---
library_name: transformers
license: bsd-3-clause
base_model: MIT/ast-finetuned-speech-commands-v2
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- precision
- recall
- f1
model-index:
- name: ast-finetuned-en-alphabets
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.9650943396226415
    - name: Recall
      type: recall
      value: 0.9481132075471698
    - name: F1
      type: f1
      value: 0.9476170056358736
---

<!-- 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-en-alphabets

This model is a fine-tuned version of [MIT/ast-finetuned-speech-commands-v2](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1933
- Precision: 0.9651
- Recall: 0.9481
- F1: 0.9476

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.3705        | 1.0   | 113  | 0.2441          | 0.9566    | 0.9434 | 0.9432 |
| 0.1728        | 2.0   | 226  | 0.1617          | 0.9608    | 0.9481 | 0.9478 |
| 0.0321        | 3.0   | 339  | 0.1838          | 0.9651    | 0.9481 | 0.9476 |
| 0.011         | 4.0   | 452  | 0.1933          | 0.9651    | 0.9481 | 0.9476 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0