Spleeter 是什么
Spleeter是Deezer的音源分离库，用Python编写的预训练模型，使用Tensorflow。它使训练音源分离模型变得容易（假设你有一个孤立的音源数据集），并提供已经训练好的最先进的模型来执行各种类型的分离：
- 声乐（唱腔）/伴奏分离（2个音轨）
- 声乐/鼓/贝司/其他分离(4个音轨)
- 声乐/鼓/贝斯/钢琴/其他分离 (5个音轨)
2音轨和4音轨模型在 musdb 数据集上有很高的性能。Spleeter也非常快，因为它在GPU上运行时，可以将音频文件分离成4个音轨，比实时速度快100倍。
我们设计了Spleeter，所以你可以直接从命令行中使用它，也可以直接在你自己的 development pipeline 中作为一个Python库。它可以用pip安装或与Docker一起使用。
使用Spleeter的项目和软件
自从它被发布以来，有多个 fork 通过指导用户界面（GUI）或独立的免费或付费的网站展示Spleeter。
Spleeter 的预训练模型也已经被专业的音频软件所使用。这里有一个非详尽的列表：
- iZotope RX 8 中的音乐平衡功能
- SpectralLayers 7 中 Unmix 功能里的 SpectralLayers
- Acoustica 7 中的 Acon Digital
- 在 VirtualDJ 音源隔离功能中
- 在 NeuralMix 和 djayPRO 应用程序套件中的 Algoriddim
快速入门
想尝试一下，但不想安装任何东西？我们已经建了一个 Google Colab。
准备好进入它了吗？只需几行字，你就可以安装Spleeter，并从一个示例音频文件中分离出人声和伴奏部分。你首先需要安装 ffmpeg 和 libsndfile。它可以在大多数平台上使用Conda完成：
# install dependencies using conda
conda install -c conda-forge ffmpeg libsndfile
# install spleeter with pip
pip install spleeter
# download an example audio file (if you don't have wget, use another tool for downloading)
wget https://github.com/deezer/spleeter/raw/master/audio_example.mp3
# separate the example audio into two components
spleeter separate -p spleeter:2stems -o output audio_example.mp3
:warning: 注意，我们不再推荐使用conda来安装spleeter。
:warning: 苹果M1芯片有已知的问题，主要是由于TensorFlow的兼容性问题。在这些问题被修复之前，你可以使用这个变通办法。
你应该会在output/audio_example文件夹中得到两个分离的音频文件（vocals.wav和accompatiment.wav）。
关于详细的文档，请查看仓库的wiki
开发和测试
这个项目是用Poetry管理的，要运行测试套件，你可以执行以下一组命令：
# Clone spleeter repository
git clone https://github.com/Deezer/spleeter && cd spleeter
# Install poetry
pip install poetry
# Install spleeter dependencies
poetry install
# Run unit test suite
poetry run pytest tests/
疑难解答
Spleeter是一个复杂的软件，尽管我们不断努力改善和测试它，但你可能会遇到意想不到的问题。如果是这种情况，请先查看常见问题页面以及当前开放的问题列表。
Windows用户
似乎有时快捷键命令 spleeter 在Windows上不能正常工作。这是一个已知的问题，我们希望能很快解决。在此期间，在命令行中用 python -m spleeter separate 代替 spleeter separate ，它应该可以工作。
在线使用 Google Colab
打开 Colab ，保存副本，开始使用。
这里预先写好了项目的整个代码，点击“代码执行程序”-“全部运行”，将所有的代码都运行一下。
程序会自动运行，安装各种依赖、库文件，并将一个预设的audio_example.mp3 音频文件进行人声、伴奏分离。
我们试一下分离自己上传的音频文件，这里准备了一个叫 op-audio.mp3 的文件，点击左边的上传按钮即可将文件上传到云端。
/*装载 Google 云端硬盘*/
from google.colab import drive
drive.mount('/content/drive')
/*在右边新建一个代码块，照着上面的分离代码修改一下，再运行一遍。*/
spleeter separate -o output/ op-audio.mp3
注意这里不要改上面代码，不然——
!wget /content/op-audio.mp3
/content/op-audio.mp3: Scheme missing.
