使用ML.NET实现猜动画片台词

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;}}}}
}

转载于:https://www.cnblogs.com/BeanHsiang/p/9052751.html