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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-Arabic-colab |
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results: [] |
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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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# wav2vec2-large-xls-r-300m-Arabic-colab |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0001 |
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- Wer: 0.0813 |
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- Cer: 0.0362 |
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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: 0.0005 |
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- train_batch_size: 16 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 250 |
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- num_epochs: 30.0 |
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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 | Wer | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 0.0166 | 1.0 | 51 | 0.0020 | 0.0944 | 0.0431 | |
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| 0.015 | 2.0 | 102 | 0.0018 | 0.0939 | 0.0434 | |
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| 0.0223 | 3.0 | 153 | 0.0034 | 0.0705 | 0.0311 | |
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| 0.0351 | 4.0 | 204 | 0.0089 | 0.1050 | 0.0414 | |
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| 0.0473 | 5.0 | 255 | 0.0051 | 0.1224 | 0.0614 | |
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| 0.0406 | 6.0 | 306 | 0.0084 | 0.1185 | 0.0547 | |
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| 0.0412 | 7.0 | 357 | 0.0030 | 0.0640 | 0.0254 | |
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| 0.0301 | 8.0 | 408 | 0.0157 | 0.0708 | 0.0219 | |
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| 0.0295 | 9.0 | 459 | 0.0027 | 0.0716 | 0.0298 | |
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| 0.0239 | 10.0 | 510 | 0.0077 | 0.0868 | 0.0354 | |
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| 0.0266 | 11.0 | 561 | 0.0017 | 0.0733 | 0.0301 | |
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| 0.0154 | 12.0 | 612 | 0.0015 | 0.0961 | 0.0385 | |
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| 0.0187 | 13.0 | 663 | 0.0006 | 0.1100 | 0.0465 | |
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| 0.0156 | 14.0 | 714 | 0.0015 | 0.1030 | 0.0426 | |
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| 0.013 | 15.0 | 765 | 0.0014 | 0.1068 | 0.0451 | |
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| 0.0136 | 16.0 | 816 | 0.0013 | 0.1066 | 0.0434 | |
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| 0.0123 | 17.0 | 867 | 0.0008 | 0.1240 | 0.0587 | |
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| 0.0098 | 18.0 | 918 | 0.0006 | 0.1140 | 0.0570 | |
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| 0.0108 | 19.0 | 969 | 0.0005 | 0.0843 | 0.0364 | |
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| 0.009 | 20.0 | 1020 | 0.0002 | 0.0954 | 0.0438 | |
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| 0.0083 | 21.0 | 1071 | 0.0003 | 0.0828 | 0.0377 | |
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| 0.0085 | 22.0 | 1122 | 0.0002 | 0.0648 | 0.0267 | |
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| 0.0073 | 23.0 | 1173 | 0.0003 | 0.0843 | 0.0373 | |
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| 0.0057 | 24.0 | 1224 | 0.0003 | 0.0822 | 0.0367 | |
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| 0.0039 | 25.0 | 1275 | 0.0002 | 0.0733 | 0.0329 | |
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| 0.0045 | 26.0 | 1326 | 0.0005 | 0.0754 | 0.0335 | |
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| 0.008 | 27.0 | 1377 | 0.0008 | 0.0803 | 0.0361 | |
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| 0.0045 | 28.0 | 1428 | 0.0001 | 0.0772 | 0.0340 | |
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| 0.0043 | 29.0 | 1479 | 0.0001 | 0.0808 | 0.0359 | |
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| 0.0042 | 30.0 | 1530 | 0.0001 | 0.0813 | 0.0362 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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