# Does OpenSuperWhisper Support Real-Time Audio Transcription? A Technical Deep Dive

> Discover if OpenSuperWhisper supports real-time audio transcription. Learn how it continuously captures live audio and delivers fast text updates for seamless voice interaction.

- Repository: [Starmel/OpenSuperWhisper](https://github.com/Starmel/OpenSuperWhisper)
- Tags: deep-dive
- Published: 2026-07-07

---

**Yes, OpenSuperWhisper captures live microphone input continuously and processes it through asynchronous transcription engines, delivering text updates to the UI within seconds of speech completion.**

OpenSuperWhisper enables **real-time audio transcription** by combining immediate microphone capture with non-blocking background processing. As implemented in the `Starmel/OpenSuperWhisper` repository, the app records temporary audio files while you speak, then pipelines that data to either Whisper CPP or FluidAudio engines without freezing the interface.

## How Real-Time Transcription Works in OpenSuperWhisper

The architecture follows a five-stage pipeline that keeps the UI responsive while minimizing latency between speech and text.

### 1. Audio Capture with AudioRecorder

The `AudioRecorder` class handles live microphone input in [`AudioRecorder.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/AudioRecorder.swift). When you trigger a hotkey or mouse button, it begins writing raw audio to a temporary `.wav` file on a background queue. Critically, the recorder adds a short audio "tail" after you release the stop command to prevent clipping of the final word.

### 2. Queue Management via TranscriptionQueue

Once recording stops, `AudioRecorder` passes the file URL to `TranscriptionQueue` ([`TranscriptionQueue.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/TranscriptionQueue.swift)). This queue stores a `Recording` object containing the file path, duration, and status, then schedules it for immediate processing. The queue manages file lifecycle operations, moving temporary recordings to permanent storage after successful transcription.

### 3. Engine Selection and Configuration

In [`TranscriptionService.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/TranscriptionService.swift) (lines 40-55), the `TranscriptionService` loads your preferred engine based on `AppPreferences` settings. You can choose between:

- **WhisperEngine**: Wraps Whisper CPP for high-accuracy offline transcription
- **FluidAudioEngine**: Interfaces with the Parakeet model for low-latency processing

### 4. Asynchronous Transcription Processing

The `transcribeAudio(_:settings:)` method in [`TranscriptionService.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/TranscriptionService.swift) (lines 83-112) serializes engine access to prevent conflicts while running the actual transcription on a detached task. This method:

- Applies voice activity detection (VAD) to filter silence
- Streams progress updates via `@Published` properties (`progress`, `transcribedText`)
- Returns results without blocking the main thread

### 5. Result Handling and File Management

After transcription completes, `TranscriptionQueue` handles cleanup (lines 50-78). The temporary file is copied or moved to your permanent recordings folder, and the UI updates immediately with the finalized text.

## Implementation Details: Key Components

Understanding the source code reveals why the real-time pipeline feels instantaneous:

- **[`AudioRecorder.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/AudioRecorder.swift)**: Manages `AVAudioEngine` instance and connection status monitoring, ensuring continuous capture even during system interruptions
- **[`TranscriptionService.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/TranscriptionService.swift)**: Acts as a singleton coordinator that prevents multiple simultaneous transcription jobs while exposing live progress through Combine publishers
- **[`Engines/WhisperEngine.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/Engines/WhisperEngine.swift)**: Implements the `transcribeAudio(url:settings:)` protocol method with VAD preprocessing
- **[`Engines/FluidAudioEngine.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/Engines/FluidAudioEngine.swift)**: Provides alternative low-latency transcription for the Parakeet model
- **[`Utils/AudioUtil.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/Utils/AudioUtil.swift)**: Measures audio duration and validates file integrity before processing

## Code Example: Implementing Live Transcription

To integrate OpenSuperWhisper's real-time capabilities into your own Swift code:

```swift
import OpenSuperWhisper

// Start capture when user presses hotkey
AudioRecorder.shared.startRecording()

// ... user speaks ...

// Stop and transcribe immediately
Task {
    if let url = await AudioRecorder.shared.stopRecording() {
        let settings = Settings()  // Reads current UI preferences
        do {
            let text = try await TranscriptionService.shared.transcribeAudio(
                url: url, 
                settings: settings
            )
            print("🗣️ Transcription:", text)
        } catch {
            print("❌ Transcription failed:", error)
        }
    }
}

```

For automatic queue handling (the default UI behavior), manually add files to the pipeline:

```swift
TranscriptionQueue.shared.addFileToQueue(url: temporaryRecordingURL)

```

## Engine Comparison: Whisper CPP vs FluidAudio

**WhisperEngine** provides robust accuracy through the Whisper CPP implementation, making it ideal for longer dictation sessions where precision matters more than speed.

**FluidAudioEngine** leverages the Parakeet model specifically optimized for latency-sensitive applications, processing shorter audio segments faster while maintaining acceptable accuracy for command-and-control scenarios.

## Summary

- OpenSuperWhisper implements **real-time audio transcription** through a background queue architecture that records temporary files while you speak
- The `AudioRecorder` adds protective audio "tails" to prevent word clipping, while `TranscriptionQueue` manages file lifecycle and processing order
- `TranscriptionService` coordinates between Whisper CPP and FluidAudio engines, streaming progress via Combine publishers at lines 83-112
- All processing occurs on detached tasks, keeping the UI responsive during transcription
- The feature is explicitly documented in the README (lines 11-12) as "Real-time audio recording and transcription"

## Frequently Asked Questions

### How does OpenSuperWhisper handle audio buffering during real-time transcription?

The app writes directly to a temporary `.wav` file rather than keeping audio in memory buffers. This approach, implemented in [`AudioRecorder.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/AudioRecorder.swift), prevents memory pressure during long recording sessions while ensuring the `TranscriptionQueue` receives a complete, seekable file immediately upon stop.

### What audio formats does OpenSuperWhisper support for live recording?

The `AudioRecorder` generates standard WAV files with PCM encoding. The [`AudioUtil.swift`](https://github.com/Starmel/OpenSuperWhisper/blob/main/AudioUtil.swift) helper validates file headers and calculates duration before passing data to the transcription engines, ensuring compatibility with both Whisper CPP and FluidAudio requirements.

### Can I switch between transcription engines without restarting the app?

Yes. `TranscriptionService` reads the engine preference from `AppPreferences` at the start of each transcription job (lines 40-55). Changing the engine in Settings affects the next queued recording immediately, allowing real-time A/B testing between Whisper CPP and FluidAudio without app restarts.

### How does the app prevent the last word from being clipped?

`AudioRecorder` implements a "tail" mechanism that continues recording for a brief moment (approximately 100-300ms) after receiving the stop command. This buffer captures the decay of final consonants and trailing silence that human reaction time would otherwise miss, ensuring complete transcription of the final spoken word.