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https://github.com/ropensci/googleLanguageR

R client for the Google Translation API, Google Cloud Natural Language API and Google Cloud Speech API
https://github.com/ropensci/googleLanguageR

cloud-speech-api cloud-translation-api google-api-client google-cloud google-cloud-speech google-nlp googleauthr natural-language-processing peer-reviewed r r-package rstats sentiment-analysis speech-api translation-api

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R client for the Google Translation API, Google Cloud Natural Language API and Google Cloud Speech API

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# googleLanguageR - R client for the Google Translation API, Natural Language API, Speech-to-Text API and Text-to-Speech API



[![CRAN](https://www.r-pkg.org/badges/version/googleLanguageR)](https://cran.r-project.org/package=googleLanguageR)

[![Build

Status](https://travis-ci.org/ropensci/googleLanguageR.png?branch=master)](https://travis-ci.org/ropensci/googleLanguageR)

[![codecov.io](https://codecov.io/github/ropensci/googleLanguageR/coverage.svg?branch=master)](http://codecov.io/github/ropensci/googleLanguageR?branch=master)

[![](https://badges.ropensci.org/127_status.svg)](https://github.com/ropensci/onboarding/issues/127)



## Language tools for R via Google Machine Learning APIs



Read the [introduction blogpost on rOpenSci's blog](https://ropensci.org/blog/2017/10/03/googlelanguager/)



This package contains functions for analysing language through the

[Google Cloud Machine Learning

APIs](https://cloud.google.com/products/machine-learning/)



Note all are paid services, you will need to provide your credit card

details for your own Google Project to use them.



The package can be used by any user who is looking to take advantage of

Google’s massive dataset to train these machine learning models. Some

applications include:



  - Translation of speech into another language text, via speech-to-text

    then translation and having the results spoen back to you

  - Talking Shiny apps

  - Identification of sentiment within text, such as from Twitter feeds

  - Pulling out the objects of a sentence, to help classify texts and

    get metadata links from Wikipedia about them.



The applications of the API results could be relevant to business or

researchers looking to scale text analysis.



## Google Natural Language API



> Google Natural Language API reveals the structure and meaning of text

> by offering powerful machine learning models in an easy to use REST

> API. You can use it to extract information about people, places,

> events and much more, mentioned in text documents, news articles or

> blog posts. You can also use it to understand sentiment about your

> product on social media or parse intent from customer conversations

> happening in a call center or a messaging app.



Read more [on the Google Natural Language

API](https://cloud.google.com/natural-language/)



## Google Cloud Translation API



> Google Cloud Translation API provides a simple programmatic interface

> for translating an arbitrary string into any supported language.

> Translation API is highly responsive, so websites and applications can

> integrate with Translation API for fast, dynamic translation of source

> text from the source language to a target language (e.g. French to

> English).



Read more [on the Google Cloud Translation

Website](https://cloud.google.com/translate/)



## Google Cloud Speech-to-Text API



> Google Cloud Speech-to-Text API enables you to convert audio to text

> by applying neural network models in an easy to use API. The API

> recognizes over 80 languages and variants, to support your global user

> base. You can transcribe the text of users dictating to an

> application’s microphone or enable command-and-control through voice

> among many other use cases.



Read more [on the Google Cloud Speech

Website](https://cloud.google.com/speech/)



## Google Cloud Text-to-Speech API



> Google Cloud Text-to-Speech enables developers to synthesize

> natural-sounding speech with 30 voices, available in multiple

> languages and variants. It applies DeepMind’s groundbreaking research

> in WaveNet and Google’s powerful neural networks to deliver the

> highest fidelity possible. With this easy-to-use API, you can create

> lifelike interactions with your users, across many applications and

> devices.



Read more [on the Google Cloud Text-to-Speech

Website](https://cloud.google.com/text-to-speech/)



## Installation



1.  Create a [Google API Console

    Project](https://cloud.google.com/resource-manager/docs/creating-managing-projects)

2.  Within your project, add a [payment method to the

    project](https://support.google.com/cloud/answer/6293589)

3.  Within your project, check the relevant APIs are activated



  - [Google Natural Language

    API](https://console.cloud.google.com/apis/api/language.googleapis.com/overview)

  - [Google Cloud Translation

    API](https://console.cloud.google.com/apis/api/translate.googleapis.com/overview)

  - [Google Cloud Speech-to-Text

    API](https://console.cloud.google.com/apis/api/speech.googleapis.com/overview)

  - [Google Cloud Text-to-Speech API](https://console.cloud.google.com/apis/library/texttospeech.googleapis.com)



4.  [Generate a service account

    credential](https://cloud.google.com/storage/docs/authentication#generating-a-private-key)

    as a JSON file by first [creating a service account](https://developers.google.com/workspace/guides/create-credentials#create_a_service_account) and then [creating credentials for a service account](https://developers.google.com/workspace/guides/create-credentials#create_credentials_for_a_service_account)

5.  Return to R, and install the official release via

    `install.packages("googleLanguageR")`, or the development version

    with `remotes::install_github("ropensci/googleLanguageR")`

    

### Docker image



Some Docker images are publicly available.  In general `gcr.io/gcer-public/googleLanguageR:$BRANCH_NAME` carries that GitHub branch's version.



