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arXiv · 2606.21854

ESPnet3: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era

Masao Someki·Alexander Polok·Carlos Carvalho·Chyi-Jiunn Lin·Da-Hee Yang·Jiatong Shi·Jinchuan Tian·Nelson Enrique Yalta Soplin·Samuele Cornell·Siddhant Arora·Francisco Teixeira·Wei Wang·William Chen·Alberto Abad·Chenda Li·Shinji Watanabe·Wangyou Zhang
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Abstract

Recent speech research involves increasingly large datasets, complex models, and diverse experimental workflows. However, existing frameworks require substantial engineering effort to support such experiments. We present ESPnet3, a speech and audio research framework built on a modular system architecture with configuration-driven dataset composition and unified Python-based workflows. ESPnet3 introduces a DataOrganizer abstraction for flexible dataset integration and dataset sharding for memory-efficient large-scale training, while allowing recipe-specific logic through lightweight stage overrides. In OWSM pre-training experiments, ESPnet3 reduces per-epoch training time by \emph{21.1 minutes} compared to ESPnet2 and achieves \emph{>80\% GPU utilization} in multi-node training. Fine-tuning experiments show that new models and datasets can be integrated with around \emph{46 lines of additional code}. ESPnet3 will be publicly released with model checkpoints and training logs.

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Masao Someki, Alexander Polok, Carlos Carvalho, Chyi-Jiunn Lin, Da-Hee Yang, Jiatong Shi, Jinchuan Tian, Nelson Enrique Yalta Soplin, Samuele Cornell, Siddhant Arora, Francisco Teixeira, Wei Wang, William Chen, Alberto Abad, Chenda Li, Shinji Watanabe, Wangyou Zhang. 2026-06-20. ESPnet3: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era. https://arxiv.org/abs/2606.21854

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