# AI use cases for TLive Omni (TaoLiveAIGC/TLive-Omni) Repo: [TaoLiveAIGC/TLive-Omni](https://github.com/TaoLiveAIGC/TLive-Omni) | Use cases: 3 | Page: https://dedupedai.com/use-cases/taoliveaigc-tlive-omni/ TLive-Omni is an omni-modal understanding model for e-commerce live streaming that takes image, video, audio, and text and returns text output. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Transcribe live-stream speech and separate speakers: https://dedupedai.com/use-cases/taoliveaigc-tlive-omni/transcribe-live-stream-speech-by-speaker/ - Locate products and read on-screen text in frames: https://dedupedai.com/use-cases/taoliveaigc-tlive-omni/locate-products-and-read-on-screen-text/ - Summarize a long live-stream video by timestamp: https://dedupedai.com/use-cases/taoliveaigc-tlive-omni/summarize-long-stream-video-by-timestamp/ ## How to transcribe live-stream speech and separate speakers with TLive Omni Repo: [TaoLiveAIGC/TLive-Omni](https://github.com/TaoLiveAIGC/TLive-Omni) | Difficulty: Advanced | Time: 120 minutes | Basis: From the README Turn a live-commerce audio track into a transcript that labels who said what, using TLive Omni's ASR features. **Who it is for:** An operations analyst at a live-commerce team who needs written transcripts of host and guest speech after each broadcast. **The problem:** Live-stream audio mixes several speakers and product talk, so plain transcripts are hard to search and review by speaker. **The outcome:** A transcript of the stream audio with speaker-attributed text that the team can search, quote, and review. ### Steps 1. 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. 2. 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. 3. Split the stream recording into audio segments and keep their timestamps so each segment can be matched to its time in the stream. 4. Run the model on each segment with a prompt that asks for speaker-labeled transcription, following the model card instructions. 5. Merge the segment outputs in time order and spot-check several speaker changes against the original audio. ### Prompt to paste into your agent ```text 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 , 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. ``` ### Caveat The README gives no install commands, hardware needs, or prompt format, so the exact run steps must come from the Hugging Face model cards. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/taoliveaigc-tlive-omni/transcribe-live-stream-speech-by-speaker/ ## How to locate products and read on-screen text in frames with TLive Omni Repo: [TaoLiveAIGC/TLive-Omni](https://github.com/TaoLiveAIGC/TLive-Omni) | Difficulty: Intermediate | Time: 90 minutes | Basis: From the README Find products in live-stream frames and read the text shown on screen, using the image tasks TLive Omni covers. **Who it is for:** A catalog or merchandising specialist at an e-commerce company who needs to match products shown on a live stream to listed items. **The problem:** Products appear briefly during a stream and prices or titles are often shown only as on-screen text, so manual matching is slow. **The outcome:** A list of product locations in each frame and the text read from the screen, ready to compare with the product catalog. ### Steps 1. Read the Live-Commerce Image Evaluation section of the README, which lists product visual grounding and text understanding as image tasks. 2. Extract still frames from the stream at the moments you want to check. 3. Open the TLive Omni model card at https://huggingface.co/TaoLiveAIGC/TLive-Omni-4B and load the model following its instructions. 4. Send each frame with a prompt that asks for product locations and the text on screen, following the model card format. 5. Compare the returned product names and on-screen text with your catalog and mark any mismatches for review. ### Prompt to paste into your agent ```text Using the TLive-Omni repo at https://github.com/TaoLiveAIGC/TLive-Omni, find the products visible in the frames at 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. ``` ### Caveat The README reports image benchmark scores but does not describe the output format for product grounding, so check the model card before building a parser. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/taoliveaigc-tlive-omni/locate-products-and-read-on-screen-text/ ## How to summarize a long live-stream video by timestamp with TLive Omni Repo: [TaoLiveAIGC/TLive-Omni](https://github.com/TaoLiveAIGC/TLive-Omni) | Difficulty: Advanced | Time: 120 minutes | Basis: From the README Produce a timestamped summary of a long stream video, using TLive Omni's grid layout and 256K context. **Who it is for:** A content lead at a live-commerce brand who needs a quick timeline of a multi-hour broadcast to pick highlights for clips. **The problem:** A multi-hour stream is too long to watch in full, and highlights need exact times to cut clips. **The outcome:** A timestamped list of what happens in the stream, including video dense captions and topic changes, to guide clip selection. ### 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 to paste into your agent ```text Using the TLive-Omni repo at https://github.com/TaoLiveAIGC/TLive-Omni, create a timestamped summary of the live-stream video at , 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. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/taoliveaigc-tlive-omni/summarize-long-stream-video-by-timestamp/