Transcribe speech to text using Apple's Speech framework. Use when implementing live microphone transcription with AVAudioEngine, recognizing recorded audio…
Speech Recognition
Transcribe live and pre-recorded audio to text using Apple's Speech framework.
Covers SpeechAnalyzer / SpeechTranscriber (iOS 26+) and
SFSpeechRecognizer (iOS 10+). Targets Swift 6.3 / iOS 26+ while preserving
fallback guidance for apps that support older OS versions.
Scope boundary: Use this skill for speech-to-text recognition, speech
authorization, microphone capture plumbing, and result handling. Hand off text
analysis, language identification after transcription, sentiment, embeddings,
and translation to natural-language; hand off audio playback UI to avkit;
hand off summarization or generation over transcripts to apple-on-device-ai.
Contents
SpeechAnalyzer Strategy (iOS 26+)
SFSpeechRecognizer Setup
Authorization
Live Microphone Transcription
Pre-Recorded Audio File Recognition
On-Device vs Server Recognition
Handling Results
Common Mistakes
Review Checklist
References
SpeechAnalyzer Strategy (iOS 26+)
Use SpeechAnalyzer for modern iOS 26+ speech analysis, especially long-form
recordings, live transcription, time-indexed transcripts, and fully on-device
flows. Keep SFSpeechRecognizer for iOS 10+ deployment targets, server-backed
locale coverage, or existing callback/delegate implementations.
Read SpeechAnalyzer patterns when
implementing an iOS 26+ transcription pipeline, model asset handling, volatile
results, or file/buffer examples.
SpeechAnalyzer setup checklist
Choose the module:
SpeechTranscriber for the newer general-purpose on-device model.
DictationTranscriber when SpeechTranscriber is unavailable for the
current device or locale and dictation-compatible support is acceptable.
SpeechDetector only in conjunction with a transcriber when voice
activity detection is worth the accuracy/power tradeoff.
Check support before creating the session:
SpeechTranscriber.isAvailable
SpeechTranscriber.supportedLocale(equivalentTo:)
SpeechTranscriber.installedLocales / supportedLocales when showing
language choices.
Pick a documented preset:
.transcription for basic accurate transcription.
.progressiveTranscription for live UI updates.
.timeIndexedProgressiveTranscription when playback highlighting needs
audioTimeRange.
Install required assets with AssetInventory.assetInstallationRequest.
Convert live audio buffers to
SpeechAnalyzer.bestAvailableAudioFormat(compatibleWith:) before yielding
AnalyzerInput.
Consume module results from their AsyncSequence in a separate task.
Finish explicitly with finalizeAndFinish(through:),
finalizeAndFinishThroughEndOfInput(), or cancelAndFinishNow().
Do not use an offlineTranscription preset; Apple does not document one.
Finishing an AsyncStream input sequence does not finish the analyzer session.
SFSpeechRecognizer Setup
Creating a recognizer with locale
import Speech
// Default locale (user's current language)
let recognizer = SFSpeechRecognizer()
// Specific locale
let recognizer = SFSpeechRecognizer(locale: Locale(identifier: "en-US"))
// Check if recognition is available for this locale
guard let recognizer, recognizer.isAvailable else {
print("Speech recognition not available")
return
}
Monitoring availability changes
final class SpeechManager: NSObject, SFSpeechRecognizerDelegate {
private let recognizer = SFSpeechRecognizer()!
override init() {
super.init()
recognizer.delegate = self
}
func speechRecognizer(
_ speechRecognizer: SFSpeechRecognizer,
availabilityDidChange available: Bool
) {
// Update UI — disable record button when unavailable
}
}
Authorization
Request both speech recognition and microphone permissions before starting
live transcription. Add these keys to Info.plist:
NSSpeechRecognitionUsageDescription
NSMicrophoneUsageDescription
import Speech
import AVFoundation
func requestPermissions() async -> Bool {
let speechStatus = await withCheckedContinuation { continuation in
SFSpeechRecognizer.requestAuthorization { status in
continuation.resume(returning: status)
}
}
guard speechStatus == .authorized else { return false }
let micStatus: Bool
if #available(iOS 17, *) {
micStatus = await AVAudioApplication.requestRecordPermission()
} else {
micStatus = await withCheckedContinuation { continuation in
AVAudioSession.sharedInstance().requestRecordPermission { granted in
continuation.resume(returning: granted)
}
}
}
return micStatus
}
Live Microphone Transcription
The standard pattern: AVAudioEngine captures microphone audio → buffers are
appended to SFSpeechAudioBufferRecognitionRequest → results stream in.
import Speech
import AVFoundation
final class LiveTranscriber {
private let recognizer = SFSpeechRecognizer(locale: Locale(identifier: "en-US"))!
private let audioEngine = AVAudioEngine()
private var recognitionRequest: SFSpeechAudioBufferRecognitionRequest?
