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RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing

This repository contains our implementation of the paper, "RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing". This work introduce RawBoost, a data boosting and augmentation method for the design of more reliable spoofing detection solutions which operate directly upon raw waveform inputs (Paper link here).

Installation

First, clone the repository locally, create and activate a conda environment, and install the requirements :

$ git clone https://github.com/TakHemlata/RawBoost-antispoofing.git
$ conda create --name RawBoost_antispoofing python=3.8.8
$ conda activate RawBoost_antispoofing
$ conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
$ pip install -r requirements.txt

Experiments

Dataset

Our experiments are performed on the logical access (LA) partition of the ASVspoof 2021 dataset (train on 2019 LA training and evaluate on 2021 LA evaluation database).

Training

To train the model run:

python main.py --track=LA --loss=WCE   --lr=0.0001 --batch_size=128

Testing

To evaluate your own model on LA evaluation dataset:

python main.py --track=LA --loss=WCE --is_eval --eval --model_path='/path/to/your/best_model.pth' --eval_output='eval_CM_scores_file.txt'

We also provide a pre-trained models. To use it you can run:

python main.py --track=LA --loss=WCE --is_eval --eval --model_path='Pre_trained_models.pth' --eval_output='RawBoost_eval_CM_scores.txt'

This repository is built on our End-to-end RawNet2 CM system (ASVspoof2021 Challenge baseline).

Contact

For any query regarding this repository, please contact:

  • Hemlata Tak: tak[at]eurecom[dot]fr
  • Massimiliano Todisco: todisco[at]eurecom[dot]fr

Citation

If you use RawBoost code in your research please use the following citation:

@inproceedings{tak2021rawboost,
  title={RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing},
  author={Tak, Hemlata and Kamble, Madhu and Patino, Jose and Todisco, Massimiliano and Evans, Nicholas},
  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2022}
}