DistilHubert-finetuned-GTzan Open-source Audio Classification Model - Finetuned on GTZAN Dataset with 77% Accuracy

Distilhubert Finetuned Gtzan

Developed by AdonaiHS

This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving 77% accuracy

Audio Classification

Transformers

Open Source License:Apache-2.0 #Music Classification #Lightweight Audio Model #High Accuracy

Downloads 28

Release Time : 7/29/2023

Model Overview

A lightweight audio classification model optimized for music genre classification tasks, based on the DistilHuBERT architecture

Model Features

Efficient Music Classification

Achieves 77% accuracy on the GTZAN dataset, suitable for music genre recognition tasks

Lightweight Architecture

Compressed model based on DistilHuBERT, reducing computational requirements while maintaining performance

Rapid Fine-tuning Capability

Achieves good results with just 3,000 training steps, suitable for quick deployment

Model Capabilities

Music audio classification

Genre recognition

Audio feature extraction

Use Cases

Music Services

Automatic Music Classification

Automatically tags uploaded music genres for music streaming platforms

77% accuracy

Playlist Generation

Automatically generates playlists of the same genre based on music features

Music Analysis

Music Feature Research

Analyzes audio feature differences between different music genres

Property	Details
Model Type	distilhubert - finetuned - gtzan
Base Model	ntu - spml/distilhubert
Generated From	Trainer
Datasets	marsyas/gtzan
Metrics	accuracy

Task	Dataset	Split	Metrics	Value
Audio Classification	GTZAN (marsyas/gtzan, config: all)	train	Accuracy	0.77

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1574	8.85	500	1.8008	0.66
1.5882	17.7	1000	1.3509	0.7
1.2416	26.55	1500	1.1347	0.72
1.037	35.4	2000	1.0163	0.74
0.9152	44.25	2500	0.9583	0.76
0.8556	53.1	3000	0.9423	0.77

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Distilhubert Finetuned Gtzan

Model Overview

Model Features

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