meetus — repo index

A standalone, local-first command-line tool: turn screen-share meeting/training recordings into a rich, AI-summarizable transcript. The extraction pipeline is deterministic and offline; the summarization step is the only LLM-facing part.

meetus stands on its own. The separate cht project does the same thing in realtime; meetus may or may not feed it, so the two are kept formally apart (the cht-facing bits here live under ctrl/cht/).

This file maps the whole repo. README.md is the detailed manual for the main process_meeting.py CLI.

Data flow

video / audio
   │  process_meeting.py                       (deterministic, offline)
   ▼
output/<run>/                                  run folder: YYYYMMDD-NNN-<stem>/
   ├── <stem>.json                             Whisper/WhisperX transcript (base)
   ├── <stem>.srt / .vtt / ...                 extra transcript formats (optional)
   ├── frames/                                 extracted scene/interval frames
   ├── <stem>_enhanced.txt                     transcript + frame refs   ◀── the product
   └── manifest.json
   │  ctrl/summarize/*.py                       (local LLM — WIP, on hold)
   ▼
<stem>_summary_simple.md / _reference.md

make batch (→ ctrl/batch.sh) runs the pipeline over a whole directory tree, mirroring the input folder structure into the output.

Status legend

✅ active · 🚧 WIP, on hold · 🗄️ deprecated (kept for reference) · 🧰 one-off / niche · 📄 docs

Layout

process_meeting.py        ✅ the CLI tool — entry point (stays at root)
Makefile                  ✅ batch convenience wrapper
meetus/                   ✅ core package
  ├─ workflow.py             orchestrator (whisper → frames → merge)
  ├─ frame_extractor.py      FFmpeg scene-detection / interval frames
  ├─ transcript_merger.py    interleave transcript + frame refs → enhanced.txt
  ├─ output_manager.py       run-folder naming + manifest.json
  ├─ cache_manager.py        per-step caching (skip done work on rerun)
  └─ deprecated/          🗄️ old screen-text idea (OCR/vision/hybrid) — unwired, reference only
       ├─ ocr_processor.py       (was --ocr-engine)
       ├─ vision_processor.py    (was --use-vision)
       ├─ hybrid_processor.py    (was --use-hybrid)
       └─ prompts/               vision context prompts
ctrl/                     control plane / operational scripts
  ├─ batch.sh             ✅ recursive batch runner (mirrors tree, continues past failures)
  ├─ transcribe_oneoff.sh 🧰 high-quality re-transcription over an existing run
  ├─ summarize/           🚧 last step — local-LLM summarization (WIP, on hold)
  │    ├─ summarize_simple.py    minimal map-and-append; reads every referenced frame
  │    ├─ compile_meeting.py     REFINE-pattern technical reference; on-demand frames
  │    └─ summarize_meeting.py   map→extract(validated facts)→reduce
  └─ cht/                 🧰 bridge to the separate realtime `cht` project
       └─ interleave_cht_frames.py   whisperx JSON + cht frames/index.json → enhanced.txt
def/                      📄 design/decision notes, in order (the feature history)
README.md                 📄 manual for process_meeting.py
INDEX.md                  📄 this file
MARIAN.md                 📄 genesis brainstorm (explains why the deprecated OCR path exists)
local-run.sh              📄 personal scratch invocations (gitignored)

Notes

Default flow uses --embed-images (frames referenced for the LLM to read) + --scene-detection --scene-threshold 10 --diarize. make batch adds --transcript-formats srt and writes outputs next to the sources. See README.md.
meetus/deprecated/ is no longer imported or reachable from the CLI (its flags were removed and workflow.py no longer imports it). Kept for reference only; the realtime continuation of the idea is the separate cht project.
ctrl/summarize/ scripts are standalone; run them under an env with the openai client (e.g. ~/wdir/llm/.venv) against a local OpenAI-compatible server.

Loose ends

README.md's "Output Files" example still shows the old run-folder format YYYYMMDD_HHMMSS-video (actual: YYYYMMDD-NNN-<stem>). Minor; worth a tidy.

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