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Distilhubert Finetuned Gtzan

Developed by NicolasDenier
An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset with 91% accuracy
Downloads 17
Release Time : 7/19/2023

Model Overview

This model is a fine-tuned version of DistilHuBERT, specifically designed for music genre classification tasks. It achieves efficient audio feature extraction through the compressed HuBERT architecture and performs excellently on the GTZAN dataset.

Model Features

Efficient Compressed Architecture
Lightweight architecture based on DistilHuBERT, reducing computational resource requirements while maintaining performance
High Accuracy
Achieves 91% accuracy on the GTZAN test set, demonstrating excellent performance
Fast Training
Through fine-tuning the pre-trained model, good performance can be achieved with just 18 training epochs

Model Capabilities

Music Genre Classification
Audio Feature Extraction
Music Content Analysis

Use Cases

Music Services
Automatic Music Classification
Automatically tag uploaded music genres for music streaming platforms
Automatic classification with 91% accuracy
Music Research
Music Feature Analysis
Study the differences in audio features across different music genres
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