Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update README.md #1

Merged
merged 1 commit into from
Dec 18, 2023
Merged

Update README.md #1

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 8 additions & 5 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,12 +1,15 @@
# 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.


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.

# Note

install this version of fairseq
## install this version of fairseq
`pip install git+https://github.com/facebookresearch/fairseq.git@a54021305d6b3c4c5959ac9395135f63202db8f1`

use numpy

float -> float64
## fix numpy package if necessary
float -> float64