SVDDChallenge / CtrSVDD2024_Baseline

Baseline system for SVDD 2024 Challenge CtrSVDD track

SVDD Challenge Baseline Systems

This repository contains the baseline system implementations for the SVDD Challenge 2024. To form a comprehensive evaluation, we implemented the front-end features, back-end systems and the evaluation metrics. The baseline systems are implemented in Python and are available as open-source software.

Updates

• Jun 3, 2024: CtrSVDD paper is accepted by INTERSPEECH 2024! We update all five baseline system implementations in models/model.py, corresponding model weights in weights/ and update our training script.
• March 12, 2024: Since during baseline training, our code contains flipped labels, you need to manually flip the sign of the predicted scores if you are only inferencing from our provided baseline systems. To do so, please add a line in eval.py after 31: pred *= -1.0.
• March 6, 2024: We update training logs in weights/training_logs. You could see them using tensorboard (see 'Visualize Training Logs of Provided Baseline Systems'). Also, we realize our training code contains flipped labels (the bonafides are labeled as 0, not 1). The code has been fixed to reflect the correct implementation. EER may change slightly due to this.

Getting Started

Setting up environment:

conda create -n svdd_baseline python=3.10
conda activate svdd_baseline
pip install -r requirements.txt

Then you can run the training script with the following command:

python train.py --base_dir {Where the data is} --gpu {GPU ID} --encoder {Encoder Type} --batch_size {Batch size}

You can use --load_from flag to resume training.

After training, you can evaluate your model using the following command:

python eval.py --base_dir {Where the data is} --model_path {The model's weights file} --gpu {GPU ID} --encoder {Encoder Type} --batch_size {Batch size}

The main functions in train and eval specify more options that you can tune.

Within base_dir, the code expects to see train_set, dev_set and test_set directories, along with train.txt and dev.txt as open-sourced. train_set, dev_set and test_set should directly contain *.flac files.

Visualize Training Logs of Provided Baseline Systems

Run the following command within the CtrSVDD2024_Baseline directory.

pip install tensorboard
tensorboard --logdir weights/training_logs