Update README.md

README.md

@@ -27,25 +27,31 @@ This repo releases the Robust HyPoradise dataset in paper "Large Language Models
 
 **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).
+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 before running data generation:
-- 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)` 
 
-**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`.
+**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
+"""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}
 }
-```
+"""