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266 lines
11 KiB
Markdown
Executable File
266 lines
11 KiB
Markdown
Executable File
[简体中文](README.md) | English
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<div align="center">
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<img src="design/icon_1024.PNG" alt="VSR Logo" width="128" height="128">
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</div>
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## Project Introduction
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[](https://hub.docker.com/r/eritpchy/video-subtitle-remover)
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Video-subtitle-remover (VSR) is an AI-based software that removes hardcoded subtitles from videos.
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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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**Instructions:**
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- If you have questions, please join the discussion group: QQ Group 210150985 (full), 806152575 (full), 816881808 (full), 295894827
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- Download the compressed package, extract and run it directly. If it cannot run, follow the tutorial below to try installing from source
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**Download:** <a href="https://github.com/YaoFANGUK/video-subtitle-remover/releases">Release</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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| `vsr-windows-cpu.7z` | 3.12 | 3.0.0 | 2.7.0 | Universal | Universal |
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| `vsr-windows-directml.7z` | 3.12 | 3.0.0 | 2.4.1 | Windows non-Nvidia GPU | Universal |
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| `vsr-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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| `vsr-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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| `vsr-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 compute capabilities for each GPU model. Refer to [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:1.4.0-cuda11.8 python backend/main.py -i test/test.mp4 -o test/test_no_sub.mp4
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# Nvidia 40 Series Graphics Cards
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docker run -it --name vsr --gpus all eritpchy/video-subtitle-remover:1.4.0-cuda12.6 python backend/main.py -i test/test.mp4 -o test/test_no_sub.mp4
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# Nvidia 50 Series Graphics Cards
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docker run -it --name vsr --gpus all eritpchy/video-subtitle-remover:1.4.0-cuda12.8 python backend/main.py -i test/test.mp4 -o test/test_no_sub.mp4
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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:1.4.0-directml python backend/main.py -i test/test.mp4 -o test/test_no_sub.mp4
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# CPU
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docker run -it --name vsr --gpus all eritpchy/video-subtitle-remover:1.4.0-cpu python backend/main.py -i test/test.mp4 -o test/test_no_sub.mp4
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# Export video
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docker cp vsr:/vsr/test/test_no_sub.mp4 ./
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```
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**Command Line:**
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```
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Video Subtitle Remover Command Line Tool
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options:
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-h, --help show this help message and exit
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--input INPUT, -i INPUT
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Input video file path
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--output OUTPUT, -o OUTPUT
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Output video file path (optional)
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--subtitle-area-coords YMIN YMAX XMIN XMAX, -c YMIN YMAX XMIN XMAX
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Subtitle area coordinates (ymin ymax xmin xmax). Can be specified multiple times for multiple areas.
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--inpaint-mode {sttn-auto,sttn-det,lama,opencv}
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Inpaint mode, default is sttn-auto
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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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<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 four running modes: CUDA (NVIDIA GPU acceleration), CPU (no GPU), DirectML (AMD, Intel and other GPU/APU acceleration), and macOS (Apple Silicon).
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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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- For Linux systems, you also need to install:
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```shell
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# for cuda 12.x
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pip install onnxruntime-gpu==1.22.0
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# for cuda 11.x
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pip install onnxruntime-gpu==1.20.1 --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-11/pypi/simple/
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```
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> For more details, see: [Install ONNX Runtime](https://onnxruntime.ai/docs/install/#install-onnx-runtime-gpu-cuda-12x)
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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 torch_directml==0.2.5.dev240914
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```
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##### (3) CPU Only (For systems without GPU or those not wanting to use GPU acceleration)
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- Suitable for systems without GPU or those that do not wish to use GPU.
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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 torch==2.7.0 torchvision==0.22.0
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pip install -r requirements.txt
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```
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##### (4) Running on macOS (Apple Silicon)
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- Suitable for macOS (Apple Silicon) devices
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- For macOS (Intel), please use the CPU mode. Forcing GPU usage will only be slower.
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- On macOS (Apple Silicon), the accuracy of the PP-OCRv4-Server model for subtitle detection seems suboptimal. We recommend using an alternative model.
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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 torch==2.7.0 torchvision==0.22.0
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pip install -r requirements.txt
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```
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> Tested with Python 3.13
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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, skipping may cause missed subtitles or damage to frames without subtitles
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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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- 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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> If you are not satisfied with the subtitle removal results, you can check the training methods in the design folder, use the code in backend/tools/train to train, and then replace the old model with the trained model.
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3. 7z file extraction error
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Solution: Upgrade the 7-zip extraction program to the latest version.
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## Sponsor
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<img src="https://github.com/YaoFANGUK/video-subtitle-extractor/raw/main/design/sponsor.png" width="600">
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