DistilHubert-Finetuned GTZAN Open-Source Audio Classification Model - High-Accuracy Identification of Music Genres

Distilhubert Finetuned Gtzan

Developed by Kodamn47

An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre dataset with an accuracy of 88%

Audio Classification

Transformers

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

Downloads 14

Release Time : 9/12/2023

Model Overview

This model is a fine-tuned version of DistilHuBERT, specifically designed for music genre classification tasks. It performs exceptionally well on the GTZAN dataset, achieving a validation accuracy of 88%.

Model Features

Efficient Music Classification

Achieves 88% accuracy on the GTZAN dataset, effectively identifying 10 different music genres

Lightweight Architecture

Based on DistilHuBERT, more lightweight and efficient compared to the original HuBERT model

Rapid Fine-tuning

Requires only 10 training epochs to achieve high performance

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Music Content Analysis

Use Cases

Music Recommendation Systems

Automatic Music Classification

Automatically tags uploaded music with genres for music streaming platforms

Automatic classification with 88% accuracy

Music Analysis

Music Collection Organization

Helps users automatically organize song genres in their music libraries

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.74	1.0	112	0.6136	0.81
0.6137	2.0	225	0.7364	0.76
0.5996	3.0	337	0.5322	0.88
0.516	4.0	450	0.9805	0.73
0.4013	5.0	562	0.5349	0.86
0.1779	6.0	675	0.6328	0.82
0.1356	7.0	787	0.5007	0.85
0.1938	8.0	900	0.5199	0.86
0.3675	9.0	1012	0.4209	0.9
0.1299	9.96	1120	0.4487	0.88

Property	Details
Model Type	distilhubert - finetuned - gtzan
Training Data	marsyas/gtzan
Metrics	accuracy

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Distilhubert Finetuned Gtzan

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 distilhubert-finetuned-gtzan

🚀 Quick Start

✨ Features

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

📄 License