DistilHubert-Finetuned GTZAN Open-Source Audio Classification Model - Free Deployment, Music Classification Accuracy Reaches 84%

Distilhubert Finetuned Gtzan

Developed by fmagot01

An audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving 84% accuracy

Audio Classification

Transformers

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

Downloads 14

Release Time : 9/12/2023

Model Overview

This model is an audio classification model fine-tuned on the GTZAN music genre classification dataset based on the distilhubert architecture, primarily used for automatic music classification tasks

Model Features

Efficient Audio Feature Extraction

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

High Accuracy

Achieves 84% accuracy on the GTZAN test set

Lightweight

Fewer parameters compared to the original HuBERT model, with higher inference efficiency

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Music Content Analysis

Use Cases

Music Streaming Services

Automatic Music Classification

Automatically add genre labels to tracks in music libraries

Accuracy reaches 84%

Music Recommendation Systems

Genre-based Recommendations

Recommend similar music based on user genre preferences

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9649	1.0	113	1.7979	0.47
1.3283	2.0	226	1.1892	0.64
0.9491	3.0	339	0.9385	0.76
0.8013	4.0	452	0.8300	0.72
0.6314	5.0	565	0.6822	0.8
0.322	6.0	678	0.7438	0.8
0.4727	7.0	791	0.5545	0.83
0.1464	8.0	904	0.5576	0.86
0.2615	9.0	1017	0.5783	0.83
0.1233	10.0	1130	0.6319	0.84

Property	Details
Model Type	Fine - tuned version of ntu - spml/distilhubert
Training Data	marsyas/gtzan
Metrics	accuracy
Model Index Name	distilhubert - finetuned - gtzan

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Distilhubert Finetuned Gtzan

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