
This repo releases the trained LLaMA-adapter weights in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."

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}
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["generative error correction", "large language model", "LLaMA"], "datasets": ["PeacefulData/Robust-HyPoradise"], "metrics": ["wer"], "pipeline_tag": "text-generation", "inference": false, "fine-tuning": true}
generative error correction large language model LLaMA text-generation en dataset:PeacefulData/Robust-HyPoradise license:apache-2.0 region:us