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Real Time Transcription

Real Time Transcription

Argmax Pro SDK includes the WhisperKitPro framework which implements an advanced streaming inference algorithm described in our ICML 2025 paper.

Key features:

  • Accuracy in real-time mode is identical to pre-recorded transcription
  • Dual output text streams: Build trust in stable and accurate results with Confirmed Text while maximizing responsiveness with Hypothesis Text.
  • Streaming API design that exposes event-based callbacks, minimizing the burden on the caller

In the video above, Confirmed Text is rendered in white while Hypothesis Text is rendered in gray.

This page includes minimal CLI examples to help you get familiar with the Real-time Transcription API in Argmax Pro SDK.

If you are looking for an end-to-end example app, check out our open-source Argmax Playground project which is also published to TestFlight and used for the demo video above.

Introduction

Real-time transcription streams input audio and the corresponding output text continuously during a live recording session:

  1. Input audio stream: Capturing audio in small user-defined intervals
  2. Inference: Incremental speech-to-text inference on the input stream
  3. Output text streams:
    • Confirmed: Finalized portion of the transcript that gets longer over time.
    • Hypothesis: Preliminary transcript that may still be refined as more audio context arrives.

This approach creates an ultra low-latency user experience where words appear on the screen almost as they're spoken, with occasional refinements to the most recent words as the model gathers more context.



Basic Example

This is a complete and self-contained CLI example project that demonstrates the usage of Argmax Pro SDK for real-time transcription from a microphone input stream.

Step 0: Verify Pro SDK setup

Argmax Pro SDK access must be set up with SwiftPM before going through this example. If unsure, please see Upgrading to Pro SDK (Step 1 only).

Step 1: Create project directory

Create a project directory as shown below and insert the code shared below into ArgmaxTestCommand.swift and Package.swift

ArgmaxSDKRealTimeTranscriptionBasicExample
├── Package.swift
└── Sources
    └── ArgmaxTestCLI
        └── ArgmaxTestCommand.swift

Package.swift:

// swift-tools-version: 5.10
// The swift-tools-version declares the minimum version of Swift required to build this package.
 
import PackageDescription
 
let package = Package(
    name: "Argmax Test CLI",
    platforms: [
        .macOS(.v14)
    ],
    products: [
        .executable(
            name: "argmax-test-cli",
            targets: ["ArgmaxTestCLI"]
        )
    ],
    dependencies: [
        .package(id: "argmaxinc.argmax-sdk-swift", .upToNextMinor(from: "1.7.0")),
        .package(url: "https://github.com/apple/swift-argument-parser.git", exact: "1.3.0")
    ],
    targets: [
        .executableTarget(
            name: "ArgmaxTestCLI",
            dependencies: [
                .product(name: "Argmax", package: "argmaxinc.argmax-sdk-swift"),
                .product(name: "ArgumentParser", package: "swift-argument-parser")
            ]
        ),
    ]
)

ArgmaxTestCommand.swift:

import Foundation
@preconcurrency import ArgumentParser
@preconcurrency import Argmax
import Combine
 
@main
struct ArgmaxTestCommand: AsyncParsableCommand {
    static let configuration = CommandConfiguration(
        abstract: "An example CLI tool for Argmax Pro SDK",
        subcommands: [Transcribe.self]
    )
 
    struct Transcribe: AsyncParsableCommand {
        static let configuration = CommandConfiguration(
            abstract: "Real-time transcription using system microphone"
        )
 
        @Option(help: "Argmax Pro SDK API key")
        var apiKey: String
 
        @Option(help: "Model name: e.g. `parakeet-v2_476MB`, `tiny.en`, `large-v3-v20240930_626MB`. Default: `parakeet-v2_476MB`")
        var modelName: String = "parakeet-v2_476MB"
        
        @Option(help: "Mode: e.g. `alwaysOn`, `voiceTriggered`, `batteryOptimized`. Default: `voiceTriggered`")
        var mode: String = "voiceTriggered"
 
        func run() async throws {
 
            print("Initializing Argmax Pro SDK...")
 
            let sdkConfig = ArgmaxConfig(apiKey: apiKey)
            // Temporarily disable keychain access for debug
            sdkConfig.keychainPersistence = false
            await ArgmaxSDK.with(sdkConfig)
 
            print("Downloading \(modelName) model using ModelStore...")
            let modelStore = ModelStore()
            let repoType: RepoType
            
