Soumi Maiti


2023

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ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
Brian Yan | Jiatong Shi | Yun Tang | Hirofumi Inaguma | Yifan Peng | Siddharth Dalmia | Peter Polák | Patrick Fernandes | Dan Berrebbi | Tomoki Hayashi | Xiaohui Zhang | Zhaoheng Ni | Moto Hira | Soumi Maiti | Juan Pino | Shinji Watanabe
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

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 https://github.com/espnet/espnet.

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CMU’s IWSLT 2023 Simultaneous Speech Translation System
Brian Yan | Jiatong Shi | Soumi Maiti | William Chen | Xinjian Li | Yifan Peng | Siddhant Arora | Shinji Watanabe
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This paper describes CMU’s submission to the IWSLT 2023 simultaneous speech translation shared task for translating English speech to both German text and speech in a streaming fashion. We first build offline speech-to-text (ST) models using the joint CTC/attention framework. These models also use WavLM front-end features and mBART decoder initialization. We adapt our offline ST models for simultaneous speech-to-text translation (SST) by 1) incrementally encoding chunks of input speech, re-computing encoder states for each new chunk and 2) incrementally decoding output text, pruning beam search hypotheses to 1-best after processing each chunk. We then build text-to-speech (TTS) models using the VITS framework and achieve simultaneous speech-to-speech translation (SS2ST) by cascading our SST and TTS models.