How to evaluate an AI model for lip sync
By Drizzle team
2 min read
Use the same authorized portrait and approved audio where possible. That makes the comparison about animation rather than different scripts, voices, and recording quality.
Prepare a short diagnostic audio sample
Include a brand name, a number, a sentence-ending pause, and words with visible lip closures such as “b” or “p.” Use clean speech without loud background music. Keep the portrait front-facing and well lit.
Obtain permission for the real face and voice. Owning a recording does not automatically grant permission to synthesize new performances.
Check more than mouth alignment
Watch normally first, then inspect questionable moments slowly. Look at teeth, jaw shape, blinking, expression, shoulders, and what happens during silence. Compare whether the face stays stable through longer vowels and pauses.
A model that handles a neutral sentence may behave differently with fast speech or a large emotional change. Test the actual style the campaign needs.
Distinguish avatar and scene generation
Audio-driven avatar workflows such as OmniHuman 1.5 or Fabric 1.0 start from a different task than a model generating speech with an entire scene. Consult the linked provider documentation for current inputs and access.
Count prepared audio, rejected takes, and editing in the comparison. This guide provides an evaluation method, not a claimed benchmark winner or a list of models included in Drizzle.