| --- |
| 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 |
