@inproceedings{yan-etal-2023-espnet,
    title = "{ESP}net-{ST}-v2: Multipurpose Spoken Language Translation Toolkit",
    author = "Yan, Brian  and
      Shi, Jiatong  and
      Tang, Yun  and
      Inaguma, Hirofumi  and
      Peng, Yifan  and
      Dalmia, Siddharth  and
      Pol{\'a}k, Peter  and
      Fernandes, Patrick  and
      Berrebbi, Dan  and
      Hayashi, Tomoki  and
      Zhang, Xiaohui  and
      Ni, Zhaoheng  and
      Hira, Moto  and
      Maiti, Soumi  and
      Pino, Juan  and
      Watanabe, Shinji",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.38",
    doi = "10.18653/v1/2023.acl-demo.38",
    pages = "400--411",
    abstract = "ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offline speech-to-text translation (ST), 2) simultaneous speech-to-text translation (SST), and 3) offline speech-to-speech translation (S2ST) {--} each task is supported with a wide variety of approaches, differentiating ESPnet-ST-v2 from other open source spoken language translation toolkits. This toolkit offers state-of-the-art architectures such as transducers, hybrid CTC/attention, multi-decoders with searchable intermediates, time-synchronous blockwise CTC/attention, Translatotron models, and direct discrete unit models. In this paper, we describe the overall design, example models for each task, and performance benchmarking behind ESPnet-ST-v2, which is publicly available at \url{https://github.com/espnet/espnet}.",
}

ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offline speech-to-text translation (ST), 2) simultaneous speech-to-text translation (SST), and 3) offline speech-to-speech translation (S2ST) {--} each task is supported with a wide variety of approaches, differentiating ESPnet-ST-v2 from other open source spoken language translation toolkits. This toolkit offers state-of-the-art architectures such as transducers, hybrid CTC/attention, multi-decoders with searchable intermediates, time-synchronous blockwise CTC/attention, Translatotron models, and direct discrete unit models. In this paper, we describe the overall design, example models for each task, and performance benchmarking behind ESPnet-ST-v2, which is publicly available at \url{https://github.com/espnet/espnet}.
Yan, Brian Shi, Jiatong Tang, Yun Inaguma, Hirofumi Peng, Yifan Dalmia, Siddharth Pol{\'a}k, Peter Fern es, Patrick Berrebbi, Dan Hayashi, Tomoki Zhang, Xiaohui Ni, Zhaoheng Hira, Moto Maiti, Soumi Pino, Juan Watanabe, Shinji
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit