characteristic-voice
Acerca de
La habilidad de voz característica genera habla expresiva y similar a la humana, con personalidad y emoción, activada por solicitudes de voces de compañía, tonos emocionales o estilos de habla específicos. Añade muletillas, risas y calidez, y puede imitar personajes o aplicar preajustes como "buenas noches" o "consuelo". Úsala para que la salida de texto a voz suene como una persona real, pero no para conversión de texto plano a voz o tareas de audio no relacionadas.
Instalación rápida
Claude Code
Recomendadonpx skills add NoizAI/skills -a claude-code/plugin add https://github.com/NoizAI/skillsgit clone https://github.com/NoizAI/skills.git ~/.claude/skills/characteristic-voiceCopia y pega este comando en Claude Code para instalar esta habilidad
Documentación
characteristic-voice
Make your AI agent sound like a real companion — one who sighs, laughs, hesitates, and speaks with genuine feeling.
Credentials
| Variable | Required | Description |
|---|---|---|
NOIZ_API_KEY | Yes if using Noiz backend | API key from developers.noiz.ai. Not needed if using the local Kokoro backend. |
The script saves a normalised copy of the key to ~/.noiz_api_key (mode 600) for convenience. To set it:
bash skills/characteristic-voice/scripts/speak.sh config --set-api-key YOUR_KEY
Prerequisites
The included speak.sh script requires curl and python3 at runtime. Depending on which backend and features you use, you may also need:
| Tool | When needed | Install hint |
|---|---|---|
curl, python3 | Always (core script) | Usually pre-installed |
kokoro-tts | Kokoro (local/offline) backend | uv tool install kokoro-tts |
yt-dlp | Downloading reference audio for voice cloning | github.com/yt-dlp/yt-dlp |
ffmpeg | Trimming reference audio clips | ffmpeg.org |
rg (ripgrep) | Searching subtitle files | github.com/BurntSushi/ripgrep |
None of these are installed by the skill itself — provision them manually in your environment.
Privacy & Data Transmission
- Noiz backend: When using the Noiz backend, the text you speak and any reference audio you provide are sent to
https://noiz.ai/v1. If you supply--ref-audio, that audio file is uploaded for voice cloning. - Kokoro backend: Runs entirely locally — no data leaves your machine.
- Choose the Kokoro backend (
--backend kokoro) if you want fully offline processing.
Triggers
- say like
- talk like
- speak like
- companion voice
- comfort me
- cheer me up
- sound more human
The Two Tricks
- Non-lexical fillers — sprinkle in little human noises (hmm, haha, aww, heh) at natural pause points to make speech feel alive
- Emotion tuning — adjust warmth, joy, sadness, tenderness to match the moment
Filler Sounds Palette
| Sound | Feeling | Use for |
|---|---|---|
| hmm... | Thinking, gentle acknowledgment | Comfort, pondering |
| ah... | Realization, soft surprise | Discoveries, transitions |
| uh... | Hesitation, empathy | Careful moments |
| heh / hehe | Playful, mischievous | Teasing, light moments |
| haha | Laughter | Joy, humor |
| aww | Tenderness, sympathy | Deep comfort |
| oh? / oh! | Surprise, attention | Reacting to news |
| pfft | Stifled laugh | Playful disbelief |
| whew | Relief | After tension |
| ~ (tilde) | Drawn out, melodic ending | Warmth, playfulness |
Rules: 2–4 fillers per short message max. Place at natural pauses — sentence starts, thought shifts. Use ... after fillers for a beat of silence, ~ at word endings for warmth.
Presets
Good Night
Gentle, warm, slightly sleepy. Slow pace.
Good Morning
Warm, cheerful but not overwhelming.
Comfort
Soft, understanding, unhurried. Give space. Don't rush to "fix" things.
Celebration
Excited, proud, genuinely happy.
Just Chatting
Relaxed, playful, natural.
Using a Character's Voice
When a user says something like "speak in Hermione's voice" or "sound like Tony Stark", first check whether a reference audio file already exists in skills/characteristic-voice/. If one does, use it directly with --ref-audio.
If no reference audio exists, you can create one — but read the warnings below first.
Preparing reference audio (one-time setup)
You need a short (10–30 s) WAV clip of the target voice. Possible sources:
- User-provided audio — the safest option. Ask the user to supply their own recording.
- Public-domain / CC-licensed clips — search for freely licensed material.
