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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: 0.4248
- 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: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9212 | 1.0 | 112 | 0.6617 | 0.79 |
| 0.4306 | 2.0 | 225 | 0.5650 | 0.81 |
| 0.3493 | 3.0 | 337 | 0.3763 | 0.88 |
| 0.0369 | 4.0 | 450 | 0.5402 | 0.83 |
| 0.0018 | 5.0 | 562 | 0.4543 | 0.9 |
| 0.0025 | 6.0 | 675 | 0.5821 | 0.85 |
| 0.0009 | 7.0 | 787 | 0.4905 | 0.89 |
| 0.0001 | 8.0 | 900 | 0.5396 | 0.86 |
| 0.0871 | 9.0 | 1012 | 0.7212 | 0.86 |
| 0.0001 | 10.0 | 1125 | 0.4179 | 0.9 |
| 0.0001 | 11.0 | 1237 | 0.5138 | 0.9 |
| 0.0001 | 12.0 | 1350 | 0.4133 | 0.9 |
| 0.0001 | 13.0 | 1462 | 0.4273 | 0.9 |
| 0.0001 | 14.0 | 1575 | 0.4278 | 0.9 |
| 0.0001 | 14.93 | 1680 | 0.4248 | 0.9 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
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