YouTube2Text - Video Transcription API

A powerful text extraction service that converts YouTube video content into clean, timestampless transcripts for content analysis, research, and processing workflows.

Overview

YouTube2Text transforms YouTube videos into readable text by removing subtitle timing markers and metadata, delivering pure content suitable for:

• Content analysis and insights
• Text summarization workflows
• Research and documentation
• Content generation pipelines
• Natural language processing tasks

Quick Start

Begin with a demo API key from https://api.youtube2text.org. For consistent access and higher usage limits, upgrade to a subscription plan.

API Reference

Base URL: https://api.youtube2text.org
Transcription Endpoint: /transcribe

Request Format

Send POST requests with these parameters:

Parameter	Type	Required	Description
url	string	Yes	Complete YouTube video URL
maxChars	number	No	Character limit (default: 150,000)

Authentication

Include your API key in the request header:

x-api-key: YOUR_API_KEY

HTTP Status Codes

Code	Meaning
200	Transcription successful
400	Invalid request parameters
401	Authentication failed
404	Video or transcript not found
429	Rate limit exceeded
500	Server error

Error Types

• VALIDATION_ERROR: Parameter validation failed
• UNAUTHORIZED: Invalid API credentials
• VIDEO_NOT_FOUND: YouTube video unavailable
• TRANSCRIPT_UNAVAILABLE: No captions available
• INVALID_URL: Malformed video URL
• RATE_LIMIT_EXCEEDED: Quota or rate limit reached
• INTERNAL_ERROR: Server-side issue

Examples

This directory contains examples of how to use the YouTube2Text API with different AI models and in different programming languages.

Python

• Anthropic Claude Integration
• OpenAI Integration
• Google Gemini Integration

JavaScript

• Anthropic Claude Integration
• OpenAI Integration
• Google Gemini Integration

TypeScript

• Anthropic Claude Integration
• OpenAI Integration
• Google Gemini Integration

Automation Integration

Workflow Automation

The API integrates with popular automation platforms:

• Zapier: Connect via MCP integration for triggered workflows
• n8n: Use HTTP request nodes or MCP connectors for process automation
• Make (Integromat): HTTP modules for video processing pipelines

Example Workflow Ideas

1. Content Pipeline: YouTube → Transcription → Summary → Social Media Posts
2. Research Automation: Video URLs → Transcripts → Analysis → Report Generation
3. Content Monitoring: Channel Watching → New Videos → Auto-transcription → Alerts

Response Examples

Successful Response

{
  "result": {
    "videoId": "dQw4w9WgXcQ",
    "title": "Rick Astley - Never Gonna Give You Up (Official Video)",
    "pubDate": "2009-10-25T07:57:33-07:00",
    "content": "We're no strangers to love You know the rules and so do I...",
    "contentSize": 1337,
    "truncated": false
  }
}

Error Response

{
  "error": {
    "code": "RATE_LIMIT_EXCEEDED",
    "message": "Monthly quota exceeded",
    "status": 429,
    "retryAfterSeconds": 3600,
    "details": "Upgrade plan for higher limits"
  }
}

Best Practices

• Store API keys securely using environment variables
• Implement proper error handling for all status codes
• Respect rate limits and implement retry logic with exponential backoff
• Cache transcripts locally when possible to avoid redundant API calls
• Monitor usage to stay within quota limits
• Use appropriate maxChars limits for your use case

Support

For additional examples, troubleshooting, and advanced integration patterns, visit the project repository or API documentation.