Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models

Published on Mar 20

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Abstract

Full-duplex speech language models require high-quality multi-speaker conversational data, which is scarce, necessitating a robust open-source data processing pipeline to address challenges in natural dialogue dynamics and system accuracy.

AI-generated summary

As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction. However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predominantly single-speaker or limited in volume. Addressing the complex dynamics of natural dialogue, such as overlapping and back-channeling remains a challenge, with standard processing pipelines suffering from diarization errors and ASR hallucinations. To bridge this gap, we present a robust and scalable open-source data processing pipeline designed for full-duplex model.

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