Why your YouTube transcript scraper returns the wrong language (and how to fix it)

What happened

The article describes a problem where a YouTube transcript scraper returns captions in the wrong language, affecting the accuracy of AI pipelines. It explains the causes, such as the order of caption tracks and dubbed videos, and provides a solution using auto-generated captions and an Apify Actor.

Why it matters

This issue is critical for AI developers and data scientists who rely on accurate transcripts for training models, creating RAG indices, or summarizing content. Incorrect captions can lead to poor model performance and unreliable results.

• Use auto-generated captions to identify the original language of a video.
• Leverage Apify Actors to handle YouTube's bot detection and proxy issues.
• Prioritize human-made captions over auto-generated ones for better accuracy.
• Be aware of dubbed videos and the default language settings that can affect caption retrieval.

The bigger picture

The practical impact is that developers can avoid silent bugs in their AI pipelines by using the correct captions, ensuring better model accuracy and data quality.

The article highlights the importance of correctly retrieving YouTube captions for AI applications and provides practical solutions to avoid common pitfalls, ensuring accurate and reliable data for machine learning models.

Technical details

• YouTube's caption list is not ordered by the original language.
• Auto-generated captions are in the language of the spoken content.
• The Apify Actor uses residential proxies and retries to handle YouTube's bot detection.
• The library 'youtube-transcript-api' is used to fetch and process captions.

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