This repository contains the code for 'In defence of metric learning for speaker recognition.'
pip install -r requirements.txt
python ./trainSpeakerNet.py --model ResNetSE34 --encoder SAP --trainfunc amsoftmax --optimizer adam --save_path data/exp1 --batch_size 200 --max_frames 200 --scale 30 --margin 0.3 --train_list /home/joon/train_list.txt --test_list /home/joon/test_list.txt --train_path /home/joon/voxceleb2 --test_path /home/joon/voxceleb1
Softmax (softmax)
AM-Softmax (amsoftmax)
AAM-Softmax (aamsoftmax)
GE2E (ge2e)
Prototypical (proto)
Triplet (triplet)
Contrastive (contrastive)
Angular Prototypical (angleproto)
ResNetSE34 (SAP)
VGGVox40 (SAP, TAP, MAX)
The VoxCeleb datasets are used for these experiments.
The train list should contain the identity and the file path, one line per utterance, as follows:
id00000 id00000/youtube_key/12345.wav
id00012 id00012/21Uxsk56VDQ/00001.wav
The test list can be downloaded from here.
Please cite the following if you make use of the code.
@article{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},
journal={arXiv preprint arXiv:2003.11982},
year={2020}
}
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