LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
<|reference_start|>LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models: 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.<|reference_end|>
@article{chen2024llast:,
    title={LLaST: Improved End-to-end Speech Translation System Leveraged by Large
  Language Models},
    author={Xi Chen, Songyang Zhang, Qibing Bai, Kai Chen, Satoshi Nakamura},
    journal={arXiv preprint arXiv:2407.15415},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15415},
    primaryClass={cs.CL}
}