# Javis — Local Voice AI Agent Wake-word activated, Ollama-backed, MCP-enabled voice assistant with audiobook-driven voice cloning. ## Architecture ``` microphone ─▶ VAD / wake word ─▶ STT (faster-whisper) │ ▼ Ollama LLM (llama3.1:8b) ◀─▶ MCP servers (filesystem, fetch, time, …) │ ▼ TTS (XTTS v2, cloned voice) │ ▼ speaker ``` - **STT** — faster-whisper, CUDA float16, default `large-v3`. - **TTS** — Coqui XTTS v2 (community fork). Zero-shot cloning plus optional fine-tune from audiobook snippets. - **LLM** — local Ollama at `http://localhost:11434`, `llama3.1:8b` (native tool calling). - **MCP** — host/client only, reads `mcp_servers.json` (VS Code-compatible). - **Wake word** — openWakeWord, default `hey_jarvis` (custom "Javis" model trainable later). - **GUI** — PySide6 + qasync. ## Prerequisites - Windows 10/11 - Python 3.10 or 3.11 (XTTS does not yet support 3.12) - NVIDIA GPU + recent CUDA drivers (CPU works but slow) - A running Ollama instance with the chosen model pulled: ```powershell ollama pull llama3.1:8b ``` - For MCP servers: Node.js (for `npx`) and/or `uv` (`uvx`). ## Install ```powershell cd Javis python -m venv .venv .\.venv\Scripts\Activate.ps1 pip install -e . # all Python deps (a CPU torch may be pulled in as a fallback) # Detects your GPU / driver and swaps in the matching PyTorch wheel (skip on CPU-only) python -m javis --install-deps copy config.example.yaml config.yaml copy mcp_servers.example.json mcp_servers.json # Verify: Ollama reachable, model pulled, CUDA/torch loadable, audio libs present python -m javis --doctor ```
Manual install (if you prefer to pick the torch wheel yourself) ```powershell # CUDA torch first (skip if CPU-only) — pick the index matching your driver pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu124 pip install -r requirements.txt pip install -e . ``` If torch later fails to import (DLL errors), run `python -m javis --repair`.
## Run ```powershell .\run.bat # GUI python -m javis # GUI python -m javis --headless # no GUI (tool calls with approval "ask" are denied — set "allow" per server) python -m javis --doctor # check environment ``` First launch opens a configuration dialog (models, microphone with live level meter, persona). Untick *Show this dialog on every start* to skip it next time; reopen it via **Tools → Startup configuration…**. Closing the window hides Javis to the system tray so wake word and timers keep running — quit via **File → Quit** or the tray menu. Push-to-talk: `Ctrl+Space` in the window, `Ctrl+Alt+Space` system-wide (Windows). ## Voice profiles 1. Open the **Voices** panel. 2. Drag an audiobook file (mp3/wav/flac) in. 3. Javis will VAD-split, transcribe, and build a clean dataset. 4. Test with zero-shot cloning (instant) or click **Fine-tune** for higher quality. ## MCP servers Edit `mcp_servers.json`. Schema matches VS Code's `mcp.json`, so you can copy/paste server entries. First-time tool calls require approval (configurable per server). ## Custom wake word The default wake phrase is *hey Jarvis*. To use your own name (e.g. **Rufus**), train a small openWakeWord model on synthetic speech (< 1 h, Colab or WSL2), drop `rufus.onnx` into `~/.javis/models/wakeword/` and set `wake_word.model: rufus`. Slaves fetch the model from the master automatically. Step-by-step: [docs/WAKE_WORD.md](docs/WAKE_WORD.md). ## Satellites (multi-room) [javis-slave](javis-slave/README.md) turns a Windows tablet or a **Raspberry Pi** into a satellite: local wake word, audio streamed to this master over the LAN, spoken reply played back locally. Enable `api.enabled: true` here, generate a pairing code in the **Devices** tab, then follow [javis-slave/RASPBERRY_PI.md](javis-slave/RASPBERRY_PI.md) for a headless Pi (`sudo ./setup-pi.sh`) or run `run.bat` on a tablet. [javis-slave-mcp](javis-slave-mcp/README.md) exposes the satellites' volume/mute/power controls as tools so you can say "mach die Küche leiser". ## Project layout ``` Javis/ src/javis/ core/ config, logger, state machine, event bus audio_io/ devices, mic stream, VAD, wake word stt/ faster-whisper tts/ backend interface, XTTS implementation voices/ profiles, audiobook ingest, fine-tune llm/ Ollama client, conversation memory mcp_client/ host, schema bridge agent/ orchestrator, system prompts gui/ PySide6 main window + panels ``` ## Status Initial scaffold. End-to-end voice loop works once dependencies and a voice reference clip are provided.