How to Optimize Voice Bot Latency for AI Phone Support

TL;DR

Most AI phone bots feel sluggish because developers ignore the 3-layer latency stack: STT processing (200-400ms), LLM inference (500-1500ms), and TTS synthesis (300-600ms). Combined, that's 1-2.5 seconds of dead air per turn. This guide shows how to cut total latency to <800ms using streaming STT, prompt caching, concurrent TTS generation, and VAPI's native interruption handling. You'll build a production voice agent that responds faster than human operators.

Prerequisites

API Access & Credentials:

VAPI API key (obtain from dashboard.vapi.ai)
Twilio Account SID and Auth Token (console.twilio.com)
Node.js ≥ 18.x (for async/await and native fetch)

Infrastructure Requirements:

Server with ≤ 50ms network latency to VAPI/Twilio regions (use AWS us-east-1 or equivalent)
Minimum 2GB RAM for concurrent session handling
SSL certificate for webhook endpoints (ngrok acceptable for dev, not production)

Technical Knowledge:

Understanding of WebSocket connections and streaming protocols
Familiarity with PCM audio formats (16kHz, 16-bit)
Experience with async event handling and race condition prevention

Monitoring Tools:

APM solution (DataDog, New Relic) for latency tracking
Webhook testing tool (Postman, curl) for payload validation

This is NOT a beginner tutorial. You should already have a working voice bot before optimizing latency.

vapi: Get Started with VAPI → Get vapi

Step-by-Step Tutorial

Most voice bots hit 2-4 second latencies because developers stack STT → LLM → TTS sequentially. Production systems need parallel processing and early audio streaming. Here's how to build it.

Architecture & Flow

mermaid

graph LR
    A[User Speech] --> B[STT Streaming]
    B --> C[LLM Partial Response]
    C --> D[TTS Chunked Audio]
    B -.Parallel.-> E[VAD Detection]
    E --> F[Interrupt Handler]
    F --> D
    D --> G[Audio Buffer]
    G --> H[User Hears Response]

The critical path: STT streams partials → LLM generates chunks → TTS synthesizes in parallel → audio buffers flush on barge-in. Each component must be non-blocking.

Configuration & Setup

Configure your assistant with streaming-optimized providers. Deepgram for STT (lowest latency), GPT-4 Turbo for LLM (streaming support), and ElevenLabs for TTS (chunked synthesis).

javascript

const assistantConfig = {
  model: {
    provider: "openai",
    model: "gpt-4-turbo",
    temperature: 0.7,
    maxTokens: 150, // Shorter responses = lower latency
    stream: true // CRITICAL: Enable streaming
  },
  transcriber: {
    provider: "deepgram",
    model: "nova-2",
    language: "en",
    smartFormat: false, // Disable formatting for speed
    endpointing: 200 // ms silence before considering turn complete
  },
  voice: {
    provider: "11labs",
    voiceId: "21m00Tcm4TlvDq8ikWAM",
    model: "eleven_turbo_v2", // Fastest model
    optimizeStreamingLatency: 4, // Max optimization
    stability: 0.5,
    similarityBoost: 0.75
  },
  firstMessage: "Hey, how can I help?",
  serverUrl: process.env.WEBHOOK_URL,
  serverUrlSecret: process.env.WEBHOOK_SECRET
};

Why these settings matter: endpointing: 200 cuts 300-500ms vs default 800ms. maxTokens: 150 prevents rambling responses. optimizeStreamingLatency: 4 trades voice quality for 40% faster audio generation.

Step-by-Step Implementation

Step 1: Set up webhook handler with streaming response

javascript

const express = require('express');
const crypto = require('crypto');
const app = express();

app.use(express.json());

// Validate webhook signature
function validateSignature(req) {
  const signature = req.headers['x-vapi-signature'];
  const body = JSON.stringify(req.body);
  const hash = crypto
    .createHmac('sha256', process.env.WEBHOOK_SECRET)
    .update(body)
    .digest('hex');
  return signature === hash;
}

// Track active sessions to prevent race conditions
const activeSessions = new Map();

app.post('/webhook/vapi', async (req, res) => {
  if (!validateSignature(req)) {
    return res.status(401).json({ error: 'Invalid signature' });
  }

  const { message } = req.body;
  const callId = req.body.call?.id;

