I built a pipeline that turns a topic into a 20-30 minute documentary

A developer built AI Video Factory, an MIT-licensed Python pipeline that turns a single topic into a complete 20-30 minute documentary, handling script research and writing, per-scene visuals, local TTS narration, music, karaoke captions, FFmpeg assembly, QC, thumbnails, and YouTube metadata. The pipeline validates every cited URL with a real HTTP check, writes scripts in timed beats to hold target runtime, and prefers real stock footage over AI-generated visuals for long-form coherence. It runs against any OpenAI-compatible endpoint with local TTS and no cloud dependency.

I've been working on AI Video Factory, an MIT-licensed Python pipeline that turns a topic into a complete 20-30 minute documentary: researched script, per-scene visuals, local TTS narration, music bed, karaoke captions, FFmpeg assembly, QC, thumbnail, and YouTube metadata. You give it a topic. It researches the topic, writes a script with citations, pulls per-scene visuals from Pexels/Pixabay/NASA, narrates it with local TTS, lays down a music bed, burns in karaoke captions, assembles everything in FFmpeg, runs a QC gate, and outputs a thumbnail plus YouTube metadata. Seven presets: business autopsy, history reconstruction, science doc, horror anthology, and more. 1. LLM-written scripts invent citations that look completely legit. The first scripts came back with references that sounded real but pointed nowhere. Now every cited URL gets a real HTTP check against a 200 response. Anything that fails the check gets cut or rewritten. 2. A "25 minute" video kept coming out at 14 minutes. Runtime drift is a real problem with generated scripts. Scripts are now written in timed beats, and underweight beats get extended at roughly 150 wpm until the target runtime holds. 3. Real stock footage beats AI visuals for most scenes. This one honestly surprised me. AI-generated visuals look impressive frame by frame but feel off across a 20-minute runtime. The pipeline prefers real footage, with local still generation and procedural fallbacks when nothing fits. Scripting runs against any OpenAI-compatible endpoint LM Studio, llama-server , TTS is local, and nothing cloud is required. Roadmap includes Whisper-based caption alignment, more music profiles, optional Veo/Sora providers, and multilingual narration. The repo is here: https://github.com/summitsingh/ai-video-factory https://github.com/summitsingh/ai-video-factory I genuinely don't know if anyone besides me wants this. Tell me what you'd throw at it first.