Use case · Pipeline
Diarized transcription.
Every speaker named.
From a meeting recording or a call file to a fully-attributed transcript — speech recognition, speaker diarization, and speaker identification stitched into one on-device pipeline. No cloud APIs, no per-minute pricing, no data leaving the device.
What you can build
Four shapes of the same pipeline.
Each shape stitches an ASR + a diarizer + an optional speaker-ID enrolment store. The components are interchangeable; what you choose depends on the audio source and your latency budget.
Meeting minutes
"Alice said …" / "Bob said …" attribution from a single Zoom export.
Call-center analytics
Agent vs. caller turns, sentiment per speaker, on-device for compliance.
Podcast transcripts
Host + guests identified across the episode, word-level timestamps.
Legal / interview records
Court-grade attribution with no audio ever leaving the device.
Deeper reading