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.

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.