pytorch_lightning_FAD

This is a general framework for fake audio detection using pytorch lightning the dataset used here is asvspoof2019

env

python 3.9

pip install -r requirement.txt

run sample

first thing first, change the dir in the "utils/loadData/asvspoof_data_DA.py"

the run this line

bash run.sh a_train_log/aasist 0.01 6

usage

1. data module

if you want to use anthor data input format, please reference file 'utils/loadData/asvspoof_data_DA.py' to write the datamodule.

if you won't change anything in the "models/tl_model.py", please make sure that the train set return three elements (tensor, label, filename), and the dev/test set return two elements (tensor, filename)

then change the "--data_module" config when you run the "run.sh"

2. model

if you want to use another model architecture, add it in to the folder "models".

if you won't change anything in the "models/tl_model.py", please make sure the model you create return at least two elements (prediction and hidden state) and change the model class name to "Model"

then change the "--module_model" config when you run the "run.sh"

3. tl_model

if you want to modify something in the train/eval/test/inference stage (like modification about the loss culculation), create a new file and reference file "models/tl_model.py"

then change the "--tl_model" config when you run the "run.sh"

Generalized Fake Audio Detection via Deep Stable Learning arxiv

reweight leaner is in utils/ideas/ check usage in the tl_model_file (models/tl_model_postft_loss.py) and model file (models/wav2vec/l5_aasist_step_stable.py) and follow the usage of this framework mentioned above

[This method offers only slight performance improvements for small models after fine-tuning many many hyperparameters, so to be honest, if you wanna have a try, do not expect it will work at the very first run using random hyperparameters if you are not lucky enough.]

Mixture of Experts Fusion for Fake Audio Detection Using Frozen wav2vec 2.0

model file is utils/ideas/MoEF/w2v2_moe_fz24_aasist.py and the MoEF module is in utils/ideas/MoEF/moef.py