Luentotulkki - MIT-licenced private university lecture transcriber for Finnish

I made a convenience tool that did the video watching for me in my previous article: Ripped for your pleasure - this makes the pipeline a nice package for your convenience. Feel free to mod it and utilize it commercially, since it's a bunch of open source tools bunched together.

Kalle Tolonen
3.9.2026

This does about the same job as the Teams summary generator, but only to any video you have url/embedded access to. It has atm some hard coded prompts for my kind of 'lets get to the point already' optimizations for the summary/pdf generation, but you can ofc mod it to your needs.

It does capture and insert the slides too at the right time to what's discussed too, but publishing a pic of that would be copyright infringement, so I wont do that.

Example generated pdf page with details redacted from a lecture summary

Geeky stuff (how the magic happens)

1. Stream Ingestion & Resolving (`catch_stream.py`)
   - Reads the copied browser DevTools cURL command or URL directly from the clipboard.
   - Converts individual HLS chunk/segment URLs (`seg-*.ts`) into the master playlist (`index.m3u8`).
   - Extracts authentication headers (User-Agent, Referer, Cookie, Origin) and downloads the stream with FFmpeg.
   - Auto-numbers the target video sequentially (e.g., `03_luento.mp4`).

2. Slide Keyframe Extraction (`extract_slides.py`)
   - Scans the video stream using FFmpeg visual scene detection (`gt(scene, 0.15)`).
   - Extracts high-quality JPEG screenshots of each slide change (`slide_MMmSSs.jpg`).
   - Enforces a minimum time interval to avoid duplicate frames during animations.

3. Audio Preprocessing (`rip-transcribe.sh`)
   - Extracts audio track from the video file using FFmpeg.
   - Preprocesses audio for speech recognition: resamples to 16 kHz mono, applies a speech bandpass filter (75 Hz–7.5 kHz), and levels volume (`speechnorm`).

4. Metal GPU Transcription (`transcribe.py`)
   - Transcribes audio on Apple Silicon GPU using `mlx-whisper` (`whisper-large-v3-turbo`).
   - Matches slide timestamps with spoken speech and chronologically embeds slide tags: `![Dia (MM:SS)](...)`.
   - Generates three outputs: raw timestamped transcript (`.txt`), clean transcript (`_clean.txt`), and subtitle file (`.srt`).

5. 3-Pass Local LLM Editorial Pipeline (`Ollama` + `qwen3.6:35b-a3b`)
   - Pass A (Error Correction): Fixes phonetic transcription slips, typos, compound word errors, and spoken filler words (`_korjattu.txt`).
   - Pass B (Drafting): Synthesizes the lecture into a structured course summary with embedded slide placements.
   - Pass C (Academic Polish): Removes repetition, polishes language to formal Finnish academic prose, and enforces clear formatting for assignment requirements and deadlines.

6. Heading Deduction & Folder Packaging (`rip-transcribe.sh`)
   - Extracts the top-level topic `# <Otsikko>` from the LLM summary and converts it to a clean slug (e.g., `kyberturvallisuuden_johtaminen`).
   - Creates a dedicated folder: `${NUM}_${SLUG}/` (e.g., `03_kyberturvallisuuden_johtaminen/`).
   - Moves video, slides, all transcript formats, and Markdown into the folder.
   - Rewrites slide links to clean relative paths (`slides/slide_*.jpg`).

7. Vector PDF Rendering (`md_to_pdf.py`)
   - Cleans any remaining LLM thinking tags or terminal ANSI artifacts.
   - Converts Markdown to HTML with styled typography and slide figures.
   - Renders a publication-grade vector PDF directly inside the lecture folder using headless Chrome/Chromium (`--headless=new`).

Github available here.