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Speech Recognition

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dpearson2699
speech-recognition

Transcribe speech to text using Apple's Speech framework. Use when implementing live microphone transcription with AVAudioEngine, recognizing recorded audio files, handling speech and microphone authorization, choosing on-device vs server-backed SFSpeechRecognizer behavior, or adopting SpeechAnalyzer, SpeechTranscriber, DictationTranscriber, AssetInventory, and async result streams on iOS 26+.

Overview

Publisherdpearson2699
Repositoryswift-ios-skills
Skill namespeech-recognition
Stars
1.2K
Forks
61
Bundled files
2
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  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by dpearson2699 on GitHub. Read the source before you install it.

Installation

Install the Speech Recognition AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dpearson2699/swift-ios-skills/tree/main/skills/speech-recognition
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dpearson2699/swift-ios-skills.git /tmp/swift-ios-skills
mkdir -p .claude/skills
cp -r /tmp/swift-ios-skills/skills/speech-recognition .claude/skills/speech-recognition
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speech Recognition in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Speech Recognition on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Speech Recognition is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Speech Recognition

Transcribe live and pre-recorded audio to text using Apple's Speech framework. Covers SpeechAnalyzer / SpeechTranscriber (iOS 26+) and SFSpeechRecognizer (iOS 10+) fallback guidance.

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+)

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

  1. 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.
  2. Check support before creating the session:
    • SpeechTranscriber.isAvailable
    • SpeechTranscriber.supportedLocale(equivalentTo:)
    • SpeechTranscriber.installedLocales / supportedLocales when showing language choices.
  3. Pick a documented preset:
    • .transcription for basic accurate transcription.
    • .progressiveTranscription for live UI updates.
    • .timeIndexedProgressiveTranscription when playback highlighting needs audioTimeRange.
  4. Install required assets with AssetInventory.assetInstallationRequest.
  5. Convert live audio buffers to SpeechAnalyzer.bestAvailableAudioFormat(compatibleWith:) before yielding AnalyzerInput.
  6. Consume module results from their AsyncSequence in a separate task.
  7. 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

swift
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

swift
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
swift
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.

swift
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:

swift
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.

swift
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

With shouldReportPartialResults, replace the displayed partial transcript until SFSpeechRecognitionResult.isFinal commits it. This is separate from SpeechTranscriber.Result.isFinal, whose volatile attributed range must be replaced by the final result for that range. The live example and references/speechanalyzer-patterns.md contain the canonical loops.

Accessing alternative transcriptions and confidence

swift
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+)

swift
let request = SFSpeechAudioBufferRecognitionRequest()
request.addsPunctuation = true

Contextual strings

Improve recognition of domain-specific terms:

swift
let request = SFSpeechAudioBufferRecognitionRequest()
request.contextualStrings = ["SwiftUI", "Xcode", "CloudKit"]

Common Mistakes

MistakeFix
Live audio requests only speech authorizationRequire both speech and microphone permission.
Recognizer availability is checked onceObserve delegate availability changes and model loss of service.
Final/error/rollover paths perform different cleanupUse one stopTranscribing() owner for engine stop, tap removal, endAudio, and task cancellation.
On-device mode is forced for every localeCheck supportsOnDeviceRecognition and provide a fallback.
One SFSpeechRecognizer task is used for long-form capturePrefer SpeechAnalyzer on iOS 26+ or roll bounded segments while preserving committed transcript.
Finishing analyzer input is treated as session completionExplicitly finalize through the last sample or cancel-and-finish.
Volatile SpeechAnalyzer results are appendedReplace the volatile range until a final result commits it.
A second recognition task starts before the first endsCancel/finish the active task and complete cleanup before replacement.

Load references/speechanalyzer-patterns.md for complete analyzer finalization and volatile-result code.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Speech Recognition AI skill do?

Transcribe speech to text using Apple's Speech framework. Use when implementing live microphone transcription with AVAudioEngine, recognizing recorded audio files, handling speech and microphone authorization, choosing on-device vs server-backed SFSpeechRecognizer behavior, or adopting SpeechAnalyzer, SpeechTranscriber, DictationTranscriber, AssetInventory, and async result streams on iOS 26+.

Why use Speech Recognition on TypingMind?

Because you install it once and use it with any model. Speech Recognition is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Speech Recognition in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dpearson2699/swift-ios-skills/tree/main/skills/speech-recognition. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Speech Recognition?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Speech Recognition?

As many as you like. As long as a model supports skills, you can use Speech Recognition with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Speech Recognition AI skill free?

It is published on GitHub by dpearson2699. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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