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229 lines
8.8 KiB
Markdown
Executable File
229 lines
8.8 KiB
Markdown
Executable File
[简体中文](README.md) | English
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## Project Introduction
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Video-subtitle-remover (VSR) is an AI-based software that removes hardcoded subtitles from videos. It mainly implements the following functionalities:
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- **Lossless resolution**: Removes hardcoded subtitles from videos and generates files without subtitles.
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- Fills in the removed subtitle text area using a powerful AI algorithm model (non-adjacent pixel filling and mosaic removal).
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- Supports custom subtitle positions by only removing subtitles in the defined location (input position).
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- Supports automatic removal of all text throughout the entire video (without inputting a position).
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- Supports multi-selection of images for batch removal of watermark text.
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<p style="text-align:center;"><img src="https://github.com/YaoFANGUK/video-subtitle-remover/raw/main/design/demo.png" alt="demo.png"/></p>
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> Download the .zip package directly, extract, and run it. If it cannot run, follow the tutorial below to try installing the conda environment and running the source code.
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**Download Links:**
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Windows GPU Version v1.1.0 (GPU):
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- Baidu Cloud Disk: <a href="https://pan.baidu.com/s/1zR6CjRztmOGBbOkqK8R1Ng?pwd=vsr1">vsr_windows_gpu_v1.1.0.zip</a> Extraction Code: **vsr1**
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- Google Drive: <a href="https://drive.google.com/drive/folders/1NRgLNoHHOmdO4GxLhkPbHsYfMOB_3Elr?usp=sharing">vsr_windows_gpu_v1.1.0.zip</a>
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**Pre-built Package Comparison**:
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| Pre-built Package Name | Python | Paddle | Torch | Environment | Supported Compute Capability Range |
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|----------------------------------|--------|--------|--------|-----------------------------------|------------------------------------|
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| `vse-windows-directml.7z` | 3.12 | 3.0.0 | 2.4.1 | Windows without Nvidia GPU | Universal |
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| `vse-windows-nvidia-cuda-11.8.7z`| 3.12 | 3.0.0 | 2.7.0 | CUDA 11.8 | 3.5 – 8.9 |
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| `vse-windows-nvidia-cuda-12.6.7z`| 3.12 | 3.0.0 | 2.7.0 | CUDA 12.6 | 5.0 – 8.9 |
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| `vse-windows-nvidia-cuda-12.8.7z`| 3.12 | 3.0.0 | 2.7.0 | CUDA 12.8 | 5.0 – 9.0+ |
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> NVIDIA provides a list of supported compute capabilities for each GPU model. You can refer to the following link: [CUDA GPUs](https://developer.nvidia.com/cuda-gpus) to check which CUDA version is compatible with your GPU.
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**Docker Versions:**
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```shell
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# Nvidia 10, 20, 30 Series Graphics Cards
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docker run -it --name vsr --gpus all eritpchy/video-subtitle-remover:1.1.1-cuda11.8
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# Nvidia 40 Series Graphics Cards
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docker run -it --name vsr --gpus all eritpchy/video-subtitle-remover:1.1.1-cuda12.6
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# Nvidia 50 Series Graphics Cards
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docker run -it --name vsr --gpus all eritpchy/video-subtitle-remover:1.1.1-cuda12.8
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# AMD / Intel Dedicated or Integrated Graphics
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docker run -it --name vsr --gpus all eritpchy/video-subtitle-remover:1.1.1-directml
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# Demo video, input
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/vsr/test/test.mp4
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docker cp vsr:/vsr/test/test_no_sub.mp4 ./
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```
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## Demonstration
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- GUI:
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<p style="text-align:center;"><img src="https://github.com/YaoFANGUK/video-subtitle-remover/raw/main/design/demo2.gif" alt="demo2.gif"/></p>
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- <a href="https://b23.tv/guEbl9C">Click to view demo video👇</a>
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<p style="text-align:center;"><a href="https://b23.tv/guEbl9C"><img src="https://github.com/YaoFANGUK/video-subtitle-remover/raw/main/design/demo.gif" alt="demo.gif"/></a></p>
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## Source Code Usage Instructions
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#### 1. Install Python
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Please ensure that you have installed Python 3.12+.
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- Windows users can go to the [Python official website](https://www.python.org/downloads/windows/) to download and install Python.
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- MacOS users can install using Homebrew:
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```shell
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brew install python@3.12
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```
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- Linux users can install via the package manager, such as on Ubuntu/Debian:
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```shell
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sudo apt update && sudo apt install python3.12 python3.12-venv python3.12-dev
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```
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#### 2. Install Dependencies
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It is recommended to use a virtual environment to manage project dependencies to avoid conflicts with the system environment.
