How to Scrape YouTube Transcripts for AI Analysis (RAG Pipelines)

The biggest untapped dataset for AI isn't on Wikipedia or Reddit. It's on YouTube.

Every minute, 500 hours of video are uploaded to YouTube. Most of this contains high-density information—tutorials, lectures, news, reviews—that is completely invisible to text-based LLMs.

If you're building an AI application in 2025, video-to-text is your competitive advantage.

Imagine building:

• A "Chat with this Lecture" app for students.
• An automated stock trading bot that analyzes CEO interviews.
• A content repurposing tool that turns videos into blog posts.

To do this, you need the transcript.

In this guide, we'll show you how to extract transcripts from any YouTube video using SociaVault, clean the data, and prepare it for a RAG (Retrieval-Augmented Generation) pipeline.

Why Not Just Use Whisper?

You could download the audio and run it through OpenAI's Whisper. It's accurate, but it's slow and expensive.

• Cost: Audio processing costs money (GPU time or API credits).
• Speed: Transcribing a 1-hour video takes minutes.
• Bandwidth: You have to download the video/audio file first.

The Better Way: YouTube already has the transcript. Most videos have auto-generated captions (or manual ones). They are just sitting there, hidden in the metadata. Extracting them takes milliseconds and costs almost nothing.

Step 1: Extracting the Transcript

We'll use SociaVault's YouTube Transcript endpoint. It returns the text with precise timestamps.

Python Example

import requests
import os

API_KEY = os.getenv("SOCIAVAULT_API_KEY")

def get_video_transcript(video_id):
    url = "https://api.sociavault.com/v1/scrape/youtube/transcript"
    params = {"videoId": video_id}
    headers = {"Authorization": f"Bearer {API_KEY}"}

    response = requests.get(url, params=params, headers=headers)
    
    if response.status_code == 200:
        return response.json()['transcript']
    else:
        raise Exception(f"Error: {response.text}")

# Example: A Lex Fridman podcast
video_id = "x4e2L3_K8_E" 
transcript_segments = get_video_transcript(video_id)

# Output format:
# [
#   {"text": "welcome to the podcast", "start": 0.0, "duration": 2.1},
#   {"text": "today I'm talking to...", "start": 2.1, "duration": 3.5},
#   ...
# ]

Step 2: Cleaning and Formatting

The raw output is a list of short segments. For an LLM, we usually want:

1. Full Text: For summarization.
2. Chunked Text with Timestamps: For "Ask a Question" (RAG) so we can link back to the exact moment in the video.

def process_transcript(segments):
    full_text = " ".join([seg['text'] for seg in segments])
    
    # Create 30-second chunks for RAG
    chunks = []
    current_chunk = {"text": "", "start": segments[0]['start']}
    
    for seg in segments:
        current_chunk["text"] += " " + seg['text']
        
        # If chunk is longer than 30 seconds, save it
        if seg['start'] - current_chunk['start'] > 30:
            chunks.append(current_chunk)
            current_chunk = {"text": "", "start": seg['start']}
            
    # Append last chunk
    if current_chunk["text"]:
        chunks.append(current_chunk)
        
    return full_text, chunks

full_text, time_chunks = process_transcript(transcript_segments)

Step 3: The AI Use Cases

Now that you have the text, here is what you can build.

Use Case A: The "TL;DW" Summarizer

Send the full_text to GPT-4 or Claude 3.

import openai

def summarize_video(text):
    prompt = f"""
    Analyze the following YouTube video transcript.
    Provide a bullet-point summary of the key takeaways.
    Ignore filler words.
    
    Transcript:
    {text[:10000]} # Truncate to fit context window if needed
    """
    
    response = openai.ChatCompletion.create(
        model="gpt-4-turbo",
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.choices[0].message.content

Use Case B: RAG (Chat with Video)

This is how you build "ChatPDF" but for YouTube.

1. Embed: Turn each time_chunk into a vector using OpenAI text-embedding-3-small.
2. Store: Save vectors in Pinecone or Supabase (pgvector).
3. Query: When user asks "What did he say about aliens?", search your vector DB.
4. Answer: Feed the matching chunks to the LLM.
5. Cite: The LLM answers and you provide the YouTube link with timestamp: https://youtu.be/VIDEO_ID?t=123.

Handling Multiple Languages

SociaVault's transcript endpoint supports language codes.

# Get Spanish transcript
params = {
    "videoId": "...",
    "language": "es"
}

If a video doesn't have the requested language, you can extract the English transcript and use an LLM to translate it. This is often better than YouTube's auto-translate.

Conclusion

Video data is the next frontier for AI applications. By using SociaVault to extract transcripts, you turn opaque video files into searchable, analyzable text data.

You don't need expensive GPU clusters to transcribe audio. You just need the right API to unlock the data that's already there.

Start building your AI video app: Get your API Key