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---
license: bsd-3-clause
base_model: MIT/ast-finetuned-speech-commands-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ast-arabic
results: []
---
<!-- 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-arabic
This model is a fine-tuned version of [MIT/ast-finetuned-speech-commands-v2](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6961
- Accuracy: 0.7871
- Precision: 0.8186
- Recall: 0.7871
- F1: 0.7848
- Binary: 0.8501
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log | 0.19 | 50 | 4.5626 | 0.0027 | 0.0001 | 0.0027 | 0.0003 | 0.0860 |
| No log | 0.38 | 100 | 4.4915 | 0.0054 | 0.0037 | 0.0054 | 0.0034 | 0.0954 |
| No log | 0.58 | 150 | 4.3465 | 0.0216 | 0.0105 | 0.0216 | 0.0123 | 0.1488 |
| No log | 0.77 | 200 | 4.1317 | 0.0647 | 0.0355 | 0.0647 | 0.0372 | 0.2356 |
| No log | 0.96 | 250 | 3.8709 | 0.1267 | 0.0963 | 0.1267 | 0.0880 | 0.3245 |
| No log | 1.15 | 300 | 3.4181 | 0.2318 | 0.2405 | 0.2318 | 0.1927 | 0.4442 |
| No log | 1.34 | 350 | 2.8697 | 0.3612 | 0.3714 | 0.3612 | 0.3150 | 0.5426 |
| No log | 1.53 | 400 | 2.3609 | 0.4663 | 0.4425 | 0.4663 | 0.4157 | 0.6248 |
| No log | 1.73 | 450 | 1.9516 | 0.5606 | 0.5573 | 0.5606 | 0.5246 | 0.6903 |
| 3.5112 | 1.92 | 500 | 1.6670 | 0.6092 | 0.6428 | 0.6092 | 0.5849 | 0.7240 |
| 3.5112 | 2.11 | 550 | 1.4376 | 0.6739 | 0.6988 | 0.6739 | 0.6544 | 0.7709 |
| 3.5112 | 2.3 | 600 | 1.3049 | 0.6658 | 0.7019 | 0.6658 | 0.6495 | 0.7636 |
| 3.5112 | 2.49 | 650 | 1.1820 | 0.6846 | 0.7214 | 0.6846 | 0.6731 | 0.7784 |
| 3.5112 | 2.68 | 700 | 1.0847 | 0.7035 | 0.7220 | 0.7035 | 0.6913 | 0.7916 |
| 3.5112 | 2.88 | 750 | 1.0370 | 0.7116 | 0.7415 | 0.7116 | 0.7004 | 0.7973 |
| 3.5112 | 3.07 | 800 | 0.9833 | 0.7062 | 0.7132 | 0.7062 | 0.6903 | 0.7935 |
| 3.5112 | 3.26 | 850 | 0.9453 | 0.7116 | 0.7384 | 0.7116 | 0.6986 | 0.7973 |
| 3.5112 | 3.45 | 900 | 0.9140 | 0.7412 | 0.7724 | 0.7412 | 0.7313 | 0.8181 |
| 3.5112 | 3.64 | 950 | 0.8602 | 0.7493 | 0.7719 | 0.7493 | 0.7403 | 0.8237 |
| 1.0914 | 3.84 | 1000 | 0.8340 | 0.7520 | 0.7857 | 0.7520 | 0.7469 | 0.8256 |
| 1.0914 | 4.03 | 1050 | 0.8317 | 0.7628 | 0.8014 | 0.7628 | 0.7602 | 0.8332 |
| 1.0914 | 4.22 | 1100 | 0.7983 | 0.7628 | 0.7863 | 0.7628 | 0.7584 | 0.8332 |
| 1.0914 | 4.41 | 1150 | 0.8015 | 0.7601 | 0.7852 | 0.7601 | 0.7529 | 0.8313 |
