VoxCeleb trainer

This repository contains the framework for training speaker recognition models described in the paper 'In defence of metric learning for 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

• AM-Softmax:

python ./trainSpeakerNet.py --model ResNetSE34L --log_input True --encoder_type SAP --trainfunc amsoftmax --save_path exps/exp1 --nClasses 5994 --batch_size 200 --scale 30 --margin 0.3

• Angular prototypical:

python ./trainSpeakerNet.py --model ResNetSE34L --log_input True --encoder_type SAP --trainfunc angleproto --save_path exps/exp2 --nPerSpeaker 2 --batch_size 200

The arguments can also be passed as --config path_to_config.yaml. 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 1.1771.

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_ap.model

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 and the test list for VoxCeleb1 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={Interspeech},
  year={2020}
}

[2] Clova baseline system for the VoxCeleb Speaker Recognition Challenge 2020

@article{heo2020clova,
  title={Clova baseline system for the {VoxCeleb} Speaker Recognition Challenge 2020},
  author={Heo, Hee Soo and Lee, Bong-Jin and Huh, Jaesung and Chung, Joon Son},
  journal={arXiv preprint arXiv:2009.14153},
  year={2020}
}

License

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