We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.
I noticed this model takes a very long time to train. Also the resources during file reading and model training are never exhausted. Are we sure multi threading has been implemented properly?
Hi @bballboy8,
I think so, I think the issue is GPU utilization is not great as this model is working on the time domain. You can try to reimplement it using the freq. domain and see better utilization.
adiyoss
commented
2 years ago
I noticed this model takes a very long time to train. Also the resources during file reading and model training are never exhausted. Are we sure multi threading has been implemented properly?