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Distilhubert Finetuned Gtzan

Developed by VinayHajare
An audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving 89% accuracy
Downloads 20
Release Time : 7/29/2023

Model Overview

This model is a fine-tuned version of the lightweight HuBERT model (distilhubert) on the GTZAN music genre classification dataset, specifically designed for audio classification tasks, particularly music genre recognition.

Model Features

High Accuracy
Achieves 89% accuracy on the GTZAN test set
Lightweight
Based on the DistilHuBERT architecture, more lightweight compared to the original HuBERT
Music Genre Recognition
Optimized specifically for music audio data

Model Capabilities

Music Genre Classification
Audio Feature Extraction
Music Content Analysis

Use Cases

Music Analysis
Automatic Music Genre Classification
Automatically identifies the genre of music audio
89% accuracy
Music Recommendation System
Serves as a feature extraction component for music recommendation systems
Audio Processing
Audio Content Analysis
Analyzes audio content features
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