📱 The Watson Swift SDK enables developers to quickly add Watson Cognitive Computing services to their Swift applications.
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README.md

Watson Developer Cloud Swift SDK

Build Status Carthage Compatible Documentation CLA assistant

Overview

The Watson Developer Cloud Swift SDK makes it easy for mobile developers to build Watson-powered applications. With the Swift SDK you can leverage the power of Watson's advanced artificial intelligence, machine learning, and deep learning techniques to understand unstructured data and engage with mobile users in new ways.

There are many resources to help you build your first cognitive application with the Swift SDK:

Contents

General

Services

Requirements

  • iOS 8.0+
  • Xcode 8.0+
  • Swift 3.0+

Installation

Dependency Management

We recommend using Carthage to manage dependencies and build the Swift SDK for your application.

You can install Carthage with Homebrew:

$ brew update
$ brew install carthage

To use the Watson Developer Cloud Swift SDK in your application, specify it in your Cartfile:

github "watson-developer-cloud/swift-sdk"

In a production app, you may also want to specify a version requirement.

Then run the following command to build the dependencies and frameworks:

$ carthage update --platform iOS

Finally, drag-and-drop the built frameworks into your Xcode project and import them as desired.

App Transport Security

App Transport Security was introduced with iOS 9 to enforce secure Internet connections. To securely connect to IBM Watson services, please add the following exception to your application's Info.plist file.

<key>NSAppTransportSecurity</key>
<dict>
    <key>NSExceptionDomains</key>
    <dict>
        <key>watsonplatform.net</key>
        <dict>
            <key>NSTemporaryExceptionRequiresForwardSecrecy</key>
            <false/>
            <key>NSIncludesSubdomains</key>
            <true/>
            <key>NSTemporaryExceptionAllowsInsecureHTTPLoads</key>
            <true/>
            <key>NSTemporaryExceptionMinimumTLSVersion</key>
            <string>TLSv1.0</string>
        </dict>
    </dict>
</dict>

Service Instances

IBM Watson Developer Cloud offers a variety of services for developing cognitive applications. The complete list of Watson Developer Cloud services is available from the services catalog. Services are instantiated using the IBM Bluemix cloud platform.

Follow these steps to create a service instance and obtain its credentials:

  1. Log in to Bluemix at https://bluemix.net.
  2. Create a service instance:
    1. From the Dashboard, select "Use Services or APIs".
    2. Select the service you want to use.
    3. Click "Create".
  3. Copy your service credentials:
    1. Click "Service Credentials" on the left side of the page.
    2. Copy the service's username and password (or api_key for Alchemy).

You will need to provide these service credentials in your mobile application. For example:

let textToSpeech = TextToSpeech(username: "your-username-here", password: "your-password-here")

Note that service credentials are different from your Bluemix username and password.

See Getting Started for more information on getting started with the Watson Developer Cloud and Bluemix.

Custom Service URLs

In some instances, users will need to use their own custom URL to access the Watson services. Thus, to make it easier to update, we have exposed the service URL as a public property of each class.

You can set a custom service URL like so:

let dialog = Dialog(username: "your-username-here", password: "your-password-here")
dialog.serviceURL = "your-custom-service-url"

Custom Headers

There are different headers that can be sent to the Watson services. For example, Watson services log requests and their results for the purpose of improving the services, but you can include the X-Watson-Learning-Opt-Out header to opt out of this.

We have exposed a defaultHeaders public property in each class to allow users to easily customize their headers:

let naturalLanguageClassifier = NaturalLanguageClassifier(username: username, password: password)
naturalLanguageClassifier.defaultHeaders = ["X-Watson-Learning-Opt-Out": "true"]

Sample Applications

Xcode 7 Compatibility

Unfortunately, the version of Swift used to develop the SDK is not backwards compatible with Xcode 7. We are not committed to maintaining Xcode 7 support but may occasionally publish a v0.7.x release with critical bug fixes.

To continue using the Swift SDK with Xcode 7, we recommend following the v0.7.x release branch with the following change to your Cartfile:

github "watson-developer-cloud/swift-sdk" ~> 0.7.0

Objective-C Compatibility

Please see this tutorial for more information about consuming the Watson Developer Cloud Swift SDK in an Objective-C application.

