Over time, I’ve developed a reliable and efficient workflow to transform video content—whether it’s native files or YouTube videos—into meaningful articles or documents. This process has become an essential part of my research and writing approach, enabling me to draw insights and craft compelling narratives. Here’s how it works:
1. Extracting Transcripts
- I use specialized tools and commands to generate transcripts from videos. These tools allow me to convert spoken content into text, ensuring I have a written reference for the material.
- The process supports both offline video files and online platforms like YouTube, providing flexibility regardless of the video source.
Here is the code that works for me very well:
conda activate py_3.11
yt-dlp "https://www.youtube.com/watch?v=jRudP40MMhE"
# had to rename file manually
transcribe-anything "Warren Buffett - Why Smart People Become Poor (How We Avoid It).webm" --device insane
2. Organizing Content
- Once the transcription is complete, I review and structure it to identify key themes, arguments, or quotes.
- By highlighting significant parts, I create a framework that serves as a foundation for deeper research or article drafting.
3. Research Integration
- The transcript serves as raw data that I can integrate with other research material. Whether I’m writing a detailed article, creating a report, or drafting a blog post, the transcript provides authentic, accurate context.
- I often cross-reference the transcript with external sources to validate facts or enrich the narrative.
4. Writing and Document Creation
- Using the transcript as a starting point, I draft articles, documents, or other content pieces. The structured approach allows me to seamlessly convert spoken insights into written ideas while maintaining the original essence of the video.
5. Refining and Finalizing
- Finally, I edit the document to refine the language, improve flow, and ensure clarity. This step polishes the final output, making it ready for publication or personal use.
Why This Workflow Works for Me
This system works well because it:
- Ensures Accuracy: Transcripts provide verbatim content, minimizing the risk of misinterpretation.
- Saves Time: Automated transcription tools reduce the manual effort of taking notes or replaying videos repeatedly.
- Supports Flexibility: The process adapts to various formats, from video lectures to interviews, making it versatile.
- Enhances Output Quality: By grounding my writing in actual spoken content, I maintain authenticity and relevance in my work.
This workflow has proven reliable, enabling me to efficiently extract insights from videos and convert them into impactful written content. It’s a process I can depend on for producing research-backed and thoughtfully crafted materials.
Further updates
Downloading sub-titles where present is also possible with yt-dlp
yt-dlp --write-auto-sub --convert-subs=srt --skip-download "https://www.youtube.com/watch?v=tOGK1nlHdFo"
yt-dlp --skip-download --write-subs --write-auto-subs --sub-lang en --sub-format ttml --convert-subs srt --output "transcript.%(ext)s" "https://www.youtube.com/watch?v=tOGK1nlHdFo"
yt-dlp --skip-download --write-subs --write-auto-subs --sub-lang en --sub-format ttml --convert-subs srt --output "transcript.%(ext)s" "https://www.youtube.com/watch?v=tOGK1nlHdFo" && sed -i '' -e '/^[0-9][0-9]:[0-9][0-9]:[0-9][0-9].[0-9][0-9][0-9] --> [0-9][0-9]:[0-9][0-9]:[0-9][0-9].[0-9][0-9][0-9]$/d' -e '/^[[:digit:]]\{1,3\}$/d' -e 's/<[^>]*>//g' ./transcript.en.srt && sed -e 's/<[^>]*>//g' -e '/^[[:space:]]*$/d' transcript.en.srt > output.txt && rm transcript.en.srt