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.
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how to solve cuda out of memory when execute train.py ? have tried to reduce batch size to 1 but problem still persist #86
Try by increasing the default L value to 16 in dset.swave. You can change the R value which may affect the model's performance. Also you can change the segment size and stride values.
Prashanth-vr
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1 year ago
please any help on this