madhavmk / noise2noise-audio_denoising_without_clean_training_data Goto Github PK

Hello

Please let me know the model size and computation.

Any other benchmarking available please post here

Regards
Yugesh

Hello,
First of all great work!

Can you please add a requirements.txt file for linux as well? The present one does not work directly with the conda create command. This may also be because a lot of packages require channels different from the base channels, if you can specify those channels in the readme that would make it more convenient.

Thank you!

I would like to ask how long it takes for such an experiment to run a type of noise, and the minimum equipment requirements,Thanks!!

What's with the requirements? olefile, webSockets and Certifi?

pysocks, pycparser, etc...

Security and Cryptography: The inclusion of packages like cryptography and argon2-cffi suggests that the program may involve cryptographic operations, such as encryption, decryption, hashing, or secure password handling.

Hello, I've been trying out your noise2noise denoising on jupyter notebook, and I found that the audio saved is locked to 3 seconds, even if the audio processed is more than 3 seconds long. Is there a way to control this? Thank you.

I am getting this error

RuntimeError: stft requires the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release.

on running
train_losses, test_losses = train(dcunet20, train_loader, test_loader, loss_fn, optimizer, scheduler, 4)

this cell.

I am using pytorch on GPU which support CUDA-11.7 , python-3.11