metadata

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, VB-DEMAND, LS-FreeSound, NOIZEUS.

IMPORTANT: The vast speech feature size mentioned above is because Whisper requires a fix input length of 30s. Please do the follwing step before running data generation:

Modified the model code 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, one is the jiwer package that is locally imported in generate_robust_hp.py, another one is the whisper decoding script decoding.py that should be put under 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.

@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}
}