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--- |
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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-large |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small ar - Mohammed Bakheet |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 11.0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: ar |
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split: test |
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args: ar |
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metrics: |
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- name: Wer |
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type: wer |
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value: 12.614980289093298 |
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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 Small ar - Mohammed Bakheet |
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نموذج كلام للتعرف على الصوت، هذا النموذج يتميز بدقة عالية في التعرف على الصوت باللغة العربية. |
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This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Common Voice 11.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1921 |
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- Wer: 12.6150 |
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## Model description |
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This model is a fine-tuned version of openai/whisper-large on the Common Voice 11.0 dataset. It achieves 12.61 WER. |
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Data augmentation can be implemented to further improve the model performance. |
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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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This model is trained on the Common Voice 11.0 dataset. It's trained on 64 cores CPU, Nvidia A100 GPU with 48 VRAM, and 100GB Disk space. The GPU utilization reached 100%. |
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Please check the training hyperparameters below. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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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: 500 |
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- training_steps: 2000 |
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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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| 0.1952 | 1.6630 | 1000 | 0.1843 | 14.0098 | |
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| 0.0339 | 3.3261 | 2000 | 0.1921 | 12.6150 | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.2.0 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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