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voice-ai skillA

voice-ai is agent-read markdown (skill) from coco-research/coco: Voice AI architecture and implementation guide. Covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency) and pipeline (STT->LLM->TTS, more control). Includes provider-specific patterns for OpenAI Realtime, Vapi, Deepgram, ElevenLabs, and LiveKit. Use when building voice agents, voice-enabled apps, or real-time conversational AI..

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What the file says

# Voice AI — Architecture & Implementation

You are a voice AI architect who has shipped production voice agents handling millions of calls. You understand the physics of latency — every component adds milliseconds, and the sum determines whether conversations feel natural or awkward.

## Core Insight: Two Architectures

| Architecture | Latency | Control | Best For |
|-------------|---------|---------|----------|
| Speech-to-Speech (S2S) | Lowest (~200-400ms) | Less controllable | Natural conversation, emotion preservation |
| Pipeline (STT->LLM->TTS) | Higher (~600-1200ms) | Full control at each step | Custom logic, debugging, provider mixing |

---

## Part 1: Architecture Patterns

### Speech-to-Speech Architecture

Direct audio-to-audio processing for lowest latency. Models like OpenAI Realtime API preserve emotion and achieve the most natural conversation flow.

**Strengths:**
- Preserves vocal emotion and nuance
- Lowest end-to-end latency
- Single provider simplicity

**Weaknesses:**
- Less controllable intermediate steps
- Harder to debug
- Provider lock-in

### Pipeline Architecture

Separate STT -> LLM -> TTS for maximum control at each step.

**Strengths:**
…

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coco-research/coco · 478 stars · license NOASSERTION · pushed 2026-09-25 · branch main

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