# Music Classification

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

An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN dataset for music genre classification tasks.

Audio classification model fine-tuned on the GTZAN music classification dataset based on the DistilHuBERT architecture, achieving 86% accuracy

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%.

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 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

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

Audio representation learning model based on HuBERT architecture, pre-trained on the complete AudioSet dataset

A music classification model fine-tuned on the GTZAN dataset based on distilhubert, achieving 88% accuracy

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

This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving an accuracy of 83%.

An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset with an accuracy of 86%

An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset with an accuracy of 89%

This model is an audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music classification dataset, primarily used for music genre classification tasks.

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

A lightweight audio feature extraction model fine-tuned on GTZAN music classification dataset based on DistilHuBERT

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

An audio classification model based on the HuBERT architecture, fine-tuned on the GTZAN music genre classification dataset with an accuracy of 88%

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 based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset with an accuracy of 94.75%

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

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

This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving an accuracy of 83%

An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset

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

Audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert

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

An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset with 91% accuracy

This model is an audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving an accuracy of 76.25%

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 a fine-tuned version based on the DistilHuBERT architecture 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

A speech classification model fine-tuned on the GTZAN dataset based on OpenAI's whisper-base model, primarily used for music genre classification tasks.

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

A model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving 88% 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

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

This is an audio classification model fine-tuned on the GTZAN music classification dataset based on DistilHuBERT, achieving an accuracy of 82%