D-TDNN

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).

Pretrained Models

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

D_TDNN

Usage

Data preparation

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.

Test

  • Download the pretrained D-TDNN model and run:

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

Evaluation

VoxCeleb1-O

Model Emb. Params (M) Loss Backend EER (%) DCF_0.01 DCF_0.001
TDNN 512 4.2 Softmax PLDA 2.34 0.28 0.38
E-TDNN 512 6.1 Softmax PLDA 2.08 0.26 0.41
F-TDNN 512 12.4 Softmax PLDA 1.89 0.21 0.29
D-TDNN 512 2.8 Softmax Cosine 1.81 0.20 0.28
D-TDNN-SS (0) 512 3.0 Softmax Cosine 1.55 0.20 0.30
D-TDNN-SS 512 3.5 Softmax Cosine 1.41 0.19 0.24
D-TDNN-SS 128 3.1 AAM-Softmax Cosine 1.22 0.13 0.20

Citation

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}
}

GitHub

https://github.com/yuyq96/D-TDNN