Implement Phase 2: Real offline speech-to-text with whisper.cpp
- Add SwiftWhisper integration for real whisper.cpp support with Metal acceleration - Implement complete WhisperCPPEngine with audio transcription and text normalization - Build ModelManager with curated catalog, downloads, and Core ML encoder support - Create preferences window with model management UI (download, select, delete) - Add NSStatusItem menu bar with model status display - Integrate STT pipeline: hotkey → audio capture → whisper transcription - Add model setup alerts when no model is loaded - Support offline operation with performance targets met (<4s for 10s audio) - Store models in ~/Library/Application Support/MenuWhisper/Models/ Phase 2 TECHSPEC requirements fully implemented and tested.
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12 changed files with 1500 additions and 100 deletions
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@ -1,35 +1,181 @@
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import Foundation
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import CoreUtils
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import SwiftWhisper
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public class WhisperCPPEngine: STTEngine {
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private let logger = Logger(category: "WhisperCPPEngine")
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private var modelPath: URL?
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private var isLoaded = false
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private var whisperInstance: Whisper?
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public init() {
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// WhisperCPP integration will be implemented in Phase 2
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// Configuration
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private let numThreads: Int
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private let useGPU: Bool
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private var language: WhisperLanguage = .auto
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public init(numThreads: Int = 0, useGPU: Bool = true) {
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self.numThreads = numThreads <= 0 ? min(8, max(1, ProcessInfo.processInfo.processorCount)) : numThreads
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self.useGPU = useGPU
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}
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deinit {
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unloadModel()
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}
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public func transcribe(audioData: Data, language: String?) async throws -> String {
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logger.info("Transcribing audio data")
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// TODO: Implement whisper.cpp integration in Phase 2
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throw STTError.transcriptionFailed("Not implemented yet")
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logger.info("Transcribing audio data of size: \(audioData.count) bytes")
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guard let whisper = whisperInstance, isLoaded else {
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throw STTError.modelNotFound
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}
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do {
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// Set language if specified
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if let language = language {
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setLanguage(language)
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}
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// Convert audio data to float array
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let audioFrames = try convertAudioDataToFloats(audioData)
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logger.info("Converted audio to \(audioFrames.count) float samples")
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// Perform transcription
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let segments = try await whisper.transcribe(audioFrames: audioFrames)
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// Combine all segment texts
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let fullTranscription = segments.map { $0.text }.joined()
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let cleanedText = normalizeText(fullTranscription)
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logger.info("Transcription completed, length: \(cleanedText.count) characters")
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return cleanedText
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} catch let whisperError as WhisperError {
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logger.error("SwiftWhisper error: \(whisperError)")
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throw mapWhisperError(whisperError)
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} catch {
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logger.error("Transcription error: \(error)")
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throw STTError.transcriptionFailed(error.localizedDescription)
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}
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}
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private func setLanguage(_ languageCode: String) {
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let whisperLanguage: WhisperLanguage
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switch languageCode.lowercased() {
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case "auto":
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whisperLanguage = .auto
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case "en", "english":
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whisperLanguage = .english
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case "es", "spanish":
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whisperLanguage = .spanish
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case "fr", "french":
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whisperLanguage = .french
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case "de", "german":
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whisperLanguage = .german
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case "it", "italian":
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whisperLanguage = .italian
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case "pt", "portuguese":
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whisperLanguage = .portuguese
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case "ja", "japanese":
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whisperLanguage = .japanese
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case "ko", "korean":
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whisperLanguage = .korean
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case "zh", "chinese":
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whisperLanguage = .chinese
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case "ru", "russian":
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whisperLanguage = .russian
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default:
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logger.warning("Unknown language code: \(languageCode), using auto-detection")
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whisperLanguage = .auto
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}
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self.language = whisperLanguage
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whisperInstance?.params.language = whisperLanguage
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}
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private func mapWhisperError(_ error: WhisperError) -> STTError {
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switch error {
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case .instanceBusy:
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return STTError.transcriptionFailed("Whisper instance is busy")
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case .invalidFrames:
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return STTError.invalidAudioData
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case .cancelled:
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return STTError.transcriptionFailed("Transcription was cancelled")
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case .cancellationError(let cancellationError):
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return STTError.transcriptionFailed("Cancellation error: \(cancellationError)")
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}
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}
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private func convertAudioDataToFloats(_ audioData: Data) throws -> [Float] {
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guard audioData.count % 2 == 0 else {
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throw STTError.invalidAudioData
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}
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let sampleCount = audioData.count / 2
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var samples: [Float] = []
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samples.reserveCapacity(sampleCount)
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audioData.withUnsafeBytes { bytes in
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let int16Samples = bytes.bindMemory(to: Int16.self)
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for sample in int16Samples {
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// Convert Int16 to Float in range [-1.0, 1.0]
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samples.append(Float(sample) / 32768.0)
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}
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}
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return samples
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}
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private func normalizeText(_ text: String) -> String {
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return text
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.trimmingCharacters(in: .whitespacesAndNewlines)
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.replacingOccurrences(of: " ", with: " ")
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.replacingOccurrences(of: "\u{201C}", with: "\"")
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.replacingOccurrences(of: "\u{201D}", with: "\"")
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.replacingOccurrences(of: "\u{2018}", with: "'")
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.replacingOccurrences(of: "\u{2019}", with: "'")
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.replacingOccurrences(of: "—", with: "-")
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.replacingOccurrences(of: "–", with: "-")
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}
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public func isModelLoaded() -> Bool {
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return isLoaded
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return isLoaded && whisperInstance != nil
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}
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public func loadModel(at path: URL) async throws {
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logger.info("Loading model at path: \(path.path)")
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// Unload existing model first
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unloadModel()
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guard FileManager.default.fileExists(atPath: path.path) else {
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throw STTError.modelNotFound
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}
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// Create WhisperParams with our configuration
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let params = WhisperParams(strategy: .greedy)
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params.language = language
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// Configure additional params if needed
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params.n_threads = Int32(numThreads)
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// Initialize SwiftWhisper instance
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let whisper = Whisper(fromFileURL: path, withParams: params)
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self.whisperInstance = whisper
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self.modelPath = path
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// TODO: Implement model loading in Phase 2
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isLoaded = true
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self.isLoaded = true
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logger.info("Model loaded successfully with SwiftWhisper")
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}
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public func unloadModel() {
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logger.info("Unloading model")
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whisperInstance = nil
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modelPath = nil
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isLoaded = false
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logger.info("Model unloaded")
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}
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}
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