metadata
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: 0.92
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: 0.2954
- Accuracy: 0.92
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: 20
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|---|---|---|---|---|
| 1.9615 | 0.9778 | 11 | 0.8039 | 0.81 |
| 0.6265 | 1.9556 | 22 | 0.5248 | 0.84 |
| 0.3419 | 2.9333 | 33 | 0.5014 | 0.81 |
| 0.172 | 4.0 | 45 | 0.3780 | 0.91 |
| 0.0895 | 4.9778 | 56 | 0.4103 | 0.85 |
| 0.033 | 5.9556 | 67 | 0.3093 | 0.9 |
| 0.0173 | 6.9333 | 78 | 0.2954 | 0.92 |
| 0.0083 | 8.0 | 90 | 0.3354 | 0.88 |
| 0.0042 | 8.9778 | 101 | 0.2688 | 0.92 |
| 0.001 | 9.7778 | 110 | 0.2712 | 0.92 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1