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Distilhubert Finetuned Gtzan
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Distilhubert Finetuned Gtzan

Developed by kfahn
An audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving 85% accuracy
Audio Classification
Transformers
Open Source License:Apache-2.0#Music Classification#Audio Feature Extraction#Lightweight HuBERT
Downloads 15
Release Time : 7/2/2023

Model Overview

This model is a fine-tuned version of DistilHuBERT, specifically designed for music genre classification tasks, and performs excellently on the GTZAN dataset.

Model Features

Efficient Music Classification
Achieves 85% accuracy on the GTZAN music dataset, effectively identifying 10 different music genres
Lightweight Architecture
Based on the distilled architecture of DistilHuBERT, reducing computational resource requirements while maintaining performance
Rapid Fine-tuning
Requires only 8 training epochs to achieve good performance, suitable for quick deployment

Model Capabilities

Music Genre Classification
Audio Feature Extraction

Use Cases

Music Streaming Services
Automatic Music Classification
Automatically adds genre labels to tracks in music libraries
85% accuracy in automatic classification
Music Recommendation Systems
Genre-based Recommendations
Provides recommendations based on genre preferences in users' listening history
Improves recommendation relevance
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