A media localization workflow needed to re-voice video across languages without losing the original speaker's identity. We built an end-to-end AI dubbing pipeline that preserves timbre and emotion and syncs automatically to the video.
Traditional dubbing is slow and expensive, and machine approaches tend to flatten the speaker's voice and emotion — losing what makes the original compelling.
Analysis of the original audio to capture timing, prosody and speaker characteristics.
Translation that preserves meaning and tone for natural-sounding localized speech.
Speech synthesis that imitates the original speaker's timbre and emotional delivery.
Alignment of synthesized speech back to the video for lip-and-timing consistency.
Figures reflect outcomes measured on this engagement. Client withheld under NDA.
The pipeline chains specialized models — analysis, translation, synthesis and sync — rather than relying on a single black box, which is what lets it preserve voice and emotion while scaling throughput.
We scope a clear plan with milestones and architecture options — and right-sized GPU hardware if AI workloads are involved.