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(1) Create and activate the virtual environment:
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```shell
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python -m venv videoEnv
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```
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- Windows:
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```shell
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videoEnv\\Scripts\\activate
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```
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- MacOS/Linux:
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```shell
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source videoEnv/bin/activate
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```
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#### 3. Create and Activate Project Directory
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Change to the directory where your source code is located:
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```shell
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cd <source_code_directory>
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```
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> For example, if your source code is in the `tools` folder on the D drive and the folder name is `video-subtitle-remover`, use:
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> ```shell
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> cd D:/tools/video-subtitle-remover-main
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> ```
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#### 4. Install the Appropriate Runtime Environment
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This project supports two runtime modes: CUDA (NVIDIA GPU acceleration) and DirectML (AMD, Intel, and other GPUs/APUs).
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##### (1) CUDA (For NVIDIA GPU users)
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> Make sure your NVIDIA GPU driver supports the selected CUDA version.
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- Recommended CUDA 11.8, corresponding to cuDNN 8.6.0.
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- Install CUDA:
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- Windows: [Download CUDA 11.8](https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_522.06_windows.exe)
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- Linux:
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```shell
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wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
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sudo sh cuda_11.8.0_520.61.05_linux.run
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```
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- CUDA is not supported on MacOS.
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- Install cuDNN (CUDA 11.8 corresponds to cuDNN 8.6.0):
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- [Windows cuDNN 8.6.0 Download](https://developer.download.nvidia.cn/compute/redist/cudnn/v8.6.0/local_installers/11.8/cudnn-windows-x86_64-8.6.0.163_cuda11-archive.zip)
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- [Linux cuDNN 8.6.0 Download](https://developer.download.nvidia.cn/compute/redist/cudnn/v8.6.0/local_installers/11.8/cudnn-linux-x86_64-8.6.0.163_cuda11-archive.tar.xz)
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- Follow the installation guide in the NVIDIA official documentation.
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- Install PaddlePaddle GPU version (CUDA 11.8):
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```shell
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pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
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```
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- Install Torch GPU version (CUDA 11.8):
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```shell
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pip install torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu118
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```
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- Install other dependencies:
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```shell
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pip install -r requirements.txt
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```
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##### (2) DirectML (For AMD, Intel, and other GPU/APU users)
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- Suitable for Windows devices with AMD/NVIDIA/Intel GPUs.
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- Install ONNX Runtime DirectML version:
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```shell
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pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
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pip install -r requirements.txt
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pip install -r requirements_directml.txt
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```
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#### 4. Run the program
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- Run the graphical interface
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```shell
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python gui.py
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```
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- Run the command line version (CLI)
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```shell
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python ./backend/main.py
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```
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## Common Issues
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1. How to deal with slow removal speed
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You can greatly increase the removal speed by modifying the parameters in backend/config.py:
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```python
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MODE = InpaintMode.STTN # Set to STTN algorithm
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STTN_SKIP_DETECTION = True # Skip subtitle detection
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```
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2. What to do if the video removal results are not satisfactory
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Modify the values in backend/config.py and try different removal algorithms. Here is an introduction to the algorithms:
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> - **InpaintMode.STTN** algorithm: Good for live-action videos and fast in speed, capable of skipping subtitle detection
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> - **InpaintMode.LAMA** algorithm: Best for images and effective for animated videos, moderate speed, unable to skip subtitle detection
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> - **InpaintMode.PROPAINTER** algorithm: Consumes a significant amount of VRAM, slower in speed, works better for videos with very intense movement
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- Using the STTN algorithm
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```python
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MODE = InpaintMode.STTN # Set to STTN algorithm
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# Number of neighboring frames, increasing this will increase memory usage and improve the result
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STTN_NEIGHBOR_STRIDE = 10
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# Length of reference frames, increasing this will increase memory usage and improve the result
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STTN_REFERENCE_LENGTH = 10
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# Set the maximum number of frames processed simultaneously by the STTN algorithm, a larger value leads to slower processing but better results
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# Ensure that STTN_MAX_LOAD_NUM is greater than STTN_NEIGHBOR_STRIDE and STTN_REFERENCE_LENGTH
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STTN_MAX_LOAD_NUM = 30
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```
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- Using the LAMA algorithm
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```python
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MODE = InpaintMode.LAMA # Set to LAMA algorithm
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LAMA_SUPER_FAST = False # Ensure quality
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```
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3. CondaHTTPError
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Place the .condarc file from the project in the user directory (C:/Users/<your_username>). If the file already exists in the user directory, overwrite it.
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Solution: https://zhuanlan.zhihu.com/p/260034241
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4. 7z file extraction error
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Solution: Upgrade the 7-zip extraction program to the latest version.
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