Video

Scripts for captioning and temporally grounding video files using HF Buckets and Jobs.

What the output looks like — a frame from Joan Avoids a Cold (1947, Prelinger Archives) with the event Marlin-2B produced for that moment:

Quick Start

Scripts run directly from their Hub URL — no clone or local checkout needed:

# Caption every video in a bucket: dense scene captions + timestamped events
hf jobs uv run --image vllm/vllm-openai:latest --flavor a10g-small \
    -s HF_TOKEN \
    -v hf://buckets/user/my-videos:/input:ro \
    https://huggingface.co/datasets/uv-scripts/video/raw/main/marlin-caption.py \
    /input hf://buckets/user/my-videos/captions

# Temporal grounding: when does an event happen?
hf jobs uv run --image vllm/vllm-openai:latest --flavor a10g-small \
    -s HF_TOKEN \
    -v hf://buckets/user/my-videos:/input:ro \
    https://huggingface.co/datasets/uv-scripts/video/raw/main/marlin-caption.py \
    /input hf://buckets/user/out --find "a person enters the room"

Scripts

marlin-caption.py

Runs NemoStation/Marlin-2B (2B video VLM, gated — accept the license on the model page first) over a directory of videos via vLLM. Output is a resumable parquet dataset: one row per ~60s chunk with scene, caption, and an events column of <start - end> descriptions in seconds. Re-running skips completed rows; failed rows are recorded, not dropped (--retry-errors re-attempts them).

Videos longer than ~60s are split into chunks and event timestamps offset back to global film time. This is required for correct timestamps, not an optimisation: Marlin was trained on short clips and compresses any input onto a ~60s timeline.

--find "event" switches to grounding mode: each chunk returns a candidate (span_start, span_end). Spans are candidates, not detections — the model cannot say "not present", so filter or verify downstream. When the event is real, spans are precise to fractions of a second.

Cost: ~3s of GPU per minute of film on a10g-small at batch scale — about $0.05 per hour of footage.

Memory: defaults encode a measured config (--mm-processor-cache-gb 0, in-flight window capped at 24). vLLM's multimodal cache grows without bound on distinct videos and will OOM a 15 GB node if re-enabled. On a10g-large and up, --window-max 64 is safe.

Run --help on the script for all options.