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voice-ai-development Build real-time voice AI apps — OpenAI Realtime API, Vapi voice agents, Deepgram STT, ElevenLabs TTS, LiveKit real-time infra, and WebRTC fundamentals, tuned for low latency. USE WHEN building voice agents, voice-enabled apps, or real-time audio pipelines and choosing/optimizing STT/TTS/transport providers.
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عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
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name voice-ai-development description Build real-time voice AI apps — OpenAI Realtime API, Vapi voice agents, Deepgram STT, ElevenLabs TTS, LiveKit real-time infra, and WebRTC fundamentals, tuned for low latency. USE WHEN building voice agents, voice-enabled apps, or real-time audio pipelines and choosing/optimizing STT/TTS/transport providers. cluster devops-infra version 1.0.0 origin antigravity-awesome-skills (MIT) risk unknown source vibeship-spawner-skills (Apache 2.0) date_added "2026-02-27T00:00:00.000Z"
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.
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
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
})
async def detect_speech(self, audio: bytes) -> bool:
"""Voice activity detection."""
# Use WebRTC VAD or Silero VAD
return self.vad.is_speech(audio)
async def stop_speaking(self):
"""Handle interruption."""
self.is_speaking = False
# Clear audio queue
# Stop TTS generation
Latency optimization tips:
1. Use streaming everywhere (STT, LLM, TTS)
2. Start TTS before LLM finishes (~50 char buffer)
3. Use PCM audio format (no encoding overhead)
4. Keep WebSocket connections alive
5. Use regional endpoints close to users
Validation Checks
Non-Streaming TTS Message: Non-streaming TTS adds significant latency.
Fix action: Use tts.synthesize_stream() or tts.convert_as_stream()
Hardcoded Sample Rate Message: Hardcoded sample rate may cause format mismatches.
Fix action: Define sample rates as constants, document expected formats
WebSocket Without Reconnection Message: WebSocket connections need reconnection logic.
Fix action: Add retry loop with exponential backoff
Missing VAD Configuration Message: VAD needs tuning for good user experience.
Fix action: Configure threshold and silence_duration_ms
Blocking Audio Processing Message: Audio processing should be async to avoid blocking.
Fix action: Use async def and await for audio operations
Missing Interruption Handling Message: Voice agents should handle user interruptions.
Fix action: Add barge-in detection and cancel current response
Audio Queue Without Clear Message: Audio queues should be clearable for interruptions.
Fix action: Add method to clear queue on interruption
WebSocket Without Error Handling Message: WebSocket operations need error handling.
Fix action: Wrap in try/except for ConnectionClosed
Collaboration
Delegation Triggers
agent graph|workflow|state -> langgraph (Need complex agent logic behind voice)
extract|structured|json -> structured-output (Need to extract structured data from voice)
observability|tracing|monitoring -> langfuse (Need to monitor voice agent quality)
frontend|web|react -> nextjs-app-router (Need web interface for voice agent)
Intelligent Voice Agent Skills: voice-ai-development, langgraph, structured-output
1. Design agent graph with tools
2. Add voice interface layer
3. Use structured output for tool responses
4. Optimize for voice latency
Monitored Voice Agent Skills: voice-ai-development, langfuse
1. Build voice agent with provider of choice
2. Add Langfuse callbacks
3. Track latency, quality, conversation flow
4. Iterate based on metrics
Phone-based Agent Skills: voice-ai-development, twilio
1. Set up Vapi or custom agent
2. Connect to Twilio for PSTN
3. Handle inbound/outbound calls
4. Implement call routing logic
Related Skills Works well with: langgraph, structured-output, langfuse
When to Use
User mentions or implies: voice ai
User mentions or implies: voice agent
User mentions or implies: speech to text
User mentions or implies: text to speech
User mentions or implies: realtime voice
User mentions or implies: vapi
User mentions or implies: deepgram
User mentions or implies: elevenlabs
User mentions or implies: livekit
User mentions or implies: openai realtime
Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
المزيد من هذا المستودع Arcplume runs Grok through the Grok Build CLI's own OAuth-authenticated session (grok login) for image generation, with strict preflight validation, secret-safe handling, and headless CLI-driven execution -- no separate XAI_API_KEY billing. Video falls back to the billed xAI API. USE WHEN a user wants to generate an image via a locally logged-in Grok Build CLI session, e.g. 'generate an image with grok', 'use grok build', or 'use my logged-in grok session'.
Shared reference for the Selemene cluster: the two report surfaces (deterministic Rust reports vs. narrative witness-pipeline readings), the @selemene/bridge CLI contract, the output manifest format, and non-prescriptive witnessing tone. USE WHEN deciding which Selemene surface to invoke or when routing between birth/compatibility/transit reports and solo/dyadic readings.
Route Selemene Engine tasks to the right surface: deterministic reports (birth/compatibility/transit) via selemene-core and the @selemene/bridge CLI, or narrative witness readings via selemene-report. USE WHEN the user wants anything under the Selemene/Noesis umbrella but has not named the exact surface.
