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How to summarize a long live-stream video by timestamp with TLive Omni

Produce a timestamped summary of a long stream video, using TLive Omni's grid layout and 256K context.

Who it is forA content lead at a live-commerce brand who needs a quick timeline of a multi-hour broadcast to pick highlights for clips.

The problemA multi-hour stream is too long to watch in full, and highlights need exact times to cut clips.

The outcomeA timestamped list of what happens in the stream, including video dense captions and topic changes, to guide clip selection.

What you need first

  • Access to the TLive-Omni-4B or TLive-Omni-9B model weights on Hugging Face (https://huggingface.co/TaoLiveAIGC)
  • A Python environment; the README names Python as the repo language but gives no install steps
  • Hardware for running the model; the README does not state minimum GPU or memory needs, so check the model cards
  • A recording of a live stream, image, or audio clip to test with

Steps

  1. Read the Live-Commerce Video Evaluation section of the README to see the video tasks covered, including dense captions and temporal grounding.
  2. Read the Architecture section to understand how audio and video tokens are grouped into timestamped grids for long streams.
  3. Open the TLive Omni model card at https://huggingface.co/TaoLiveAIGC and load the model that fits your hardware.
  4. Submit the video in chunks that fit within the 256K token context, asking for timestamped dense captions per chunk.
  5. Join the chunk outputs in time order and check three timestamps against the original video before cutting clips.

Prompt for your agent

Using the TLive-Omni repo at https://github.com/TaoLiveAIGC/TLive-Omni, create a timestamped summary of the live-stream video at <path to video>, listing time, what happens, and any product mentioned. Verify that three timestamps match the source video before reporting.

Caveat. The README says the model supports up to 256K tokens at inference but does not say how long a video that covers, so test with a short clip first.

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