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Speaker embedding(verification and recognition) using Pytorch

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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:

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

Authors

[email protected]( or [email protected])

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