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Distilhubert Finetuned Gtzan
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Distilhubert Finetuned Gtzan

Developed by jalal-elzein
An audio classification model fine-tuned on the GTZAN music classification dataset based on the DistilHuBERT architecture, achieving 88% accuracy
Audio Classification
Transformers
Open Source License:Apache-2.0#Music Classification#High Accuracy#Lightweight Audio Model
Downloads 14
Release Time : 7/1/2023

Model Overview

This model is a fine-tuned version of DistilHuBERT, specifically designed for music genre classification tasks, and performs excellently on the GTZAN dataset.

Model Features

Efficient Lightweight Architecture
Lightweight design based on DistilHuBERT, reducing computational resource requirements while maintaining performance
High Accuracy
Achieves 88% accuracy on the GTZAN music classification dataset
Rapid Fine-tuning Capability
Requires only 7 training epochs to reach optimal performance

Model Capabilities

Music Genre Classification
Audio Feature Extraction
Music Content Analysis

Use Cases

Music Recommendation Systems
Automatic Music Classification
Automatically tagging song genres for music streaming platforms
88% classification accuracy
Music Analysis
Music Content Analysis
Identifying audio features for music research or market analysis
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