| 1.0914 | 4.6 | 1200 | 0.7584 | 0.7709 | 0.7959 | 0.7709 | 0.7685 | 0.8388 |
| 1.0914 | 4.79 | 1250 | 0.7518 | 0.7763 | 0.7957 | 0.7763 | 0.7699 | 0.8426 |
| 1.0914 | 4.99 | 1300 | 0.7484 | 0.7520 | 0.7698 | 0.7520 | 0.7449 | 0.8256 |
| 1.0914 | 5.18 | 1350 | 0.7518 | 0.7466 | 0.7711 | 0.7466 | 0.7397 | 0.8218 |
| 1.0914 | 5.37 | 1400 | 0.7379 | 0.7682 | 0.7961 | 0.7682 | 0.7654 | 0.8369 |
| 1.0914 | 5.56 | 1450 | 0.7356 | 0.7601 | 0.7770 | 0.7601 | 0.7533 | 0.8313 |
| 0.6112 | 5.75 | 1500 | 0.7351 | 0.7763 | 0.8018 | 0.7763 | 0.7720 | 0.8426 |
| 0.6112 | 5.94 | 1550 | 0.7230 | 0.7655 | 0.7886 | 0.7655 | 0.7631 | 0.8350 |
| 0.6112 | 6.14 | 1600 | 0.7222 | 0.7709 | 0.7977 | 0.7709 | 0.7656 | 0.8388 |
| 0.6112 | 6.33 | 1650 | 0.7054 | 0.7790 | 0.8022 | 0.7790 | 0.7744 | 0.8445 |
| 0.6112 | 6.52 | 1700 | 0.7286 | 0.7736 | 0.8016 | 0.7736 | 0.7693 | 0.8407 |
| 0.6112 | 6.71 | 1750 | 0.6991 | 0.7925 | 0.8141 | 0.7925 | 0.7907 | 0.8539 |
| 0.6112 | 6.9 | 1800 | 0.7096 | 0.7655 | 0.7907 | 0.7655 | 0.7633 | 0.8350 |
| 0.6112 | 7.09 | 1850 | 0.7010 | 0.7844 | 0.8062 | 0.7844 | 0.7821 | 0.8482 |
| 0.6112 | 7.29 | 1900 | 0.7026 | 0.7574 | 0.7813 | 0.7574 | 0.7514 | 0.8294 |
| 0.6112 | 7.48 | 1950 | 0.6973 | 0.7817 | 0.7992 | 0.7817 | 0.7785 | 0.8464 |
| 0.4147 | 7.67 | 2000 | 0.7035 | 0.7736 | 0.7911 | 0.7736 | 0.7707 | 0.8407 |
| 0.4147 | 7.86 | 2050 | 0.6894 | 0.7844 | 0.8089 | 0.7844 | 0.7827 | 0.8482 |
| 0.4147 | 8.05 | 2100 | 0.7046 | 0.7763 | 0.8009 | 0.7763 | 0.7731 | 0.8426 |
| 0.4147 | 8.25 | 2150 | 0.6994 | 0.7817 | 0.8028 | 0.7817 | 0.7786 | 0.8464 |
| 0.4147 | 8.44 | 2200 | 0.6890 | 0.7763 | 0.7994 | 0.7763 | 0.7723 | 0.8426 |
| 0.4147 | 8.63 | 2250 | 0.6988 | 0.7763 | 0.7954 | 0.7763 | 0.7722 | 0.8426 |
| 0.4147 | 8.82 | 2300 | 0.7021 | 0.7763 | 0.7964 | 0.7763 | 0.7731 | 0.8426 |
| 0.4147 | 9.01 | 2350 | 0.6974 | 0.7817 | 0.8048 | 0.7817 | 0.7784 | 0.8464 |
| 0.4147 | 9.2 | 2400 | 0.6861 | 0.7817 | 0.8041 | 0.7817 | 0.7807 | 0.8464 |
| 0.4147 | 9.4 | 2450 | 0.6930 | 0.7763 | 0.7994 | 0.7763 | 0.7734 | 0.8426 |
| 0.3165 | 9.59 | 2500 | 0.6954 | 0.7763 | 0.8001 | 0.7763 | 0.7741 | 0.8426 |
| 0.3165 | 9.78 | 2550 | 0.6953 | 0.7817 | 0.8045 | 0.7817 | 0.7794 | 0.8464 |
| 0.3165 | 9.97 | 2600 | 0.6940 | 0.7817 | 0.8053 | 0.7817 | 0.7794 | 0.8464 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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