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Distilhubert Finetuned Gtzan
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Distilhubert Finetuned Gtzan

Developed by sanchit-gandhi
This is an audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving an accuracy of 82%
Audio Classification
Transformers
Open Source License:Apache-2.0#Music Classification#High Accuracy#Lightweight Audio Model
Downloads 255
Release Time : 6/20/2023

Model Overview

This model is a distilled version of the lightweight HuBERT model, specifically fine-tuned for music genre classification tasks

Model Features

Efficient and Lightweight
Based on distillation technology, it retains the core capabilities of the HuBERT model but with fewer parameters
High Accuracy
Achieves 82% accuracy on the GTZAN music classification dataset
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 Music Classification
Automatically classify tracks in a music library by genre
82% accuracy
Music Recommendation System
Feature extraction and similarity calculation based on music content
Audio Analysis
Audio Content Understanding
Extract high-level semantic features from audio
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