# GTZAN Fine-tuning

This model is an audio classification model fine-tuned on the GTZAN music genre classification dataset based on DistilHuBERT, with an accuracy of 84%.

This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on ntu-spml/distilhubert, achieving an accuracy of 85%.

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.

An audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving 84% accuracy

This model is a fine-tuned version of DistilHuBERT on the GTZAN music classification dataset, primarily used for music genre classification tasks.

This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, with an accuracy of 88%

A lightweight audio classification model fine-tuned on the GTZAN music classification dataset based on the DistilHuBERT architecture

An audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving 83% accuracy

This model is a fine-tuned version of DistilHuBERT on the GTZAN music classification dataset, primarily used for music genre classification tasks.

An audio classification model fine-tuned on the GTZAN music classification dataset based on the DistilHuBERT architecture, achieving 88% accuracy

Audio emotion classification model fine-tuned on the GTZAN dataset based on Hubert_emotion, with 65% accuracy

An audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving 85% accuracy