在设备上训练声音分类器

Question

我正在尝试在 iOS 设备上训练 CoreML 声音分类器，并且一直在努力寻找有关该主题的学习资源。声音分类器用于确定音乐的 sn-p 是否与其他歌曲的集合相似。因此分类器的输出只是“匹配”/“不匹配”的标签。

使用 CreateML 应用工作流程进行训练非常简单。我只是想在 iOS 设备上获得相同类型的培训，但据我所知（如果我错了，请纠正我）iOS 不支持 createML。

我一直在尝试调整来自各种来源的代码，以使其在 iOS 操场上工作。我只能找到关于训练图像分类器的资源，这两个是最有帮助的（1，2）。

请在下面查看我到目前为止提出的代码。

import UIKit
import CoreML

func convertDataToArray<T>(count: Int, data: Data) -> [T] {
    let array = data.withUnsafeBytes { (pointer: UnsafePointer<T>) -> [T] in
        let buffer = UnsafeBufferPointer(start: pointer, count: count / MemoryLayout<Float32>.size)
        return Array<T>(buffer)
    }
    return array
}

// Get files (names and paths) in directory
public func getAllFilesInDirectory(bundle: Bundle, directory: String, extensionWanted: String) -> (names: [String], paths: [URL]) {
    let cachesURL = URL(fileURLWithPath: "/Users/...../Playgrounds/MLPlayground.playground/Resources")
    let directoryURL = cachesURL.appendingPathComponent(directory)

    do {
        try FileManager.default.createDirectory(atPath: directoryURL.relativePath, withIntermediateDirectories: true)
        
        // Get the directory contents urls (including subfolders urls)
        let directoryContents = try FileManager.default.contentsOfDirectory(at: directoryURL, includingPropertiesForKeys: nil, options: [])

        // Filter the directory contents
        let filesPath = directoryContents.filter{ $0.pathExtension == extensionWanted }
        let fileNames = filesPath.map{ $0.deletingPathExtension().lastPathComponent }

        return (names: fileNames, paths: filesPath);

    } catch {
        print("Failed to fetch contents of directory: \(error.localizedDescription)")
    }

    return (names: [], paths: [])
}


let bundle = Bundle.main
var featureProviders = [MLFeatureProvider]()

let matchDir = getAllFilesInDirectory(bundle: bundle, directory: "Match", extensionWanted: "m4a")
let noMatchDir = getAllFilesInDirectory(bundle: bundle, directory: "No Match", extensionWanted: "m4a")


// I have ommited the full path directories for Stack Overflow
try! MLModel.compileModel(at: URL(fileURLWithPath: "/Users/...../Playgrounds/MLPlayground.playground/Resources/UpdateableML.mlmodel"))

let modelDir = URL(fileURLWithPath: "/Users/....../Playgrounds/MLPlayground.playground/Resources/UpdateableML.mlmodel")
let outputDir = URL(fileURLWithPath: "/Users/....../Playgrounds/MLPlayground.playground/Resources/Output/outputmodel.mlmodel")




func getFeatureProvider(forLabel: String, directory: URL) {
    let data = try! Data(contentsOf: directory.appendingPathComponent("\(forLabel).m4a"))
    
    // MultiArray (Float32 15600)
    let mlInputData = try! MLMultiArray(shape: [15600], dataType: .float32)
    
    let songDataArray: [Float32] = convertDataToArray(count: data.count, data: data)
    let count = songDataArray.count

    for i in 0..<mlInputData.count {
        mlInputData[i] = NSNumber(value: songDataArray[i])
    }
    
    let soundValue = MLFeatureValue(multiArray: mlInputData)
    let outputValue = MLFeatureValue(string: forLabel)
    
    let dataPointFeatures: [String: MLFeatureValue] = ["audioSamples": soundValue, "classLabel": outputValue]
    
    if let provider = try? MLDictionaryFeatureProvider(dictionary: dataPointFeatures) {
        featureProviders.append(provider)
    } else {
        print("Failed to get provider")
    }
}


// Get features
for s in matchDir.names {
    getFeatureProvider(forLabel: s, directory: matchDir.paths.first!.deletingLastPathComponent())
}
for s in noMatchDir.names {
    getFeatureProvider(forLabel: s, directory: noMatchDir.paths.first!.deletingLastPathComponent())
}



var batchProvider = MLArrayBatchProvider(array: featureProviders)




func updateModel(at url: URL, with trainingData: MLBatchProvider, completionHandler: @escaping (MLUpdateContext) -> Void) {
    let updateTask = try! MLUpdateTask(
        forModelAt: url,
        trainingData: trainingData,
        configuration: nil,
        completionHandler: completionHandler
    )
    updateTask.resume()
}



func saveUpdatedModel(_ updateContext: MLUpdateContext) {
    let updatedModel = updateContext.model
    let fileManager = FileManager.default
    do {
        try fileManager.createDirectory(
            at: outputDir,
            withIntermediateDirectories: true,
            attributes: nil)
        
        try updatedModel.write(to: outputDir)
        print("Updated model saved to:\n\t\(outputDir)")
    } catch let error {
        print("Could not save updated model to the file system: \(error)")
        return
    }
}



func updateWith(trainingData: MLBatchProvider, completionHandler: @escaping () -> Void) {
    updateModel(at: modelDir, with: trainingData) { context in
        print("Update Complete")
        saveUpdatedModel(context)
        completionHandler()
    }
}


updateWith(trainingData: batchProvider, completionHandler: {
    print("Final Complete")
})

我现在有两个问题：

• 我在函数“updateModel”处收到来自 MLUpdateTask 的以下错误：

Fatal error: 'try!' expression unexpectedly raised an error: Error Domain=com.apple.CoreML Code=0 "Unable to load model at file:///Users/....../Playgrounds/CuratorMLPlayground.playground/Resources/UpdateableML.mlmodel with error: Error opening file stream: /Users/....../Playgrounds/CuratorMLPlayground.playground/Resources/UpdateableML.mlmodel/coremldata.bin: unspecified iostream_category error"

• 我不知道我是否在函数“getFeatureProvider”中正确获取音频数据，因为“songDataArray”的大小大约为 260000，而模型/“mlInputData”的形状为 15600？有人可以向我解释一下吗？

更新： 我已将其复制到我实际的 iOS 应用程序项目中。我现在收到以下错误代替上述错误。

Fatal error: 'try!' expression unexpectedly raised an error: Error Domain=com.apple.CoreML Code=0 "Invalid URL for .mlmodel." UserInfo={NSLocalizedDescription=Invalid URL for .mlmodel.}:

但是，我几乎可以肯定 URL 正确指向了 mlmodel

Answer 1

我已经设法解决了与 mlUpdate 任务相关的错误，问题是我引用的是 .mlmodel 而不是编译版本，即 .mlmodelc 。从 Xcode 构建 iOS 应用程序时，会自动生成此文件。

我现在收到以下错误：

Fatal error: 'try!' expression unexpectedly raised an error: Error Domain=com.apple.CoreML Code=6 "Pipeline is not marked as updatable to perform update." UserInfo={NSLocalizedDescription=Pipeline is not marked as updatable to perform update.}:

因此，我可以得出结论，现在只需建立一个更好的模型。我现在假设如果我有合适的模型，更新/个性化设备上的代码会起作用。

所以现在这只是建立一个可以在这里工作的模型的问题。感谢another answer by Matthjis，我现在意识到我在 CreateML 中制作的模型无法更新，因为它是一个 GLM 分类器。

感谢this git repo，我想我也发现了快速加载音频数据的正确方法。