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# HypothesesParadise
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This repo releases the Robust HyPoradise dataset in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."
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Considering the file size, the uploaded training data does not contain the speech features (vast size).
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Alternatively, we have provided a script named `add_speech_feats_to_train_data.py` to generate them from raw speech (.wav).
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You need to specify the raw speech path from utterance id in the script.
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Here are the available speech data: [CHiME-4](https://entuedu-my.sharepoint.com/:f:/g/personal/yuchen005_e_ntu_edu_sg/EuLgMQbjrIJHk7dKPkjcDMIB4SYgXKKP8VBxyiZk3qgdgA),
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[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/).
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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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```bib
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# HypothesesParadise
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This repo releases the Robust HyPoradise dataset in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."
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UPDATE (Apr-18-2024): We have released the training data, which follows the same format as test data.
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Considering the file size, the uploaded training data does not contain the speech features (vast size).
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Alternatively, we have provided a script named `add_speech_feats_to_train_data.py` to generate them from raw speech (.wav).
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You need to specify the raw speech path from utterance id in the script.
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Here are the available speech data: [CHiME-4](https://entuedu-my.sharepoint.com/:f:/g/personal/yuchen005_e_ntu_edu_sg/EuLgMQbjrIJHk7dKPkjcDMIB4SYgXKKP8VBxyiZk3qgdgA),
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[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/).
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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.
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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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```bib
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