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.
Due to large memory consumption a long audio file should be split. What I'm doing is splitting the file and doing voice separation on each chunk separately.
My problem is in assembly: Speaker 1 of chunk i can become Speaker 2 of chunk j. Is there a way to identify speakers across files?
Due to large memory consumption a long audio file should be split. What I'm doing is splitting the file and doing voice separation on each chunk separately.
My problem is in assembly: Speaker 1 of chunk i can become Speaker 2 of chunk j. Is there a way to identify speakers across files?
Arik