Teaching Your Raspberry Pi to Speak with IBM Watson

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It turns out there’s a lot you can do with a Raspberry Pi and IBM Bluemix. In the first article in this series, we explored how to connect up a Raspberry Pi to IBM Bluemix, IBM Watson and Node-RED. If you haven’t yet got your Raspberry Pi set up with Bluemix and Node-RED, go through part one first and then come back! I personally used a Raspberry Pi 3 for this, however I’ll assume a Raspberry Pi 2 can work just as well.

In this article, we are going to explore how to bring in text to speech to our Node-RED flow from the previous example.

Raspberry Pi and IBM Bluemix Adventures Part Two
Artwork by SitePoint/PatCat, Logo rights: IBM and Raspberry Pi Foundation

Connecting Text to Speech in IBM Bluemix

In order to have access to IBM Watson’s text to speech services, we need to add the “Text to Speech” service in Bluemix. Let’s head over to the Bluemix Services page and find the “Text to Speech” service (be careful not to choose “Speech to Text”… that’s different!).

The Text to Speech service in IBM Bluemix

That should take us to the Text to Speech service Bluemix page.

On that page, we’ll see various options for adding this service to our IBM Bluemix arsenal. We make sure our space that we are using for our Raspberry Pi is selected (I called mine “Dev” in the last article) and leave the app unbound. We can give the service a name (I called mine “The Voice”) and give the credentials a name (I left it as is). The only plan I had available was “Standard”, so I left that one as is too. Once we’re happy with our settings, we click “Create”.

Creating our text to speech instance

Once the service is created in our space, we’ll be taken to the page for that service. We click the “Service Credentials” menu item on the left to access the username and password we will need to give Node-RED in order to have access to our new IBM Watson Text to Speech service. Copy down the username and password from this page:

Copying our credentials

Adding New IBM Watson Services to Node-RED

In order to access the IBM Watson Text to Speech service in Node-RED, we will need to install some new nodes. To do so, we SSH into our Pi (or open the terminal from our Pi directly) and type in:

cd ~/.node-red

This brings us to the Node-RED app folder. From within here, we install a new collection of Node-RED nodes called node-red-node-watson. This includes access to a whole range of IBM Watson services, including the Text to Speech that we need. To install it, we run the following command on our Pi from the Node-RED folder:

sudo npm install node-red-node-watson

Installing the ALSA Dev Library

The IBM Watson Text to Speech functionality will convert our Node-RED app’s text into spoken audio files, but we also need a way to get the Pi to play these files — otherwise those words will never be heard! In order for this to work on your Pi, you may need to run the following command to install the dev ALSA library:

sudo apt-get install libasound2-dev

Restarting Node-RED

In order for the new Node-RED node changes to come into effect, we need to restart Node-RED. To do so, we run the following two commands:



Our New Node-RED Flow

We now have all the parts in place to be able to perform text to speech and have it playback — it is now time to put these into Node-RED and get it to run.

When we open up Node-RED after installing the node-red-node-watson package and restarting, we should see a bunch of new nodes under “IBM_Watson” on the left-hand side:

Our new IBM Watson section in Node-RED

Let’s scroll down, find the “text to speech” node and drag it into our Node-RED sheet:

The text to speech node

Once that’s in our sheet, we double click it to open up its settings:

Double click the node

This is the section where we add in our credentials that we copied earlier from IBM Bluemix (the username and password). We paste those into here and choose a language and a voice for our Pi’s voice. We leave the file format as “WAV”, then we click “OK”:

The settings for our text to speech node

Our text to speech is ready to work its magic. We now need a way to tell it what to say. Lets get it to tell us what the temperature of our Pi is, just as we previously were sending to IBM Bluemix’s system in the last article.

