Local VoiceMode LLM

Give your AI agent a voice — and ears — that run entirely on your CPU.

Quick Start · Benchmarks · Integrations · Config

A complete, local voice pipeline for AI agents. One command installs everything: Silero VAD for detecting when you speak, Parakeet TDT 0.6B for transcription, and Supertonic TTS 3 for synthesis. No cloud, no API keys, no GPU required.

It drops a talk skill into Claude Code, OpenCode CLI, OpenClaw, Hermes Agent, and Codex, then installs and starts the speech backends for you. Pick your agent, run the installer, start talking.

Why CPU-only?

Because you don’t need a GPU for great voice — and the one you have is busy.

Every engine here runs on ONNX, tuned for CPU inference on Intel, AMD, and Apple Silicon. No CUDA, no ROCm, no driver hell. It runs the same on a laptop, in WSL, inside Docker, or on a CI machine. On a typical multi-GPU rig, that means your VRAM stays fully committed to the LLM doing the actual thinking, while the voice layer hums along on cores you weren’t using anyway.

The numbers below are measured, not estimated — and reproducible.

Engine	Runtime	CPU latency	Footprint
Silero VAD	ONNX	~0.1 ms/frame	~1.3 MB
Parakeet TDT 0.6B v3	ONNX INT8	~280 ms short reply · 8–21× realtime	~600 MB
Supertonic TTS 3	ONNX FP16	~1.7 s short reply · 1.6–2.8× realtime	~196 MB
Supertonic TTS 2 (optional)	ONNX	~0.8 s short reply · 3.4–10.5× realtime	~252 MB

Benchmarks

Measured on an Intel Core i7-12700KF (12C/20T desktop), CPU-only, median of 5 runs. Reproduce it against your own services:

python benchmarks/run_benchmark.py     # writes benchmarks/RESULTS.md

Stage	Input	Latency	vs. realtime
Silero VAD	32 ms frame	0.09 ms	~347×
Parakeet STT	2.4 s utterance	307 ms	7.9×
Parakeet STT	6.6 s utterance	441 ms	14.9×
Parakeet STT	13.4 s utterance	729 ms	18.4×
Supertonic · normal (8 steps)	→ 2.4 s audio	1.39 s	1.7×
Supertonic · normal (8 steps)	→ 13.4 s audio	5.18 s	2.6×
Supertonic · high (20 steps)	→ 2.4 s audio	2.46 s	~1×
Supertonic · high (20 steps)	→ 13.4 s audio	10.2 s	1.3×

Supertonic defaults to 8 denoising steps — short replies in ~1.4 s, faster than realtime. Set TTS_QUALITY=high for 20 steps when quality matters more than speed. A TTS→STT round-trip transcribes back verbatim.

The voice overhead around your LLM is ~1.5–2 s (STT + TTS combined). In practice, the slowest part of the loop is the LLM itself.

Parakeet can use onnxruntime-gpu if you have spare VRAM — but the whole point is to leave the GPU for the model that’s answering you. The installer auto-detects your hardware: on a Linux box with an NVIDIA GPU it asks whether to use CUDA (and defaults to CPU if you decline or run non-interactively); Apple Silicon and CPU-only hosts stay on the ONNX-CPU path (this GPU toggle is Linux + NVIDIA only). Force it either way with ./setup.sh --gpu or ./setup.sh --cpu.

Supertonic 2 — optional, even faster on CPU

Supertonic 2 is an optional backend (bash integrations/supertonic2/install.sh, then TTS_ENGINE=supertonic2). Measured back-to-back on the same i7-12700KF, both CPU-only (median of 5, voice F4), it synthesizes ~3.2× faster than Supertonic 3 at the default 8 steps:

Reply	Audio	Supertonic 3 (8 steps)	Supertonic 2 (8 steps)	Speed-up
short (10 words)	2.4 s	1.98 s · 0.82 RTF	0.78 s · 0.29 RTF	2.6×
medium (22 words)	6.6 s	3.12 s · 0.48 RTF	0.99 s · 0.15 RTF	3.2×
long (45 words)	13.4 s	5.44 s · 0.41 RTF	1.44 s · 0.10 RTF	3.8×

At high quality (20 steps) the gap widens to ~3.4× (mean RTF 0.31 vs 1.07). Both engines share the same OpenAI-compatible API and voices (F1–F5 / M1–M5), so switching is just TTS_ENGINE; Supertonic 2 runs on :8880 and coexists with Supertonic 3 (:8766), falling back to it automatically. Full numbers and the reproduce script: benchmarks/TTS_BACKENDS.md · python benchmarks/compare_tts_backends.py.

