voice-ai-development — Платформа для быстрой разработки голосовых приложений

--- name: voice-ai-development description: "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." 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 ```python 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 ```python # 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 ```python 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.