How to vibe codeMacWhisper

Prerequisites

• Mac with Apple SiliconExisting hardware

  Required for Metal GPU acceleration to run Whisper models locally at usable speeds

• XcodeFree

  Required to build and run native macOS Swift applications

• Node.jsFree

  Required to run helper tooling and AI coding agent runtimes

AI coding tools

• Claude Code

  Best-in-class agentic CLI tool for scaffolding multi-file Swift projects and debugging native compilation errors

• Cursor

  Ideal for iterative SwiftUI view design and managing file diffs visually

Build guide

1. 01Scaffold Native macOS App Shell

   Initialize a new SwiftUI macOS application project configured for Apple Silicon with standard window navigation, sidebar file drop zone, and local settings storage.

   Create a new SwiftUI macOS desktop application structure using Swift package manager or Xcode project layout. Set up a split-view layout featuring a sidebar for navigation (Files, Settings, About), a central drop zone supporting drag-and-drop of audio and video files (mp3, wav, mp4, m4a, mov), and a bottom status bar showing processing progress. Implement a local settings view using AppStorage to save user preferences such as default Whisper model size (tiny, base, small, medium, large-v3, turbo), output format checkboxes (SRT, VTT, TXT, JSON), and language selection. Ensure the app adheres to macOS Human Interface Guidelines with a clean translucent sidebar and Dark/Light mode support. Provide all necessary Swift file structures and Xcode configuration notes.

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2. 02Integrate WhisperKit / whisper.cpp Engine

   Embed whisper.cpp or WhisperKit into the Swift project to handle offline speech-to-text inference utilizing Apple Metal GPU acceleration.

   Integrate whisper.cpp or WhisperKit into the Swift project to handle local offline audio transcription. Write a Swift wrapper class (TranscriptionEngine) that manages model file downloads from Hugging Face into the application's Application Support directory, checks for existing weights, and initializes the model with Metal GPU acceleration enabled. Expose asynchronous async/await functions for processing an audio file URL with progress reporting callbacks (0.0 to 1.0). Handle edge cases such as missing model weights, unsupported sample rates, and out-of-memory errors gracefully by catching exceptions and passing descriptive error messages back to the UI state. Provide complete, paste-ready Swift code.

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3. 03Audio Extraction and Conversion Pipeline

   Implement AVFoundation media extraction to convert imported video and audio files into 16kHz mono WAV files required by Whisper models.

   Build an audio preparation pipeline using AVFoundation and CoreAudio in Swift. When a user drops a video file (e.g., MP4, MOV) or compressed audio file (e.g., M4A, MP3), the pipeline must extract the audio track, resample it to 16kHz mono PCM WAV format, and store it in a temporary caching directory. Implement cleanup routines to delete temporary files when processing completes or fails. Ensure the UI displays an informative progress indicator during the extraction phase before handing the WAV file over to the TranscriptionEngine. Handle errors gracefully if the media file is corrupted or lacks an audio track.

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4. 04Transcription Viewer and Export Engine

   Build a rich text viewing interface for transcripts with timestamps and export format generators for SRT, VTT, TXT, and JSON.

   Create a SwiftUI view for displaying completed transcription results, featuring a split layout with timestamped segments on the left and searchable text on the right. Clicking a segment should copy its timestamp or highlight the text. Implement export utility functions that convert the array of transcribed segments into standard formats: plain text (.txt), SubRip subtitles (.srt), WebVTT (.vtt), and structured JSON. Add an 'Export' button that triggers a native macOS NSSavePanel allowing the user to save the generated files to their desired directory. Include a 'Copy to Clipboard' action with visual confirmation feedback.

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5. 05Batch Processing Queue and CLI Companion

   Add multi-file batch queueing management and a lightweight command-line interface (CLI) wrapper for terminal automation.

   Extend the application to support batch processing of multiple audio and video files simultaneously using an actor-based concurrency queue in Swift. Display a queue status list showing individual file progress states (Pending, Extracting, Transcribing, Completed, Failed). Additionally, create a secondary target or command-line executable script in Swift using ArgumentParser that allows running transcriptions headlessly from the terminal by passing an input file path and model size flag. Test that both the GUI queue and CLI tool share the same core transcription engine without locking the UI main thread.

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Cost vs paying for MacWhisper