Apify youtube transcripts llm training data

YouTube Transcripts as LLM Training Data

Turn a list of YouTube videos into a clean text corpus for LLM fine-tuning, RAG, or analysis. One run takes an array of URLs and returns one row per video: the full transcript as a single text field, plus the provenance metadata (title, channel, upload date, views) a documented dataset needs.

When to use this skill

• The user wants LLM training data, a fine-tuning corpus, or RAG documents sourced from YouTube videos.
• They want bulk transcripts from a channel dump, a playlist export, or a keyword-collected URL list.
• They want spoken-video content as text without running their own speech-to-text.
• They need a single-language corpus from mixed-language sources (translate everything to one target).

Not for: discovering which videos to include (collect URLs first; see the chain table in references/actor-index.md), videos without caption tracks (those return error rows), or verbatim audio ground truth (captions are YouTube's own tracks, not fresh ASR).

What you get (one row per video)

The corpus fields: non_timestamped (the full transcript as one string, the training-text column), language_code, source_type (Manual or Auto-generated, a quality signal), snippet_count, total_seconds. The provenance fields for dataset documentation: title, channel_name, channel_url, upload_date, view_count, like_count, tags, categories, url, video_id. Optional alignment data: timestamped (snippets with text, start, duration). Failures arrive as error rows (success false), so a bad URL never kills the batch.

Prerequisites

• Apify account (sign up at https://apify.com?fpr=9n7kx3&fp_sid=skillrepo).
• Authentication via apify login, or an APIFY_TOKEN environment variable (Apify Console, Settings, Integrations).

The Actor

• Store page: https://apify.com/johnvc/YoutubeTranscripts?fpr=9n7kx3&fp_sid=skillrepo
• Actor ID: johnvc/YoutubeTranscripts
• Pricing: pay per video transcribed; failed videos are free (see references/gotchas.md).

Run it with the Apify CLI

Batch corpus run, English preferred, metadata on for provenance:

apify actors call "johnvc/YoutubeTranscripts" -i '{"youtube_url":["https://www.youtube.com/watch?v=jNQXAC9IVRw","https://www.youtube.com/watch?v=dQw4w9WgXcQ"],"languages":["en"],"include_metadata":true}' \
  --json \
  --output-dataset \
  --user-agent apify-awesome-skills/apify-youtube-transcripts-llm-training-data \
  2>/dev/null

Mixed-language sources normalized into a Spanish corpus:

apify actors call "johnvc/YoutubeTranscripts" -i '{"youtube_url":["https://www.youtube.com/watch?v=dQw4w9WgXcQ"],"translate_to":"es"}' \
  --json \
  --output-dataset \
  --user-agent apify-awesome-skills/apify-youtube-transcripts-llm-training-data \
  2>/dev/null

Every call carries the three flags this repo expects: --json, --user-agent apify-awesome-skills/apify-youtube-transcripts-llm-training-data, and 2>/dev/null. The --output-dataset flag prints the dataset rows (the transcript data) on success instead of just run metadata.

Run it from Claude or another AI agent (MCP)

The Actor is MCP-ready. Add the hosted server URL:

https://mcp.apify.com/?tools=actors,docs,johnvc/YoutubeTranscripts

Then ask, for example: "Pull the transcripts for these 50 videos and build me a JSONL training file with text and metadata columns." MCP setup docs: https://docs.apify.com/platform/integrations/mcp

Workflow

1. Collect the URL list. Channel exports, playlist dumps, or keyword discovery (see the chain table); dedupe by video_id before running.
2. Decide the corpus language policy. Same-language sources: set languages to that code. Mixed sources: set translate_to to normalize, and note the translation caveat below.
3. Run the batch. Pass the whole array in one call; the Actor processes videos in parallel and records failures as error rows without stopping.
4. Filter for quality. Drop rows where success is false. If caption quality matters, prefer rows with source_type Manual, or set transcript_type to manual up front.
5. Export the corpus. Map non_timestamped to your text column and carry title, channel_name, upload_date, url, and language_code as metadata columns; write JSONL or CSV.
6. Document provenance. The metadata fields exist so the dataset card can say what the corpus contains and where it came from.

Inputs

• youtube_url (array of URLs, the batch)
• languages (ordered array of ISO 639-1 codes, default ["en"])
• translate_to (single ISO code, normalizes the corpus language; see limits)
• transcript_type (manual restricts to human-written captions, a quality filter)
• include_metadata (boolean, default true; keep it on for provenance)
• list_only (boolean; free dry run that shows the available languages per video)

Cost

Billing is per video successfully transcribed, at a fraction of a cent per video; a 10,000-video corpus costs well under a dollar. Failed videos are not charged. Details and the live-price check are in references/gotchas.md.

Honest limits

• Captions are YouTube's own tracks. Auto-generated captions contain recognition errors; filter on source_type Manual when accuracy matters more than coverage.
• Translation (translate_to) covers only the languages YouTube exposes for auto-translation, roughly 18 codes. Rows without a translated_to field were NOT translated; exclude them or re-plan the corpus language.
• Respect the licensing context of the content you collect; a transcript corpus inherits the source videos' copyright status. That judgment belongs to the dataset builder, not this API.

Troubleshooting

• Many error rows in one channel batch: that channel likely disables captions; check a few URLs with "list_only": true.
• Corpus language is inconsistent: some rows fell back to a different track. Group by language_code and re-run outliers with translate_to.
• Batch runs long: thousands of URLs take minutes; split into parallel runs of about 500 URLs if wall-clock matters.

See references/gotchas.md for cost guardrails and error recovery, and references/actor-index.md for the Actor routing table.

Related Actors

• Google Short Videos API (discover short-form videos by keyword to feed this corpus builder): https://apify.com/johnvc/google-short-videos-api?fpr=9n7kx3&fp_sid=skillrepo