Implementing Real-time Streaming With Vapi: Build Voice Apps

Posted on Dec 10

• Originally published at callstack.tech

Most voice apps break when network jitter hits 200ms+ or users interrupt mid-sentence. Here's how to build a production-grade streaming voice application using VAPI's WebRTC voice integration with Twilio as the telephony layer. You'll handle real-time audio processing, implement proper barge-in detection, and manage session state without race conditions. Stack: VAPI for voice AI, Twilio for call routing, Node.js for webhook handling. Outcome: sub-500ms response latency with graceful interruption handling.

Most streaming implementations fail because they treat VAPI like a REST API. It's not. You're building a stateful WebSocket connection that handles bidirectional audio streams. Here's what breaks in production: developers configure the assistant but forget to set up the event handlers BEFORE initiating the connection.

The transcriber config is critical. Default models add 200-400ms latency. Nova-2 cuts that to 80-120ms but costs 3x more. Budget accordingly.

Audio flows through VAPI's platform, NOT your server. Your webhook server only handles function calls and events. Trying to proxy audio through your backend adds 500ms+ latency and breaks streaming.

Race condition warning: If you process partial transcripts, you'll send duplicate requests to your LLM. Wait for transcriptType === "final" before triggering actions.

Timeout trap: VAPI expects webhook responses within 5 seconds. If your function call takes longer, return immediately and use a callback pattern. Otherwise, the call drops.

Test on actual mobile networks, not just WiFi. Latency spikes from 100ms to 800ms on 4G. Your VAD threshold needs adjustment - default 0.3 triggers on breathing sounds. Bump to 0.5 for production.

Audio processing pipeline from microphone input to speaker output.

Most voice apps break in production because devs skip local webhook testing. Here's how to catch issues before deployment.

Source: Dev.to