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Build and deploy self-hosted Text-to-Speech API using MeloTTS from Hugging Face and create a LiveKit plugin for voice agents. Use this skill when building TTS systems, LiveKit voice agents, or self-hosted speech synthesis solutions.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | tts-livekit-plugin |
| description | Build and deploy self-hosted Text-to-Speech API using MeloTTS from Hugging Face and create a LiveKit plugin for voice agents. Use this skill when building TTS systems, LiveKit voice agents, or self-hosted speech synthesis solutions. |
This skill provides a complete solution for building self-hosted Text-to-Speech (TTS) systems integrated with LiveKit voice agents.
Creates a Self-Hosted TTS API Server
Builds a LiveKit TTS Plugin
Provides Complete Testing
Includes Full Documentation
api/)GET /: Health checkGET /voices: List available voicesPOST /synthesize: Full audio synthesisPOST /synthesize/stream: Streaming synthesisplugin/)livekit.agents.tts.TTS base classChunkedStream for audio streamingtests/)test_api.py: API server tests
test_plugin.py: Plugin integration tests
examples/)docs/)cd api
pip install -r requirements.txt
python -m unidic download
python server.py
Server runs on http://localhost:8000
cd examples
python test_api_client.py
from melotts_plugin import TTS
tts = TTS(
api_base_url="http://localhost:8000",
language="EN",
speaker="EN-US",
speed=1.0
)
stream = tts.synthesize("Hello from LiveKit!")
┌─────────────────┐ HTTP POST ┌──────────────────┐
│ LiveKit Agent │ ──────────────────► │ TTS API │
│ │ │ Server │
│ ┌───────────┐ │ │ │
│ │ MeloTTS │ │ Audio Stream │ ┌────────────┐ │
│ │ Plugin │ │ ◄────────────────── │ │ MeloTTS │ │
│ └───────────┘ │ (WAV chunks) │ │ Model │ │
└─────────────────┘ │ └────────────┘ │
└──────────────────┘
| Language | Code | Speakers |
|---|---|---|
| English | EN | EN-US, EN-BR, EN-AU, EN-IN |
| Spanish | ES | ES |
| French | FR | FR |
| Chinese | ZH | ZH |
| Japanese | JP | JP |
| Korean | KR | KR |
All tests use real implementations - no mocks:
# Start API server
cd api && python server.py
# Run API tests
cd tests && pytest test_api.py -v
# Run plugin tests
cd tests && pytest test_plugin.py -v
Multiple deployment options:
See docs/DEPLOYMENT.md for detailed guides.
The plugin is a drop-in replacement for other TTS providers:
# Instead of:
# from livekit.plugins import openai
# tts = openai.TTS()
# Use:
from melotts_plugin import TTS
tts = TTS(api_base_url="http://localhost:8000")
# Same interface, self-hosted!
API Server:
LiveKit Plugin:
For production:
See docs/DEPLOYMENT.md#security for details.
Use this skill when you need to:
Server won't start:
python -m unidic downloadPlugin connection errors:
api_base_url configurationAudio quality issues:
See documentation for more troubleshooting tips.
Apache 2.0 License
docs/examples/This skill provides everything needed for production-ready, self-hosted TTS with LiveKit integration. All code is fully functional with no mocks or placeholders.
Build self-hosted TTS APIs using HuggingFace models (Parler-TTS, F5-TTS, XTTS-v2) and create LiveKit voice agent plugins with streaming support. Use when creating production-ready text-to-speech systems that need: (1) Self-hosted TTS with full control, (2) LiveKit voice agent integration, (3) Streaming audio for low-latency conversations, (4) Custom voice characteristics, (5) Cost-effective alternatives to cloud TTS providers like ElevenLabs or Google TTS.
Build self-hosted speech-to-text APIs using Hugging Face models (Whisper, Wav2Vec2) and create LiveKit voice agent plugins. Use when building STT infrastructure, creating custom LiveKit plugins, deploying self-hosted transcription services, or integrating Whisper/HF models with LiveKit agents. Includes FastAPI server templates, LiveKit plugin implementation, model selection guides, and production deployment patterns.
Guide for creating effective prompts and instructions for LiveKit voice agents. Use when building conversational AI agents with the LiveKit Agents framework, including (1) Creating new voice agent prompts from scratch, (2) Improving existing agent instructions, (3) Optimizing prompts for text-to-speech output, (4) Integrating tool/function calling capabilities, (5) Building multi-agent systems with handoffs, (6) Ensuring voice-friendly formatting and brevity for natural conversations, (7) Iteratively improving prompts based on testing and feedback, (8) Building industry-specific agents (debt collection, healthcare, banking, customer service, front desk).
Comprehensive guide for building functional tools for LiveKit voice agents using the @function_tool decorator. Use when creating tools for LiveKit agents to enable capabilities like API calls, database queries, multi-agent coordination, or any external integrations. Covers tool design, RunContext handling, interruption patterns, parameter documentation, testing, and production best practices.
Build and review production-grade web and mobile frontends using LiveKit with Next.js. Covers real-time video/audio/data communication, WebRTC connections, track management, and best practices for LiveKit React components.
Guide for building production-ready LiveKit voice AI agents with multi-agent workflows and intelligent handoffs. Use when creating real-time voice agents that need to transfer control between specialized agents, implement supervisor escalation, or build complex conversational systems.