I wired a fully offline voice loop to Ollama + LM Studio — 100% CPU, no GPU, nothing leaves your machine (Silero VAD + Parakeet STT + Supertonic TTS 3)

I kept wanting to *talk* to my local models instead of typing, but every voice setup wanted a GPU, shipped my audio to the cloud, or was macOS-only. So I built one that's none of those — and I benchmarked it, so these are real measured numbers, not vibes. **One command installs the whole stack and wires it into your agent. Then you just talk.** Everything runs on CPU and stays off your GPU (your GPU is busy running the actual LLM): - **Silero VAD** — knows when you start/stop talking, no push-to-talk. ~0.09 ms/frame. - **Parakeet TDT 0.6B v3** — local ONNX INT8 STT, 25 languages, OpenAI-compatible on :5093. A 2.5 s clip transcribes in ~280 ms (~9× realtime). - **Supertonic TTS 3** — local ONNX FP16 synthesis, multilingual, voices F1–F5 / M1–M5. A short reply renders in ~1.7 s (1.6–2.8× realtime), and a TTS→STT round-trip comes back word-for-word. **Measured on a plain i7-12700KF, CPU only, no GPU touched** — both my 3090s were full serving the LLM itself in vLLM, which is exactly the point: voice runs on CPU, VRAM stays with your model. **Works with whatever agent you use — one install drops a `talk` skill into all of them:** Claude Code, Hermes Agent, OpenClaw, OpenCode, and Codex. The same installer also auto-installs and starts the STT + TTS backends for you. **Data flow — nothing leaves the box:** you -> Silero VAD (CPU) -> Parakeet STT (CPU) -> your LLM (Ollama / LM Studio / vLLM) -> Supertonic 3 (CPU) -> speakers **Install (macOS / Linux):** git clone https://github.com/groxaxo/opencode-voice-service cd opencode-voice-service && ./setup.sh **Windows (PowerShell):** .\setup.ps1 The installer is interactive (pick components + agent integrations) and auto-starts via systemd / launchd / Task Scheduler. Free and MIT-licensed. **GitHub:** https://github.com/groxaxo/opencode-voice-service Runs fine on a 4-year-old ThinkPad with no GPU. Happy to answer VAD-tuning or ONNX-performance questions.