private var recognitionTask: SFSpeechRecognitionTask?
func startTranscribing() throws {
// Cancel any in-progress task
recognitionTask?.cancel()
recognitionTask = nil
// Configure audio session
let audioSession = AVAudioSession.sharedInstance()
try audioSession.setCategory(.record, mode: .measurement, options: .duckOthers)
try audioSession.setActive(true, options: .notifyOthersOnDeactivation)
// Create request
let request = SFSpeechAudioBufferRecognitionRequest()
request.shouldReportPartialResults = true
self.recognitionRequest = request
// Start recognition task
recognitionTask = recognizer.recognitionTask(with: request) { result, error in
if let result {
let text = result.bestTranscription.formattedString
print("Transcription: \(text)")
if result.isFinal {
self.stopTranscribing()
}
}
if let error {
print("Recognition error: \(error)")
self.stopTranscribing()
}
}
// Install audio tap
let inputNode = audioEngine.inputNode
let recordingFormat = inputNode.outputFormat(forBus: 0)
inputNode.installTap(onBus: 0, bufferSize: 1024, format: recordingFormat) {
buffer, _ in
request.append(buffer)
}
audioEngine.prepare()
try audioEngine.start()
}
func stopTranscribing() {
audioEngine.stop()
audioEngine.inputNode.removeTap(onBus: 0)
recognitionRequest?.endAudio()
recognitionRequest = nil
recognitionTask?.cancel()
recognitionTask = nil
}
}
Pre-Recorded Audio File Recognition
Use SFSpeechURLRecognitionRequest for audio files on disk:
func transcribeFile(at url: URL) async throws -> String {
guard let recognizer = SFSpeechRecognizer(), recognizer.isAvailable else {
throw SpeechError.unavailable
}
let request = SFSpeechURLRecognitionRequest(url: url)
request.shouldReportPartialResults = false
return try await withCheckedThrowingContinuation { continuation in
var didResume = false
recognizer.recognitionTask(with: request) { result, error in
guard !didResume else { return }
if let error {
didResume = true
continuation.resume(throwing: error)
} else if let result, result.isFinal {
didResume = true
continuation.resume(
returning: result.bestTranscription.formattedString
)
}
}
}
}
On-Device vs Server Recognition
SFSpeechRecognizer can use on-device recognition for supported locales on
iOS 13+. If supportsOnDeviceRecognition is false, the recognizer requires a
network connection. requiresOnDeviceRecognition only has effect when the
recognizer supports it.
let recognizer = SFSpeechRecognizer(locale: Locale(identifier: "en-US"))!
// Check if on-device is supported for this locale
if recognizer.supportsOnDeviceRecognition {
let request = SFSpeechAudioBufferRecognitionRequest()
request.requiresOnDeviceRecognition = true // Force on-device
}
SFSpeechRecognizer requests may still be a poor fit for long-form capture.
Apple documents a roughly one-minute task limit for speech recognition and
other service limits. For long recordings on iOS 26+, prefer SpeechAnalyzer;
otherwise chunk or restart recognition before the limit and preserve transcript
state across tasks.
Handling Results
Partial vs final results
let request = SFSpeechAudioBufferRecognitionRequest()
request.shouldReportPartialResults = true // default is true
recognizer.recognitionTask(with: request) { result, error in
guard let result else { return }
if result.isFinal {
// Final transcription — recognition is complete
let final = result.bestTranscription.formattedString
} else {
// Partial result — may change as more audio is processed
let partial = result.bestTranscription.formattedString
}
}
Accessing alternative transcriptions and confidence
recognizer.recognitionTask(with: request) { result, error in
guard let result else { return }
// Best transcription
let best = result.bestTranscription
// All alternatives (sorted by confidence, descending)
for transcription in result.transcriptions {
for segment in transcription.segments {
print("\(segment.substring): \(segment.confidence)")
}
}
}
Adding punctuation (iOS 16+)
let request = SFSpeechAudioBufferRecognitionRequest()
request.addsPunctuation = true
Contextual strings
Improve recognition of domain-specific terms:
let request = SFSpeechAudioBufferRecognitionRequest()
request.contextualStrings = ["SwiftUI", "Xcode", "CloudKit"]
Common Mistakes
Not requesting both speech and microphone authorization
// ❌ DON'T: Only request speech authorization for live audio
SFSpeechRecognizer.requestAuthorization { status in
// Missing microphone permission — audio engine will fail
self.startRecording()
}
// ✅ DO: Request both permissions before recording
SFSpeechRecognizer.requestAuthorization { status in
guard status == .authorized else { return }
AVAudioSession.sharedInstance().requestRecordPermission { granted in
guard granted else { return }
self.startRecording()
}
}
Not handling availability changes
// ❌ DON'T: Assume recognizer stays available after initial check
let recognizer = SFSpeechRecognizer()!
