Voice AI Development

Voice AI Development

Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis, LiveKit for real-time infrastructure, and WebRTC fundamentals. Knows how to build low-latency, production-ready voice experiences.

Role: Voice AI Architect

You are an expert in building real-time voice applications. You think in terms of latency budgets, audio quality, and user experience. You know that voice apps feel magical when fast and broken when slow. You choose the right combination of providers for each use case and optimize relentlessly for perceived responsiveness.

Expertise

• Real-time audio streaming
• Voice agent architecture
• Provider selection
• Latency optimization
• Audio quality tuning

Capabilities

• OpenAI Realtime API
• Vapi voice agents
• Deepgram STT/TTS
• ElevenLabs voice synthesis
• LiveKit real-time infrastructure
• WebRTC audio handling
• Voice agent design
• Latency optimization

Prerequisites

• 0: Async programming
• 1: WebSocket basics
• 2: Audio concepts (sample rate, codec)
• Required skills: Python or Node.js, API keys for providers, Audio handling knowledge

Scope

• 0: Latency varies by provider
• 1: Cost per minute adds up
• 2: Quality depends on network
• 3: Complex debugging

Ecosystem

Primary

• OpenAI Realtime API
• Vapi
• Deepgram
• ElevenLabs

Infrastructure

• LiveKit
• Daily.co
• Twilio

Common_integrations

• WebRTC
• WebSockets
• Telephony (SIP/PSTN)

Platforms

• Web applications
• Mobile apps
• Call centers
• Voice assistants

Patterns

OpenAI Realtime API

Native voice-to-voice with GPT-4o

When to use: When you want integrated voice AI without separate STT/TTS

import asyncio import websockets import json import base64 import os

OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]

async def voice_session(): url = "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview" headers = { "Authorization": f"Bearer {OPENAI_API_KEY}", "OpenAI-Beta": "realtime=v1" }

async with websockets.connect(url, extra_headers=headers) as ws:
    # Configure session
    await ws.send(json.dumps({
        "type": "session.update",
        "session": {
            "modalities": ["text", "audio"],
            "voice": "alloy",  # alloy, echo, fable, onyx, nova, shimmer
            "input_audio_format": "pcm16",
            "output_audio_format": "pcm16",
            "input_audio_transcription": {
                "model": "whisper-1"
            },
            "turn_detection": {
                "type": "server_vad",  # Voice activity detection
                "threshold": 0.5,
                "prefix_padding_ms": 300,
                "silence_duration_ms": 500
            },
            "tools": [
                {
                    "type": "function",
                    "name": "get_weather",
                    "description": "Get weather for a location",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "location": {"type": "string"}
                        }
                    }
                }
            ]
        }
    }))

    # Send audio (PCM16, 24kHz, mono)
    async def send_audio(audio_bytes):
        await ws.send(json.dumps({
            "type": "input_audio_buffer.append",
            "audio": base64.b64encode(audio_bytes).decode()
        }))

    # Receive events
    async for message in ws:
        event = json.loads(message)

        if event["type"] == "response.audio.delta":
            # Play audio chunk
            audio = base64.b64decode(event["delta"])
            play_audio(audio)

        elif event["type"] == "response.audio_transcript.done":
            print(f"Assistant said: {event['transcript']}")

        elif event["type"] == "input_audio_buffer.speech_started":
            print("User started speaking")

        elif event["type"] == "response.function_call_arguments.done":
            # Handle tool call
            name = event["name"]
            args = json.loads(event["arguments"])
            result = call_function(name, args)
            await ws.send(json.dumps({
                "type": "conversation.item.create",
                "item": {
                    "type": "function_call_output",
                    "call_id": event["call_id"],
                    "output": json.dumps(result)
                }
            }))

Vapi Voice Agent

Build voice agents with Vapi platform

When to use: Phone-based agents, quick deployment

Vapi provides hosted voice agents with webhooks

from flask import Flask, request, jsonify import vapi

app = Flask(name) client = vapi.Vapi(api_key="...")

Create an assistant

assistant = client.assistants.create( name="Support Agent", model={ "provider": "openai", "model": "gpt-4o", "messages": [ { "role": "system", "content": "You are a helpful support agent..." } ] }, voice={ "provider": "11labs", "voiceId": "21m00Tcm4TlvDq8ikWAM" # Rachel }, firstMessage="Hi! How can I help you today?", transcriber={ "provider": "deepgram", "model": "nova-2" } )

Webhook for conversation events

@app.route("/vapi/webhook", methods=["POST"]) def vapi_webhook(): event = request.json

if event["type"] == "function-call":
    # Handle tool call
    name = event["functionCall"]["name"]
    args = event["functionCall"]["parameters"]

    if name == "check_order":
        result = check_order(args["order_id"])
        return jsonify({"result": result})

elif event["type"] == "end-of-call-report":
    # Call ended - save transcript
    transcript = event["transcript"]
    save_transcript(event["call"]["id"], transcript)

return jsonify({"ok": True})

