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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-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- alexstokes/masri_audio_transcription |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small - Egyptian Arabic |
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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: Egyptian Arabic Speech Recognition |
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type: alexstokes/masri_audio_transcription |
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args: 'split: train' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 41.667657904127516 |
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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 - Egyptian Arabic |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Egyptian Arabic Speech Recognition dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9560 |
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- Wer: 41.6677 |
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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: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- training_steps: 4000 |
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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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| 0.0518 | 7.6336 | 1000 | 0.7113 | 42.9166 | |
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| 0.004 | 15.2672 | 2000 | 0.8712 | 41.2157 | |
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| 0.001 | 22.9008 | 3000 | 0.9327 | 42.0245 | |
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| 0.0006 | 30.5344 | 4000 | 0.9560 | 41.6677 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.4.1.post303 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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