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Distilhubert Finetuned Gtzan

Developed by timjwhite
An audio classification model fine-tuned on the GTZAN music classification dataset based on the DistilHuBERT architecture, achieving 88% accuracy
Downloads 16
Release Time : 7/11/2023

Model Overview

This model is a variant of DistilHuBERT, specifically fine-tuned for music genre classification tasks, suitable for audio signal processing and analysis scenarios.

Model Features

Efficient Distilled Architecture
Lightweight distilled version based on HuBERT, reducing computational resource requirements while maintaining performance
High Accuracy
Achieves 88% classification accuracy on the GTZAN test set
Fast Inference
Faster inference speed compared to the original HuBERT model

Model Capabilities

Music Genre Classification
Audio Feature Extraction
Music Content Analysis

Use Cases

Music Information Retrieval
Automatic Classification for Music Streaming Platforms
Automatically adds genre labels to uploaded music
Automatic classification with 88% accuracy
Music Recommendation Systems
Content-Based Music Recommendation
Generates recommendations by analyzing audio features
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