Chapter 3: Voice Input Processing

April 13, 2026 · View on GitHub

Welcome to Chapter 3: Voice Input Processing. In this part of OpenAI Realtime Agents Tutorial: Voice-First AI Systems, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

Input quality and turn-boundary accuracy are the biggest predictors of perceived voice-agent quality.

Learning Goals

By the end of this chapter, you should be able to:

design a robust audio input pipeline
tune voice activity detection (VAD) for your environment
handle interruption and partial-turn scenarios correctly
track metrics that reveal input regressions early

Input Pipeline Stages

microphone capture
buffering and chunk framing
optional preprocessing (normalization/noise reduction)
VAD-based turn detection
commit audio segment to session
begin response generation

VAD Strategy Choices

Mode	Best For	Risk
automatic VAD	consumer voice UX with minimal friction	clipping in noisy environments if tuned poorly
push-to-talk	controlled enterprise or noisy contexts	higher user interaction cost
hybrid	mixed environments and advanced clients	more implementation complexity

Interruption Handling (Barge-In)

When user speech starts while assistant is speaking:

stop output quickly
preserve minimal state needed for continuity
commit new user input immediately
avoid long blocking operations before acknowledgement

Input Reliability Controls

enforce expected sample format at ingestion
cap maximum segment duration to prevent oversized turns
detect prolonged silence and reset capture state gracefully
log dropped frames and jitter indicators

Quality Pitfalls

Pitfall	User Impact	Mitigation
aggressive VAD	clipped speech and repeated clarifications	relax sensitivity and add hysteresis
conservative VAD	laggy turn transitions	reduce release delay
no interruption support	assistant talks over user	prioritize barge-in cancellation path
poor noise handling	wrong intent extraction	add preprocessing and environment presets

Metrics to Track

speech-start to commit latency
clipped-turn rate
interruption success rate
speech-to-first-token latency
retry rate after misunderstood turns

Source References

Summary

You now have a robust input architecture pattern that supports low-latency conversation without sacrificing turn accuracy.

Next: Chapter 4: Conversational AI

Source Code Walkthrough

`src/app/App.tsx`

The App function in src/app/App.tsx handles a key part of this chapter's functionality:

import { useHandleSessionHistory } from "./hooks/useHandleSessionHistory";

function App() {
  const searchParams = useSearchParams()!;

  // ---------------------------------------------------------------------
  // Codec selector – lets you toggle between wide-band Opus (48 kHz)
  // and narrow-band PCMU/PCMA (8 kHz) to hear what the agent sounds like on
  // a traditional phone line and to validate ASR / VAD behaviour under that
  // constraint.
  //
  // We read the `?codec=` query-param and rely on the `changePeerConnection`
  // hook (configured in `useRealtimeSession`) to set the preferred codec
  // before the offer/answer negotiation.
  // ---------------------------------------------------------------------
  const urlCodec = searchParams.get("codec") || "opus";

  // Agents SDK doesn't currently support codec selection so it is now forced 
  // via global codecPatch at module load 

  const {
    addTranscriptMessage,
    addTranscriptBreadcrumb,
  } = useTranscript();
  const { logClientEvent, logServerEvent } = useEvent();

  const [selectedAgentName, setSelectedAgentName] = useState<string>("");
  const [selectedAgentConfigSet, setSelectedAgentConfigSet] = useState<
    RealtimeAgent[] | null
  >(null);

  const audioElementRef = useRef<HTMLAudioElement | null>(null);

This function is important because it defines how OpenAI Realtime Agents Tutorial: Voice-First AI Systems implements the patterns covered in this chapter.

