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:

使用ML.NET实现猜动画片台词

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:

使用ML.NET实现猜动画片台词

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: "")]
public string DubbingText;
[Column(ordinal: "", 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 != ) continue;
var segments = segmenter.Cut(parts[]);
writer.WriteLine("{0}\t{1}", string.Join(" ", segments), parts[]);
}
}
}
}

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:

使用ML.NET实现猜动画片台词

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: "")]
public string DubbingText;
[Column(ordinal: "", 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 != ) continue;
var segments = segmenter.Cut(parts[]);
writer.WriteLine("{0}\t{1}", string.Join(" ", segments), parts[]);
}
}
}
} 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;
}
}
}
}
}
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