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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: 0.89
---
<!-- 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: 0.5513
- Accuracy: 0.89
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4812 | 1.0 | 100 | 0.4780 | 0.86 |
| 0.4555 | 2.0 | 200 | 0.6969 | 0.795 |
| 0.106 | 3.0 | 300 | 0.6725 | 0.85 |
| 0.0063 | 4.0 | 400 | 0.5885 | 0.875 |
| 0.0004 | 5.0 | 500 | 0.5513 | 0.89 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.2
- Datasets 2.14.7
- Tokenizers 0.15.0
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