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--- |
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library_name: transformers |
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language: |
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- en |
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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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datasets: |
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- Hani89/medical_asr_recording_dataset |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Base - Safa |
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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: 'medical-speech-transcription-and-intent ' |
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type: Hani89/medical_asr_recording_dataset |
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args: 'config: en, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 6.061930783242259 |
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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 - Safa |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the medical-speech-transcription-and-intent dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1192 |
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- Wer: 6.0619 |
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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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| 0.0544 | 3.0030 | 1000 | 0.1282 | 7.2423 | |
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| 0.005 | 6.0060 | 2000 | 0.1124 | 6.0109 | |
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| 0.0006 | 9.0090 | 3000 | 0.1178 | 5.9891 | |
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| 0.0004 | 12.0120 | 4000 | 0.1192 | 6.0619 | |
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### Framework versions |
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- Transformers 4.50.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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