Transcribe hours-long audio or video into an accurate, speaker-labeled (diarized), timestamped transcript with correctly-timed SRT/VTT captions – work a general model can't do on a raw file. Accepts a file_ref from upload_file or a url (YouTube, podcast episode, RSS feed, Google Drive/Dropbox share). Audio is never used to train models. Returns { job_id, status }; fetch the result with get_transcription.

Fetch a transcription by job_id: full text, per-speaker timestamped segments, summary, duration_seconds, billed_minutes, and language. Set wait_ms (up to 25000) to long-poll so short clips come back in one call; otherwise poll until status is 'done'.

Upload local audio/video the agent is holding (as base64 bytes or a file path) and get back a file_ref to pass to transcribe. Use this when the media has no public URL. Requires the Pepys R2 storage backend.

List this account's recent transcription jobs with their job_id, status, title, and duration, so you can resume, fetch, or export an earlier result instead of re-transcribing.

Given a podcast RSS feed or Apple Podcasts show URL, list its episodes (title, publish date, episode_guid, audio_url) so you can pick exactly which one to transcribe.

Batch-transcribe a whole podcast feed in one call – fan out every episode, or the latest N, to individual jobs. Returns a set of job_ids. Paid capability (throughput/abuse gate).

Export a finished transcript as SRT, VTT, TXT, Markdown, or JSON, with correct caption timings. Segment-level export is free; word-level-timed export (word_level:true) is a paid unlock. (DOCX/PDF are available in the Pepys web app.)

Search inside a long transcript for a phrase and get back only the matching timestamped segments – locate a quote or topic in an hours-long recording without loading the whole transcript into context.

Return the account's remaining transcription credits (in minutes) so you can check headroom before starting a large batch and avoid running out mid-run.