googleLanguageR

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

Language tools for R via Google Machine Learning APIs

Read the introduction blogpost on rOpenSci’s blog

This package contains functions for analysing language through the
Google Cloud Machine Learning
APIs

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.

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).

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.

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.

Installation

Create a Google API Console
Project
Within your project, add a payment method to the
project
Within your project, check the relevant APIs are activated

Generate a service account
credential
as a JSON file by first creating a service account and then creating credentials for a service account
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
gcr.io/gcer-public/googleLanguageR:master - latest GitHub master version
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:

library(googleLanguageR)

You can also authenticate directly using the gl_auth function pointing
at your JSON auth file:

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.

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
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.

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
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:

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

See more examples and details on the
website
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:

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

See more examples and details on the
website
or via vignette("text-to-speech", package = "googleLanguageR")