DistilHubert-finetuned-gtzan Open-source Model - Free Deployment for Precise Music Genre Classification

Distilhubert Finetuned Gtzan

Developed by artyomboyko

A model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, used for music genre classification tasks

Audio Classification

Transformers

Open Source License:Apache-2.0 #Music Classification #High Accuracy #Audio Feature Extraction

Downloads 16

Release Time : 8/23/2023

Model Overview

This model is an audio classification model fine-tuned on the GTZAN music dataset, capable of recognizing 10 different music genres

Model Features

High Accuracy

Achieves 91% accuracy on the GTZAN evaluation set

Lightweight

Based on the DistilHuBERT architecture, more lightweight compared to the original HuBERT model

Music Classification

Specifically optimized for music genre classification tasks

Model Capabilities

Music Genre Classification

Audio Feature Extraction

Use Cases

Music Analysis

Music Streaming Classification

Automatically add genre labels to songs on music streaming platforms

91% accuracy

Music Recommendation System

Improve recommendation algorithms based on music genre features

🚀 distilhubert-finetuned-gtzan

This model is a fine - tuned version of [ntu - spml/distilhubert](https://huggingface.co/ntu - spml/distilhubert) on the GTZAN dataset, achieving a loss of 0.4989 and an accuracy of 0.91 on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of [ntu - spml/distilhubert](https://huggingface.co/ntu - spml/distilhubert) on the marsyas/gtzan dataset. It achieves the following results on the evaluation set:

Loss: 0.4989
Accuracy: 0.91

📚 Documentation

Model Information

Property	Details
Model Type	Fine - tuned version of [ntu - spml/distilhubert](https://huggingface.co/ntu - spml/distilhubert)
Training Data	marsyas/gtzan
Metrics	Accuracy
License	Apache - 2.0

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 4e - 06
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training Results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2359	1.0	112	0.4776	0.87
0.1235	2.0	225	0.4872	0.84
0.2083	3.0	337	0.4910	0.85
0.19	4.0	450	0.4953	0.87
0.1128	5.0	562	0.4801	0.87
0.1644	6.0	675	0.4703	0.87
0.0699	7.0	787	0.4692	0.85
0.1082	8.0	900	0.4708	0.87
0.0898	9.0	1012	0.4347	0.89
0.1071	10.0	1125	0.5310	0.85
0.0727	11.0	1237	0.4765	0.87
0.0338	12.0	1350	0.4859	0.87
0.0233	13.0	1462	0.4713	0.87
0.0248	14.0	1575	0.5068	0.88
0.0263	15.0	1687	0.4874	0.88
0.0185	16.0	1800	0.4925	0.88
0.0142	17.0	1912	0.4766	0.89
0.0178	18.0	2025	0.4850	0.89
0.0153	19.0	2137	0.4660	0.88
0.012	20.0	2250	0.4831	0.88
0.0113	21.0	2362	0.4965	0.89
0.0106	22.0	2475	0.5098	0.89
0.011	23.0	2587	0.5093	0.89
0.009	24.0	2700	0.4989	0.91
0.0094	25.0	2812	0.4999	0.89
0.0441	26.0	2925	0.5197	0.88
0.0079	27.0	3037	0.5115	0.89
0.0072	28.0	3150	0.5136	0.88
0.007	29.0	3262	0.5394	0.88
0.0068	30.0	3375	0.5374	0.88
0.0061	31.0	3487	0.5221	0.88
0.0533	32.0	3600	0.5775	0.87
0.0055	33.0	3712	0.5632	0.88
0.0059	34.0	3825	0.5584	0.87
0.0051	35.0	3937	0.5444	0.88
0.0051	36.0	4050	0.5373	0.88
0.0045	37.0	4162	0.5723	0.87
0.0058	38.0	4275	0.5773	0.87
0.0043	39.0	4387	0.5455	0.88
0.0044	40.0	4500	0.5686	0.88
0.004	41.0	4612	0.5622	0.87
0.004	42.0	4725	0.5797	0.88
0.0042	43.0	4837	0.5621	0.88
0.0037	44.0	4950	0.5734	0.87
0.0048	45.0	5062	0.5774	0.88
0.0039	46.0	5175	0.5901	0.87
0.0043	47.0	5287	0.5743	0.88
0.0043	48.0	5400	0.5757	0.87
0.0037	49.0	5512	0.5710	0.88
0.0036	49.78	5600	0.5759	0.87

Framework Versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

📄 License

This model is licensed under the Apache - 2.0 license.

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