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---
language:
- en
- ar
tags:
- automatic-speech-recognition
- whisper
- medical
- asr
- fp16
license: apache-2.0
datasets:
- yashtiwari/PaulMooney-Medical-ASR-Data
model-index:
- name: Whisper Large-v3 Medical
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Medical ASR
type: yashtiwari/PaulMooney-Medical-ASR-Data
metrics:
- type: wer
value: 4.12
metrics:
- wer
base_model:
- openai/whisper-large-v3
pipeline_tag: automatic-speech-recognition
---
# Whisper Large-v3 Medical
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on medical speech data. It is trained for **Automatic Speech Recognition (ASR)** of **doctor-patient dialogues and medical narratives**, with support for **English** and **Arabic**.
## 🩺 Use Cases
- Transcribing clinical interviews.
- Building medical dictation tools.
- Precision: float16 (better for inference), float32 (better for fine-tuning)
## 📊 Performance
- **WER (Word Error Rate): 4.12% **
- Optimized for clean and domain-specific spoken medical data.
## 🔧 Model Details
- Base model: [`openai/whisper-large-v3`](https://huggingface.co/openai/whisper-large-v3)
- Fine-tuned on: [`yashtiwari/PaulMooney-Medical-ASR-Data`](https://huggingface.co/datasets/yashtiwari/PaulMooney-Medical-ASR-Data)
- Languages: Multilingual
- Framework: 🤗 Transformers
## 🧪 How to Use
```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
model_id = "yehiazak/whisper-largev3-medical"
# Load FP16 model
model = WhisperForConditionalGeneration.from_pretrained(model_id, revision="fp16")
# Load FP32 model
model = WhisperForConditionalGeneration.from_pretrained(model_id)
processor = WhisperProcessor.from_pretrained(model_id)
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