For real-time scenarios, GenAI Processors offers the following optimization strategies:
- streaming: Use
LiveProcessorProcesses audio and video streams frame-by-frame instead of waiting for full inputs - hardware acceleration: Enables PyAudio's
use_pcm_mimetype=TrueParameters reduce audio codec overhead - lightweight model: Selection
gemini-2.5-flashetc. optimized version of the model to reduce inference latency - asynchronous piping: By
async forCyclic parallel execution of data acquisition, processing, and output processes
Measurements show that this method can control the end-to-end delay within 300ms, which meets the real-time interaction requirements.
This answer comes from the articleGenAI Processors: lightweight Python library supports efficient parallel processing of multimodal contentThe
May not be reproduced without permission:AI productivity tools " What are the viable options for optimizing the response latency of real-time AI applications?
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