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--- |
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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.en |
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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: English Whisper Model |
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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 |
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type: Hani89/medical_asr_recording_dataset |
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args: 'split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 8.218579234972678 |
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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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# English Whisper Model |
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This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the Medical dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1241 |
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- Wer: 8.2186 |
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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: 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: 3000 |
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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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| 0.0573 | 3.0030 | 1000 | 0.1333 | 8.6703 | |
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| 0.0073 | 6.0060 | 2000 | 0.1185 | 8.2914 | |
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| 0.0009 | 9.0090 | 3000 | 0.1241 | 8.2186 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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