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facebookresearch / svoice

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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More efficient wav file selection #51

Closed vxfla closed 2 years ago

vxfla commented 3 years ago

In some data sets, each speaker recorded many audio files, and they were concentrated in the same path. It seems that the current script can only select audio files from the same path for mixing. For the above types of data sets, such script is inconvenient to use, since you can't just move all files into a folder. I modified some code for file selection. It can work on data sets with the following structure. e.g. python make_dataset.py --in_path ./dataset -dataset |-spk1 |   |-xxx1.wav |   |_xxx2.wav |-spk2 |   |-dict |     |-xxx1.wav |   |_xxx2.wav The new script will randomly select speakers firstly, then randomly select a wav file of the speaker.

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