Apple Silicon (Apple M5)

Measured on a MacBook Air (Apple M5), median of 3 runs. On Apple Silicon, TTS can run on the Neural Engine (CPU_AND_NE) while ONNX Parakeet handles STT.

Stage	Input	Latency	vs. realtime
Parakeet STT · ONNX (CPU)	2.4 s utterance	0.26 s	9×
Parakeet STT · ONNX (CPU)	14.0 s utterance	0.54 s	26×
Parakeet STT · CoreML (ANE)	2.4 s utterance	0.075 s	33×
Parakeet STT · CoreML (ANE)	14.0 s utterance	1.23 s	11×
Supertonic 3 · CoreML (ANE)	→ 1.2 s audio	0.16 s	~7×
Supertonic 3 · CoreML (ANE)	→ 3.5 s audio	0.21 s	16.6×

On the Neural Engine, Supertonic 3 TTS synthesizes 8–30× faster than CPU ONNX. For STT, ONNX Parakeet is faster than the CoreML recognizer on longer audio (0.54 s vs 1.23 s) and is identical across macOS/Linux/Windows — so it stays the default everywhere; the CoreML recognizer only edges ahead on very short clips (~0.075 s vs ~0.26 s), a gap well below conversational perception.

Architecture

  Mic ──▶ Silero VAD ──▶ WAV ──▶ Parakeet STT (:5093, ONNX, CPU)
    (local ONNX)                          │
                                          ▼
                                  Agent / OpenCode / Claude Code
                                          │
                                          ▼
                     ┌──────────────────────────────────────┐
                     │ Supertonic TTS (:8766) — default     │  ONNX, CPU
                     │ Supertonic 2  (:8880)  — optional    │  ONNX, CPU
                     │ NeuTTS (:8020)         — fallback 1   │  local GGUF
                     │ xAI (api.x.ai)         — fallback 2   │  cloud API
                     └──────────────────────────────────────┘
                                          │
                                          ▼
                                  playback ──▶ listen again

  Browser ──▶ Dashboard (:7862) ──▶ Supertonic :8766  (TTS test)
                (frontend/)      ──▶ Parakeet   :5093  (STT test)
                                 ──▶ systemctl         (GPU/CPU toggle)

Ports: Supertonic uses :8766 (not :8765) so it can coexist with an existing Chatterbox server — override with SUPERTONIC_PORT=8765 to replace it. Parakeet STT runs on :5093; if a precompiled speech-server is already there, setup.sh detects it and leaves it alone.

Features

Three CPU-native engines — Silero VAD, Parakeet STT (25 languages), Supertonic TTS (EN/ES/KO/PT/FR)
Multi-engine TTS with fallbacks — Supertonic (local ONNX) → NeuTTS (local GGUF) → xAI (cloud, last resort); local engines are always tried before the cloud
Pipelined talk loop — TTS finishes, mic opens instantly (TALK_AUTO_LISTEN=1)
Barge-in — interrupt playback by speaking (opt-in, TALK_BARGE_IN=1)
Five agents, one skill — Claude Code, OpenCode CLI, OpenClaw, Hermes, Codex
Web dashboard — test and tune every setting live at :7862, no npm, no build step
Cross-platform — macOS, Linux, Windows
Non-destructive installs — existing services are preserved; re-running setup.sh is safe

Platform support

Platform	Installer	Auto-start	Audio
macOS	`setup.sh`	launchd	`afplay`
Linux	`setup.sh`	systemd (user)	`ffplay` / `aplay` / `paplay`
Windows	`setup.ps1`	Task Scheduler	`ffplay` / SoundPlayer

