Distilhubert-finetuned-gtzan Open-source Model - Free Deployment for Accurate Music Genre Classification

Distilhubert Finetuned Gtzan

Developed by pollner

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

Audio Classification

Transformers

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

Downloads 24

Release Time : 6/21/2023

Model Overview

A lightweight audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN dataset, specifically designed for music genre classification tasks.

Model Features

Lightweight Architecture

Lightweight design based on DistilHuBERT, reducing computational resource requirements while maintaining performance.

High Accuracy

Achieves 80% accuracy on the GTZAN evaluation set.

Efficient Training

Uses linear learning rate scheduling and the Adam optimizer for stable and efficient training.

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Use Cases

Music Analysis

Music Streaming Platform Classification

Automatically add genre labels to songs in a music library.

80% classification accuracy

Music Recommendation System

Improve recommendation algorithms based on music genre features.

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1981	1.0	57	2.1804	0.37
1.7932	2.0	114	1.7160	0.62
1.3257	3.0	171	1.2539	0.67
1.1239	4.0	228	1.1187	0.68
0.7457	5.0	285	0.9367	0.73
0.6922	6.0	342	0.7564	0.81
0.5718	7.0	399	0.8179	0.78
0.3729	8.0	456	0.7299	0.79
0.2667	9.0	513	0.6415	0.82
0.4672	10.0	570	0.8068	0.78
0.1392	11.0	627	0.7228	0.81
0.1069	12.0	684	0.7787	0.79
0.0659	13.0	741	0.7720	0.8
0.0291	14.0	798	0.7609	0.79
0.0263	15.0	855	0.8363	0.8
0.0177	16.0	912	0.8796	0.78
0.0166	17.0	969	0.8844	0.79
0.0139	18.0	1026	0.8909	0.8
0.0132	19.0	1083	0.9017	0.8
0.0131	20.0	1140	0.9006	0.8

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Distilhubert Finetuned Gtzan

Model Overview

Model Features

Model Capabilities

Use Cases

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🔧 Technical Details

Training hyperparameters

Training results

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