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README.md

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 # 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. 
+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/).
 
+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`.
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 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.
 
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