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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
- precision
- recall
- 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: 0.93
- name: Precision
type: precision
value: 0.9386363636363637
- name: Recall
type: recall
value: 0.93
- name: F1
type: f1
value: 0.9311080732133363
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. -->
[<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/raspuntinov_ai/huggingface/runs/igv2809l)
# 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: 0.3715
- Accuracy: 0.93
- Precision: 0.9386
- Recall: 0.93
- F1: 0.9311
## 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: 16
- eval_batch_size: 16
- seed: 42
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8552 | 1.0 | 57 | 0.5962 | 0.83 | 0.8693 | 0.83 | 0.8207 |
| 0.448 | 2.0 | 114 | 0.5167 | 0.85 | 0.8736 | 0.85 | 0.8534 |
| 0.1634 | 3.0 | 171 | 0.5433 | 0.86 | 0.8780 | 0.86 | 0.8570 |
| 0.1673 | 4.0 | 228 | 0.4743 | 0.88 | 0.8836 | 0.88 | 0.8769 |
| 0.0065 | 5.0 | 285 | 0.4956 | 0.91 | 0.9212 | 0.91 | 0.9060 |
| 0.0279 | 6.0 | 342 | 0.5635 | 0.89 | 0.8971 | 0.89 | 0.8879 |
| 0.104 | 7.0 | 399 | 0.6799 | 0.86 | 0.8832 | 0.86 | 0.8564 |
| 0.001 | 8.0 | 456 | 0.4927 | 0.91 | 0.9246 | 0.91 | 0.9109 |
| 0.0002 | 9.0 | 513 | 0.3899 | 0.92 | 0.9245 | 0.92 | 0.9187 |
| 0.0002 | 10.0 | 570 | 0.3715 | 0.93 | 0.9386 | 0.93 | 0.9311 |
| 0.0002 | 11.0 | 627 | 0.4695 | 0.92 | 0.9245 | 0.92 | 0.9180 |
| 0.0001 | 12.0 | 684 | 0.4150 | 0.93 | 0.9370 | 0.93 | 0.9291 |
| 0.0468 | 13.0 | 741 | 0.4483 | 0.92 | 0.9294 | 0.92 | 0.9182 |
| 0.0001 | 14.0 | 798 | 0.3852 | 0.93 | 0.9334 | 0.93 | 0.9288 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1 |