Introduction
The dialogflow service allows using the Google Dialogflow platform within your application. Dialogflow is used to translate human speech into intents (intent recognition). In other words, not only does it (try to) convert an audio stream into readable text, it also processes this text into an intent (possibly with some additional parameters). For example, an audio stream can translate to the string "I am 15 years old", which is, in turn, converted to the intent 'answer_age' with the parameter 'age = 15'.
Docker name: dialogflow
Input
Required sensors: Mic audio
Required actuators: None
Required service(s): stream_audio
or audio file
audio length + audio type (bytestream)
The following drivers need to be running if testing locally;
computer-robot,computer-speaker,andcomputer-microphone.
Output
The output solely depends on your project and the set-up of your intents and entities of the Dialogflow agent.
confidence level - 0-100
transcript - string
entities + their type
intent - string
Parameters
Dialogflow Keyfile path - string
Dialogflow Project ID - string
Service Configuration
Our service communicates with a Dialogflow agent to achieve its intended purpose, and it does so by using a project ID and a key file, however, if you happen to have them, you may skip this section.
The following steps will help you get the required items:
Create a Dialogflow agent by clicking the following link: https://dialogflow.cloud.google.com
Use the ‘Create Agent' button at the left top to start your first project. Press the settings icon next to your agent's name at the left top to see the Project ID.
Follow the steps here to retrieve your private key file in JSON format.
Initialisation
Using the service
In order to use our service for your purposes, there are two classes with which you must interact, namely BasicSICConnector and ActionRunner. You can find the details of these classes here. You may also need a class to manage speech_recognition attempts and a callback function for retrieving a recognized entity from the detection result.
In order to run this service, the following steps must be taken into consideration:
You have the correct agent name and keyfile(path) as parameters for an instance of the class Example in the example file and are passing it as parameters when creating an instance of BasicSICConnector.
You have the Dialogflow services running and you have the relevant local devices running.
Example
The following file, https://bitbucket.org/socialroboticshub/connectors/src/master/python/speech_recognition_example.py, is available for the purpose of demonstration. Two questions are dealt with in this example, the first being an entity question where the point of interest is the name of the user, and the second being a yes, no, or don’t know question.
Creating an intent
In order to deal with the first question, an intent needs to be set up. Intent is something you want to recognise from an end-user, in our example that would be the name of the person. The following steps will set an intent of your Dialogflow agent:
Begin by naming your intent, you can name it whatever you wish; we have named it ‘answer_name’.
In the section Action and parameters, you should name the intent to what you will use in the program. In the given example, we have used the name ‘answer_name’.
In the section context, this section indicates that one is looking for a particular intent. Match the (input)context with the name of the intent, ‘answer-name’ has been assigned in this particular example. Thereafter, set the output(context) to 0, this option is set at 5 by default.
In the section Training Phrases, there are empty input strings, which when filled with expressions you would expect one to express, Dialogflow will learn from these to make a model which would also include phrases similar to the list of expressions you gave. Our example has used a list of phrases that you can find in figure 1.
Select a word from an expression as a parameter by double-clicking on it and selecting the appropriate entity from the list. It will then automatically appear below ‘Action and parameters' as well; the ‘parameter name’ there will be passed in the result (we use 'name’ here).
Our complete intent example thus looks like this (note: using sys.given-name is usually preferred):
With these steps, the intent of your Dialogflow agent would be set up successfully for the example file. All that remains now is to ensure to pass your agent name and keyfile(path) as parameters when creating an instance of class Example.
For each intent that you create, make sure that: abstract away + concrete example
Events
There are no events that this service creates.
Known Issues
There is a rare bug where sometimes Dialogflow will suddenly only respond with ‘UNAUTHENTICATED’ errors. Restarting Docker and/or your entire machine seems to be the only way to resolve this.