---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-common_voice
  results: []
---

<!-- 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-common_voice

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5704
- Accuracy: 0.8775
- F1: 0.8773
- Recall: 0.8775
- Precision: 0.8773
- Mcc: 0.8469
- Auc: 0.9835

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision | Mcc    | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 1.0165        | 1.0   | 200  | 1.2394          | 0.5575   | 0.4931 | 0.5575 | 0.4662    | 0.4677 | 0.8879 |
| 0.8119        | 2.0   | 400  | 0.5971          | 0.7575   | 0.7576 | 0.7575 | 0.7668    | 0.6992 | 0.9592 |
| 0.1652        | 3.0   | 600  | 0.5719          | 0.815    | 0.8161 | 0.8150 | 0.8273    | 0.7711 | 0.9713 |
| 0.0829        | 4.0   | 800  | 0.8850          | 0.8      | 0.8017 | 0.8    | 0.8346    | 0.7590 | 0.9730 |
| 0.0102        | 5.0   | 1000 | 0.7974          | 0.8375   | 0.8386 | 0.8375 | 0.8590    | 0.8024 | 0.9778 |
| 0.0004        | 6.0   | 1200 | 0.5919          | 0.86     | 0.8607 | 0.86   | 0.8632    | 0.8254 | 0.9815 |
| 0.0           | 7.0   | 1400 | 0.5652          | 0.88     | 0.8798 | 0.8800 | 0.8803    | 0.8502 | 0.9833 |
| 0.0           | 8.0   | 1600 | 0.5665          | 0.875    | 0.8749 | 0.875  | 0.8749    | 0.8438 | 0.9834 |
| 0.0           | 9.0   | 1800 | 0.5695          | 0.8775   | 0.8773 | 0.8775 | 0.8773    | 0.8469 | 0.9835 |
| 0.0           | 10.0  | 2000 | 0.5704          | 0.8775   | 0.8773 | 0.8775 | 0.8773    | 0.8469 | 0.9835 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1