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--- |
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library_name: transformers |
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language: |
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- ar |
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license: apache-2.0 |
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base_model: openai/whisper-base |
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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: Whisper base AR - BA |
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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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# Whisper base AR - BA |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0853 |
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- Wer: 0.1969 |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.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_steps: 500 |
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- num_epochs: 15 |
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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 | |
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|:-------------:|:-------:|:-----:|:---------------:|:------:| |
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| 2.0599 | 0.5858 | 1000 | 0.0910 | 0.2075 | |
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| 1.6156 | 1.1716 | 2000 | 0.0921 | 0.1917 | |
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| 1.5706 | 1.7575 | 3000 | 0.0891 | 0.1953 | |
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| 1.3401 | 2.3433 | 4000 | 0.0880 | 0.1882 | |
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| 1.2238 | 2.9291 | 5000 | 0.0865 | 0.1886 | |
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| 1.0654 | 3.5149 | 6000 | 0.0860 | 0.1922 | |
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| 1.0904 | 4.1008 | 7000 | 0.0859 | 0.2000 | |
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| 1.2607 | 4.6866 | 8000 | 0.0872 | 0.1882 | |
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| 1.147 | 5.2724 | 9000 | 0.0870 | 0.1944 | |
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| 1.1237 | 5.8582 | 10000 | 0.0856 | 0.1905 | |
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| 1.0093 | 6.4441 | 11000 | 0.0849 | 0.2001 | |
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| 0.9993 | 7.0299 | 12000 | 0.0839 | 0.1888 | |
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| 0.8718 | 7.6157 | 13000 | 0.0844 | 0.1894 | |
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| 0.8877 | 8.2015 | 14000 | 0.0838 | 0.1908 | |
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| 0.8187 | 8.7873 | 15000 | 0.0843 | 0.1957 | |
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| 0.8235 | 9.3732 | 16000 | 0.0838 | 0.1975 | |
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| 0.7972 | 9.9590 | 17000 | 0.0835 | 0.1911 | |
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| 0.8203 | 10.5448 | 18000 | 0.0844 | 0.1866 | |
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| 0.8593 | 11.1306 | 19000 | 0.0843 | 0.1916 | |
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| 0.8279 | 11.7165 | 20000 | 0.0840 | 0.1905 | |
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| 0.806 | 12.3023 | 21000 | 0.0827 | 0.1897 | |
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| 0.8343 | 12.8881 | 22000 | 0.0832 | 0.1891 | |
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| 0.7252 | 13.4739 | 23000 | 0.0830 | 0.1845 | |
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| 0.7685 | 14.0598 | 24000 | 0.0830 | 0.1919 | |
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| 0.7085 | 14.6456 | 25000 | 0.0829 | 0.1975 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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