Quick Start

macOS / Linux

git clone https://github.com/groxaxo/opencode-voice-service.git
cd opencode-voice-service
chmod +x setup.sh && ./setup.sh

Run with no arguments for an interactive menu — choose which components (Parakeet, Supertonic) and which agents (Claude Code, OpenCode, OpenClaw, Hermes, Codex) to install. Or go straight through:

./setup.sh                                       # full install, all components + agents
./setup.sh --skip-parakeet                       # skip Parakeet STT
./setup.sh --skip-supertonic                     # skip Supertonic TTS
./setup.sh --integrations=claudecode,opencode    # only these agents

# Optional: cloud TTS fallback
export XAI_API_KEY=xai-...

Backends are installed and running when it finishes. That’s the whole setup.

Windows (PowerShell)

git clone https://github.com/groxaxo/opencode-voice-service.git
cd opencode-voice-service
.\setup.ps1

Same component/agent prompts, then registers Task Scheduler tasks that start Parakeet and Supertonic on login.

Prerequisites: Python 3.11+ (winget install Python.Python.3.12), Git (winget install Git.Git), and optionally ffmpeg for playback (winget install Gyan.FFmpeg).

What gets installed

Component	Location	Port	Auto-start
Voice venv (VAD + ONNX)	`~/.config/opencode/tts-venv/`	—	—
Parakeet STT	`~/.config/opencode/parakeet-stt/`	5093	launchd / systemd / Task Scheduler
Supertonic TTS	`~/.config/opencode/supertonic-tts/`	8766	launchd / systemd / Task Scheduler
Supertonic 2 (opt-in)	`~/.config/opencode/supertonic2-tts/`	8880	`integrations/supertonic2/install.sh`
Web dashboard	`frontend/` (repo)	7862	manual (`bash frontend/start.sh`)
`talk` skill	per-agent (see below)	—	—

Optional: Supertonic 2. Supertonic Express 2 (model onnx-community/Supertonic-TTS-2-ONNX) is a 66M-param, CPU-only, multilingual ONNX TTS with the same OpenAI-compatible API. Add it with bash integrations/supertonic2/install.sh, then select it with TTS_ENGINE=supertonic2 — it runs on :8880 alongside Supertonic 3 and falls back to it automatically. See integrations/supertonic2/.

Agent integrations

The installer copies the talk skill into each selected agent’s skill directory. Same SKILL.md descriptor everywhere — it tells the agent when to invoke voice (talk, voice, speak, habla, audio, tts), how to run the VAD → STT → TTS loop, and where the services live.

Agent	Skill path	Activation
Claude Code	`~/.claude/skills/talk/`	`skill("talk")` or auto-detected
OpenCode CLI	`~/.config/opencode/skills/talk/`	`skill("talk")`
OpenClaw	`~/.openclaw/skills/talk/`	`skill("talk")`
Hermes Agent	`~/.hermes/skills/talk/`	`skill("talk")`
Codex	`~/.codex/skills/talk/`	auto-detected via symlink

More installer options:

./setup.sh --venv-only          # only create the voice venv
./setup.sh --skip-voices        # skip reference voice generation
./setup.sh --no-integrations    # skip all agent integrations
./setup.sh --force              # overwrite existing plists/tasks (destructive)
./setup.sh --uninstall          # stop services, remove plists
./setup.sh --uninstall --force  # also remove installed dirs

Talk to a local LLM (Ollama)

Point the voice loop at a model running in your local Ollama — speak to it, hear it reply, entirely offline. If Ollama is already installed, one command wires it up (no Ollama rebuild):

bash integrations/ollama/install.sh     # installs the `ollama-voice` command + voice backends
ollama-voice                            # talk to your default model — speak after the tone, Ctrl-C to exit
ollama-voice llama3.2 --text            # choose a model; type instead of speaking (mic-free test)

ollama-voice drives the listen → chat → speak loop against Ollama's HTTP API (the same one ollama run uses), reusing this project's CPU STT/TTS — so any model you can ollama run, you can talk to. See integrations/ollama/ for configuration and a native ollama voice subcommand (build-from-source) alternative.

