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Distilhubert Finetuned Gtzan Finetuned Gtzan

Developed by calvpang
An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset with an accuracy of 89%
Downloads 13
Release Time : 8/10/2023

Model Overview

This model is a fine-tuned version of DistilHuBERT, specifically designed for music genre classification tasks. It performs exceptionally well on the GTZAN dataset and is suitable for audio content analysis applications.

Model Features

High Accuracy
Achieves 89% classification accuracy on the GTZAN test set
Lightweight Architecture
Based on the distilled version of DistilHuBERT, offering high computational efficiency
Music Genre Recognition
Optimized specifically for music audio data

Model Capabilities

Music Genre Classification
Audio Feature Extraction
Audio Content Analysis

Use Cases

Music Recommendation Systems
Automatic Music Classification
Automatically tagging music genres for music streaming platforms
Improves music classification accuracy and enhances recommendation effectiveness
Audio Content Analysis
Music Library Management
Automating genre classification for large music libraries
Saves manual classification time and improves management efficiency
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