  // Handle partial transcripts for early processing
  if (message.type === 'transcript' && message.transcriptType === 'partial') {
    // Start preparing response before full transcript arrives
    const sessionState = activeSessions.get(callId) || { buffer: '' };
    sessionState.buffer += message.transcript;
    activeSessions.set(callId, sessionState);
    
    return res.status(200).json({ received: true });
  }

  // Process complete transcript
  if (message.type === 'transcript' && message.transcriptType === 'final') {
    const session = activeSessions.get(callId);
    
    // Clear buffer to prevent duplicate processing
    if (session) {
      session.buffer = '';
      activeSessions.set(callId, session);
    }
    
    return res.status(200).json({ received: true });
  }

  // Clean up session on call end
  if (message.type === 'end-of-call-report') {
    activeSessions.delete(callId);
    
    // Log latency metrics
    const { duration, endedReason } = message;
    console.log(`Call ${callId}: ${duration}s, reason: ${endedReason}`);
  }

  res.status(200).json({ received: true });
});

app.listen(3000, () => console.log('Webhook server running on port 3000'));

Step 2: Configure Twilio for optimal audio routing

Twilio adds 150-300ms overhead if misconfigured. Use these settings:

javascript

const twilioConfig = {
  codec: 'PCMU', // Lower latency than Opus for phone calls
  record: false, // Recording adds 50-100ms
  timeout: 30,
  answerOnBridge: true, // Reduces connection time
  ringTone: 'us' // Immediate feedback
};

Step 3: Implement barge-in handling

The assistant config already enables native barge-in via endpointing: 200. When VAD detects speech, Vapi automatically cancels TTS playback and flushes audio buffers. No custom cancellation logic needed.

Error Handling & Edge Cases

Race condition: Overlapping transcripts

The activeSessions Map prevents processing duplicate partials. Each session tracks buffer state and clears on final transcript.

Network jitter: Variable STT latency

Mobile networks cause 100-400ms jitter. The endpointing: 200 setting adapts by waiting for consistent silence, not fixed duration.

False VAD triggers: Background noise

Default VAD threshold (0.3) triggers on breathing. Increase to 0.5 in noisy environments by adding vadThreshold: 0.5 to transcriber config.

Testing & Validation

Measure end-to-end latency with timestamps:

javascript

// In webhook handler
if (message.type === 'transcript' && message.transcriptType === 'final') {
  const latency = Date.now() - message.timestamp;
  console.log(`STT latency: ${latency}ms`);
  
  if (latency > 1000) {
    console.warn(`High latency detected: ${latency}ms`);
  }
}

Target: <800ms STT, <1200ms LLM, <600ms TTS = <2.6s total response time.

System Diagram

Audio processing pipeline from microphone input to speaker output.

mermaid

graph LR
    PhoneCall[Phone Call]
    AudioCapture[Audio Capture]
    STT[Speech-to-Text]
    LLM[Large Language Model]
    TTS[Text-to-Speech]
    AudioOutput[Audio Output]
    ErrorHandling[Error Handling]
    Retry[Retry Mechanism]
    
    PhoneCall-->AudioCapture
    AudioCapture-->STT
    STT-->LLM
    LLM-->TTS
    TTS-->AudioOutput
    
    STT-->|Error|ErrorHandling
    LLM-->|Error|ErrorHandling
    TTS-->|Error|ErrorHandling
    
    ErrorHandling-->|Retry|Retry
    Retry-->STT
    Retry-->LLM
    Retry-->TTS

Testing & Validation

Most latency issues only surface under real network conditions. Local testing with synthetic delays won't catch jitter, packet loss, or mobile network variance.

Local Testing

Use ngrok to expose your webhook server for real-world testing. This catches race conditions that localhost testing misses.

javascript

// Test webhook latency with timestamp tracking
app.post('/webhook/vapi', (req, res) => {
  const receivedAt = Date.now();
  const sentAt = req.body.timestamp; // VAPI includes this in webhook payload
  const networkLatency = receivedAt - sentAt;
  
  console.log(`Webhook latency: ${networkLatency}ms`);
  
  if (networkLatency > 200) {
    console.warn('High network latency detected - check ngrok tunnel');
  }
  
  // Validate signature before processing
  if (!validateSignature(req.body, req.headers['x-vapi-signature'])) {
    return res.status(401).json({ error: 'Invalid signature' });
  }
  
  res.status(200).json({ received: true });
});

Real-world problem: Ngrok free tier adds 50-100ms latency. Upgrade to paid or use a VPS for production testing.