Web Dashboard

A single-page control panel for testing and tuning all three components live. No npm, no framework — open it in a browser.

cd frontend && bash start.sh
# → http://localhost:7862

Panel	Controls
TTS Test	Voice (F1–F5 / M1–M5), language, inference steps (1–20), speed (0.5–2×) → plays in-browser
STT Test	Record from mic or upload a WAV → transcribes via Parakeet
VAD Settings	Threshold, min silence, pre-speech padding, max duration → saved to `frontend-config.json`
Backend Settings	GPU/CPU toggle per service → writes a systemd drop-in, restarts immediately, live status badges

A FastAPI proxy on :7862 forwards requests to Supertonic and Parakeet so you don’t hit CORS. Dependencies install into the existing tts-venv on first launch.

Usage

CLI

talk.sh listen                          # record + transcribe → stdout
talk.sh speak "Hello"                   # synthesize, then auto-listen
TTS_ENGINE=xai talk.sh speak "…"        # force xAI cloud TTS
TTS_ENGINE=supertonic talk.sh speak "…" # force local Supertonic
talk.sh status                          # health check
talk.sh devices                         # list mics

(Skill lives at ~/.config/opencode/skills/talk/. On Windows, use talk.ps1 with the same verbs.)

Agent talk loop

The agent runs:

Once: talk.sh listen → first user message
Each turn: talk.sh speak '<reply>' → plays audio, then records; stdout is the next user message
Never call listen after speak — it’s built in.

Full rules in skill/SKILL.md.

Configuration

Variable	Default	Description
`STT_ENGINE`	`local`	STT backend — Parakeet on `:5093` (ONNX/CPU on Linux, CoreML on macOS)
`STT_URL`	`http://127.0.0.1:5093/v1/audio/transcriptions`	Local Parakeet endpoint
`TTS_ENGINE`	`supertonic`	`supertonic` (local ONNX) → `neutts` (local GGUF) → `xai` (cloud, last resort)
`SUPERTONIC_URL`	`http://127.0.0.1:8766`	Supertonic endpoint
`SUPERTONIC_VOICE`	`F4`	`F1`–`F5` / `M1`–`M5`
`TTS_QUALITY`	`normal`	`normal` = 8 steps (fast) · `high` = 20 steps (best)
`SUPERTONIC_STEPS`	(from quality)	Denoising steps `1`–`20`; overrides the preset
`XAI_API_KEY`	(env)	Bearer token for xAI cloud fallback
`XAI_TTS_VOICE`	`eve`	`ara` · `eve` · `leo` · `rex` · `sal`
`TALK_AUTO_LISTEN`	`1`	Run `listen` after `speak`
`TALK_BARGE_IN`	`0`	Interrupt TTS on speech
`TALK_IDLE_TIMEOUT_S`	`300`	Session-silence window — end listen after N s of no speech (`0` = off)
`VAD_THRESHOLD`	`0.5`	Speech sensitivity — lower = catches softer speech, higher = ignores background noise/speech (also in dashboard)
`VAD_MIN_SILENCE_MS`	`700`	End-of-turn silence — 700 ms tolerates mid-sentence pauses; lower (~500) for snappier turns (also in dashboard)
`MIC_QUERY`	(empty)	Mic name substring; empty = auto-detect (honors the OS system-default input, skips virtual adapters)
`PORT`	`7862`	Dashboard port

Tuning the mic for your room

Silero VAD listens through one microphone with no speaker separation — it captures whatever crosses the speech threshold, including a TV, music, or other people talking nearby. In a quiet one-on-one setting it's accurate out of the box; in a noisy room you may need to tune two knobs:

Symptom	Fix
Picks up background speech / TV / other people	Raise `VAD_THRESHOLD` toward `0.6`–`0.7` (stricter — only clearer, louder speech triggers)
Misses your speech / clips soft talkers	Lower `VAD_THRESHOLD` toward `0.3`–`0.4` (more sensitive)
Cuts you off during a natural pause	Raise `VAD_MIN_SILENCE_MS` (e.g. `900`) so longer pauses don't end the turn
Feels sluggish to respond after you stop	Lower `VAD_MIN_SILENCE_MS` toward `500` for snappier endpointing
Grabs the wrong microphone	Set `MIC_QUERY` to a substring of your mic's name (e.g. `MIC_QUERY="Headset"`); see `talk.sh devices`

# Example: noisy room, want it to only react to clear, deliberate speech
VAD_THRESHOLD=0.65 VAD_MIN_SILENCE_MS=800 talk.sh listen

All values are also adjustable live in the Web Dashboard (saved to frontend-config.json).

