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---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
- f1
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-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
- name: F1
type: f1
value: 0.89
pipeline_tag: audio-classification
---
<!-- 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-10-10-0.4593-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: 0.5658
- Accuracy: 0.87
- F1: 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 2024
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.8357 | 0.9956 | 56 | 0.6582 | 0.82 | 0.82 |
| 0.4742 | 1.9911 | 112 | 0.6527 | 0.81 | 0.81 |
| 0.3344 | 2.9867 | 168 | 0.9048 | 0.76 | 0.76 |
| 0.0659 | 4.0 | 225 | 0.6998 | 0.84 | 0.8400 |
| 0.0966 | 4.9956 | 281 | 0.6737 | 0.83 | 0.83 |
| 0.0026 | 5.9911 | 337 | 0.5133 | 0.89 | 0.89 |
| 0.0038 | 6.9867 | 393 | 0.5704 | 0.86 | 0.8600 |
| 0.0005 | 8.0 | 450 | 0.5722 | 0.86 | 0.8600 |
| 0.0003 | 8.9956 | 506 | 0.5632 | 0.87 | 0.87 |
| 0.0003 | 9.9556 | 560 | 0.5658 | 0.87 | 0.87 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |