Allow protocol defined types for model inputs and outputs

.github/workflows/development-tests.yml

@@ -2,7 +2,6 @@ name: Development Tests
 
 on:
   pull_request:
-    branches: ["main"]
   pull_request_review:
     types: [submitted]
   workflow_dispatch:

.github/workflows/unit-tests.yml

@@ -75,8 +75,19 @@ jobs:
           sleep 15
           xcrun simctl list devices
       - name: Build and Test - ${{ matrix.run-config['name'] }}
+        id: test-step
         if: ${{ matrix.run-config['condition'] == true }}
+        continue-on-error: true
         run: |
           set -o pipefail
           xcodebuild clean build-for-testing -scheme whisperkit-Package -destination '${{ matrix.run-config['clean-destination'] }}' | xcpretty
           xcodebuild test -only-testing WhisperKitTests/UnitTests -scheme whisperkit-Package -destination '${{ matrix.run-config['test-destination'] }}'
+
+      - name: Upload Test Results
+        if: failure() && steps.test-step.outcome == 'failure'
+        uses: actions/upload-artifact@v4
+        with:
+          name: test-results-${{ matrix.run-config['name'] }}
+          path: |
+            ~/Library/Developer/Xcode/DerivedData/**/Logs/Test/*.xcresult
+          retention-days: 5

Sources/WhisperKit/Core/Audio/AudioProcessor.swift

@@ -93,7 +93,7 @@ public extension AudioProcessing {
     }
 
     static func padOrTrimAudio(fromArray audioArray: [Float], startAt startIndex: Int = 0, toLength frameLength: Int = 480_000, saveSegment: Bool = false) -> MLMultiArray? {
-        guard startIndex >= 0 && startIndex < audioArray.count else {
+        guard startIndex >= 0, startIndex < audioArray.count else {
             Logging.error("startIndex is outside the buffer size")
             return nil
         }
@@ -178,7 +178,6 @@ public class AudioProcessor: NSObject, AudioProcessing {
     }
 
     public var audioBufferCallback: (([Float]) -> Void)?
-    public var maxBufferLength = WhisperKit.sampleRate * WhisperKit.chunkLength // 30 seconds of audio at 16,000 Hz
     public var minBufferLength = Int(Double(WhisperKit.sampleRate) * 0.1) // 0.1 second of audio at 16,000 Hz
 
     // MARK: - Loading and conversion
@@ -229,7 +228,11 @@ public class AudioProcessor: NSObject, AudioProcessing {
             guard let buffer = AVAudioPCMBuffer(pcmFormat: audioFile.processingFormat, frameCapacity: frameCount) else {
                 throw WhisperError.loadAudioFailed("Unable to create audio buffer")
             }
-            try audioFile.read(into: buffer, frameCount: frameCount)
+            do {
+                try audioFile.read(into: buffer, frameCount: frameCount)
+            } catch {
+                throw WhisperError.loadAudioFailed("Failed to read audio file: \(error)")
+            }
             outputBuffer = buffer
         } else {
             // Audio needs resampling to 16khz

Sources/WhisperKit/Core/Audio/VoiceActivityDetector.swift

@@ -117,7 +117,7 @@ open class VoiceActivityDetector {
         }
     }
 
-    // MARK - Utility
+    // MARK: - Utility
 
     func voiceActivityClipTimestamps(in waveform: [Float]) -> [Float] {
         let nonSilentChunks = calculateActiveChunks(in: waveform)

Sources/WhisperKit/Core/AudioEncoder.swift

@@ -3,17 +3,22 @@
 
 import CoreML
 
+public protocol AudioEncoderOutputType {}
+extension MLMultiArray: AudioEncoderOutputType {}
+
 /// AudioEncoding protocol defines the requirements for an audio encoding implementation.
+@available(macOS 13, iOS 16, watchOS 10, visionOS 1, *)
 public protocol AudioEncoding {
     /// The size of the embedding produced by the encoder.
     var embedSize: Int? { get }
 
     /// Encodes the given audio features asynchronously.
     /// - Parameter features: The audio features to be encoded.
-    /// - Returns: An optional `MLMultiArray` containing the encoded features.
-    func encodeFeatures(_ features: MLMultiArray) async throws -> MLMultiArray?
+    /// - Returns: An optional tensor containing the encoded features.
+    func encodeFeatures(_ features: any FeatureExtractorOutputType) async throws -> (any AudioEncoderOutputType)?
 }
 
+/// Backwards-compatible AudioEncoder implementation
 @available(macOS 13, iOS 16, watchOS 10, visionOS 1, *)
 public class AudioEncoder: AudioEncoding, WhisperMLModel {
     public var model: MLModel?
@@ -36,8 +41,15 @@ public class AudioEncoder: AudioEncoding, WhisperMLModel {
 
     public init() {}
 
+    public func encodeFeatures(_ features: any FeatureExtractorOutputType) async throws -> (any AudioEncoderOutputType)? {
+        guard let features = features as? MLMultiArray else {
+            throw WhisperError.audioProcessingFailed("AudioEncoder input must be MLMultiArray")
+        }
+
+        return try await encodeFeatures(features)
+    }
+
     public func encodeFeatures(_ features: MLMultiArray) async throws -> MLMultiArray? {
-        // Make sure features is shape MultiArray (Float32 1 × {80,128} × 3000)
         guard let model else {
             throw WhisperError.modelsUnavailable()
         }

