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Language Detection and Textual content to Speech in SwiftUI Apps

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With the arrival of machine studying and synthetic intelligence, the iOS SDK already comes with various frameworks for builders to develop apps with machine learning-related options. On this tutorial, let’s discover two built-in ML APIs for changing textual content to speech and performing language detection.

Utilizing Textual content to Speech in AVFoundation

Let’s say, if we’re constructing an app that reads customers’ enter message, you must implement some form of text-to-speech capabilities.

text-to-speech-app

The AVFoundation framework has include some text-to-speech APIs. To make use of these APIs, we have now to first import the framework:

Subsequent, create an occasion of AVSpeechSynthesizer:

To transform the textual content message to speech, you possibly can write the code like this:

You create an occasion of AVSpeechUtterance with the textual content for the synthesizer to talk. Optionally, you possibly can configure the pitch, charge, and voice. For the voice parameter, we set the language to English (U.S.). Lastly, you go the utterance object to the speech synthesizer to learn the textual content in English.

The built-in speech synthesizer is able to talking a number of languages reminiscent of Chinese language, Japanese, and French. To inform the synthesizer the language to talk, it’s a must to go the proper language code when creating the occasion of AVSpeechSynthesisVoice.

To search out out all of the language codes that the machine helps, you possibly can name up the speechVoices() technique of AVSpeechSynthesisVoice:

Listed below are a few of the supported language codes:

  • Japanese – ja-JP
  • Korean – ko-KR
  • French – fr-FR
  • Italian – it-IT
  • Cantonese – zh-HK
  • Mandarin – zh-TW
  • Putonghua – zh-CN

In some circumstances, chances are you’ll have to interrupt the speech synthesizer. You’ll be able to name up the stopSpeaking technique to cease the synthesizer:

Performing Language Identification Utilizing Pure Language Framework

As you possibly can see within the code above, we have now to determine the language of the enter message earlier than the speech synthesizer can convert the textual content to speech appropriately. Wouldn’t it’s nice if the app can robotically detect the language of the enter message?

The NaturalLanguage framework supplies a wide range of pure language processing (NLP) performance together with language identification.

To make use of the NLP APIs, first import the NaturalLanguage framework:

You simply want a pair traces of code to detect the language of a textual content message:

The code above creates an occasion of NLLanguageRecognizer after which invokes the processString to course of the enter message. As soon as processed, the language recognized is saved within the dominantLanguage property:

Here’s a fast instance:

natural-language-detection

For the pattern, NLLanguageRecognizer acknowledges the language as English (i.e. en). In case you change the inputMessage to Japanese like beneath, the dominantLanguage turns into ja:

The dominantLanguage property might haven’t any worth if the enter message is like this:

Wrap Up

On this tutorial, we have now walked you thru two of the built-in machine studying APIs for changing textual content to speech and figuring out the language of a textual content message. With these ML APIs, you possibly can simply incorporate the text-to-speech function in your iOS apps.


Founding father of AppCoda. Writer of a number of iOS programming books together with Starting iOS Programming with Swift and Mastering SwiftUI. iOS App Developer and Blogger. Observe me at Fb, Twitter and Google+.



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