前面几篇主要内容出自微软官方，经我特意修改的案例的文章：使用ML.NET实现情感分析[新手篇]使用ML.NET预测纽约出租车费.NETCore玩转机器学习使用ML.NET实现情感分析 前面几篇主要内容出
前面几篇主要内容出自微软官方，经我特意修改的案例的文章：
使用ML.NET实现情感分析[新手篇]
使用ML.NET预测纽约出租车费
.NET Core玩转机器学习
使用ML.NET实现情感分析[新手篇]后补
相信看过后大家对ML.NET有了一定的了解了，由于目前还是0.1的版本，也没有更多官方示例放出来，大家普遍觉得提供的特性还不够强大，所以处在观望状态也是能理解的。
本文结合Azure提供的语音识别服务，向大家展示另一种ML.NET有趣的玩法——猜动画片台词。
这个场景特别容易想像，是一种你说我猜的游戏，我会事先用ML.NET对若干动画片的台词进行分类学习，然后使用麦克风，让使用者随便说一句动画片的台词（当然得是数据集中已存在的，没有的不要搞事情呀！），然后来预测出自哪一部。跟随我动手做做看。
准备工作
这次需要使用Azure的认知服务中一项API——Speaker Recognition，目前还处于免费试用阶段，打开https://azure.microsoft.com/zh-cn/try/cognitive-services/?api=speaker-recognition，能看到如下页面：
点击获取API密钥，用自己的Azure账号登录，然后就能看到自己的密钥了，类似如下图：
创建项目
这一次请注意，我们要创建一个.NET Framework 4.6.1或以上版本的控制台应用程序，通过NuGet分别引用三个类库：Microsoft.ML，JiebaNet.Analyser，Microsoft.CognitiveServices.Speech。
然后把编译平台修改成x64，而不是Any CPU。（这一点非常重要）
代码分解
在Main函数部分，我们只需要关心几个主要步骤，先切词，然后训练模型，最后在一个循环中等待使用者说话，用模型进行预测。
static void Main(string[] args){ Segment(_dataPath, _dataTrainPath); var model = Train(); Evaluate(model); ConsoleKeyInfo x; do { var speech = Recognize(); speech.Wait(); Predict(model, speech.Result); Console.WriteLine("\nRecognition done. Your Choice (0: Stop Any key to continue): "); x = Console.ReadKey(true); } while (x.Key != ConsoleKey.D0);}初始化的变量主要就是训练数据，Azure语音识别密钥等。注意YourServiceRegion的值是“westus”，而不是网址。
const string SubscriptiOnKey= "你的密钥";const string YourServiceRegion = "westus";const string _dataPath = @".\data\dubs.txt";const string _dataTrainPath = @".\data\dubs_result.txt";定义数据结构和预测结构和我之前的文章一样，没有什么特别之处。
public class DubbingData{ [Column(ordinal: "0")] public string DubbingText; [Column(ordinal: "1", name: "Label")] public string Label;}public class DubbingPrediction{ [ColumnName("PredictedLabel")] public string PredictedLabel;}切记部分注意对分隔符的过滤。
public static void Segment(string source, string result){ var segmenter = new JiebaSegmenter(); using (var reader = new StreamReader(source)) { using (var writer = new StreamWriter(result)) { while (true) { var line = reader.ReadLine(); if (string.IsNullOrWhiteSpace(line)) break; var parts = line.Split(new[] { '\t' }, StringSplitOptions.RemoveEmptyEntries); if (parts.Length != 2) continue; var segments = segmenter.Cut(parts[0]); writer.WriteLine("{0}\t{1}", string.Join(" ", segments), parts[1]); } } }}训练部分依然使用熟悉的多分类训练器StochasticDualCoordinateAscentClassifier。TextFeaturizer用于对文本内容向量化处理。
public static PredictionModel Train(){ var pipeline = new LearningPipeline(); pipeline.Add(new TextLoader(_dataTrainPath, useHeader: false, separator: "tab")); pipeline.Add(new TextFeaturizer("Features", "DubbingText")); pipeline.Add(new Dictionarizer("Label")); pipeline.Add(new StochasticDualCoordinateAscentClassifier()); pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" }); var model = pipeline.Train(); return model;}验证部分这次重点是看损失程度分数。
public static void Evaluate(PredictionModel model){ var testData = new TextLoader(_dataTrainPath, useHeader: false, separator: "tab"); var evaluator = new ClassificationEvaluator(); var metrics = evaluator.Evaluate(model, testData); Console.WriteLine(); Console.WriteLine("PredictionModel quality metrics evaluation"); Console.WriteLine("------------------------------------------"); //Console.WriteLine($"TopKAccuracy: {metrics.TopKAccuracy:P2}"); Console.WriteLine($"LogLoss: {metrics.LogLoss:P2}");}预测部分没有什么大变化，就是对中文交互进行了友好展示。
public static void Predict(PredictionModel model, string sentence){ IEnumerable sentences = new[] { new DubbingData { DubbingText = sentence } }; var segmenter = new JiebaSegmenter(); foreach (var item in sentences) { item.DubbingText = string.Join(" ", segmenter.Cut(item.DubbingText)); } IEnumerable predictiOns= model.Predict(sentences); Console.WriteLine(); Console.WriteLine("Category Predictions"); Console.WriteLine("---------------------"); var sentencesAndPredictiOns= sentences.Zip(predictions, (sentiment, prediction) => (sentiment, prediction)); foreach (var item in sentencesAndPredictions) { Console.WriteLine($"台词: {item.sentiment.DubbingText.Replace(" ", string.Empty)} | 来自动画片: {item.prediction.PredictedLabel}"); } Console.WriteLine();}Azure语音识别的调用如下。
static async Task Recognize(){ var factory = SpeechFactory.FromSubscription(SubscriptionKey, YourServiceRegion); var lang = "zh-cn"; using (var recognizer = factory.CreateSpeechRecognizer(lang)) { Console.WriteLine("Say something..."); var result = await recognizer.RecognizeAsync().ConfigureAwait(false); if (result.RecognitionStatus != RecognitionStatus.Recognized) { Console.WriteLine($"There was an error. Status:{result.RecognitionStatus.ToString()}, Reason:{result.RecognitionFailureReason}"); return null; } else { Console.WriteLine($"We recognized: {result.RecognizedText}"); return result.RecognizedText; } }}运行过程如下：
