We are currently exploring the <LiveKit (AI voice)> framework to build a customer-facing support agent. While the WebRTC integration is solid, we are noticing a slight lag in the "Time to First Token" during the speech-to-speech transition. What are the best practices for configuring the VAD parameters to ensure the response feels instantaneous for our global users?
3 answers
To achieve sub-second latency with the <LiveKit (AI voice)> setup, you should look into the "Semantic VAD" configuration. Unlike standard silence-based detection, semantic VAD uses a classifier to understand if the user has actually finished a sentence or just paused for breath. This prevents the agent from interrupting mid-thought. Additionally, ensure your worker nodes are geographically distributed; if your agent is running in a single region, the round-trip time for audio packets can easily add 200ms of perceived lag regardless of how fast your LLM is.
That makes sense, but would using a smaller LLM like GPT-4o-mini improve the prefill speed significantly?
We switched to the AssemblyAI plugin for turn detection and it cut our false triggers by nearly 30%.
I second the AssemblyAI suggestion. Their neural turn detection combined with
Actually, Scott, while the model size helps, the bottleneck in voice is often the TTS chunking. I recommend using the 'eagerness' parameter in the SDK to start streaming audio the moment the first few tokens are generated, rather than waiting for a full sentence.