Linux Compatibility

The following services offer basic support in Linux: Conversation, Language Translator, Natural Language Classifier, Personality Insights V3, Tone Analyzer, and Tradeoff Analytics. Please note some services are not yet fully supported such as Alchemy Language, Alchemy Data News, Document Conversion, Text to Speech, Speech to Text, and Visual Recognition.

To include the Watson SDK to your Linux projects, add the following to your Package.swift file:

dependencies: [
    .Package(url: "https://github.com/watson-developer-cloud/swift-sdk",
             majorVersion: 0)
]

To build the project, run swift build from the command line.

Contributing

We would love any and all help! If you would like to contribute, please read our CONTRIBUTING documentation with information on getting started.

License

This library is licensed under Apache 2.0. Full license text is available in LICENSE.

This SDK is intended for use with an Apple iOS product and intended to be used in conjunction with officially licensed Apple development tools.

AlchemyData News

AlchemyData News provides news and blog content enriched with natural language processing to allow for highly targeted search and trend analysis. Now you can query the world's news sources and blogs like a database.

The following example demonstrates how to use the AlchemyData News service:

import AlchemyDataNewsV1

let apiKey = "your-apikey-here"
let alchemyDataNews = AlchemyDataNews(apiKey: apiKey)

let start = "now-1d" // yesterday
let end = "now" // today
let query = [
    "q.enriched.url.title": "O[IBM^Apple]",
    "return": "enriched.url.title,enriched.url.entities.entity.text,enriched.url.entities.entity.type"
]
let failure = { (error: Error) in print(error) }

alchemyDataNews.getNews(from: start, to: end, query: query, failure: failure) { news in
    print(news)
}

Refine your query by referring to the Count and TimeSlice Queries and API Fields documentation.

The following links provide more information about the IBM AlchemyData News service:

AlchemyLanguage

AlchemyLanguage is a collection of text analysis functions that derive semantic information from your content. You can input text, HTML, or a public URL and leverage sophisticated natural language processing techniques to get a quick high-level understanding of your content and obtain detailed insights such as directional sentiment from entity to object.

AlchemyLanguage has a number of features, including:

  • Entity Extraction
  • Sentiment Analysis
  • Keyword Extraction
  • Concept Tagging
  • Relation Extraction
  • Taxonomy Classification
  • Author Extraction
  • Language Detection
  • Text Extraction
  • Microformats Parsing
  • Feed Detection

The following example demonstrates how to use the AlchemyLanguage service:

import AlchemyLanguageV1

let apiKey = "your-apikey-here"
let alchemyLanguage = AlchemyLanguage(apiKey: apiKey)

let url = "https://github.com/watson-developer-cloud/swift-sdk"
let failure = { (error: Error) in print(error) }
alchemyLanguage.getTextSentiment(fromContentAtURL: url, failure: failure) { sentiment in
    print(sentiment)
}

The following links provide more information about the IBM AlchemyLanguage service:

Conversation

With the IBM Watson Conversation service you can create cognitive agents--virtual agents that combine machine learning, natural language understanding, and integrated dialog scripting tools to provide outstanding customer engagements.

The following example shows how to start a conversation with the Conversation service:

import ConversationV1

let username = "your-username-here"
let password = "your-password-here"
let version = "YYYY-MM-DD" // use today's date for the most recent version
let conversation = Conversation(username: username, password: password, version: version)

let workspaceID = "your-workspace-id-here"
let failure = { (error: Error) in print(error) }
var context: Context? // save context to continue conversation
conversation.message(withWorkspace: workspaceID, failure: failure) { response in
    print(response.output.text)
    context = response.context
}

The following example shows how to continue an existing conversation with the Conversation service:

let text = "Turn on the radio."
let failure = { (error: Error) in print(error) }
let request = MessageRequest(text: text, context: context)
conversation.message(withWorkspace: workspaceID, request: request, failure: failure) {
    response in
    print(response.output.text)
    context = response.context
}

The Conversation service allows users to define custom variables and values in their application's payload. For example, a Conversation workspace that guides users through a pizza order might include a user-defined variable for pizza toppings: "pizza_toppings": ["ketchup", "ham", "onion"].

Unfortunately, the Swift SDK does not have advance knowledge of the user-defined variables so it cannot conveniently parse them as properties or model classes. Instead, users of the SDK can manually parse user-defined variables. All models in the Conversation framework include a json: [String: Any] property to allow users to access the underlying JSON payload and manually parse user-defined variables.

