Files

T

Antigravity Agent f8b21af4be Wiki cleanup: error-doc removal, dedup merge, link normalization

10_Wiki/Topics 대규모 정리:
- 오류 캡처/미완성 stub 문서 227개 제거
- 교차폴더 중복 43클러스터 병합 (63파일 → redirect)
- 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건
- 카테고리 MOC 6개 신규 생성
- Graph 섹션 미해결 related-keyword 링크 10,058건 제거

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 23:52:15 +09:00

7.6 KiB

Raw Blame History

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

title

Voice Assistant Architecture

매 한 줄

"매 voice assistant 의 핵심 = 'ASR → LLM → TTS pipeline 의 sub-500ms latency 의 streaming end-to-end'.". 2023 Whisper / GPT-4 era 의 cascaded pipeline 의 mainstream → 2026 Realtime API era 매 native speech-to-speech model (GPT-4o Realtime, Gemini 2 Live) 의 emerge — 매 latency 의 ~300ms 의 reduce 의 cascade 의 obsolete 의 begin. 매 production 의 hybrid pipelines 의 still dominant 의 control / cost reasons.

매 핵심

매 Cascaded pipeline (classical)

VAD (Voice Activity Detection): Silero / WebRTC VAD — speech 의 boundary 의 detect.
ASR: Whisper Large v3 / Deepgram Nova-3 / AssemblyAI Universal-2 — streaming partial transcripts.
LLM: Claude Opus 4.7 / GPT-5 / Llama 3.3 — reasoning + persona.
TTS: ElevenLabs Flash v2 / Cartesia Sonic / OpenAI tts-1-hd — streaming audio chunks.
Turn-taking logic: end-of-turn detection, interruption handling, barge-in.

매 Speech-to-Speech (S2S) 모델

GPT-4o Realtime: WebSocket, ~320ms first-byte audio.
Gemini 2 Live API: WebRTC, native multimodal.
Moshi (Kyutai): open-source full-duplex, ~200ms.
매 advantage: emotion / prosody / pause 의 preserve. 매 disadvantage: tool-calling / structured output 의 weaker.

매 Production patterns

Edge VAD + cloud ASR: 매 unnecessary upload 의 cut.
Streaming everywhere: ASR partial → LLM partial prompt → TTS chunk-by-chunk.
Interruption handling: 매 user 의 speak 의 detect → LLM stream 의 cancel + TTS audio 의 stop.
Function calling layer: tool 의 invoke 의 structured (calendar, search, IoT).

💻 패턴

Pattern 1: LiveKit Agents (full pipeline, 2026 standard)

from livekit.agents import AgentSession, Agent
from livekit.plugins import openai, deepgram, cartesia, silero

async def entrypoint(ctx):
    session = AgentSession(
        vad=silero.VAD.load(),
        stt=deepgram.STT(model="nova-3"),
        llm=openai.LLM.with_anthropic(model="claude-opus-4-7"),
        tts=cartesia.TTS(model="sonic-2", voice="warm-female"),
    )
    agent = Agent(instructions="You are a helpful kitchen timer assistant.")
    await session.start(agent=agent, room=ctx.room)
    await session.generate_reply(instructions="Greet the user warmly.")

Pattern 2: OpenAI Realtime API (S2S WebSocket)

import asyncio, websockets, json, base64

async def main():
    async with websockets.connect(
        "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview-2026",
        extra_headers={"Authorization": f"Bearer {API_KEY}", "OpenAI-Beta": "realtime=v1"},
    ) as ws:
        await ws.send(json.dumps({
            "type": "session.update",
            "session": {
                "voice": "alloy",
                "turn_detection": {"type": "server_vad"},
                "tools": [{"type": "function", "name": "get_weather", ...}],
            },
        }))
        async for msg in ws:
            event = json.loads(msg)
            if event["type"] == "response.audio.delta":
                play_audio(base64.b64decode(event["delta"]))

