LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models

• URL: http://arxiv.org/abs/2407.15415v1
• Date: Mon, 22 Jul 2024 06:42:00 GMT
• Title: LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
• Authors: Xi Chen, Songyang Zhang, Qibing Bai, Kai Chen, Satoshi Nakamura,
• Abstract summary: LLaST is a framework for building high-performance Large Language model based Speech-to-text Translation systems. Our approach includes LLM-based speech translation architecture design, ASR-augmented training, multilingual data augmentation, and dual-LoRA optimization.
• Score: 38.60622303744585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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