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README.md
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---
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license:
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language:
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- "zh"
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pipeline_tag: text-generation
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inference: false
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fine-tuning: true
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- generative error correction
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- large language model
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- LLaMA
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---
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This repo releases the trained LLaMA-adapter weights in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."
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If you consider this work would be related or useful for your research, please consider to cite the work in ICLR 2024. Thank you.
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```bib
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@
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title={Large Language Models are Efficient Learners of Noise-Robust Speech Recognition},
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author={Hu, Yuchen and Chen, Chen and Yang, Chao-Han Huck and Li, Ruizhe and Zhang, Chao and Chen, Pin-Yu and Chng,
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year={2024}
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}
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```
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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inference: false
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fine-tuning: true
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- generative error correction
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- large language model
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- LLaMA
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metrics:
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- wer
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---
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This repo releases the trained LLaMA-adapter weights in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."
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If you consider this work would be related or useful for your research, please kindly consider to cite the work in ICLR 2024. Thank you.
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```bib
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@inproceedings{hu2024large,
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title={Large Language Models are Efficient Learners of Noise-Robust Speech Recognition},
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author={Hu, Yuchen and Chen, Chen and Yang, Chao-Han Huck and Li, Ruizhe and Zhang, Chao and Chen, Pin-Yu and Chng, Eng Siong},
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booktitle={International Conference on Learning Representations},
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year={2024}
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}
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```
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