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---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.450
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.450-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: 0.88
---
<!-- 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-10-10-0.450-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.450](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.450) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4457
- Accuracy: 0.88
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5804 | 1.0 | 57 | 0.5356 | 0.84 |
| 0.2655 | 2.0 | 114 | 0.5664 | 0.76 |
| 0.1767 | 3.0 | 171 | 0.3925 | 0.88 |
| 0.4169 | 4.0 | 228 | 0.8874 | 0.78 |
| 0.0685 | 5.0 | 285 | 0.6067 | 0.83 |
| 0.0725 | 6.0 | 342 | 0.5612 | 0.81 |
| 0.1003 | 7.0 | 399 | 0.6928 | 0.82 |
| 0.004 | 8.0 | 456 | 0.4814 | 0.86 |
| 0.0122 | 9.0 | 513 | 0.6141 | 0.86 |
| 0.0009 | 10.0 | 570 | 0.4017 | 0.91 |
| 0.0828 | 11.0 | 627 | 0.4937 | 0.88 |
| 0.0025 | 12.0 | 684 | 0.8455 | 0.82 |
| 0.0005 | 13.0 | 741 | 0.4439 | 0.89 |
| 0.0001 | 14.0 | 798 | 0.4956 | 0.87 |
| 0.0001 | 15.0 | 855 | 0.4362 | 0.88 |
| 0.0001 | 16.0 | 912 | 0.4146 | 0.89 |
| 0.0299 | 17.0 | 969 | 0.4241 | 0.9 |
| 0.0001 | 18.0 | 1026 | 0.4375 | 0.87 |
| 0.0001 | 19.0 | 1083 | 0.4502 | 0.88 |
| 0.0001 | 20.0 | 1140 | 0.4457 | 0.88 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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