mirror of
https://github.com/YaoFANGUK/video-subtitle-remover.git
synced 2026-02-21 00:44:46 +08:00
Update README.md
This commit is contained in:
天涯古巷
134
README.md
134
README.md
@@ -55,87 +55,81 @@ conda activate videoEnv
|
||||
|
||||
请确保你已经安装 python 3.8+，使用conda创建项目虚拟环境并激活环境 (建议创建虚拟环境运行，以免后续出现问题)
|
||||
|
||||
- GPU用户(有N卡)：
|
||||
|
||||
- 安装CUDA和cuDNN
|
||||
- 安装CUDA和cuDNN
|
||||
|
||||
<details>
|
||||
<summary>Linux用户</summary>
|
||||
<details>
|
||||
<summary>Linux用户</summary>
|
||||
<h5>(1) 下载CUDA 11.7</h5>
|
||||
<pre><code>wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run</code></pre>
|
||||
<h5>(2) 安装CUDA 11.7</h5>
|
||||
<pre><code>sudo sh cuda_11.7.0_515.43.04_linux.run</code></pre>
|
||||
<p>1. 输入accept</p>
|
||||
<img src="https://i.328888.xyz/2023/03/31/iwVoeH.png" width="500" alt="">
|
||||
<p>2. 选中CUDA Toolkit 11.7（如果你没有安装nvidia驱动则选中Driver，如果你已经安装了nvidia驱动请不要选中driver），之后选中install，回车</p>
|
||||
<img src="https://i.328888.xyz/2023/03/31/iwVThJ.png" width="500" alt="">
|
||||
<p>3. 添加环境变量</p>
|
||||
<p>在 ~/.bashrc 加入以下内容</p>
|
||||
<pre><code># CUDA
|
||||
export PATH=/usr/local/cuda-11.7/bin${PATH:+:${PATH}}
|
||||
export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}</code></pre>
|
||||
<p>使其生效</p>
|
||||
<pre><code>source ~/.bashrc</code></pre>
|
||||
<h5>(3) 下载cuDNN 8.4.1</h5>
|
||||
<p>国内：<a href="https://pan.baidu.com/s/1Gd_pSVzWfX1G7zCuqz6YYA">cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz</a> 提取码：57mg</p>
|
||||
<p>国外：<a href="https://github.com/YaoFANGUK/video-subtitle-extractor/releases/download/1.0.0/cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz">cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz</a></p>
|
||||
<h5>(4) 安装cuDNN 8.4.1</h5>
|
||||
<pre><code> tar -xf cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz
|
||||
mv cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive cuda
|
||||
sudo cp ./cuda/include/* /usr/local/cuda-11.7/include/
|
||||
sudo cp ./cuda/lib/* /usr/local/cuda-11.7/lib64/
|
||||
sudo chmod a+r /usr/local/cuda-11.7/lib64/*
|
||||
sudo chmod a+r /usr/local/cuda-11.7/include/*</code></pre>
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Windows用户</summary>
|
||||
<h5>(1) 下载CUDA 11.7</h5>
|
||||
<pre><code>wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run</code></pre>
|
||||
<a href="https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_516.01_windows.exe">cuda_11.7.0_516.01_windows.exe</a>
|
||||
<h5>(2) 安装CUDA 11.7</h5>
|
||||
<pre><code>sudo sh cuda_11.7.0_515.43.04_linux.run</code></pre>
|
||||
<p>1. 输入accept</p>
|
||||
<img src="https://i.328888.xyz/2023/03/31/iwVoeH.png" width="500" alt="">
|
||||
<p>2. 选中CUDA Toolkit 11.7（如果你没有安装nvidia驱动则选中Driver，如果你已经安装了nvidia驱动请不要选中driver），之后选中install，回车</p>
|
||||
<img src="https://i.328888.xyz/2023/03/31/iwVThJ.png" width="500" alt="">
|
||||
<p>3. 添加环境变量</p>
|
||||
<p>在 ~/.bashrc 加入以下内容</p>
|
||||
<pre><code># CUDA
|
||||
export PATH=/usr/local/cuda-11.7/bin${PATH:+:${PATH}}
|
||||
export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}</code></pre>
|
||||
<p>使其生效</p>
|
||||
<pre><code>source ~/.bashrc</code></pre>
|
||||
<h5>(3) 下载cuDNN 8.4.1</h5>
|
||||
<p>国内：<a href="https://pan.baidu.com/s/1Gd_pSVzWfX1G7zCuqz6YYA">cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz</a> 提取码：57mg</p>
|
||||
<p>国外：<a href="https://github.com/YaoFANGUK/video-subtitle-extractor/releases/download/1.0.0/cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz">cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz</a></p>
|
||||
<h5>(4) 安装cuDNN 8.4.1</h5>
|
||||
<pre><code> tar -xf cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive.tar.xz
|
||||
mv cudnn-linux-x86_64-8.4.1.50_cuda11.6-archive cuda
|
||||
sudo cp ./cuda/include/* /usr/local/cuda-11.7/include/
|
||||
sudo cp ./cuda/lib/* /usr/local/cuda-11.7/lib64/
|
||||
sudo chmod a+r /usr/local/cuda-11.7/lib64/*
|
||||
sudo chmod a+r /usr/local/cuda-11.7/include/*</code></pre>
|
||||
<h5>(3) 下载cuDNN 8.2.4</h5>
|
||||
<p><a href="https://github.com/YaoFANGUK/video-subtitle-extractor/releases/download/1.0.0/cudnn-windows-x64-v8.2.4.15.zip">cudnn-windows-x64-v8.2.4.15.zip</a></p>
|
||||
<h5>(4) 安装cuDNN 8.2.4</h5>
|
||||
<p>
|
||||
将cuDNN解压后的cuda文件夹中的bin, include, lib目录下的文件复制到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\对应目录下
|
||||
</p>
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Windows用户</summary>
|
||||
<h5>(1) 下载CUDA 11.7</h5>
|
||||
<a href="https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_516.01_windows.exe">cuda_11.7.0_516.01_windows.exe</a>
|
||||
<h5>(2) 安装CUDA 11.7</h5>
|
||||
<h5>(3) 下载cuDNN 8.2.4</h5>
|
||||
<p><a href="https://github.com/YaoFANGUK/video-subtitle-extractor/releases/download/1.0.0/cudnn-windows-x64-v8.2.4.15.zip">cudnn-windows-x64-v8.2.4.15.zip</a></p>
|
||||
<h5>(4) 安装cuDNN 8.2.4</h5>
|
||||
<p>
|
||||
将cuDNN解压后的cuda文件夹中的bin, include, lib目录下的文件复制到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\对应目录下
|
||||
</p>
|
||||
</details>
|
||||
|
||||
|
||||
- 安装paddlepaddle:
|
||||
- 安装GPU版本Paddlepaddle:
|
||||
|
||||
- windows:
|
||||
- windows:
|
||||
|
||||
```shell
|
||||
python -m pip install paddlepaddle-gpu==2.4.2.post117 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html
|
||||
```
|
||||
```shell
|
||||
python -m pip install paddlepaddle-gpu==2.4.2.post117 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html
|
||||
```
|
||||
|
||||
- Linux:
|
||||
- Linux:
|
||||
|
||||
```shell
|
||||
python -m pip install paddlepaddle-gpu==2.4.2.post117 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
|
||||
```
|
||||
```shell
|
||||
python -m pip install paddlepaddle-gpu==2.4.2.post117 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
|
||||
```
|
||||
|
||||
> 如果安装cuda 10.2，请对应安装7.6.5的cuDNN，并使用对应cuda版本的paddlepaddle，**请不要使用cuDNN v8.x 和 cuda 10.2的组合**
|
||||
|
||||
> 如果安装cuda 11.2，请对应安装8.1.1的cuDNN，并使用对应cuda版本的paddlepaddle，**30系列以上的显卡驱动可能不支持 cuda 11.2及以下版本的安装**
|
||||
- 安装GPU版本Pytorch:
|
||||
|
||||
```shell
|
||||
conda install pytorch==2.0.1 torchvision==0.15.2 pytorch-cuda=11.7 -c pytorch -c nvidia
|
||||
```
|
||||
或者使用
|
||||
```shell
|
||||
pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu117
|
||||
```
|
||||
|
||||
- 安装GPU版本Pytorch:
|
||||
|
||||
```shell
|
||||
conda install pytorch==2.0.1 torchvision==0.15.2 pytorch-cuda=11.7 -c pytorch -c nvidia
|
||||
```
|
||||
或者使用
|
||||
```shell
|
||||
pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu117
|
||||
```
|
||||
- 安装其他依赖:
|
||||
|
||||
- 安装其他依赖:
|
||||
```shell
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
```shell
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
|
||||
#### 4. 运行程序
|
||||
|
||||
@@ -145,3 +139,9 @@ conda activate videoEnv
|
||||
python ./backend/main.py
|
||||
```
|
||||
|
||||
## 常见问题
|
||||
1. CondaHTTPError
|
||||
|
||||
将项目中的.condarc放在用户目录下(C:\Users\<你的用户名>)，如果用户目录已经存在该文件则覆盖
|
||||
|
||||
解决方案：https://zhuanlan.zhihu.com/p/260034241
|
||||
|
||||
Reference in New Issue
Block a user