Add MedASR medical speech recognition notebook

This notebook demonstrates converting Google's MedASR model to OpenVINO
with FP16 and INT8 quantization for efficient medical speech recognition.

Features:
- HuggingFace authentication with notebook_login for gated model access
- Model conversion using torch.export and ov.convert_model
- INT8 quantization with NNCF using real audio calibration data
- Comprehensive accuracy validation (97.98% token-level accuracy)
- Performance benchmarking on CPU and GPU
- Model compression: 402 MB -> 102 MB (3.9x reduction)

The notebook includes complete workflow from model loading to deployment,
with support for 10-second audio chunks (static shape [1, 998, 128]).

Author: padatta

notebooks/medasr-medical-asr/README.md

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+# MedASR Medical Speech Recognition with OpenVINO
+
+This notebook demonstrates converting Google's MedASR (Medical Automatic Speech Recognition) model to OpenVINO format with FP16 and INT8 quantization for efficient medical speech-to-text transcription.
+
+## Overview
+
+MedASR is a specialized speech recognition model optimized for medical terminology. This tutorial shows how to:
+
+- Load the MedASR model from HuggingFace
+- Convert it to OpenVINO IR format for optimal inference performance
+- Apply INT8 quantization using NNCF for model compression
+- Compare accuracy and performance across PyTorch, FP16, and INT8 versions
+
+## Key Features
+
+- **Model Compression**: 3.9x size reduction (402 MB → 102 MB) with INT8 quantization
+- **High Accuracy**: 97.98% token-level accuracy maintained after INT8 quantization
+- **Medical Terminology**: Optimized for accurate medical speech recognition
+
+## Tutorial Contents
+
+1. **Installation** - Install required packages (OpenVINO, NNCF, Transformers, etc.)
+2. **Load Model** - Load Google's MedASR model from HuggingFace
+3. **Prepare Audio Data** - Download and preprocess test audio (optimized for 10s chunks)
+4. **PyTorch Inference** - Establish baseline accuracy with original model
+5. **Convert to OpenVINO FP16** - Convert using torch.export and ov.convert_model
+6. **INT8 Quantization** - Apply NNCF quantization with real audio calibration
+7. **Accuracy Comparison** - Validate quantization quality across all versions
+8. **Performance Benchmarking** - Measure inference speed on CPU and GPU
+
+## Results
+
+- **Model Size**: 402 MB (FP16) → 102 MB (INT8) = **3.9x compression**
+- **Accuracy**: 97.98% token match between INT8 and PyTorch
+- **Model Shape**: Static [1, 998, 128] optimized for 10-second audio chunks
+
+## Installation
+
+```bash
+pip install -q "openvino>=2024.4.0" "nncf>=2.13.0" "torch>=2.1" "transformers>=5.4.0" "librosa" "soundfile" "huggingface_hub"
+```
+
+## Important Notes
+
+⚠️ **Gated Model Access**: The MedASR model is gated on HuggingFace. You must:
+1. Request access at https://huggingface.co/google/medasr
+2. Authenticate with your HuggingFace token before running the notebook
+
+## Use Cases
+
+- Medical transcription systems
+- Clinical documentation automation
+- Healthcare voice assistants
+- Medical education and training platforms