mahwizzzz/medwhishper

MedWhisper: Fine-Tuned Whisper-Small for Medical Transcription

MedWhisper is a fine-tuned version of OpenAI's Whisper-small model, optimized specifically for automatic speech recognition (ASR) in the medical domain.

---

Note: The training dataset used for this model has since been published and is now maintained at Mozilla Data Collective under Proxima AI. The original Hugging Face dataset repo now redirects to this canonical listing.

Model Overview

MedWhisper builds upon the strong baseline of Whisper-small and adapts it for medical speech, achieving significant reductions in error rates on evaluation data. This makes it well-suited for applications such as medical dictation, clinical note transcription, and other healthcare communication tasks.

---

Performance Metrics on Evaluation Set

Metric	Value
Loss	0.0303
CER (Character Error Rate)	1.90%
WER (Word Error Rate)	2.65%
SER (Sentence Error Rate)	10.68%

These metrics demonstrate the model's high transcription accuracy in the medical domain.

---

Training Details

• Base model: openai/whisper-small
• Dataset: Custom medical transcription corpus (proprietary)
• Epochs: 50
• Batch size: 8 (train and eval), with gradient accumulation to total batch size 64
• Optimizer: AdamW (betas=(0.9, 0.999), epsilon=1e-8)
• Learning rate: 1e-5 with linear scheduler and 500 warmup steps
• Mixed precision: Native AMP (Automatic Mixed Precision)
• Seed: 42

Training Progress Highlights

Step	Epoch	Train Loss	Val Loss	CER (%)	WER (%)	SER (%)
1000	3.6	0.0105	0.0343	3.23	4.56	17.58
2000	7.3	0.0022	0.0307	1.77	2.59	12.73
8000	29.1	0.0002	0.0289	1.75	2.47	10.91
13000	47.3	0.0001	0.0303	1.90	2.65	10.68

---

Framework & Dependencies

• Transformers 4.54.0.dev0
• PyTorch 2.7.1+cu126
• Datasets 3.6.0
• Tokenizers 0.21.2

---

Acknowledgments

medwhisper is provided under the MIT license. If you use this model in your work, please acknowledge the creators and consider referencing this model as follows:

@model{medwhisper,
title = {medwhisper Model},
author = {Mahwiz Khalil},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/mahwizzzz/medwhishper}},
}