theolepage/sslsv
Toolkit for training and evaluating Self-Supervised Learning (SSL) frameworks for Speaker Verification (SV).
This toolkit helps speech researchers and machine learning engineers develop and evaluate self-supervised learning models for speaker verification. It takes raw audio data and applies advanced algorithms to produce robust speaker representations (embeddings). These embeddings can then be used to verify a person's identity from their voice, even with limited labeled data.
Available on PyPI.
Use this if you are a speech researcher or ML engineer developing speaker verification systems and want to experiment with various state-of-the-art self-supervised learning frameworks to improve model performance.
Not ideal if you are an application developer looking for a ready-to-use API or pre-trained model for speaker verification without needing to delve into the underlying deep learning research.
Stars
36
Forks
9
Language
Python
License
MIT
Category
Last pushed
Feb 12, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/theolepage/sslsv"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
AdaptiveMotorControlLab/CEBRA
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
PaddlePaddle/PASSL
PASSL包含 SimCLR，MoCo v1/v2，BYOL，CLIP，PixPro，simsiam, SwAV, BEiT，MAE 等图像自监督算法以及 Vision...
YGZWQZD/LAMDA-SSL
30 Semi-Supervised Learning Algorithms
ModSSC/ModSSC
ModSSC: A Modular Framework for Semi Supervised Classification
microsoft/Semi-supervised-learning
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)