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---
license: apache-2.0
language_creators:
- expert-generated
task_categories:
- text-generation
tags:
- generative error correction
- large language model
- LLaMA
pretty_name: Robust HyPoradise
size_categories:
- 100K<n<1M
language:
- en
---
# HypothesesParadise
This repo releases the Robust HyPoradise dataset in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."
NEW (Apr-18): We have released the training data, which follows the same format as test data.
Considering the file size, the uploaded training data does not contain the speech features (vast size).
Alternatively, we have provided a script named `add_speech_feats_to_train_data.py` to generate them from raw speech (.wav).
You need to specify the raw speech path from utterance id in the script.
Here are the available speech data: [CHiME-4](https://entuedu-my.sharepoint.com/:f:/g/personal/yuchen005_e_ntu_edu_sg/EuLgMQbjrIJHk7dKPkjcDMIB4SYgXKKP8VBxyiZk3qgdgA),
[VB-DEMAND](https://datashare.ed.ac.uk/handle/10283/2791), [LS-FreeSound](https://github.com/archiki/Robust-E2E-ASR), [NOIZEUS](https://ecs.utdallas.edu/loizou/speech/noizeus/).
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}
}
``` |