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YouTube to text

Have you ever come across that perfect YouTube video—perhaps a tutorial on advanced Revit modeling techniques or a presentation on sustainability in projects—and thought, "Wow, if only I could have this transcribed, it would be fantastic"? You know when you find a tutorial in English with valuable information, but watching a full hour of video just doesn't fit into your tight schedule?

Well, this is exactly where the magic happens. Combining tools for extracting YouTube content with artificial intelligence for transcription opens up a world of possibilities that goes far beyond simply "downloading a video." We're talking about transforming any audiovisual content into searchable, translatable, and organizable text.

But before we dive into the possibilities, let's understand how this works in practice. The process is simpler than it seems: first, we extract the audio from the YouTube video, then we apply Whisper (which we just learned about) to convert that audio into text. It's like having an assistant who watches the video, takes notes on everything, and gives you a complete summary.

The tool that makes this possible is called yt-dlp—an evolution of the famous youtube-dl, but supercharged [3]. Think of it as a "superpower" for YouTube: it can extract not only the full video but also just the audio, in different qualities, from thousands of video sites, not just YouTube [4]. And the best part? It's completely free and constantly updated by the community.

What's interesting about yt-dlp is its flexibility. You can download a full 4K video if you want, but you can also extract just the audio in MP3 to save space and time [5]. For our transcription purposes, we usually just need the audio—why download gigabytes of video when we only want to convert the speech to text?

Let's pause for a moment to absorb this: we're talking about a combination that can transform virtually any educational video, technical tutorial, or presentation into organized textual content. Imagine the possibilities for a professional who needs to stay constantly updated.

Practical Example 1: Basic Tutorial Transcription

In Google Colab, you can create a complete YouTube video transcription system with surprisingly few lines of code:

# Install the necessary tools
!pip install -q yt-dlp
!pip install -q openai-whisper

import yt_dlp
import whisper

# Download only the audio from the video
def download_youtube_audio(video_url):
    ydl_opts = {
        'format': 'bestaudio/best',
        'outtmpl': 'extracted_audio.%(ext)s',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'mp3',
            'preferredquality': '192',
        }],
    }
    
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        ydl.download([video_url])
    
    return 'extracted_audio.mp3'

# Transcribe the audio
model = whisper.load_model("base")
url = "https://www.youtube.com/watch?v=YOUR_VIDEO_ID_HERE"

audio_file = download_youtube_audio(url)
result = model.transcribe(audio_file)
print(result["text"])

This example downloads the audio from any YouTube video and automatically transcribes it. It's perfect for those technical tutorials you want to consult later without having to rewatch the whole thing.

Practical Example 2: Content Analysis with a Word Cloud

Here's where it gets more interesting—let's not just transcribe, but also visually analyze the content:

# Add analysis tools
!pip install -q wordcloud matplotlib

from wordcloud import WordCloud
import matplotlib.pyplot as plt

# After transcribing (using the previous code)
transcribed_text = result["text"]

# Create a word cloud to visualize the main themes
wordcloud = WordCloud(width=800, height=400, 
                     background_color='white').generate(transcribed_text)

plt.figure(figsize=(12, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.title("Main Themes of the Video")
plt.show()

# Show basic statistics
words = transcribed_text.split()
print(f"Total words: {len(words)}")
print(f"Estimated reading time: {len(words)//200} minutes")

Imagine using this to analyze a series of videos on a specific topic—like sustainability in construction or new BIM technologies. You can quickly identify which concepts appear most frequently and focus your study on those areas.

The beauty of this approach lies in its scalability. You can process an hour-long lecture in a few minutes, or even an entire playlist of educational videos. The result is a text file that you can search, translate, summarize, or share with your team.

But here's an important reflection: this technology doesn't replace the experience of watching the video, especially when it comes to visual content like demonstrations in Revit. It complements your learning, offering a quick way to review, search, and organize information.

The combination of yt-dlp + Whisper also opens doors to more sophisticated applications. You could, for example, create a system that monitors specific YouTube channels, automatically transcribes new videos, and sends summaries to your team. Or develop a personal knowledge library where all important audiovisual content is available in a searchable text format [6].

What particularly fascinates me is how this democratizes access to knowledge. That video in English about advanced sustainability techniques? Now you can transcribe it and use translation tools. That long lecture about the future of construction? Turn it into text and extract only the most relevant points for your context.

Of course, there are important ethical considerations here. Always respect copyrights and the platforms' terms of use. Use these techniques for educational or research purposes, or for content you have permission to process. The technology is powerful, but it must be used responsibly.

This ability to extract and process information from online videos naturally leads us to think about how we can organize and structure all this knowledge in a way that is easily accessible to AI tools. And that's exactly what we'll explore next: how different file formats can be "friends" or "enemies" of language models, and how choosing the right formats can make all the difference in the effectiveness of our AI systems.

References Cited in This Section​

[3] GitHub. (2025). yt-dlp/yt-dlp: A feature-rich command-line audio/video downloader. Available at: https://github.com/yt-dlp/yt-dlp

[4] RapidSeedbox. (2025). How to Use YT-DLP: Guide and Commands (2025). Available at: https://www.rapidseedbox.com/blog/yt-dlp-complete-guide

[5] Stack Overflow. (2025). How to extract only audio from downloading video? Python yt-dlp. Available at: https://stackoverflow.com/questions/75867758/how-to-extract-only-audio-from-downloading-video-python-yt-dlp

[6] Devangtomar. (2023). Transcribing YouTube Videos using OpenAI's Whisper. Hashnode. Available at: https://devangtomar.hashnode.dev/transcribing-youtube-videos-using-openais-whisper-efb88f-efb88f-a29d264d6fb1