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This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset. It achieves the following results on the evaluation set: Loss: 0.4179 Accuracy: 0.9
Model source
Source description
This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset. It achieves the following results on the evaluation set:
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Sources
1 sourceVerified Sep 13
Model artifacts
1 artifactSource excerpts
2 excerptsMore information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8812 | 1.0 | 113 | 0.5635 | 0.81 |
| 0.4718 | 2.0 | 226 | 0.9129 | 0.77 |
| 0.3250 | 3.0 | 339 | 0.5048 | 0.89 |
| 0.1794 | 4.0 | 452 | 0.4237 | 0.88 |
| 0.0462 | 5.0 | 565 | 0.9291 | 0.83 |
--- library_name: transformers license: bsd-3-clause base_model: MIT/ast-finetuned-audioset-10-10-0.4593 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.9 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan 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 GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.4179 - Accuracy: 0.9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8812 | 1.0 | 113 | 0.5635 | 0.81 | | 0.4718 | 2.0 | 226 | 0.9129 | 0.77 | | 0.3250 | 3.0 | 339 | 0.5048 | 0.89 | | 0.1794 | 4.0 | 452 | 0.4237 | 0.88 | | 0.0462 | 5.0 | 565 | 0.9291 | 0.83 | | 0.0005 | 6.0 | 678 | 0.5636 | 0.86 | | 0.0002 | 7.0 | 791 | 0.4091 | 0.91 | | 0.0001 | 8.0 | 904 | 0.4560 | 0.89 | | 0.0001 | 9.0 | 1017 | 0.4239 | 0.9 | | 0.0002 | 10.0 | 1130 | 0.4179 | 0.9 | ### Framework versions - Transformers 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 3.6.0 - Tokenizers 0.22.2
Source context: 2 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo tong-li-fsx/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
| 0.0005 | 6.0 | 678 | 0.5636 | 0.86 |
| 0.0002 | 7.0 | 791 | 0.4091 | 0.91 |
| 0.0001 | 8.0 | 904 | 0.4560 | 0.89 |
| 0.0001 | 9.0 | 1017 | 0.4239 | 0.9 |
| 0.0002 | 10.0 | 1130 | 0.4179 | 0.9 |