The following example shows how to extract a user-defined pizza_toppings variable from the context of a Conversation response:

conversation.message(withWorkspace: workspaceID, request: request, failure: failure) {
    response in
    let pizzaToppings = response.context.json["pizza_toppings"] as! [String]
    print(pizzaToppings) // ["ketchup", "ham", "onion"]
}

The following links provide more information about the IBM Conversation service:

Discovery

The IBM Discovery Service allows for rapid automated ingestion and feature enrichment of unstructured data. Enrichments of documents ingested include concepts, relationship extraction and sentiment analysis through Natural Language Processing. With the IBM Discovery service you can take advantage of IBM Watson algorithms to take your unstructured data, enrich it, and query it to return the information you need from it.

The following example shows how to instantiate a Discovery object:

import DiscoveryV1

let username = "your-username-here"
let password = "your-password-here"
let version = "YYYY-MM-DD" // use today's date for the most recent version
let discovery = Discovery(username: username, password: password, version: version)
}

The following example demonstrates how to create a Discovery environment and collection with the default configuration, and add documents to the collection.

let failure = { (error: Error) in print(error) }

// Create and store the environment ID for you to access later:
var environmentID: String?
let environmentName = "your-environment-name-here"

discovery.createEnvironment(
    withName: environmentName,
    withSize: .zero,
    withDescription: testDescription,
    failure: failure) 
{   
    environment in
    self.environmentID = environment.environmentID
}

// Wait for the environment to be ready before creating a collection:
bool environmentReady = false
while (!environmentReady) {
    discovery.getEnvironment(withName: environmentName, failure: failure)
    { 
        environment in
        if environment.status == "active" {
         self.environmentReady = true
        }
    }
}

// Create a collection and store the collection ID for you to access later:
var collectionID: String?

let collectionName = "your-collection-name-here"
discovery.createCollection(
    withEnvironmentID: environmentID!,
    withName: collectionName,
    withDescription: collectionDescription,
    withConfigurationID: configurationID,
    failure: failure)
{   
    collection in
    self.collectionID = collection.collectionID
}

// Wait for the collection to be "available" before adding a document:
bool collectionReady = false
while (!collectionReady) {
    discovery.listCollectionDetails(
    withEnvironmentID: environmentID!,
    withCollectionID: collectionID!,
    failure: failWithError) 
{
    collection in
    if collection.status == CollectionStatus.active {
        self.collectionReady = true
    }
}

// Add a document to the collection with the saved environment and collection ID:
guard let file = Bundle(for: type(of: self)).url(forResource: "your-Document-Name", withExtension: "document-type") else {
    XCTFail("Unable to locate your-Document-Name.document-type")
    return
}
discovery.addDocumentToCollection(
    withEnvironmentID: environmentID!,
    withCollectionID: collectionID!,
    file: file,
    failure: failWithError) 
{
    document in
    NSLog(document)
}

The following example demonstrates how to perform a query on the Discovery instance using the KennedySpeech.html we have within our DiscoveryV1Tests folder:

/// String to search for within the documents.
let query = "United Nations"

/// Find the max sentiment score for entities within the enriched text.
let aggregation = "max(enriched_text.entities.sentiment.score)"

/// Specify which portion of the document hierarchy to return.
let returnHierarchies = "enriched_text.entities.sentiment,enriched_text.entities.text"

discovery.queryDocumentsInCollection(
    withEnvironmentID: environmentID!,
    withCollectionID: collectionID!,
    withQuery: query,
    withAggregation: aggregation,
    return: returnHierarchies,
    failure: failWithError) 
{ 
    queryResponse in
    if let results = queryResponse.results {
        for result in results {
            if let entities = result.entities {
                for entity in entities {
                        NSLog(entity)
                }
            }
        }
    }
}

The following links provide more information about the IBM Discovery service:

Document Conversion

The IBM Watson Document Conversion Service converts a single HTML, PDF, or Microsoft Word™ document. The input document is transformed into normalized HTML, plain text, or a set of JSON-formatted Answer units that can be used with other Watson services, like the Watson Retrieve and Rank Service.

