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[D] What is the best implementation of a trainable TTS network for creating custom TTS voices?

In this instance, TTS refers to Text-To-Speech.

As the title implies I am looking for the best way to train a network to produce high-quality text to speech results in a custom voice pulled from training data. Assuming access to large amounts of high-quality speech data from a single speaker, the English language, powerful machines, and extended training times what is the best implementation/codebase to use?

I have done quite a lot of research into this but have found my results to be quite confusing. Tacotron-2 seems to me to provide the highest quality results with an open-source implementation. However, implementations such as ESPnet(1) seem to be geared more towards testing different methods rather than developing your own custom voice. I am not new to Machine Learning but I am new to applying ML to audio or language-related problems thus I am very behind on my understanding of the state of such lines of research.

If I was looking to replicate something like the results from “Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions”(2) where they used 20+ hours of data from an English to produce very natural sounding speech(3) what would be my best option? I just figured I would ask the experts of Reddit before I took the plunge on setting up a codebase and dataset only to realize there were significantly better options available.

Thanks!

(1) https://github.com/espnet/espnet

(2)(paper link) https://arxiv.org/abs/1712.05884

(3)(audio sample link) https://google.github.io/tacotron/publications/tacotron2/index.html

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.