`src/app/components/Transcript.tsx`

The Transcript function in src/app/components/Transcript.tsx handles a key part of this chapter's functionality:

import React, { useEffect, useRef, useState } from "react";
import ReactMarkdown from "react-markdown";
import { TranscriptItem } from "@/app/types";
import Image from "next/image";
import { useTranscript } from "@/app/contexts/TranscriptContext";
import { DownloadIcon, ClipboardCopyIcon } from "@radix-ui/react-icons";
import { GuardrailChip } from "./GuardrailChip";

export interface TranscriptProps {
  userText: string;
  setUserText: (val: string) => void;
  onSendMessage: () => void;
  canSend: boolean;
  downloadRecording: () => void;
}

function Transcript({
  userText,
  setUserText,
  onSendMessage,
  canSend,
  downloadRecording,
}: TranscriptProps) {
  const { transcriptItems, toggleTranscriptItemExpand } = useTranscript();
  const transcriptRef = useRef<HTMLDivElement | null>(null);
  const [prevLogs, setPrevLogs] = useState<TranscriptItem[]>([]);
  const [justCopied, setJustCopied] = useState(false);
  const inputRef = useRef<HTMLInputElement | null>(null);

  function scrollToBottom() {
    if (transcriptRef.current) {
      transcriptRef.current.scrollTop = transcriptRef.current.scrollHeight;

This function is important because it defines how OpenAI Realtime Agents Tutorial: Voice-First AI Systems implements the patterns covered in this chapter.

`src/app/components/Transcript.tsx`

The scrollToBottom function in src/app/components/Transcript.tsx handles a key part of this chapter's functionality:

  const inputRef = useRef<HTMLInputElement | null>(null);

  function scrollToBottom() {
    if (transcriptRef.current) {
      transcriptRef.current.scrollTop = transcriptRef.current.scrollHeight;
    }
  }

  useEffect(() => {
    const hasNewMessage = transcriptItems.length > prevLogs.length;
    const hasUpdatedMessage = transcriptItems.some((newItem, index) => {
      const oldItem = prevLogs[index];
      return (
        oldItem &&
        (newItem.title !== oldItem.title || newItem.data !== oldItem.data)
      );
    });

    if (hasNewMessage || hasUpdatedMessage) {
      scrollToBottom();
    }

    setPrevLogs(transcriptItems);
  }, [transcriptItems]);

  // Autofocus on text box input on load
  useEffect(() => {
    if (canSend && inputRef.current) {
      inputRef.current.focus();
    }
  }, [canSend]);

This function is important because it defines how OpenAI Realtime Agents Tutorial: Voice-First AI Systems implements the patterns covered in this chapter.

`src/app/components/Transcript.tsx`

The TranscriptProps interface in src/app/components/Transcript.tsx handles a key part of this chapter's functionality:

import { GuardrailChip } from "./GuardrailChip";

export interface TranscriptProps {
  userText: string;
  setUserText: (val: string) => void;
  onSendMessage: () => void;
  canSend: boolean;
  downloadRecording: () => void;
}

function Transcript({
  userText,
  setUserText,
  onSendMessage,
  canSend,
  downloadRecording,
}: TranscriptProps) {
  const { transcriptItems, toggleTranscriptItemExpand } = useTranscript();
  const transcriptRef = useRef<HTMLDivElement | null>(null);
  const [prevLogs, setPrevLogs] = useState<TranscriptItem[]>([]);
  const [justCopied, setJustCopied] = useState(false);
  const inputRef = useRef<HTMLInputElement | null>(null);

  function scrollToBottom() {
    if (transcriptRef.current) {
      transcriptRef.current.scrollTop = transcriptRef.current.scrollHeight;
    }
  }

  useEffect(() => {
    const hasNewMessage = transcriptItems.length > prevLogs.length;
    const hasUpdatedMessage = transcriptItems.some((newItem, index) => {

This interface is important because it defines how OpenAI Realtime Agents Tutorial: Voice-First AI Systems implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[App]
    B[Transcript]
    C[scrollToBottom]
    D[TranscriptProps]
    E[useHandleSessionHistory]
    A --> B
    B --> C
    C --> D
    D --> E