livekit-voice-pipeline

To build a real-time voice AI agent, you orchestrate audio input through VAD, Speech-to-Text, Large Language Models, and Text-to-Speech output. This pipeline integrates STT, LLM, and TTS components within the LiveKit framework to process and respond to user queries naturally.

Yes, you can dynamically configure and swap various STT, LLM, and TTS models in the voice pipeline. The framework supports integrations for services like Deepgram, OpenAI, Cartesia, and ElevenLabs to customize your voice AI agent's speech recognition and generation.

LiveKit voice AI supports advanced features including turn detection and interruption handling. These capabilities allow the voice agent to manage conversational dynamics effectively, enabling natural interactions with latency optimization for real-time communication applications.

Latency optimization for real-time voice AI is handled by orchestrating the STT-LLM-TTS pipeline within the LiveKit framework. This approach manages audio processing and turn detection to minimize delays, ensuring responsive and interactive voice communication.

Yes, developing a voice AI agent requires the LiveKit agent SDK. You also need various plugin integrations for your chosen STT, LLM, and TTS services to establish the full end-to-end voice processing pipeline within the LiveKit environment.