Tools/pierrenade/short-video-generator-AI

short-video-generator-AI

Free open-source project designed for turning youtube-viedos into viral short videos. Highlight detection, subtitles, translation, voiceover, all in one for your content.

1.1kemergingPythonMIT Licensenew this week

The Lens

By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.

Updated Sep 2026

This turns a long YouTube video into vertical clips without you scrubbing a timeline. It downloads the source, transcribes locally with faster-whisper, classifies the content type, then scores every transcript segment against a virality rubric covering hooks, emotional peaks, and quotables. Overlapping candidates collapse and the top few render as 9:16 verticals, each able to open with an AI-written hook. It targets OpusClip, which charges a subscription for the same job.

You are not stuck in a terminal. There is a CLI, a local web page for queueing videos and setting flags visually, and an API for your own code. Transcription runs on your machine, so the only external cost is one LLM provider for classification and scoring. Gemini has a free tier with daily limits, MuAPI is pay per use, OpenAI is paid only.

Python 3.10 or newer and an env file. Whisper model size runs from tiny to large-v3 depending on your GPU. Solo creators and anyone repurposing a podcast are the audience. MIT, free, no watermarks, no per-clip credits.

The catch is that the headline promises more than the pipeline documents. Subtitles, translation, and voiceover are all advertised up top, but the seven documented steps end at cropping and the optional hook. Confirm they exist before building a workflow on them.

Free vs Self-Hosted vs Paid

fully free

Free tier: The pipeline is MIT and complete. Download, transcription, content classification, virality scoring, deduplication, and 9:16 rendering with an optional AI hook all ship in the repository, with no watermark and no per-clip credits.

Self-hosted: The only mode, and the compute is yours. Python 3.10 or newer plus the requirements file. Run it three ways: a CLI, a local web page for queueing several videos and setting render flags visually, or an API you can call from your own code. faster-whisper runs locally and the model size is your dial, with tiny fast on a laptop CPU and large-v3 wanting a GPU. Rendering is the other CPU cost. Budget machine time, not money.

Paid: No tier from the project, but the classification and scoring steps need an LLM provider. Gemini has a free tier with daily request limits that covers casual use, MuAPI bills pay per use with no subscription, and OpenAI is paid only. Pick Gemini and you can run this at zero marginal cost until you hit the daily cap.

Unverified: The repository headline advertises subtitles, translation, and voiceover. None of the three appears in the documented seven-step pipeline or the feature list. Treat them as unconfirmed until you test them.

Free to run if you pick the Gemini free tier and own the GPU. Compare that to a monthly OpusClip seat.

What to do by team size

Solo
free; Gemini free tier plus a local whisper model covers it
Small team
free; one machine with a GPU serves a small content team
Medium team
free; move scoring to a paid provider key once daily limits bite
Large team
Not a team product; evaluate a hosted editor instead
Self-hosting ops:moderate

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Score
52/100 · C+
Adoption12/30
Maintenance10/25
Community5/20
License15/15
Analysis10/10

A low score is not a verdict on quality. Young and niche tools start low by design. How we calculate scores

License: MIT License

Use freely, including commercial. Just keep the license.

Commercial use: ✓ Yes

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