Webhook Validation

Test signature validation with curl to catch auth failures before production:

bash

curl -X POST https://your-ngrok-url.ngrok.io/webhook/vapi \
  -H "Content-Type: application/json" \
  -H "x-vapi-signature: test_signature" \
  -d '{"timestamp": 1234567890, "callId": "test-call"}'

Expected response: 401 Invalid signature. If you get 200, your validation is broken.

Real-World Example

Barge-In Scenario

User calls support line. Agent starts explaining a 45-second refund policy. User interrupts at 8 seconds: "I just need the tracking number."

What breaks: Most implementations buffer the full TTS response before streaming. When the user interrupts, the agent keeps talking for 2-3 seconds because the audio buffer hasn't flushed. The STT fires late because endpointing is set too conservatively (800ms silence threshold). By the time the interruption registers, the user has already repeated themselves twice.

What actually happens in production:

javascript

// Barge-in handler - flushes TTS buffer on partial transcript
app.post('/webhook/vapi', (req, res) => {
  const { message } = req.body;
  
  if (message.type === 'transcript' && message.transcriptType === 'partial') {
    const callId = message.call.id;
    const session = activeSessions[callId];
    
    if (session && session.buffer.isStreaming) {
      // User spoke while agent was talking - INTERRUPT
      session.buffer.flush(); // Stops TTS mid-sentence
      session.buffer.isStreaming = false;
      
      console.log(`[${callId}] Barge-in detected: "${message.transcript}"`);
      
      // Reset turn-taking state
      session.lastUserSpeech = Date.now();
      session.agentShouldWait = true;
    }
  }
  
  res.sendStatus(200);
});

The endpointing value in transcriber controls when STT considers speech "done." Default 800ms causes 600-900ms lag on interruptions. Drop to 300ms for phone support:

javascript

const assistantConfig = {
  transcriber: {
    provider: "deepgram",
    model: "nova-2-phonecall",
    language: "en",
    endpointing: 300 // Aggressive barge-in detection
  }
};

Event Logs

Real webhook payload sequence during barge-in (timestamps show the race condition):

javascript

// T+0ms: Agent starts speaking
{
  "message": {
    "type": "speech-update",
    "status": "started",
    "text": "Your refund will be processed within 5-7 business days..."
  },
  "call": { "id": "call_abc123" }
}

// T+1847ms: User interrupts (partial transcript fires)
{
  "message": {
    "type": "transcript",
    "transcriptType": "partial",
    "transcript": "I just need the track"
  },
  "call": { "id": "call_abc123" }
}

// T+1850ms: TTS buffer still streaming (NOT flushed yet)
// Agent continues: "...and you'll receive an email confirmation..."

// T+2100ms: Buffer flush completes
{
  "message": {
    "type": "speech-update",
    "status": "stopped",
    "reason": "interrupted"
  }
}

The 253ms gap (1847ms → 2100ms) is where users hear the agent talking over them. This happens because:

Partial transcript fires when user starts speaking
Your server receives webhook ~50ms later (network latency)
Buffer flush command sent back to Vapi
Audio pipeline drains remaining chunks (~200ms)

Fix: Pre-emptively lower optimizeStreamingLatency to 3 (more aggressive chunking) and reduce endpointing to 250ms for phone calls where barge-in is critical.

Edge Cases

Multiple rapid interruptions: User says "wait... no... actually..." within 2 seconds. Each partial transcript triggers a buffer flush. If you don't debounce, the agent never finishes a sentence.

javascript

// Debounce rapid interruptions
let lastInterruptTime = 0;
const INTERRUPT_COOLDOWN = 1500; // ms

if (message.transcriptType === 'partial') {
  const now = Date.now();
  if (now - lastInterruptTime < INTERRUPT_COOLDOWN) {
    console.log('Ignoring rapid interrupt');
    return res.sendStatus(200);
  }
  lastInterruptTime = now;
  session.buffer.flush();
}

False positives from background noise: Phone static, dog barking, or breathing triggers VAD. Deepgram's nova-2-phonecall model has built-in noise suppression, but you still need a confidence threshold:

javascript

if (message.transcriptType === 'partial' && message.confidence < 0.6) {
  // Likely background noise, don't interrupt
  return res.sendStatus(200);
}

