short-video-maker README

The project's README from github.com/mushigaite/short-video-maker. As one markdown file: README.md.

AI shorts generator

Python Stars Forks

A free open-source project designed to turn youtube-videos into viral short videos. Highlight detection, subtitles, translation, voiceover, all in one for your content: no pre-clip credits or any watermarks. Designed for creators who want an alternative to short-video SaaS tools like OpusClip or Vidyo.ai for free.

Examples of a processed video

image
"The Speech that Made Obama President" 		image
"How Tom Overcame Social Anxiety - The Mindset That Changed Everything" 		image
"How to stay calm when you know you'll be stressed | Daniel Levitin | TED"

Features 🪁

How It Works

  1. Download: Fetches the source video from YouTube, or uses the local file path directly
  2. Transcribe: faster-whisper produces a timestamped transcript locally — same step regardless of which LLM_PROVIDER is selected
  3. Detect content type: The chosen LLM classifies the video (podcast, interview, tutorial, vlog, etc.) and pacing, so the highlight prompt can be tuned per content style
  4. Highlight ranking: The chosen LLM (openai / gemini / muapi) scans the transcript through a virality framework — hook moments, emotional peaks, opinion bombs, revelations, conflict, quotables, story peaks, practical value — and emits ranked candidates with scores 0–100
  5. Dedupe: Overlapping candidates are collapsed by score
  6. Top-N selection: The top --n candidates are selected
  7. Auto-crop: Each highlight is rendered as a vertical short at the requested --ratio, with an optional AI-generated hook at the start unless --no-hook is set

Requirements

Quick start

  1. Clone the repo:
git clone https://github.com/mushigaite/short-video-generator-AI.git
cd short-video-generator-AI
  1. Create and activate a virtual environment:

Windows:

python -m venv venv
venv\Scripts\activate

Linux/MacOS:

python -m venv venv
venv\Scripts\activate
  1. Install dependencies
pip install -r requirements.txt
  1. Set up .env
# Used LLM provider(openai/gemini/MuAPI)
LLM_PROVIDER=openai

# Enter the API key for the chosen provider
OPENAI_API_KEY=your_openai_key_here
OPENAI_MODEL=gpt-4o-mini          # optional
GEMINI_API_KEY=your_gemini_key_here
GEMINI_MODEL=gemini-2.5-flash      # optional
MUAPI_API_KEY=your_muapi_key_here

# Whisper settings
LOCAL_WHISPER_MODEL=base # tiny / base / small / medium / large-v3
LOCAL_WHISPER_DEVICE=auto    # auto / cpu / cuda

If you uncertain about the provider:

Provider Free tier? Get key
Gemini ✅ Yes, but daily limit https://aistudio.google.com
OpenAI ❌ Paid https://platform.openai.com
MuAPI ❌ Paid, but pay-per-use, no subscription https://muapi.ai

Usage

Basic usage:

python main.py "https://www.youtube.com/watch?v=video_id"

Renderred clips are saved to output folder

With flags:

python main.py "https://www.youtube.com/watch?v=video_id" \
      --n 3 \
      --ratio 9:16 \
      --resolution 1080 \

You can also provide a local video file instead of a YouTube link, for example:

python main.py "/Users/Admin/Folder/video.mp4" \
      --n 4 \
      --ratio 9:16 \
      --resolution 720 \
      --language zh \

CLI Flags

Flag Default Notes
--n 3 How many clips to render
--ratio 9:16 Any ratio / 9:16 for short videos / 1:1 for square
--resolution 720 Source video download resolution: 360 / 480 / 720 / 1080
--language auto Force Whisper language code (e.g. en)
--no-hook - Excludes AI generated hook from the start of the clip

Web version set-up

Instead of the CLI, you can use a local web interface to queue multiple videos at once and adjust flags visually. The LLM provider and API keys are still configured in .env — the web version only handles video input and render flags.

python3 server.py

Serve the frontend:

cd web
python3 -m http.server 8000

Then, open in your browser:

http://localhost:8000/shorts-generator-ui.html

Contributing

Feel free to open an issue or fork the repository for your projects.

License

This project is licensed under the MIT License.

short-video-maker (mushigaite/short-video-maker)

short-video
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629 ·GitHub
Short-video-maker
What is this?short-video-maker turns a YouTube link (or a local video file) into a ready-to-post vertical short by automatically detecting the most viral highlight moments, then adding subtitles, translation and AI voiceover — no watermarks or per-clip credits.
Is this a CLI tool, or does it have a web interface too?It's primarily a Python CLI, but it also ships a local web UI so you can queue multiple videos and adjust render flags visually instead of typing commands.
How do I get it running?Clone the repo, create and activate a Python virtual environment, install requirements.txt, set up a .env file with your LLM API key, then run the CLI with a YouTube link or serve the web frontend.
Do I need an OpenAI or Gemini API key?Yes — you need a key from one LLM provider (OpenAI, Gemini, or MuAPI) for highlight detection and hooks. Gemini offers a free tier with a daily limit, while OpenAI and MuAPI are paid, pay-per-use for MuAPI.
Any catch with the free Gemini option?The free Gemini tier comes with a daily request limit, so heavy usage will eventually push you toward a paid provider like OpenAI or MuAPI.
Does it run on Windows, Mac, and Linux?Yes, the setup instructions give separate virtual-environment commands for Windows and for Linux/MacOS, so it's cross-platform.
How does it pick which moments are the highlights?An LLM scans the transcript using a virality framework (hook moments, emotional peaks, conflict, quotables, story peaks, practical value), scores each candidate 0-100, dedupes overlapping clips by score, and keeps the top N.
What does the hook option do?When enabled, it adds a short, context-aware AI-generated hook at the start of the clip to grab viewers before the main highlight plays; you can disable it with --no-hook.
Can I process a local video file instead of a YouTube link?Yes, you can pass a local file path directly in place of a YouTube URL and it goes through the same highlight, subtitle, and crop pipeline.
How is transcription handled?faster-whisper generates a timestamped transcript locally, and this step is identical no matter which LLM provider you choose for highlight ranking.
What are similar projects?autoclip, MoneyPrinterTurbo, herk-edit-kit, youtube-automation-agent, storycast, youtube-agentic-ai-studio, Easel, OpenReels
Which topics is it in?short-video, ai-video, video-editing, whisper, youtube, content-creation, text-to-speech, translation, video-generation, multilingual, video, gemini
Creator
mushigaite
Size
1.3 MB
Stars
629
Updated
CommitOct 17
Age
13d
Commits · 90 Days