SASV2_Baseline

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SASV2 baseline, a track on ASVspoof5 phase2 challenge

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# Towards single integrated spoofing-aware speaker verification embeddings
<img src="img/overview.png" width="800">

## `Get started`. Installation dependencies.

`requirements.txt` is included in each stage directory (All of them are the same so you can use any of them).

If you use the Anaconda virtual environment,
```
conda create -n sasv python=3.9 cudatoolkit=11.3
conda activate sasv
```
Install all dependency packages,
```
pip3 install -r requirements.txt
```

## `Stage 1`. Speaker classification-based Pre-training.

In [Stage 1](https://github.com/sasv-challenge/ASVSpoof5-SASVBaseline/tree/main/stage1), the ability to discriminate between target and bona fide non-target speakers can be learned using the `VoxCeleb2` database which contains data collected from thousands of bona fide speakers. In this repository, we provide the pre-trained weights of the following models:

* ECAPA-TDNN:    [ecapa_tdnn.model](https://drive.google.com/file/d/1wazmfXOP5jv4Ynn3SNYhNKJs2e_z86ue/view?usp=share_link) (64MB)
* MFA_Conformer: [mfa_conformer.model](https://drive.google.com/file/d/1R8koiGMFzCRl0v8gPBYYSD21ZKOks9nm/view?usp=share_link) (89MB)
* SKA-TDNN:      [ska_tdnn.model](https://drive.google.com/file/d/1buMtltChZxdQyBqQkUQwohgqV_vubGfU/view?usp=share_link) (123MB)

| Model           | params       | SASV-EER (%)  | SV-EER (%)    | SPF-EER (%)  |
| :---:           | :---:        | :---:         | :---:         | :---:        |
| `ECAPA-TDNN`    | 16.7M        | 20.66         | 0.74          | 27.30        |
| `MFA-Conformer` | 20.9M        | 20.22         | 0.41          | 26.52        |
| `SKA-TDNN`      | 29.4M        | 16.74         | 0.38          | 22.38        |

You can evaluate the pre-trained weights using the following commands:
```bash
cd stage3

python trainSASVNet.py
        --eval \
        --test_list ./protocols/ASVspoof2019.LA.asv.eval.gi.trl.txt \
        --test_path /path/to/dataset/ASVSpoof/ASVSpoof2019/LA/ASVspoof2019_LA_eval/wav \
        --model ECAPA_TDNN \
        --initial_model /path/to/weight/ecapa_tdnn.model

python trainSASVNet.py
        --eval \
        --test_list ./protocols/ASVspoof2019.LA.asv.eval.gi.trl.txt \
        --test_path /path/to/dataset/ASVSpoof/ASVSpoof2019/LA/ASVspoof2019_LA_eval/wav \
        --model MFA_Conformer \
        --initial_model /path/to/weight/mfa_conformer.model

python trainSASVNet.py
        --eval \
        --test_list ./protocols/ASVspoof2019.LA.asv.eval.gi.trl.txt \
        --test_path /path/to/dataset/ASVSpoof/ASVSpoof2019/LA/ASVspoof2019_LA_eval/wav \
        --model SKA_TDNN \
        --initial_model /path/to/weight/ska_tdnn.model
```

## `Stage 2`.  Copy-synthesis Training.
In [Stage 2](https://github.com/sasv-challenge/ASVSpoof5-SASVBaseline/tree/main/stage2), we augment the model with the ability to discriminate between bona fide and spoofed inputs by using large-scale data generated through an oracle speech synthesis system, referred to as copy synthesis.
This repository has the copy-synthesis training using copy-synthesized data from `VoxCeleb2 dev` or `ASVspoof2019 LA train/train+dev`.

## `Stage 3`. In-domain Fine-tuning.
Even though training in Stages 1 and 2 learn to discriminate bona fide non-target and spoof non-target inputs, there is a remaining domain mismatch with the evaluation protocol. Furthermore, artefacts from the acoustic model have yet to be learned. Hence, in [Stage 3](https://github.com/sasv-challenge/ASVSpoof5-SASVBaseline/tree/main/stage3), we fine-tune the model using in-domain bona fide and spoofed data contained within the `ASVspoof2019 LA` dataset.

You can download each pre-trained weight from the above links:

## Citation
If you utilize this repository, please cite the following paper,
```
@inproceedings{chung2020in,
  title={In defence of metric learning for speaker recognition},
  author={Chung, Joon Son and Huh, Jaesung and Mun, Seongkyu and Lee, Minjae and Heo, Hee Soo and Choe, Soyeon and Ham, Chiheon and Jung, Sunghwan and Lee, Bong-Jin and Han, Icksang},
  booktitle={Proc. Interspeech},
  year={2020}
}
```

```
@inproceedings{jung2022pushing,
  title={Pushing the limits of raw waveform speaker recognition},
  author={Jung, Jee-weon and Kim, You Jin and Heo, Hee-Soo and Lee, Bong-Jin and Kwon, Youngki and Chung, Joon Son},
  booktitle={Proc. Interspeech},
  year={2022}
}
```

```
@inproceedings{mun2022frequency,
  title={Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification},
  author={Mun, Sung Hwan and Jung, Jee-weon and Han, Min Hyun and Kim, Nam Soo},
  booktitle={Proc. IEEE SLT},
  year={2022}
}
```

SASV2_Baseline

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