name: livekit-stt-selfhosted description: 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.

LiveKit Self-Hosted STT Plugin

Build self-hosted speech-to-text APIs and LiveKit voice agent plugins using Hugging Face models.

Overview

This skill provides templates and guidance for:

1. Building a self-hosted STT API server using FastAPI + Whisper/HF models
2. Creating a LiveKit plugin that connects to your self-hosted API
3. Deploying and scaling in production

Quick Start

Option 1: Build Both (API + Plugin)

When user wants complete setup:

1. Create API Server:

python scripts/setup_api_server.py my-stt-server --model openai/whisper-medium
cd my-stt-server
pip install -r requirements.txt
python main.py

2. Create Plugin:

python scripts/setup_plugin.py custom-stt
cd livekit-plugins-custom-stt
pip install -e .

3. Use in LiveKit Agent:

from livekit.plugins import custom_stt

stt=custom_stt.STT(api_url="ws://localhost:8000/ws/transcribe")

Option 2: API Server Only

When user only needs the API server:

• Use scripts/setup_api_server.py with desired model
• See references/api_server_guide.md for implementation details
• Template in assets/api-server/

Option 3: Plugin Only

When user has existing API and needs LiveKit plugin:

• Use scripts/setup_plugin.py with plugin name
• See references/plugin_implementation.md for details
• Template in assets/plugin-template/

Model Selection

Help user choose the right model:

For detailed comparison and optimization tips, see references/models_comparison.md.

Implementation Workflow

Building the API Server

1. Use the template: Start with assets/api-server/main.py

2. Key components:

   • FastAPI app with WebSocket endpoint
   • Model loading at startup (kept in memory)
   • Audio buffer management
   • WebSocket protocol for streaming

3. Customization points:

   • Model selection (change MODEL_ID in .env)
   • Audio processing parameters
   • Batch size and optimization
   • Error handling

For complete implementation guide, see references/api_server_guide.md.

Building the LiveKit Plugin

1. Use the template: Start with assets/plugin-template/

2. Required implementations:

   • _recognize_impl() - Non-streaming recognition
   • stream() - Return SpeechStream instance
   • SpeechStream class - Handle streaming

3. Key considerations:

   • Audio format conversion (16kHz, mono, 16-bit PCM)
   • WebSocket connection management
   • Event emission (interim/final transcripts)
   • Error handling and cleanup

For complete implementation guide, see references/plugin_implementation.md.

Deployment

Development

# API Server
uvicorn main:app --host 0.0.0.0 --port 8000 --reload

# Test WebSocket
ws://localhost:8000/ws/transcribe

Production

Docker (Recommended):

docker-compose up

Kubernetes: Use manifests in deployment guide

Cloud Platforms: AWS ECS, GCP Cloud Run, Azure Container Instances

For complete deployment guide including scaling, monitoring, and security, see references/deployment.md.

WebSocket Protocol

Client → Server

• Audio: Binary (16-bit PCM, 16kHz)
• Config: {"type": "config", "language": "en"}
• End: {"type": "end"}

Server → Client

• Interim: {"type": "interim", "text": "..."}
• Final: {"type": "final", "text": "...", "language": "en"}
• Error: {"type": "error", "message": "..."}

Common Tasks

Change Model

Edit .env:

MODEL_ID=openai/whisper-small  # Faster model

Add Language Support

In plugin usage:

stt=custom_stt.STT(language="es")  # Spanish
stt=custom_stt.STT(detect_language=True)  # Auto-detect

Enable GPU

In API server:

DEVICE=cuda:0  # Use GPU

Scale Horizontally

Deploy multiple API server instances behind load balancer. See references/deployment.md for Nginx configuration.

Troubleshooting

Out of Memory

• Use smaller model (whisper-small or whisper-tiny)
• Reduce batch_size in pipeline
• Enable low_cpu_mem_usage=True

Slow Transcription

• Ensure GPU is enabled (DEVICE=cuda:0)
• Use FP16 precision (automatic on GPU)
• Increase batch_size
• Use smaller model

Connection Issues

• Verify WebSocket support in load balancer
• Check firewall rules
• Increase timeout settings

Scripts

• scripts/setup_api_server.py - Generate API server from template
• scripts/setup_plugin.py - Generate LiveKit plugin from template

References

Load these as needed for detailed information:

• references/api_server_guide.md - Complete API implementation guide
• references/plugin_implementation.md - LiveKit plugin development
• references/models_comparison.md - Model selection and optimization
• references/deployment.md - Production deployment best practices

Assets

Ready-to-use templates:

• assets/api-server/ - Complete FastAPI server with Whisper
• assets/plugin-template/ - LiveKit STT plugin structure

Best Practices

1. Keep models in memory - Load once at startup, not per request
2. Use appropriate model size - Balance quality vs. speed for your use case
3. Process audio in chunks - 1-second chunks work well for streaming
4. Implement proper cleanup - Close WebSocket connections gracefully
5. Monitor metrics - Track latency, throughput, GPU utilization
6. Use Docker - Ensures consistent deployments
7. Enable authentication - Secure production APIs
8. Scale horizontally - Use load balancer for high availability