当然你也可以像示例一样分离存储在 github 仓库中的音频文件，像这样
最后分离出来两个文件，accompaniment.wav为伴奏，vocals.wav为人声。
安装 spleeterGUI 图形界面软件
spleeterGUI 是基于 spleeter 进行深入开发的适用于 windows 平台的图形化界面软件。
下载地址：https://github.com/boy1dr/SpleeterGui
最新的安装程序可以从这里下载 https://makenweb.com/#spleetergui
不需要安装python或spleeter，这个应用程序包含一个预装了spleeter的便携式python版本。
这个项目的目的是使Windows用户能够轻松地下载和运行Spleeter，而不需要使用命令行工具来完成。
支持的语言： 阿拉伯语、中文、英语、法语、印地语、意大利语、日语、俄语、西班牙语。
Intel Pentium & Celeron CPU不能运行spleeter
如果你运行的不是intel i5/7/9或Ryzen 5/7，或者不确定你的CPU是否支持AVX，请在尝试安装spleeter之前使用AVX检查工具（上文）。
打开软件后选择 parts to separate（分离声部，一般就是2），设置好文件保存路径（save to），选择需要分离的音频文件（支持多个音频文件），即可快速导出！
加载多个音频文件时，输出路径下会输出多个原文件名的文件夹，内含 accompaniment.wav 和 vocals.wav。
运行过程中遇到问题请前往 Github Issue 和 spleeter_help 搜索。
问题
httpx.ReadTimeout: The read operation timed out
Can’t load save_path when it is None
解决方法
删除“…/SpleeterGUI/pretrained_models“文件夹中的模型文件夹（如“2stems”）。
下载“https://github.com/deezer/spleeter/releases“中“Spleeter public release“的文件，把它们解压到“pretrained_models“文件夹。
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
-
点击“计算机” →“ 属性” → “高级系统设置” → “高级” → “性能 ”→ “设置”
-
进入设置后，再点击进入“数据执行保护”
-
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
-
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
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
- 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）
如果文件被删除，则软链接文件失去指向，变为不可用ln 原文件名 链接文件名
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
- 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：
| CPython | Visual C++ | Visual Studio | Visual Studio下载地址 |
|---|---|---|---|
| 2.6, 2.7, 3.0, 3.1, 3.2 | 9.0 | Visual Studio 2008 | x86下载 x64下载 |
| 3.3, 3.4 | 10.0 | Visual Studio 2010 | x86下载 x64下载 |
| 3.5 | 14.0 | Visual Studio 2015 | 下载 |
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
-
Python >= 3.6
-
Clone this repository
-
Install python requirements. Please refer requirements.txt
i. You may need to install espeak first: apt-get install espeak
-
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 -
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
- Windows 7 用户选择 Python 3.8
- 克隆仓库
git clone git@github.com:jaywalnut310/vits.git
- 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)
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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)
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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)
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Collecting pooch>=1.0
Downloading http://mirrors.aliyun.com/pypi/packages/84/8c/4da580db7fb4cfce8f5ed78e7d2aa542e6f201edd69d3d8a96917a8ff63c/pooch-1.7.0-py3-none-any.whl (60 kB)
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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)
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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)
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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)
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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)
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Collecting pycparser
Downloading http://mirrors.aliyun.com/pypi/packages/62/d5/5f610ebe421e85889f2e55e33b7f9a6795bd982198517d912eb1c76e1a53/pycparser-2.21-py2.py3-none-any.whl (118 kB)
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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)
