Expert in building voice AI applications - from real-time voice agents to voice-enabled apps...
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.
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
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
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
Why bad: Adds seconds of latency. User perceives as slow. Loses conversation flow.
Instead: Stream everything:
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.
Why bad: May not be best quality. Single point of failure. Harder to optimize.
Instead: Mix best providers:
Works well with: langgraph, structured-output, langfuse
