/End-to-End-Dual-Branch-Network-Towards-Synthetic-Speech-Detection

Primary LanguagePython

End-to-End Dual-Branch Network Towards Synthetic Speech Detection

Prerequisites

  • NVIDIA GPU+CUDA CuDNN
  • Install Torch1.8 and dependencies

Training and Test Details

  • Please adjust the file location before training and testing;

  • Data Preparation

    • Change the Feature Engineering/CQT/cqt_extract.py, Feature Engineering/LFCC/extract_lfcc.m and Feature Engineering/LFCC/reload_data.py
    • Run the Feature Engineering/CQT/cqt_extract.py, Feature Engineering/LFCC/extract_lfcc.m and Feature Engineering/LFCC/reload_data.py

  • When you train the network

    • Change the dual-branch_sum_loss.py or dual-branch_alternative_loss.py
    • Run the dual-branch_sum_loss.py or dual-branch_alternative_loss.py

  • When you test the network

    • Change the Result_sum_loss/test_dual.py or Result_alternative_loss/test_dual.py
    • Run the Result_sum_loss/test_dual.py or Result_alternative_loss/test_dual.py

Acknowledgements

The code of this work is adapted from https://github.com/yzyouzhang/AIR-ASVspoof, https://github.com/yzyouzhang/Empirical-Channel-CM and https://github.com/joaomonteirof/e2e_antispoofing.