The following example demonstrates how to convert a document with the Document Conversation service:

import DocumentConversionV1

let username = "your-username-here"
let password = "your-password-here"
let version = "2015-12-15"
let documentConversion = DocumentConversion(username: username, password: password, version: version)

// load document
let filename = "your-document-filename"
guard let document = Bundle.main.url(forResource: filename, withExtension: "xml") else {
    print("Failed to locate document.")
    return
}

// convert document
let config = try! documentConversion.writeConfig(type: ReturnType.text)
let failure = { (error: Error) in print(error) }
documentConversion.convertDocument(document, withConfigurationFile: config, failure: failure) {
    text in
    print(text)
}

The following links provide more information about the IBM Document Conversion service:

Language Translator

The IBM Watson Language Translator service lets you select a domain, customize it, then identify or select the language of text, and then translate the text from one supported language to another.

Note that the Language Translator service was formerly known as Language Translation. It is recommended to migrate to Language Translator, however, existing Language Translation service instances are currently supported by the LanguageTranslatorV2 framework. To use a legacy Language Translation service, set the serviceURL property before executing the first API call to the service.

The following example demonstrates how to use the Language Translator service:

import LanguageTranslatorV2

let username = "your-username-here"
let password = "your-password-here"
let languageTranslator = LanguageTranslator(username: username, password: password)

// set the serviceURL property to use the legacy Language Translation service
// languageTranslator.serviceURL = "https://gateway.watsonplatform.net/language-translation/api"

let failure = { (error: Error) in print(error) }
languageTranslator.translate("Hello", from: "en", to: "es", failure: failure) {
    translation in
    print(translation)
}

The following links provide more information about the IBM Watson Language Translator service:

Natural Language Classifier

The IBM Watson Natural Language Classifier service enables developers without a background in machine learning or statistical algorithms to create natural language interfaces for their applications. The service interprets the intent behind text and returns a corresponding classification with associated confidence levels. The return value can then be used to trigger a corresponding action, such as redirecting the request or answering a question.

The following example demonstrates how to use the Natural Language Classifier service:

import NaturalLanguageClassifierV1

let username = "your-username-here"
let password = "your-password-here"
let naturalLanguageClassifier = NaturalLanguageClassifier(username: username, password: password)

let classifierID = "your-trained-classifier-id"
let text = "your-text-here"
let failure = { (error: Error) in print(error) }
naturalLanguageClassifier.classify(text, withClassifierID: classifierID, failure: failure) {
    classification in
    print(classification)
}

The following links provide more information about the Natural Language Classifier service:

Personality Insights

The IBM Watson Personality Insights service enables applications to derive insights from social media, enterprise data, or other digital communications. The service uses linguistic analytics to infer personality and social characteristics, including Big Five, Needs, and Values, from text.

The following example demonstrates how to use the Personality Insights service:

import PersonalityInsightsV3

let username = "your-username-here"
let password = "your-password-here"
let version = "yyyy-mm-dd" // use today's date for the most recent version
let personalityInsights = PersonalityInsights(username: username, password: password, version: version)

let text = "your-input-text"
let failure = { (error: Error) in print(error) }
personalityInsights.getProfile(fromText: text, failure: failure) { profile in
    print(profile)                      
}

The following links provide more information about the Personality Insights service:

Retrieve and Rank

The IBM Watson Retrieve and Rank service combines Apache Solr and a machine learning algorithm, two information retrieval components, into a single service in order to provide users with the most relevant search information.

The following example demonstrates how to instantiate a Retrieve and Rank object.

import RetrieveAndRankV1

let username = "your-username-here"
let password = "your-password-here"
let retrieveAndRank = RetrieveAndRank(username: username, password: password)

The following example demonstrates how to create a Solr Cluster, configuration, and collection.

let failure = { (error: Error) in print(error) }

// Create and store the Solr Cluster so you can access it later.
var cluster: SolrCluster!
let clusterName = "your-cluster-name-here"
retrieveAndRank.createSolrCluster(withName: clusterName, failure: failure) {
    solrCluster in
    cluster = solrCluster
}

// Load the configuration file.
guard let configFile = Bundle.main.url(forResource: "your-config-filename", withExtension: "zip") else {
    print("Failed to locate configuration file.")
    return
}

// Create the configuration. Make sure the Solr Cluster status is READY first.
let configurationName = "your-config-name-here"
retrieveAndRank.uploadSolrConfiguration(
    withName: configurationName,
    toSolrClusterID: cluster.solrClusterID,
    zipFile: configFile,
    failure: failure
)

