Speech / Video Intelligence Platform
AI-powered system that converts long-form audio/video into structured insights, enabling semantic search and faster content understanding.
Tech Stack
Problem Statement
Long-form audio and video content is difficult to consume efficiently. Users lack tools to extract structured insights such as summaries, topics, and searchable knowledge from raw media.
System Architecture
Designed a multi-stage NLP pipeline with asynchronous FastAPI orchestration. The system processes input media through transcription, summarization, embedding generation, and retrieval layers. A React frontend interfaces with backend job queues and displays structured outputs.
Approach
Used Faster-Whisper for high-performance transcription, followed by hierarchical summarization using BART. Generated embeddings using SentenceTransformers and stored them in FAISS for semantic retrieval. Designed the system to support modular pipeline stages.
Implementation Details
Built async FastAPI endpoints managing job queues and pipeline execution. Integrated embedding-based clustering for topic segmentation. Developed a React dashboard with real-time status tracking and structured visualization of insights.
Challenges & Solutions
Handling long transcripts efficiently, maintaining pipeline modularity, and ensuring fast semantic search performance over large embedding spaces.
Results & Impact
Enabled structured extraction of insights from long-form media with real-time tracking and semantic Q&A capabilities, significantly improving content accessibility.
Key Learnings
Gained deep understanding of RAG pipelines, multi-stage NLP systems, and designing scalable async architectures for AI workloads.