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Distilhubert Finetuned Gtzan
D
Distilhubert Finetuned Gtzan
Developed by Ducco
An audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving 83% accuracy
Downloads 16
Release Time : 7/20/2023
Model Overview
This model is a distilled version of the HuBERT model, specifically fine-tuned for music genre classification tasks, capable of recognizing 10 different music genres.
Model Features
Efficient Distilled Architecture
A lightweight version based on HuBERT, maintaining high accuracy while reducing computational resource requirements
Music Genre Recognition
Optimized specifically for the 10 music genres in the GTZAN dataset
Fast Inference
The distilled architecture enables faster model inference, suitable for real-time applications
Model Capabilities
Music Classification
Audio Feature Extraction
Music Genre Recognition
Use Cases
Music Streaming Services
Automatic Music Classification
Automatically add genre labels to tracks in a music library
Achieves 83% accuracy
Music Recommendation Systems
Genre-based Recommendations
Analyze genre preferences based on user listening habits
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