PyTorch implementation of Densely Connected Time Delay Neural Network

Related tags

Deep Learning speech speaker-recognition speaker-verification speaker-diarization time-delay-neural-network speaker-embedding speaker-adaptation temporal-convolutional-network d-tdnn
Overview

Densely Connected Time Delay Neural Network

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).

What's New ⚠️

  • [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.

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 & D-TDNN-SS

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}
}

Revision of the Paper ⚠️

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.

Comments
  • size mismatch while loading pre-trained weights

    size mismatch while loading pre-trained weights

    RuntimeError: Error(s) in loading state_dict for DTDNN: Missing key(s) in state_dict: "xvector.tdnn.linear.bias", "xvector.dense.linear.bias". size mismatch for xvector.tdnn.linear.weight: copying a param with shape torch.Size([128, 30, 5]) from checkpoint, the shape in current model is torch.Size([128, 150]). size mismatch for xvector.block1.tdnnd1.linear1.weight: copying a param with shape torch.Size([128, 128, 1]) from checkpoint, the shape in current model is torch.Size([128, 128]). size mismatch for xvector.block1.tdnnd1.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd2.linear1.weight: copying a param with shape torch.Size([128, 192, 1]) from checkpoint, the shape in current model is torch.Size([128, 192]). size mismatch for xvector.block1.tdnnd2.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd3.linear1.weight: copying a param with shape torch.Size([128, 256, 1]) from checkpoint, the shape in current model is torch.Size([128, 256]). size mismatch for xvector.block1.tdnnd3.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd4.linear1.weight: copying a param with shape torch.Size([128, 320, 1]) from checkpoint, the shape in current model is torch.Size([128, 320]). size mismatch for xvector.block1.tdnnd4.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd5.linear1.weight: copying a param with shape torch.Size([128, 384, 1]) from checkpoint, the shape in current model is torch.Size([128, 384]). size mismatch for xvector.block1.tdnnd5.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block1.tdnnd6.linear1.weight: copying a param with shape torch.Size([128, 448, 1]) from checkpoint, the shape in current model is torch.Size([128, 448]). size mismatch for xvector.block1.tdnnd6.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.transit1.linear.weight: copying a param with shape torch.Size([256, 512, 1]) from checkpoint, the shape in current model is torch.Size([256, 512]). size mismatch for xvector.block2.tdnnd1.linear1.weight: copying a param with shape torch.Size([128, 256, 1]) from checkpoint, the shape in current model is torch.Size([128, 256]). size mismatch for xvector.block2.tdnnd1.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd2.linear1.weight: copying a param with shape torch.Size([128, 320, 1]) from checkpoint, the shape in current model is torch.Size([128, 320]). size mismatch for xvector.block2.tdnnd2.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd3.linear1.weight: copying a param with shape torch.Size([128, 384, 1]) from checkpoint, the shape in current model is torch.Size([128, 384]). size mismatch for xvector.block2.tdnnd3.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd4.linear1.weight: copying a param with shape torch.Size([128, 448, 1]) from checkpoint, the shape in current model is torch.Size([128, 448]). size mismatch for xvector.block2.tdnnd4.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd5.linear1.weight: copying a param with shape torch.Size([128, 512, 1]) from checkpoint, the shape in current model is torch.Size([128, 512]). size mismatch for xvector.block2.tdnnd5.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd6.linear1.weight: copying a param with shape torch.Size([128, 576, 1]) from checkpoint, the shape in current model is torch.Size([128, 576]). size mismatch for xvector.block2.tdnnd6.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd7.linear1.weight: copying a param with shape torch.Size([128, 640, 1]) from checkpoint, the shape in current model is torch.Size([128, 640]). size mismatch for xvector.block2.tdnnd7.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd8.linear1.weight: copying a param with shape torch.Size([128, 704, 1]) from checkpoint, the shape in current model is torch.Size([128, 704]). size mismatch for xvector.block2.tdnnd8.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd9.linear1.weight: copying a param with shape torch.Size([128, 768, 1]) from checkpoint, the shape in current model is torch.Size([128, 768]). size mismatch for xvector.block2.tdnnd9.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd10.linear1.weight: copying a param with shape torch.Size([128, 832, 1]) from checkpoint, the shape in current model is torch.Size([128, 832]). size mismatch for xvector.block2.tdnnd10.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd11.linear1.weight: copying a param with shape torch.Size([128, 896, 1]) from checkpoint, the shape in current model is torch.Size([128, 896]). size mismatch for xvector.block2.tdnnd11.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.block2.tdnnd12.linear1.weight: copying a param with shape torch.Size([128, 960, 1]) from checkpoint, the shape in current model is torch.Size([128, 960]). size mismatch for xvector.block2.tdnnd12.linear2.weight: copying a param with shape torch.Size([64, 128, 3]) from checkpoint, the shape in current model is torch.Size([64, 384]). size mismatch for xvector.transit2.linear.weight: copying a param with shape torch.Size([512, 1024, 1]) from checkpoint, the shape in current model is torch.Size([512, 1024]). size mismatch for xvector.dense.linear.weight: copying a param with shape torch.Size([512, 1024, 1]) from checkpoint, the shape in current model is torch.Size([512, 1024]).

