The official implementation of SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model
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Updated
Jul 16, 2024 - Python
The official implementation of SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model
Implements ML audio separation algorithm on audio from YouTube or Spotify resulting in "stems" for download (e.g. vocals, drums, bass) in MP3, WAV or FLAC.
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)
Unofficial (Golang) Go bindings for the Hugging Face Inference API
Whole Audio Analysis with Python
A library built for easier audio self-supervised training, downstream tasks evaluation
基于PaddlePaddle实现的音频分类,支持EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE等各种模型,还有多种预处理方法
Democratizing Artificial Intelligence
The repository contains software and a neural network model specifically developed for the MineGuard project
building AVA from ex-machina; a lightweight multi-modal system from scratch, just for learning & experimentation
Quran Recitation Audio Classification project aims to classify different recitations of the Quran using machine learning techniques. It involves preprocessing audio data, extracting features, training models, and evaluating their performance
Official Implementation of the work "Audio Mamba: Bidirectional State Space Model for Audio Representation Learning"
tensorflow for speech-music-detection task,acc 96%+
Audio Pattern Recognition project - Music Genres Classification
🚤 Label data at scale. Fun and precision included.
Cross platform audio feature extraction and sound classification tool
This project explores the use of deep learning models, particularly CNNs, to classify drum sounds accurately.
transform your music creation with AI
A summary of python tasks made for my thesis project @ AAU
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