YouTube Transcript Scraper

Extract YouTube video transcripts as clean, structured JSON - timestamped segments plus joined plaintext, with the available caption languages and the video's title, channel and duration. Pass video URLs or ids; no YouTube API key.

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What you get per video

• Segments - each with start, dur (seconds) and text
• text - the full transcript as one joined string, ready to feed an LLM
• availableLanguages - every caption track (code + name + auto-generated flag)
• language / isAutoGenerated - the track that was returned
• Video metadata - title, author, channelId, lengthSeconds, viewCount

Features

• Batch - pass many videos in one run; one dataset item per video.
• Language choice - request a specific caption language with lang (a prefix like en matches en-US), or take the default track.
• Manual or auto captions - human-written subtitles when present, auto-generated otherwise, flagged with isAutoGenerated.
• Two output shapes - timestamped segments for subtitles/alignment, or plain text for summarisation.

Input

Field	Type	Default	Description
videos	array	required	Watch URLs, youtu.be / shorts links, or 11-char video ids.
lang	string	-	Preferred caption language code, e.g. en. Blank = default track.
format	select	segments	segments (with timestamps) or text (plain).
proxy_country	string	-	Exit-IP country (ISO-2). Blank = a US exit (recommended).

Example output (one dataset item)

{
  "videoId": "dQw4w9WgXcQ",
  "title": "Rick Astley - Never Gonna Give You Up (Official Video)",
  "author": "Rick Astley",
  "lengthSeconds": 213,
  "language": "en",
  "languageName": "English",
  "isAutoGenerated": false,
  "availableLanguages": [
    { "languageCode": "en", "name": "English", "isAutoGenerated": false },
    { "languageCode": "de-DE", "name": "German (Germany)", "isAutoGenerated": false }
  ],
  "segmentCount": 62,
  "segments": [
    { "start": 8.46, "dur": 4.2, "text": "We're no strangers to love" },
    { "start": 12.66, "dur": 3.0, "text": "You know the rules and so do I" }
  ],
  "text": "We're no strangers to love You know the rules and so do I ..."
}

Use cases

• LLM / RAG pipelines - turn videos into text for summarisation, Q&A and embeddings.
• Subtitles & captions - export timestamped segments to SRT/VTT or align to audio.
• Content analysis - keyword, topic and sentiment analysis over spoken content.
• Accessibility & search - make video libraries readable and full-text searchable.

Pricing

$1 per 1,000 transcripts - one transcript = one processed video. Videos whose transcript could not be fetched are listed in the run's ERRORS record (key-value store) and are not billed.

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