To begin, we’ll drag in a new function node into our sheet:

Drag a new function node into the sheet

Double click that new node and enter in the following code to its function:

msg.payload = "My current CPU temperature is " + 
              msg.payload.replace("temp=","").replace("'C\n","") +
              " degrees celsius";
return msg;

This function is working to format our message just like the one we used in the last article, however rather than formatting it into a JSON string, we are formatting it into a human readable sentence. The sentence will say, “My current CPU temperature is X degrees celsius”. We store this sentence within the msg.payload variable, which is what the IBM Watson Text to Speech node expects. We can also give the function node a label, I called mine “Temperature Text”:

Our function to turn it into a readable sentence

We link up our getCPUTemp node’s output to the input for the new function node:

Linking up our getCPUtemp output to the new function

Followed by linking up the output of our function node to the input of our Text to Speech node:

Linking up our output sentence to our speech input

Then, we bring another function node into our sheet. Within that node, we set up a simple function that will set the msg.speech variable to the msg.payload which is what our text to speech node returns:

msg.payload = msg.speech;
return msg;

Feel free to call that node whatever you’d like, I called it “Format Speech”. Then click “OK”:

Set up msg.speech

Storing and Retrieving Our Sound File

We now have a sound buffer that is generated and ready to be stored somewhere so we can play it. To store it in a file, we use the “file” node which can be found underneath the “storage” heading. Drag the one with no output into your sheet:

Drag a file node into the sheet

We double click on that new node and enter in the following settings:

  • We set the filename to /home/pi/temp/speech.wav (where pi is the username currently running Node-RED).
  • The action should be “overwrite file”.
  • We ensure “Add newline (\n) to each payload?” is unchecked.
  • “Create directory if it doesn’t exist” should be checked so that Node-RED can create a directory for the file if needed.
  • We name our node — name it whatever you’d prefer! I named mine “Create WAV”.
  • Click “OK”!
Our various settings for our sound file

This has created a simple flow that will generate a WAV file for us each time IBM Watson has translated some text for us. Ensure you’ve got each of the nodes connected like so:

Our nodes should be connected together

Playing Sound

Before we try to run this app, we need to adjust the frequency at which it runs. To do this, we double-click our “timestamp” node:

Double clicking the timestamp node

Let’s set the frequency to something more reasonable — remember, IBM Watson’s voice needs time to actually speak the line each time! Also, note that IBM will charge for the service if it goes over one million characters in a month, so you don’t want to overuse this — especially if you’re requesting it so frequently that the voice never gets heard! One minute intervals worked well for me:

Setting the interval to 1 second

Now, we drag in a “delay” node. This will wait a little while for IBM Watson to have time to perform the text to speech before we try to play it. There are ways to run nodes in Node-RED when the sound file is updated but I found them to be a little buggy at times — a simple delay seemed to work best.

Dragging in a delay node

We double-click the node to open its settings, change the delay to 1 second and click “OK”:

Setting delay to one second

We connect up the “Format Speech” output (or whatever you named the function) to our delay like so:

Connecting up the format speech output

Then, we drag in a new node — the file reading node. It will be above the “file” node we dragged in earlier but has both an input and output:

Dragging in a file reading node

Double click that new node and enter the following settings:

  • We set the filename to /home/pi/temp/speech.wav (the same as the one we had created earlier).
  • We ensure the output is “a buffer”.
  • We give it a name of “Read WAV” (this can be whatever you’d like to call it of course).
  • Click OK!
Changing our audio reading settings

We connect up our delay node to the “Read WAV” node and then drag in our final node — the “exec” node. This one is underneath the “advanced” section and can run Unix commands. This is how we will play our audio on the Pi.

Dragging in an exec node

We double click that new node to change its settings. We set the command to:

aplay /home/pi/temp/speech.wav

This plays the sound file on our Pi! We don’t need the radio buttons to be selected or anything, we just name the node (I named mine “Play Audio”) and click OK:

Setting the command to play our audio file

In Action

With that, we have all pieces in place for this to work:

Our final setup

One setting you may need to change on your Pi is where it is set up to output the audio. I used the following command to direct audio to the 3.5mm jack:

amixer cset numid=3 1

You can find out more about changing your Raspberry Pi’s audio settings on the Raspberry Pi audio config page.

With the audio set up correctly and everything in place, click the “Deploy” button in the top right of your Node-RED interface. If all goes to plan, it should run successfully and your Raspberry Pi should begin speaking to you!

