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<DubbingData, DubbingPrediction> Train(){ ???var pipeline = new LearningPipeline(); ???pipeline.Add(new TextLoader<DubbingData>(_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<DubbingData, DubbingPrediction>(); ???return model;}验证部分这次重点是看损失程度分数。
public static void Evaluate(PredictionModel<DubbingData, DubbingPrediction> model){ ???var testData = new TextLoader<DubbingData>(_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<DubbingData, DubbingPrediction> model, string sentence){ ???IEnumerable<DubbingData> sentences = new[] ???{ ???????new DubbingData ???????{ ???????????DubbingText = sentence ???????} ???}; ???var segmenter = new JiebaSegmenter(); ???foreach (var item in sentences) ???{ ???????item.DubbingText = string.Join(" ", segmenter.Cut(item.DubbingText)); ???} ???IEnumerable<DubbingPrediction> 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($"台词: {item.sentiment.DubbingText.Replace(" ", string.Empty)} | 来自动画片: {item.prediction.PredictedLabel}"); ???} ???Console.WriteLine();}Azure语音识别的调用如下。
static async Task<string> Recognize(){ ???var factory = SpeechFactory.FromSubscription(SubscriptionKey, YourServiceRegion); ???var lang = "zh-cn"; ???using (var recognizer = factory.CreateSpeechRecognizer(lang)) ???{ ???????Console.WriteLine("Say something..."); ???????var result = await recognizer.RecognizeAsync().ConfigureAwait(false); ???????if (result.RecognitionStatus != RecognitionStatus.Recognized) ???????{ ???????????Console.WriteLine($"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<DubbingData, DubbingPrediction> Train() ???????{ ???????????var pipeline = new LearningPipeline(); ???????????pipeline.Add(new TextLoader<DubbingData>(_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<DubbingData, DubbingPrediction>(); ???????????return model; ???????} ???????public static void Evaluate(PredictionModel<DubbingData, DubbingPrediction> model) ???????{ ???????????var testData = new TextLoader<DubbingData>(_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($"LogLoss: {metrics.LogLoss:P2}"); ???????} ???????public static void Predict(PredictionModel<DubbingData, DubbingPrediction> model, string sentence) ???????{ ???????????IEnumerable<DubbingData> sentences = new[] ???????????{ ???????????????new DubbingData ???????????????{ ???????????????????DubbingText = sentence ???????????????} ???????????}; ???????????var segmenter = new JiebaSegmenter(); ???????????foreach (var item in sentences) ???????????{ ???????????????item.DubbingText = string.Join(" ", segmenter.Cut(item.DubbingText)); ???????????} ???????????IEnumerable<DubbingPrediction> predictions = model.Predict(sentences); ???????????Console.WriteLine(); ???????????Console.WriteLine("Category Predictions"); ???????????Console.WriteLine("---------------------"); ???????????var sentencesAndPredictions = sentences.Zip(predictions, (sentiment, prediction) => (sentiment, prediction)); ???????????foreach (var item in sentencesAndPredictions) ???????????{ ???????????????Console.WriteLine($"台词: {item.sentiment.DubbingText.Replace(" ", string.Empty)} | 来自动画片: {item.prediction.PredictedLabel}"); ???????????} ???????????Console.WriteLine(); ???????} ???????static async Task<string> 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; ???????????????} ???????????} ???????} ???}}使用ML.NET实现猜动画片台词
原文地址：https://www.cnblogs.com/BeanHsiang/p/9052751.html