# Audio Feature Extraction

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%.

wav2vec 2.0 is a self-supervised learning framework for speech representation learning, capable of learning speech features from unlabeled audio data.

Gender classifier based on the HuBERT large model with 98.61% accuracy

A fine-tuned voice gender classification model based on facebook/wav2vec2-base, achieving 98.92% accuracy on the evaluation set

A fine-tuned audio emotion classification model based on facebook/wav2vec2-base, achieving 73.98% 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.

This model, fine-tuned from Wav2Vec2-Large-Robust, can predict the speaker's age and gender from raw audio.

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 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 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%

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

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 DistilHuBERT, achieving 85% accuracy

A speech emotion recognition model fine-tuned on the RAVDESS dataset based on DistilHuBERT architecture, achieving 92.36% accuracy

An audio classification model fine-tuned based on facebook/wav2vec2-base-960h, with specific uses and training data not clearly specified.

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 audio datasets based on MIT/ast-finetuned-audioset-10-10-0.4593

MERT-v1-330M is an advanced music understanding model trained based on the MLM paradigm, with 330M parameters, supporting a 24K Hz audio sampling rate and 75 Hz feature rate, suitable for various music information retrieval tasks.

Age group classification model fine-tuned on the COSCAN-speech dataset based on wav2vec2-base, achieving 99.8% accuracy on the validation set

A speech processing model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 53.57% on the evaluation set