Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Overview

Introduction

This repository contains my unofficial reimplementation of the standard ECAPA-TDNN, which is the speaker recognition in VoxCeleb2 dataset.

This repository is modified based on voxceleb_trainer.

Best Performance in this project (with AS-norm)

Dataset Vox1_O Vox1_E Vox1_H
EER 0.86 1.18 2.17
minDCF 0.0686 0.0765 0.1295

System Description

I will write a technique report about this system and all the details later. Please wait.

Dependencies

Note: That is the setting based on my device, you can modify the torch and torchaudio version based on your device.

Start from building the environment

conda create -n ECAPA python=3.7.9 anaconda
conda activate ECAPA
pip install -r requirements.txt

Start from the existing environment

pip install -r requirements.txt

Data preparation

Please follow the official code to perpare your VoxCeleb2 dataset from the 'Data preparation' part in this repository.

Dataset for training usage:

  1. VoxCeleb2 training set;

  2. MUSAN dataset;

  3. RIR dataset.

Dataset for evaluation:

  1. VoxCeleb1 test set for Vox1_O

  2. VoxCeleb1 train set for Vox1_E and Vox1_H (Optional)

Training

Then you can change the data path in the trainECAPAModel.py. Train ECAPA-TDNN model end-to-end by using:

python trainECAPAModel.py --save_path exps/exp1 

Every test_step epoches, system will be evaluated in Vox1_O set and print the EER.

The result will be saved in exps/exp1/score.txt. The model will saved in exps/exp1/model

In my case, I trained 80 epoches in one 3090 GPU. Each epoch takes 37 mins, the total training time is about 48 hours.

Pretrained model

Our pretrained model performs EER: 0.96 in Vox1_O set without AS-norm, you can check it by using:

python trainECAPAModel.py --eval --initial_model exps/pretrain.model

With AS-norm, this system performs EER: 0.86, we will release the code of AS-norm later.

We also update the score.txt file in exps/pretrain_score.txt, it contains the training loss, training acc and EER in Vox1_O in each epoch for your reference.


Reference

@inproceedings{desplanques2020ecapa,
  title={{ECAPA-TDNN: Emphasized Channel Attention, propagation and aggregation in TDNN based speaker verification}},
  author={Desplanques, Brecht and Thienpondt, Jenthe and Demuynck, Kris},
  booktitle={Interspeech 2020},
  pages={3830--3834},
  year={2020}
}
@inproceedings{chung2020in,
  title={In defence of metric learning for speaker recognition},
  author={Chung, Joon Son and Huh, Jaesung and Mun, Seongkyu and Lee, Minjae and Heo, Hee Soo and Choe, Soyeon and Ham, Chiheon and Jung, Sunghwan and Lee, Bong-Jin and Han, Icksang},
  booktitle={Interspeech},
  year={2020}
}

Acknowledge

We study many useful projects in our codeing process, which includes:

clovaai/voxceleb_trainer.

lawlict/ECAPA-TDNN.

speechbrain/speechbrain

ranchlai/speaker-verification

Thanks for these authors to open source their code!

Notes

If you meet the problems about this repository, Please ask me from the 'issue' part in Github (using English) instead of sending the messages to me from bilibili, so others can also benifit from it. Thanks for your understanding!

If you improve the result based on this repository by some methods, please let me know. Thanks!

Owner
Tao Ruijie
NUS ECE PhD student
Tao Ruijie
Custom Implementation of Non-Deep Networks

ParNet Custom Implementation of Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Official Repository https

Pritama Kumar Nayak 20 May 27, 2022
A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

Biomedical Computer Vision @ Uniandes 52 Dec 19, 2022
Pytorch reimplementation of PSM-Net: "Pyramid Stereo Matching Network"

This is a Pytorch Lightning version PSMNet which is based on JiaRenChang/PSMNet. use python main.py to start training. PSM-Net Pytorch reimplementatio

XIAOTIAN LIU 1 Nov 25, 2021
Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Wilson 1.7k Dec 30, 2022
General purpose Slater-Koster tight-binding code for electronic structure calculations

tight-binder Introduction General purpose tight-binding code for electronic structure calculations based on the Slater-Koster approximation. The code

9 Dec 15, 2022
Source code for the Paper: CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints}

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints Installation Run pipenv install (at your own risk with --skip-lo

Autonomous Learning Group 65 Dec 27, 2022
A unified framework to jointly model images, text, and human attention traces.

connect-caption-and-trace This repository contains the reference code for our paper Connecting What to Say With Where to Look by Modeling Human Attent

Meta Research 73 Oct 24, 2022
Unofficial Implementation of MLP-Mixer in TensorFlow

mlp-mixer-tf Unofficial Implementation of MLP-Mixer [abs, pdf] in TensorFlow. Note: This project may have some bugs in it. I'm still learning how to i

Rishabh Anand 24 Mar 23, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
Defending against Model Stealing via Verifying Embedded External Features

Defending against Model Stealing Attacks via Verifying Embedded External Features This is the official implementation of our paper Defending against M

20 Dec 30, 2022
A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor

Phase-SLAM A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor This open source is written by MATLAB Run Mode Open

Xi Zheng 14 Dec 19, 2022
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

Stanford Intelligent Systems Laboratory 9 Jun 06, 2022
这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Facenet+Retinaface:人脸识别模型在Keras当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络,分别是retinaface和fa

Bubbliiiing 31 Nov 15, 2022
Learning multiple gaits of quadruped robot using hierarchical reinforcement learning

Learning multiple gaits of quadruped robot using hierarchical reinforcement learning We propose a method to learn multiple gaits of quadruped robot us

Yunho Kim 17 Dec 11, 2022
Implementation for the "Surface Reconstruction from 3D Line Segments" paper.

Surface Reconstruction from 3D Line Segments Surface reconstruction from 3d line segments. Langlois, P. A., Boulch, A., & Marlet, R. In 2019 Internati

85 Jan 04, 2023
Collection of sports betting AI tools.

sports-betting sports-betting is a collection of tools that makes it easy to create machine learning models for sports betting and evaluate their perf

George Douzas 109 Dec 31, 2022
This is a Image aid classification software based on python TK library development

This is a Image aid classification software based on python TK library development.

EasonChan 1 Jan 17, 2022
How Do Adam and Training Strategies Help BNNs Optimization? In ICML 2021.

AdamBNN This is the pytorch implementation of our paper "How Do Adam and Training Strategies Help BNNs Optimization?", published in ICML 2021. In this

Zechun Liu 47 Sep 20, 2022
Official Pytorch implementation of "Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video", CVPR 2021

TCMR: Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video Qualtitative result Paper teaser video Introduction This r

Hongsuk Choi 215 Jan 06, 2023
Benchmarks for the Optimal Power Flow Problem

Power Grid Lib - Optimal Power Flow This benchmark library is curated and maintained by the IEEE PES Task Force on Benchmarks for Validation of Emergi

A Library of IEEE PES Power Grid Benchmarks 207 Dec 08, 2022