| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-generation |
| inference: false |
| fine-tuning: true |
| tags: |
| - generative error correction |
| - large language model |
| - LLaMA |
| metrics: |
| - wer |
| datasets: |
| - PeacefulData/Robust-HyPoradise |
| --- |
| |
| This repo releases the trained LLaMA-adapter weights in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition." |
|
|
| **GitHub:** https://github.com/YUCHEN005/RobustGER |
|
|
| **Data:** https://huggingface.co/datasets/PeacefulData/Robust-HyPoradise |
|
|
| **Model:** This repo |
|
|
| 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. |
|
|
| ```bib |
| @inproceedings{hu2024large, |
| title={Large Language Models are Efficient Learners of Noise-Robust Speech Recognition}, |
| 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}, |
| booktitle={International Conference on Learning Representations}, |
| year={2024} |
| } |
| ``` |
