voice-ai-development

# 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": { "type": "function_call_output", "call_id": event["call_id"], "output": json.dumps(result) } })) ### 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 ### LiveKit Real-time Infrastructure WebRTC infrastructure for voice apps **When to use**: Building custom real-time voice apps from livekit import api, rtc import asyncio # Server-side: Create room and tokens lk_api = api.LiveKitAPI( url="wss://your-livekit.livekit.cloud", api_key="...", api_secret="..." ) async def create_room(room_name: str): room = await lk_api.room.create_room( api.CreateRoomRequest(name=room_name) ) return room def create_token(room_name: str, participant_name: str): token = api.AccessToken( api_key="...", api_secret="..." ) token.with_identity(participant_name) token.with_grants(api.VideoGrants( room_join=True, room=room_name )) return token.to_jwt() # Agent-side: Connect and process audio async def voice_agent(room_name: str): room = rtc.Room() @room.on("track_subscribed") def on_track(track, publication, participant): if track.kind == rtc.TrackKind.KIND_AUDIO: # Process incoming audio audio_stream = rtc.AudioStream(track) asyncio.create_task(process_audio(audio_stream)) token = create_token(room_name, "agent") await room.connect("wss://your-livekit.livekit.cloud", token) # Publish agent's audio source = rtc.AudioSource(sample_rate=24000, num_channels=1) track = rtc.LocalAudioTrack.create_audio_track("agent-voice", source) await room.local_participant.publish_track(track) # Send audio from TTS async def speak(text: str): for audio_chunk in text_to_speech(text): await source.capture_frame(rtc.AudioFrame( data=audio_chunk, sample_rate=24000, num_channels=1, samples_per_channel=len(audio_chunk) // 2 )) return room, speak # Process audio with STT async def process_audio(audio_stream): async for frame in audio_stream: # Send to Deepgram or other STT await transcriber.send(frame.data) ### Full Voice Agent Pipeline Complete voice agent with all components **When to use**: Custom production voice agent import asyncio from dataclasses import dataclass from typing import AsyncIterator @dataclass class VoiceAgentConfig: stt_provider: str = "deepgram" tts_provider: str = "elevenlabs" llm_provider: str = "openai" vad_enabled: bool = True interrupt_enabled: bool = True class VoiceAgent: def __init__(self, config: VoiceAgentConfig): self.config = config self.is_speaking = False self.conversation_history = [] async def process_audio_stream( self, audio_in: AsyncIterator[bytes], audio_out: asyncio.Queue ): """Main audio processing loop.""" # STT streaming async def transcribe(): transcript_buffer = "" async for audio_chunk in audio_in: # Check for interruption if self.is_speaking and self.config.interrupt_enabled: if await self.detect_speech(audio_chunk): await self.stop_speaking() result = await self.stt.transcribe(audio_chunk) if result.is_final: yield result.transcript # Process transcripts async for user_text in transcribe(): if not user_text.strip(): continue self.conversation_history.append({ "role": "user", "content": user_text }) # Generate response with streaming self.is_speaking = True async for audio_chunk in self.generate_response(user_text): await audio_out.put(audio_chunk) self.is_speaking = False async def generate_response(self, text: str) -> AsyncIterator[bytes]: """Stream LLM response through TTS.""" # Stream LLM tokens llm_stream = self.llm.stream_chat(self.conversation_history) # Buffer for TTS (need ~50 chars for good prosody) text_buffer = "" full_response = "" async for token in llm_stream: text_buffer += token full_response += token # Send to TTS when we have enough text if len(text_buffer) > 50 or token in ".!?": async for audio in self.tts.synthesize_stream(text_buffer): yield audio text_buffer = "" # Flush remaining if text_buffer: async for audio in self.tts.synthesize_stream(text_buffer): yield audio self.conversation_history.append({ "role": "assistant", "content": full_response