---
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: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.94
---



# 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: 0.2095
- Accuracy: 0.94

## Model description

MIT/ast-finetuned-audioset-10-10-0.4593 model has been used with the head replaced for classification of the 10 music genres.

## Intended uses & limitations

More information needed

## Training and evaluation data

GTZAN dataset has been used for training. 20% split was used for evaluation.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.3629        | 1.0877 | 62   | 2.0026          | 0.38     |
| 1.816         | 2.1754 | 124  | 1.0936          | 0.76     |
| 1.0625        | 3.2632 | 186  | 0.5384          | 0.87     |
| 0.5878        | 4.3509 | 248  | 0.3465          | 0.91     |
| 0.2426        | 5.4386 | 310  | 0.3506          | 0.88     |
| 0.1584        | 6.5263 | 372  | 0.2532          | 0.92     |
| 0.1067        | 7.6140 | 434  | 0.2333          | 0.9      |
| 0.0741        | 8.7018 | 496  | 0.2248          | 0.91     |
| 0.0431        | 9.7895 | 558  | 0.2095          | 0.94     |


### Framework versions

- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2