This repository contains the framework for training speaker recognition models described in the paper 'In defence of metric learning for speaker recognition' and 'Pushing the limits of raw waveform speaker recognition'.
pip install -r requirements.txt
The following script can be used to download and prepare the VoxCeleb dataset for training.
python ./dataprep.py --save_path data --download --user USERNAME --password PASSWORD
python ./dataprep.py --save_path data --extract
python ./dataprep.py --save_path data --convert
In order to use data augmentation, also run:
python ./dataprep.py --save_path data --augment
In addition to the Python dependencies, wget and ffmpeg must be installed on the system.
- ResNetSE34L with AM-Softmax:
python ./trainSpeakerNet.py --config ./configs/ResNetSE34L_AM.yaml
- RawNet3 with AAM-Softmax
python ./trainSpeakerNet.py --config ./configs/RawNet3_AAM.yaml
- ResNetSE34L with Angular prototypical:
python ./trainSpeakerNet.py --config ./configs/ResNetSE34L_AP.yaml
You can pass individual arguments that are defined in trainSpeakerNet.py by --{ARG_NAME} {VALUE}.
Note that the configuration file overrides the arguments passed via command line.
A pretrained model, described in [1], can be downloaded from here.
You can check that the following script returns: EER 2.1792. You will be given an option to save the scores.
python ./trainSpeakerNet.py --eval --model ResNetSE34L --log_input True --trainfunc angleproto --save_path exps/test --eval_frames 400 --initial_model baseline_lite_ap.model
A larger model trained with online data augmentation, described in [2], can be downloaded from here.
The following script should return: EER 1.0180.
python ./trainSpeakerNet.py --eval --model ResNetSE34V2 --log_input True --encoder_type ASP --n_mels 64 --trainfunc softmaxproto --save_path exps/test --eval_frames 400 --initial_model baseline_v2_smproto.model
Pretrained RawNet3, described in [3], can be downloaded via git submodule update --init --recursive.
The following script should return EER 0.8932.
python ./trainSpeakerNet.py --eval --config ./configs/RawNet3_AAM.yaml --initial_model models/weights/RawNet3/model.pt
Softmax (softmax)
AM-Softmax (amsoftmax)
AAM-Softmax (aamsoftmax)
GE2E (ge2e)
Prototypical (proto)
Triplet (triplet)
Angular Prototypical (angleproto)
ResNetSE34L (SAP, ASP)
ResNetSE34V2 (SAP, ASP)
VGGVox40 (SAP, TAP, MAX)
--augment True enables online data augmentation, described in [2].
You can add new models and loss functions to models and loss directories respectively. See the existing definitions for examples.
-
Use
--mixedprecflag to enable mixed precision training. This is recommended for Tesla V100, GeForce RTX 20 series or later models. -
Use
--distributedflag to enable distributed training.-
GPU indices should be set before training using the command
export CUDA_VISIBLE_DEVICES=0,1,2,3. -
If you are running more than one distributed training session, you need to change the
--portargument.
-
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 train list for VoxCeleb2 can be download from here. The test lists for VoxCeleb1 can be downloaded from here.
- Model definitions
VGG-M-40in [1] isVGGVoxin the repository.Thin ResNet-34in [1] isResNetSE34in the repository.Fast ResNet-34in [1] isResNetSE34Lin the repository.H / ASPin [2] isResNetSE34V2in the repository.
-
For metric learning objectives, the batch size in the paper is
nPerSpeakermultiplied bybatch_sizein the code. For the batch size of 800 in the paper, use--nPerSpeaker 2 --batch_size 400,--nPerSpeaker 3 --batch_size 266, etc. -
The models have been trained with
--max_frames 200and evaluated with--max_frames 400. -
You can get a good balance between speed and performance using the configuration below.
python ./trainSpeakerNet.py --model ResNetSE34L --trainfunc angleproto --batch_size 400 --nPerSpeaker 2
Please cite [1] if you make use of the code. Please see here for the full list of methods used in this trainer.
[1] In defence of metric learning for speaker recognition
@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={Proc. Interspeech},
year={2020}
}
[2] The ins and outs of speaker recognition: lessons from VoxSRC 2020
@inproceedings{kwon2021ins,
title={The ins and outs of speaker recognition: lessons from {VoxSRC} 2020},
author={Kwon, Yoohwan and Heo, Hee Soo and Lee, Bong-Jin and Chung, Joon Son},
booktitle={Proc. ICASSP},
year={2021}
}
[3] Pushing the limits of raw waveform speaker recognition
@inproceedings{jung2022pushing,
title={Pushing the limits of raw waveform speaker recognition},
author={Jung, Jee-weon and Kim, You Jin and Heo, Hee-Soo and Lee, Bong-Jin and Kwon, Youngki and Chung, Joon Son},
booktitle={Proc. Interspeech},
year={2022}
}
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