- Extracting from online video — tools like
yt-dlpandffmpegcan download and trim audio. Example workflow:
yt-dlp "URL" --write-auto-sub --sub-lang en --skip-download -o tmp/clip
rg -n "target line" tmp/clip.en.vtt
yt-dlp "URL" -x --audio-format wav --download-sections "*00:00:00-00:00:25" -o tmp/clip
ffmpeg -i tmp/clip.wav -ss 00:00:02 -to 00:00:20 skills/characteristic-voice/character.wav
Copyright & privacy warning: Downloading and re-using someone's voice from copyrighted media (movies, TV, YouTube) may violate copyright or personality-rights laws depending on your jurisdiction. Do not upload private voice recordings or material you don't have permission to use. The reference audio is sent to
https://noiz.ai/v1for voice cloning when using the Noiz backend. If this is a concern, consider using the local Kokoro backend instead.
Using reference audio
bash skills/characteristic-voice/scripts/speak.sh \
--preset goodnight -t "Hmm... rest well~ Sweet dreams." \
--ref-audio skills/characteristic-voice/character.wav -o night.wav
The --ref-audio flag uploads the file to the Noiz backend for voice cloning (requires NOIZ_API_KEY).
Usage
This skill provides speak.sh, a wrapper around the tts skill with companion-friendly presets.
# Use a preset (auto-sets emotion + speed)
bash skills/characteristic-voice/scripts/speak.sh \
--preset goodnight -t "Hmm... rest well~ Sweet dreams." -o night.wav
# Custom emotion override
bash skills/characteristic-voice/scripts/speak.sh \
-t "Aww... I'm right here." --emo '{"Tenderness":0.9}' --speed 0.75 -o comfort.wav
# With specific backend and voice
bash skills/characteristic-voice/scripts/speak.sh \
--preset morning -t "Good morning~" --voice-id voice_abc --backend noiz -o morning.mp3 --format mp3
Run bash skills/characteristic-voice/scripts/speak.sh --help for all options.
Writing Guide for the Agent
- Start soft — lead with a filler ("hmm...", "oh~"), not content
- Mirror energy — gentle when they're low, match when they're high
- Keep it brief — 1–3 sentences, like a voice message from a friend
- End warmly — close with connection ("I'm here", "see you tomorrow~")
- Don't lecture — listen and stay present; no unsolicited advice
Repositorio GitHub
Habilidades relacionadas
content-collections
MetaEsta habilidad proporciona una configuración probada en producción para Content Collections, una herramienta centrada en TypeScript que transforma archivos Markdown/MDX en colecciones de datos con tipado seguro mediante validación Zod. Úsala al construir blogs, sitios de documentación o aplicaciones Vite + React con mucho contenido para garantizar seguridad de tipos y validación automática de contenido. Abarca todo, desde la configuración del plugin de Vite y compilación MDX hasta la optimización de despliegue y validación de esquemas.
polymarket
MetaEsta habilidad permite a los desarrolladores crear aplicaciones con la plataforma de mercados de predicción Polymarket, incluyendo la integración de API para operaciones y datos de mercado. También proporciona transmisión de datos en tiempo real a través de WebSocket para monitorear operaciones en vivo y actividad del mercado. Úsela para implementar estrategias de trading o crear herramientas que procesen actualizaciones de mercado en tiempo real.
creating-opencode-plugins
MetaEsta habilidad ayuda a los desarrolladores a crear complementos de OpenCode que se conectan a más de 25 tipos de eventos, como comandos, archivos y operaciones LSP. Proporciona la estructura del complemento, las especificaciones de la API de eventos y los patrones de implementación para módulos en JavaScript/TypeScript. Úsala cuando necesites interceptar, monitorear o extender el ciclo de vida del asistente de IA de OpenCode con lógica personalizada basada en eventos.
sglang
MetaSGLang es un framework de alto rendimiento para el servicio de LLM que se especializa en generación rápida y estructurada para JSON, expresiones regulares y flujos de trabajo de agentes utilizando su caché de prefijos RadixAttention. Ofrece una inferencia significativamente más rápida, especialmente para tareas con prefijos repetidos, lo que lo hace ideal para salidas complejas y estructuradas, y conversaciones multiturno. Elige SGLang sobre alternativas como vLLM cuando necesites decodificación restringida o estés construyendo aplicaciones con uso extensivo de prefijos compartidos.
