R client for the Google Translation API, Google Cloud Natural Language API and Google Cloud Speech API
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:
The applications of the API results could be relevant to business or researchers looking to scale text analysis.
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
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
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
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
install.packages("googleLanguageR"), or the development version with
remotes::install_github("ropensci/googleLanguageR")
Some Docker images are publicly available. In general
gcr.io/gcer-public/googleLanguageR:$BRANCH_NAMEcarries 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
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
Homein the file explorer, create a new
TEXTfile and call it
.Renviron)
Set the file location of your download Google Project JSON file in a
GL_AUTHargument:
#.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_authfunction 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
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:
[0, +inf]and score between
-1.0(negative sentiment) and
1.0(positive sentiment).
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.
textsSee 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 viagl_translateNote this is a lot more refined than the free version on Google’s translation website.
textSee 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_speechfunction.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 ## 1 to administer medicine to animals is frequency of very diffi… 0.9180294See 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_talkfunction.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")