techwavedev 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
},
"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"] == "resp
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
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.
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Voice Ai Development"
Storing Results
After completing work, store AI agent orchestration decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
--type decision --project <project> \
--tags voice-ai-development ai-agents
Multi-Agent Collaboration
This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
--project <project>
Control Tower Integration
Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.
Blockchain Identity
Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.