VideoHighlighter (Freeware)

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

A local desktop tool: drop raw video, score strong moments (scene, motion, audio, objects, actions, transcript), see why they scored on a signal timeline and report, then export a highlight reel and separate clips. Nothing is uploaded for analysis.

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.

Features

Detects:

• Scenes using OpenCV.
• Motion peaks and scene changes.
• Objects
• Actions
• Audio peaks.

Generates transcript subtitles via OpenAI Whisper (local). Cuts top-scoring segments into a highlight video and optional separate clips. Fully configurable: frame skip, highlight duration, keywords. Optional GUI for easy interaction.

Not sure which detector to reach for? See docs/DETECTION-GUIDE.md — what object recognition, action recognition, CLIP search and the composition engine are each good at, and where each one falls down.

Want real-time detection? Everything above runs offline, after the fact. VideoHighlighter Pro adds live object and action overlays during playback, teach-by-example categories, open-vocabulary detection and counter detection. See what's different →

Preview

Timeline Viewer

Demo

demo-final.mp4

Workflow Stages

Pro edition

This edition already includes live face detection, VR side-by-side playback and rendering, offline analysis, CLIP search, the composition engine, and the training scripts.

VideoHighlighter Pro adds:

• Live object and action overlays — real-time detection during playback, including on side-by-side VR footage.
• Teach a category by pointing — draw a box around anything, name it, and it is scored live from then on. No dataset, no training run.
• Find more like this — pick a region in one frame and search the whole video for it.
• Open-vocabulary detection — type a plain word and find it, with no trained model for it.
• Counter / scoreboard detection — if the footage has an on-screen counter, every tick proves an event, so Pro can show which real moments the detector missed.

This edition remains free and AGPL-3.0 licensed.

Installation

Windows (recommended)

1. One-click: download VideoHighlighter-Windows-Setup.zip from Releases, extract it, and double-click Install-VideoHighlighter.bat. It downloads both archive parts and unpacks them (~4 GB download).
2. Manual: download both VideoHighlighter-Windows-*.7z.001 and .7z.002 into the same folder, then extract the .001 file with 7-Zip.

No Python or dependencies required — run VideoHighlighter.exe inside the extracted folder.

macOS

Not a supported product download. The prebuilt app we sell and support is Windows-only. You can try building from source on macOS if you know the stack; we do not ship or support a Mac release yet.

Linux / building from source

1. Python & FFmpeg FFmpeg must be installed and available in your system PATH.

Usage

• Windows: run VideoHighlighter.exe from the extracted build.
• From source (Linux / advanced): python main.py

Footage, transcripts, and local models stay on disk. Analysis does not require an API key for the basic pipeline.

Discord

VideoHighlighter occasionally has feelings about your footage. When it does: Join the Discord and yell in #support, I'm usually around.

Notes

OpenAI Whisper is MIT licensed — freely usable.

Google Translate API is optional. If using unofficial libraries (googletrans), no API key is needed, but results may break if Google changes endpoints.

This project does not include any paid API keys. Users must provide their own if using official services.

License

This repository is released under the GNU Affero General Public License v3.0 (AGPLv3). You are free to use, modify, and distribute the code, provided that any modified versions, including those offered over a network, make their complete source code available under the same license.

Project Background

This project started as a personal tool to automatically generate subtitles for videos, for my young 7 years old son. Over time, it evolved into a highlights generator for movies, sports, and personal videos.

The primary goal remains practical: speed up video analysis, generate highlights you can explain, and create accessible subtitles automatically — without uploading footage you would rather keep local.