RawNet2

Project on making the AntiSpoofing pipeline. This rep contains my implementation of RawNet2 and all the needed metrics

How to install?

Make sure to follow this guide

git clone https://github.com/aizamaksutova/RawNet2.git
cd RawNet2
pip install -r requirements.txt

How to inference?

First, you should create a directory (e.g. inference/) with all the wavs you want to rate as spoof or bona-fide.

Then perform these:

python3 test.py -m checkpoint.pth -inf inference/

-inf inference is a directory where your samples are stored.

For inferencing my model exactly you should do these steps:

python3 data.py
python3 test.py -m model_rawnet.pth -inf inference/

You can look at the results in your output logs

How to train the model by yourself?

In order to train the model you would need to perform simple steps, but wait for a long time for them to actually download all the data which is a ASV Dataset for antispoofing model training

#you need to have a kaggle.json file from your kaggle account
chmod a+x prepare_data.sh
./prepare_data.sh
python3 train.py -c config.json

All the other parameters are manually stored in the config.json, but you can look up the config options in train.py in order to change everything right from terminal.

Wandb report

Here is the link to my wandb report with all the architecture explanation and wavs with their scores on being spoofed or bona-fide