Ast Finetuned Audioset 10 10 0.4593 Heart Sounds

🚀 ast-finetuned-audioset-10-10-0.4593-heart-sounds

This model is a fine - tuned version of [MIT/ast - finetuned - audioset - 10 - 10 - 0.4593](https://huggingface.co/MIT/ast - finetuned - audioset - 10 - 10 - 0.4593) on the audiofolder dataset. It can achieve high accuracy in audio classification tasks, providing reliable performance for related applications.

[](https://wandb.ai/vldmrl - org/HeartDiseaseDetector/runs/n9fzp3eh)

🚀 Quick Start

This model is a fine - tuned version of [MIT/ast - finetuned - audioset - 10 - 10 - 0.4593](https://huggingface.co/MIT/ast - finetuned - audioset - 10 - 10 - 0.4593) on the audiofolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.1499
• Accuracy: 0.9695

✨ Features

• High - accuracy audio classification: Achieves an accuracy of 0.9695 on the evaluation set.
• Fine - tuned from a well - known base model: Based on [MIT/ast - finetuned - audioset - 10 - 10 - 0.4593](https://huggingface.co/MIT/ast - finetuned - audioset - 10 - 10 - 0.4593).

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e - 05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 8

Training results

Framework versions

• Transformers 4.49.0
• Pytorch 2.0.1+cu118
• Datasets 3.3.2
• Tokenizers 0.21.0

📄 License

This project is licensed under the bsd - 3 - clause license.