Turn any YouTube channel into RAG‑ready transcripts (no YouTube API key)

Description and use

An Apify Actor automatically fetches accurate transcripts from any YouTube channel, cleans HTML entities, splits into RAG‑ready chunks with timestamps, and outputs structured data ready for vector‑store ingestion.

Problem

Obtaining accurate, clean transcripts from arbitrary YouTube channels for RAG pipelines is difficult because the official API does not expose caption text for non‑owned videos, scrapers often return only one video at a time, may provide auto‑translated captions instead of the spoken language, and require manual cleaning of HTML entities, chunking, and timestamp handling.

Outcome

The solution reliably returns 20/20 transcripts in the correct spoken language within ~30 seconds, correctly handles right‑to‑left languages, and flags videos without captions with a NO_CAPTIONS row. It eliminates manual cleaning and chunking, enabling seamless RAG pipeline integration.

Workflow

• 1. Deploy the Apify Actor "panix/youtube-transcript-scraper" via the web console, Python, or an n8n workflow.
• 2. Provide a list of channel URLs, @handles, playlists or Shorts URLs, and set parameters such as maximum videos and inclusion of chunks.
• 3. The Actor fetches each video’s transcript, detects the spoken language, cleans HTML entities, splits into non‑crossing chapter chunks, and attaches timestamps and deep links.
• 4. Output is returned as JSON rows containing transcript text, segments, and optional formats (SRT, WebVTT, Markdown, RAG chunks).
• 5. For automated pipelines, consume the JSON via the Apify API, iterate over rows, and upsert each chunk into a vector store (Pinecone, Qdrant, Supabase pgvector, Chroma) using the dedupeKey and chunkIndex as the record ID.

Evidence

The official YouTube Data API doesn’t give you caption text for videos you don’t own.

Source

I tested it on 20 videos in 20 languages in one run: English, German, French, Spanish, Hindi, Arabic, Urdu, Japanese, Italian, Filipino, Polish, Romanian, Dutch, Persian, Portuguese, Bengali, Tamil, Telugu, Ukrainian and Marathi.

Source

20/20 came back in the language actually spoken, in about 30 seconds.

Source