AI-enabled processing path for multimedia data
For the LLM adaptation challenges of audio and video, Supametas.AI provides hierarchical processing solutions:
- base layer: Automatic Speech Recognition (ASR) transcription to time-stamped text, supports Chinese/English and other languages
- reinforcement layer: speaker separation (distinguishing host/guest), emotion labeling (recognizing tone changes), key frame extraction (video key frames)
- application layer (computing): Generate structured dialog tree formats suitable for digital human training or podcast summarization
Example: After uploading the meeting recording.mp3, 1) Enable "Multi-speaker Recognition" in the Advanced Settings 2) Set the output format to "Dialogue Scene JSON" 3) Export the structured data containing [Timestamp, Speaker, Text, Sentiment Value]. This is the first time we've done this. Processing 1 hour of audio only consumes about 2000 Token.
This answer comes from the articleSupametas.AI: Extracting Unstructured Data into LLM Highly Available DataThe
May not be reproduced without permission:AI productivity tools " How to overcome the technical barrier that audio and video content is difficult to be processed by text-based AI models?
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