// Recognition may fail if network drops or locale changes
// ✅ DO: Monitor availability via delegate
recognizer.delegate = self
func speechRecognizer(
_ speechRecognizer: SFSpeechRecognizer,
availabilityDidChange available: Bool
) {
recordButton.isEnabled = available
}
Not stopping the audio engine when recognition ends
// ❌ DON'T: Leave audio engine running after recognition finishes
recognizer.recognitionTask(with: request) { result, error in
if result?.isFinal == true {
// Audio engine still running, wasting resources and battery
}
}
// ✅ DO: Clean up all audio resources
recognizer.recognitionTask(with: request) { result, error in
if result?.isFinal == true || error != nil {
self.audioEngine.stop()
self.audioEngine.inputNode.removeTap(onBus: 0)
self.recognitionRequest?.endAudio()
self.recognitionRequest = nil
}
}
Assuming on-device recognition is available for all locales
// ❌ DON'T: Force on-device without checking support
let request = SFSpeechAudioBufferRecognitionRequest()
request.requiresOnDeviceRecognition = true // Ignored unless the recognizer supports it
// ✅ DO: Check support before requiring on-device
if recognizer.supportsOnDeviceRecognition {
request.requiresOnDeviceRecognition = true
} else {
// Fall back to server-based or inform user
}
Not handling the one-minute recognition limit
// ❌ DON'T: Start one long continuous recognition session
func startRecording() {
// SFSpeechRecognizer tasks can be cut off after about 60 seconds
}
// ✅ DO: roll the segment before the limit and let cleanup end audio once
func scheduleRecognitionRollover() {
recognitionTimer = Timer.scheduledTimer(withTimeInterval: 55, repeats: false) { [weak self] _ in
self?.commitLatestPartialText()
self?.stopTranscribing() // owns endAudio(), tap removal, and task cancellation
try? self?.startTranscribing()
}
}
SFSpeechRecognitionTask exposes finish(), cancel(), state, and error;
do not invent task properties such as recognitionTask to restart work. Keep
the active SFSpeechAudioBufferRecognitionRequest in your manager and call
endAudio() from one cleanup path only.
Treating SpeechAnalyzer input completion as session completion
// ❌ DON'T: Only finish the AsyncStream and expect result streams to close
inputBuilder.finish()
// ✅ DO: explicitly finish or cancel the analyzer session
let lastSampleTime = try await analyzer.analyzeSequence(inputSequence)
if let lastSampleTime {
try await analyzer.finalizeAndFinish(through: lastSampleTime)
} else {
try analyzer.cancelAndFinishNow()
}
Duplicating volatile SpeechAnalyzer results
// ✅ Replace volatile text with the finalized result for the same audio range
for try await result in transcriber.results {
if result.isFinal {
volatileTranscript = AttributedString()
finalizedTranscript.append(result.text)
} else {
volatileTranscript = result.text
}
}
Creating multiple simultaneous recognition tasks
// ❌ DON'T: Start a new task without canceling the previous one
func startRecording() {
recognitionTask = recognizer.recognitionTask(with: request) { ... }
// Previous task is still running — undefined behavior
}
// ✅ DO: Cancel existing task before creating a new one
func startRecording() {
recognitionTask?.cancel()
recognitionTask = nil
recognitionTask = recognizer.recognitionTask(with: request) { ... }
}
Review Checklist
NSSpeechRecognitionUsageDescription is in Info.plist
NSMicrophoneUsageDescription is in Info.plist (if using live audio)
Authorization is requested before starting recognition
SFSpeechRecognizerDelegate is set to handle availabilityDidChange
Audio engine is stopped and tap removed when recognition ends
recognitionRequest.endAudio() is called when done recording
Previous recognitionTask is canceled before starting a new one
supportsOnDeviceRecognition is checked before requiring on-device mode
Partial results are handled separately from final (isFinal) results
SFSpeechRecognizer one-minute/service limits are accounted for
For iOS 26+: AssetInventory assets are installed before using SpeechAnalyzer
For iOS 26+: SpeechTranscriber.isAvailable and locale support are checked
For iOS 26+: live buffers are converted to the analyzer-compatible format
For iOS 26+: analyzer sessions are explicitly finalized or canceled
For iOS 26+: volatile results are replaced by finalized results, not duplicated
References
Speech framework
SpeechAnalyzer
SpeechTranscriber
SpeechTranscriber.Preset
DictationTranscriber
SpeechDetector
SFSpeechRecognizer
SFSpeechAudioBufferRecognitionRequest
SFSpeechURLRecognitionRequest
SFSpeechRecognitionResult
SFSpeechRecognitionRequest
AssetInventory
Asking Permission to Use Speech Recognition
Recognizing Speech in Live Audio
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