Welcome to the Azure-Voice-to-Text project! Developed during a hackathon, this innovative project utilizes Azure Cognitive Services to integrate text-to-speech and text classification functionalities. Our solution aims to enhance medical consultations by recording, transcribing, and analyzing conversations efficiently.
Poharogapp is a web application designed to streamline medical consultations in hospitals, whether they are in-person or virtual. The application provides a robust platform for recording and transcribing medical consultations, transforming spoken words into actionable text. This transcribed text is then analyzed using Azure's advanced AI capabilities, providing valuable insights and classifications.
The application leverages the following technologies:
Flask: A lightweight Python web framework that serves as the backbone of the web application, offering flexibility and ease of development. Azure Cognitive Services: Includes text-to-speech for converting audio to text and text classification for analyzing the transcribed content. Python: The primary programming language used for developing the application logic and integrating Azure services.
Medical Consultation Recording:
Allows users to record both in-person and virtual medical consultations. Ensures high-quality audio capture for accurate transcription. Transcription:
Utilizes Azure's text-to-speech service to convert recorded audio into text. Provides a clear and accurate textual representation of the consultation. Text Classification:
Processes transcribed text through our API, leveraging Azure's AI model. Categorizes and analyzes consultation content to provide actionable insights. User Interface:
Intuitive web interface for easy interaction with the application. Supports user authentication to secure access to sensitive data.
To set up and run the Azure-Voice-to-Text project locally, follow these steps:
Clone the Repository:
Clone the repository from GitHub link. bash Copy code git clone https://github.com/nicolasvargaszz/azure-voice-to-text.git cd azure-voice-to-text Install Dependencies:
Ensure you have Python 3.8 or higher installed. Install the necessary dependencies by running: bash Copy code pip install -r requirements.txt Configure Azure Credentials:
Configure these credentials in the application. You can typically do this by setting environment variables or updating a configuration file. Run the Flask Application:
Launch the Flask application by executing: bash Copy code python app.py Access the Web Interface:
Open your web browser and navigate to http://localhost:5000 to access the Poharogapp interface. 📈 Conclusion The Azure-Voice-to-Text project combines Azure's powerful text-to-speech and text classification services to provide a comprehensive solution for medical consultations. By recording, transcribing, and analyzing consultations, Poharogapp enhances the efficiency and effectiveness of healthcare professionals, making medical consultations more accessible and informative.
User Feedback Integration: Implement a feedback system to continuously improve the transcription and classification accuracy based on user input. Enhanced Security: Strengthen security features to protect sensitive medical data and ensure compliance with data protection regulations. Additional Languages: Expand the application to support multiple languages for a broader user base. Integration with Electronic Health Records (EHR): Explore integration with existing EHR systems to provide a seamless experience for healthcare providers. 📬 Contact For further inquiries or support, please reach out via the following channels:
Twitter: @nicoelingeniero GitHub: Nicolas Vargas