---
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: MIT/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.92
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# MIT/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: 0.2989
- 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6403        | 1.0   | 113  | 0.4655          | 0.87     |
| 0.2874        | 2.0   | 226  | 0.6336          | 0.82     |
| 0.1274        | 3.0   | 339  | 0.4641          | 0.87     |
| 0.0465        | 4.0   | 452  | 0.5623          | 0.83     |
| 0.0285        | 5.0   | 565  | 0.3886          | 0.89     |
| 0.0042        | 6.0   | 678  | 0.2969          | 0.92     |
| 0.0006        | 7.0   | 791  | 0.3001          | 0.92     |
| 0.0003        | 8.0   | 904  | 0.3014          | 0.92     |
| 0.0003        | 9.0   | 1017 | 0.3009          | 0.92     |
| 0.0022        | 10.0  | 1130 | 0.2989          | 0.92     |


### Framework versions

- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 2.16.1
- Tokenizers 0.22.1