VideoHighlighter README

The project's README from github.com/Aseiel/VideoHighlighter. As one markdown file: README.md.

VideoHighlighter

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VideoHighlighter

English | 中文 | 日本語 | Español

Find and explain the moments that matter in footage you won't upload — then export a cut, on your machine.

A local desktop app. Drop in raw video; it scores strong moments from scene, motion, audio, objects, actions and the transcript, shows you why each one scored on a signal timeline and in a report, then exports a highlight reel and the individual clips. Nothing is uploaded for analysis, and the basic pipeline needs no API key.

Free and open source (AGPL-3.0). Windows, macOS, Linux. Intel, AMD and NVIDIA GPUs.

It's free. To make sure you see new releases in future, please click the motivation button: the ⭐ at the top of the page. It's the cheapest payment we accept.

What people use it for

Not sure which detector fits your footage? docs/DETECTION-GUIDE.md covers what each one is good at and where it falls down.

What makes it different

Every run explains itself. The report is the arithmetic behind each kept moment — the per-signal point breakdown, what fired, what scored well and still missed the cut. It names the claims that came from the transcript and were never measured, instead of scoring them anyway. Open a real one →

Composition rules. You say what a combination of detections means for your footage — one class inside another, counted, held over a window — and that becomes an event under a name you choose. Because a rule re-reads detections that already exist, editing one and re-running costs milliseconds.

Preview

VideoHighlighter

Timeline Viewer

Timeline Viewer

Demo

https://github.com/user-attachments/assets/5c85af94-9228-4537-926a-1ed7a91fa5ee

Install

Grab a build from Releases:

Portable builds, GPU setup, where it writes, and fixing an oversized UI: docs/INSTALL.md.

Documentation

Choosing a detector Objects, actions, CLIP search, composition rules — what each is for
Why these moments What the report contains and why it is built that way
The Auto pipeline Card → ingest → script → music → reel, resumable
Installing Every platform, GPU backends, settings
Training a model Label your own class and train it
Community models Install models other people trained, publish your own
Intel GPU · AMD GPU Vendor-specific acceleration
Remote ollama Run the local LLM on another box on your LAN

Pro edition

VideoHighlighter — this repository — is free software under AGPL-3.0, and stays that way. It includes offline analysis, live face detection, VR side-by-side playback, CLIP search, the composition engine, model training and the model hub.

VideoHighlighter Pro is a separate paid edition that adds real-time work on top: live object and action overlays during playback, teaching a category by drawing a box around it, find-more-like-this search, open-vocabulary detection and counter/scoreboard detection.

Explanation is not among them. The report, the findings and the advisor are identical in both editions — a cloud tool gives you a button and a result you cannot interrogate; answering "why", locally, is what this is instead.

Community

VideoHighlighter occasionally has feelings about your footage. When it does: join the Discord and yell in #support, I'm usually around. Bugs and ideas are welcome in Issues.

License

Copyright (C) 2026 Przemysław Kreft and Meric Donmezer.

Released under the GNU Affero General Public License v3.0 — use, modify and distribute it freely, provided modified versions, including ones offered over a network, make their complete source available under the same license. Full text in LICENSE; notice in COPYRIGHT.

Contributors keep copyright in their own work — see CONTRIBUTING.md and CLA.md. VideoHighlighter is also offered under a separate commercial license by the copyright holders.

Background

This started as a personal tool to generate subtitles for videos, for my 7-year-old son. Over time it turned into a highlights generator for movies, sports and personal footage. The goal is unchanged: speed up video analysis, generate highlights you can explain, and create subtitles automatically — without uploading footage you would rather keep local.

Stars History

VideoHighlighter (Aseiel/VideoHighlighter)

Searching Database…

这是干什么的?一款免费开源的桌面应用,能找出并解读你视频里的精彩片段,然后导出精华集锦和剪辑片段——是 Twelve Labs、Runway 和 Descript 的离线替代品。
这是桌面应用还是云服务?一款本地桌面应用,支持 Windows、macOS 和 Linux:你把原始视频放进去,检测、评分和导出全部在你自己的电脑上完成。
在 Windows、macOS 或 Linux 上怎么安装?从 Releases 下载:Windows 上运行 00-VideoHighlighter-Windows-Setup.exe(按用户安装,无需管理员权限);macOS 上把 .dmg 拖进「应用程序」即可;从源码安装:pip install -r requirements.txt && python main.py。自带 FFmpeg,无需单独安装。
Windows 安装包触发了 SmartScreen 警告,这正常吗?正常——安装包还没有做代码签名,你点击「更多信息」→「仍要运行」即可。macOS 上还需要用 xattr -dr com.apple.quarantine /Applications/VideoHighlighter.app 清除一次隔离标记。
分析视频的时候,我的素材会被上传到别处吗?不会——分析、检测和本地 Whisper 转录完全离线运行,基础流程连 API 密钥都不需要。你甚至可以把 Ollama 大模型放在局域网里的另一台电脑上。
如果我修改它或者把它部署到网上,AGPL-3.0 许可证意味着什么?你可以自由使用、修改和分发它,但修改后的版本——包括通过网络提供服务的版本——必须以同一许可证公开完整源代码。另外也提供单独的商业授权。
必须要有 NVIDIA 显卡吗,AMD 和 Intel 的显卡能用吗?三家都可以——它能在 Windows、macOS 和 Linux 上搭配 Intel、AMD 和 NVIDIA 显卡运行,还提供各厂商专属的加速指南,支持 CUDA 和 OpenVINO 后端。
付费的 Pro 版本有没有锁住什么功能?没有。免费版已经包含离线分析、实时人脸检测、VR 左右分屏播放、CLIP 搜索、合成引擎、模型训练和社区模型中心。Pro 是单独的付费版本,只增加实时相关功能:播放时的实时叠加画面、画框教学来识别某类目标、以图搜图、开放词汇检测以及计数/记分牌检测——而且两个版本生成的可解释报告完全一致。
作者为什么要做这个?它最初是作者给 7 岁儿子给视频生成字幕的个人工具,后来慢慢发展成面向电影、体育和个人素材的集锦生成器。初心一直没变:快速、可解释的视频分析,以及不用上传你想留在本地的素材就能自动生成字幕。
它底层跑的是哪些模型?包括场景、运动、音频、物体和动作检测器,基于 CLIP 的视觉搜索,以及本地 Whisper 转录;视觉大模型通过 Ollama 运行(LLaVA,仓库话题里还列出了 CUDA 和 OpenVINO 后端)。docs/DETECTION-GUIDE.md 介绍了每个检测器擅长什么、短板在哪里。
Creator
Aseiel
Size
238.6 MB
Stars
240
Age
1y
Commits · 90 Days