Pattern 3: Pipecat custom pipeline

from pipecat.pipeline import Pipeline
from pipecat.services import AnthropicLLM, ElevenLabsTTS, DeepgramSTT
from pipecat.transports import DailyTransport

pipeline = Pipeline([
    DailyTransport(room_url=URL).input(),
    DeepgramSTT(api_key=DG_KEY, model="nova-3"),
    AnthropicLLM(api_key=ANTH_KEY, model="claude-opus-4-7"),
    ElevenLabsTTS(api_key=EL_KEY, voice_id="..."),
    DailyTransport(room_url=URL).output(),
])
await pipeline.run()

Pattern 4: Interruption handling

class InterruptManager:
    def __init__(self):
        self.current_response = None

    async def on_user_speech_started(self):
        if self.current_response:
            self.current_response.cancel()  # cancel LLM stream
            await self.tts.stop()           # stop audio playback
            await self.tts.flush_buffer()

    async def on_user_speech_ended(self, transcript: str):
        self.current_response = asyncio.create_task(self.respond(transcript))

Pattern 5: Function calling 의 mid-conversation

tools = [{
    "name": "set_timer",
    "description": "Set a kitchen timer",
    "input_schema": {"type": "object", "properties": {"minutes": {"type": "integer"}}},
}]

async def handle_tool_call(name, args):
    if name == "set_timer":
        timer = Timer(minutes=args["minutes"])
        timer.start()
        return f"Timer set for {args['minutes']} minutes"

# LLM streams tool_use → execute → feed result back → continue speaking

Pattern 6: VAD-gated ASR (cost saving)

import silero_vad

vad = silero_vad.load_silero_vad()

async def stream_audio(audio_iter):
    buffer = []
    for chunk in audio_iter:
        speech_prob = vad(chunk, 16000)
        if speech_prob > 0.5:
            buffer.append(chunk)
        elif buffer:
            # End of utterance — send buffered audio to ASR
            await asr.transcribe(b"".join(buffer))
            buffer = []

Pattern 7: Latency budget breakdown (target <800ms total)

User speaks → VAD endpoint detect:    150ms
ASR partial → final transcript:       100ms  (streaming, parallel)
LLM TTFT (time-to-first-token):       250ms
TTS first audio chunk:                150ms
Network + jitter buffer:              150ms
Total perceived latency:              ~800ms

매 결정 기준

상황	Approach
Lowest latency / natural prosody	OpenAI Realtime / Gemini Live (S2S)
Tool-heavy / structured	Cascaded (Claude/GPT-5 + LiveKit Agents)
Open-source / on-prem	Whisper + Llama 3.3 + XTTS-v2
Phone / telephony	Twilio + LiveKit Agents
Browser-only client	Web Speech API + WebRTC (limited)
Multilingual	Deepgram Nova-3 + Cartesia Sonic

기본값: LiveKit Agents 의 cascaded pipeline (Deepgram + Claude/GPT-5 + Cartesia) for production flexibility.

🔗 Graph

부모: Real-Time-Systems
Adjacent: Whisper · ElevenLabs · LiveKit · WebRTC

🤖 LLM 활용

언제: pipeline component 의 selection / latency 의 budget 의 calculation / interruption logic 의 design. 언제 X: 매 audio quality 의 subjective evaluation — 매 human listening test 의 필요.

❌ 안티패턴

No streaming: full transcript 의 wait → 매 multi-second latency.
VAD 의 missing: 매 silence 의 ASR 의 send → cost + latency.
No interrupt handling: 매 user 의 speak 의 의 assistant 의 talk over.
Synchronous tool calls: 매 long tool 의 block 의 audio response — 매 ack message ("checking...") + parallel.
Over-engineered S2S: 매 simple Q&A 의 Realtime API 의 use → cost 5-10x without benefit.
No turn-detection tuning: 매 default endpointing 의 cut user mid-sentence.

🧪 검증 / 중복

Verified: LiveKit Agents docs (2026), OpenAI Realtime API docs, Pipecat documentation, "Building Voice Agents" (Anthropic cookbook).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — full voice assistant architecture guide

7.6 KiB Raw Blame History