ZIP-0417: Real-Time Conversational AI (Zen-Live)

Abstract

This proposal specifies Zen-Live, the real-time conversational variant of the Zen model family. Zen-Live enables sub-200ms latency voice conversations with AI agents, live video understanding (e.g., pointing a camera at a bird and asking "what species is this?"), and interactive tutoring sessions. The model uses speculative decoding, streaming output, and an optimized audio codec to achieve real-time performance while maintaining the quality of the Zen-Pro model.

Motivation

The whitepaper's vision (Section 08) of conversational AI companions requires real-time interaction -- not the multi-second latency typical of API-based LLM calls. When a user is on a nature walk with their Zoo companion app, they need:

Instant voice responses (< 200ms end-to-end latency)
Live video understanding (identify species from camera feed)
Interruptible conversation (user can speak while agent is responding)
Emotional expressiveness (tone of voice conveys excitement about a rare sighting)

Specification

Architecture

Microphone ─> Audio Codec ─> Token Encoder ─> ┐
Camera ─> Frame Sampler ─> Vision Encoder ─> ─┤
Text Input ─> Tokenizer ─────────────────────> ├─> Zen-Live Model ─> Streaming Output
                                               │     (speculative    ├─> Audio Codec ─> Speaker
                                               │      decoding)      ├─> Text Stream
                                               └─────────────────────└─> Video Annotations

Latency Budget

Component	Budget
Audio capture + encoding	20ms
Network (edge inference)	10ms
Model inference (first token)	80ms
Audio synthesis (first chunk)	40ms
Network (return)	10ms
Audio playback buffer	40ms
Total end-to-end	< 200ms

Speculative Decoding

Zen-Live uses a small draft model (1.5B params) to generate candidate continuations, which the main model (7B+ params) verifies in a single forward pass. This achieves 3-4x throughput improvement for real-time streaming.

Audio Codec

Custom neural audio codec with:

24kHz sample rate, 16-bit depth
1.5 kbps encoding rate (extremely efficient for voice)
Emotional encoding: tone, pace, emphasis encoded as separate streams
Speaker identity preservation for consistent agent voice

Capabilities

Voice conversation: Natural back-and-forth voice dialogue
Live video QA: Real-time visual question answering from camera feed
Interruption handling: Graceful handling of user interruptions mid-response
Multilingual voice: Real-time conversation in 30+ languages
Emotional expression: Agent voice conveys appropriate emotions

Research Papers

zen-live_whitepaper -- Zen-Live real-time model whitepaper
zen-audio-architecture -- Audio encoder/decoder architecture
zen-voice-clone -- Voice cloning and synthesis technology

Implementation

hanzo/jin: Jin multimodal framework with real-time streaming
hanzo/llm: LLM Gateway with streaming inference support
hanzo/chat: Chat interface with voice conversation mode

Timeline

Originated: May 2024 (Zen-Live architecture)
Research: zen-live_whitepaper published Q3 2024
Implementation: Zen-Live deployed via Hanzo LLM Gateway Q4 2024