![]() ![]() The big key here is that the system is able to take the “knowledge” that the speaker encoder learns from the voice and apply it to the text.Īfter being separately encoded, the speech and the text are combined in a common embedding space, and then decoded together to create the final output waveform. And indeed, there are many proposed solutions for Text-to-Speech that work quite well, being based on Deep Learning. Text-to-speech systems have gotten a lot of research attention in the Deep Learning community over the past few years. In the paper, the three components are trained independently. (3) Use a Vocoder to transform the spectrogram into an audio waveform that we can listen to. Combine the two vectors of speech and text, and decode them into a Spectrogram (2) Given a piece of text, also encode it into a vector representation. (1) Given a small audio sample of the voice we wish to use, encode the voice waveform into a fixed dimensional vector representation The output should then be an audio of Batman’s voice saying the words “I love pizza”!įrom a technical view, the system is then broken down into 3 sequential components: Thus, Google researchers designed the voice cloning system to have 2 inputs: the text we want to be read and a sample of the voice which we want to read the text.įor example, if we wanted Batman to read the phrase “I love pizza”, then we’d give the system two things: text that says “I love pizza” and a short sample of Batman’s voice so it knows what Batman should sound like. It’s clear that in order for a computer to be able to read out-loud with any voice, it needs to somehow understand 2 things: what it’s reading and how it reads it. So if you wanted create audio of your voice, or someone else’s, the only way to do it would have been to collect a whole new dataset.ĪI research from Google nicknamed Voice Cloning makes it possible for a computer to read out-loud using any voice. The set of speakers who recorded that speech is fixed - you can’t have unlimited speakers! You’d have to collect a dataset of text-speech pairs. ![]() This used to present a restriction when doing TTS with Deep Learning. Should it be a man or a woman? A loud voice or a soft one? One very interesting choice that one makes when creating such a system is the selection of which voice to use for the generated audio. The goal of a good TTS system is to have a computer do it automatically. A human performs this task simply by reading. ![]() Text-to-Speech (TTS) Synthesis refers to the artificial transformation of text to audio. Want to be inspired? Come join my Super Quotes newsletter.
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