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Distilhubert Finetuned Gtzan
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Distilhubert Finetuned Gtzan

Developed by Terps
This model is a fine-tuned version of NTU-SPML's DistilHuBERT on the GTZAN music classification dataset, primarily used for music genre classification tasks.
Audio Classification
Transformers
Open Source License:Apache-2.0#Music Classification#Lightweight HuBERT#GTZAN Fine-tuning
Downloads 15
Release Time : 9/27/2023

Model Overview

This is a fine-tuned lightweight speech representation model specifically designed for music genre classification tasks. Based on the DistilHuBERT architecture, it achieves 87% accuracy on the GTZAN dataset.

Model Features

Efficient Music Classification
Achieves 87% classification accuracy on the GTZAN dataset, suitable for music genre recognition tasks
Lightweight Architecture
Based on the distilled DistilHuBERT architecture, more lightweight compared to the original HuBERT model
Fast Inference
Evaluation speed reaches 1.893 samples per second, suitable for real-time applications

Model Capabilities

Music Genre Classification
Audio Feature Extraction
Music Content Analysis

Use Cases

Music Recommendation Systems
Automatic Music Classification
Automatically classify uploaded music files for music streaming platforms
87% classification accuracy
Music Analysis
Music Library Management
Help individuals or organizations automatically organize music collections
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