# Audio Classification

This model is used to classify audio clips as either 'Suno' music or 'People' music.

A fine-tuned audio classification model based on openai/whisper-tiny, excelling in voice command datasets

A fine-tuned audio classification model based on DistilHuBERT, achieving 100% accuracy on the evaluation set

Audio classification model based on Wav2Vec2 for music genre recognition

This model is an audio classification model based on the AST architecture and fine-tuned on the GTZAN music classification dataset, achieving an accuracy of 89%

An audio classification model fine-tuned on the minds14 dataset based on facebook/wav2vec2-base

This model is a fine-tuned version of Audio Spectrogram Transformer (AST) on the GTZAN music classification dataset for audio classification tasks, achieving an accuracy of 88%.

A speech emotion recognition system fine-tuned on Wav2Vec2 Large XLSR-53 model, capable of identifying 7 common emotions

A synthetic speech detection model fine-tuned based on MIT/ast-finetuned-audioset-10-10-0.4593, used to identify whether an audio is synthetic speech.

This model is a fine-tuned audio classification model based on DistilHuBERT, specifically designed to distinguish between music and non-music audio.

A 5-class audio classification model fine-tuned on the superb dataset based on the wav2vec pre-trained model

A speech emotion analysis model fine-tuned based on facebook/wav2vec2-base, achieving 70.87% accuracy on the evaluation set

An audio classification model fine-tuned on an audio folder dataset based on the wav2vec2-base model, achieving 99.82% accuracy on the validation set

An audio classification model fine-tuned from facebook/wav2vec2-base-960h for detecting violent sounds

An audio classification model fine-tuned based on facebook/wav2vec2-base, achieving 58.92% accuracy on the evaluation set

A Vision Transformer (ViT)-based audio processing model, pre-trained on AudioSet-2M using self-supervised masked autoencoder (MAE) method and fine-tuned on AudioSet-20k

Audio classification model based on wav2vec2 architecture, fine-tuned on the GTZAN dataset for music and speech classification tasks

A foundational speech recognition model based on the wav2vec 2.0 architecture, pre-trained on 960 hours of English speech data

A foundational speech recognition model based on the wav2vec 2.0 architecture, pre-trained on 960 hours of English speech data

An audio classification model based on facebook/wav2vec2-base-960h, capable of identifying the composer of classical music audio clips

This model is an audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN music genre classification dataset, achieving an accuracy of 89%.

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

MERT is an acoustic music understanding model based on self-supervised learning, using pseudo-labels provided by a teacher model for pre-training.

An audio classification model fine-tuned on the GTZAN music classification dataset based on distilhubert, achieving 89% accuracy

This model is an audio classification model fine-tuned on the GTZAN dataset based on facebook/wav2vec2-base, primarily used for music genre classification tasks.

An audio classification model fine-tuned based on facebook/wav2vec2-base for distinguishing between music and speech

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

An audio classification model fine-tuned on the GTZAN dataset based on openai/whisper-tiny, achieving 91% accuracy

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

Audio Spectrogram Transformer (AST) model fine-tuned on the AudioSet dataset for audio classification tasks

An audio classification model fine-tuned based on facebook/wav2vec2-base for music genre recognition

A fine-tuned speech classification model based on facebook/wav2vec2-base for audio folder dataset classification tasks

This model is a fine-tuned audio classification model based on facebook/wav2vec2-base on the superb dataset, achieving 99.87% accuracy on the evaluation set.

An audio classification model fine-tuned on the SUPERB dataset based on facebook/wav2vec2-base, achieving 99.98% accuracy

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving 73.22% accuracy on the evaluation set

Bird sound recognition model fine-tuned based on Microsoft's WavLM-Large model

An audio classification model fine-tuned based on facebook/wav2vec2-base for identifying bird sounds

This model is a fine-tuned version of the AST model on the shEMO dataset, primarily used for speech emotion recognition tasks.

This model is a fine-tuned audio classification model based on MIT/ast-finetuned-audioset-10-10-0.4593, achieving 60.76% accuracy on the evaluation set.

This model is a fine-tuned audio classification model based on facebook/wav2vec2-base on the superb dataset, achieving an accuracy of 0.6578 on the evaluation set.