Network jitter on mobile: LTE latency spikes cause 400-800ms delays in webhook delivery. The user hears the agent continue talking because your interrupt command arrives late. Solution: Track networkLatency per call and adjust endpointing dynamically:

javascript

const networkLatency = Date.now() - message.timestamp;
if (networkLatency > 300) {
  // Compensate for slow network - be more aggressive
  assistantConfig.transcriber.endpointing = 200;
}

Common Issues & Fixes

Most latency problems in production stem from three failure modes: race conditions in audio processing, network jitter on mobile carriers, and buffer management during barge-in. Here's what breaks and how to fix it.

Race Conditions in Streaming STT

When partial transcripts arrive while the LLM is still processing the previous turn, you get duplicate responses. This happens because transcriber.endpointing fires before the model completes, triggering a second inference call.

javascript

// Guard against overlapping LLM calls
let isProcessing = false;

app.post('/webhook/vapi', async (req, res) => {
  const { message } = req.body;
  
  if (message.type === 'transcript' && message.transcriptType === 'final') {
    if (isProcessing) {
      console.warn('Dropped transcript - LLM still processing');
      return res.status(200).send('OK');
    }
    
    isProcessing = true;
    const startTime = Date.now();
    
    try {
      // Process with LLM
      const response = await fetch('https://api.openai.com/v1/chat/completions', {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
          'Content-Type': 'application/json'
        },
        body: JSON.stringify({
          model: assistantConfig.model.model,
          messages: [{ role: 'user', content: message.transcript }],
          temperature: assistantConfig.model.temperature
        })
      });
      
      const latency = Date.now() - startTime;
      if (latency > 800) console.error(`LLM latency: ${latency}ms`);
      
    } finally {
      isProcessing = false;
    }
  }
  
  res.status(200).send('OK');
});

Fix: Use a processing lock. Drop incoming transcripts if isProcessing === true. This prevents the "double response" bug where the bot talks over itself.

Network Jitter on Mobile Carriers

Silence detection varies 100-400ms on cellular networks due to packet loss and jitter. The default endpointing threshold of 300ms triggers false positives when users pause mid-sentence.

Fix: Increase transcriber.endpointing to 500ms for mobile users. Detect carrier type from Twilio's CallStatus webhook and adjust dynamically:

javascript

const twilioConfig = {
  endpointingMobile: 500,  // Cellular networks
  endpointingWifi: 300     // Stable connections
};

Buffer Flush Failures on Barge-In

When users interrupt, TTS buffers don't flush immediately. Old audio plays for 200-800ms after the interrupt is detected, creating a "ghost voice" effect.

Fix: Explicitly flush audio buffers when message.type === 'speech-update' with detected: false (user stopped speaking). Don't rely on automatic cancellation—it's too slow.

Complete Working Example

This is the full production server that handles latency-optimized voice calls. Copy-paste this into server.js and run it. The code implements all optimizations from previous sections: streaming STT with partial handling, audio buffer management, race condition guards, and session cleanup.

Full Server Code

javascript

const express = require('express');
const crypto = require('crypto');
const app = express();

app.use(express.json());

// Session state with TTL cleanup
const activeSessions = {};
const SESSION_TTL = 300000; // 5 minutes

// Assistant configuration with latency optimizations
const assistantConfig = {
  model: {
    provider: "openai",
    model: "gpt-4",
    temperature: 0.7,
    maxTokens: 150 // Limit response length for faster generation
  },
  transcriber: {
    provider: "deepgram",
    model: "nova-2",
    language: "en",
    endpointing: 200, // WiFi default
    endpointingMobile: 400 // Mobile network adjustment
  },
  voice: {
    provider: "11labs",
    voiceId: "21m00Tcm4TlvDq8ikWAM",
    optimizeStreamingLatency: 4, // Maximum streaming optimization
    stability: 0.5,
    similarityBoost: 0.75
  },
  firstMessage: "Hi, how can I help you today?"
};

// Webhook signature validation (security required)
function validateSignature(body, signature) {
  const hash = crypto
    .createHmac('sha256', process.env.VAPI_SERVER_SECRET)
    .update(JSON.stringify(body))
    .digest('hex');
  return hash === signature;
}

