med-whisper-large-final
This model is a fine-tuned version of openai/whisper-large-v3 on the primock_data dataset.
Model description
Fine tuned version of whisper-large-v3 through transfer learning on Doctor/Patient consultations
Intended uses & limitations
Medical transcription
Training and evaluation data
Na0s/Medical_Augmented_data
Training procedure
Exhaustive transfer learning
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Performance Overview:
| | Model Name | WER | CER | Number of Parameters |
|---|---|---|---|
| Whisper Tiny | 0.46 | 0.27 | 39M |
| Whisper Base | 0.42 | 0.26 | 74M |
| Whisper Small | 0.39 | 0.26 | 244M |
| Whisper Medium | 0.37 | 0.23 | 769M |
| Whisper Large v3 | 0.33 | 0.18 | 1.55B |
| Whisper Medical | 0.19 | 0.10 | 1.55B |
Performance of foundation Whispers vs Medical Whisper on the Validation set.
| Model Name | WER | CER | Number of Parameters |
|---|---|---|---|
| Whisper Medical | 0.24 | 0.13 | 1.55B |
Table: Performance of Whisper Medical on the Test set.
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1