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：
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。
SubscriptionKey = YourServiceRegion = _dataPath = _dataTrainPath = ;
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：https://files.cnblogs.com/files/BeanHsiang/dubs.zip
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;
}
}
}
}
}
相关文章：
原文地址：http://www.cnblogs.com/BeanHsiang/p/9052751.html