Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
v1.1.0: Add setup.sh one-command installer (embeds systemd services inline). Fix manage.sh hardcoded devices — now uses VOICE_MIC/VOICE_SPEAKER env vars. Remove unused json import. Fix fragile test-tts heredoc. Remove cmake from runtime requires. Clean up SKILL.md: quick start section, fixed file structure, removed missing systemd/ dir reference.
What This Skill Does
Offline voice assistant pipeline for NVIDIA Jetson devices. Chains wake word detection, voice activity detection, GPU-accelerated speech-to-text, LLM inference, and neural TTS. Only the LLM call requires internet access.
Running STT and TTS fully on-device eliminates round-trip latency and keeps audio data off third-party servers, which cloud-dependent pipelines cannot offer.
When to use it
- Hands-free home automation control on a Jetson-based device
- Private voice queries without sending audio to cloud STT services
- Multilingual voice assistant that auto-detects spoken language
- Always-on desk assistant with LED visual state feedback
- Embedded voice interface for robotics or kiosk projects
Example Workflow
Here's how your AI assistant might use this skill in practice.
User says 'Hey Jarvis, what's the weather like today?'
- 1openWakeWord detects the wake phrase and triggers a two-tone beep with blue LED
- 2_measure_ambient() samples 480ms of room noise to set dynamic VAD thresholds
- 3transcribe_stream() captures speech via VAD, sends audio to whisper.cpp CUDA server, returns transcript
- 4ask_llm() sends transcript plus 20-turn conversation history to OpenRouter API and receives response
- 5Piper TTS synthesizes the response offline and aplay outputs it while LED shows cyan then off
Spoken answer played through ALSA speaker, LEDs return to off state
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.