deepspeaker-pytorch's Introduction

Deep Speaker from Baidu Research - Pytorch Implementation

This is a slightly modified pytorch implementation of the model(modified Resnet + triplet loss) presented by Baidu Research in Deep Speaker: an End-to-End Neural Speaker Embedding System.

This code was tested using Voxceleb database. Voxceleb database paper shows shows 7.8% EER using CNN. But in my code can't reach that point. This code contained a lot of editable point such as preprocessing, model, scoring(length nomalization and file processing) and etc.

I hope this code helps researcher reach higher score.

Credits

Original paper:

Baidu Research paper:

@article{DBLP:journals/corr/LiMJLZLCKZ17,
  author    = {Chao Li and Xiaokong Ma and Bing Jiang and Xiangang Li and Xuewei Zhang and Xiao Liu and Ying Cao and Ajay Kannan and Zhenyao Zhu},
  title     = {Deep Speaker: an End-to-End Neural Speaker Embedding System},
  journal   = {CoRR},
  volume    = {abs/1705.02304},
  year      = {2017},
  url       = {http://arxiv.org/abs/1705.02304},
  timestamp = {Wed, 07 Jun 2017 14:41:04 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/LiMJLZLCKZ17},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

Also, use the part of code:

liorshk's git repository
- Baseline code - Facenet pytorch implimetation
hbredin's git repository
- Voxceleb Database reader

Features

In test, length normalization
- This means extracting many input from single wave and averaging. This makes the results slightly better.
In training, except pandas and preloading list.
- This makes different training accuracy each epoch, but it does not matter.

To do list

Work accomplished so far:

Model implementation
Data pipeline implementation - "Voxceleb"(Please note:Pytorch dataloader is so weak(High-load preprocessing and many thread))
Code cleanup
Modified preprocessing
Modified model
Modified scoring