WaveNet is an artificial neural network designed to generate raw audio. Here's how the technology - one text-to-speech tool of many available - is improving our ability to hear and process the words around us.
Many people use text-to-speech services on a daily basis, as well as virtual assistants. But what they might not know is that these two share a lot of features when it comes to the way they work. As technology improves, so does the quality of apps we use in our everyday lives.
The same thing applies to TTS apps and VAs. There are a couple of companies that show exceptional results in the field, and one of them is Google with its WaveNet technology.
What is Google WaveNet?
WaveNet is an artificial neural network designed to generate raw audio. The team behind it is DeepMind, which is a firm from London focusing on artificial intelligence. The introduction of the technology made quite a change for Google Cloud platform, and it took everything to the next level.
One of the main advantages that Google’s DeepMind introduced compared to the previous text-to-speech systems is that it sounds better. When it was introduced in 2016, TTS systems were not able to create a natural-sounding voice.
WaveNet text-to-speech outperformed it in every single way. The idea behind this tech is quite simple. The software is able to use raw audio files such as WAV as input and benefits from connectivity with Google API and an API key.
Today, we have numerous ways to use this technology, thanks to our ability to harness these complex algorithms. Many companies across the world are competing with one another to deliver the best possible product. And this is a good thing. For end users, it only means more options that make it easier to find a program that suits their needs.
How WaveNet works
WaveNet is a version of FNN or feedforward neural network also known as a deep convolutional neural network. CNN takes the raw signal from the input and can then synthesize the output one sample at a time.
Of course, the basis behind everything is machine learning, natural language processing, deep learning, and machine intelligence. In previous iterations of text-to-speech apps, the idea was to create a database of phonemes, and the app would pick the right one, or at least the one that represented the closest to the sound needed.
But creating this type of puzzle is not easy. The software needs to understand how language works, including its rhythm and dynamics, or the sounds coming out of your speaker would come across as fake.
As with the majority of text-to-speech programs, WaveNet also uses real audio waveforms – think parametric or concatenative, to name but a few. This way, the software can analyze the rules of the language (or rather sounds), and how it changes with time.
This allows the program to generate patterns that will sound like human speech based on the speech samples. What is impressive is that the software will produce the output based on the info that’s fed to the software.
Here’s what that means in the real world: If you speak Italian, for example, the program can help you produce Italian speech. This represented a huge change at the time and paved the way for other text-to-speech APIs.
Examples of WaveNet in action
When Google introduced the software, it required too much processing power to be used in real life. But all of this changed in the ensuing years. This API first helped power Google Assistant voices, which the company offered across multiple platforms.
WaveNet also is a great tool if you’re looking for TTS software. The voice sounds more realistic, which makes the entire experience more enjoyable. You can use it to listen to the latest news, transcripts of podcasts or anything else you can imagine.
That’s just the beginning. The entire idea behind the process can also help speech-impaired people get their voices back. Voice synthesis is the term used for voice imitation, and its potential is astonishing. For example, people who are speech-impaired can, in theory, use a sample of their voice and integrate it with text-to-speech tools. This can give them their voice back.
We don’t yet know all that the future holds for TTS programs, but we can assume it will be wonderful. One of the best things about this area of innovation is that there are many different companies working on TTS products.
When everyone works toward the same goal, it’s more likely we will see incredible results.
Speechify – Speech synthesis
Among the programs you need to check out as soon as possible is Speechify. It is a text-to-speech app, and you can use it on almost any device. It is available for iOS, Android, Mac and even as an extension for Google Chrome.
Speechify can run through any type of content. It can read you PDFs, docs, emails or anything else you have on your device. One of the main advantages of the app is its versatility and customizability.
You can change the speed of the reading, pick different speech voices, adjust the pitch and so on. It is also worth mentioning that Speechify offers an OCR function, which means you can take a photo of your book, and the app will read it for you.
The app is specifically designed for people with dyslexia, ADD, those learning a new language or anyone who wants to be productive while reading a book. It is an all-in-one app that will change the way you feel about reading.
Speechify is easy to use, and you won’t need a comprehensive tutorial to figure it out.
FAQ
What is WaveNet used for?
It is a deep neural network that can create raw audio. It is a text-to-speech synthesis that offers realistic-sounding WaveNet voices, and it can be trained using real recordings of speech. As a result, it has successfully outperformed Google Cloud text-to-speech.
Today, the software is used for Google Assistant voices.
What is the WaveNet model?
The model is based on the PixelCNN architecture. To deal with long-range dependencies necessary to create raw output, the architecture uses dilated causal convulsions.
The addition of dilated CNNS allows easier and faster training, and it can go a thousand layers back in time. It can also work 20 times faster than real-time.
What is the difference between WaveNet and Convolutional Neural Networks?
The software is based on the deep convolutional neural network or CNN. This means that WaveNet is just one application of CNN. A similar technology is used by other companies such as Microsoft or Amazon (along with SSML), and it offers high quality and great results.
When seeking out the best text-to-speech app, turn to Speechify. Although other platforms offer select benefits, Speechify is seamless to use, hassle free and intuitive for any user seeking to turn text into spoken word.
