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---
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-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: 0.87
---
<!-- 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-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: 0.3848
- Accuracy: 0.87
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8911 | 1.0 | 113 | 1.7770 | 0.52 |
| 0.9154 | 2.0 | 226 | 0.8861 | 0.77 |
| 0.5408 | 3.0 | 339 | 0.5815 | 0.83 |
| 0.3854 | 4.0 | 452 | 0.5075 | 0.86 |
| 0.4656 | 5.0 | 565 | 0.4716 | 0.87 |
| 0.3679 | 6.0 | 678 | 0.4578 | 0.87 |
| 0.3263 | 7.0 | 791 | 0.4368 | 0.87 |
| 0.4072 | 8.0 | 904 | 0.4078 | 0.88 |
| 0.2734 | 9.0 | 1017 | 0.3847 | 0.88 |
| 0.3517 | 10.0 | 1130 | 0.4185 | 0.88 |
| 0.3147 | 11.0 | 1243 | 0.3946 | 0.86 |
| 0.2572 | 12.0 | 1356 | 0.3899 | 0.88 |
| 0.3696 | 13.0 | 1469 | 0.3843 | 0.87 |
| 0.256 | 14.0 | 1582 | 0.3872 | 0.87 |
| 0.3737 | 15.0 | 1695 | 0.3914 | 0.88 |
| 0.1702 | 16.0 | 1808 | 0.3863 | 0.87 |
| 0.2974 | 17.0 | 1921 | 0.3857 | 0.87 |
| 0.1916 | 18.0 | 2034 | 0.3855 | 0.87 |
| 0.223 | 19.0 | 2147 | 0.3848 | 0.87 |
| 0.1942 | 20.0 | 2260 | 0.3848 | 0.87 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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