ASVspoof2021_AIR

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ASVspoof2021_AIR

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Official implementation of our ASVspoof 2021 paper, "UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021"

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# ASVspoof2021_AIR
## UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021

This repository contains our implementation of the paper, "UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021".
[Paper link here](https://www.isca-speech.org/archive/asvspoof_2021/chen21_asvspoof.html) [Video link here](https://www.youtube.com/watch?v=-wKMOTp8Tt0)

The code here is a little bit unorganized, please feel free to open any issues. The contribution from this work mainly derives from our previous research works. Please feel free to check out the paper and code there.

## Reference to our prior research works
[1] Y. Zhang, F. Jiang and Z. Duan, "One-Class Learning Towards Synthetic Voice Spoofing Detection," in IEEE Signal Processing Letters, vol. 28, pp. 937-941, 2021, doi: 10.1109/LSP.2021.3076358. [[link](https://ieeexplore.ieee.org/document/9417604)] [[arXiv](https://arxiv.org/pdf/2010.13995.pdf)] [[code](https://github.com/yzyouzhang/AIR-ASVspoof)] [[video](https://www.youtube.com/watch?v=pX9aq8CaIvk)]

[2] Y. Zhang, G. Zhu, F. Jiang, Z. Duan, "An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems," Proc. Interspeech 2021, pp. 4309-4313, 2021, doi: 10.21437/Interspeech.2021-1820. [[link](https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html)] [[arXiv](https://arxiv.org/pdf/2104.01320.pdf)] [[code](https://github.com/yzyouzhang/Empirical-Channel-CM)] [[video](https://www.youtube.com/watch?v=t6qtehKer6w)]

### Requirements
python==3.6

pytorch==1.1.0

### Data Channel Augmentation
We use impulse responses from [this repo](https://github.com/idiap/acoustic-simulator) and the code we adapted is in the `channel_simulation` folder. Please refer to the paper for the augmentation process.

### Data Preprocess
The raw wave files of the ASVspoof 2019 dataset is stored in `/data/neil/DS_10283_3336`.
The `raw_dataset.py` is a pytorch dataloader for the original speech dataset.
The `feature_extraction.py` is to write feature extraction methods, currently I only have LFCC calculation. You may include CQCC, melspectrogram, CQT, MFCC, etc. in the future for model fusion. The `utils_dsp.py` are some helper functions for feature extraction.
The `process.py` is using the functions in feature extraction and output the preprocessed speech dataset.

### Data Preparation
The `dataset.py` is a pytorch dataloader for the preprocessed speech dataset. 
Make sure you change the directory path to the path on your machine.

### Model and loss functions
The `model.py` include some model architectures.
The `loss.py` defines some loss functions, including what we proposed.

### Run the training code
In the `main_train.py`, please read the argparse to get familiar with the options.
Before running the `main_train.py`, please change the `path_to_features` according to the files' location on your machine.
```
python3 main_train.py --add_loss ang_iso -o /data/xinhui/models/ocsoftmax --gpu 3
```
### Run the test code with trained model
I included the test code in `generate_score.py`. The `eval_metrics.py` and `evaluate_tDCF_asvspoof19.py` are some helper functions to calculate the evaluation metrics.
Before running, please change the `model_dir` to the location of the model you would like to test with.
```
python3 generate_score.py
```

## Citations
```
@inproceedings{chen21_asvspoof,
  author={Xinhui Chen and You Zhang and Ge Zhu and Zhiyao Duan},
  title={{UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021}},
  year=2021,
  booktitle={Proc. 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge},
  pages={75--82},
  doi={10.21437/ASVSPOOF.2021-12}
}
```

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