# Audio feature extraction

voc2vec is a foundational model specifically designed for non-verbal human data, built on the HuBERT framework and pre-trained on 125 hours of non-verbal audio data.

voc2vec is a foundational model specifically designed for non-linguistic human data, built upon the wav2vec 2.0 framework.

This model takes raw audio signals as input and outputs age predictions and gender probabilities (child/female/male), along with the pooled state of the last transformer layer.

This is an audio classification model based on the AST (Audio Spectrogram Transformer) architecture, fine-tuned on the GTZAN music genre classification dataset with an accuracy rate of 92%.

A foundational speech recognition model based on the Wav2Vec2 architecture, trained on 960 hours of English speech data, suitable for speech classification tasks.

This model is an automatic speech recognition model fine-tuned on the AI Light Dance dataset based on facebook/wav2vec2-large-xlsr-53.

This model is a fine-tuned automatic speech recognition model based on facebook/wav2vec2-large-xlsr-53 on the GARY109/AI_Light_Dance - ONSET-CHORD2 dataset.