voice-ai-development — quality + safety report

---
name: voice-ai-development
description: 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.
risk: unknown
source: vibeship-spawner-skills (Apache 2.0)
date_added: 2026-02-27
---

# 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

OPENAI_API_KEY = "sk-..."

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():

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