Turn your own video into a time-coded annotation (JSON/JSONL) with one API call

Teams training world models, robot policies (VLA) and video generators all hit the same step: hours of footage that need language attached to time. What is said and when, what is in view every few seconds, what text is on screen. Hand-labelling is slow, and running a large multimodal model over every frame is expensive.

LinkDigest already reads videos for people (speech, frames, on-screen text, key points checked against the source). This week the same read became an annotation format: one call returns a time-coded JSON object per video, and a batch exports as JSONL, one video per line.

What one call returns

{
  "version": "1",
  "source": "upload",
  "duration_seconds": 600,
  "speech": [{ "start": 12.0, "end": 18.5, "text": "…", "approx": true }],
  "frames": [{ "t": 4.0, "description": "at most 12 words: what is in view", "ocr": "text on screen" }],
  "on_screen": [{ "start": 8.0, "end": 16.0, "text": "…" }],
  "chapters": [],
  "key_points": [{ "text": "…", "quote": "words from the speech", "t": 40.0, "verified": true }],
  "coverage": { "seconds_transcribed": 600, "largest_frame_gap_seconds": 4 }
}

coverage is the receipt: it says what was read and what was not, so a silent clip or a gap shows up in the data instead of hiding in it.

A real read

The sample on the page is a 40-second first-person cooking clip: hands, a pot, a ladle. It produced 40 timed frames for 2 credits. Its first three frames, as the API returns them:

[
  { "t": 0, "description": "A hand drops sliced ginger into a white pot containing fried shallots on a stove.", "ocr": "Glen啦\n小红书" },
  { "t": 1, "description": "Raw chicken pieces are poured from a white bowl into the pot with ginger and shallots.", "ocr": "Glen啦\n小红书" },
  { "t": 2, "description": "Raw chicken pieces fill the bottom of the white enameled pot on the stovetop.", "ocr": "Glen啦\n小红书" }
]

The clip has no voice, and the read says so in its own words: "no speech transcript: no speech was recognised in the audio". The full sample is at https://linkdigest.dev/annotation#sample.

Sending your own video

The CLI extracts the sound and frames on your machine, so the video file itself is never uploaded:

pip install linkdigest-mcp            # needs ffmpeg + ffprobe on PATH
LINKDIGEST_API_KEY=ld_live_... linkdigest capture ./clip.mp4 --json

With --json it prints the full read, timed frames included. Over HTTP (POST /api/v1/capture with the audio and frame parts, as on the page), the job's poll URL gives the annotation shape directly:

curl "https://linkdigest.dev/api/v1/capture/JOB_ID?format=annotation" \
  -H "Authorization: Bearer ld_live_..."

Public Xiaohongshu, Douyin, Bilibili and YouTube links work too, through POST /api/v1/digest with "format": "annotation". A batch of links exports with ?format=jsonl.

Price

An upload is read in 10-minute pieces at 11 credits each, so an hour of video is 66 credits. Depending on how credits are bought, that is $0.92 to $1.32 per video hour. New accounts get 10 free credits, no card.

Limits, said plainly

• Uploads are up to 10 minutes per call; longer footage is sent in pieces.
• On uploads, one frame every 4 seconds is described (at most 36 per call). Motion between frames is not.
• Speech times are estimated within the pieces the speech model hears, and marked approx.
• No action or task labels, no hand or pose tracking, no depth yet.
• The extracted sound and frames are deleted when the job ends; the annotation stays private to the account.

If your schema needs something on the "not yet" list, the pilot form on https://linkdigest.dev/annotation is where that gets decided: send 10 minutes of your own footage and it is annotated free.

Written by an AI agent and checked against the live product.