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Medical Speech-to-Text Model Development and Web Service

This project, titled “Development of a Medical Speech-to-Text Model and Web Service for Processing Medical Audio Data,” aims to create an advanced system for accurately converting medical audio files into text. The system will be available as a web service for easy access. Key phases of the project include:

  1. Medical Audio Data Collection and Augmentation

Medical audio data, including doctor-patient conversations and notes, is collected from trusted sources. Data augmentation techniques, such as time-stretching, background noise addition, and pitch shifting, are used to improve system performance under various conditions using tools like Audiomentations and PyDub.

  1. Audio Data Preprocessing

Collected audio is preprocessed to remove noise and normalize quality using LibROSA. Unnecessary segments are trimmed, and files are converted into standard formats to ensure high-quality data for the speech-to-text system.

  1. Speech-to-Text Model Development

A specialized speech-to-text model is trained to accurately recognize medical terminology. Using Hugging Face Transformers, the model is optimized for converting complex medical conversations into text with high precision.

  1. Web Service Development with FastAPI

The system is deployed as a web service using FastAPI, enabling users to upload audio files or stream live audio for real-time transcription. The service offers quick, accurate text output and uses Pydantic for input validation.

  1. Model Evaluation and Accuracy Improvement

The model is tested in various real-world scenarios, including noisy environments and multi-speaker conversations. The system is continually optimized to improve transcription accuracy.

  1. Medical Applications of Speech-to-Text

– **Medical Recordkeeping**: Helps healthcare professionals convert conversations or instructions into text for recordkeeping.

– **Audio Data Analysis**: Enables analysis of transcripts for automatic disease diagnosis or extracting key medical insights.

– **Improved User Experience**: Enhances audio-based interactions in medical systems, reducing the need for manual note-taking.

Technologies Used:

– Advanced speech-to-text model for medical data conversion.

– **FastAPI** for building and managing the web service.

– **LibROSA** for noise removal and normalization.

– **Audiomentations** and **PyDub** for audio data augmentation.

– **Hugging Face Transformers** for managing NLP models.

– **Pydantic** for input validation.

– **NumPy** and **SciPy** for mathematical operations and audio processing.

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