sungwon23 / bsrnn Goto Github PK

Can this model be used for speaker separation, and how can it be done?

Hi, does your code have real-time mode? What I mean is there possibility in your code for batch audio processing?

I think the lstm t/k sequence should be lstm_t_i lstm_k_i lstm_t_i+1 lstm_k_i+1 ?

https://github.com/sungwon23/BSRNN/blob/main/module.py#L46-L60

Hello，thank you for your sharing and generous！
But I got a problem when I reproduct your result with totally the same code. I use VoiceBank+Demand dataset , sampling rate 16kHz. And I split the 28 speakers in the trainset into train and test for 26 and 2 spkears ,respectively. I set batch size as 18, and train epoch 100 in total.
When I test the model in VoiceBank+Demand testset, BUT I got PESQ at 1.97, STOI 0.92, it's weird and just like the model doesn't has been trained. Are you use the same testset in train and test? Or can you share your opinion about this question?
Thanks a lot.

Hello! I tried to change the code to dp training form and found this problem
in: module.py line40. I printed the input tensor for model and found that the data form of dp multi-GPU is different from the original single GPU

First of all, thank you for your work.
I would like to ask if the results obtained by that code can get the results described in the paper and if they can beat MTFAA and FRCRN in terms of objective scores?

Hello! I want to confirm to you that is this code already a causal system? If not, which part leads to non-causal processing? Thanks.