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Fastaudio

FastAudio is a Learnable Audio Frontend team Magnum's designed for the ASVspoof 2021 challenge. It was developed using the Speechbrain framework. The solution was produced by Quchen Fu and Zhongwei Teng, researchers in the Magnum Research Group at Vanderbilt University. The Magnum Research Group is part of the Institute for Software Integrated Systems.

The ASVspoof 2021 Competition challenges teams to develop countermeasures capable of discriminating between bona fide and spoofed or deepfake speech. The model achieved a 0.2531 min t-DCF score in LA Track on the open Leaderboard.

fastaudio-2

Requirements

Show details

  • speechbrain==0.5.7
  • pandas
  • wandb
  • torch==1.8.0+cu111
  • torchaudio==0.8.0
  • nnAudio==0.2.6

How it works

Environment

  1. Create a virtual environment with python3.8 installed(virtualenv)
  2. git clone --recursive https://github.com/QuchenFu/Fastaudio
  3. use pip install -r requirements.txt to install the requirements files.
  4. cd leaf-audio-pytorch/ and pip install -e .
  5. pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

Data pre-processing

.
├── data                       
│   │
│   ├── PA                  
│   │   └── ...
│   └── LA           
│       ├── ASVspoof2019_LA_asv_protocols
│       ├── ASVspoof2019_LA_asv_scores
│       ├── ASVspoof2019_LA_cm_protocols
│       ├── ASVspoof2019_LA_train
│       ├── ASVspoof2019_LA_dev
│       └── ASVspoof2021_LA_eval
│
└── Fastaudio
  1. Download the data here
  2. Unzip and save the data to a folder data in the same directory as Fastaudio
  3. python3.8 preprocess.py
  4. Change args['data_type'] = ['labeled','unlabeled'][1] in preprocess.py to args['data_type'] = ['labeled','unlabeled'][0]
  5. python3.8 preprocess.py

Train

  1. python3.8 train_spoofspeech.py yaml/SpoofSpeechClassifier.yaml --data_parallel_backend --data_parallel_count=2

Inference

  1. Modify the TRAIN in train_spoofspeech.py to False.
  2. python3.8 train_spoofspeech.py yaml/SpoofSpeechClassifier.yaml --data_parallel_backend --data_parallel_count=2

Evaluate

  1. python3.8 eval.py

Metrics

Accuracy metric

min t−DCF =min{βPcm (s)+Pcm(s)}

Reference

If you use this repository, please consider citing:

@inproceedings{Fu2021FastAudioAL,
  title={FastAudio: A Learnable Audio Front-End for Spoof Speech Detection},
  author={Quchen Fu and Zhongwei Teng and Jules White and M. Powell and Douglas C. Schmidt},
  booktitle={2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2022},
  organization={IEEE}
}

@inproceedings{Teng2021ComplementingHF,
  title={Complementing Handcrafted Features with Raw Waveform Using a Light-weight Auxiliary Model},
  author={Zhongwei Teng and Quchen Fu and Jules White and M. Powell and Douglas C. Schmidt},
  year={2021}
}