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Distilhubert Finetuned Gtzan

Developed by GFazzito
Audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert
Downloads 18
Release Time : 7/21/2023

Model Overview

This model is a lightweight audio classification model based on the distilhubert architecture, specifically fine-tuned for music genre classification tasks. It achieves 82% accuracy on the GTZAN dataset.

Model Features

Lightweight Architecture
Lightweight design based on DistilHuBERT, suitable for resource-constrained environments
High Accuracy
Achieves 82% accuracy on the GTZAN music classification task
Fast Fine-tuning
Requires only 10 training epochs to achieve good performance

Model Capabilities

Music Genre Classification
Audio Feature Extraction

Use Cases

Music Analysis
Automatic Music Genre Classification
Classify music clips by genre
Accuracy reaches 82%
Music Recommendation System
Used as a feature extraction component for music recommendation systems
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