            // Pick the right model repo
            if modelName.lowercased().contains("parakeet") {
                // Use Nvidia Parakeet models
                repoType = .parakeetRepo
            } else if #available(macOS 15, *) {
                // Use Pro Whisper models
                repoType = .proRepo
            } else if #available(macOS 13, *) {
                // Use Open-source Whisper models
                repoType = .openSourceRepo
            } else {
                fatalError("Oldest supported macOS is 13.")
            }
            
            // Track download progress
            let _ = modelStore.$progress.sink { progress in
                if let progress = progress {
                    let percentage = Int(progress.fractionCompleted * 100)
                    print("\rDownload progress: \(percentage)%", terminator: "")
                    fflush(stdout)
                }
            }
            
            let downloadURL = try await modelStore.downloadModel(
                name: modelName,
                repo: repoType
            )
            
            // To cancel download if needed:
            // modelStore.cancelDownload()
            
            let modelFolder = downloadURL.path(percentEncoded: false)
            print("\nDownload completed: \(modelFolder)")
 
            let liveTranscriber = try await setupLiveTranscriber(modelFolder: modelFolder)
 
            // Set stream transcription mode
            let streamMode: StreamTranscriptionMode
            switch mode {
            case "alwaysOn":
                streamMode = .alwaysOn
            case "voiceTriggered":
                // minProcessInterval is 0 by default which is too fast for most use cases
                streamMode = .voiceTriggered(minProcessInterval: 0.3)
            case "batteryOptimized":
                streamMode = .batteryOptimized
            default:
                streamMode = .voiceTriggered(minProcessInterval: 0.3)
            }
            print("\nStream mode: \(streamMode)")
 
            try await transcribeStream(liveTranscriber: liveTranscriber, mode: streamMode)
        }
 
        private func setupLiveTranscriber(modelFolder: String) async throws -> LiveTranscriber {
            print("Initializing WhisperKit Pro...")
            let whisperConfig = WhisperKitProConfig(
                modelFolder: modelFolder,
                verbose: true,
                logLevel: .debug
            )
            let whisperKitPro = try await WhisperKitPro(whisperConfig)
 
            print("Loading WhisperKit Pro models... It may take up to 1 minute during first load after download")
            try await whisperKitPro.loadModels()
 
            print("Creating LiveTranscriber...")
            let liveTranscriber = LiveTranscriber(whisperKit: whisperKitPro)
 
            return liveTranscriber
        }
 
        private func transcribeStream(liveTranscriber: LiveTranscriber, mode: StreamTranscriptionMode) async throws {
            print("Transcribing while streaming audio from microphone...")
 
            // Create Argmax stream source for device (microphone)
            // On iOS: pass nil to use default microphone
            // let deviceSource = ArgmaxSource(streamType: .device())
 
            // Or on macOS: pass AudioDeviceID to select specific input device,
            //  use AudioProcessor.getAudioDevices() to list avaialble devices
            let macbookMicrophone = AudioProcessor.getAudioDevices().first(where: { $0.name == "MacBook Pro Microphone" })
            let deviceSource = ArgmaxSource(streamType: .device(macbookMicrophone?.id))
 
            // Configure decoding options
            let options = DecodingOptionsPro(
                base: .init(
                        task: .transcribe,
                        wordTimestamps: true,
                        chunkingStrategy: .vad
                    ),
                transcribeInterval: 0.1,
                streamTranscriptionMode: mode,
                alignTimestampsToGlobal: true
            )
 
            // Register stream with options and optional audio callback
            try await liveTranscriber.registerStream(
                streamSource: deviceSource,
                options: options,
                audioCallback: { audioData in
                    // Optional: Process audio data for visualization, etc.
                    // print("Received \(audioData.count) audio samples")
                }
            )
 
            // Start transcription
            let deviceResults = try await liveTranscriber.startTranscription(for: deviceSource)
            
