• Feature: 80-dim fbank
• Training: batch_size 96 * 4, 4 gpu(Tesla V100)
• Metrics: EER(%), MinDCF(p-target=0.05)

• Train set: Voxceleb2-dev, 5994 speakers
• Test set: Voxceleb1-O

Pretrained models are accessible on ModelScope.

• Voxceleb: iic/speech_sdpn_ecapa_tdnn_sv_en_voxceleb_16k

Here is a simple example for directly extracting embeddings. It downloads the pretrained model from ModelScope and generates embeddings.

# Install modelscope
pip install modelscope
# SDPN trained on VoxCeleb
model_id=iic/speech_sdpn_ecapa_tdnn_sv_en_voxceleb_16k
# Run inference
python speakerlab/bin/infer_sv_ssl.py --model_id $model_id

If you are using SDPN model in your research, please cite:

@article{chen2024sdpn,
  title={Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision},
  author={Chen, Yafeng and Zheng, Siqi and Wang, Hui and Cheng, Luyao and others},
  booktitle={ICASSP},
  year={2025}
}