---
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-mlcommons-speech-commands
  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: 0.9743628199079283
    - name: Recall
      type: recall
      value: 0.9743424814179531
    - name: F1
      type: f1
      value: 0.9743165983480835
---

<!-- 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-mlcommons-speech-commands

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: 0.1346
- Precision: 0.9744
- Recall: 0.9743
- F1: 0.9743

## 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: 32
- eval_batch_size: 32
- 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|
| 0.0799        | 1.0   | 3496  | 0.1498          | 0.9596    | 0.9573 | 0.9577 |
| 0.0624        | 2.0   | 6992  | 0.1141          | 0.9689    | 0.9687 | 0.9685 |
| 0.0091        | 3.0   | 10488 | 0.1285          | 0.9713    | 0.9713 | 0.9711 |
| 0.0384        | 4.0   | 13984 | 0.1237          | 0.9743    | 0.9743 | 0.9742 |
| 0.0019        | 5.0   | 17480 | 0.1346          | 0.9744    | 0.9743 | 0.9743 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1