ast-finetuned-gtzan
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: 0.3877
- Accuracy: 0.9164
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5193 | 1.0 | 169 | 0.4598 | 0.8361 |
| 0.2436 | 2.0 | 338 | 0.5951 | 0.8462 |
| 0.0787 | 3.0 | 507 | 0.6161 | 0.8562 |
| 0.0333 | 4.0 | 676 | 0.4487 | 0.8963 |
| 0.0035 | 5.0 | 845 | 0.4108 | 0.9197 |
| 0.0002 | 6.0 | 1014 | 0.3863 | 0.9197 |
| 0.0001 | 7.0 | 1183 | 0.3851 | 0.9231 |
| 0.0032 | 8.0 | 1352 | 0.3877 | 0.9164 |
Framework versions
- Transformers 5.15.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2