voice-ai-development

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...
category	Development & Code Tools
source	antigravity
tags	["python","node","api","ai","agent","llm","gpt","workflow","design","langgraph"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/voice-ai-development

Voice AI Development

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.

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

Requirements

• Python or Node.js
• API keys for providers
• Audio handling knowledge

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
                },
                : [
                    {
                        : ,
                        : ,
                        : ,
                        : {
                            : ,
                            : {
                                : {: }
                            }
                        }
                    }
                ]
            }
        }))

        
          ():
             ws.send(json.dumps({
                : ,
                : base64.b64encode(audio_bytes).decode()
            }))

        
          message  ws:
            event = json.loads(message)

             event[] == 

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"])
             jsonify({: result})

     event[] == :
        
        transcript = event[]
        save_transcript(event[][], transcript)

     jsonify({: })


call = client.calls.create(
    assistant_id=assistant.,
    customer={
        : 
    },
    phoneNumber={
        : 
    }
)


web_call = client.calls.create(
    assistant_id=assistant.,
    =
)

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(ap