Voicelayer Mcp

Your AI agent can't hear you. VoiceLayer gives it ears and a voice.

npm License: Apache-2.0 MCP Tools Tests

Voice I/O for AI coding assistants. Press F5, speak to Claude Code, get on-device transcription in under 1.5 seconds. Your AI speaks back. Works with any MCP client.

  You ──🎤──> whisper.cpp ──> Claude Code ──> edge-tts ──🔊──> You
         STT (local)           MCP tools         TTS (free)

Local-first. Free. Open-source. No cloud APIs, no API keys, no data leaves your machine. Part of the Golems ecosystem.

Website | Docs | npm

VoiceLayer runs as a persistent singleton daemon on a Unix socket — every Claude session connects through a lightweight socat shim instead of spawning its own process. 2 canonical MCP tools plus 9 backward-compatible aliases ship with full ToolAnnotations.

Architecture

                  ┌─────────────────────────────────────┐
                  │         VoiceLayer Daemon            │
                  │     /tmp/voicelayer-mcp.sock         │
                  │                                      │
                  │  MCP JSONRPC ──> Tool Handlers       │
                  │  (Content-Length     ├── voice_speak  │
                  │   framing)          └── voice_ask    │
                  │                                      │
                  │  TTS: edge-tts (retry + 30s timeout) │
                  │  STT: whisper.cpp / Wispr Flow       │
                  │  VAD: Silero ONNX (speech detection)  │
                  │  IPC: Voice Bar ← NDJSON events      │
                  └──────────┬──────────────────────────┘
                             │ Unix socket
              ┌──────────────┼──────────────┐
              │              │              │
         Claude Code    Claude Code    Cursor/Codex
         (socat shim)  (socat shim)   (socat shim)

Why a daemon? The original design spawned a new Bun process per Claude session. With 17+ repos open, that meant 17 competing processes (700+ MB RAM), fighting over one Voice Bar socket, crashing edge-tts with PATH issues, and leaving orphans that never died. The daemon architecture — shipped in PRs #67-72 — replaced all of that with a single process and socat shims.

Metric	Before (spawn-per-session)	After (daemon)
Processes	N per session (17+ typical)	1 daemon + socat shims
RAM	~700 MB (17 x 41 MB)	~50 MB
Orphan cleanup	Manual pkill	PID lockfile auto-kills stale
edge-tts failures	Random (PATH, contention)	Retry with 30s hard timeout
voice_ask hang	Up to 300s (5 min!)	30s default + outer guard

Quick Start

# Install from npm
bun add -g voicelayer-mcp

# Prerequisites
brew install sox socat
pip3 install edge-tts
brew install whisper-cpp  # optional — local STT

# Download a whisper model (recommended)
mkdir -p ~/.cache/whisper
curl -L -o ~/.cache/whisper/ggml-large-v3-turbo.bin \
  https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo.bin

Or install from source:

git clone https://github.com/EtanHey/voicelayer.git
cd voicelayer && bun install

Start VoiceBar

# Build + install /Applications/VoiceBar.app (also retires the old
# standalone daemon LaunchAgent via launchd/install.sh).
voicelayer build-app        # or: bash flow-bar/build-app.sh

# Launch the installed app; the app owns the child daemon
voicelayer bar

voicelayer build-app is the canonical builder — it compiles VoiceBar from source, installs to /Applications/VoiceBar.app (override with --install-path), refuses to overwrite a running VoiceBar, and runs launchd/install.sh afterward. voicelayer bar then opens the installed /Applications/VoiceBar.app (it no longer builds a bare dev binary); if the app isn't installed it tells you to run voicelayer build-app first.

VoiceLayer's daily-driver supervision chain is launchd -> VoiceBar.app -> child MCP daemon. The standalone com.voicelayer.mcp-daemon LaunchAgent is retired because a launchd-owned daemon cannot reliably inherit VoiceBar's microphone permission. launchd/install.sh now removes that old LaunchAgent; it does not install or bootstrap a daemon plist. VoiceBar launches the daemon child from the installed bundle or checkout and restarts it on crashes, clean exits, and broken-mic silence signals.

Disabling VoiceLayer

DISABLE_VOICELAYER=1 and /tmp/.voicelayer-daemon-disabled are hard kill-switches for the MCP daemon child.

