her-voice

name	Her Voice
description	Give your agent a voice. Use when the user wants the agent to speak, read aloud, or have voice responses.
metadata	{"openclaw":{"emoji":"🎙️","requires":{"bins":"[Truncated]"}}}

Her Voice 🎙️

Give your agent a voice. Audio responses powered by Kokoro TTS — a compact, naturally expressive model running entirely on-device.

✨ Features

Highly optimized response time thanks to on-the-fly audio streaming technology. 100% free, no API keys required. Inspired by Samantha and Sky.

• ⚡ On-the-fly Streaming — Audio plays as it generates, very low latency
• 👄 The Voice of an angel — Cutting-edge local text-to-speech model Kokoro TTS
• 🧠 TTS Daemon — Keep the model warm in RAM for instant responses (can be disabled to save RAM)
• 🖥️ Persist Mode — Drag & drop audio, paste text, use as a voice station
• 🔧 Fully Configurable — Voice, speed, visualizer, notification sounds
• 🍎 MLX + PyTorch — Native Metal acceleration on Apple Silicon, PyTorch fallback everywhere else
• 🎨 Real-time Visualizer — Floating 60fps LED bars that react to speech (macOS only)

First-Run Setup

python3 SKILL_DIR/scripts/setup.py

Note: SKILL_DIR is the root directory of this skill — the agent resolves it automatically when running commands.

The setup wizard will:

1. Detect platform and select TTS engine (MLX on Apple Silicon, PyTorch elsewhere)
2. Find or install the appropriate TTS backend (mlx-audio or kokoro)
3. Install espeak-ng (Homebrew on macOS, apt on Linux)
4. Patch espeak loader if needed (macOS compatibility)
5. Compile the native visualizer binary (macOS only)
6. Download the Kokoro model
7. Create config at ~/.her-voice/config.json

Check status anytime:

python3 SKILL_DIR/scripts/setup.py status

Post-Setup: Names & Pronunciation

After setup, configure the agent and user names:

python3 SKILL_DIR/scripts/config.py set agent_name "Jackie"
python3 SKILL_DIR/scripts/config.py set user_name "Matúš"
python3 SKILL_DIR/scripts/config.py set user_name_tts "Mah-toosh"

TTS pronunciation tip: If the user's name is non-English, figure out a phonetic English spelling that Kokoro will pronounce correctly. Store it in user_name_tts and use that spelling whenever speaking the name aloud. The real name stays in user_name for display purposes.

Speaking Text

# Basic usage
python3 SKILL_DIR/scripts/speak.py "Hello, world!"

# Skip visualizer for this call
python3 SKILL_DIR/scripts/speak.py --no-viz "Quick note"

# Save to file instead of playing
python3 SKILL_DIR/scripts/speak.py --save /tmp/output.wav "Save this"

# Override voice or speed
python3 SKILL_DIR/scripts/speak.py --voice af_bella --speed 1.2 "Faster!"

# Pipe text from stdin
echo "Piped text" | python3 SKILL_DIR/scripts/speak.py

Options

Flag	Description
--no-viz	Skip the visualizer for this call
--persist	Keep visualizer open after playback ends
--save PATH	Save audio to WAV file instead of playing
--voice NAME	Override the configured voice
--speed N	Override the configured speed multiplier
--mode MODE	Override visualizer mode (v2 or classic)

Agent Workflow

When the user wants voice responses:

1. Check voice mode — is voice enabled or did the user ask for it?
2. Play notification sound (instant feedback while TTS generates):

   afplay /System/Library/Sounds/Blow.aiff &
   

3. Speak the response:

   python3 SKILL_DIR/scripts/speak.py "Response text here"
   

4. Always provide text alongside voice — accessibility matters.

Notification Sound

The notification sound plays instantly (~0.1s) while TTS generates (~0.3-3s). This gives the user immediate feedback that the agent is responding.

Configure in ~/.her-voice/config.json:

{
  "notification_sound": {
    "enabled": true,
    "sound": "Blow"
  }
}

Available macOS sounds: Blow, Bottle, Frog, Funk, Glass, Hero, Morse, Ping, Pop, Purr, Sosumi, Submarine, Tink. Located in /System/Library/Sounds/.

TTS Daemon

The daemon keeps the Kokoro model warm in RAM, eliminating ~1.1s of startup overhead per call.

The daemon auto-resolves the mlx-audio venv — no need to find the venv Python manually.

# Start (persists in background)
nohup python3 SKILL_DIR/scripts/daemon.py start > /tmp/her-voice-daemon.log 2>&1 & disown

# Status
python3 SKILL_DIR/scripts/daemon.py status

# Stop
python3 SKILL_DIR/scripts/daemon.py stop

# Restart
python3 SKILL_DIR/scripts/daemon.py restart

speak.py auto-detects the daemon: uses it if available, falls back to direct model loading.

The daemon is optional. Without it, speech still works — just ~1s slower per call as the model loads each time. Skip the daemon to save ~2.3GB RAM.

Note: The daemon writes its PID file and socket after the model is fully loaded and ready to accept connections. They live in ~/.her-voice/ with restricted permissions (owner-only access). The daemon won't survive a reboot — start it again after restart if needed.

