The ezsummarizer project is focused on developing a comprehensive architecture that leverages cutting-edge technologies to summarize audio, text, and video content. This system integrates various tools and platforms to efficiently manage user interactions, media processing, and data storage, resulting in precise and relevant summaries across multiple content types. The architecture is scalable and adaptable, suitable for applications such as podcast transcription, video and audio content summarization, and article summarization.
- Vercel: Used for deployment, ensuring a seamless and scalable hosting environment.
- Hugging Face: Employed for natural language processing, hosting, and providing datasets for text summarization tasks.
- Supabase: Powers the backend services, handling user authentication, database management, and API integration.
- Assembly AI: Converts speech to text, enabling the summarization of audio and video content.
- Dropbox: Serves as the storage solution for media files and processed summaries.
- JavaScript Web Speech API: Facilitates real-time speech recognition, enhancing the user experience for live audio summarization.
- Azure: Hosts machine learning models, providing robust and scalable processing power for summarization tasks.
- Audio Summarization: Converts spoken content from audio files into concise text summaries.
- Video Summarization: Extracts key points from video content, summarizing them in text format.
- Text Summarization: Summarizes large text documents into brief, coherent summaries.
- Real-time Speech Recognition: Supports real-time audio input and generates live summaries.
- Scalability: Designed to handle large volumes of media content efficiently, making it suitable for enterprise-level applications.
To run the ezsummarizer project locally, follow these steps:
- Clone this repository to your local machine:
git clone [https://github.com/sudhy8/ezsummarizer.git] cd ezsummarizer - Install the necessary dependencies:
pip install -r requirements.txt
- Set up the required environment variables for Vercel, Supabase, Dropbox, and Azure.
- Ensure you have access to the relevant APIs (Hugging Face, Assembly AI, JavaScript Web Speech API) and configure them in the project settings.
- Deploy the application on Vercel:
vercel deploy
- Upload media content via the user interface or use real-time speech input for summarization.
- The system will process the input using the integrated services and generate summaries.
- Access the summaries from the Dropbox storage or directly from the application interface.
This project demonstrates a solid workflow that integrates various services to provide accurate and relevant summaries for audio, video, and text content. The architecture's scalability and adaptability ensure that it can handle various media types effectively.