Musical-genres-Classification-Hubert-V1 Open-source Model - Accurately Classify Music Genres with an Accuracy Rate of 84%

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Musical Genres Classification Hubert V1

Developed by SeyedAli

A music genre classification model fine-tuned on distilhubert, achieving 84% accuracy on the GTZAN dataset

Audio Classification

Transformers

Open Source License:Apache-2.0 #Music genre classification #High-precision audio analysis #HuBERT fine-tuning

Downloads 1,961

Release Time : 9/25/2023

Model Overview

This model is an audio classification model based on the distilhubert architecture, specifically designed for music genre recognition tasks. After fine-tuning on the GTZAN dataset, it can accurately identify 10 different music genres.

Model Features

Efficient and lightweight

Based on the DistilHuBERT architecture, it reduces computational resource requirements while maintaining high accuracy

High accuracy

Achieves 84% classification accuracy on the GTZAN test set

Fast training

Only requires 10 training epochs to achieve good performance

Model Capabilities

Music genre recognition

Audio feature extraction

Music content analysis

Use Cases

Music recommendation systems

Automatic music classification

Automatically tags uploaded music with genre categories for music platforms

84% accuracy

Music research

Music style analysis

Assists musicologists in large-scale music style feature research

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0025	1.0	113	1.8276	0.57
1.3273	2.0	226	1.2564	0.64
0.9558	3.0	339	0.9493	0.76
0.8668	4.0	452	0.7986	0.76
0.8293	5.0	565	0.6570	0.8
0.3777	6.0	678	0.6068	0.78
0.4111	7.0	791	0.4885	0.84
0.1512	8.0	904	0.5045	0.8
0.3596	9.0	1017	0.4681	0.85
0.088	10.0	1130	0.5068	0.84

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

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Musical Genres Classification Hubert V1

Model Overview

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