虽然这看上去有点幼稚，不过一样让你开心一笑了，不是么？请期待更多有趣的案例。
本文使用的数据集：下载
完整的代码如下：
using System;using Microsoft.ML.Models;using Microsoft.ML.Runtime;using Microsoft.ML.Runtime.Api;using Microsoft.ML.Trainers;using Microsoft.ML.Transforms;using System.Collections.Generic;using System.Linq;using Microsoft.ML;using JiebaNet.Segmenter;using System.IO;using Microsoft.CognitiveServices.Speech;using System.Threading.Tasks;namespace DubbingRecognition{ class Program { public class DubbingData { [Column(ordinal: "0")] public string DubbingText; [Column(ordinal: "1", name: "Label")] public string Label; } public class DubbingPrediction { [ColumnName("PredictedLabel")] public string PredictedLabel; } const string SubscriptiOnKey= "你的密钥"; const string YourServiceRegion = "westus"; const string _dataPath = @".\data\dubs.txt"; const string _dataTrainPath = @".\data\dubs_result.txt"; static void Main(string[] args) { Segment(_dataPath, _dataTrainPath); var model = Train(); Evaluate(model); ConsoleKeyInfo x; do { var speech = Recognize(); speech.Wait(); Predict(model, speech.Result); Console.WriteLine("\nRecognition done. Your Choice (0: Stop Any key to continue): "); x = Console.ReadKey(true); } while (x.Key != ConsoleKey.D0); } public static void Segment(string source, string result) { var segmenter = new JiebaSegmenter(); using (var reader = new StreamReader(source)) { using (var writer = new StreamWriter(result)) { while (true) { var line = reader.ReadLine(); if (string.IsNullOrWhiteSpace(line)) break; var parts = line.Split(new[] { '\t' }, StringSplitOptions.RemoveEmptyEntries); if (parts.Length != 2) continue; var segments = segmenter.Cut(parts[0]); writer.WriteLine("{0}\t{1}", string.Join(" ", segments), parts[1]); } } } } public static PredictionModel Train() { var pipeline = new LearningPipeline(); pipeline.Add(new TextLoader(_dataTrainPath, useHeader: false, separator: "tab")); //pipeline.Add(new ColumnConcatenator("Features", "DubbingText")); pipeline.Add(new TextFeaturizer("Features", "DubbingText")); //pipeline.Add(new TextFeaturizer("Label", "Category")); pipeline.Add(new Dictionarizer("Label")); pipeline.Add(new StochasticDualCoordinateAscentClassifier()); pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" }); var model = pipeline.Train(); return model; } public static void Evaluate(PredictionModel model) { var testData = new TextLoader(_dataTrainPath, useHeader: false, separator: "tab"); var evaluator = new ClassificationEvaluator(); var metrics = evaluator.Evaluate(model, testData); Console.WriteLine(); Console.WriteLine("PredictionModel quality metrics evaluation"); Console.WriteLine("------------------------------------------"); //Console.WriteLine($"TopKAccuracy: {metrics.TopKAccuracy:P2}"); Console.WriteLine($"LogLoss: {metrics.LogLoss:P2}"); } public static void Predict(PredictionModel model, string sentence) { IEnumerable sentences = new[] { new DubbingData { DubbingText = sentence } }; var segmenter = new JiebaSegmenter(); foreach (var item in sentences) { item.DubbingText = string.Join(" ", segmenter.Cut(item.DubbingText)); } IEnumerable predictiOns= model.Predict(sentences); Console.WriteLine(); Console.WriteLine("Category Predictions"); Console.WriteLine("---------------------"); var sentencesAndPredictiOns= sentences.Zip(predictions, (sentiment, prediction) => (sentiment, prediction)); foreach (var item in sentencesAndPredictions) { Console.WriteLine($"台词: {item.sentiment.DubbingText.Replace(" ", string.Empty)} | 来自动画片: {item.prediction.PredictedLabel}"); } Console.WriteLine(); } static async Task Recognize() { var factory = SpeechFactory.FromSubscription(SubscriptionKey, YourServiceRegion); var lang = "zh-cn"; using (var recognizer = factory.CreateSpeechRecognizer(lang)) { Console.WriteLine("Say something..."); var result = await recognizer.RecognizeAsync().ConfigureAwait(false); if (result.RecognitionStatus != RecognitionStatus.Recognized) { Console.WriteLine($"There was an error. Status:{result.RecognitionStatus.ToString()}, Reason:{result.RecognitionFailureReason}"); return null; } else { Console.WriteLine($"We recognized: {result.RecognizedText}"); return result.RecognizedText; } } } }}