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Extract YouTube Transcripts with AI Fallback — No YouTube API Key Needed

#python #api #youtube #ai

Need to extract transcripts from YouTube videos programmatically? If you've tried before, you know it's surprisingly hard:

  • The YouTube Data API doesn't actually give you transcripts (just metadata)
  • youtube-transcript-api (Python package) breaks regularly and doesn't handle all videos
  • Many videos don't have captions at all — no amount of scraping helps
  • Rate limits, quotas, and API key management add friction

I built a solution that handles all of these cases with a single API call — including an AI fallback for videos without captions.

The Quick Version 

curl "https://api.getcontentapi.com/api/v1/youtube/transcript?url=https://youtube.com/watch?v=dQw4w9WgXcQ" \
  -H "X-API-Key: your_api_key"
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That's it. You get back:

{
  "success": true,
  "data": {
    "title": "Rick Astley - Never Gonna Give You Up",
    "channel": "Rick Astley",
    "duration": 212,
    "language": "en",
    "word_count": 284,
    "full_text": "We're no strangers to love...",
    "transcript": [
      {"start": 0.0, "duration": 4.2, "text": "We're no strangers to love"},
      {"start": 4.2, "duration": 3.8, "text": "You know the rules and so do I"},
      ...
    ]
  }
}
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How It Works

ContentAPI uses a multi-strategy approach to get transcripts:

Strategy 1: Native Captions

First, we check if the video has official captions (manual or auto-generated). This covers ~90% of videos and is the fastest method.

Strategy 2: Community Captions

Some videos have community-contributed subtitles. We check for these as a fallback.

Strategy 3: AI Transcription (Whisper)

For the remaining ~10% of videos with no captions at all, we extract the audio and run it through OpenAI's Whisper model. This works on any video in any language.

All of this happens automatically — you just make one API call.

Python Examples

Basic Transcript Extraction 

import requests

API_KEY = "your_api_key"
BASE_URL = "https://api.getcontentapi.com/api/v1"

def get_transcript(video_url):
    response = requests.get(
        f"{BASE_URL}/youtube/transcript",
        params={"url": video_url},
        headers={"X-API-Key": API_KEY}
    )
    return response.json()["data"]

transcript = get_transcript("https://youtube.com/watch?v=dQw4w9WgXcQ")
print(f"Title: {transcript['title']}")
print(f"Words: {transcript['word_count']}")
print(f"Full text:\n{transcript['full_text']}")
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Using the Python SDK 

pip install contentapi
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from contentapi import ContentAPI

client = ContentAPI(api_key="your_api_key")

# Get transcript
transcript = client.youtube_transcript("https://youtube.com/watch?v=...")
print(transcript.title)
print(transcript.full_text)

# Get with timestamps
for segment in transcript.segments:
    minutes = int(segment.start // 60)
    seconds = int(segment.start % 60)
    print(f"[{minutes:02d}:{seconds:02d}] {segment.text}")
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Get Video Summary Instead

Don't want the full transcript? Get an AI-generated summary:

response = requests.get(
    f"{BASE_URL}/youtube/summary",
    params={"url": "https://youtube.com/watch?v=..."},
    headers={"X-API-Key": API_KEY}
)

summary = response.json()["data"]
print(summary["summary"])
print("Key points:", summary["key_points"])
print("Topics:", summary["topics"])
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Get Video Chapters 

response = requests.get(
    f"{BASE_URL}/youtube/chapters",
    params={"url": "https://youtube.com/watch?v=..."},
    headers={"X-API-Key": API_KEY}
)

for chapter in response.json()["data"]["chapters"]:
    print(f"{chapter['start_time']}s - {chapter['title']}")
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Get Comments 

response = requests.get(
    f"{BASE_URL}/youtube/comments",
    params={"url": "https://youtube.com/watch?v=..."},
    headers={"X-API-Key": API_KEY}
)

for comment in response.json()["data"]["comments"]:
    print(f"{comment['author']} ({comment['likes']} likes): {comment['text']}")
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Real-World Use Cases

1. Build a YouTube Knowledge Base 

from contentapi import ContentAPI
import json

client = ContentAPI(api_key="your_api_key")

# Process a playlist worth of videos
video_urls = [
    "https://youtube.com/watch?v=video1",
    "https://youtube.com/watch?v=video2",
    "https://youtube.com/watch?v=video3",
]

knowledge_base = []
for url in video_urls:
    transcript = client.youtube_transcript(url)
    knowledge_base.append({
        "title": transcript.title,
        "channel": transcript.channel,
        "content": transcript.full_text,
        "url": url,
        "duration": transcript.duration,
    })

# Save for RAG pipeline
with open("youtube_kb.json", "w") as f:
    json.dump(knowledge_base, f, indent=2)
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2. Auto-Generate Blog Posts from Videos 

from contentapi import ContentAPI
from openai import OpenAI

content_client = ContentAPI(api_key="your_contentapi_key")
openai_client = OpenAI(api_key="your_openai_key")

# Get transcript
transcript = content_client.youtube_transcript(
    "https://youtube.com/watch?v=..."
)

# Generate blog post
response = openai_client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "system",
            "content": "Convert this video transcript into a well-structured "
                       "blog post with headers, key takeaways, and a summary."
        },
        {
            "role": "user",
            "content": f"Video: {transcript.title}\n\n{transcript.full_text}"
        }
    ]
)

blog_post = response.choices[0].message.content
print(blog_post)
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3. Multi-Language Transcript Search 

# ContentAPI returns the detected language
transcript = client.youtube_transcript(
    "https://youtube.com/watch?v=spanish_video"
)

print(f"Detected language: {transcript.language}")
# "es" — Spanish

# The full_text is in the original language
# Use any translation API to convert if needed
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Comparison with Alternatives

Feature youtube-transcript-api YouTube Data API ContentAPI
API key needed No Yes (Google) Yes (free)
Rate limits Aggressive 10,000 units/day 5,000 req/month free
No-caption videos ❌ Fails ❌ No transcripts ✅ AI fallback
Video metadata ❌ No ✅ Yes ✅ Yes
Summaries ❌ No ❌ No ✅ AI-powered
Chapters ❌ No ❌ No ✅ Yes
Comments ❌ No ✅ Yes ✅ Yes
Maintenance You You Managed

Beyond YouTube

The same API also handles web pages, Twitter/X, and Reddit — so if your pipeline needs content from multiple sources, it's one SDK:

from contentapi import ContentAPI

client = ContentAPI(api_key="your_api_key")

# All through the same interface
youtube = client.extract("https://youtube.com/watch?v=...")
webpage = client.extract("https://example.com/article")
tweet = client.extract("https://x.com/user/status/123456")
reddit = client.extract("https://reddit.com/r/python/comments/abc")
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MCP Server for AI Assistants

If you use Claude, Cursor, or Windsurf, there's an MCP server that lets your AI assistant extract YouTube transcripts directly:

npx contentapi-mcp-server
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Then just ask your AI: "Get me the transcript from this YouTube video: [url]"

Getting Started

  1. Sign up free: getcontentapi.com — no credit card needed
  2. Install the SDK: pip install contentapi
  3. Extract your first transcript: 
from contentapi import ContentAPI
client = ContentAPI(api_key="your_key")
t = client.youtube_transcript("https://youtube.com/watch?v=dQw4w9WgXcQ")
print(t.full_text)
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Free tier: 5,000 requests/month. Enough to process ~170 videos per day.

Resources:

Have you worked with YouTube transcripts before? What was your biggest pain point? I'd love to hear about your use cases in the comments! 💬

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