---
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: 0.0148
- Accuracy: 0.9925
- F1: 0.9925
- Recall: 0.9925
- Precision: 0.9926
- Mcc: 0.9906
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 0.2357        | 1.0   | 200  | 0.0914          | 0.96     | 0.9603 | 0.96   | 0.9637    | 0.9508 | 0.9997 |
| 0.2769        | 2.0   | 400  | 0.1397          | 0.965    | 0.9653 | 0.9650 | 0.9673    | 0.9567 | 0.9982 |
| 0.0736        | 3.0   | 600  | 0.0468          | 0.9875   | 0.9875 | 0.9875 | 0.9880    | 0.9845 | 0.9998 |
| 0.0672        | 4.0   | 800  | 0.0554          | 0.985    | 0.9849 | 0.985  | 0.9854    | 0.9814 | 0.9999 |
| 0.0024        | 5.0   | 1000 | 0.1224          | 0.97     | 0.9703 | 0.9700 | 0.9724    | 0.9630 | 0.9997 |
| 0.0           | 6.0   | 1200 | 0.0109          | 0.995    | 0.9950 | 0.9950 | 0.9951    | 0.9938 | 1.0000 |
| 0.0           | 7.0   | 1400 | 0.0255          | 0.9925   | 0.9925 | 0.9925 | 0.9926    | 0.9906 | 1.0000 |
| 0.0           | 8.0   | 1600 | 0.0169          | 0.9925   | 0.9925 | 0.9925 | 0.9926    | 0.9906 | 1.0000 |
| 0.0           | 9.0   | 1800 | 0.0154          | 0.9925   | 0.9925 | 0.9925 | 0.9926    | 0.9906 | 1.0000 |
| 0.0           | 10.0  | 2000 | 0.0148          | 0.9925   | 0.9925 | 0.9925 | 0.9926    | 0.9906 | 1.0000 |


### Framework versions

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