AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
This repository provides the overall framework for training and evaluating audio anti-spoofing systems proposed in ‘AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks’
Getting started
requirements.txt must be installed for execution. We state our experiment environment for those who prefer to simulate as similar as possible.
pip install -r requirements.txt
- Our environment (for GPU training)
- Based on a docker image:
pytorch:1.6.0-cuda10.1-cudnn7-runtime - GPU: 1 NVIDIA Tesla V100
- About 16GB is required to train AASIST using a batch size of 24
- gpu-driver: 418.67
- Based on a docker image:
Data preparation
We train/validate/evaluate AASIST using the ASVspoof 2019 logical access dataset.