Building Enterprise Realtime Voice Agents from Scratch: A Technical Tutorial
Abstract
We present a technical tutorial for building enterprise-grade realtime voice agents from first principles. While over 25 open-source speech-to-speech models and numerous voice agent frameworks exist, no single resource explains the complete pipeline from individual components to a working streaming voice agent with function calling capabilities. Through systematic investigation, we find that (1) native speech-to-speech models like Qwen2.5-Omni, while capable of high-quality audio generation, are too slow for realtime interaction ($\sim$13s time-to-first-audio); (2) the industry-standard approach uses a cascaded streaming pipeline: STT $\rightarrow$ LLM $\rightarrow$ TTS, where each component streams its output to the next; and (3) the key to ``realtime'' is not any single fast model but rather \textit{streaming and pipelining} across components. 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 P50 time-to-first-audio of 947ms (best case 729ms) with cloud LLM APIs, and comparable latency with self-hosted vLLM on NVIDIA A10G GPU. We release the full codebase as a tutorial with working, tested code for every component.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper provides a comprehensive tutorial for building enterprise-grade realtime voice agents from scratch, emphasizing the importance of streaming and pipelining in achieving low latency. The technical contributions and methodology are significant, offering valuable insights and practical tools for researchers and practitioners in the field of audio machine learning.
Methodology
The paper presents a systematic approach to building enterprise-grade realtime voice agents by dissecting the components of speech-to-text (STT), language model (LLM), and text-to-speech (TTS) into a cascaded streaming pipeline. The authors emphasize the importance of streaming and pipelining rather than relying on a single fast model, which is a critical insight for achieving low latency in voice interactions. The tutorial format is effective, providing a step-by-step guide that includes empirical evaluations of various models, thus making the methodology accessible and practical for developers.
Results & evaluation
The experiments conducted are robust, comparing the performance of native speech-to-speech models against a cascaded pipeline approach. The authors provide detailed latency measurements for each component, demonstrating the effectiveness of their proposed architecture in achieving sub-1-second time-to-first-audio (TTFA). The empirical results are well-documented, showcasing the advantages of their approach in real-world scenarios, which adds credibility to their findings.
Reproducibility
The paper includes a comprehensive codebase released as open-source, which is a significant advantage for reproducibility. The detailed tutorial format, along with the release of tested code for each component, allows other researchers and practitioners to replicate the results and build upon the work. However, the paper could benefit from clearer documentation on the specific environments and dependencies required to run the code effectively.
Limitations
One limitation noted is the reliance on cloud APIs for some components, which may introduce variability in performance due to network latency. Additionally, the findings are based on specific models and configurations, which may not generalize across all potential implementations. The authors also acknowledge that native speech-to-speech models are not yet viable for real-time applications, which highlights the current constraints in the field.
Broader impact
This work has significant implications for the development of voice-based AI agents in enterprise settings, particularly in applications such as customer service, healthcare, and task management. By providing a clear framework and practical guidance, the paper can facilitate the adoption of real-time voice agents, potentially transforming user interactions across various industries. The paper provides a comprehensive tutorial for building enterprise-grade realtime voice agents from scratch, emphasizing the importance of streaming and pipelining in achieving low latency. The technical contributions and methodology are significant, offering valuable insights and practical tools for researchers and practitioners in the field of audio machine learning.
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