First off, WaveGANs work by training two neural networks a generator and a discriminator to play a game of cat-and-mouse. The generator tries to create fake speech waves that sound like the real thing, while the discriminator tries to figure out which ones are fake and which ones are real.

Here’s how it works in more detail: 1) We start with some text input (like “Hello, my name is Bob”) and convert it into a series of numbers using a technique called spectrogram analysis. This gives us an image-like representation of the sound waves that make up our speech.

2) The generator takes this spectrogram as its input and tries to create a new set of fake sound waves based on what it’s learned from real speech data. It does this by using a neural network with multiple layers (kinda like a really fancy calculator).

3) The discriminator then takes both the original spectrogram and the generator’s output, and tries to figure out which one is fake and which one is real. If it can’t tell them apart, we know that our generator has done a pretty good job of creating realistic-sounding speech waves!

4) The two networks keep playing this game back and forth for several rounds (called “epochs”), with the goal being to improve both the quality of the fake speech and the ability of the discriminator to tell them apart. This is where things get a little bit technical, but basically we’re trying to find a balance between making our fake speech sound as realistic as possible while also not overdoing it (which can lead to some pretty weird-sounding results).

5) Once our WaveGAN has been trained for several epochs, we can use it to generate new speech waves based on any text input that we want. This is where the magic happens suddenly, we have a way of creating realistic-sounding voices without having to record them ourselves!

If you’re interested in learning more about this technology and how it works, I highly recommend checking out the original paper or watching some of the videos online. Trust me, it’s pretty ***** cool!