DistilHubert-finetuned-GTzan Open Source Audio Classification Model - Accurately Realize Music Classification Function

Distilhubert Finetuned Gtzan

Developed by mcamara

An audio classification model based on the DistilHuBERT architecture fine-tuned on the GTZAN music classification dataset

Audio Classification

Transformers

Open Source License:Apache-2.0 #Music Classification #High Accuracy #Lightweight Audio Model

Downloads 18

Release Time : 7/24/2023

Model Overview

This model is a fine-tuned version of the DistilHuBERT architecture, specifically designed for music genre classification tasks, achieving 89% accuracy on the GTZAN dataset.

Model Features

Efficient Music Classification

Achieves 89% accuracy on the GTZAN dataset, effectively identifying 10 different music genres

Lightweight Architecture

Based on the distilled version of DistilHuBERT, more lightweight and efficient 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 Streaming Services

Automatic Music Classification

Automatically add genre labels to songs in a music library

89% accuracy

Music Recommendation Systems

Genre-based Music Recommendation

Analyze genre preferences based on user listening history

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0742	0.02	2	0.4905	0.89

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Distilhubert Finetuned Gtzan

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