# Audio classification

This is the ONNX version of the AST (Audio Spectrogram Transformer) model, designed specifically for audio classification tasks and fine-tuned on the AudioSet dataset.

This model is an audio classification model based on the Audio Spectrogram Transformer (AST) architecture. After pre-training on the Audioset dataset, it was fine-tuned on the GTZAN music genre classification dataset.

An audio classification model based on the wav2vec2 architecture, suitable for climate-related sound classification tasks

Speech encoder module extracted from SeamlessM4Tv2-Large, excelling in cross-language and multilingual sequence-level audio classification tasks

This is an audio classification model for classifying Vietnamese dialects, achieving an F1 score of 0.8217 on the evaluation set.

A baby cry classification model fine-tuned based on wav2vec2-large-xlsr-53-english, achieving 81.5% accuracy

A language identification model fine-tuned based on the Whisper Medium model, specifically designed for language classification tasks on the FLEURS dataset

This model is used to estimate the direction of arrival (DOA) of fixed sound sources, trained on the SOFA dataset and fine-tuned using the AST model.

A speech recognition model fine-tuned on the speech_commands dataset based on facebook/wav2vec2-base, achieving 97.8% accuracy

This model is a fine-tuned audio classification model based on MIT/ast-finetuned-speech-commands-v2 on the digital speech commands dataset, primarily used for recognizing 0-9 digit speech commands