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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": {