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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.15415 |
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| _version_ | 1866916331633246208 |
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| author | Chen, Xi Zhang, Songyang Bai, Qibing Chen, Kai Nakamura, Satoshi |
| author_facet | Chen, Xi Zhang, Songyang Bai, Qibing Chen, Kai Nakamura, Satoshi |
| contents | We introduces LLaST, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech translation(E2E ST) models by exploring model architecture design and optimization techniques tailored for LLMs. Our approach includes LLM-based speech translation architecture design, ASR-augmented training, multilingual data augmentation, and dual-LoRA optimization. Our approach demonstrates superior performance on the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs. We believe this effective method will serve as a strong baseline for speech translation and provide insights for future improvements of the LLM-based speech translation framework. We release the data, code and models in https://github.com/openaudiolab/LLaST. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15415 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models Chen, Xi Zhang, Songyang Bai, Qibing Chen, Kai Nakamura, Satoshi Computation and Language We introduces LLaST, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech translation(E2E ST) models by exploring model architecture design and optimization techniques tailored for LLMs. Our approach includes LLM-based speech translation architecture design, ASR-augmented training, multilingual data augmentation, and dual-LoRA optimization. Our approach demonstrates superior performance on the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs. We believe this effective method will serve as a strong baseline for speech translation and provide insights for future improvements of the LLM-based speech translation framework. We release the data, code and models in https://github.com/openaudiolab/LLaST. |
| title | LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.15415 |