            // Consume results
            let dateFormatter = DateFormatter()
            dateFormatter.dateFormat = "HH:mm:ss.SSS"
            let transcribeTask = Task {
                var accumulatedConfirmedText = ""
                for try await result in deviceResults {
                    let timestamp = dateFormatter.string(from: Date())
                    var hypothesisText = ""
                    // LiveResult parameters:
                    // - text: Transcribed text (confirmed or hypothesis)
                    // - seconds: Elapsed time since transcription started (monotonic)
                    // - result: Complete TranscriptionResultPro (segments, timings, metadata)
                    switch result {
                    case .confirm(let text, let seconds, let result):
                        accumulatedConfirmedText += " " + text
                    case .hypothesis(let text, let seconds, let result):
                        hypothesisText = text
                    }
                    print("[\(timestamp)] \(accumulatedConfirmedText)\u{001B}[34m\(hypothesisText)\u{001B}[0m")
                }
                return accumulatedConfirmedText
            }
            
            // Captures ^+c signal from terminal, stop recording and clean up
            signal(SIGINT, SIG_IGN)
            let signalSource = DispatchSource.makeSignalSource(signal: SIGINT, queue: DispatchQueue.main)
            signalSource.setEventHandler(handler: DispatchWorkItem(block: {
                print("Stop recording...")
                print("Finalizing transcription...")
                Task.detached {
                    try? await liveTranscriber.stopAndRemoveStream(for: deviceSource)
                    let accumulatedConfirmedText = try! await transcribeTask.value
                    print("\n\nTranscription: \n\n\(accumulatedConfirmedText)\n")
                }
            }))
            
            signalSource.resume()
            let _ = try! await transcribeTask.value
        }
    }
}

Step 2: Build and run in Terminal

Run the following command in your Terminal from within the top-level project directory:

Example usage:

swift run argmax-test-cli transcribe --api-key <API_KEY>

If you observe error: no registry configured for 'argmaxinc' scope, go back to Step 0.

Here is an example output upon successful build and launch with --model-name large-v3-v20240930_turbo:


Advanced Example

The key differences between the Basic Example and Advanced Example are:

FeatureBasic ExampleAdvanced Example
Instance ManagementAutomatically manages WhisperKitPro instance via LiveTranscriberManual setup and configuration of WhisperKitPro
Session LifecycleHandles session creation and lifecycleManual TranscribeStreamSession lifecycle management
Audio Source AbstractionsProvides convenient abstractions (ArgmaxSource, CustomSource)Direct access to audioProcessor for custom audio handling
CleanupSimplifies cleanup with stopAndRemoveAllTranscriptions()Manual cleanup of streams and sessions
API SurfaceUser-friendly, higher-level APILower-level, more flexible but requires more code

For most use cases, the LiveTranscriber API is recommended. Use the low-level API when you need the additional control and are comfortable managing the complexity.

To set up the Advanced Example, simply modify the ArgmaxTestCommand.swift file from the Basic Example to the following:

ArgmaxTestCommand.swift

import Foundation
@preconcurrency import ArgumentParser
@preconcurrency import Argmax
import Combine
 
@main
struct ArgmaxTestCommand: AsyncParsableCommand {
    static let configuration = CommandConfiguration(
        abstract: "An example CLI tool for Argmax Pro SDK",
        subcommands: [Transcribe.self]
    )
 
    struct Transcribe: AsyncParsableCommand {
        static let configuration = CommandConfiguration(
            abstract: "Real-time transcription using system microphone"
        )
 
        @Option(help: "Argmax Pro SDK API key")
        var apiKey: String
 
        @Option(help: "Model name: e.g. `parakeet-v2_476MB`, `tiny.en`, `large-v3-v20240930_626MB`. Default: `parakeet-v2_476MB`")
        var modelName: String = "parakeet-v2_476MB"
 
        @Option(help: "Mode: e.g. `alwaysOn`, `voiceTriggered`, `batteryOptimized`. Default: `voiceTriggered`")
        var mode: String = "voiceTriggered"
 
        func run() async throws {
 
            print("Initializing Argmax Pro SDK...")
 
            let sdkConfig = ArgmaxConfig(apiKey: apiKey)
            // Temporarily disable keychain access for debug
            sdkConfig.keychainPersistence = false
            await ArgmaxSDK.with(sdkConfig)
 
            print("Downloading \(modelName) model using ModelStore...")
            let modelStore = ModelStore()
            let repoType: RepoType
 