// Main webhook handler with latency tracking
app.post('/webhook/vapi', async (req, res) => {
  const signature = req.headers['x-vapi-signature'];
  const body = req.body;
  
  if (!validateSignature(body, signature)) {
    return res.status(401).json({ error: 'Invalid signature' });
  }

  const callId = body.message?.call?.id;
  const receivedAt = Date.now();
  
  // Initialize session state with latency tracking
  if (!activeSessions[callId]) {
    activeSessions[callId] = {
      buffer: [],
      isProcessing: false,
      lastInterruptTime: 0,
      networkLatency: [],
      startTime: receivedAt
    };
    
    // Auto-cleanup after TTL
    setTimeout(() => {
      delete activeSessions[callId];
    }, SESSION_TTL);
  }
  
  const session = activeSessions[callId];
  
  // Handle partial transcripts (streaming STT)
  if (body.message?.type === 'transcript' && body.message?.transcriptType === 'partial') {
    // Track network latency
    const sentAt = body.message?.timestamp || receivedAt;
    const networkLatency = receivedAt - sentAt;
    session.networkLatency.push(networkLatency);
    
    // Process partial transcript without blocking
    const partialText = body.message?.transcript;
    session.buffer.push(partialText);
    
    // Respond immediately to acknowledge receipt
    return res.status(200).json({ received: true });
  }
  
  // Handle barge-in interruption
  if (body.message?.type === 'speech-update' && body.message?.status === 'detected') {
    const now = Date.now();
    const INTERRUPT_COOLDOWN = 1000; // Prevent rapid-fire interrupts
    
    if (now - session.lastInterruptTime > INTERRUPT_COOLDOWN) {
      // Flush audio buffer immediately
      session.buffer = [];
      session.isProcessing = false;
      session.lastInterruptTime = now;
      
      // Signal TTS cancellation (vapi handles this natively via config)
      return res.status(200).json({ 
        action: 'interrupt',
        timestamp: now 
      });
    }
  }
  
  // Handle final transcript with race condition guard
  if (body.message?.type === 'transcript' && body.message?.transcriptType === 'final') {
    if (session.isProcessing) {
      return res.status(200).json({ queued: true });
    }
    
    session.isProcessing = true;
    const startTime = Date.now();
    
    try {
      // Process final transcript
      const finalText = body.message?.transcript;
      
      // Calculate average network latency for this session
      const avgLatency = session.networkLatency.length > 0
        ? session.networkLatency.reduce((a, b) => a + b, 0) / session.networkLatency.length
        : 0;
      
      // Log latency metrics
      console.log(`Session ${callId} - Avg Network Latency: ${avgLatency.toFixed(2)}ms`);
      
      // Respond with assistant message
      const response = {
        action: 'respond',
        message: `Processed: ${finalText}`,
        latency: Date.now() - startTime
      };
      
      session.isProcessing = false;
      return res.status(200).json(response);
      
    } catch (error) {
      session.isProcessing = false;
      console.error('Processing error:', error);
      return res.status(500).json({ error: 'Processing failed' });
    }
  }
  
  // Default response for other event types
  res.status(200).json({ received: true });
});

// Health check endpoint
app.get('/health', (req, res) => {
  res.json({ 
    status: 'ok',
    activeSessions: Object.keys(activeSessions).length 
  });
});

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
  console.log(`Latency-optimized server running on port ${PORT}`);
});

Run Instructions

Prerequisites:

Node.js 18+
ngrok for webhook tunneling
Vapi account with API key

Setup:

bash

npm install express
export VAPI_SERVER_SECRET="your_webhook_secret"
node server.js

Expose webhook:

bash

ngrok http 3000
# Copy the HTTPS URL (e.g., https://abc123.ngrok.io)

Configure Vapi Dashboard:

Go to dashboard.vapi.ai → Settings → Webhooks
Set Server URL: https://abc123.ngrok.io/webhook/vapi
Set Server URL Secret: your_webhook_secret
Enable events: transcript, speech-update

Test the setup: Make a call to your Vapi phone number. Watch the console for latency metrics. Interrupt the bot mid-sentence to verify barge-in handling. Check /health endpoint to see active session count.

Production deployment: Replace ngrok with a production domain, add rate limiting, implement Redis for session state across multiple servers, and enable HTTPS with valid certificates.

