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Distilhubert Finetuned Gtzan

Developed by Marco-Cheung
This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving an accuracy of 83%
Downloads 17
Release Time : 7/22/2023

Model Overview

A lightweight audio classification model based on the DistilHuBERT architecture, specifically optimized for music genre classification tasks

Model Features

High Accuracy
Achieves 83% classification accuracy on the GTZAN test set
Lightweight
Based on the distilled HuBERT architecture with a smaller model size
Specialized for Music Classification
Specifically optimized for music genre classification tasks

Model Capabilities

Music Genre Classification
Audio Feature Extraction

Use Cases

Music Analysis
Automatic Music Genre Tagging
Automatically add genre labels to tracks in a music library
Accuracy reaches 83%
Music Recommendation System
Improve recommendation algorithms based on music genre features
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