            // Pick the right model repo
            if modelName.lowercased().contains("parakeet") {
                // Use Nvidia Parakeet models
                repoType = .parakeetRepo
            } else if #available(macOS 15, *) {
                // Use Pro Whisper models
                repoType = .proRepo
            } else if #available(macOS 13, *) {
                // Use Open-source Whisper models
                repoType = .openSourceRepo
            } else {
                fatalError("Oldest supported macOS is 13.")
            }
 
            // Track download progress
            let _ = modelStore.$progress.sink { progress in
                if let progress = progress {
                    let percentage = Int(progress.fractionCompleted * 100)
                    print("\rDownload progress: \(percentage)%", terminator: "")
                    fflush(stdout)
                }
            }
 
            let downloadURL = try await modelStore.downloadModel(
                name: modelName,
                repo: repoType
            )
 
            // To cancel download if needed:
            // modelStore.cancelDownload()
 
            let modelFolder = downloadURL.path(percentEncoded: false)
            print("\nDownload completed: \(modelFolder)")
 
            let whisperKitPro = try await setupWhisperKitPro(modelFolder: modelFolder)
 
            // Set stream transcription mode
            let streamMode: StreamTranscriptionMode
            switch mode {
            case "alwaysOn":
                streamMode = .alwaysOn
            case "voiceTriggered":
                streamMode = .voiceTriggered(minProcessInterval: 0.3)
            case "batteryOptimized":
                streamMode = .batteryOptimized
            default:
                streamMode = .voiceTriggered(minProcessInterval: 0.3) // fallback to default
            }
            print("\nStream mode: \(streamMode)")
 
            try await transcribeStream(whisperKitPro: whisperKitPro)
        }
 
        private func setupWhisperKitPro(modelFolder: String) async throws -> WhisperKitPro {
            let config = WhisperKitProConfig(
                modelFolder: modelFolder,
                verbose: false,
                logLevel: .debug
            )
 
            let whisperKitPro = try await WhisperKitPro(config)
            return whisperKitPro
        }
 
        private func transcribeStream(whisperKitPro: WhisperKitPro) async throws {
            print("Transcribing with low-level WhisperKitPro API...")
 
            // 1. Configure decoding options
            let options = DecodingOptionsPro(
                base: .init(
                        verbose: true,
                        task: .transcribe,
                        skipSpecialTokens: true,
                        wordTimestamps: true,
                        chunkingStrategy: .vad
                    ),
                transcribeInterval: 0.1
            )
 
            // 2. Create audio stream
            let (stream, continuation) = whisperKitPro.audioProcessor.startStreamingRecordingLive()
 
            // 3. Create transcription session
            let session = whisperKitPro.makeStreamSession(options: options)
 
            // 4. Start processing
            await session.start(audioInputStream: stream)
 
            // 5. Process results
            let dateFormatter = DateFormatter()
            dateFormatter.dateFormat = "HH:mm:ss.SSS"
            let transcribeTask = Task {
                for try await result in session.results {
                    let timestamp = dateFormatter.string(from: Date())
                    if let hypothesis = result.hypothesisText {
                        print("[\(timestamp)] Hypothesis: \(hypothesis)")
                    }
                    if !result.text.isEmpty {
                        print("[\(timestamp)] Confirmed: \(result.text)")
                    }
 
//                    // Access word-level timestamps for words in Hypothesis Text
//                    if !result.hypothesisSegments.isEmpty {
//                        print("\nHypothesis Text Word Timestamps:")
//                        result.hypothesisSegments.forEach { segment in
//                            segment.words?.forEach { word in
//                                print("\(word) - \(word.start), \(word.end))")
//                            }
//                        }
//                    }
                }
            }
 
            // 6. Handle termination signal
            signal(SIGINT, SIG_IGN)
            let signalSource = DispatchSource.makeSignalSource(signal: SIGINT, queue: DispatchQueue.main)
            signalSource.setEventHandler(handler: DispatchWorkItem(block: {
                print("Stop recording...")
                Task {
                    // Cleanup - finish the stream
                    continuation.finish()
                }
            }))
            signalSource.resume()
 
            // Wait for transcription task to complete
            try await transcribeTask.value
        }
    }
}

Advanced Features

Modes

Real-time Transcription continuously processes audio which is suboptimal in cases where there is no active talker. Downsides may include:

  • False positive predictions from background noise
  • Device resources being utilized indiscriminately

For this purpose, we have built StreamTranscriptionMode which lets developers set adaptive behavior based on input audio and other use case-related intent.