// Create and store your Solr collection name.
let collectionName = "your-collection-name-here"
retrieveAndRank.createSolrCollection(
    withName: collectionName,
    forSolrClusterID: cluster.solrClusterID,
    withConfigurationName: configurationName,
    failure: failure
)

// Load the documents you want to add to your collection.
guard let collectionFile = Bundle.main.url(forResource: "your-collection-filename", withExtension: "json") else {
    print("Failed to locate collection file.")
    return
}

// Upload the documents to your collection.
retrieveAndRank.updateSolrCollection(
    withName: collectionName,
    inSolrClusterID: cluster.solrClusterID,
    contentFile: collectionFile,
    contentType: "application/json",
    failure: failure
)

The following example demonstrates how to use the Retrieve and Rank service to retrieve answers without ranking them.

retrieveAndRank.search(
    withCollectionName: collectionName,
    fromSolrClusterID: cluster.solrClusterID,
    query: "your-query-here",
    returnFields: "your-return-fields-here",
    failure: failure)
{
    response in
    print(response)
}

The following example demonstrates how to create and train a Ranker.

// Load the ranker training data file.
guard let rankerTrainingFile = Bundle.main.url(forResource: "your-ranker-training-data-filename", withExtension: "json") else {
    print("Failed to locate collection file.")
    return
}

// Create and store the ranker.
var ranker: RankerDetails!
retrieveAndRank.createRanker(
    withName: "your-ranker-name-here",
    fromFile: rankerTrainingFile,
    failure: failure)
{
    rankerDetails in
    ranker = rankerDetails
}

The following example demonstrates how to use the service to retrieve and rank the results.

retrieveAndRank.searchAndRank(
    withCollectionName: collectionName,
    fromSolrClusterID: cluster.solrClusterID,
    rankerID: ranker.rankerID,
    query: "your-query-here",
    returnFields: "your-return-fields-here",
    failure: failure)
{
    response in
    print(response)
}

The following links provide more information about the Retrieve and Rank service:

Speech to Text

The IBM Watson Speech to Text service enables you to add speech transcription capabilities to your application. It uses machine intelligence to combine information about grammar and language structure to generate an accurate transcription. Transcriptions are supported for various audio formats and languages.

The SpeechToText class is the SDK's primary interface for performing speech recognition requests. It supports the transcription of audio files, audio data, and streaming microphone data. Advanced users, however, may instead wish to use the SpeechToTextSession class that exposes more control over the WebSockets session.

Recognition Request Settings

The RecognitionSettings class is used to define the audio format and behavior of a recognition request. These settings are transmitted to the service when initating a request.

The following example demonstrates how to define a recognition request that transcribes Opus-formatted audio data with interim results until the stream terminates:

var settings = RecognitionSettings(contentType: .wav)
settings.interimResults = true
settings.continuous = true

See the class documentation or service documentation for more information about the available settings.

Microphone Audio and Compression

The Speech to Text framework makes it easy to perform speech recognition with microphone audio. The framework internally manages the microphone, starting and stopping it with various function calls (such as recognizeMicrophone(settings:model:customizationID:learningOptOut:compress:failure:success) and stopRecognizeMicrophone() or startMicrophone(compress:) and stopMicrophone()).

Knowing when to stop the microphone depends upon the recognition request's continuous setting:

  • If false, then the service ends the recognition request at the first end-of-speech incident (denoted by a half-second of non-speech or when the stream terminates). This will coincide with a final transcription result. So the success or onResults callback should be configured to stop the microphone when a final transcription result is received.

  • If true, then the microphone will typically be stopped by user-feedback. For example, your application may have a button to start/stop the request, or you may stream the microphone for the duration of a long press on a UI element.

To reduce latency and bandwidth, the microphone audio is compressed to Opus format by default. To disable compression, set the compress parameter to false.

