sslsv is a PyTorch-based deep learning toolkit consisting of a collection of Self-Supervised Learning (SSL) frameworks for learning speaker representations, applicable to various speaker-related downstream tasks, notably Speaker Verification (SV).

Its main objectives are to: (1) provide implementations of state-of-the-art SSL frameworks by adapting algorithms from the computer vision domain; and (2) evaluate them within a consistent and comparable environment.

An overview of the general training and evaluation framework is provided in the figure below.

• June 2025 – 👏 Release of results and checkpoints (v2.0).
• June 2025 – 🔖 Support for Python 3.13 and PyTorch 2.7.
• December 2024 – 🧪 Implementation of SimCLR MultiViews and MoCo Margins.
• November 2024 – 💡 Implementation of Self-Supervised Positive Sampling (SSPS).
• July 2024 – 🌱 Implementation of more losses for SimCLR Margins (SphereFace, CurricularFace, MagFace, AdaFace).
• May 2024 – 📚 Documentation of the complete codebase.
• April 2024 – 🛠️ Complete refactoring, including typing, tests, and coding style (v2.0).
• January 2024 – 🚀 Implementation of the W-MSE framework.
• July 2023 – ⚡ Support for PyTorch Distributed Data Parallel (DDP).
• June 2023 – 🧠 Evaluation on language, emotion, age, and gender recognition tasks.
• April 2023 – 📊 Additional benchmarks (SITW, VOiCES) and metrics (CLLR, ActDCF, AvgRPrec).
• March 2023 – 📏 Support for cosine scoring normalizations and PLDA evaluations.
• January 2023 – 🧪 Implementation of SimCLR Margins (CosFace and ArcFace).
• December 2022 – 🚀 Implementation of SSL frameworks: LIM, CPC, SimCLR, MoCo, Barlow Twins, VICReg, VIbCReg, DeepCluster, SwAV, SimSiam, BYOL, and DINO.
• June 2022 – 🌠 First release of sslsv (v1.0).

General

• Data:
  • Supervised and Self-supervised datasets (siamese and DINO sampling)
  • Audio augmentation (noise and reverberation)
• Training:
  • CPU, GPU and multi-GPUs (DataParallel and DistributedDataParallel)
  • Checkpointing, resuming, early stopping and logging
  • Tensorboard and wandb
• Evaluation:
  • Speaker verification
    • Backend: Cosine scoring and PLDA
    • Metrics: EER, MinDCF, ActDFC, CLLR, AvgRPrec
  • Classification (emotion, language, ...)
• Notebooks: DET curve, scores distribution, t-SNE on embeddings, ...
• Misc: scalable config, typing, documentation and tests

sslsv runs on Python 3.13.3 with the following dependencies.

Note: developers will also need pytest, pre-commit and twine to work on this project.

Speaker recognition:

• VoxCeleb1 (train and test)
• VoxCeleb2 (train)
• SITW (test)
• VOiCES (test)

Language recognition:

• VoxLingua107

Emotion recognition:

• CREMA-D

Data-augmentation:

• MUSAN
• Room Impulse Response and Noise Database

Data used for main experiments (conducted on VoxCeleb1 and VoxCeleb2 + data-augmentation) can be automatically downloaded, extracted and prepared using the following scripts.

python tools/prepare_data/prepare_voxceleb.py data/
python tools/prepare_data/prepare_augmentation.py data/

The resulting data folder shoud have the structure presented below.

data
├── musan_split/
├── simulated_rirs/
├── voxceleb1/
├── voxceleb2/
├── voxceleb1_test_O
├── voxceleb1_test_H
├── voxceleb1_test_E
├── voxsrc2021_val
├── voxceleb1_train.csv
└── voxceleb2_train.csv

Other datasets have to be manually downloaded and extracted but their train and trials files can be created using the corresponding scripts from the tools/prepare_data/ folder.