.voiceTriggered

Voice-triggered mode processes audio only when there is sufficiently high energy in the input audio. To see it in action, please refer to CLI example video above.

This mode's behavior is customizable with silenceThreshold and maxBufferLength but the default values work for a wide range of use cases.

.batteryOptimized

Battery-optimized mode is built for use cases where battery life and thermal sustainability are the top optimization objective. This mode is built on top of the voice-triggered mode but it inserts additional adaptive delays to throttle inference speed while keeping latency as low as possible.

.alwaysOn

This mode disables adaptive mode and is not recommended unless there is a specific use case requirement for it.

Background Processing on iOS

Real-time transcription can be sustained even after your app is backgrounded if the following is inserted into your app's Info.plist:

+	<key>UIBackgroundModes</key>
+	<array>
+		<string>audio</string>
+	</array>

This works for whisperKitPro.audioProcessor.startStreamingRecordingLive() because it uses AVAudioSession under the hood. Other audio sources built with AVAudioSession will also work in the background with this change.

Multiple Audio Streams

This feature allows multiple input audio streams to be real-time transcribed by the same LiveTranscriber instance. An example use case is concurrent real-time transcription of system audio and microphone for meeting transcriptions.

Before implementing multi-stream transcription, ensure that the ArgmaxTestCommand from Step 1 works correctly, particularly its transcribeStream function which demonstrates the basic LiveTranscriber usage.

Multi-Stream Architecture

The same LiveTranscriber instance can efficiently handle multiple audio streams simultaneously. Each stream gets its own registered source that shares the same LiveTranscriber instance but maintains independent processing context, allowing them to run concurrently without interference.

Example Implementation

private func transcribeMultipleStreams() async throws {
    // 1. Setup LiveTranscriber (same as single stream example)
    let liveTranscriber = try await setupLiveTranscriber()
    
    // 2. Create stream sources for each audio input
    
    // System audio stream (custom stream - for capturing system/app audio)
    let (systemStream, systemContinuation) = createSystemAudioStream() // Your implementation
    let systemSource = CustomSource(
        id: "system-audio",
        audioStream: systemStream,
        audioContinuation: systemContinuation
    )
    
    // Device microphone stream (using built-in device source)
    let deviceSource = ArgmaxSource(streamType: .device())
    
    // 3. Configure decoding options
    let options = DecodingOptionsPro(
        base: .init(
                verbose: true,
                task: .transcribe,
                skipSpecialTokens: true,
                wordTimestamps: true,
                chunkingStrategy: .vad
            ),
        transcribeInterval: 0.1
    )
    
    // 4. Register both streams with LiveTranscriber
    try await liveTranscriber.registerStream(streamSource: systemSource, options: options)
    try await liveTranscriber.registerStream(streamSource: deviceSource, options: options)
    
    // 5. Start transcription and get results stream for each source
    let systemResults = try await liveTranscriber.startTranscription(for: systemSource)
    let deviceResults = try await liveTranscriber.startTranscription(for: deviceSource)
    
    // 6. Process results from both streams concurrently
    // LiveResult parameters:
    // - text: Transcribed text (confirmed or hypothesis)
    // - seconds: Elapsed time since transcription started (monotonic)
    // - result: Complete TranscriptionResultPro (segments, timings, metadata)
    try await withTaskGroup(of: Void.self) { group in
        // System audio results processing
        group.addTask {
            for try await result in systemResults {
                switch result {
                case .confirm(let text, let seconds, let result):
                    print("[SYSTEM] Confirmed: \(text) at \(seconds)s")
                case .hypothesis(let text, let seconds, let result):
                    print("[SYSTEM] Hypothesis: \(text) at \(seconds)s")
                }
            }
        }
        
        // Device microphone results processing
        group.addTask {
            for try await result in deviceResults {
                switch result {
                case .confirm(let text, let seconds, let result):
                    print("[DEVICE] Confirmed: \(text) at \(seconds)s")
                case .hypothesis(let text, let seconds, let result):
                    print("[DEVICE] Hypothesis: \(text) at \(seconds)s")
                }
            }
        }
        
        // Wait for all processing to complete
        try await group.waitForAll()
    }
    
    // 7. Cleanup - stop and remove all transcriptions
    try await liveTranscriber.stopAndRemoveAllTranscriptions()
}

Key Considerations

  • Stream Management: Each audio stream requires its own ArgmaxSource (either built-in like .device() or custom like CustomSource) registered with the LiveTranscriber.

