Distilhubert Finetuned Distilhubert
This model is a fine-tuned version of DistilHuBERT on the GTZAN music classification dataset, primarily used for music genre classification tasks.
Downloads 14
Release Time : 7/31/2023
Model Overview
This is a DistilHuBERT model fine-tuned on the GTZAN dataset, specifically designed for music genre classification tasks. It achieves 90% accuracy on the evaluation set.
Model Features
Efficient Music Classification
Based on the DistilHuBERT architecture, it reduces computational complexity while maintaining high accuracy
High Accuracy
Achieves 90% classification accuracy on the GTZAN evaluation set
Lightweight
Compared to the original HuBERT model, the Distil version is more suitable for resource-constrained environments
Model Capabilities
Music Genre Classification
Audio Feature Extraction
Use Cases
Music Analysis
Music Streaming Service Classification
Automatically classify uploaded music files for music streaming platforms
Accuracy reaches 90%
Music Recommendation System
Serves as a front-end classifier for music recommendation systems
Featured Recommended AI Models
Š 2025AIbase