Voice Agents Implementation Summary

Current Status: Voice Fully Implemented! 🎉

What's Implemented and Working

1. Backend Infrastructure ✅

   • All voice WebSocket handlers configured and production-ready
   • OpenAI real-time API integration with proper error handling
   • Session management with automatic cleanup
   • Smart truncation logic for voice responses

2. Frontend Components ✅

   • VoiceRecordButtonRealtime with all visual states
   • useVoice hook with complete session management
   • AudioStreamProcessor for real-time audio capture
   • AudioStreamPlayer for queue-based audio playback
   • Full audio encoding/decoding infrastructure

3. Complete Voice Flow ✅

   • Tap microphone → switch to real-time model
   • Session connects to OpenAI with timeout handling
   • Speech transcribed and sent to agents
   • Agents respond with both text and voice
   • Smart audio truncation based on content type
   • Voice cancellation and audio controls

Recent Fixes and Improvements

1. SST Configuration (June 2025)

   • All voice handlers properly configured with dot notation (voice.action.name)
   • Added proper memorySize, timeout, bind, and vpc settings
   • All handlers production-ready with 30s timeouts

2. Session Connection

   • Added waitForSession() method to ensure OpenAI session is created
   • Backend now waits for session confirmation before returning success
   • Proper error handling and timeout management

3. Audio Infrastructure

   • AudioStreamProcessor for real-time microphone capture
   • AudioStreamPlayer with queue-based playback
   • PCM16 encoding/decoding with proper resampling

Remaining Optimization Opportunities

1. Voice UX: Manual tap-to-end is cumbersome

   • Solution: Auto-stop after 2-4 seconds silence (future enhancement)

2. Agent Awareness: Agents don't know input was voice

   • Solution: Add metadata (inputType, duration, confidence) to messages

3. Hybrid Mode: Currently requires expensive realtime model

   • Solution: Use Whisper for input, realtime only for responses

Future Enhancement: Hybrid Voice Mode 🚀

The current implementation requires switching to a real-time model before using voice. A better UX would be:

1. Voice Input on ANY Model

   • Click microphone with any model selected
   • Use existing Whisper transcription for voice → text
   • Send as regular text message

2. Smart Voice Response

   • Track if input was voice (lastInputWasVoice)
   • Auto-switch to real-time model only for voice response
   • Use requestVoiceResponse() with smart truncation

3. Benefits

   • Natural voice interaction without model switching
   • Preserves user's model choice for text interactions
   • Only uses real-time models when voice response is needed
   • More cost-effective (real-time models only for audio)

Implementation Plan

// In VoiceRecordButtonRealtime
if (!isRealtimeModel(model)) {
  // Use regular voice recording
  startVoiceRecording({
    onTranscript: async (transcript) => {
      await sendMessage(transcript);
      setLastInputWasVoice(true);
      
      // Optional: auto-switch for response
      if (autoSwitchForVoiceResponse) {
        await changeModel(ChatModels.GPT4O_REALTIME_PREVIEW);
      }
    }
  });
}

Next Steps (Future Enhancements)

1. ✅ Enable Nova's voice responses COMPLETED
2. Implement smart silence detection
3. Add voice metadata to messages
4. Build hybrid voice mode (voice input on any model)

What We Built (30 mins as requested!)

Frontend Components

1. Enhanced useVoice Hook (packages/client/app/hooks/useVoice.ts)

   • Added requestVoiceResponse(text) method
   • Smart truncation logic for different content types
   • Added stopSpeaking() method

2. VoiceResponseManager (in SessionBottom.tsx)

   • Listens for chat completions
   • Detects voice-initiated messages
   • Triggers voice responses automatically

3. Voice Button Integration

   • Replaced old VoiceRecordButton with VoiceRecordButtonRealtime
   • Tracks voice vs text input
   • Passes agent context

Backend Handlers

1. voice:text:input (voiceTextInput.ts)

   • Receives truncated text for voice response
   • Sends to OpenAI realtime API
   • Triggers response generation

2. voice:response:cancel (voiceResponseCancel.ts)

   • Cancels ongoing voice responses
   • Handles cleanup gracefully

Smart Truncation Rules

// Implemented in requestVoiceResponse
const MAX_SPOKEN_WORDS = 75;

if (words.length > MAX_SPOKEN_WORDS) {
  if (isList) {
    // "I found 12 items. Here are the first few..."
  } else if (hasCodeBlock) {
    // "I've written some code for you."
  } else {
    // First 75 words + "I've written more details below."
  }
}

The Flow

1. User taps mic → speaks question
2. Real-time transcription → message sent
3. VoiceResponseManager detects voice input
4. When agent responds, triggers requestVoiceResponse
5. Smart truncation applied
6. Voice streams back to user
7. Both text and voice delivered!

Testing Instructions (Production Ready)

1. Open chat with Nova
2. Tap microphone button (should show green connected state)
3. Ask: "What are the benefits of meditation?"
4. Nova should:
   • Show text response immediately
   • Start speaking the response simultaneously
   • Use smart truncation for long responses
   • Allow you to stop speaking with button click

Extended Testing:

• Ask for code: "Write a Python function" → Should say "I've written some code for you"
• Ask for lists: "Give me 10 tips" → Should count items and read first few
• Test cancellation: Stop Nova mid-speech

What's Next

1. ✅ Test the implementation! → Ready for production testing
2. Add silence detection for auto-stop (UX enhancement)
3. Add voice metadata to messages (agent awareness)
4. Implement hybrid voice mode (cost optimization)
5. Add interruption handling (advanced UX)

Technical Achievement

• Integrated OpenAI's real-time API
• Bi-directional audio streaming
• Smart content-aware truncation
• Seamless voice/text experience
• Ready for production testing!

Code Quality

• ✅ TypeScript fully typed
• ✅ No compilation errors
• ✅ Proper error handling
• ✅ WebSocket cleanup
• ✅ Memory management