metadata
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: 0.93
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 on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.3427
- Accuracy: 0.93
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7202 | 1.0 | 113 | 0.6222 | 0.81 |
| 0.5233 | 2.0 | 226 | 0.9347 | 0.77 |
| 0.4073 | 3.0 | 339 | 0.7779 | 0.8 |
| 0.1514 | 4.0 | 452 | 0.4372 | 0.89 |
| 0.006 | 5.0 | 565 | 0.4375 | 0.9 |
| 0.0028 | 6.0 | 678 | 0.4320 | 0.9 |
| 0.0003 | 7.0 | 791 | 0.3487 | 0.93 |
| 0.0001 | 8.0 | 904 | 0.3310 | 0.93 |
| 0.0002 | 9.0 | 1017 | 0.3413 | 0.93 |
| 0.0002 | 9.9156 | 1120 | 0.3427 | 0.93 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0