DistilHubert - Finetuned - GTZAN Open-Source Audio Classification Model, Based on GTZAN Dataset with 89% Accuracy

Distilhubert Finetuned Gtzan

Developed by VinayHajare

An audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving 89% accuracy

Audio Classification

Transformers

Open Source License:Apache-2.0 #Audio Classification #Music Genre Recognition #High Accuracy

Downloads 20

Release Time : 7/29/2023

Model Overview

This model is a fine-tuned version of the lightweight HuBERT model (distilhubert) on the GTZAN music genre classification dataset, specifically designed for audio classification tasks, particularly music genre recognition.

Model Features

High Accuracy

Achieves 89% accuracy on the GTZAN test set

Lightweight

Based on the DistilHuBERT architecture, more lightweight compared to the original HuBERT

Music Genre Recognition

Optimized specifically for music audio data

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Music Content Analysis

Use Cases

Music Analysis

Automatic Music Genre Classification

Automatically identifies the genre of music audio

89% accuracy

Music Recommendation System

Serves as a feature extraction component for music recommendation systems

Audio Processing

Audio Content Analysis

Analyzes audio content features

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2163	1.0	113	2.0720	0.34
1.7237	2.0	226	1.5361	0.59
1.3254	3.0	339	1.2044	0.65
1.0757	4.0	452	1.0578	0.66
1.0683	5.0	565	0.8947	0.78
0.9307	6.0	678	0.7716	0.82
1.0313	7.0	791	0.7210	0.82
0.6988	8.0	904	0.6506	0.8
0.8053	9.0	1017	0.5944	0.81
0.6243	10.0	1130	0.5637	0.87
0.6238	11.0	1243	0.5212	0.89
0.4493	12.0	1356	0.5167	0.89

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

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Distilhubert Finetuned Gtzan

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