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arXiv · 2603.05413

Building Enterprise Realtime Voice Agents from Scratch: A Technical Tutorial

Jielin Qiu·Zixiang Chen·Liangwei Yang·Ming Zhu·Zhiwei Liu·Juntao Tan·Wenting Zhao·Rithesh Murthy·Roshan Ram·Akshara Prabhakar·Shelby Heinecke·Caiming Xiong·Silvio Savarese·Huan Wang
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Abstract

We present a technical tutorial for building enterprise-grade realtime voice agents from first principles. While end-to-end speech-to-speech models may ultimately provide the best latency for voice agents, fully self-hosted end-to-end solutions are not yet available. We evaluate the closest candidate, Qwen3-Omni, across three configurations: its cloud-only DashScope Realtime API achieves $\sim$702ms audio-to-audio latency with streaming, but is not self-hostable; its local vLLM deployment supports only the Thinker (text generation from audio, 516ms), not the Talker (audio synthesis); and its local Transformers deployment runs the full pipeline but at $\sim$146s -- far too slow for realtime. The cascaded streaming pipeline (STT $\rightarrow$ LLM $\rightarrow$ TTS) therefore remains the practical architecture for self-hosted realtime voice agents, and the focus of this tutorial. We build a complete voice agent using Deepgram (streaming STT), vLLM-served LLMs with function calling (streaming text generation), and ElevenLabs (streaming TTS), achieving a measured time-to-first-audio of 755ms (best case 729ms) with full function calling support. We release the full codebase as a 9-chapter progressive tutorial with working, tested code for every component.

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Jielin Qiu, Zixiang Chen, Liangwei Yang, Ming Zhu, Zhiwei Liu, Juntao Tan, Wenting Zhao, Rithesh Murthy, Roshan Ram, Akshara Prabhakar, Shelby Heinecke, Caiming Xiong, Silvio Savarese, Huan Wang. 2026-03-05. Building Enterprise Realtime Voice Agents from Scratch: A Technical Tutorial. https://arxiv.org/abs/2603.05413

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