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Distilhubert Finetuned Gtzan
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Distilhubert Finetuned Gtzan

Developed by mory91
A lightweight audio feature extraction model fine-tuned on GTZAN music classification dataset based on DistilHuBERT
Audio Classification
Transformers
Open Source License:Apache-2.0#Music Classification#Lightweight Audio Model#High Accuracy
Downloads 48
Release Time : 7/29/2023

Model Overview

This model is a lightweight distilled version of HuBERT, specifically fine-tuned for music genre classification tasks, suitable for audio feature extraction and music classification applications

Model Features

Lightweight and Efficient
Maintains original model performance while significantly reducing model size through knowledge distillation
Music Classification Optimized
Specifically fine-tuned for music genre classification on GTZAN dataset
High Accuracy
Achieves 87% classification accuracy on evaluation set

Model Capabilities

Audio Feature Extraction
Music Genre Classification
Audio Representation Learning

Use Cases

Music Analysis
Automatic Music Genre Classification
Classify music clips into genres (e.g., rock, jazz, classical)
Evaluation accuracy 87%
Music Recommendation System
Serve as feature extraction component for music recommendation systems
Audio Processing
Audio Content Analysis
Extract high-level semantic features from audio for subsequent analysis
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