PyTorch implementation of Densely Connected Time Delay Neural Network (D-TDNN) in our paper "Densely Connected Time Delay Neural Network for Speaker Verification" (INTERSPEECH 2020).

• [2021-02-14] We add an impl option in TimeDelay, now you can choose:

  • 'conv': implement TDNN by F.conv1d.
  • 'linear': implement TDNN by F.unfold and F.linear.

  Check this commit for more information. Note the pre-trained models of 'conv' have not been uploaded yet.

• [2021-02-04] TDNN (default implementation) in this repo is slower than nn.Conv1d, but we adopted it because:

  • TDNN in this repo was also used to create F-TDNN models that are not perfectly supported by nn.Conv1d (asymmetric paddings).
  • nn.Conv1d(dilation>1, bias=True) is slow in training.

  However, we do not use F-TDNN here, and we always set bias=False in D-TDNN. So, we are considering uploading a new version of TDNN soon (2021-02-14 updated).

• [2021-02-01] Our new paper is accepted by ICASSP 2021.

  Y.-Q. Yu, S. Zheng, H. Suo, Y. Lei, and W.-J. Li, "CAM: Context-Aware Masking for Robust Speaker Verification"

  CAM outperforms statistics-and-selection (SS) in terms of speed and accuracy.

We provide the pretrained models which can be used in many tasks such as:

• Speaker Verification
• Speaker-Dependent Speech Separation
• Multi-Speaker Text-to-Speech
• Voice Conversion

You can either use Kaldi toolkit:

• Download VoxCeleb1 test set and unzip it.
• Place prepare_voxceleb1_test.sh under $kaldi_root/egs/voxceleb/v2 and change the $datadir and $voxceleb1_root in it.
• Run chmod +x prepare_voxceleb1_test.sh && ./prepare_voxceleb1_test.sh to generate 30-dim MFCCs.
• Place the trials under $datadir/test_no_sil.

Or checkout the kaldifeat branch if you do not want to install Kaldi.

• Download the pretrained D-TDNN model and run:

python evaluate.py --root $datadir/test_no_sil --model D-TDNN --checkpoint dtdnn.pth --device cuda

VoxCeleb1-O

If you find D-TDNN helps your research, please cite

@inproceedings{DBLP:conf/interspeech/YuL20,
  author    = {Ya-Qi Yu and
               Wu-Jun Li},
  title     = {Densely Connected Time Delay Neural Network for Speaker Verification},
  booktitle = {Annual Conference of the International Speech Communication Association (INTERSPEECH)},
  pages     = {921--925},
  year      = {2020}
}

References:

[16] X. Li, W. Wang, X. Hu, and J. Yang, "Selective Kernel Networks," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 510-519.