• Example format of a train file (voxceleb1_train.csv)

  File,Speaker
  voxceleb1/id10001/1zcIwhmdeo4/00001.wav,id10001
  ...
  voxceleb1/id11251/s4R4hvqrhFw/00009.wav,id11251
  

• Example format of a trials file (voxceleb1_test_O)

  1 voxceleb1/id10270/x6uYqmx31kE/00001.wav voxceleb1/id10270/8jEAjG6SegY/00008.wav
  ...
  0 voxceleb1/id10309/0cYFdtyWVds/00005.wav voxceleb1/id10296/Y-qKARMSO7k/00001.wav
  

1. Clone this repository: git clone https://github.com/theolepage/sslsv.git.
2. Install dependencies: pip install -r requirements.txt.

Note: sslsv can also be installed as a standalone package via pip with pip install sslsv or with pip install . (in the project root folder) to get the latest version.

• Start a training (2 GPUs): ./train_ddp.sh 2 <config_path>.
• Evaluate your model (2 GPUs): ./evaluate_ddp.sh 2 <config_path>.

Note: use sslsv/bin/train.py and sslsv/bin/evaluate.py for non-distributed mode to run with a CPU, a single GPU or multiple GPUs (DataParallel).

You can visualize your experiments with tensorboard --logdir models/your_model/.

Use wandb online and wandb offline to toggle wandb. To log your experiments you first need to provide your API key with wandb login API_KEY.

Documentation is currently being developed...

• Configs: models/ssl/voxceleb2/
• Train set: VoxCeleb2
• Evaluation: VoxCeleb1-O (Original)
• Encoder: Fast ResNet-34 and ECAPA-TDNN

• Configs: models/ssps/voxceleb2/
• Train set: VoxCeleb2
• Evaluation: VoxCeleb1-O (Original)
• Encoder: ECAPA-TDNN

sslsv contains third-party components and code adapted from other open-source projects, including: voxceleb_trainer, voxceleb_unsupervised and solo-learn.

If you use sslsv, please consider starring this repository on GitHub and citing one the following papers.

@InProceedings{lepage2025SSPS,
  title     = {SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification},
  author    = {Lepage, Theo and Dehak, Reda},
  year      = {2025},
  booktitle = {Interspeech 2025},
  url       = {https://arxiv.org/abs/2505.14561},
}

@Article{lepage2025SSLSVBootstrappedPositiveSampling,
  title     = {Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling},
  author    = {Lepage, Theo and Dehak, Reda},
  year      = {2025},
  journal   = {arXiv preprint library},
  url       = {https://arxiv.org/abs/2501.17772},
}

@InProceedings{lepage2024AdditiveMarginSSLSV,
  title     = {Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations},
  author    = {Lepage, Theo and Dehak, Reda},
  year      = {2024},
  booktitle = {The Speaker and Language Recognition Workshop (Odyssey 2024)},
  pages     = {38--42},
  doi       = {10.21437/odyssey.2024-6},
  url       = {https://www.isca-archive.org/odyssey_2024/lepage24_odyssey.html},
}

@InProceedings{lepage2023ExperimentingAdditiveMarginsSSLSV,
  title     = {Experimenting with Additive Margins for Contrastive Self-Supervised Speaker Verification},
  author    = {Lepage, Theo and Dehak, Reda},
  year      = {2023},
  booktitle = {Interspeech 2023},
  pages     = {4708--4712},
  doi       = {10.21437/Interspeech.2023-1479},
  url       = {https://www.isca-archive.org/interspeech_2023/lepage23_interspeech.html},
}

@InProceedings{lepage2022LabelEfficientSSLSV,
  title     = {Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning},
  author    = {Lepage, Theo and Dehak, Reda},
  year      = {2022},
  booktitle = {Interspeech 2022},
  pages     = {4018--4022},
  doi       = {10.21437/Interspeech.2022-802},
  url       = {https://www.isca-archive.org/interspeech_2022/lepage22_interspeech.html},
}

This project is released under the MIT License.