Higangssh/ghostmeet

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Última actividad de origen registrada: 31 de julio de 2026 a las 14:29

Catálogo de SkillsMP actualizado: 26 de octubre de 2026 a las 11:37

skills recopiladas: 1

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<img src="assets/logo.jpg" width="200" alt="ghostmeet logo"> # ghostmeet **Your invisible AI meeting assistant** — Live captions and smart summaries, right in your browser. [Quick Start](#quick-start) • [How It Works](#how-it-works) • [Features](#features) • [API](#api) > **Renewing as MeetButler.** ghostmeet is becoming MeetButler, a desktop app for Windows > and macOS that records in-person meetings as well as web calls, fully on your machine. > It is under development in `app/` and `crates/` and not ready to use yet. The Chrome > extension and Python backend described below are v0.1, now in [`legacy/`](legacy/), and > still work as documented. --- <img src="assets/demo.gif" alt="ghostmeet demo" width="800"> ## What is ghostmeet? ghostmeet silently captures audio from any browser tab — Google Meet, Zoom, Teams, or anything with sound — and transcribes it in real-time using Whisper. When the meeting ends, click **Summarize** and AI extracts key decisions, action items, and next steps. It runs as a **Chrome Extension side panel**. Other participants can't see it. Like a ghost in your meeting. 👻 - **100% local** — audio never leaves your machine - **No accounts** — no sign-up, no cloud, no subscriptions - **Works everywhere** — any tab that plays audio ## Features - 🎙️ **Real-time transcription** — Whisper STT, updates every 10 seconds - ⏱️ **Built for long meetings** — cost per pass stays flat, so a 4-hour session behaves like a 4-minute one - 📋 **AI-powered summaries** — Key decisions, action items, next steps - 💾 **Nothing is lost** — transcripts are written to SQLite as they happen and survive a restart - 🌏 **Per-meeting language** — pick the language in the side panel, or let Whisper detect it - 🔒 **Self-hosted** — your audio stays on your machine, and the server listens on loopback only - 🐳 **One-command setup** — `docker compose up` and you're ready - 👻 **Invisible** — side panel UI, no one in the meeting knows ## How It Works ``` Browser Tab (Zoom / Meet / Teams) │ audio ▼ Chrome Extension ├── service worker — asks Chrome for a tab stream id └── offscreen page — records it, and plays it back so you still hear the meeting │ ▼ WebSocket (webm/opus, 1s chunks) Local Backend (FastAPI) ├── one demuxer per session ──→ PCM appended to disk ├── Whisper reads only the newest window, never the whole recording ├── segments ──→ SQLite + live captions in the side panel └── Claude API (on demand) ──→ Meeting Summary ``` Everything runs on your machine. The only external call is to Claude API when you click Summarize (optional — transcription works without it). Audio is decoded once as it arrives and kept on disk, and each transcription pass reads only a bounded window of it. That is what keeps a long meeting from getting slower and slower, and keeps memory flat no matter how long you record. ## Quick Start ### Prerequisites - **Docker** (recommended) or Python 3.10+ - **Chrome** browser ### 1) Start the backend ```bash git clone https://github.com/Higangssh/ghostmeet.git cd ghostmeet/legacy # Copy and edit config (add your Anthropic API key for summaries) cp .env.example .env # Start with Docker docker compose up -d ``` Backend is ready when you see `http://0.0.0.0:8877` in the logs. <details> <summary>Manual install (without Docker)</summary> ```bash python3 -m venv .venv && source .venv/bin/activate pip install -r requirements.txt python -m backend ``` Note: First run downloads the Whisper model (~150MB for `base`). </details> ### 2) Install Chrome Extension 1. Open **chrome://extensions** in Chrome 2. Enable **Developer mode** (toggle in top-right) 3. Click **Load unpacked** 4. Select the `legacy/extension/` folder from this repo 5. Pin the 👻 icon in your toolbar ### 3) Use it 1. **Join a meeting** — Open Google Meet, Zoom, Teams (or any tab with audio) 2. **Click 👻** — Side panel opens on the right 3. **Pick a language** (optional) — or leave it on Auto-detect 4. **Click ▶ Start** — Live captions appear as people speak. The tab stays audible. 5. **Click ■ Stop** — The panel says "Transcription complete" once the last pass finishes 6. **Click 📋 Summarize** — AI generates a structured summary Reopening the side panel mid-meeting brings the transcript so far back with it. That's it. No sign-up, no config, no cloud. ## Configuration Set these in `.env` or `docker-compose.yml`: | Variable | Default | Description | |----------|---------|-------------| | `GHOSTMEET_MODEL` | `base` | Whisper model size (`tiny` / `base` / `small` / `medium` / `large`) | | `GHOSTMEET_DEVICE` | `auto` | Compute device (`auto` / `cpu` / `cuda`) | | `GHOSTMEET_COMPUTE_TYPE` | `float32` | Precision (`int8` is much faster on CPU) | | `GHOSTMEET_LANGUAGE` | auto-detect | Default language (`en` / `ko` / `ja` / etc.) — the side panel can override it per