# Disable daemon child launch/restart
touch /tmp/.voicelayer-daemon-disabled

# Re-enable daemon child launch/restart
rm -f /tmp/.voicelayer-daemon-disabled

When the flag exists, VoiceBar treats exit 0 as an explicit terminal stop. All other child exits reschedule a restart.

Updating VoiceLayer

voicelayer update is a cross-machine updater that runs on whichever Mac you invoke it on (it auto-detects a git checkout vs a global package install). It updates the package, rebuilds /Applications/VoiceBar.app, runs launchd/install.sh, pulls the Qwen3-TTS model into ~/.voicelayer if missing, and restarts the VoiceBar stack.

voicelayer update --dry-run            # print the plan without running it
voicelayer update                      # package + app + model + restart

# Optionally sync personal runtime data (voices, vocabulary, daemon secret):
voicelayer update --data-mode direct       --data-source other-mac.local:/Users/<you>
voicelayer update --data-mode brain-drive  --data-source /Volumes/BrainDrive/VoiceLayerBackup/<you>

Personal-data sync is opt-in (--data-mode skip is the default). When enabled, it rsyncs ~/.voicelayer/voices, voices.json, pronunciation.yaml, daemon.secret, and the STT vocabulary from the source host.

Configure MCP Clients

Add to your .mcp.json (in any repo where you use Claude Code):

{
  "mcpServers": {
    "voicelayer": {
      "command": "socat",
      "args": ["STDIO", "UNIX-CONNECT:/tmp/voicelayer-mcp.sock"]
    }
  }
}

Or migrate all repos at once:

bash scripts/migrate-to-daemon.sh         # migrates every .mcp.json under ~/Gits
bash scripts/migrate-to-daemon.sh --dry-run  # preview without changes

Grant microphone access to your terminal (macOS: System Settings > Privacy > Microphone).

Voice Tools

Primary tools

Tool	Behavior	Blocking	readOnly	destructive	idempotent
voice_speak	TTS with auto-mode (announce/brief/consult/think), replay, toggle	No	false	false	true
voice_ask	Speak question + record mic + transcribe response	Yes	false	false	false

Backward-compatible aliases

Alias	Maps to	idempotent
qa_voice_announce	voice_speak(mode='announce')	true
qa_voice_brief	voice_speak(mode='brief')	true
qa_voice_consult	voice_speak(mode='consult')	true
qa_voice_say	voice_speak(mode='announce')	true
qa_voice_think	voice_speak(mode='think')	false
qa_voice_replay	voice_speak(replay_index=N)	true
qa_voice_toggle	voice_speak(enabled=bool)	true
qa_voice_converse	voice_ask	false
qa_voice_ask	voice_ask	false

All 11 tools include MCP ToolAnnotations. No VoiceLayer tools are destructive. All have openWorldHint: false.

How voice_ask Works

1. Waits for any playing voice_speak audio to finish
2. Speaks the question via edge-tts (with retry on failure)
3. Records mic at device native rate, resamples to 16kHz
4. Silero VAD detects speech onset and silence end
5. whisper.cpp transcribes locally (~200-400ms on Apple Silicon)
6. Returns transcription to the AI agent

Reliability Features

• PID lockfile (/tmp/voicelayer-mcp.pid): On startup, detects and kills any orphan MCP server from a previous session
• edge-tts retry: Health check (cached 60s) + automatic retry with 30s hard timeout per attempt
• Outer timeout guard: Promise.race wrapper around the entire voice_ask flow — if anything hangs, returns an error instead of blocking forever
• Session booking: Lockfile mutex prevents mic conflicts between concurrent sessions

Recording Controls

Method	How
Stop signal	touch ~/.local/state/voicelayer/stop-{token}
VAD silence	Configurable: quick (0.5s), standard (1.5s), thoughtful (2.5s)
Timeout	30s default, configurable 5-3600s per call
Push-to-talk	press_to_talk: true — no VAD, stop on signal only

STT Backends

Backend	Type	Latency	Setup
whisper.cpp	Local (default)	~200-400ms	brew install whisper-cpp + model download
Wispr Flow	Cloud (fallback)	~500ms + network	Set QA_VOICE_WISPR_KEY env var

Auto-detected. Override with QA_VOICE_STT_BACKEND=whisper|wispr|auto.

Voice Bar (macOS)

Floating SwiftUI widget providing visual feedback during voice interactions. Connects to the daemon via NDJSON over /tmp/voicelayer.sock.

