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Backend API

FastAPI backend for real-time AI voice conversations using LiveKit and OpenAI.

Setup

python -m venv venv
venv\Scripts\activate  # Windows
pip install -r requirements.txt

Configuration

Create .env file:

LIVEKIT_API_KEY=your_livekit_key
LIVEKIT_API_SECRET=your_livekit_secret
LIVEKIT_URL=ws://localhost:7880
OPENAI_API_KEY=sk-your-openai-key

Run

uvicorn main:app --reload --port 8000

API will be available at http://localhost:8000

API Endpoints

GET /

Health check endpoint.

Response:

{
  "status": "ok",
  "ai_enabled": true,
  "livekit_configured": true
}

POST /token

Generate LiveKit access token for room connection.

Request:

{
  "room_name": "voice-conversation",
  "participant_name": "user-123"
}

Response:

{
  "token": "...",
  "url": "ws://localhost:7880"
}

POST /transcribe

Transcribe audio to text using OpenAI Whisper.

Request:

  • Content-Type: multipart/form-data
  • Body: Audio file (webm format)

Response:

{
  "text": "Hello, how are you?"
}

Notes:

  • Returns empty text if audio is too small (< 5000 bytes)

POST /respond

Generate AI response with audio using GPT-4o-mini and OpenAI TTS.

Request:

{
  "text": "Hello, how are you?"
}

Response:

{
  "response_text": "I'm doing great, thank you for asking! How can I help you today?",
  "audio": "base64_encoded_audio_data..."
}

Notes:

  • Audio is returned as base64-encoded MP3
  • Uses GPT-4o-mini for response generation
  • Uses TTS-1-HD for high-quality speech synthesis

Error Handling

All endpoints return standard HTTP error codes:

  • 400 - Bad request (invalid input)
  • 500 - Server error (OpenAI/LiveKit not configured or processing failed)

Dependencies

  • FastAPI - Web framework
  • OpenAI - AI services (Whisper, GPT-4o-mini, TTS)
  • LiveKit - WebRTC infrastructure
  • python-dotenv - Environment variable management