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