What you can do with TLive Omni

3 use cases

• Transcribe live-stream speech and separate speakers

  Turn a live-commerce audio track into a transcript that labels who said what, using TLive Omni's ASR features.

  • TLive Omni
  • Sales and marketing
  • Advanced
  View prompt

  Using the TLive-Omni repo at https://github.com/TaoLiveAIGC/TLive-Omni and its model card on Hugging Face, produce a speaker-attributed transcript of <path to live-stream audio>, with timestamps where the model provides them. Verify that speaker labels stay consistent across the first five minutes of the transcript before reporting the result.

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• Locate products and read on-screen text in frames

  Find products in live-stream frames and read the text shown on screen, using the image tasks TLive Omni covers.

  • TLive Omni
  • Sales and marketing
  • Intermediate
  View prompt

  Using the TLive-Omni repo at https://github.com/TaoLiveAIGC/TLive-Omni, find the products visible in the frames at <path to frame folder> and return each product's location and any on-screen text as a table. Verify by checking three frames by eye against the table before reporting.

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• Summarize a long live-stream video by timestamp

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

  • TLive Omni
  • Sales and marketing
  • Advanced
  View prompt

  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.

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How to use TLive Omni

1. Check you have what it needs: 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.
2. Open TaoLiveAIGC/TLive-Omni on GitHub and read the README and license.
3. Open the TLive-Omni-4B or TLive-Omni-9B model page at https://huggingface.co/TaoLiveAIGC and read the model card for how to load the weights.
4. Read the Live-Commerce Audio Evaluation section of the README to see which speech tasks the model covers, including live ASR and speaker-attributed ASR.
5. Split the stream recording into audio segments and keep their timestamps so each segment can be matched to its time in the stream.
6. Run the model on each segment with a prompt that asks for speaker-labeled transcription, following the model card instructions.

These steps come from our top use case, Transcribe live-stream speech and separate speakers, which also has a copy-paste agent prompt.

TLive Omni alternatives · Best AI repos for Sales and marketing

About this repo

• Who it is forBuilt for e-commerce teams and developers who need to analyze live-stream recordings, products, and speech, and it is best for readers comfortable loading a model from Hugging Face.
• Why it is hot
• LanguagePython
• Sourcegithub.com/TaoLiveAIGC/TLive-Omni · Back to Hot repos

Requirements

• 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

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