sslsv

Collection of self-supervised learning (SSL) methods for speaker verification (SV).

Resources

Models

• sslsv.model.ThinResNet34
  "Delving into VoxCeleb: environment invariant speaker recognition" (arxiv)
  Joon Son Chung, Jaesung Huh, Seongkyu Mun

Losses

• sslsv.losses.InfoNCE
  "Representation Learning with Contrastive Predictive Coding" (arxiv)
  Aaron van den Oord, Yazhe Li, Oriol Vinyals

• sslsv.losses.VICReg
  "VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning" (arxiv)
  Adrien Bardes, Jean Ponce, Yann LeCun

• sslsv.losses.BarlowTwins
  "Barlow Twins: Self-Supervised Learning via Redundancy Reduction" (arxiv)
  Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny

Datasets

VoxCeleb1 and VoxCeleb2 are used for our experiments and we rely on MUSAN and Room Impulse Response and Noise Database for data augmentation.

To download, extract and prepare all datasets run python prepare_data.py data/. The data/ directory will have the structure detailed below.

data
├── musan_split/
├── simulated_rirs/
├── voxceleb1/
├── voxceleb2/
├── trials
├── voxceleb1_train_list
└── voxceleb2_train_list

Trials and train lists files are also automatically created with the following formats.

• trials

  1 id10270/x6uYqmx31kE/00001.wav id10270/8jEAjG6SegY/00008.wav
  ...
  0 id10309/0cYFdtyWVds/00005.wav id10296/Y-qKARMSO7k/00001.wav
  

• voxceleb1_train_list and voxceleb2_train_list

  id00012 voxceleb2/id00012/21Uxsk56VDQ/00001.wav
  ...
  id09272 voxceleb2/id09272/u7VNkYraCw0/00027.wav
  

Please refer to prepare_data.py script if you want further details about data preparation.

Usage

Start self-supervised training with python train.py configs/vicreg_b256.yml.

To-Do

• DDP: adapt losses and supervised sampler
• Refactor evaluation (use AudioDataset class for handling test data)
• Documentation, comments, typing

Credits

Some parts of the code (data preparation, data augmentation and model evaluation) were adapted from VoxCeleb trainer repository.