jefflai108 / Contrastive-Predictive-Coding-PyTorch

Contrastive Predictive Coding for Automatic Speaker Verification
MIT License
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Can you provide the train & test dataset? #1

Closed guanyuelee closed 4 years ago

guanyuelee commented 5 years ago

Thanks for your sharing of CPC code. I read the code and found that the provided Dataset class reads .h5 files. From open ASR website and the information provided from the paper, I can only download those files with extension .frac or .txt. Can you explicitly explain the configuration of your dataset?

jefflai108 commented 5 years ago

Hi @guanyuelee Sorry for the late reply. I was on vacation.

I uploaded the script to store raw wav files to hdf5 (.h5) format: https://github.com/jefflai108/Contrastive-Predictive-Coding-PyTorch/blob/master/wav2raw.py

Basically, all raw wave files are stored in a single hdf5 file. The benefit of doing so is a cleaner PyTorch Dataset class code. It is my personal practice and you could, of course, do it your preferred way.

Let me know if you have further question :)

martinmamql commented 4 years ago

Dear Jeff,

Thank you so much for providing this great repository! Sincerely appreciate your great implementation!

However, after reading all the closed issues and trying out for initializing the training, I am still a bit confused about the training and test dataset. I try to run run.sh and the following error reported:

Traceback (most recent call last): File "main.py", line 164, in <module> main() File "main.py", line 109, in main training_set = RawDataset(args.train_raw, args.train_list, args.audio_window) File "/home/ubuntu/Contrastive-Predictive-Coding-PyTorch/src/data_reader/dataset.py", line 353, in __init__ with open(list_file) as f: FileNotFoundError: [Errno 2] No such file or directory: 'LibriSpeech/list/train.txt' May I request what might be the possible solution of this? Thank you so much for your clarification!

Sincerely, Martin

martinmamql commented 4 years ago

Sorry the format is obscured. I have attached a screenshot below. Screenshot from 2020-01-30 14-42-21