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# occm | ||
My attempt to employ one-class learning to detect synthetic speech. | ||
In fancy terms, I am trying to transform a hyper-plane classifier into a sphere covering positive samples. | ||
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For feature extraction (frontend), I use wav2vec model from Meta and finetune it with a subset of real/synthetic samples. | ||
For classification (backend), I work with several models SE-Resnet, AASIST, etc. | ||
This work is under progress. All suggestions are welcome. | ||
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# Note | ||
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install this version of fairseq | ||
## install this version of fairseq | ||
`pip install git+https://github.com/facebookresearch/fairseq.git@a54021305d6b3c4c5959ac9395135f63202db8f1` | ||
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use numpy | ||
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float -> float64 | ||
## fix numpy package if necessary | ||
float -> float64 |