WhisperASR

繁體中文

A native macOS app for audio transcription using Breeze-ASR-25 (Whisper large-v2 fine-tuned for Taiwanese Mandarin and code-switching) via whisper.cpp with Metal GPU acceleration.

Screenshots

| Transcription Progress | Bilingual Transcript | |---|---| | | |

| App Picker | Settings | |---|---| | | |

Features

Live transcription + translation

• App audio recording — capture audio from any running app via ScreenCaptureKit (M4A/AAC at 48 kHz)
• Live transcription — see transcribed text in real-time while recording
• Live translation — per-segment translation displayed inline below each transcribed line, via OpenAI-compatible API
• Smart auto-scroll — live transcription view automatically follows new segments
• Live results reuse — when recording stops, live transcription results are kept (no re-transcription)
• Zoom meeting detection — automatically prompts to stop recording when a Zoom meeting ends

File transcription

• Drag-and-drop audio/video files (MP3, WAV, M4A, MP4, AAC, FLAC, OGG, WMA, AIFF, CAF)
• Batch processing — queue multiple files at once
• Sequential transcription queue — files wait in queue and transcribe one at a time

Bilingual output

• Post-transcription translation — translate completed transcriptions into any configured language with a single click
• Configurable languages — auto-detect or set source language; choose target language for translation
• Search — global sidebar filter across all transcriptions, plus in-file find (Cmd+F) with match highlighting and navigation

Playback

• Audio playback with play/pause, seek bar, and skip ±5s controls
• Synced text highlighting — the current sentence highlights as audio plays
• Click-to-seek — click any segment to jump to that point in the audio

Privacy & performance

• Metal GPU acceleration via whisper.cpp — fully on-device, no audio ever leaves your Mac
• Bring your own API — translation uses any OpenAI-compatible endpoint, including local models
• Re-transcribe and retry on failure with copyable error messages
• Custom model path — configure via Settings

Requirements

• macOS 14.0+
• Apple Silicon Mac (arm64) — the included xcframework is built for arm64
• Python 3 with torch, transformers, numpy, huggingface_hub (only if converting the model yourself; not needed if downloading the pre-converted GGML file)

Setup

1. Build whisper.cpp (if not already included)

The repo includes a pre-built CWhisper.xcframework. To rebuild it from source:

bash Scripts/build_whisper_lib.sh

This clones whisper.cpp, builds it with Metal + Accelerate, and packages the static libraries into an xcframework.

2. Get the Breeze-ASR-25 GGML model

Option A (recommended): Download the pre-converted GGML file directly from HuggingFace (~3 GB) and place it at Models/ggml-model.bin:

https://huggingface.co/danielkao0421/Breeze-ASR-25-ggml/blob/main/ggml-model.bin

Option B: Convert from the original model (requires Python 3 + torch, transformers, numpy, huggingface_hub):

bash Scripts/convert_model.sh

This downloads the Breeze-ASR-25 model from HuggingFace, clones the necessary repos, and converts it to GGML format at Models/ggml-model.bin.

3. Build and run

swift build
swift run

Or open in Xcode:

open Package.swift

Then build and run from Xcode (Cmd+R).

4. Build release app bundle (optional)

bash Scripts/build_release.sh

This builds an optimized release binary, generates a proper .icns icon, and packages everything into WhisperASR.app with Info.plist. To install:

cp -r WhisperASR.app /Applications/

If macOS blocks the app with a "cannot be opened" warning (because it is not signed), run the following command with the path to wherever WhisperASR.app is located:

xattr -cr /path/to/WhisperASR.app

Usage

1. Add files — drag audio/video files onto the sidebar, or click the + button
2. Record app audio — click the record button, select a running app, and start recording; recently used apps are listed first
3. Live transcription & translation — enable live transcription in the recording dialog to see text as you record; set a target language in Settings to see inline translations below each segment
4. Wait for transcription — files are queued and transcribed one at a time with progress and ETA
5. Review — click a completed item to see the transcript with timestamps
6. Translate — click the translate button to translate a completed transcription into any configured language
7. Search — use the sidebar search bar to filter across all files, or press Cmd+F to find within a transcript
8. Play audio — use the player controls at the bottom; text highlights in sync
9. Export — click the export button (top-right) to save as SRT or plain text

Settings

Open Settings (Cmd+,) to configure:

• Target Language — choose a language to translate transcriptions into
• OpenAI Translation API — only the API key is required; endpoint defaults to OpenAI, model defaults to gpt-4o-mini. Any OpenAI-compatible endpoint (including local models) works.
• Whisper Model — path to a custom ggml-*.bin model file; defaults to Models/ggml-model.bin in the project directory

Project Structure

Sources/
├── WhisperASRApp.swift        # App entry point
├── ContentView.swift          # NavigationSplitView layout
├── SidebarView.swift          # File list with drag-and-drop & context menu
├── DetailView.swift           # Transcript display, progress, export
├── PlayerView.swift           # Audio playback controls
├── RecordingView.swift        # App audio recording UI
├── AudioRecorder.swift        # ScreenCaptureKit audio capture
├── AppState.swift             # App state management & transcription queue
├── Models.swift               # Data models
├── TranscriptionService.swift # whisper.cpp C API integration
├── TranslationService.swift   # OpenAI-compatible translation API
├── TranscriptionStore.swift   # JSON file-per-item persistence
├── AudioLoader.swift          # AVAssetReader audio loading
├── AudioPlayerManager.swift   # AVPlayer wrapper
├── AppIconGenerator.swift     # Programmatic app icon rendering
└── SettingsView.swift         # Language, translation & model settings
Scripts/
├── build_whisper_lib.sh       # Build whisper.cpp xcframework
├── convert_model.sh           # Convert HuggingFace model to GGML
└── build_release.sh           # Build release .app bundle with icon
Frameworks/
└── CWhisper.xcframework/      # Pre-built whisper.cpp static library

License

This project uses whisper.cpp (MIT) and the Breeze-ASR-25 model by MediaTek Research.