It's important to specify the correct audio format for recognition requests that use the microphone:

// compressed microphone audio uses the Opus format
let settings = RecognitionSettings(contentType: .opus)

// uncompressed microphone audio uses a 16-bit mono PCM format at 16 kHz
let settings = RecognitionSettings(contentType: .l16(rate: 16000, channels: 1))

Transcribe Recorded Audio

The following example demonstrates how to use the Speech to Text service to transcribe a WAV audio file.

import SpeechToTextV1

let username = "your-username-here"
let password = "your-password-here"
let speechToText = SpeechToText(username: username, password: password)

let audio = Bundle.main.url(forResource: "filename", withExtension: "wav")!
var settings = RecognitionSettings(contentType: .wav)
settings.interimResults = true
let failure = { (error: Error) in print(error) }
speechToText.recognize(audio, settings: settings, failure: failure) {
    results in
    print(results.bestTranscript)
}

Transcribe Microphone Audio

Audio can be streamed from the microphone to the Speech to Text service for real-time transcriptions. The following example demonstrates how to use the Speech to Text service to transcribe microphone audio:

import SpeechToTextV1

let username = "your-username-here"
let password = "your-password-here"
let speechToText = SpeechToText(username: username, password: password)

func startStreaming() {
    var settings = RecognitionSettings(contentType: .opus)
    settings.continuous = true
    settings.interimResults = true
    let failure = { (error: Error) in print(error) }
    speechToText.recognizeMicrophone(settings: settings, failure: failure) { results in
        print(results.bestTranscript)
    }
}

func stopStreaming() {
    speechToText.stopRecognizeMicrophone()
}

Session Management and Advanced Features

Advanced users may want more customizability than provided by the SpeechToText class. The SpeechToTextSession class exposes more control over the WebSockets connection and also includes several advanced features for accessing the microphone. Before using SpeechToTextSession, it's helpful to be familiar with the Speech to Text WebSocket interface.

The following steps describe how to execute a recognition request with SpeechToTextSession:

  1. Connect: Invoke connect() to connect to the service.
  2. Start Recognition Request: Invoke startRequest(settings:) to start a recognition request.
  3. Send Audio: Invoke recognize(audio:) or startMicrophone(compress:)/stopMicrophone() to send audio to the service.
  4. Stop Recognition Request: Invoke stopRequest() to end the recognition request. The service will automatically stop the request if the continuous setting is not set to true. If the recognition request is already stopped, then sending a stop message will have no effect.
  5. Disconnect: Invoke disconnect() to wait for any remaining results to be received and then disconnect from the service.

All text and data messages sent by SpeechToTextSession are queued, with the exception of connect() which immediately connects to the server. The queue ensures that the messages are sent in-order and also buffers messages while waiting for a connection to be established. This behavior is generally transparent.

A SpeechToTextSession also provides several (optional) callbacks. The callbacks can be used to learn about the state of the session or access microphone data.

  • onConnect: Invoked when the session connects to the Speech to Text service.
  • onMicrophoneData: Invoked with microphone audio when a recording audio queue buffer has been filled. If microphone audio is being compressed, then the audio data is in Opus format. If uncompressed, then the audio data is in 16-bit PCM format at 16 kHz.
  • onPowerData: Invoked every 0.025s when recording with the average dB power of the microphone.
  • onResults: Invoked when transcription results are received for a recognition request.
  • onError: Invoked when an error or warning occurs.
  • onDisconnect: Invoked when the session disconnects from the Speech to Text service.

The following example demonstrates how to use SpeechToTextSession to transcribe microphone audio:

import SpeechToTextV1

let username = "your-username-here"
let password = "your-password-here"
let speechToTextSession = SpeechToTextSession(username: username, password: password)

func startStreaming() {
    // define callbacks
    speechToTextSession.onConnect = { print("connected") }
    speechToTextSession.onDisconnect = { print("disconnected") }
    speechToTextSession.onError = { error in print(error) }
    speechToTextSession.onPowerData = { decibels in print(decibels) }
    speechToTextSession.onMicrophoneData = { data in print("received data") }
    speechToTextSession.onResults = { results in print(results.bestTranscript) }

    // define recognition request settings
    var settings = RecognitionSettings(contentType: .opus)
    settings.interimResults = true
    settings.continuous = true

    // start streaming microphone audio for transcription
    speechToTextSession.connect()
    speechToTextSession.startRequest(settings: settings)
    speechToTextSession.startMicrophone()
}

func stopStreaming() {
    speechToTextSession.stopMicrophone()
    speechToTextSession.stopRequest()
    speechToTextSession.disconnect()
}

Customization

Customize the language model interface to include and tailor domain-specific data and terminology. Improve the accuracy of speech recognition for domains within health care, law, medicine, information technology, and so on.

