SimulSeamless: FBK at IWSLT 2024 Simultaneous Speech Translation
This work addresses real-time translation needs for multilingual users, but it is incremental as it adapts existing models without retraining.
The paper tackled simultaneous speech-to-text translation by combining AlignAtt with SeamlessM4T, achieving acceptable or better results compared to previous submissions across multiple language pairs.
This paper describes the FBK's participation in the Simultaneous Translation Evaluation Campaign at IWSLT 2024. For this year's submission in the speech-to-text translation (ST) sub-track, we propose SimulSeamless, which is realized by combining AlignAtt and SeamlessM4T in its medium configuration. The SeamlessM4T model is used "off-the-shelf" and its simultaneous inference is enabled through the adoption of AlignAtt, a SimulST policy based on cross-attention that can be applied without any retraining or adaptation of the underlying model for the simultaneous task. We participated in all the Shared Task languages (English->{German, Japanese, Chinese}, and Czech->English), achieving acceptable or even better results compared to last year's submissions. SimulSeamless, covering more than 143 source languages and 200 target languages, is released at: https://github.com/hlt-mt/FBK-fairseq/.