Below is a video I posted up on Twitter of my Raspberry Pi speaking to me through a Starbucks cup:

(How did I do that you ask? I used a magical device called a Boombox Portable Vibration Speaker!)


The IBM Watson text to speech capabilities bring a whole new set of possibilities for developers working with IBM Bluemix and the Raspberry Pi! You could connect up the speech to almost anything, including other IBM Watson services. We will continue to explore more capabilities in the next article in this series, thank you to those currently following along!

If you have been giving the Raspberry Pi and IBM Bluemix combo a go, I’d love to hear what you create with it! Let me know in the comments below, or get in touch with me on Twitter at @thatpatrickguy.

Frequently Asked Questions (FAQs) about Teaching Your Raspberry Pi to Speak with IBM Watson

How can I get started with IBM Watson Text-to-Speech on Raspberry Pi?

To get started with IBM Watson Text-to-Speech on Raspberry Pi, you first need to set up your Raspberry Pi and install the necessary software. This includes the IBM Watson Developer Cloud Python SDK, which allows your Raspberry Pi to interact with IBM Watson services. You also need to create an IBM Cloud account and an instance of the Text-to-Speech service. Once you have these set up, you can start writing Python scripts to convert text to speech.

What languages does IBM Watson Text-to-Speech support?

IBM Watson Text-to-Speech supports a wide range of languages, including English, Spanish, French, Italian, German, Brazilian Portuguese, Japanese, and Mandarin Chinese. This makes it a versatile tool for creating applications that need to support multiple languages.

Can I customize the voice in IBM Watson Text-to-Speech?

Yes, IBM Watson Text-to-Speech allows you to customize the voice output. You can choose from a variety of voices, each with its own unique characteristics. You can also adjust the pitch, speed, and pronunciation of the voice to suit your needs.

How can I integrate IBM Watson Text-to-Speech with other services?

IBM Watson Text-to-Speech can be integrated with other services through APIs. This allows you to use it in conjunction with other IBM Watson services, such as Speech-to-Text, Visual Recognition, and Natural Language Understanding. You can also integrate it with third-party services to create more complex applications.

What are the limitations of IBM Watson Text-to-Speech?

While IBM Watson Text-to-Speech is a powerful tool, it does have some limitations. For example, it may not always accurately pronounce certain words or phrases, especially in languages other than English. It also requires an internet connection to function, which may not always be available.

How can I troubleshoot issues with IBM Watson Text-to-Speech?

If you encounter issues with IBM Watson Text-to-Speech, you can start by checking the error messages that are returned by the service. These can often provide clues about what is going wrong. You can also consult the IBM Watson documentation and forums for additional help.

Is IBM Watson Text-to-Speech free to use?

IBM Watson Text-to-Speech offers a free tier that allows you to convert a certain amount of text to speech each month. However, if you need to convert larger amounts of text, you will need to pay for a premium plan.

How can I improve the quality of the speech output?

You can improve the quality of the speech output by fine-tuning the settings of the Text-to-Speech service. This includes adjusting the pitch, speed, and pronunciation of the voice. You can also use SSML tags to add pauses, emphasize certain words, and control the overall flow of the speech.

Can I use IBM Watson Text-to-Speech for commercial purposes?

Yes, you can use IBM Watson Text-to-Speech for commercial purposes. However, you should be aware that the use of the service is subject to IBM’s terms of service, which may include certain restrictions.

What are some potential applications of IBM Watson Text-to-Speech?

IBM Watson Text-to-Speech can be used in a wide range of applications, from creating voice assistants and chatbots to providing voiceover for videos and presentations. It can also be used in accessibility applications to make content more accessible to people with visual impairments.

Patrick CatanzaritiPatrick Catanzariti
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PatCat is the founder of Dev Diner, a site that explores developing for emerging tech such as virtual and augmented reality, the Internet of Things, artificial intelligence and wearables. He is a SitePoint contributing editor for emerging tech, an instructor at SitePoint Premium and O'Reilly, a Meta Pioneer and freelance developer who loves every opportunity to tinker with something new in a tech demo.

bluemixEmerging Techibm watsonpatrickctext to speech
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