  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.
