Datasets:
File size: 3,046 Bytes
d12634e db60e59 d12634e db60e59 38525b1 d12634e 46e8f98 db60e59 d12634e db60e59 f9543e1 b9df713 02bf225 b9df713 02bf225 b9df713 02bf225 b9df713 78ccfc5 66619d3 78ccfc5 527fc0c 66619d3 1da76e4 db60e59 ae02e05 66619d3 db60e59 ae02e05 db60e59 ae02e05 66619d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
---
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."
**GitHub:** https://github.com/YUCHEN005/RobustGER
**Model:** https://huggingface.co/PeacefulData/RobustGER
**Data:** This repo
**UPDATE (Apr-18-2024):** 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/).
**IMPORTANT:** The vast speech feature size mentioned above is because Whisper requires a fix input length of 30s that is too long. Please do the follwing step to remove it before running ***add_speech_feats_to_train_data.py***:
- Modified the [whisper model code](https://github.com/openai/whisper/blob/main/whisper/model.py#L167) "x = (x + self.positional_embedding).to(x.dtype)" to be "x = (x + self.positional_embedding[:x.shape[1], :]).to(x.dtype)"
**UPDATE (Apr-29-2024):** To support customization, We release the script ***generate_robust_hp.py*** for users to generate train/test data from their own ASR datasets.
We also release two necessary packages for generation: "my_jiwer" and "decoding.py".
To summary, you will need to do the following three steps before running ***generate_robust_hp.py***:
- Modified the [whisper model code](https://github.com/openai/whisper/blob/main/whisper/model.py#L167) "x = (x + self.positional_embedding).to(x.dtype)" to be "x = (x + self.positional_embedding[:x.shape[1], :]).to(x.dtype)"
- Specify the absolute path of "my_jiwer" directory in ***generate_robust_hp.py*** (sys.path.append)
- Put our whisper decoding script "decoding.py" under your locally installed whisper directory "<your-path>/whisper/whisper"
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}
}
""" |