Awesome — here’s a high-level, advanced streaming architecture for a low-latency voice assistant that processes speech “on the fly.” It’s vendor-agnostic and built for real-time, reliability, and scale.

1) Realtime I/O Edge

Client (mobile/web)

Capture: 16 kHz mono PCM (Opus/WebRTC for network), chunked at 20–40 ms.

Transport: WebRTC (ideal), or WebSocket (fallback) with bi-directional streaming.

Barge-in: When user speaks, locally duck/stop current TTS playback and signal server (/barge-in).

Echo cancel / AGC / NS: Use WebRTC audio processing to reduce far-end echo.

Edge Gateway

Terminates WebRTC/WebSocket; forwards audio frames to STT.

Enforces auth, rate limits, and QoS (drop late frames, jitter buffer 100–200 ms).

Pushes telemetry (RTT, jitter, packet loss).

2) Realtime STT + Turn-Taking

Streaming STT Service

Accepts audio frames; emits:

Partial transcripts (word-level with timestamps)

Stabilized segments (endpointed phrases with punctuation)

Features:

Adaptive endpointing: VAD + pause detector (e.g., 350–600 ms silence, intonation drop).

CTC stabilizer / confusion network to revise partials.

Custom biasing: dynamic phrase lists from domain KB (names, SKUs).

Output topic(s): stt.partial, stt.segment.

Turn Manager

Consumes STT events; decides when to hand off to NLU/LLM.

Maintains speaker state (user vs. TTS playback) to avoid self-transcribe.

Rules:

If segment.confidence >= θ OR silence >= τ, commit segment to LLM.

If user resumes during TTS → barge-in: cancel TTS, mark LLM turn aborted (or continue silently as context).

3) NLU / LLM Layer (Token-Streaming)

Dialogue Orchestrator

Receives committed segments; aggregates context window (last N turns).

Intent router:

Direct to tools/skills (DB lookup, CRM, scheduling) when intent confidence ≥ threshold.

Otherwise to LLM with function-calling enabled.

Runs guardrails (PII redaction, jailbreak filters, safety).

LLM Service

Streaming decoding (server-side).

Speculative decoding (draft model + target model) to cut latency.

Distillation cache: cache short canonical responses (greetings, confirmations) in Redis for sub-100 ms first tokens.

Context sources:

Long-term memory (Vector DB for embeddings of prior sessions, docs)

Short-term scratchpad (dialogue state store)

Tool results (synchronous if <150 ms; async otherwise with incremental yields)

Tooling/Skills Bus

gRPC / RPC microservices for domain actions (search, calendar, order status).

Deadlines per call (e.g., 200–400 ms). If slow, return placeholder + stream update later.

4) TTS (Low-Latency, Streamed Synthesis)

Streaming TTS Service

Accepts partial text tokens; starts synthesizing immediately.

Chunked audio out (50–100 ms buffers).

Prosody control via SSML (breath, emphasis); fast style switch for follow-ups.

Neural VAD on output to expose playback markers (for barge-in & echo suppression).

Audio Mixer

Resolves:

Barge-in: cross-fade/stop current playback on user speech

Earcons (beeps) for turn start/end

Latency smoothing: prebuffer 150–250 ms on client

5) Data Plane & State

Event Bus (NATS/Kafka)

Topics: audio.in, stt.partial, stt.segment, turn.start, llm.tokens, tts.audio, barge-in.

Enables loose coupling and backpressure handling.

State Store (Redis)

Per-session state: dialogue context ids, last user segment offsets, barge-in flags.

Token caches: LLM prefix cache, tool result cache.

Knowledge & Memory

Vector DB (pgvector/FAISS/Milvus) for RAG over product docs, policies, FAQs.

Session memory policy:

Ephemeral short-term buffer (windowed)

Opt-in long-term with PII minimization & TTLs.

6) Latency Budget (Targets)

Capture → STT partial: 80–150 ms

STT segment commit: +150–300 ms after pause

First LLM token: ≤150 ms (with speculative + cache), ≤400 ms worst-case

First TTS audio: ≤150 ms after first LLM tokens

Ear-to-mouth (user stop → assistant start): 700–1200 ms typical

7) Control Logic (concise pseudocode)
onAudioFrame(frame):
  stt.push(frame)

onSttPartial(text, ts):
  dialog_orchestrator.observe_partial(text)        # for anticipation (optional)

onSttSegment(segment):
  turn_mgr.commit(segment)
  if turn_mgr.should_respond():
     dialog_orchestrator.start_turn(segment.context)

DialogOrchestrator.start_turn(ctx):
  emit(turn.start)
  plan = nlu_router.route(ctx)
  result_stream = llm.generate_stream(plan, tools=tooling)
  for tokens in result_stream:
     tts.speak_stream(tokens)       # begin audio immediately
     if barge_in_detected(): break

onBargeIn():
  tts.stop()
  llm.cancel_current()
  turn_mgr.reset_for_user_speaking()

8) Advanced Tricks for Snappiness

Anticipatory planning: while user is still talking, run cheap intent classifier on partials to prefetch tools.

Speculative replies: produce a short “backchannel” (e.g., “Got it—checking that now…”) while the main tool call runs.

Segment reflow: if late STT edits arrive, edit future tokens (LLM streaming with “rewrite” head) or subtly correct in speech.

Domain lexicon priming: pass biasing hints to STT and glossary to LLM.

Multi-bandwidth modes: automatically drop from WebRTC to WS PCM, then to DTMF fallbacks on poor networks.

Server-side AEC: if you must play TTS on device speakers, feed reference signal to STT (far-end echo suppression).

9) Reliability, Safety, and Observability

Health gates: If STT stalls >1 s, switch to backup STT; if LLM >800 ms to first token, serve cached acknowledgement line.

Circuit breakers around tools; idempotent tool calls with request ids.

Metrics: E2E latency percentiles, ASR WER, barge-in rate, interruption timing, token/s, dropout rate, turn success.

Tracing: OpenTelemetry across STT→LLM→TTS with session & turn ids.

Content safety: classifier pre- and post-LLM; redact PII in logs; on-device logging only with consent.

10) Deployment & Scale

Microservices per major box (Gateway, STT, Turn Manager, Orchestrator, LLM, TTS).

GPU pools:

STT: small GPUs, many streams (FP16)

LLM: larger GPUs, KV-cache pinning, tensor parallel

TTS: CPU or small GPU depending on model

Autoscaling on active sessions; session affinity for KV-cache reuse.

Regions & edge POPs to keep RTT low (<50 ms).

11) Interfaces & Protocols (quick reference)

Client ↔ Edge: WebRTC (Opus) or WebSocket (PCM/Opus)

Internal: gRPC streaming for audio/tokens; NATS/Kafka for events

Data: Redis, Postgres(+pgvector), Object store for recordings (encrypted)

If you want, I can tailor this to your current stack (what you’re using for STT/LLM/TTS) and give you a reference implementation outline with concrete tech picks, Docker compose, and example client code.