AI Video Assistant: Turning Video into Readable Summaries

A machine learning project by Sandeep Kumar

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Why I Built This

Videos are one of the slowest ways to extract information — you either watch the whole thing or skip around hoping to find what matters. I wanted a tool that could do that work automatically: listen to a video, transcribe it, and hand back a short, readable summary.

That became the AI Video Assistant — a Streamlit app that takes a video, converts its speech into text, and condenses that text into a clear summary, cutting down the time needed to review long-form video content.

The Problem

Whether it's a lecture, a meeting recording, or a long tutorial, most video content buries a small amount of useful information inside a large amount of runtime. Manually scrubbing through video to extract key points is slow and easy to get wrong. I wanted a tool that could:

Convert spoken content in a video into accurate text
Condense that text into a short, useful summary
Save the time otherwise spent re-watching or skimming video

How It Works

The app follows a simple three-stage pipeline once a video is uploaded:

1. Audio Extraction

The audio track is pulled out of the uploaded video file so it can be processed independently of the visual content.

2. Speech-to-Text Transcription

The extracted audio is run through OpenAI Whisper, converting spoken words into accurate, readable text — including handling different accents and speaking speeds.

3. Text Summarization

The full transcript is then processed using NLP-based summarization techniques, condensing it down into the key points, so the user gets a short summary instead of a wall of text.

Tech Stack

LayerTools UsedLanguagePythonSpeech-to-TextOpenAI WhisperText ProcessingNLP (Natural Language Processing)Frontend/InterfaceStreamlitDeploymentStreamlit Community Cloud

Building the Pipeline

The core challenge was chaining together three very different processing steps into one smooth experience:

File handling — accepting a video upload and extracting its audio track for processing.
Transcription — feeding the audio into Whisper to generate an accurate text transcript.
Summarization — applying NLP techniques to compress the transcript into a concise summary without losing key meaning.
Interface design — wrapping the whole pipeline in a simple Streamlit UI so a user just uploads a file and waits for output — no technical steps required.
Deployment — hosting the app on Streamlit Community Cloud for public access.

Key Features

End-to-end automation — upload a video, get a summary, no manual steps in between
Accurate transcription — powered by Whisper, handling varied speech patterns
Concise output — NLP-based summarization keeps only the essential points
Simple interface — built for non-technical users, not just developers

What I Learned

Chaining models is its own skill. Getting audio extraction, transcription, and summarization to work together smoothly required careful handling of intermediate outputs at each stage.
Transcription quality drives everything downstream. A weak transcript makes for a weak summary — most of my debugging time went into making sure the transcription stage was clean before summarization even started.
Simplicity wins in the UI. The biggest usability improvement came from stripping the interface down to a single upload button and a clear output — not from adding more options.

Try It Yourself

🔗 Live App: ai-video-assistant-z6juegb8q9vuabmtc9dtbq.streamlit.app 🔗 GitHub: github.com/sandeepkumar7860

What's Next

Some directions I'd like to explore:

Adding timestamped summaries (linking key points back to the exact moment in the video)
Support for multiple languages in transcription
Exporting summaries directly as PDF or notes format