Fully offline speech-to-text deployment program
For medical and financial scenarios that require data not to be out of this world, the following steps can be taken to build a secure environment:
- environmental isolation::
- Build offline images with Docker:
docker build --network none -t local-asr . - Disable all network interfaces (ifdown eth0)
- Turn off automatic model download (settings)
HF_HUB_OFFLINE=1)
- Build offline images with Docker:
- <strong]Resource preparation::
- Pre-download the whisper model to the . /models
- Install all dependencies offline (pip download -r requirements.txt)
- Using locally cached ffmpeg binary packages
- security enhancement::
- Configuring Memory Cryptography (dm-crypt)
- Enable transcription log auditing
- Add an auto-wipe cache parameter (
auto_flush=True)
- <strong]Validation Methods::
- (of a computer) run
netstat -tulnpConfirm no external connections - Verification using wireshark packet capture
- Check. /cache directory is free of sensitive data
- (of a computer) run
The solution has passed Equalization Level 3 security testing and is suitable for handling HIPAA/GDPR sensitive data. Deployment takes about 2 hours and requires 10GB of reserved storage.
This answer comes from the articleOpen source tool for real-time speech to textThe
May not be reproduced without permission:AI productivity tools " How to achieve fully offline voice transcription in privacy-sensitive scenarios?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil