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 failedUNAUTHORIZED: Invalid API credentialsVIDEO_NOT_FOUND: YouTube video unavailableTRANSCRIPT_UNAVAILABLE: No captions availableINVALID_URL: Malformed video URLRATE_LIMIT_EXCEEDED: Quota or rate limit reachedINTERNAL_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
JavaScript
TypeScript
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
- Content Pipeline: YouTube → Transcription → Summary → Social Media Posts
- Research Automation: Video URLs → Transcripts → Analysis → Report Generation
- 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
maxCharslimits for your use case
Support
For additional examples, troubleshooting, and advanced integration patterns, visit the project repository or API documentation.