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--- |
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library_name: transformers |
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base_model: openai/whisper-large-turbo |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Farhang87/whisperlargeturbo-Medical-ASR-Data |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large Turbo Medical |
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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 ASR |
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type: Farhang87/whisperlargeturbo-Medical-ASR-Data |
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metrics: |
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- name: Wer |
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type: wer |
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value: 4.344677769732078 |
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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 Large Turbo Medical |
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This model is a fine-tuned version of [openai/whisper-large-turbo](https://huggingface.co/openai/whisper-large-turbo) on the Medical ASR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0672 |
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- Wer: 4.3447 |
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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: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use 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: 50 |
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- training_steps: 500 |
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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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| 0.2994 | 0.5405 | 100 | 0.2101 | 9.9928 | |
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| 0.1405 | 1.0811 | 200 | 0.1212 | 5.7929 | |
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| 0.0859 | 1.6216 | 300 | 0.0929 | 4.4895 | |
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| 0.044 | 2.1622 | 400 | 0.0739 | 3.9585 | |
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| 0.0248 | 2.7027 | 500 | 0.0672 | 4.3447 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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