The following example demonstrates an example of how to customize the language model:

import SpeechToTextV1

let username = "your-username-here"
let password = "your-password-here"
let speechToText = SpeechToText(username: username, password: password)

guard let corpusFile = loadFile(name: "healthcare-short", withExtension: "txt") else {
    NSLog("Failed to load file needed to create the corpus.")
    return
}

let newCorpusName = "swift-sdk-unit-test-corpus"

speechToText.addCorpus(
    withName: newCorpusName,
    fromFile: corpusFile,
    customizationID: trainedCustomizationID,
    failure: failWithError) {
}

// Get the custom corpus to build the trained customization
speechToText.getCorpus(
    withName: corpusName,
    customizationID: trainedCustomizationID,
    failure: failWithError) { corpus in

    print(corpus.name)
    // Check that the corpus is finished processing 
    print("finished processing: \(corpus.status == .analyzed)")
    print(corpus.totalWords)
    print(corpus.outOfVocabularyWords)
    // Check the corpus has no error
    print("errors: \(corpus.error == nil)")
}

There is also an option to add words to a trained customization:

import SpeechToTextV1

let username = "your-username-here"
let password = "your-password-here"
let speechToText = SpeechToText(username: username, password: password)
let error = NSError(domain: "testing", code: 0)

var trainedCustomizationName = "your-customization-name-here"
var customizationStatus = CustomizationStatus?


// Look up the customization to add the words to
speechToText.getCustomizations(failure: failure) { customizations in
    for customization in customizations {
        if customization.name == self.trainedCustomizationName {
            self.trainedCustomizationID = customization.customizationID
            customizationStatus = customization.status
        }
    }
}

guard let customizationStatus = customizationStatus else {
    throw error 
}

// Check the customization status
switch customizationStatus {
case .available, .ready:
    break // do nothing, because the customization is trained
case .pending: // train -> then fail (wait for training)
    print("Training the `trained customization` used for tests.")
    self.trainCustomizationWithCorpus()
    print("The customization has been trained and is ready for use.")
case .training: // fail (wait for training)
    let message = "Please wait a few minutes for the trained customization to finish " +
    "training. You can try running the tests again afterwards."
    print(message)
    throw error
case .failed: // training failed => delete & retry
    let message = "Creating a trained ranker has failed. Check the errors " +
    "within the corpus and customization and retry."
    print(message)
    throw error
}

// Add custom words to the corpus
if customizationStatus == .available {
    let customWord1 = NewWord(word: "HHonors", soundsLike: ["hilton honors", "h honors"], displayAs: "HHonors")
    let customWord2 = NewWord(word: "IEEE", soundsLike: ["i triple e"])

    speechToText.addWords(
        customizationID: trainedCustomizationID, 
        words: [customWord1, customWord2], 
        failure: failWithError) {
        print("added words to corpus")
    }
}

Additional Information

The following links provide more information about the IBM Speech to Text service:

Text to Speech

The IBM Watson Text to Speech service synthesizes natural-sounding speech from input text in a variety of languages and voices that speak with appropriate cadence and intonation.

The following example demonstrates how to use the Text to Speech service:

import TextToSpeechV1
import AVFoundation

let username = "your-username-here"
let password = "your-password-here"
let textToSpeech = TextToSpeech(username: username, password: password)
var audioPlayer = AVAudioPlayer() // see note below

let text = "your-text-here"
let failure = { (error: Error) in print(error) }
textToSpeech.synthesize(text, failure: failure) { data in
    audioPlayer = try! AVAudioPlayer(data: data)
    audioPlayer.prepareToPlay()
    audioPlayer.play()
}

// A note about AVAudioPlayer: The AVAudioPlayer object will stop playing
// if it falls out-of-scope. Therefore, it's important to declare it as a
// property or otherwise keep it in-scope beyond the completion handler.

The Text to Speech service supports a number of voices for different genders, languages, and dialects. The following example demonstrates how to use the Text to Speech service with a particular voice:

import TextToSpeechV1

let username = "your-username-here"
let password = "your-password-here"
let textToSpeech = TextToSpeech(username: username, password: password)
var audioPlayer = AVAudioPlayer() // see note below

let text = "your-text-here"
let failure = { (error: Error) in print(error) }
textToSpeech.synthesize(text, voice: SynthesisVoice.gb_Kate.rawValue, failure: failure) { data in
    audioPlayer = try! AVAudioPlayer(data: data)
    audioPlayer.prepareToPlay()
    audioPlayer.play()
}

// A note about AVAudioPlayer: The AVAudioPlayer object will stop playing
// if it falls out-of-scope. Therefore, it's important to declare it as a
// property or otherwise keep it in-scope beyond the completion handler.

