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mdd-arabic-e5-xlsr1b-specaugment-beam
omarZACK/mdd-arabic-e5-xlsr1b-specaugment-beam
mdd-arabic-e5-xlsr1b-specaugment-beam is a automatic speech recognition model from omarZACK. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Aug 20, 2026
- Updated
- Aug 21, 2026
- SHA
- f7b486d5
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
mdd-arabic-e5-xlsr1b-specaugment-beam
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2205
- Per: 0.2245
- Accuracy: 0.7621
- Precision: 0.2869
- Recall: 0.2746
- F1 Macro: 0.2804
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | F1 Macro | Validation Loss | Per | Precision | Recall |
|---|---|---|---|---|---|---|---|---|
| 2.1605 | 1.0 | 728 | 0.7864 | 0.2971 | 0.7895 | 0.1988 | 0.2976 | 0.3045 |
| 1.0854 | 2.0 | 1456 | 0.7764 | 0.2950 | 0.9990 | 0.2084 | 0.3008 | 0.2896 |
| 0.7894 | 3.0 | 2184 | 0.7773 | 0.2879 | 1.0016 | 0.2078 | 0.2959 | 0.2804 |
| 0.6151 | 4.0 | 2912 | 0.7765 | 0.2978 | 1.0822 | 0.2104 | 0.3033 | 0.2927 |
| 0.6811 | 5.0 | 3640 | 0.7697 | 0.2807 | 1.0894 | 0.2127 | 0.2847 | 0.2778 |
| 0.5452 | 6.0 | 4368 | 0.7715 | 0.2888 | 1.0083 | 0.2130 | 0.2928 | 0.2857 |
| 0.4816 | 7.0 | 5096 | 1.1389 | 0.2206 | 0.7654 | 0.2902 | 0.2862 | 0.2874 |
| 0.4420 | 8.0 | 5824 | 1.2157 | 0.2247 | 0.7617 | 0.2824 | 0.2741 | 0.2777 |
| 0.4235 | 9.0 | 6552 | 1.1786 | 0.2312 | 0.7564 | 0.2765 | 0.2693 | 0.2721 |
| 0.3895 | 10.0 | 7280 | 1.2156 | 0.2285 | 0.7600 | 0.2879 | 0.2785 | 0.2826 |
| 0.5042 | 11.0 | 8008 | 1.2205 | 0.2245 | 0.7621 | 0.2869 | 0.2746 | 0.2804 |
Framework versions
- Transformers 5.15.0
- Pytorch 2.13.0+cu130
- Datasets 5.0.1
- Tokenizers 0.22.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Aug 20, 2026
- Updated
- Aug 21, 2026