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How to transcribe live-stream speech and separate speakers with TLive Omni

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

Who it is forAn operations analyst at a live-commerce team who needs written transcripts of host and guest speech after each broadcast.

The problemLive-stream audio mixes several speakers and product talk, so plain transcripts are hard to search and review by speaker.

The outcomeA transcript of the stream audio with speaker-attributed text that the team can search, quote, and review.

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. 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 for your agent

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.

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.

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