The following links provide more information about the IBM Text To Speech service:

Tone Analyzer

The IBM Watson Tone Analyzer service can be used to discover, understand, and revise the language tones in text. The service uses linguistic analysis to detect three types of tones from written text: emotions, social tendencies, and writing style.

Emotions identified include things like anger, fear, joy, sadness, and disgust. Identified social tendencies include things from the Big Five personality traits used by some psychologists. These include openness, conscientiousness, extraversion, agreeableness, and emotional range. Identified writing styles include confident, analytical, and tentative.

The following example demonstrates how to use the Tone Analyzer service:

import ToneAnalyzerV3

let username = "your-username-here"
let password = "your-password-here"
let version = "YYYY-MM-DD" // use today's date for the most recent version
let toneAnalyzer = ToneAnalyzer(username: username, password: password, version: version)

let text = "your-input-text"
let failure = { (error: Error) in print(error) }
toneAnalyzer.getTone(ofText: text, failure: failure) { tones in
    print(tones)
}

The following links provide more information about the IBM Watson Tone Analyzer service:

Tradeoff Analytics

The IBM Watson Tradeoff Analytics service helps people make better choices when faced with multiple, often conflicting, goals and alternatives. By using mathematical filtering techniques to identify the best candidate options based on different criteria, the service can help users explore the tradeoffs between options to make complex decisions. The service combines smart visualization and analytical recommendations for easy and intuitive exploration of tradeoffs.

The following example demonstrates how to use the Tradeoff Analytics service:

import TradeoffAnalyticsV1

let username = "your-username-here"
let password = "your-password-here"
let tradeoffAnalytics = TradeoffAnalytics(username: username, password: password)

// define columns
let price = Column(
    key: "price",
    type: .numeric,
    goal: .minimize,
    isObjective: true
)
let ram = Column(
    key: "ram",
    type: .numeric,
    goal: .maximize,
    isObjective: true
)
let screen = Column(
    key: "screen",
    type: .numeric,
    goal: .maximize,
    isObjective: true
)
let os = Column(
    key: "os",
    type: .categorical,
    isObjective: true,
    range: Range.categoricalRange(categories: ["android", "windows-phone", "blackberry", "ios"]),
    preference: ["android", "ios"]
)

// define options
let galaxy = Option(
    key: "galaxy",
    values: ["price": .int(50), "ram": .int(45), "screen": .int(5), "os": .string("android")],
    name: "Galaxy S4"
)
let iphone = Option(
    key: "iphone",
    values: ["price": .int(99), "ram": .int(40), "screen": .int(4), "os": .string("ios")],
    name: "iPhone 5"
)
let optimus = Option(
    key: "optimus",
    values: ["price": .int(10), "ram": .int(300), "screen": .int(5), "os": .string("android")],
    name: "LG Optimus G"
)

// define problem
let problem = Problem(
    columns: [price, ram, screen, os],
    options: [galaxy, iphone, optimus],
    subject: "Phone"
)

// define failure function
let failure = { (error: Error) in print(error) }

// identify optimal options
tradeoffAnalytics.getDilemma(for: problem, failure: failure) { dilemma in
    print(dilemma.resolution)
}

The following links provide more information about the IBM Watson Tradeoff Analytics service:

Visual Recognition

The IBM Watson Visual Recognition service uses deep learning algorithms to analyze images (.jpg or .png) for scenes, objects, faces, text, and other content, and return keywords that provide information about that content. The service comes with a set of built-in classes so that you can analyze images with high accuracy right out of the box. You can also train custom classifiers to create specialized classes.

The following example demonstrates how to use the Visual Recognition service:

The following example demonstrates how to use the Visual Recognition service to detect faces in an image:

import VisualRecognitionV3

let apiKey = "your-apikey-here"
let version = "YYYY-MM-DD" // use today's date for the most recent version
let visualRecognition = VisualRecognition(apiKey: apiKey, version: version)

let url = "your-image-url"
let failure = { (error: Error) in print(error) }
visualRecognition.classify(image: url, failure: failure) { classifiedImages in
    print(classifiedImages)
}

The following links provide more information about the IBM Watson Visual Recognition service: