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Distilhubert Finetuned Gtzan

Developed by pranjalks
This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving an accuracy of 83%.
Downloads 18
Release Time : 8/20/2023

Model Overview

A lightweight audio processing model specifically designed for music genre classification, fine-tuned on the GTZAN dataset based on the DistilHuBERT architecture.

Model Features

Efficient and Lightweight
Lightweight architecture based on DistilHuBERT, reducing computational resource requirements while maintaining performance
High Accuracy
Achieves 83% accuracy on the GTZAN music classification dataset
Fast Training
Requires only 15 training epochs to achieve good performance

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 capability with 83% accuracy
Music Recommendation System
Personalized recommendations based on music genre analysis
Education & Research
Music Analysis Teaching
Used for teaching demonstrations in Music Information Retrieval (MIR) related courses
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