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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: openai/whisper-large-v3 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- OUTCOMESAI/medical_speech_corpus |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small 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: OUTCOMESAI/medical_speech_corpus zh-en |
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type: OUTCOMESAI/medical_speech_corpus |
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metrics: |
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- name: Wer |
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type: wer |
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value: 44.25531914893617 |
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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 Medical |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the OUTCOMESAI/medical_speech_corpus zh-en dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6201 |
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- Wer: 44.2553 |
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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: 5e-07 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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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: 100 |
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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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| 7.4337 | 25.0 | 50 | 0.6201 | 44.2553 | |
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| 5.7447 | 50.0 | 100 | 0.6113 | 51.2340 | |
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### Framework versions |
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- Transformers 4.48.0.dev0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.1.1.dev0 |
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- Tokenizers 0.21.0 |
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