• Teleprompter with word-level highlighting and auto-scroll
• Waveform visualization during recording
• Expandable pill UI — collapses to dot after 5s idle
• Draggable, position persisted across launches
• Global hotkey: F5 (hold for push-to-talk)

bun add -g voicelayer-mcp
voicelayer build-app            # Build + install /Applications/VoiceBar.app
voicelayer hotkey install       # Install Dictation-key -> F18 relay
voicelayer bar                  # Launch the installed VoiceBar.app

Hotkey Notes:

• Requires Input Monitoring permission (System Settings > Privacy & Security)
• On keyboards where the physical key is Apple's Dictation key, voicelayer hotkey install installs a hidutil LaunchAgent to map the Dictation key to VoiceBar's internal F18 relay. Physical F5 is handled natively by VoiceBar.
• The installer preserves non-VoiceBar hidutil mappings and is safe to rerun. Shift+F5 re-pastes the latest transcript.
• voicelayer hotkey status prints the LaunchAgent state and the current hidutil key mapping.

Settings

Open VoiceBar's Settings window for in-app configuration:

• General — a permissions and hotkey setup panel (Microphone, Accessibility, Input Monitoring, and the hidutil relay) with per-row status plus quick "Open" and "Set up" actions.
• Audio — microphone input device + a Performance effort picker (see below).
• Dictionary — STT corrections and prompt terms (the same vocabulary managed by voicelayer vocab).

Performance (STT effort tiers). The Audio tab exposes three decoding-effort tiers that trade latency for accuracy on the same large-v3-turbo model (they only change whisper.cpp's beam search/best-of, not the model):

Tier	whisper.cpp args	Notes
Fast	-bo 1 -bs 1	Lowest decode cost
Balanced	-bo 3 -bs 3	Middle ground
Accurate	-bo 5 -bs 5	Default; widest beam

The selection persists to ~/.local/state/voicelayer/whisper-performance.json. Override per-process with QA_VOICE_WHISPER_PERFORMANCE_EFFORT=fast|balanced|accurate.

Advanced: Voice Cloning

Three-tier TTS engine cascade for cloned voices:

1. XTTS-v2 fine-tuned (cadence + timbre)
2. F5-TTS MLX zero-shot (local, no daemon)
3. Qwen3-TTS daemon (HTTP-based)
4. edge-tts fallback (always available)

voicelayer extract <youtube-url>   # Extract voice samples
voicelayer clone <name>            # Build voice profile
voicelayer daemon --port 8880      # Run Qwen3-TTS server

The Qwen3 daemon now uses bearer auth from ~/.voicelayer/daemon.secret (created on first launch with mode 0600). The TypeScript bridge reads the same file automatically. Override the location with VOICELAYER_TTS_DAEMON_SECRET_FILE, VOICELAYER_TTS_AUTH_TOKEN_FILE, or voicelayer daemon --daemon-secret-file ... if you need a custom launcher path. The daemon only accepts Host: 127.0.0.1:8880 / Host: localhost:8880, rejects non-local Origin headers, and only reads reference_wav files that resolve under ~/.voicelayer/voices/.

Environment Variables

Variable	Default	Description
QA_VOICE_STT_BACKEND	auto	STT backend: whisper, wispr, or auto
QA_VOICE_WHISPER_MODEL	auto-detected	Path to whisper.cpp GGML model
QA_VOICE_WHISPER_PERFORMANCE_EFFORT	accurate	STT decode effort: fast, balanced, or accurate
QA_VOICE_WISPR_KEY	--	Wispr Flow API key (cloud fallback)
QA_VOICE_TTS_VOICE	en-US-JennyNeural	edge-tts voice ID
QA_VOICE_TTS_RATE	+0%	Base speech rate
VOICELAYER_TTS_DAEMON_SECRET_FILE	~/.voicelayer/daemon.secret	Preferred override for the shared Qwen3 daemon bearer secret file
VOICELAYER_TTS_AUTH_TOKEN_FILE	~/.voicelayer/daemon.secret	Backward-compatible override for the shared Qwen3 daemon bearer secret file

Testing

bun test                              # 585 Bun tests + 1 skip (latest verified on PR #190 pre-push gate)
bash flow-bar/run_tests.sh            # 144 Swift tests for VoiceBar
git config core.hooksPath .githooks   # install repo pre-push hook once per clone (#181, #182)

Test coverage includes: MCP protocol framing, tool handlers, TTS synthesis + retry, VAD speech detection, session booking, process lock lifecycle, socket client reconnection, edge-tts health checks, schema validation, Hebrew STT eval baselines, daemon resilience, ToolAnnotations, SSML sanitization, and secure path hardening.

