LaST-Net

Model weights for LaST-Net: Length-Aware Layer and Scale-Adaptive Temporal Network for Speech Deepfake Detection.

Training and inference code: JunXue-tech/LaST-Net.

Checkpoint

best.pt is the original epoch-52 checkpoint selected by the lowest mean development EER across 1, 2, 4 and 6 seconds on ASVspoof 2019 LA. It includes the fine-tuned XLS-R 300M frontend, LaST-Net backend, optimizer state and original training metadata.

Usage

Follow the environment setup in the code repository. From that repository:

python download_model.py
python infer.py example.wav --checkpoint checkpoints/best.pt \
  --ssl-path /path/to/xlsr2_300m.pt --seconds 6

The model constructor requires the original fairseq-format XLS-R 300M checkpoint, available from the official XLS-R repository, before loading the fine-tuned parameters from best.pt.

Input audio must be mono at 16 kHz. The supplied inference code evaluates 1–6 second inputs using prefix cropping and repetition of shorter recordings. Higher bonafide_log_score values favor bona fide speech. Scores are not calibrated probabilities.

Evaluation

Duration-averaged EER (%) across 1–6 second inputs: 19LA 1.29, 21LA 4.98, 21DF 3.62, and In-the-Wild 7.58. Per-duration results and evaluation commands are provided in the code repository. Results depend on the evaluation protocol and preprocessing.

Author

Jun Xue — junxue@whu.edu.cn