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--- |
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license: apache-2.0 |
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language_creators: |
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- expert-generated |
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task_categories: |
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- text-generation |
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tags: |
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- generative error correction |
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- large language model |
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- LLaMA |
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pretty_name: Robust HyPoradise |
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size_categories: |
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- 100K<n<1M |
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language: |
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- en |
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--- |
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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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**GitHub:** https://github.com/YUCHEN005/RobustGER |
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**Model:** https://huggingface.co/PeacefulData/RobustGER |
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**Data:** This repo |
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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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**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 before running data generation: |
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- Modified the [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)` |
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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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@inproceedings{hu2024large, |
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title={Large Language Models are Efficient Learners of Noise-Robust Speech Recognition}, |
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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}, |
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booktitle={International Conference on Learning Representations}, |
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year={2024} |
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} |
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``` |
