AI Engineer and Analyst

github

Amitha Rajakumar  • Peerlist

News Summarization and TTS Model: Project Overview The News Summarization and TTS Model is an AI-powered system that extracts news articles, generates concise summaries, analyzes sentiment, and converts text into Hindi speech. The project integrates Natural Language Processing (NLP), Machine Learning, and Text-to-Speech (TTS) technologies to enhance news accessibility and comprehension. Key Features News Extraction – Fetches news articles from APIs like NewsAPI. Text Summarization – Uses transformers-based models for concise summaries. Sentiment Analysis – Classifies news as positive, negative, or neutral using ML techniques. Comparative Analysis – Compares multiple sources covering the same topic. Text-to-Speech (TTS) – Converts Hindi summaries into natural-sounding speech using gTTS. Structured JSON Output – Formats the output for website integration. Technology Stack Frontend: Streamlit (User Interface) Backend: Flask (API) Machine Learning: transformers, nltk, scikit-learn Web Scraping: BeautifulSoup TTS: gTTS, pyttsx3 Deployment: GitHub Actions, Hugging Face Spaces Use Cases News Aggregators – Summarizes trending news for easy consumption. Accessibility Solutions – Converts news into Hindi speech for visually impaired users. Data Analysis – Helps researchers analyze sentiment trends. Deployment & Challenges The model is deployed on Hugging Face Spaces with automated syncing via GitHub Actions. Challenges include authentication issues, API rate limits, and model optimization. Future improvements involve multi-language support, real-time updates, and improved speech synthesis. This project enhances news accessibility, user engagement, and AI-driven content processing.