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My Awesome Model

Developed by AK-12
An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset with an accuracy of 94.75%
Audio Classification
Transformers
Open Source License:Apache-2.0#Music Classification#High Accuracy#Audio Feature Extraction
Downloads 15
Release Time : 7/27/2023

Model Overview

This model is a fine-tuned version of DistilHuBERT (a lightweight variant of HuBERT) on the GTZAN music dataset, specifically designed for music genre classification tasks.

Model Features

High Accuracy
Achieves 94.75% classification accuracy on the GTZAN test set
Lightweight Architecture
Based on the distilled DistilHuBERT architecture, more lightweight compared to the original HuBERT
Music Genre Recognition
Optimized specifically for 10-class music genre classification tasks

Model Capabilities

Music Audio Classification
Genre Recognition
Audio Feature Extraction

Use Cases

Music Analysis
Music Streaming Classification
Automatically tag genre labels for songs in music libraries
94.75% accuracy
Music Recommendation System
Enhance recommendation effects based on genre features
Audio Processing
Audio Content Analysis
Identify music type features in audio
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