* `gcr.io/gcer-public/googleLanguageR:CRAN` - the latest CRAN version  [![CRAN](https://www.r-pkg.org/badges/version/googleLanguageR)](https://cran.r-project.org/package=googleLanguageR)

* `gcr.io/gcer-public/googleLanguageR:master` - latest GitHub master version [![Build

Status](https://travis-ci.org/ropensci/googleLanguageR.png?branch=master)](https://travis-ci.org/ropensci/googleLanguageR)

* `gcr.io/gcer-public/googleLanguageR:feature` - a feature branch from GitHub



## Usage



### Authentication



The best way to authenticate is to use an environment file. See

`?Startup`. I usually place this in my home directory. (e.g. if using

RStudio, click on `Home` in the file explorer, create a new `TEXT` file

and call it `.Renviron`)



Set the file location of your download Google Project JSON file in a

`GL_AUTH` argument:



    #.Renviron

    GL_AUTH=location_of_json_file.json



Then, when you load the library you should auto-authenticate:



``` r

library(googleLanguageR)

```



You can also authenticate directly using the `gl_auth` function pointing

at your JSON auth file:



``` r

library(googleLanguageR)

gl_auth("location_of_json_file.json")

```



You can then call the APIs via the functions:



  - `gl_nlp()` - Natural Langage API

  - `gl_speech()` - Cloud Speech-to-Text API

  - `gl_translate()` - Cloud Translation API

  - `gl_talk()` - Cloud Text-to-Speech API



## Natural Language API



The Natural Language API returns natural language understanding

technolgies. You can call them individually, or the default is to return

them all. The available returns are:



  - *Entity analysis* - Finds named entities (currently proper names and

    common nouns) in the text along with entity types, salience,

    mentions for each entity, and other properties. If possible, will

    also return metadata about that entity such as a Wikipedia URL. If

    using the **v1beta2** endpoint this also includes sentiment for each

    entity.

  - *Syntax* - Analyzes the syntax of the text and provides sentence

    boundaries and tokenization along with part of speech tags,

    dependency trees, and other properties.

  - *Sentiment* - The overall sentiment of the text, represented by a

    magnitude `[0, +inf]` and score between `-1.0` (negative sentiment)

    and `1.0` (positive sentiment).



### Demo for Entity Analysis



You can pass a vector of text which will call the API for each element.

The return is a list of responses, each response being a list of tibbles

holding the different types of

analysis.



``` r

texts <- c("to administer medicince to animals is frequently a very difficult matter, and yet sometimes it's necessary to do so", 

         "I don't know how to make a text demo that is sensible")

nlp_result <- gl_nlp(texts)



# two results of lists of tibbles

str(nlp_result, max.level = 2)

```



See more examples and details [on the

website](http://code.markedmondson.me/googleLanguageR/articles/nlp.html)

or via `vignette("nlp", package = "googleLanguageR")`



## Google Translation API



You can detect the language via `gl_translate_detect`, or translate and

detect language via `gl_translate`



Note this is a lot more refined than the free version on Google’s

translation

website.



``` r

text <- "to administer medicine to animals is frequently a very difficult matter, and yet sometimes it's necessary to do so"

## translate British into Danish

gl_translate(text, target = "da")$translatedText

```



See more examples and details [on the

website](http://code.markedmondson.me/googleLanguageR/articles/translation.html)

or via `vignette("translate", package = "googleLanguageR")`



## Google Cloud Speech-to-Text API



The Cloud Speech-to-Text API provides audio transcription. Its

accessible via the `gl_speech` function.



A test audio file is installed with the package which reads:



> “To administer medicine to animals is frequently a very difficult

> matter, and yet sometimes it’s necessary to do so”



The file is sourced from the University of Southampton’s speech

detection (`http://www-mobile.ecs.soton.ac.uk/newcomms/`) group and is

fairly difficult for computers to parse, as we see below:



``` r

## get the sample source file

test_audio <- system.file("woman1_wb.wav", package = "googleLanguageR")



## its not perfect but...:)

gl_speech(test_audio)$transcript



    ## # A tibble: 1 x 2

    ##   transcript                                                    confidence

    ##                                                                 

    ## 1 to administer medicine to animals is frequency of very diffi… 0.9180294

```



See more examples and details [on the

website](http://code.markedmondson.me/googleLanguageR/articles/speech.html)

or via `vignette("speech", package = "googleLanguageR")`



## Google Cloud Text-to-Speech API



The Cloud Text-to-Speech API turns text into talk audio files. Its

accessible via the `gl_talk` function.



To use, supply your text to the function:



``` r

gl_talk("This is a talking computer.  Hello Dave.")

```



See more examples and details [on the

website](http://code.markedmondson.me/googleLanguageR/articles/text-to-speech.html)

or via `vignette("text-to-speech", package =

"googleLanguageR")`



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