Python in Action: See How Easily You Can Extract Audio from A Video in HLS and M3U8. Powering Educational Media Apps
Have you ever wanted to extract the audio from a video, transcript it and process it into a compelling document with Markdown formatting?
Imagine an app that not only simplifies media processing but also transforms the way we interact with educational content.
At its core, the app leverages the power of Python to effortlessly handle media files. From extracting audio from video lectures to transcribing and analyzing spoken words, the app opens up new avenues for interactive learning.
It’s designed to make learning more accessible and engaging by converting video content into more digestible, text-based formats.
Python’s role extends beyond just processing, integrating artificial intelligence to enhance any app’s capabilities. From accurately transcribing lectures to analyzing speech patterns, its ability to handle diverse tasks with minimal coding makes it an ideal choice for developers working on educational technology.
What’s the story?
As a technologist and fitness coach, I’ve always been fascinated by the potential of AI to transform everyday life. However, it was during a trip in Taiwan, that this idea came to me.
Walking through the historic streets, I observed myself engaged in traditional learning but noticed a disconnect — the digital evolution seemed missing from an educational experience. A tool that could convert any video lecture into an accessible text format, breaking down the barriers to effective education.
This journey, from concept to creation, was driven by a desire to make a tangible difference in the world of education. It’s more than just a small app; it’s a mission to empower learning while travelling, to bring the wonders of AI into the heart of learning, and to make education more inclusive and engaging.
Navigating the Digital Learning Divide
In today’s fast-paced digital world, the landscape of education is evolving rapidly. However, this evolution brings with it a significant challenge — the digital learning divide. The primary problem that learners face is accessing and effectively utilizing digital educational content. In an era where video lectures and online seminars are becoming the norm, not everyone finds it easy to engage with these digital formats.
They need a tool that can transform the abundant video material available online into a format that’s more universally accessible and useful for different learning styles.
This AI Media Processing App was developed to convert video lectures into written transcripts, thereby democratizing access to educational content and empowering all learners, regardless of their preferred learning mode or abilities.
The Power of AI-Driven Transcription
A key to unlocking digital learning: AI-driven transcription. In the face of the challenge where traditional educational resources fail to cater to diverse learning needs, a solution that leverages advanced artificial intelligence to transform any video content into accurate, readable text.
This isn’t just transcription; it’s about reimagining how educational content can be accessed and utilized.
While video lectures offer convenience, they don’t accommodate all learners. The breakthrough came with Whisper https://openai.com/research/whisper, an advanced AI transcription technology, into this app. Whisper goes beyond simple speech-to-text conversion; it can also identify languages.
It’s a tool that empowers students to learn in their preferred style — reading. It also opens doors for learners with hearing disabilities, non-native speakers, and even educators who wish to create supplemental written materials. The fusion of AI transcription with educational content is what can make an app inclusive and adaptable to the evolving landscape of digital learning.
AI-Driven Transcription Process
To effectively address the digital learning divide, the AI Media Processing App employs an AI-driven transcription framework. Here’s how it works, step by step:
Step 1: Video Content Extraction
- User Input: Users begin by inputting the URL of the video content they wish to process.
- Extraction: The app uses `yt-dlp`, a powerful Python tool, to extract the audio and video from the provided URL, ensuring compatibility with a wide array of video sources.
Step 2: AI-Driven Transcription
- Transcription Engine: The isolated audio is then fed into Whisper, an advanced AI transcription model. Whisper transcribes the audio into text, and saves it to a file.
Step 3: Output Presentation
- Text Display: The transcribed text is displayed within the app, allowing users to read and interact with the content.
- Editing and Customization: Users have the option to edit the text, adjust formatting, and even translate best with Notion https://www.notion.so/ and ChatGPT https://chat.openai.com/, making the content fully adaptable to their needs.
Step 4: Accessibility and Inclusivity
- Diverse Learning Styles: This framework caters to diverse learning preferences, making educational content accessible to readers, auditory learners, and those with hearing impairments.
- Language Inclusivity: With the potential for multilingual support, the app breaks language barriers, extending its reach to a global audience.
Technical Insights
Streamlit: Crafting a User-Friendly Interface
Streamlit stands at the forefront of the app’s interface design. This Python library is renowned for its ability to turn data scripts into shareable web apps quickly. In our app, Streamlit acts as the canvas where users interact with the tool.
yt-dlp: The Backbone of Video Processing
yt-dlp, a Python library that excels in handling video streams. It powers the app’s ability to extract media from various sources.
Whisper: Transforming Audio with AI
Once the video is processed, next is Whisper, an advanced library for audio transcription. Utilizing cutting-edge AI, Whisper converts spoken words into accurate text, a feature vital for creating accessible educational content.
ChatGPT: Transforming Plan Text to Structured Markdown
You will see a compelling prompt that you can use to get ChatGPT to help you create a new version of the transcript more readable. You can just upload the transcript.txt file to the chat.
You can test the app here:
https://hls-m3u8-audio-ai-whisper-processing-app.streamlit.app/
You can get this app code via Github:
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