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

Distilhubert Finetuned Gtzan

Developed by Maldopast

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 Model

Downloads 14

Release Time : 7/28/2023

Model Overview

A lightweight audio classification model based on the DistilHuBERT architecture, specifically fine-tuned for the GTZAN music dataset, capable of accurately identifying 10 different music genres.

Model Features

Efficient and Lightweight

Lightweight architecture based on DistilHuBERT, reducing computational resource requirements while maintaining high accuracy.

High Accuracy

Achieves 88% classification accuracy on the GTZAN evaluation set.

Fast Training

Requires only 15 training epochs to achieve good performance.

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Use Cases

Music Analysis

Music Streaming Service Classification

Automatically classify uploaded music files for music platforms.

Accuracy reaches 88%

Music Recommendation System

Build a recommendation system based on music genre features.

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9647	1.0	113	1.8614	0.52
1.3987	2.0	226	1.3098	0.61
0.8809	3.0	339	0.8631	0.76
0.7643	4.0	452	0.8114	0.77
0.5958	5.0	565	0.7013	0.81
0.4405	6.0	678	0.5860	0.84
0.2183	7.0	791	0.6114	0.82
0.1587	8.0	904	0.5141	0.85
0.0899	9.0	1017	0.4760	0.87
0.0575	10.0	1130	0.5759	0.86
0.0647	11.0	1243	0.6467	0.86
0.0061	12.0	1356	0.6372	0.88
0.0029	13.0	1469	0.6721	0.88
0.0018	14.0	1582	0.7565	0.89
0.0013	15.0	1695	0.7537	0.88

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Distilhubert Finetuned Gtzan

Model Overview

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