Service management

macOS (launchd)

launchctl kickstart -k gui/$UID/com.opencode.parakeet-stt   # restart
launchctl bootout      gui/$UID/com.opencode.parakeet-stt   # stop
launchctl kickstart -k gui/$UID/com.opencode.supertonic
launchctl bootout      gui/$UID/com.opencode.supertonic

tail -f ~/.config/opencode/parakeet-stt.log
tail -f ~/.config/opencode/supertonic.log

Linux (systemd)

systemctl --user start  opencode-parakeet-stt
systemctl --user status opencode-parakeet-stt
journalctl --user -u    opencode-parakeet-stt -f

systemctl --user start  opencode-supertonic
systemctl --user status opencode-supertonic
journalctl --user -u    opencode-supertonic -f

Windows (Task Scheduler)

Start-ScheduledTask "OpenCode-Parakeet-STT"
Stop-ScheduledTask  "OpenCode-Parakeet-STT"
Start-ScheduledTask "OpenCode-Supertonic"
Stop-ScheduledTask  "OpenCode-Supertonic"

Get-Content "$env:USERPROFILE\.config\opencode\parakeet-stt.log" -Tail 50
Get-Content "$env:USERPROFILE\.config\opencode\supertonic.log"   -Tail 50

Project layout

opencode-voice-service/
├── setup.sh / setup.ps1     # installers (macOS+Linux / Windows)
├── service/
│   ├── vad_recorder.py      # Silero VAD + sounddevice
│   ├── talk.sh              # voice conversation orchestrator
│   ├── tts.sh               # multi-engine TTS CLI
│   └── tts_lang.sh          # language detection
├── windows/talk.ps1         # Windows orchestrator
├── skill/SKILL.md           # agent skill descriptor
├── launchd/                 # macOS auto-start plists
├── frontend/                # web dashboard
├── integrations/ollama/     # talk to a local Ollama model by voice (autoinstaller + command)
└── benchmarks/              # reproducible benchmark suite

Related projects

parakeet-tdt-0.6b-v3-fastapi-openai — STT backend
supertonic-express-3 — Supertonic 3 TTS runtime
supertonic-3-v2 — Supertonic 3 FP16 ONNX model
OpenVoiceApp — iOS voice app
OpenCode

License

MIT

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Repository files navigation

Local VoiceMode LLM

Why CPU-only?

Benchmarks

Supertonic 2 — optional, even faster on CPU

Apple Silicon (Apple M5)

Architecture

Features

Platform support

Quick Start

macOS / Linux

Windows (PowerShell)

What gets installed

Agent integrations

Talk to a local LLM (Ollama)

Web Dashboard

Usage

CLI

Agent talk loop

Configuration

Tuning the mic for your room

Service management

Project layout

Related projects

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Name		Name	Last commit message	Last commit date
Latest commit History 49 Commits
benchmarks		benchmarks
docs		docs
frontend		frontend
img		img
integrations		integrations
launchd		launchd
service		service
skill		skill
systemd		systemd
windows		windows
.env.example		.env.example
.gitignore		.gitignore
README.md		README.md
setup.ps1		setup.ps1
setup.sh		setup.sh

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Local VoiceMode LLM

Why CPU-only?

Benchmarks

Supertonic 2 — optional, even faster on CPU

Apple Silicon (Apple M5)

Architecture

Features

Platform support

Quick Start

macOS / Linux

Windows (PowerShell)

What gets installed

Agent integrations

Talk to a local LLM (Ollama)

Web Dashboard

Usage

CLI

Agent talk loop

Configuration

Tuning the mic for your room

Service management

Project layout

Related projects

License

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