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---
base_model: MIT/ast-finetuned-audioset-16-16-0.442
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-16-16-0.442-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: 0.93
---
<!-- 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-16-16-0.442-finetuned-gtzan
This model is a fine-tuned version of [ast-finetuned-audioset-16-16-0.442](https://huggingface.co/ast-finetuned-audioset-16-16-0.442) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3315
- Accuracy: 0.93
## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam-8bits with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8802 | 1.0 | 45 | 0.5267 | 0.85 |
| 0.3183 | 2.0 | 90 | 0.5893 | 0.81 |
| 0.1094 | 3.0 | 135 | 0.4421 | 0.89 |
| 0.0259 | 4.0 | 180 | 0.4100 | 0.88 |
| 0.0291 | 5.0 | 225 | 0.3695 | 0.9 |
| 0.0409 | 6.0 | 270 | 0.3071 | 0.91 |
| 0.0152 | 7.0 | 315 | 0.3482 | 0.92 |
| 0.0003 | 8.0 | 360 | 0.3187 | 0.94 |
| 0.0003 | 9.0 | 405 | 0.3258 | 0.93 |
| 0.0004 | 10.0 | 450 | 0.3315 | 0.93 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|