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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
安装matplotlib
- 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。
- 如果安装了Anaconda，可以通过conda命令安装：
conda install matplotlib
- 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
- 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)
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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)
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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)
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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)
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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。
- 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。 - 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/
- 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
- 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。
- 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。
- 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)
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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
- 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。
-
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。 -
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)
------------------------------------ 126.5/126.5 kB 113.0 kB/s eta 0:00:00
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)
------------------------------------ 420.6/420.6 kB 461.6 kB/s eta 0:00:00
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)
-------------------------------------- 781.3/781.3 kB 1.3 MB/s eta 0:00:00
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
)
-------------------------------------- 233.6/233.6 kB 1.2 MB/s eta 0:00:00
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
(177 kB)
-------------------------------------- 177.2/177.2 kB 1.3 MB/s eta 0:00:00
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)
------------------------------------ 155.3/155.3 kB 619.8 kB/s eta 0:00:00
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
)
-------------------------------------- 151.7/151.7 kB 1.0 MB/s eta 0:00:00
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="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")
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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 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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/
Requirement already satisfied: transformers in c:\program files\python38\lib\site-packages (from -r requirements.txt (line 1)) (4.26.1)
Requirement already satisfied: ffmpeg-python==0.2.0 in c:\program files\python38\lib\site-packages (from -r requirements.txt (line 2)) (0.2.0)
Collecting gradio==3.13.0
Downloading http://mirrors.aliyun.com/pypi/packages/61/e2/cb14526cf49689b5cc3cb942e20747257d98a2879bf53e7ee096eae4630a/gradio-3.13.0-py3-none-any.whl (13.8 MB)
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Collecting yt-dlp
Downloading http://mirrors.aliyun.com/pypi/packages/a7/df/498c57f641e9993376cf52489047158e6d660e8bab06b72c470ad5cce2bd/yt_dlp-2023.3.4-py2.py3-none-any.whl (2.9 MB)
