# Lightweight HuBERT

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%.

A cry detection model fine-tuned based on the distilhubert architecture, achieving outstanding performance on the evaluation set with an accuracy of 98.83%

A stuttering detection model fine-tuned based on the DistilHuBERT model, achieving 90.24% accuracy on the evaluation set

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

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

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

An automatic speech recognition (ASR) model fine-tuned on Arabic speech datasets based on distilhubert

DistilHuBERT is a lightweight speech representation learning model achieved through hierarchical distillation of the HuBERT model, significantly reducing model size and computational costs while maintaining performance.