DistilHubert-finetuned-gtzan Open-source Audio Classification Model

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Distilhubert Finetuned Gtzan

Developed by MariaK

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

Audio Classification

Transformers

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

Downloads 17

Release Time : 6/8/2023

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, providing efficient audio feature extraction capabilities

High Accuracy

Achieves 83% classification accuracy on the GTZAN test set

Lightweight

More lightweight compared to the original HuBERT model

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Music Content Analysis

Use Cases

Music Recommendation Systems

Automatic Music Classification

Automatically classify music genres for music streaming platforms

83% accuracy

Music Analysis

Music Content Analysis

Analyze audio features for music research

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7582	1.0	113	1.7912	0.45
1.2332	2.0	226	1.3051	0.64
1.0058	3.0	339	1.0200	0.71
0.6894	4.0	452	0.8303	0.79
0.5041	5.0	565	0.7038	0.79
0.3281	6.0	678	0.6500	0.82
0.2457	7.0	791	0.5476	0.82
0.3409	8.0	904	0.5793	0.83
0.1521	9.0	1017	0.5568	0.82
0.3542	10.0	1130	0.5757	0.83

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Distilhubert Finetuned Gtzan

Model Overview

Model Features

Model Capabilities

Use Cases

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🔧 Technical Details

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