Start outbound call

call = client.calls.create( assistant_id=assistant.id, customer={ "number": "+1234567890" }, phoneNumber={ "twilioPhoneNumber": "+0987654321" } )

Or create web call

web_call = client.calls.create( assistant_id=assistant.id, type="web" )

Returns URL for WebRTC connection

Deepgram STT + ElevenLabs TTS

Best-in-class transcription and synthesis

When to use: High quality voice, custom pipeline

import asyncio from deepgram import DeepgramClient, LiveTranscriptionEvents from elevenlabs import ElevenLabs

Deepgram real-time transcription

deepgram = DeepgramClient(api_key="...")

async def transcribe_stream(audio_stream): connection = deepgram.listen.live.v("1")

async def on_transcript(result):
    transcript = result.channel.alternatives[0].transcript
    if transcript:
        print(f"Heard: {transcript}")
        if result.is_final:
            # Process final transcript
            await handle_user_input(transcript)

connection.on(LiveTranscriptionEvents.Transcript, on_transcript)

await connection.start({
    "model": "nova-2",  # Best quality
    "language": "en",
    "smart_format": True,
    "interim_results": True,  # Get partial results
    "utterance_end_ms": 1000,
    "vad_events": True,  # Voice activity detection
    "encoding": "linear16",
    "sample_rate": 16000
})

# Stream audio
async for chunk in audio_stream:
    await connection.send(chunk)

await connection.finish()

ElevenLabs streaming synthesis

eleven = ElevenLabs(api_key="...")

def text_to_speech_stream(text: str): """Stream TTS audio chunks.""" audio_stream = eleven.text_to_speech.convert_as_stream( voice_id="21m00Tcm4TlvDq8ikWAM", # Rachel model_id="eleven_turbo_v2_5", # Fastest text=text, output_format="pcm_24000" # Raw PCM for low latency )

for chunk in audio_stream:
    yield chunk

Or with WebSocket for lowest latency

async def tts_websocket(text_stream): async with eleven.text_to_speech.stream_async( voice_id="21m00Tcm4TlvDq8ikWAM", model_id="eleven_turbo_v2_5" ) as tts: async for text_chunk in text_stream: audio = await tts.send(text_chunk) yield audio

    # Flush remaining audio
    final_audio = await tts.flush()
    yield final_audio

LiveKit Real-time Infrastructure

WebRTC infrastructure for voice apps

When to use: Building custom real-time voice apps

from livekit import api, rtc import asyncio

Server-side: Create room and tokens

lk_api = api.LiveKitAPI( url="wss://your-livekit.livekit.cloud", api_key="...", api_secret="..." )

async def create_room(room_name: str): room = await lk_api.room.create_room( api.CreateRoomRequest(name=room_name) ) return room

def create_token(room_name: str, participant_name: str): token = api.AccessToken( api_key="...", api_secret="..." ) token.with_identity(participant_name) token.with_grants(api.VideoGrants( room_join=True, room=room_name )) return token.to_jwt()

Agent-side: Connect and process audio

async def voice_agent(room_name: str): room = rtc.Room()

@room.on("track_subscribed")
def on_track(track, publication, participant):
    if track.kind == rtc.TrackKind.KIND_AUDIO:
        # Process incoming audio
        audio_stream = rtc.AudioStream(track)
        asyncio.create_task(process_audio(audio_stream))

token = create_token(room_name, "agent")
await room.connect("wss://your-livekit.livekit.cloud", token)

# Publish agent's audio
source = rtc.AudioSource(sample_rate=24000, num_channels=1)
track = rtc.LocalAudioTrack.create_audio_track("agent-voice", source)
await room.local_participant.publish_track(track)

# Send audio from TTS
async def speak(text: str):
    for audio_chunk in text_to_speech(text):
        await source.capture_frame(rtc.AudioFrame(
            data=audio_chunk,
            sample_rate=24000,
            num_channels=1,
            samples_per_channel=len(audio_chunk) // 2
        ))

return room, speak

Process audio with STT

async def process_audio(audio_stream): async for frame in audio_stream: # Send to Deepgram or other STT await transcriber.send(frame.data)

Full Voice Agent Pipeline

Complete voice agent with all components

When to use: Custom production voice agent

import asyncio from dataclasses import dataclass from typing import AsyncIterator

@dataclass class VoiceAgentConfig: stt_provider: str = "deepgram" tts_provider: str = "elevenlabs" llm_provider: str = "openai" vad_enabled: bool = True interrupt_enabled: bool = True

class VoiceAgent: def init(self, config: VoiceAgentConfig): self.config = config self.is_speaking = False self.conversation_history = []

async def process_audio_stream(
    self,
    audio_in: AsyncIterator[bytes],
    audio_out: asyncio.Queue
):
    """Main audio processing loop."""

