A Practical Guide to Conference Analysis
In-depth insights into the conference are available through the following process:
preliminary
- Ensure that the video is in MP4 format (recommended resolution ≥ 720p)
- Install ffmpeg to ensure proper decoding of audio streams
- Prepare the analysis instruction file (see below for sample instructions)
Five-step process
- fundamental analysis::
python inference.py --modal video_audio --video_path meeting.mp4 --instruct "List speakers' emotions" - interaction detection::
--instruct "Identify who is agree/disagree" - Highlights::
--instruct "Summarize key discussion points" - Participation in assessments::
--instruct "Score engagement level 1-10" - Report Generation: Add
--output_report jsonParameters to get structured data
Optimization Recommendations
- Best camera angle: 45 degree overhead shot captures both face and limbs
- Audio quality: Directional microphones are recommended to reduce ambient noise
- Multi-scenario analysis: using
--time_range 00:10-00:30Parameter segmentation
Measurements show that the model can accurately recognize more than 85% of "speaking intent" (e.g., questioning/agreeing/complementing), which is 39% higher than traditional speech analysis systems.
This answer comes from the articleHumanOmni: a multimodal macromodel for analyzing human video emotions and actionsThe
May not be reproduced without permission:AI productivity tools " How do I use HumanOmni to analyze conference video? What are the exact steps to do so?
