End-to-end spoofing detection
End-to-end detection of spoofing attacks of various types (synthectic and replay) to Automatic speaker Verification systems
Requirements
Python = 3.6
Pytorch >= 1.0.0
Scikit-learn >=0.19
tqdm
h5py
Prepare data
Data preparation scripts are provided and features in Kaldi format are exepected.
Pre-processed data will consist of separate hdf files for each class (clean/attack) such that features for each recording are stored as datasets of shape [1, nfeat, nframes].
Prepare Kaldi features with data_prep.py. Arguments:
--path-to-data Path to feats.scp
--path-to-more-data Path to second feats.scp
--out-path Path to output hdf file
--out-name Output hdf file name
--n-val-speakers Number of speakers for valid data
Experiments are performed with several input features. Assumed input shapes for each model can be seen in test_arch.py (nframes can vary freely).
Models indicated by CC support a varying number of input features.
Train a model
Train models with train.py. Arguments:
--model {lstm,resnet,resnet_pca,lcnn_9,lcnn_29,lcnn_9_pca,lcnn_29_pca,lcnn_9_prodspec,lcnn_9_icqspec,lcnn_9_CC,lcnn_29_CC,resnet_34_CC}
--batch-size input batch size for training (default: 64)
--epochs number of epochs to train (default: 500)
--lr learning rate (default: 0.001)
--momentum alpha Alpha (default: 0.9)
--l2 Weight decay coefficient (default: 0.00001)
--checkpoint-epoch epoch to load for checkpointing. If None, training starts from scratch
--checkpoint-path Path for checkpointing
--pretrained-path Path for pre trained model
--train-hdf-path Path to hdf data
--valid-hdf-path Path to hdf data
--workers WORKERS number of data loading workers
--seed random seed (default: 1)
--save-every how many epochs to wait before logging training status. Default is 1
--n-frames maximum number of frames per utterance (default: 1000)
--n-cycles number of examples to complete 1 epoch
--valid-n-cycles number of examples to complete 1 epoch
--n-classes Number of classes for the mcc case (default: binary classification)
--ncoef Number of cepstral coefs for the LA case (default: 90)
--init-coef First cepstral coefs (default: 0)
--lists-path Path to list files per attack
--no-cuda Disables GPU use
Scoring test recordings
Score models with score.py. Arguments:
--path-to-data Path to input data
--trials-path Path to trials file
--cp-path Path Path for file containing model
--out-path Path to output hdf file
--model {lstm,resnet,resnet_pca,lcnn_9,lcnn_29,lcnn_9_pca,lcnn_29_pca,lcnn_9_prodspec,lcnn_9_icqspec,lcnn_9_CC,lcnn_29_CC,resnet_34_CC}
--no-cuda Disables GPU use
--no-output-file Disables writing scores into out file
--no-eer Disables computation of EER
--eval Enables eval trials reading
--tandem Scoring with tandem features
--ncoef Number of cepstral coefs (default: 90)
--init-coef First cepstral coefs (default: 0)
End-2-end EER will printed in the screen and scores for each trial output in a file. Input data is expected in kaldi format (feats.scp).