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--- |
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library_name: transformers |
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license: bsd-3-clause |
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base_model: |
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- MIT/ast-finetuned-speech-commands-v2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: ast-mlcommons-speech-commands |
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results: |
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- task: |
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name: Audio Classification |
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type: audio-classification |
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dataset: |
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name: audiofolder |
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type: audiofolder |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9743628199079283 |
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- name: Recall |
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type: recall |
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value: 0.9743424814179531 |
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- name: F1 |
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type: f1 |
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value: 0.9743165983480835 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ast-mlcommons-speech-commands |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1346 |
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- Precision: 0.9744 |
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- Recall: 0.9743 |
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- F1: 0.9743 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:| |
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| 0.0799 | 1.0 | 3496 | 0.1498 | 0.9596 | 0.9573 | 0.9577 | |
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| 0.0624 | 2.0 | 6992 | 0.1141 | 0.9689 | 0.9687 | 0.9685 | |
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| 0.0091 | 3.0 | 10488 | 0.1285 | 0.9713 | 0.9713 | 0.9711 | |
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| 0.0384 | 4.0 | 13984 | 0.1237 | 0.9743 | 0.9743 | 0.9742 | |
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| 0.0019 | 5.0 | 17480 | 0.1346 | 0.9744 | 0.9743 | 0.9743 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.7.0+cu128 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
