yaofanguk/video-subtitle-remover — explained in plain English
Analysis updated 2026-06-24
Remove hardcoded subtitles from a downloaded video before re-editing or re-subtitling it in another language.
Strip text watermarks from a batch of images in one command.
Target only the bottom strip of a video to speed up subtitle removal without processing the whole frame.
| yaofanguk/video-subtitle-remover | kedro-org/kedro | rany2/edge-tts | |
|---|---|---|---|
| Stars | 10,864 | 10,863 | 10,860 |
| Language | Python | Python | Python |
| Setup difficulty | hard | moderate | easy |
| Complexity | 4/5 | 3/5 | 2/5 |
| Audience | general | data | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires Python 3.11+ and a separate GPU-specific installation path (NVIDIA CUDA, AMD DirectML, or CPU-only). Docker images available for each.
Video Subtitle Remover (VSR) is a Python tool that uses AI to remove burned-in subtitles and text watermarks from videos and images. Burned-in subtitles (sometimes called hardcoded subtitles) are text overlaid directly onto the video frames, as opposed to subtitle tracks that can be toggled on and off. This tool detects where the text appears and fills in the background behind it using AI image reconstruction techniques, so the output looks like the text was never there. The tool runs entirely on your own machine with no third-party API required. It offers both a graphical interface and a command-line version. You can remove subtitles from an entire video automatically, specify exact screen coordinates to target only a particular area (such as the bottom strip where subtitles typically appear), or batch-process multiple images to remove watermarks. The README (written in Chinese) describes three AI-based fill methods with different trade-offs. The STTN mode works best on footage of real people and is the fastest option. The LAMA mode produces better results on still images and animated content. The ProPainter mode is the slowest but handles videos with a lot of fast movement more accurately. The tool supports NVIDIA GPU acceleration via CUDA, AMD and Intel GPU acceleration via DirectML on Windows, Apple Silicon on macOS, and CPU-only mode for systems without a compatible GPU. Pre-built packages for Windows are available as downloads for each GPU configuration. Docker images are also provided for each environment. Setup requires Python 3.11 or newer, and the installation steps differ depending on whether you have an NVIDIA, AMD, Intel, or no GPU. The README provides separate installation instructions for each path. The project is open source under the Apache 2.0 license, which allows free use in personal and commercial projects.
AI tool that detects and erases burned-in subtitles or text watermarks from videos and images by reconstructing the background behind the text. Runs on your own machine, no cloud upload needed.
Mainly Python. The stack also includes Python, PyTorch, CUDA.
Use freely for personal and commercial projects as long as you include the license notice. Apache 2.0.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly general.
This repo across BitVibe Labs
Verify against the repo before relying on details.