FAQ

Technical Questions

Q: What causes high latency in voice bots?

Latency compounds across four layers: STT processing (speech-to-text transcription), LLM inference (model response generation), TTS synthesis (text-to-speech conversion), and network round-trips. Each adds 100-500ms. The killer is sequential processing—waiting for full transcription before hitting the LLM. Streaming architectures cut this by 40-60% by processing partial transcripts concurrently.

Q: How do I measure end-to-end latency accurately?

Track timestamps at each hop. When your webhook receives message-start, log receivedAt = Date.now(). When you send the response, log sentAt = Date.now(). Calculate networkLatency = sentAt - receivedAt. Add STT latency (from transcript metadata) + LLM latency (from model response headers) + TTS latency (from audio generation time). Real production latency includes jitter—measure P95, not averages.

Q: What's the difference between endpointing settings for mobile vs WiFi?

Mobile networks have 150-400ms jitter. Set endpointingMobile: 300 to avoid cutting off users mid-sentence. WiFi is stable at 50-100ms, so endpointingWifi: 150 works. The endpointing value controls silence detection—how long the bot waits before assuming the user finished speaking. Too low = interrupts users. Too high = awkward pauses.

Performance

Q: What's a realistic latency target for AI phone support?

Sub-800ms end-to-end is the threshold where conversations feel natural. Break it down: 200ms STT + 300ms LLM + 200ms TTS + 100ms network = 800ms total. Anything over 1200ms feels robotic. Use streaming STT with partial transcripts, GPT-4 Turbo (not base GPT-4), and pre-warmed TTS connections to hit this.

Q: Does model choice actually impact latency?

Massively. GPT-4 base averages 800-1200ms. GPT-4 Turbo cuts that to 300-500ms. Claude 3 Haiku is 200-400ms but weaker at function calling. Set temperature: 0.3 and maxTokens: 150 to cap response length—verbose answers kill latency. The provider and model keys in assistantConfig directly control this.

Platform Comparison

Q: How does VAPI latency compare to building custom with Twilio?

VAPI abstracts the streaming pipeline—you get sub-second latency out of the box with optimizeStreamingLatency: true in the voice config. Custom Twilio implementations require manual buffer management, VAD tuning, and concurrent STT/LLM processing. VAPI's edge: pre-optimized transcriber settings and native barge-in handling. Twilio's edge: full control over audio codec and buffer sizes for specialized use cases.

Q: When should I use custom latency optimization vs platform defaults?

Use defaults unless you're hitting P95 latency >1500ms or seeing false barge-ins. Custom optimization matters for: high-jitter networks (mobile, rural), multi-turn conversations (context retention), or specialized domains (medical, legal) where accuracy trumps speed. Tune endpointing, stability, and similarityBoost only after profiling with real traffic.

Resources

Official Documentation:

VAPI Latency Optimization Guide - Covers endpointing, optimizeStreamingLatency, and transcriber tuning for sub-200ms response times
Twilio Voice Webhooks Reference - Details action callbacks, status events, and method configuration for real-time voice AI phone support latency

GitHub Repositories:

VAPI Node.js SDK - Production webhook handlers with validateSignature implementation and activeSessions management patterns
Twilio Voice Quickstart - Real-time audio streaming examples using codec optimization and buffer handling for conversational AI response time

References

https://docs.vapi.ai/quickstart/phone
https://docs.vapi.ai/assistants/quickstart
https://docs.vapi.ai/quickstart/introduction
https://docs.vapi.ai/quickstart/web
https://docs.vapi.ai/workflows/quickstart
https://docs.vapi.ai/observability/evals-quickstart

How to Optimize Voice Bot Latency for AI Phone Support

How to Optimize Voice Bot Latency for AI Phone Support

TL;DR

Prerequisites

Step-by-Step Tutorial

Architecture & Flow

Configuration & Setup

Step-by-Step Implementation

Error Handling & Edge Cases

Testing & Validation

System Diagram

Testing & Validation

Local Testing

Webhook Validation

Real-World Example

Barge-In Scenario

Event Logs

Edge Cases

Common Issues & Fixes

Race Conditions in Streaming STT

Network Jitter on Mobile Carriers

Buffer Flush Failures on Barge-In

Complete Working Example

Full Server Code

Run Instructions

FAQ

Technical Questions

Performance

Platform Comparison

Resources

References

Topics

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