Distilhubert Finetuned Gtzan
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Distilhubert Finetuned Gtzan
Developed by f0ghedgeh0g
Audio classification model fine-tuned on the GTZAN music classification dataset based on the DistilHuBERT architecture, achieving 86% accuracy
Downloads 39
Release Time : 1/2/2025
Model Overview
This model is a lightweight audio processing model optimized for music genre classification tasks, capable of recognizing 10 different music genres
Model Features
Efficient Music Classification
Optimized for the GTZAN music dataset, accurately identifying 10 music genres
Lightweight Architecture
Based on the distilled DistilHuBERT architecture, reducing computational resource requirements while maintaining performance
High Accuracy
Achieves 86% classification accuracy on the GTZAN test set
Model Capabilities
Music Genre Classification
Audio Feature Extraction
Music Content Analysis
Use Cases
Music Industry
Music Streaming Auto-Tagging
Automatically tagging uploaded music with genre categories for music platforms
86% accuracy in automatic classification
Music Recommendation System
Improving recommendation algorithms based on music content similarity analysis
Academic Research
Music Information Retrieval
Supporting musicology research and music content analysis
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