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197 stars · Python

How to use Autoclip: what you can do with it

AutoClip is a local-first Python app that turns a long video into ranked, captioned, speaker-tracked 9:16 clips using Whisper plus Ollama or your own LLM key.

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What do you want to do?

3 use cases

  • Turn a long video into captioned vertical clips offline

    Paste a YouTube link or drop a file and get ranked 9:16 clips with burned-in captions, all on your machine.

    View prompt
    Install artbyjazi/autoclip, run `autoclip doctor` until it passes, then clip <path or YouTube URL> with the local Ollama provider. Report how many clips were exported and confirm each MP4 is 9:16 with captions burned in.

  • Use your own LLM key for sharper clip selection

    Point highlight detection at Anthropic, OpenAI, Gemini or an OpenAI-compatible endpoint while video stays local.

    View prompt
    Configure artbyjazi/autoclip with `autoclip config set-secret anthropic`, re-run highlight detection on <video>, and list the top five clips with timestamps. Verify that no audio or video file was uploaded, only transcript text.

  • Run the clipper in Docker with GPU acceleration

    Start AutoClip from the bundled compose file, optionally with the GPU profile for faster transcription.

    View prompt
    Bring up artbyjazi/autoclip with `docker compose -f docker/compose.yaml --profile gpu up --build` on <host>, clip one short test video, and confirm the export finished and the GPU was used for transcription.

Quick start with Autoclip

  1. Check you have what it needs: Python 3.11 or 3.12 (not 3.13); A full ffmpeg build with libass and libx264; Node 20+ to build the UI.
  2. Open artbyjazi/autoclip on GitHub and read the README and license.
  3. Clone the repo with git clone https://github.com/artbyjazi/autoclip.git
  4. Install with cd autoclip && uv venv --python 3.11 && uv pip install -e ".[dev]"
  5. Build the UI with cd frontend && npm install && npm run build && cd ..
  6. Pull a local model with ollama pull llama3.1:8b and check your setup with autoclip doctor

These steps come from our top use case, Turn a long video into captioned vertical clips offline, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forCreators and small teams who want vertical clips from long videos without uploading footage to a hosted clipper.
  • Why it is hot197 stars, launched 85 days ago
  • LanguagePython
  • Sourcegithub.com/artbyjazi/autoclip · Back to Hot repos

Requirements

  • Python 3.11 or 3.12 (not 3.13)
  • A full ffmpeg build with libass and libx264
  • Node 20+ to build the UI
  • Ollama for fully local runs, or an Anthropic, OpenAI, Gemini or OpenAI-compatible key

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Questions about Autoclip

What is Autoclip?

AutoClip is a local-first Python app that turns a long video into ranked, captioned, speaker-tracked 9:16 clips using Whisper plus Ollama or your own LLM key. It is an open-source project on GitHub: https://github.com/artbyjazi/autoclip.

Who is Autoclip for?

Creators and small teams who want vertical clips from long videos without uploading footage to a hosted clipper.

What can I do with Autoclip?

3 use cases so far, for example: Turn a long video into captioned vertical clips offline; Use your own LLM key for sharper clip selection; Run the clipper in Docker with GPU acceleration. Each has steps and a copy-paste agent prompt.

What do I need to run Autoclip?

Python 3.11 or 3.12 (not 3.13); A full ffmpeg build with libass and libx264; Node 20+ to build the UI; Ollama for fully local runs, or an Anthropic, OpenAI, Gemini or OpenAI-compatible key.

What are alternatives to Autoclip?

Other hot AI repos in Content: Voice Studio, Anatomy, Video Agent, Brand System Skill, Invoke AI Windows Setup Guide.

Is Autoclip safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.