Real-Time Speech-to-Speech AI: Sub-200ms Voice Agents with WebRTC & Audio Codec Latents
The legacy pipeline of transcribing speech via Whisper, feeding text into an LLM, and synthesizing audio with ElevenLabs imposes an unavoidable 1200ms+ latency penalty. Modern Speech-to-Speech models tokenize raw audio directly into discrete acoustic latents, delivering human-like sub-200ms conversational responsiveness with natural barge-in interruption handling.
AM LABFull-Duplex Audio Codecs (MIMIC, SNAC & EnCodec)
By quantizing continuous speech waveforms into hierarchical codebook indices (RVQ - Residual Vector Quantization), transformers process voice directly as token sequences. This preserves inflection, whispers, laughter, and emotional tone that text-based tokenizers permanently destroy.
Silicon Telemetry & Production Verification
vllm serve real-time-voice-ai-speech-to-speech-latency --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 32768 --kv-cache-dtype fp8
Artur Moiseyev
AI Systems Architect
Independent AI Researcher and Distributed Systems Architect. Building autonomous multi-agent workflows, open reasoning models, and real-time inference infrastructure.
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