DistilHubert-Finetuned-GTZAN Open-Source Model - Free Deployment for Precise Music Genre Classification

Distilhubert Finetuned Gtzan

Developed by vineetsharma

This model is a fine-tuned version of distilhubert on the GTZAN music classification dataset, primarily used for music genre classification tasks.

Audio Classification

Transformers

Open Source License:Apache-2.0 #Music Classification #High Accuracy #Lightweight Audio Model

Downloads 17

Release Time : 7/1/2023

Model Overview

A lightweight audio classification model based on the distilhubert architecture, fine-tuned on the GTZAN dataset, specifically designed for music genre classification tasks.

Model Features

Efficient and Lightweight

Based on the DistilHuBERT architecture, it reduces model complexity while maintaining high accuracy.

High Accuracy

Achieves 86% accuracy on the GTZAN evaluation set.

Fast Training

Requires only 10 training epochs to achieve good performance.

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Use Cases

Music Analysis

Music Streaming Service Classification

Used for automatically classifying song genres in music libraries.

Accuracy reaches 86%

Music Recommendation System

Serves as a preprocessing component for music recommendation systems.

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.735	1.0	113	1.7670	0.48
1.2344	2.0	226	1.2200	0.69
1.0264	3.0	339	0.8847	0.8
0.6698	4.0	452	0.7208	0.82
0.503	5.0	565	0.6785	0.78
0.3042	6.0	678	0.5969	0.84
0.2176	7.0	791	0.5525	0.86
0.3577	8.0	904	0.5487	0.85
0.137	9.0	1017	0.5064	0.87
0.1305	10.0	1130	0.5214	0.86

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Distilhubert Finetuned Gtzan

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 distilhubert-finetuned-gtzan

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

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📄 License