  • Individual Results Streams: Each source gets its own results stream from startTranscription(for: source) - no need to filter by stream ID.

  • Resource Management: The shared LiveTranscriber instance efficiently manages computational resources across all streams while maintaining independent processing contexts.

  • Graceful Termination: Call stopAndRemoveAllTranscriptions() to stop and remove all registered streams at once, ensuring proper cleanup and graceful termination of transcription sessions.

Migrate from transcribeWhileRecording (deprecating soon)

If you're currently using the older WhisperKitPro.transcribeWhileRecording API, here's how to migrate to the new stream-based approach for better performance and cleaner code.

Old API Pattern (whisperKitPro.transcribeWhileRecording and audioProcessor.startRecordingLive)

// Old approach - manual buffer management
private func oldTranscribeStream(whisperKitPro: WhisperKitPro) async throws {
    let options = DecodingOptionsPro(
        base: .init(
                verbose: true,
                task: .transcribe,
                wordTimestamps: true,
                chunkingStrategy: .vad
            ),
        transcribeInterval: 0.1
    )
    
    // Manual buffer management
    var audioBuffer: [Float] = []
    let lock = NSLock()
    
    try whisperKitPro.audioProcessor.startRecordingLive { samples in
        lock.withLock {
            audioBuffer.append(contentsOf: samples)
        }
    }
    
    // Create recording task with callbacks
    let recordingTask = whisperKitPro.transcribeWhileRecording(
        options: options,
        audioCallback: {
            let samples = lock.withLock {
                let samples = audioBuffer
                audioBuffer.removeAll()
                return samples
            }
            return AudioSamples(samples: samples)
        },
        resultCallback: { result in
            print("Text: \(result.text)")
            if let hypothesis = result.hypothesisText {
                print("Hypothesis: \(hypothesis)")
            }
            return true
        }
    )
    
    // Start and finalize
    try await recordingTask.start()
    let results = try await recordingTask.finalize()
}

New API Pattern (whisperKitPro.makeStreamSession and audioProcessor.startStreamingRecordingLive)

// New approach - stream-based with automatic management
private func newTranscribeStream(whisperKitPro: WhisperKitPro) async throws {
    let options = DecodingOptionsPro(
        base: .init(
                verbose: true,
                task: .transcribe,
                wordTimestamps: true,
                chunkingStrategy: .vad
            ),
        transcribeInterval: 0.1
    )
    
    // Create stream (automatic audio handling)
    let (stream, continuation) = whisperKitPro.audioProcessor.startStreamingRecordingLive()
    
    // Create session
    let session = whisperKitPro.makeStreamSession(options: options)
    
    // Start processing
    await session.start(audioInputStream: stream)
    
    // Process results with async iteration
    for try await result in session.results {
        print("Text: \(result.text)")
        if let hypothesis = result.hypothesisText {
            print("Hypothesis: \(hypothesis)")
        }
    }
    
    // Graceful termination
    continuation.finish()
}

Migration Benefits

  1. Simplified Architecture: No manual buffer management or thread synchronization required
  2. Better Performance: Reduced memory overhead and improved audio processing efficiency
  3. Cleaner Error Handling: Automatic stream lifecycle management reduces potential failure points
  4. Async/Await Integration: Modern Swift concurrency support with for try await result iteration
  5. Resource Management: Automatic cleanup and better resource utilization

Key Migration Steps

  1. Replace audioProcessor.startRecordingLive() with audioProcessor.startStreamingRecordingLive()
  2. Replace whisperKitPro.transcribeWhileRecording() with whisperKitPro.makeStreamSession()
  3. Remove manual buffer management (audio buffers, locks, callback handling)
  4. Replace resultCallback with for try await result in session.results
  5. Replace recordingTask.start() and finalize() with session.start() and continuation.finish()
  6. Update error handling to use Swift's structured concurrency patterns
File TranscriptionVoice Activity Detection