Distilhubert Finetuned Gtzan
This model is a fine-tuned version of DistilHuBERT on the GTZAN music classification dataset, primarily used for music genre classification tasks.
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Release Time : 6/21/2023
Model Overview
A lightweight audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN dataset, specifically designed for music genre classification tasks.
Model Features
Lightweight Architecture
Lightweight design based on DistilHuBERT, reducing computational resource requirements while maintaining performance.
High Accuracy
Achieves 80% accuracy on the GTZAN evaluation set.
Efficient Training
Uses linear learning rate scheduling and the Adam optimizer for stable and efficient training.
Model Capabilities
Music Genre Classification
Audio Feature Extraction
Use Cases
Music Analysis
Music Streaming Platform Classification
Automatically add genre labels to songs in a music library.
80% classification accuracy
Music Recommendation System
Improve recommendation algorithms based on music genre features.
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