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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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tags: |
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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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datasets: |
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- PeacefulData/Robust-HyPoradise |
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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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**GitHub:** https://github.com/YUCHEN005/RobustGER |
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**Data:** https://huggingface.co/datasets/PeacefulData/Robust-HyPoradise |
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**Model:** This repo |
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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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``` |
