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...
risk	unknown
source	vibeship-spawner-skills (Apache 2.0)
date_added	2026-02-27

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(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
      chunk  audio_stream:
         connection.send(chunk)

     connection.finish()


eleven = ElevenLabs(api_key=)

 ():
    
    audio_stream = eleven.text_to_speech.convert_as_stream(
        voice_id=,  
        model_id=,  
        text=text,
        output_format=  
    )

     chunk  audio_stream:
         chunk


  ():
      eleven.text_to_speech.stream_async(
        voice_id=,
        model_id=
    )  tts:
          text_chunk  text_stream:
            audio =  tts.send(text_chunk)
             audio

        
        final_audio =  tts.flush()
         final_audio

Anti-Patterns

❌ Non-streaming Pipeline

Why bad: Adds seconds of latency. User perceives as slow. Loses conversation flow.

Instead: Stream everything:

• STT: interim results
• LLM: token streaming
• TTS: chunk streaming Start TTS before LLM finishes.

❌ Ignoring Interruptions

Why bad: Frustrating user experience. Feels like talking to a machine. Wastes time.

Instead: Implement barge-in detection. Use VAD to detect user speech. Stop TTS immediately. Clear audio queue.

❌ Single Provider Lock-in

Why bad: May not be best quality. Single point of failure. Harder to optimize.

Instead: Mix best providers:

• Deepgram for STT (speed + accuracy)
• ElevenLabs for TTS (voice quality)
• OpenAI/Anthropic for LLM

Limitations

• Latency varies by provider
• Cost per minute adds up
• Quality depends on network
• Complex debugging

Related Skills

Works well with: langgraph, structured-output, langfuse

When to Use

This skill is applicable to execute the workflow or actions described in the overview.