@inproceedings{xu-etal-2024-cmus,
title = "{CMU}{'}s {IWSLT} 2024 Simultaneous Speech Translation System",
author = "Xu, Xi and
Ouyang, Siqi and
Yan, Brian and
Fernandes, Patrick and
Chen, William and
Li, Lei and
Neubig, Graham and
Watanabe, Shinji",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.20",
pages = "154--159",
abstract = "This paper describes CMU{'}s submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-end speech-to-text (ST) system integrates the WavLM speech encoder, a modality adapter, and the Llama2-7B-Base model as the decoder. We employ a two-stage training approach: initially, we align the representations of speech and text, followed by full fine-tuning. Both stages are trained on MuST-c v2 data with cross-entropy loss. We adapt our offline ST model for SST using a simple fixed hold-n policy. Experiments show that our model obtains an offline BLEU score of 31.1 and a BLEU score of 29.5 under 2 seconds latency on the MuST-C-v2 tst-COMMON.",
}
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<abstract>This paper describes CMU’s submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-end speech-to-text (ST) system integrates the WavLM speech encoder, a modality adapter, and the Llama2-7B-Base model as the decoder. We employ a two-stage training approach: initially, we align the representations of speech and text, followed by full fine-tuning. Both stages are trained on MuST-c v2 data with cross-entropy loss. We adapt our offline ST model for SST using a simple fixed hold-n policy. Experiments show that our model obtains an offline BLEU score of 31.1 and a BLEU score of 29.5 under 2 seconds latency on the MuST-C-v2 tst-COMMON.</abstract>
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%0 Conference Proceedings
%T CMU’s IWSLT 2024 Simultaneous Speech Translation System
%A Xu, Xi
%A Ouyang, Siqi
%A Yan, Brian
%A Fernandes, Patrick
%A Chen, William
%A Li, Lei
%A Neubig, Graham
%A Watanabe, Shinji
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand (in-person and online)
%F xu-etal-2024-cmus
%X This paper describes CMU’s submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-end speech-to-text (ST) system integrates the WavLM speech encoder, a modality adapter, and the Llama2-7B-Base model as the decoder. We employ a two-stage training approach: initially, we align the representations of speech and text, followed by full fine-tuning. Both stages are trained on MuST-c v2 data with cross-entropy loss. We adapt our offline ST model for SST using a simple fixed hold-n policy. Experiments show that our model obtains an offline BLEU score of 31.1 and a BLEU score of 29.5 under 2 seconds latency on the MuST-C-v2 tst-COMMON.
%U https://aclanthology.org/2024.iwslt-1.20
%P 154-159
Markdown (Informal)
[CMU’s IWSLT 2024 Simultaneous Speech Translation System](https://aclanthology.org/2024.iwslt-1.20) (Xu et al., IWSLT 2024)
ACL
- Xi Xu, Siqi Ouyang, Brian Yan, Patrick Fernandes, William Chen, Lei Li, Graham Neubig, and Shinji Watanabe. 2024. CMU’s IWSLT 2024 Simultaneous Speech Translation System. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pages 154–159, Bangkok, Thailand (in-person and online). Association for Computational Linguistics.