This paper presents Ethos AT, a desktop software for automatic transcription that uses OpenAI Whisper models, enabling local processing and ensuring data privacy and accessibility for users who are not necessarily programming experts, such as oral history researchers. A comparative analysis of six Whisper models (small, medium, large, large-v2, large-v3, and turbo) was conducted to analyze performance in terms of transcription accuracy, error types, and processing time. Results indicate that larger models achieve higher lexical accuracy, while smaller ones provide faster execution with acceptable quality for general use; the turbo model showed an effective balance between accuracy and speed. Overall, Ethos AT offers a secure, efficient, and user-friendly solution for academic and institutional contexts.