ClipSummarizer: Audio & Video Highlights at Your Fingertips

This is a submission for the AssemblyAI Challenge : No More Monkey Business.

What I Built

I built a two-tier web application that allows users to submit an audio clip or a YouTube video link, and it generates summarized headlines or key highlights from the provided content. The application integrates the AssemblyAI API to transcribe and summarize the audio content into concise headlines, enabling users to quickly digest the key points of any video or audio.

Features:

1. Audio Upload: Users can upload audio files, and the backend processes them for transcription and summarization.
2. YouTube Video Link: Users can provide a YouTube video link, and the application fetches the audio from the video to process it.
3. AssemblyAI Integration: The backend sends the audio data to the AssemblyAI API for transcription and summary generation, which returns key headlines or highlights.
4. React Frontend: The user interface is built in React, where users can upload files or paste video links. It displays the generated summaries clearly and efficiently.

Demo

Github
Here are some screenshots of the app in action:

Journey

Building this project was an exciting challenge, especially integrating the AssemblyAI API into the backend. Here's a breakdown of the implementation:

1. Frontend (React):
- I created a simple and intuitive interface where users can either upload an audio file or submit a YouTube link.
- The frontend is built with React and allows users to see the progress of their requests. After submitting the data, it displays the summarized headlines returned by the backend.

2. Backend (Python):
- The backend is built using Python, where I used libraries like yt_dlp to extract audio from YouTube videos.
- Once the audio is extracted, it's sent to AssemblyAI for transcription and summarization.
- The backend then processes the response and sends the summary back to the frontend.

3. AssemblyAI API:
- I leveraged AssemblyAI's transcription API to convert the audio into text and their summarization feature to condense the content into highlights.
- The integration was seamless, and the API provided accurate transcriptions and concise summaries.

4. Challenges and Solutions:
- Audio Extraction from YouTube: Initially, I ran into issues with audio quality from YouTube. I improved this by choosing the highest-quality audio stream available using yt_dlp.
- API Response Time: AssemblyAI's API can take some time for large audio clips. To handle this, I added a progress bar to the frontend to keep users informed about the status of their request.
- Error Handling: I ensured that the frontend handles errors gracefully if the audio is too long or if the YouTube link is invalid.

This project allowed me to explore the full potential of integrating AssemblyAI’s transcription and summarization services into a real-world application, and I’m excited to see how it can help users save time and quickly extract insights from media.