DistilHubert-Finetuned GTZAN Open-Source Audio Classification Model - Accurately Identify Music Categories with an Accuracy Rate of 83%

Distilhubert Finetuned Gtzan

Developed by Leo1212

An audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving 83% accuracy

Audio Classification

Transformers

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

Downloads 25

Release Time : 9/30/2024

Model Overview

This model is a fine-tuned version of DistilHuBERT, specifically designed for music genre classification tasks, demonstrating excellent performance on the GTZAN dataset

Model Features

Efficient Music Classification

Achieves 83% accuracy on the GTZAN music dataset, effectively identifying 10 music genres

Lightweight Architecture

Based on the distilled version of HuBERT, reducing computational resource requirements while maintaining performance

Fast Training Convergence

Requires only 10 training epochs to achieve good performance, with validation loss decreasing from 2.0456 to 0.6581

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Music Content Analysis

Use Cases

Music Streaming Services

Automatic Music Classification

Automatically adds genre labels to tracks in music libraries

Automatic classification capability with 83% accuracy

Music Recommendation Systems

Genre-based Recommendations

Provides personalized recommendations based on users' preferred music genres

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1424	1.0	57	2.0456	0.45
1.5766	2.0	114	1.5045	0.68
1.2474	3.0	171	1.2068	0.68
0.9574	4.0	228	1.1053	0.67
0.8741	5.0	285	0.8743	0.78
0.721	6.0	342	0.8041	0.77
0.6497	7.0	399	0.7521	0.81
0.5037	8.0	456	0.7051	0.82
0.5083	9.0	513	0.6693	0.82
0.4939	10.0	570	0.6581	0.83

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Distilhubert Finetuned Gtzan

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