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---
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- medical_data
- Na0s/Primock_med
model-index:
- name: Final_Medical_whisper
results: []
metrics:
- cer
- wer
pipeline_tag: automatic-speech-recognition
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
<a href="https://ibb.co/4YRxh82"><img src="https://i.ibb.co/wwh15S7/DALL-E-2024-10-05-20-47-54-A-doctor-in-a-modern-clinical-setting-carefully-listening-to-a-patient-s.webp" alt="DALL-E-2024-10-05-20-47-54-A-doctor-in-a-modern-clinical-setting-carefully-listening-to-a-patient-s" border="0"></a>
# med-whisper-large-final
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/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 |