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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-large-v3 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-large-clinical |
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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: audiofolder |
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type: audiofolder |
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config: default |
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split: None |
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args: default |
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metrics: |
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- name: Wer |
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type: wer |
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value: 5.21215221530679 |
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--- |
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# whisper-large-clinical |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on a private audiofolder dataset of 18.96 hours of clinical notes text data and corresponding synthetic audio generated by a TTS API. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2757 |
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- Wer: 5.2122 |
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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-06 |
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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: 5000 |
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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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| 0.0143 | 9.0090 | 1000 | 0.2275 | 5.2605 | |
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| 0.0009 | 18.0180 | 2000 | 0.2468 | 5.1724 | |
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| 0.0003 | 27.0270 | 3000 | 0.2641 | 5.2548 | |
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| 0.0002 | 36.0360 | 4000 | 0.2728 | 5.2264 | |
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| 0.0002 | 45.0450 | 5000 | 0.2757 | 5.2122 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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