Cadre

Capstone 03 — Real-Time Voice Assistant (ASR to LLM to TTS)

A voice agent that feels right has end-to-end latency under 800ms, knows when you have stopped talking, handles barge-in, and can call a tool without stalling. Retell, Vapi, LiveKit Agents, and Pipecat all hit this bar in 2026. They do it with the same shape: a streaming ASR, a turn-detector, a streaming LLM, and a streaming TTS, all wired through WebRTC with aggressive latency budgets at every hop. Build one, measure WER and MOS and false-cutoff rate, and run it under packet loss.

Capstone 30 hr Python (agent + pipeline), TypeScript (web client)

Prerequisites: Phase 6 (speech and audio), Phase 7 (transformers), Phase 11 (LLM engineering), Phase 13 (tools), Phase 14 (agents), Phase 17 (infrastructure)

#Problem

Voice has been the fastest-moving AI UX category of 2025-2026. The technical ceiling dropped each quarter. OpenAI Realtime API, Gemini 2.5 Live, Cartesia Sonic-2, ElevenLabs Flash v3, LiveKit Agents 1.0, and Pipecat 0.0.70 all put sub-800ms first-audio-out within reach. The bar is not latency alone. It is the interaction feel: not cutting the user off, not getting cut off, recovering from a mid-sentence interruption, calling a tool mid-conversation without stalling the audio, surviving jittery mobile networks.

You cannot get there by stitching three REST calls. The architecture is pipelined streaming end to end. Build it and the failure modes become visible: a VAD tuned for phone audio firing on background TV, a turn-detector waiting for punctuation that never comes, a TTS that buffers 400ms before emitting. The capstone is to fix these one at a time under load and publish a latency-and-quality report.

#Problem

Keep going. The rest of this lesson is one step away.