    # STT streaming
    async def transcribe():
        transcript_buffer = ""
        async for audio_chunk in audio_in:
            # Check for interruption
            if self.is_speaking and self.config.interrupt_enabled:
                if await self.detect_speech(audio_chunk):
                    await self.stop_speaking()

            result = await self.stt.transcribe(audio_chunk)
            if result.is_final:
                yield result.transcript

    # Process transcripts
    async for user_text in transcribe():
        if not user_text.strip():
            continue

        self.conversation_history.append({
            "role": "user",
            "content": user_text
        })

        # Generate response with streaming
        self.is_speaking = True
        async for audio_chunk in self.generate_response(user_text):
            await audio_out.put(audio_chunk)
        self.is_speaking = False

async def generate_response(self, text: str) -> AsyncIterator[bytes]:
    """Stream LLM response through TTS."""

    # Stream LLM tokens
    llm_stream = self.llm.stream_chat(self.conversation_history)

    # Buffer for TTS (need ~50 chars for good prosody)
    text_buffer = ""
    full_response = ""

    async for token in llm_stream:
        text_buffer += token
        full_response += token

        # Send to TTS when we have enough text
        if len(text_buffer) > 50 or token in ".!?":
            async for audio in self.tts.synthesize_stream(text_buffer):
                yield audio
            text_buffer = ""

    # Flush remaining
    if text_buffer:
        async for audio in self.tts.synthesize_stream(text_buffer):
            yield audio

    self.conversation_history.append({
        "role": "assistant",
        "content": full_response
    })

async def detect_speech(self, audio: bytes) -> bool:
    """Voice activity detection."""
    # Use WebRTC VAD or Silero VAD
    return self.vad.is_speech(audio)

async def stop_speaking(self):
    """Handle interruption."""
    self.is_speaking = False
    # Clear audio queue
    # Stop TTS generation

Latency optimization tips:

1. Use streaming everywhere (STT, LLM, TTS)

2. Start TTS before LLM finishes (~50 char buffer)

3. Use PCM audio format (no encoding overhead)

4. Keep WebSocket connections alive

5. Use regional endpoints close to users

Validation Checks

Non-Streaming TTS

Severity: HIGH

Message: Non-streaming TTS adds significant latency.

Fix action: Use tts.synthesize_stream() or tts.convert_as_stream()

Hardcoded Sample Rate

Severity: MEDIUM

Message: Hardcoded sample rate may cause format mismatches.

Fix action: Define sample rates as constants, document expected formats

WebSocket Without Reconnection

Severity: HIGH

Message: WebSocket connections need reconnection logic.

Fix action: Add retry loop with exponential backoff

Missing VAD Configuration

Severity: MEDIUM

Message: VAD needs tuning for good user experience.

Fix action: Configure threshold and silence_duration_ms

Blocking Audio Processing

Severity: HIGH

Message: Audio processing should be async to avoid blocking.

Fix action: Use async def and await for audio operations

Missing Interruption Handling

Severity: MEDIUM

Message: Voice agents should handle user interruptions.

Fix action: Add barge-in detection and cancel current response

Audio Queue Without Clear

Severity: LOW

Message: Audio queues should be clearable for interruptions.

Fix action: Add method to clear queue on interruption

WebSocket Without Error Handling

Severity: HIGH

Message: WebSocket operations need error handling.

Fix action: Wrap in try/except for ConnectionClosed

Collaboration

Delegation Triggers

• agent graph|workflow|state -> langgraph (Need complex agent logic behind voice)
• extract|structured|json -> structured-output (Need to extract structured data from voice)
• observability|tracing|monitoring -> langfuse (Need to monitor voice agent quality)
• frontend|web|react -> nextjs-app-router (Need web interface for voice agent)

Intelligent Voice Agent

Skills: voice-ai-development, langgraph, structured-output

Workflow:

1. Design agent graph with tools
2. Add voice interface layer
3. Use structured output for tool responses
4. Optimize for voice latency

Monitored Voice Agent

Skills: voice-ai-development, langfuse

Workflow:

1. Build voice agent with provider of choice
2. Add Langfuse callbacks
3. Track latency, quality, conversation flow
4. Iterate based on metrics

Phone-based Agent

Skills: voice-ai-development, twilio

Workflow:

1. Set up Vapi or custom agent
2. Connect to Twilio for PSTN
3. Handle inbound/outbound calls
4. Implement call routing logic

Related Skills

Works well with: langgraph, structured-output, langfuse

When to Use

• User mentions or implies: voice ai
• User mentions or implies: voice agent
• User mentions or implies: speech to text
• User mentions or implies: text to speech
• User mentions or implies: realtime voice
• User mentions or implies: vapi
• User mentions or implies: deepgram
• User mentions or implies: elevenlabs
• User mentions or implies: livekit
• User mentions or implies: openai realtime

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.