Visualizer

A floating overlay with three animated LED bars that react to speech in real-time. 60fps, native macOS (Cocoa + AVFoundation). macOS only — on other platforms, audio plays without the visualizer.

Modes

• v2 (default) — Three-tier pure red, center raw amplitude, sides with lag
• classic — Original smooth gradient look

Controls

Key	Action
ESC	Quit
Space	Pause/Resume (file mode)
← →	Seek ±5s (file mode)
⌘V	Paste text to speak (persist mode)

Persist Mode

Keep the visualizer on screen between playbacks. Use as a standalone voice station:

# Launch in persist mode (stays open, idle breathing animation)
~/.her-voice/bin/her-voice-viz --persist

# Stream mode + persist (stays open after speech ends)
python3 SKILL_DIR/scripts/speak.py --persist "Hello!"

In persist mode:

• Drag & drop audio files (.wav, .mp3, .aiff, .m4a) onto the visualizer to play them
• ⌘V pastes clipboard text → streams directly from TTS daemon with full visualizer animation
• Idle breathing — subtle center bar pulse when waiting for input

Standalone Usage

# Play a file with visualizer
~/.her-voice/bin/her-voice-viz --audio /path/to/file.wav

# Demo mode (simulated audio)
~/.her-voice/bin/her-voice-viz --demo

# Stream raw PCM
cat audio.raw | ~/.her-voice/bin/her-voice-viz --stream --sample-rate 24000

Disable Visualizer

python3 SKILL_DIR/scripts/config.py set visualizer.enabled false

Configuration

Config file: ~/.her-voice/config.json

# View all settings
python3 SKILL_DIR/scripts/config.py status

# Get a value
python3 SKILL_DIR/scripts/config.py get voice

# Set a value (dot notation for nested keys)
python3 SKILL_DIR/scripts/config.py set speed 1.1
python3 SKILL_DIR/scripts/config.py set visualizer.mode classic

Key Settings

Key	Default	Description
agent_name	""	Agent's name (e.g. "Jackie")
user_name	""	User's real name
user_name_tts	""	Phonetic spelling for TTS (e.g. "Mah-toosh" for Matúš)
voice	af_heart	Base voice name
voice_blend	{af_heart: 0.6, af_sky: 0.4}	Voice blend weights
speed	1.05	Speech speed multiplier
language	en	Language code
tts_engine	auto	TTS engine: auto, mlx, or pytorch
model	mlx-community/Kokoro-82M-bf16	Model identifier (MLX)
visualizer.enabled	true	Show visualizer overlay
visualizer.mode	v2	Animation mode (v2/classic)
visualizer.remember_position	true	Save window position between sessions
notification_sound.enabled	true	Play sound before speaking
notification_sound.sound	Blow	macOS system sound name
daemon.auto_start	true	Advisory flag only — the daemon never self-starts. When true, the agent should start it on first voice use (saves ~1s/call, costs ~2.3GB RAM)

Voice Selection

Voice Blending

Mix multiple voices for a unique sound. Configure voice_blend in config:

{
  "voice_blend": {"af_heart": 0.6, "af_sky": 0.4}
}

The blended voice is stored as a .safetensors file in the model's voices directory (e.g., af_heart_60_af_sky_40.safetensors). Create it by running TTS once — speak.py looks for the pre-blended file automatically.

Error Handling

Error	Cause	Fix
"mlx-audio not found"	Venv missing or broken	Run setup.py
"espeak-ng not found"	Phonemizer missing	brew install espeak-ng
Compilation failed	Xcode tools missing	xcode-select --install
"Model not found"	First run, no download	Run setup.py or speak once
Daemon "not running"	Crashed or rebooted	Start daemon again
No sound output	macOS audio permissions	Check System Settings → Sound → Output
Visualizer not showing	Binary not compiled	Run setup.py
"kokoro not found"	PyTorch venv missing	Run setup.py
PyTorch CUDA error	GPU driver mismatch	pip install torch --force-reinstall in kokoro venv
"soundfile not found"	Missing dependency	pip install soundfile in kokoro venv

Requirements

• macOS + Apple Silicon recommended for best experience (MLX engine + visualizer + notification sounds)
• Linux/Intel Mac supported via PyTorch Kokoro engine (no visualizer)
• Windows is not supported
• Xcode Command Line Tools for visualizer on macOS (xcode-select --install)
• espeak-ng for phonemization (brew install espeak-ng on macOS, apt install espeak-ng on Linux)
• ~500MB disk (model + venv)
• ~2.3GB RAM when daemon is running

Uninstall

Remove all Her Voice data (config, venvs, compiled binary, daemon state):

python3 SKILL_DIR/scripts/daemon.py stop
rm -rf ~/.her-voice

How It Works

1. Kokoro 82M — A compact neural TTS model with two backends: MLX (Apple's framework for native Metal GPU acceleration on Apple Silicon) and PyTorch (works everywhere). The engine is auto-detected based on platform, or can be forced via the tts_engine config option (auto, mlx, or pytorch)
2. Streaming — Audio generates and plays simultaneously. First sound in ~0.3s (with daemon) vs ~3s batch
3. Visualizer — Native macOS app (Swift/Cocoa) reads raw PCM from stdin, plays via AVAudioEngine with real-time amplitude metering
4. Daemon — Unix socket server holding the model in RAM. Eliminates Python import + model load overhead on every call