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Distilhubert Finetuned Gtzan
This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, with an accuracy of 88%
Downloads 22
Release Time : 7/26/2023
Model Overview
A lightweight audio classification model specifically designed for music genre recognition tasks
Model Features
Efficient and Lightweight
Based on the distilled HuBERT architecture, reducing computational resource requirements while maintaining performance
High Accuracy
Achieves 88% accuracy on the GTZAN test set
Fast Fine-tuning
Only requires 2 training epochs to achieve good performance
Model Capabilities
Music genre classification
Audio feature extraction
Music content analysis
Use Cases
Music Streaming Services
Automatic Music Classification
Automatically add genre labels to tracks in music libraries
88% accuracy
Music Information Retrieval
Genre-based Music Search
Help users discover music of specific genres
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