    opened by zabir-nabil 3
  • 实验细节的疑问

    实验细节的疑问

    您好： 我想教下您的论文中，实验的实现细节： 1.实验数据：我看很多其他论文都是使用voxceleb2 dev 5994说话人作为训练集（或者voxceleb dev+voxceleb2 dev，1211+5994说话人），您有只在这部分说话人上的实验结果吗？方便透露下嘛？

    2.PLDA和Cosine Similarity：您这里实验比较这两个的EER在TDNN中是提取的是倒数第二层（分类器前一层）还是第三层(xvector)的输出啊？因为我在论文中又看到，这两个不同层embedding对不同方法性能有差异,倒数第二层的cosine方法可能会更好一些。

    Thanks！🙏

    opened by Wenhao-Yang 1
  • questions about model training

    questions about model training

    hello, yuyq96, Thank you so much for the great work you've shared. I learned that D-TDNNSS mini-batch setting 128 from D-TDNN paper. But this model is too large to train on single gpu. Could you tell me how you train it? Using nn.Parallel or DDP? Looking forward to you reply

    opened by forwiat 2
  • the difference between kaldifeat-kaldi and kaldifeat-python?

    the difference between kaldifeat-kaldi and kaldifeat-python?

    May I ask you the numerical difference between kaldifeat by kaldi implementation and kaldifeat by your python implementation? I have compared the two computed features, and I find it has some difference. I wonder that the experiment results showed in D-TDNN master and D-TDNN-kaldifeat branch is absolutely the same.

    Thanks~

    opened by mezhou 4
  • 针对论文的一些疑问

    针对论文的一些疑问

    您好，我觉得您的工作-DTDNN，在参数比较少的情况下获得了较ETDNN，FTDNN更好的结果，我认为这非常有意义。但是我对论文的实验存在两处疑惑： 1、论文中Table5中，基于softmax训练的D-TDNN模型Cosine的结果好于PLDA，在上面的TDNN，ETDNN，FTDNN的结果不一致（均是PLDA好于Cosine），请问这是什么原因导致的？ 2、对于null branch，能稍微解释一下吗？

    opened by xuanjihe 10
Releases(trials)
Owner
Ya-Qi Yu
Machine Learning
Ya-Qi Yu
GitHub
(IEEE TIP 2021) Regularized Densely-connected Pyramid Network for Salient Instance Segmentation

RDPNet IEEE TIP 2021: Regularized Densely-connected Pyramid Network for Salient Instance Segmentation PyTorch training and testing code are available.

Yu-Huan Wu 41 Oct 21, 2022
Densely Connected Search Space for More Flexible Neural Architecture Search (CVPR2020)

DenseNAS The code of the CVPR2020 paper Densely Connected Search Space for More Flexible Neural Architecture Search. Neural architecture search (NAS)

Jamin Fong 291 Nov 18, 2022
TCNN Temporal convolutional neural network for real-time speech enhancement in the time domain

TCNN Pandey A, Wang D L. TCNN: Temporal convolutional neural network for real-time speech enhancement in the time domain[C]//ICASSP 2019-2019 IEEE Int

凌逆战 16 Dec 30, 2022
[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"

MDCA Calibration This is the official PyTorch implementation for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved

MDCA Calibration 21 Dec 22, 2022
This is a model made out of Neural Network specifically a Convolutional Neural Network model

This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternative libraries that can be used for this purpose, one of which is the PyTorch library.

null 9 Oct 18, 2022
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition (PyTorch) Paper: https://arxiv.org/abs/2105.01883 Citation: @

null 260 Jan 3, 2023
This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN

DeepMind 892 Dec 28, 2022
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

Intel Labs 210 Jan 4, 2023
Pytorch Implementation of Adversarial Deep Network Embedding for Cross-Network Node Classification

Pytorch Implementation of Adversarial Deep Network Embedding for Cross-Network Node Classification (ACDNE) This is a pytorch implementation of the Adv

陈志豪 8 Oct 13, 2022
PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 20

Zhengqi Li 585 Jan 4, 2023
Pytorch implementation of "Forward Thinking: Building and Training Neural Networks One Layer at a Time"

forward-thinking-pytorch Pytorch implementation of Forward Thinking: Building and Training Neural Networks One Layer at a Time Requirements Python 2.7

Kim Heecheol 65 Oct 6, 2022
Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting This repository is the official implementation of Spectral Temporal Gr

Microsoft 306 Dec 29, 2022
🔮 Execution time predictions for deep neural network training iterations across different GPUs.

Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training Habitat is a tool that predicts a deep neural network's

Geoffrey Yu 44 Dec 27, 2022
Neural network-based build time estimation for additive manufacturing

Neural network-based build time estimation for additive manufacturing Oh, Y., Sharp, M., Sprock, T., & Kwon, S. (2021). Neural network-based build tim

Yosep 1 Nov 15, 2021
Use tensorflow to implement a Deep Neural Network for real time lane detection

LaneNet-Lane-Detection Use tensorflow to implement a Deep Neural Network for real time lane detection mainly based on the IEEE IV conference paper "To

MaybeShewill-CV 1.9k Jan 8, 2023
A real world application of a Recurrent Neural Network on a binary classification of time series data

What is this This is a real world application of a Recurrent Neural Network on a binary classification of time series data. This project includes data

Josep Maria Salvia Hornos 2 Jan 30, 2022
Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.

Lunar Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs. About Lunar can be modified to work

Zeyad Mansour 276 Jan 7, 2023
An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" in Pytorch.

GLOM An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" for MNIST Dataset. To understand this

null 50 Oct 19, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022