README: Comprehensive Overview of Speech Detection, Medical SOAP Classification, and Model Training

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Overview

This project integrates workflows for speech detection, medical SOAP classification, and machine learning model training into a cohesive pipeline. It leverages cutting-edge tools such as OpenAI Whisper, Hugging Face Transformers, and Named Entity Recognition models to process, analyze, and generate insights from speech, text, and structured datasets.

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Key Objectives

1. Speech Detection and Processing:

   • Preprocess audio files through denoising, segmentation, and transcription.
   • Use the OpenAI Whisper model for accurate speech-to-text conversions.
   • Organize outputs into structured directories.

2. Medical SOAP Classification:

   • Analyze medical dialogues to classify text into Subjective, Objective, Assessment, and Plan categories.
   • Employ NLP and entity recognition models to extract structured medical information.
   • Save categorized data for downstream analysis.

3. Model Training:

   • Fine-tune pre-trained models for specific NLP tasks such as summarization or translation.
   • Configure and evaluate models using advanced metrics like F1 score and BLEU score.
   • Save and reuse trained models for scalable applications.

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Highlights of the Workflow

1. Audio Preprocessing:

   • Convert audio formats for compatibility and remove irrelevant content.
   • Segment long audio files into manageable chunks.

2. Speech-to-Text and Transcription:

   • Generate high-quality transcriptions from audio data.
   • Clean and refine text outputs to remove placeholders and noise markers.

3. SOAP Categorization:

   • Map medical entities to SOAP components for actionable insights.
   • Aggregate data into CSV files for easy retrieval.

4. Model Fine-Tuning:

   • Train transformer models on custom datasets.
   • Evaluate performance using precision, recall, and accuracy metrics.

5. Evaluation and Organization:

   • Measure transcription quality with Word Error Rate (WER) and BLEU scores.
   • Organize data outputs into structured folders for scalable use.

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Outputs

• Denoised and Segmented Audio Files
• High-Quality Transcriptions
• SOAP Categorized Medical Data
• Fine-Tuned Models for NLP Tasks
• Evaluation Metrics Reports

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Applications

This pipeline is designed for:

• Medical data analysis and structuring.
• Multilingual speech transcription and translation.
• Machine learning model customization for domain-specific tasks.

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Summary

The project combines advanced machine learning, natural language processing, and speech analytics to create a versatile pipeline for handling and interpreting complex datasets. It offers modularity, scalability, and efficiency, making it suitable for various applications in healthcare, research, and technology.