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Distilhubert Finetuned Gtzan
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Distilhubert Finetuned Gtzan

Developed by pratik33
This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving an accuracy of 76.25%
Audio Classification
Transformers
Open Source License:Apache-2.0#Music Classification#Audio Feature Extraction#Lightweight Model
Downloads 14
Release Time : 7/12/2023

Model Overview

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

Model Features

Efficient Music Classification
Achieves 76.25% accuracy on the GTZAN music dataset
Lightweight Architecture
Based on DistilHuBERT, more lightweight compared to the original HuBERT model
Fast Inference
Suitable for real-time music classification applications

Model Capabilities

Music Genre Classification
Audio Feature Extraction
Music Content Analysis

Use Cases

Music Services
Automatic Music Classification
Automatically tagging music genres for music streaming platforms
76.25% accuracy
Music Recommendation System
Front-end processing for content-based music recommendation systems
Audio Analysis
Music Content Analysis
Analyzing audio features for musicology research
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