Use Case Analysis

Purpose

This document provides an overview of primary use cases for Cleanroom Whisper, an offline audio transcription application designed for privacy-conscious users who need reliable voice-to-text conversion without cloud dependencies.

User Personas

Privacy-Conscious Professional

Needs: Secure transcription without cloud services
Environment: Corporate, government, or security-sensitive
Priority: Data privacy, air-gap capability

Productivity Enthusiast

Needs: Fast voice-to-text for task capture
Environment: Personal productivity workflows
Priority: Speed, convenience, keyboard shortcuts

Accessibility User

Needs: Alternative text input method
Environment: Daily computer use with mobility limitations
Priority: Reliability, customizable controls, accuracy

Researcher/Interviewer

Needs: Transcribe recorded interviews
Environment: Qualitative research, journalism
Priority: Accuracy, handle longer audio, export capability

Primary Use Cases

Use Case	Actor	Workflow
Quick Voice Memo	Individual user	Hotkey -> record -> transcribe -> clipboard
Meeting Notes	Meeting participant	Record sections -> review history -> copy
Interview	Researcher/journalist	Play audio -> record -> compile transcript
Accessibility	User with mobility needs	Accessible hotkey -> dictate -> paste

Common Requirements Across All Use Cases

Requirement	Rationale
Offline operation	Privacy, security, reliability in air-gapped environments
Global hotkeys	Enable hands-free, seamless workflow
Quick transcription	Maintain productivity, minimize waiting
History access	Review, copy, and organize past transcriptions
No cloud dependency	Data privacy, control, and air-gap compatibility

Integration Scenarios

With Other Applications

Application Type	Integration Method	Use Case
Email client	Copy transcription to clipboard	Dictate emails
Note-taking apps	Paste transcription	Capture meeting notes
Task managers	Copy tasks/reminders	Voice-based task creation
Document editors	Paste sections	Dictate document sections
Chat/messaging	Copy messages	Dictate messages

With AirGap Deploy

Cleanroom Whisper can be deployed to air-gapped systems using AirGap Deploy:

Package Cleanroom Whisper binary with vendored dependencies
Include whisper.cpp source and pre-downloaded models
Transfer via AirGap Transfer if package exceeds USB capacity
Deploy and build on isolated system

See: AirGap Deploy workflow example

Out of Scope

The following are explicitly NOT supported in MVP:

Use Case	Why Not in MVP
Real-time streaming transcription	Complexity, batch processing sufficient
Multi-language auto-detection	Single language per model, user selects
Speaker diarization	whisper.cpp feature, adds complexity
Audio editing/playback	Use system audio player
Cloud backup/sync	Violates privacy design principle

Success Metrics

Metric	Target
Transcription accuracy	> 90% for clear audio
Time to transcription	< 10 seconds for 1-minute audio
User workflow disruption	Minimal (hotkey-driven)
Privacy violations	Zero (no network calls)
User errors	< 5% (clear UI, good defaults)