SiavashShams/ssamba
[SLT'24] The official implementation of SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model
This project helps audio engineers, researchers, and developers build better audio analysis systems. It takes raw audio recordings as input and produces high-quality, efficient audio representations (like numerical embeddings) that can then be used for tasks such as identifying sounds, recognizing speech, or analyzing emotional tone. The primary users are those who work with large volumes of audio data and need to extract meaningful insights.
134 stars.
Use this if you are developing or training machine learning models for audio tasks and need a robust, fast, and memory-efficient way to process raw audio into meaningful numerical data.
Not ideal if you are looking for a ready-to-use application to analyze audio without any programming or machine learning model development.
Stars
134
Forks
12
Language
Python
License
BSD-3-Clause
Category
Last pushed
Nov 05, 2025
Commits (30d)
0
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