desktop（Ｇｉｔ工具）作为工作流依然无法渲染
实测Ｇｒｉｄｅａ本地打开状态下网页加载正常，但实际上读取的是本地　localhost:4000/　链接，点开任意一篇博文地址栏都会跳到　localhost:4000/……　链接，而关闭软件主题就会渲染失败。
推测为跳转链接的问题，打开html文件，搜索　localhost:4000/　，果然文件夹读取多为　localhost:4000/　开头，而不是　（用户名）.github.io/　，这应该是为了预览本地修改而设置的，我们只要把这些　localhost:4000/　替换成　（用户名）.github.io/　就能加载云端资源了。

关于Gitalk找不到Issue Error: Network Error

Gitalk 加载中 ...
Error: Network Error
F12 网络
状态 已拦截
方法 POST
域名 https://cors-anywhere.azm.workers.dev/https://github.com/login/oauth/access_token
文件 access_token
发起者 gitalk.min.js:1(xhr)
传输 NS_ERROR_DOM_BAD_URL

Gitalk Network Error问题处理 - 腾讯云开发者社区-腾讯云
https://cloud.tencent.com/developer/article/2093779

记录一次 Gitalk 问题排查过程 - 知乎
https://zhuanlan.zhihu.com/p/350735142
（Ｈｅｘｏ博客的解决方法）

Valine评论系统

调不好咱还可以换Valine评论系统，教程如下——
Valine官网
为你的Gridea博客加上Valine评论系统 | kveln’s blog
搭建Gridea+gitee+Valine评论+Valine-Admin评论后台的个人博客
Valine: 独立博客评论系统
自动初始化 Gitalk 和 Gitment 评论
简单粗暴的评论清空（测试的时候可以这么干）：
1、打开 LeanCloud ，重新创建应用，生成新的应用凭证
2、数据存储-结构化数据-删除Comment所有数据