DistilHubert-Finetuned-GTzan Open-Source Model - Achieving Over 88% Accuracy in Music Classification

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Distilhubert Finetuned Gtzan

Developed by DanGalt

A model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving 88% accuracy

Audio Classification

Transformers

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

Downloads 13

Release Time : 7/2/2023

Model Overview

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

Model Features

Efficient Audio Feature Extraction

Based on the DistilHuBERT architecture, capable of effectively extracting audio features

High Accuracy

Achieves 88% classification accuracy on the GTZAN test set

Lightweight

Compared to the original HuBERT model, DistilHuBERT is more lightweight while maintaining good performance

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Use Cases

Music Analysis

Automatic Music Genre Classification

Automatically identifies the music genre of audio files

Test set accuracy 88%

Music Recommendation System

Serves as a feature extraction component for music recommendation systems

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.5923	1.0	113	1.7310	0.44
1.2071	2.0	226	1.2546	0.62
1.0673	3.0	339	0.9320	0.76
0.8149	4.0	452	0.8768	0.81
0.4999	5.0	565	0.7154	0.86
0.3562	6.0	678	0.6631	0.89
0.3852	7.0	791	0.7136	0.87
0.4476	8.0	904	0.7162	0.88

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Distilhubert Finetuned Gtzan

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 distilhubert-finetuned-gtzan

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Model description

Intended uses & limitations

Training and evaluation data

Training procedure

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