Recent Hardening (2026-04-27 → 2026-05-02)

One-week sprint focused on VoiceBar reliability and a recording corpus to fight STT regressions. Every line below traces to a merged PR.

Recording reliability

• Recording control clickability restored — F6 socket controls remained interactive while the pill animated (#188).
• Pill bottom anchor preserved during resize so the UI doesn't drift off-screen (#187).
• Waveform animates again on real audio input + redundant "listening" copy removed (#184).
• Waveform dynamic range restored above the silence gate (#185).
• Custom VoiceBar install paths supported (no more hard-coded /Applications/VoiceBar.app) (#186).
• VoiceBar transcription preserved through the recording RMS gate so quiet speech survives (#177).
• Stale daemon restart detection — VoiceBar transcription resumes automatically after the daemon restarts (#183).

STT quality

• No-input STT hallucinations suppressed (#189).
• Zero-RMS audio ingestion watchdog catches a silent mic before whisper.cpp guesses (#178).

VoiceBar dictation corpus (Phase 1) — #190

• Every successful VoiceBar dictation is archived under ~/.local/share/voicelayer/recordings/YYYY-MM-DD/<timestamp-id>/ with audio.wav + voicelayer-transcript.txt + metadata.json (schema v1, SHA-256 over WAV bytes).
• Atomic rename + fsync so partial writes never appear in the corpus.
• Cancelled or empty transcriptions are skipped — only real dictations land on disk.
• Re-paste hotkey moved to Shift+F5; plain F5 is now the default record-start/stop activation through VoiceBar's F18 relay.

Test infrastructure

• VoiceLayer pre-push regression gate (#181) plus exit-0 fix on the success path (#182).
• voicelayer run_tests.sh orchestrator script unifies Bun + Swift + daemon-boot + Karabiner smoke runs (#180).
• VoiceBar audio fixtures for golden-path STT regressions (#179).

Project Structure

voicelayer/
├── src/                          # TypeScript/Bun (18K lines, 69 files)
│   ├── mcp-server-daemon.ts      # Singleton daemon entry point
│   ├── mcp-server.ts             # Stdio MCP server (legacy)
│   ├── mcp-daemon.ts             # Unix socket server (dual-protocol)
│   ├── mcp-framing.ts            # Content-Length + NDJSON framing
│   ├── mcp-handler.ts            # JSONRPC request router
│   ├── process-lock.ts           # PID lockfile (orphan prevention)
│   ├── handlers.ts               # Tool handler implementations
│   ├── tts.ts                    # Multi-engine TTS with playback queue
│   ├── tts-health.ts             # edge-tts health check + retry
│   ├── input.ts                  # Mic recording + STT pipeline
│   ├── vad.ts                    # Silero VAD (ONNX inference)
│   ├── stt.ts                    # STT backend abstraction
│   ├── socket-client.ts          # Voice Bar IPC (auto-reconnect)
│   ├── session-booking.ts        # Lockfile mutex
│   ├── paths.ts                  # Centralized path constants
│   └── __tests__/                # 536 tests across 48 files
├── flow-bar/                     # SwiftUI macOS app (1.9K lines, 9 files)
│   ├── Sources/VoiceBar/         # App source
│   └── Tests/                    # Swift tests
├── scripts/
│   ├── migrate-to-daemon.sh      # Batch .mcp.json migration
│   └── edge-tts-words.py         # Word-level TTS with timestamps
├── launchd/                      # VoiceBar LaunchAgent + retired daemon cleanup
├── models/                       # Silero VAD ONNX model
└── package.json                  # v2.0.0

Platform Support

Platform	TTS	STT	Recording	Voice Bar
macOS	edge-tts + afplay	whisper.cpp (CoreML)	sox	SwiftUI app
Linux	edge-tts + mpv/ffplay	whisper.cpp	sox	--

Part of Golems

VoiceLayer is one of three open-source MCP servers in the Golems ecosystem:

Server	What it does	Tools
BrainLayer	Persistent memory for AI agents — knowledge graph + hybrid search	12
VoiceLayer	Voice I/O — local STT, neural TTS, F5 push-to-talk	11
cmuxLayer	Terminal orchestration — spawn panes, read screens, coordinate agents	22

Pair with BrainLayer to remember voice conversations across sessions.

License

Apache-2.0