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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
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: 0.92
---
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/soundofai/huggingface-audio-course/runs/nfwkhlry)
# 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: 0.3597
- 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.5671 | 0.9956 | 112 | 0.5463 | 0.85 |
| 0.7083 | 2.0 | 225 | 0.6822 | 0.78 |
| 0.2257 | 2.9956 | 337 | 0.5415 | 0.85 |
| 0.028 | 4.0 | 450 | 0.5070 | 0.9 |
| 0.0526 | 4.9956 | 562 | 0.8882 | 0.82 |
| 0.0628 | 6.0 | 675 | 0.9979 | 0.79 |
| 0.0025 | 6.9956 | 787 | 0.5942 | 0.88 |
| 0.0005 | 8.0 | 900 | 0.6327 | 0.9 |
| 0.0005 | 8.9956 | 1012 | 0.4033 | 0.9 |
| 0.0009 | 10.0 | 1125 | 0.4190 | 0.88 |
| 0.0001 | 10.9956 | 1237 | 0.3672 | 0.93 |
| 0.0001 | 12.0 | 1350 | 0.3615 | 0.91 |
| 0.0001 | 12.9956 | 1462 | 0.3631 | 0.92 |
| 0.0001 | 14.0 | 1575 | 0.3597 | 0.92 |
| 0.0001 | 14.9956 | 1687 | 0.3604 | 0.92 |
| 0.0 | 16.0 | 1800 | 0.3589 | 0.92 |
| 0.0 | 16.9956 | 1912 | 0.3597 | 0.92 |
| 0.0434 | 18.0 | 2025 | 0.3590 | 0.92 |
| 0.0 | 18.9956 | 2137 | 0.3594 | 0.92 |
| 0.0 | 19.9111 | 2240 | 0.3597 | 0.92 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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