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Requirement already satisfied: torchaudio in c:\program files\python38\lib\site-packages (from -r requirements.txt (line 5)) (0.13.1)
Collecting altair
Downloading http://mirrors.aliyun.com/pypi/packages/18/62/47452306e84d4d2e67f9c559380aeb230f5e6ca84fafb428dd36b96a99ba/altair-4.2.2-py3-none-any.whl (813 kB)
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Requirement already satisfied: future in c:\program files\python38\lib\site-packages (from ffmpeg-python==0.2.0->-r requirements.txt (line 2)) (0.18.3)
Collecting paramiko
Downloading http://mirrors.aliyun.com/pypi/packages/56/7c/9dd558ec0869fcecb661765d0a2504978dbfe85de24cbcccc847aa9b58e4/paramiko-3.1.0-py3-none-any.whl (211 kB)
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Downloading http://mirrors.aliyun.com/pypi/packages/60/0f/7a0eeea938eaf61074f29fed9717f2010e8d0e0905d36b38d3275a1e4622/h11-0.12.0-py3-none-any.whl (54 kB)
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Collecting pydub
Downloading http://mirrors.aliyun.com/pypi/packages/a6/53/d78dc063216e62fc55f6b2eebb447f6a4b0a59f55c8406376f76bf959b08/pydub-0.25.1-py2.py3-none-any.whl (32 kB)
Collecting fsspec
Downloading http://mirrors.aliyun.com/pypi/packages/4f/65/887925f1549fcb6ac3abb23a747c10f5ab083e8471fe568768b18bdb15b2/fsspec-2023.3.0-py3-none-any.whl (145 kB)
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Collecting pycryptodome
Downloading http://mirrors.aliyun.com/pypi/packages/14/7a/f764564dceaf131e7a740c618d6bdfc30e2ca264e9de410ca757f6c4c3e3/pycryptodome-3.17-cp35-abi3-win_amd64.whl (1.7 MB)
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Collecting matplotlib
Downloading http://mirrors.aliyun.com/pypi/packages/92/01/2c04d328db6955d77f8f60c17068dde8aa66f153b2c599ca03c2cb0d5567/matplotlib-3.7.1-cp38-cp38-win_amd64.whl (7.6 MB)
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Collecting pandas
Downloading http://mirrors.aliyun.com/pypi/packages/ca/4e/d18db7d5ff9d28264cd2a7e2499b8701108f0e6c698e382cfd5d20685c21/pandas-1.5.3-cp38-cp38-win_amd64.whl (11.0 MB)
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Requirement already satisfied: pillow in c:\program files\python38\lib\site-packages (from gradio==3.13.0->-r requirements.txt (line 3)) (9.4.0)
Collecting aiohttp
Downloading http://mirrors.aliyun.com/pypi/packages/48/5b/dabb02a8fe7da607c0b65d9086af36a2c77c509f3ee7efb7a80b008d7c7a/aiohttp-3.8.4-cp38-cp38-win_amd64.whl (324 kB)
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Collecting jinja2
Downloading http://mirrors.aliyun.com/pypi/packages/bc/c3/f068337a370801f372f2f8f6bad74a5c140f6fda3d9de154052708dd3c65/Jinja2-3.1.2-py3-none-any.whl (133 kB)
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Requirement already satisfied: numpy in c:\program files\python38\lib\site-packages (from gradio==3.13.0->-r requirements.txt (line 3)) (1.23.5)
Collecting ffmpy
Downloading http://mirrors.aliyun.com/pypi/packages/bf/e2/947df4b3d666bfdd2b0c6355d215c45d2d40f929451cb29a8a2995b29788/ffmpy-0.3.0.tar.gz (4.8 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 gradio==3.13.0->-r requirements.txt (line 3)) (2.28.2)
Collecting fastapi
Downloading http://mirrors.aliyun.com/pypi/packages/f5/07/8e950c4bcb953a0bcbb41e0d7b1d5496f9792edfd0dc2cd518cd7a42f948/fastapi-0.94.0-py3-none-any.whl (56 kB)
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Collecting entrypoints
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Building wheels for collected packages: ffmpy
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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。
WebUI界面
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
- 上传音频文件到Google Drive，建议一个项目一个文件夹。
- 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。
怎么用github绑定自己购买的的域名（亲测版）一文中
「ping你的http://xxxxx.github.io域名，得到一个IP；
windows操作系统下，快捷键win+R，然后输入cmd，弹出小黑框，然后输入
ping https://andyofjuly.github.io/
当然这里是你自己的域名，然后底下会出现四行ip地址，记住。例如」
而我无论pinggithub.com还是xiaxi626.github.com均超时，于是上网查了一下——
1、ping不通github，如何解决？
注意这段话「然后更改后在cmd中运行更新dns命令，ipconfig/flushdns多执行几次，最好在把网络重置一下，在ping的时候注意你所ping的ip是不是你更改后的，如果不是证明没有更新过来；」
我使用SwitchHosts+Github520，现在是140.82.114.3 github.com，ping一下github.com，仍是20.205.243.166。多次执行ipconfig/flushdns，IP地址仍不变。
2、解决：ping github.com遇到“请求超时”
接着看这篇文章。
问题：「有时候会因为某些原因，访问不了github，ping这个网站，请求超时」
解决方法：「这时候就需要修改一下hosts文件了，因为hosts文件负责解析域名并优先于DNS服务。」
此方法同上，然而我可以裸连github.com，ping github.com遇到“请求超时”。
3、为什么github网站ping不通但是可以访问
原来是「国内的github被解析到了某个CDN上，而该CDN禁用ping」。于是我找了两个镜像站ping一下
- https://hub.nuaa.cf/ 能ping通
- https://kgithub.com/ 不能
摘抄一段——
为什么要防ping
这么做的目的，主要是为了安全考虑。如果黑客想要攻击某个网站，最简单的做法就是，他们只要模拟一大批设备狂ping该网站，导致该网站处理ping包而耗费大量资源，那么正常用户想要访问该网站时，因为资源分配不均，就会变得极其慢。网站甚至有可能会宕机。
如何实现防ping
防ping的手段多种多样，Windows系统下主要有三种方法：
- 使用IPSec安全策略
- 使用第三方防火墙工具
- 使用路由与远程访问组件
防ping原理
防ping的本质是过滤icmp协议的响应报文。ping命令是基于icmp协议中的echo request报文进行工作的。本机发送一个icmp协议中的echo request到目标主机，等待目标主机的响应。如果此时把响应的结果过滤掉，那么ping也就无法继续工作，直接收到一个超时信息。
如何解释github网站ping不通
可能不是github的问题，而是国内的github被解析到了某个CDN上，而该CDN禁用ping了而已。我用国外的VPS尝试ping一下github正常，果然应该就是这个原因了。
4、网站能打开，但是ping不通，这是什么原因
某个网站是http://xxx.xxxx.cn:27110，可以用浏览器打开,我想知道这个网站的ip地址，然后运行ping命令：
ping http://xxx.xxxx.cn:27110
提示是：Ping 请求找不到主机 http://xxx.xxxx.cn:27110。请检查该名称，然后重试。
请问这是什么原因。
lich2005：「估计有防火墙吧，禁止别人 ping 自己。」
我的大神666：「ping没有端口这一说法，任何ping带端口都报错。
ping是检查主机的连通性，不是检查服务器的连通性。
一台物理主机可以有n台虚拟主机，每台虚拟主机可以有n个服务器。
你可以直接ping http://xxx.xxxx.cn」
clever101：「ping http://xxx.xxxx.cn也提示Ping 请求找不到主机 http://git.piesat.cn。请检查该名称，然后重试。」
wangdengwk：「前面不要带http:// 直接ping域名，比如 ping www.baidu.com」
Kianteck：「可以尝试使用nslookup CMD-> nslookup xxx.xxxx.cn」
既然如此，不妨试试nslookup github.com
可以看到，nslookup没报错，ping 域名与nslookup 域名 得到的IP地址相同。那如果不同怎么办？
服务器上「ping 域名与nslookup 域名 得到的IP地址」相同，客户端电脑上不同——
ping 域名与nslookup 域名 得到的IP地址不同
xman_78tom：「检查客户端的 HOSTS 文件。如果还不是，则尝试以下检查：
首先清除客户端 dns 缓存，然后 ping www.abc.com 时在客户端抓包，判断客户端是否从 192.168.1.100 获取 dns 解析。
如果 211.10.21.5 是从 192.168.1.100 处获取，则检查 dns 服务器的配置。按 dns 解析的顺序，检查主要区域和辅助区域（权威答复），（清理）服务器上的 dns 缓存，存根区域、转发、根提示。」
yjvjom：「 问题解决:
因为我在客户端指定了多个DNS服务器
DNS1:192.168.1.100
DNS2:202.96.134.133
结果在ping www.abc.com时有时得到的IP是10.1.1.100，有时是211.10.21.5,在只指定一个DNS：192.168.1.100时正常了，但是不明白为什么为出现这样的问题。
」
zhaozy1982：「有可能是内网DNS有时候无法解析
看看内网DNS是不是存在丢包，或者超连接数的限制」
顺便查了一下——「为什么网上查到的ip和自己ipconfig出来的不一样？」
5、解决ping得通IP 但是ping 不通域名的问题
摘抄一段——
能ping通ip 则代表链路是通的，但是ping不通域名只能说明是域名解析出现了问题。
可使用nslookup + 域名 看下是哪个域名服务器，我们很多时候都是使用自动获取DNS服务器，但是有些时候，使用默认的DNS服务器是ping 不通域名的，比如使用移动的 移动终端默认使用dns02.hb.chinamobile.com DNS服务器去解析域名，可能会导致解析不到域名。
此时可以将DNS 配置成固定的，可以是google的DNS 8.8.8.8 或者电信的 101.226.4.6或者114.114.114.114大部分是可以的 如果还是Ping不通 则可联系给域名服务的服务商，他们会给出dns服务器。
windows系统
更改前可以先清除dns缓存：命令窗口ipconfig/flushdns
然后设置电脑的DNS为8.8.8.8
自己遇到的情况（烦）
- ping 不通域名，ping 该IP不通
- 换改hosts用的IP通了
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
