Distilhubert Finetuned Gtzan
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
Featured Recommended AI Models
Š 2025AIbase