VoxCeleb trainer

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'.

Dependencies

pip install -r requirements.txt

Data preparation

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.

Training examples

• 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.

Pretrained models

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

Implemented loss functions

Softmax (softmax)
AM-Softmax (amsoftmax)
AAM-Softmax (aamsoftmax)
GE2E (ge2e)
Prototypical (proto)
Triplet (triplet)
Angular Prototypical (angleproto)

Implemented models and encoders

ResNetSE34L (SAP, ASP)
ResNetSE34V2 (SAP, ASP)
VGGVox40 (SAP, TAP, MAX)

Data augmentation

--augment True enables online data augmentation, described in [2].

Adding new models and loss functions

You can add new models and loss functions to models and loss directories respectively. See the existing definitions for examples.

Accelerating training

• Use --mixedprec flag to enable mixed precision training. This is recommended for Tesla V100, GeForce RTX 20 series or later models.

• Use --distributed flag 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 --port argument.

Data

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.

Replicating the results from the paper

1. Model definitions

   • VGG-M-40 in [1] is VGGVox in the repository.
   • Thin ResNet-34 in [1] is ResNetSE34 in the repository.
   • Fast ResNet-34 in [1] is ResNetSE34L in the repository.
   • H / ASP in [2] is ResNetSE34V2 in the repository.

2. For metric learning objectives, the batch size in the paper is nPerSpeaker multiplied by batch_size in the code. For the batch size of 800 in the paper, use --nPerSpeaker 2 --batch_size 400, --nPerSpeaker 3 --batch_size 266, etc.

3. The models have been trained with --max_frames 200 and evaluated with --max_frames 400.

4. 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 

Citation

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}
}

License

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