Local audio transcription for sensitive recordings
A local model reduces transmission to a cloud provider, but recordings can still leak through disks, temporary files, logs and backups.
Define the closed boundary
Document ingestion, processing, storage, access, retention and deletion. Check telemetry, model downloads and automatic backup paths.
Include upload workstations, temporary conversion files, result delivery and backup storage when describing the local processing boundary.
Fit the model to hardware
File size is not GPU memory usage. Model weights and computation dominate memory, while long recordings can be processed sequentially with global timestamps.
Benchmark the selected model and precision on the available accelerator; recording size alone does not predict inference memory requirements.
Control every copy
Use access control, encrypted transfer and storage, isolated temporary directories, content-free audit logs and explicit retention for both primary and backup copies.
Apply retention and access policy to derived text and indexes as well as the original media, because each remains sensitive content.
Evaluate quality separately
Privacy does not solve quiet speech or rare names. Pin the model and configuration and test genuine difficult recordings before routine use.
Use representative quiet, technical and multi-speaker recordings to prove recognition quality separately from the privacy architecture.
Continue with the underlying material
Need an estimate for your project?
Tell us about the project. We will break it into stages and explain the budget drivers.