Sources/WhisperKit/Core/FeatureExtractor.swift

@@ -7,9 +7,15 @@ import CoreGraphics
 import CoreML
 import Foundation
 
+public protocol FeatureExtractorOutputType {}
+extension MLMultiArray: FeatureExtractorOutputType {}
+
 public protocol FeatureExtracting {
+    associatedtype OutputType: FeatureExtractorOutputType
+
     var melCount: Int? { get }
-    func logMelSpectrogram(fromAudio inputAudio: MLMultiArray) async throws -> MLMultiArray?
+    var windowSamples: Int? { get }
+    func logMelSpectrogram(fromAudio inputAudio: MLMultiArray) async throws -> OutputType?
 }
 
 @available(macOS 13, iOS 16, watchOS 10, visionOS 1, *)
@@ -26,6 +32,14 @@ open class FeatureExtractor: FeatureExtracting, WhisperMLModel {
         return shape[1]
     }
 
+    public var windowSamples: Int? {
+        guard let inputDescription = model?.modelDescription.inputDescriptionsByName["audio"] else { return nil }
+        guard inputDescription.type == .multiArray else { return nil }
+        guard let shapeConstraint = inputDescription.multiArrayConstraint else { return nil }
+        let shape = shapeConstraint.shape.map { $0.intValue }
+        return shape[0]  // The audio input is a 1D array
+    }
+
     public func logMelSpectrogram(fromAudio inputAudio: MLMultiArray) async throws -> MLMultiArray? {
         guard let model else {
             throw WhisperError.modelsUnavailable()
@@ -40,4 +54,5 @@ open class FeatureExtractor: FeatureExtracting, WhisperMLModel {
         let output = MelSpectrogramOutput(features: outputFeatures)
         return output.melspectrogramFeatures
     }
+
 }

Sources/WhisperKit/Core/Models.swift

@@ -49,6 +49,7 @@ public extension WhisperMLModel {
 
 // MARK: - Whisper Models
 
+@frozen
 public enum ModelVariant: CustomStringConvertible, CaseIterable {
     case tiny
     case tinyEn
@@ -100,6 +101,7 @@ public enum ModelVariant: CustomStringConvertible, CaseIterable {
     }
 }
 
+@frozen
 public enum ModelState: CustomStringConvertible {
     case unloading
     case unloaded
@@ -282,6 +284,7 @@ public struct AudioChunk {
 
 // MARK: - Decoding
 
+@frozen
 public enum DecodingTask: Codable, CustomStringConvertible, CaseIterable {
     case transcribe
     case translate
@@ -296,7 +299,7 @@ public enum DecodingTask: Codable, CustomStringConvertible, CaseIterable {
     }
 }
 
-public struct DecodingInputs {
+open class DecodingInputs {
     public var initialPrompt: [Int]
     public var inputIds: MLMultiArray
     public var cacheLength: MLMultiArray
@@ -355,6 +358,7 @@ public struct DecodingCache {
     }
 }
 
+@frozen
 public enum ChunkingStrategy: String, Codable, CaseIterable {
     case none
     case vad
@@ -444,6 +448,7 @@ public struct DecodingResult {
     }
 }
 
+@frozen
 public enum WhisperError: Error, LocalizedError, Equatable {
     case tokenizerUnavailable(String = "Tokenizer is unavailable")
     case modelsUnavailable(String = "Models are unavailable")
@@ -575,6 +580,7 @@ public struct TranscriptionResult: Codable {
         Total Tokens:                  \(totalTokens)
         Tokens per Second:             \(String(format: "%.2f", tokensPerSecond)) tok/s
         Real Time Factor:              \(String(format: "%.3f", rtf))
+        Speed Factor:                  \(String(format: "%.3f", 1.0 / rtf))
         Fallbacks:                     \(timings.totalDecodingFallbacks)
         """)
     }
@@ -647,6 +653,7 @@ public typealias ModelStateCallback = (_ oldState: ModelState?, _ newState: Mode
 public typealias TranscriptionStateCallback = (_ state: TranscriptionState) -> Void
 
 /// Represents the different states of the transcription process.
+@frozen
 public enum TranscriptionState: CustomStringConvertible {
     /// The audio is being converted to the required format for transcription
     case convertingAudio
@@ -1372,6 +1379,7 @@ extension WhisperTokenizerWrapper {
 
 // MARK: Constants
 
+@frozen
 public enum Constants {
     enum Logging {
         static let subsystem = "com.argmax.whisperkit"
@@ -1502,6 +1510,8 @@ public enum Constants {
 
     public static let defaultAudioReadFrameSize: AVAudioFrameCount = 1_323_000 // 30s of audio at commonly found 44.1khz sample rate
 
+    public static let defaultWindowSamples: Int = 480_000 // 30s of audio at 16khz sample rate default for Whisper models
+
     public static let fallbackModelSupportConfig: ModelSupportConfig = {
         var config = ModelSupportConfig(
             repoName: "whisperkit-coreml-fallback",

Sources/WhisperKit/Core/Text/LogitsFilter.swift

@@ -75,8 +75,9 @@ open class TimestampRulesFilter: LogitsFiltering {
 
     public func filterLogits(_ logits: MLMultiArray, withTokens tokens: [Int]) -> MLMultiArray {
         guard let sampleBegin = sampleBegin(for: tokens),
-              sampleBegin > tokens.count
+              sampleBegin <= tokens.count
         else {
+            // Early return if we are still prefilling the prompt
             return logits
         }