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wav2vec2-base-960h-Arabic
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7209
- Wer: 1.0
- Cer: 1.0
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: 0.0005
- train_batch_size: 16
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 40
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
---|---|---|---|---|---|
0.7909 | 1.0 | 51 | 0.8018 | 1.0 | 1.0 |
0.6736 | 2.0 | 102 | 1.1252 | 1.0 | 1.0 |
0.7037 | 3.0 | 153 | 0.7432 | 1.0 | 1.0 |
0.724 | 4.0 | 204 | 1.2762 | 1.0 | 1.0 |
0.8887 | 5.0 | 255 | 0.7064 | 1.0 | 1.0 |
0.835 | 6.0 | 306 | 1.0820 | 1.0 | 1.0 |
0.8042 | 7.0 | 357 | 1.0530 | 1.0 | 1.0 |
0.7475 | 8.0 | 408 | 0.6969 | 1.0 | 1.0 |
0.6998 | 9.0 | 459 | 0.7852 | 1.0 | 1.0 |
0.7048 | 10.0 | 510 | 0.6942 | 1.0 | 1.0 |
0.7883 | 11.0 | 561 | 0.7767 | 1.0 | 1.0 |
0.6773 | 12.0 | 612 | 0.7355 | 1.0 | 1.0 |
0.7421 | 13.0 | 663 | 1.5550 | 1.0 | 1.0 |
0.7573 | 14.0 | 714 | 0.8736 | 1.0 | 1.0 |
0.6911 | 15.0 | 765 | 1.3328 | 1.0 | 1.0 |
0.7129 | 16.0 | 816 | 0.8911 | 1.0 | 1.0 |
0.6619 | 17.0 | 867 | 1.0227 | 1.0 | 1.0 |
0.6807 | 18.0 | 918 | 0.7829 | 1.0 | 1.0 |
0.6409 | 19.0 | 969 | 0.9122 | 1.0 | 1.0 |
0.6588 | 20.0 | 1020 | 0.9179 | 1.0 | 1.0 |
0.6648 | 21.0 | 1071 | 0.8469 | 1.0 | 1.0 |
0.6521 | 22.0 | 1122 | 0.7197 | 1.0 | 1.0 |
0.7189 | 23.0 | 1173 | 0.7972 | 1.0 | 1.0 |
0.664 | 24.0 | 1224 | 1.0197 | 1.0 | 1.0 |
0.6611 | 25.0 | 1275 | 1.0786 | 1.0 | 1.0 |
0.6333 | 26.0 | 1326 | 0.8307 | 1.0 | 1.0 |
0.6407 | 27.0 | 1377 | 0.7388 | 1.0 | 1.0 |
0.6322 | 28.0 | 1428 | 0.6789 | 1.0 | 1.0 |
0.6283 | 29.0 | 1479 | 0.6917 | 1.0 | 1.0 |
0.6343 | 30.0 | 1530 | 0.7045 | 1.0 | 1.0 |
0.6292 | 31.0 | 1581 | 0.6717 | 1.0 | 1.0 |
0.6209 | 32.0 | 1632 | 0.7436 | 1.0 | 1.0 |
0.6088 | 33.0 | 1683 | 0.7392 | 1.0 | 1.0 |
0.6103 | 34.0 | 1734 | 0.7387 | 1.0 | 1.0 |
0.6084 | 35.0 | 1785 | 0.7647 | 1.0 | 1.0 |
0.6042 | 36.0 | 1836 | 0.8165 | 1.0 | 1.0 |
0.605 | 37.0 | 1887 | 0.7578 | 1.0 | 1.0 |
0.6042 | 38.0 | 1938 | 0.7359 | 1.0 | 1.0 |
0.6 | 39.0 | 1989 | 0.7287 | 1.0 | 1.0 |
0.5963 | 40.0 | 2040 | 0.7209 | 1.0 | 1.0 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2