---
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
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# 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: 0.0088
- Accuracy: 0.995
- F1: 0.9950
- Recall: 0.9950
- Precision: 0.9951
- Mcc: 0.9938
- Auc: 1.0000

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 0.3329        | 1.0   | 200  | 0.1555          | 0.9525   | 0.9521 | 0.9525 | 0.9566    | 0.9418 | 0.9991 |
| 0.1007        | 2.0   | 400  | 0.1966          | 0.9525   | 0.9512 | 0.9525 | 0.9559    | 0.9420 | 0.9975 |
| 0.0243        | 3.0   | 600  | 0.0619          | 0.98     | 0.9799 | 0.9800 | 0.9805    | 0.9752 | 0.9999 |
| 0.0007        | 4.0   | 800  | 0.0194          | 0.995    | 0.9950 | 0.9950 | 0.9950    | 0.9938 | 1.0000 |
| 0.0           | 5.0   | 1000 | 0.0182          | 0.9925   | 0.9925 | 0.9925 | 0.9927    | 0.9907 | 1.0000 |
| 0.0           | 6.0   | 1200 | 0.0106          | 0.995    | 0.9950 | 0.9950 | 0.9951    | 0.9938 | 1.0000 |
| 0.0           | 7.0   | 1400 | 0.0098          | 0.995    | 0.9950 | 0.9950 | 0.9951    | 0.9938 | 1.0000 |
| 0.0           | 8.0   | 1600 | 0.0093          | 0.995    | 0.9950 | 0.9950 | 0.9951    | 0.9938 | 1.0000 |
| 0.0           | 9.0   | 1800 | 0.0089          | 0.995    | 0.9950 | 0.9950 | 0.9951    | 0.9938 | 1.0000 |
| 0.0           | 10.0  | 2000 | 0.0088          | 0.995    | 0.9950 | 0.9950 | 0.9951    | 0.9938 | 1.0000 |


### Framework versions

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