EnviousWispr is a free dictation application for macOS that converts natural speech into polished text. It addresses the need for fast, private voice-to-text input directly within any application where the user types.
The application works by letting users hold a hotkey while speaking, after which the spoken words appear as clean, polished text in the active app. Transcription occurs entirely on the device using Apple Silicon, ensuring audio never leaves the Mac. An optional AI polish feature can refine the output either on-device or via a user-chosen cloud provider. The tool operates offline, requires no account, and runs on macOS Sonoma 14 or later with an M1 processor or newer.
It supports workflows across different roles. Developers use it for PR descriptions, commit messages, and code comments while staying in their flow. Writers employ it to draft first versions, outlines, blog posts, and essays by speaking ideas aloud. Founders and executives apply it to handle email, reports, and meeting notes more quickly. The page presents nine such daily use cases that illustrate how the tool fits varied writing habits.
EnviousWispr is delivered as a downloadable macOS application with an optional Homebrew installation for terminal users via the command brew install --cask saurabhav88/tap/enviouswispr. It is open source under the GPLv3 license, with source code available on GitHub. The application is provided at no cost.
EnviousWispr is a Productivity & Work project. It focuses on converting natural spoken thoughts into clean, polished text without leaving your current app or sending audio to the cloud. It is built as an open-source project for mac users who write frequently such as developers, writers, and executives. It is available for free. EnviousWispr is available on the command line and macOS.
It is developed by Saurabh Awasthi, and it first shipped in 2026. The project is developed in the open on GitHub with 52 stars and 631 commits in the last 90 days. Among its 5 catalogued features are Hotkey Activation, On-device Transcription, and AI Polish.
Summary written by a language model from the project’s public pages.
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