meeting | | `GHOSTMEET_CHUNK_INTERVAL` | `10` | Seconds between transcription updates | | `GHOSTMEET_ANTHROPIC_KEY` | — | Required for AI summaries | | `GHOSTMEET_HOST` | `127.0.0.1` | Server bind address (loopback — the API has no auth) | | `GHOSTMEET_PORT` | `8877` | Server port | **Model size guide:** - `tiny` — fastest, least accurate (~75MB) - `base` — good balance (recommended, ~150MB) - `small` — better accuracy, slower (~500MB) - `medium` / `large` — best accuracy, needs GPU ## API | Endpoint | Method | Description | |----------|--------|-------------| | `/api/health` | GET | Health check + model info | | `/api/sessions` | GET | List all sessions | | `/api/sessions/{id}` | GET | Session details | | `/api/sessions/{id}/transcript` | GET | Full transcript | | `/api/sessions/{id}/summarize` | POST | Generate AI summary | | `/api/sessions/{id}/summary` | GET | Get generated summary | | `/ws/audio` | WS | Audio ingest (binary chunks; send `stop` as text to finish) | | `/ws/transcript/{id}` | WS | Live transcript stream | Sessions, transcripts and summaries are stored in `recordings/ghostmeet.db`, so every endpoint above keeps working after the backend restarts. ## Development MeetButler (Rust, from the repository root): ```bash cargo test --workspace # no model, no network ``` v0.1 (from `legacy/`): ```bash cd legacy python -m venv .venv ./.venv/Scripts/python.exe -m pip install -r requirements-dev.txt ./.venv/Scripts/python.exe -m pytest tests/ -q # backend suite node --test tests/extension/shared.test.mjs # extension helpers ``` The suite needs no Whisper model and no network — real opus audio goes through the real decoder, and only inference is stubbed. There is one thing tests cannot reach: `chrome.tabCapture.getMediaStreamId()` needs the `activeTab` grant that only a real toolbar click produces. Everything after that point is covered by `node tests/extension/verify-capture.mjs`, which drives the extension in a real browser against a running backend (needs `npm install playwright && npx playwright install chromium`). To check the last step by hand: start a capture on a tab with audio and confirm `audio_bytes` climbs in `/api/sessions` — and that you can still hear the tab. ## Project Structure ``` ghostmeet/ ├── app/ # MeetButler desktop app (Tauri 2), in development ├── crates/ # MeetButler engine and audio capture (Rust) ├── bench/ # Korean STT engine benchmarks └── legacy/ # v0.1, laid out as below legacy/ ├── extension/ # Chrome MV3 Extension │ ├── manifest.json # permissions + side panel config │ ├── background.js # service worker: gets a tab stream id, drives capture │ ├── offscreen.html/js # hidden page that actually records → WebSocket │ ├── sidepanel.html/js # live captions, language picker, summaries │ ├── popup.html/js # start/stop controls │ ├── shared.js # pure helpers shared by the above │ └── icons/ ├── backend/ # Python backend (FastAPI) │ ├── app.py # HTTP + WebSocket server │ ├── decoder.py # streaming webm/opus → PCM (one demuxer per session) │ ├── pcm_store.py # append-only audio on disk, windowed reads │ ├── incremental.py # bounded-window transcription, absolute timestamps │ ├── pipeline.py # receive / decode / transcribe, decoupled │ ├── transcriber.py # shared Whisper model + per-session language │ ├── store.py # SQLite: sessions, segments, summaries │ ├── summarizer.py # Claude API integration │ └── models.py # session model ├── tests/ # pytest suite + extension tests ├── docker-compose.yml # one-command deployment ├── Dockerfile # backend container └── requirements.txt # Python dependencies ``` ## OpenClaw Integration ghostmeet works as an [OpenClaw](https://github.com/openclaw/openclaw) skill. Control your meetings from chat. ```bash # Install the skill clawhub install ghostmeet ``` Then just ask your AI assistant: - **"Summarize my last meeting"** → generates AI summary from latest session - **"How many meetings did I have today?"** → lists all sessions - **"What was discussed?"** → fetches full transcript - **"Extract action items"** → pulls tasks from the summary > The skill handles session listing, transcript retrieval, and summary generation via the ghostmeet API. Recording start/stop is done through the Chrome Extension. ## Roadmap - [x] Real-time transcription (Whisper) - [x] Chrome Extension side panel UI - [x] AI meeting summaries (Claude) - [x] Long meetings — flat cost per pass, tested to 4h+ of audio - [x] Transcripts survive a restart (SQLite) - [x] Per-session language selection - [ ] Meeting context input + file attach - [ ] Speaker diarization (who said what) - [ ] In-person meetings (microphone capture) - [ ] Export to Markdown / PDF - [ ] Agent Mode — AI speaks in the meeting for you ## License [MIT](LICENSE)

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ghostmeet

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AI meeting assistant via ghostmeet. Start sessions, get live transcripts, and generate AI summaries from any browser meeting.

Idioma del texto original: inglés

2026-07-31

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