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--- |
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language: |
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- eng |
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tags: |
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- generated_from_trainer |
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base_model: openai/whisper-small-2000 |
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datasets: |
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- Kaggle/transcription_audio |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small Eng - noursene |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: medical audio trascription |
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type: Kaggle/transcription_audio |
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args: 'config: eng' |
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metrics: |
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- type: wer |
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value: 10.536550234065539 |
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name: Wer |
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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 Eng - noursene |
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This model is a fine-tuned version of [openai/whisper-small-2000](https://huggingface.co/openai/whisper-small-2000) on the medical audio trascription dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1612 |
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- Wer: 10.5366 |
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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: 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: 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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| 0.191 | 3.0257 | 500 | 0.2308 | 14.0727 | |
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| 0.0375 | 6.0514 | 1000 | 0.1570 | 10.9975 | |
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| 0.0045 | 9.0772 | 1500 | 0.1594 | 10.7598 | |
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| 0.0029 | 12.1029 | 2000 | 0.1612 | 10.5366 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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