Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Her Voice
Give your agent a voice. Use when the user wants the agent to speak, read aloud, or have voice responses.
skill openclawclawhub Free
Give your agent a voice. Use when the user wants the agent to speak, read aloud, or have voice responses.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- CHANGELOG.md, SKILL.md, assets/HerVoice.swift, scripts/config.py, scripts/daemon.py, scripts/setup.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- matusvojtek
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Her Voice ποΈ
Give your agent a voice. Audio responses powered by Kokoro TTS β a compact, naturally expressive model running entirely on-device.
β¨ Features
Highly optimized response time thanks to on-the-fly audio streaming technology. 100% free, no API keys required. Inspired by Samantha and Sky. β‘ On-the-fly Streaming β Audio plays as it generates, very low latency π The Voice of an angel β Cutting-edge local text-to-speech model Kokoro TTS π§ TTS Daemon β Keep the model warm in RAM for instant responses (can be disabled to save RAM) π₯οΈ Persist Mode β Drag & drop audio, paste text, use as a voice station π§ Fully Configurable β Voice, speed, visualizer, notification sounds π MLX + PyTorch β Native Metal acceleration on Apple Silicon, PyTorch fallback everywhere else π¨ Real-time Visualizer β 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 β the agent resolves it automatically when running commands. The setup wizard will: Detect platform and select TTS engine (MLX on Apple Silicon, PyTorch elsewhere) Find or install the appropriate TTS backend (mlx-audio or kokoro) Install espeak-ng (Homebrew on macOS, apt on Linux) Patch espeak loader if needed (macOS compatibility) Compile the native visualizer binary (macOS only) Download the Kokoro model 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 "MatΓΊΕ‘" 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
FlagDescription--no-vizSkip the visualizer for this call--persistKeep visualizer open after playback ends--save PATHSave audio to WAV file instead of playing--voice NAMEOverride the configured voice--speed NOverride the configured speed multiplier--mode MODEOverride visualizer mode (v2 or classic)
Agent Workflow
When the user wants voice responses: Check voice mode β is voice enabled or did the user ask for it? Play notification sound (instant feedback while TTS generates): afplay /System/Library/Sounds/Blow.aiff & Speak the response: python3 SKILL_DIR/scripts/speak.py "Response text here" Always provide text alongside voice β 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 β 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 β 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 β 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 β on other platforms, audio plays without the visualizer.
Modes
v2 (default) β Three-tier pure red, center raw amplitude, sides with lag classic β Original smooth gradient look
Controls
KeyActionESCQuitSpacePause/Resume (file mode)β βSeek Β±5s (file mode)βVPaste 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 βV pastes clipboard text β streams directly from TTS daemon with full visualizer animation Idle breathing β 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
KeyDefaultDescriptionagent_name""Agent's name (e.g. "Jackie")user_name""User's real nameuser_name_tts""Phonetic spelling for TTS (e.g. "Mah-toosh" for MatΓΊΕ‘)voiceaf_heartBase voice namevoice_blend{af_heart: 0.6, af_sky: 0.4}Voice blend weightsspeed1.05Speech speed multiplierlanguageenLanguage codetts_engineautoTTS engine: auto, mlx, or pytorchmodelmlx-community/Kokoro-82M-bf16Model identifier (MLX)visualizer.enabledtrueShow visualizer overlayvisualizer.modev2Animation mode (v2/classic)visualizer.remember_positiontrueSave window position between sessionsnotification_sound.enabledtruePlay sound before speakingnotification_sound.soundBlowmacOS system sound namedaemon.auto_starttrueAdvisory flag only β the daemon never self-starts. When true, the agent should start it on first voice use (saves ~1s/call, costs ~2.3GB RAM)daemon.socket_path~/.her-voice/tts.sockUnix socket path
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 β speak.py looks for the pre-blended file automatically.
Error Handling
ErrorCauseFix"mlx-audio not found"Venv missing or brokenRun setup.py"espeak-ng not found"Phonemizer missingbrew install espeak-ngCompilation failedXcode tools missingxcode-select --install"Model not found"First run, no downloadRun setup.py or speak onceDaemon "not running"Crashed or rebootedStart daemon againNo sound outputmacOS audio permissionsCheck System Settings β Sound β OutputVisualizer not showingBinary not compiledRun setup.py"kokoro not found"PyTorch venv missingRun setup.pyPyTorch CUDA errorGPU driver mismatchpip install torch --force-reinstall in kokoro venv"soundfile not found"Missing dependencypip 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
Kokoro 82M β 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) Streaming β Audio generates and plays simultaneously. First sound in ~0.3s (with daemon) vs ~3s batch Visualizer β Native macOS app (Swift/Cocoa) reads raw PCM from stdin, plays via AVAudioEngine with real-time amplitude metering Daemon β Unix socket server holding the model in RAM. Eliminates Python import + model load overhead on every call
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs1 Assets
- SKILL.md
- CHANGELOG.md
- scripts/config.py
- scripts/daemon.py
- scripts/setup.py
- assets/HerVoice.swift