AI-Powered Lecture Summarizer & Note Generator

Lecture-to-Knowledge Pipeline for Smarter Learning

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Project Description

Students and self-learners often struggle with slow manual note-taking, passive learning from lengthy lectures, and fragmented tools for revision, quizzes, and doubt-solving. Transforming long lecture videos into structured notes requires repeated effort and reduces engagement, retention, and efficiency.

Our solution introduces an AI-powered Lecture-to-Knowledge Pipeline that converts raw lecture videos into clean, structured, and visually enriched study material — automatically.

The system treats a lecture as a single source of truth and generates multiple learning artifacts, including:

• Structured summarized notes
• Key concept extraction
• Auto-generated quizzes
• Visual summaries and diagrams
• Intelligent doubt-solving assistant
• Unified manual + automated note-taking workspace

This enables faster revision, better understanding, and improved knowledge retention — all within one integrated platform.

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Features

Audio & Video Input

• Upload MP3, WAV, MP4 lecture files
• Supports long recordings
• Automatic audio cleaning

Fast Transcription

• Near real-time speech-to-text
• Lightweight distilled Transformer models
• Efficient memory management

Multi-Language Ready

• English support (Phase 1)
• Expandable to multilingual & Indic languages

Export Options

• TXT
• PDF
• Markdown (Obsidian-compatible)

AI-Powered Intelligence

• Transformer-based architecture
• Extractive + Abstractive summarization
• Semantic embedding search
• Context-aware doubt solving

Quiz & Revision Generator

• MCQs
• Short-answer questions
• Key concept extraction

Modular Architecture

• Plug-and-play model replacement
• Institution-ready scalability

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Tech Stack

Backend

• Python
• FastAPI

Machine Learning

• PyTorch / TensorFlow
• Scikit-learn
• Transformer-based models
• Sentence Transformers (MiniLM embeddings)

Deployment

• Docker
• AWS / GCP (Cloud-ready)

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Machine Learning Details

Models Used

• Speech-to-Text: Distilled Transformer ASR
• Summarization: BERT / TextRank / LLM-based
• Embeddings: sentence-transformers/all-MiniLM-L6-v2
• Q&A Model: Instruction-tuned LLM (e.g., Qwen)

Evaluation Metrics

• WER (Word Error Rate) – Transcription
• ROUGE Score – Summarization
• BLEU Score – Text generation
• Accuracy / F1 Score – Quiz performance
• Inference Latency – System performance

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To build a smart, scalable, and accessible AI learning ecosystem that transforms lectures into structured knowledge — instantly and intelligently.