It's unfortunate that there aren't any high quality projects for the other way around - text to speech.
By high quality I mean something as good as Tacotron 2 [1]. However, I have no illusions of Google ever releasing code or trained models.
Someone will likely re-implement it from the paper, but it's unlikely to be anywhere near as good as those samples, due to worse tuning and lack of a good training set[2].
This effect can be seen if you compare Baidu's samples [3] for Deep Voice 3 with those from an implementation by a third party [4].
[1]: https://google.github.io/tacotron/publications/tacotron2/ind...
[2]: Common voice, for example, will not work well, since you want lots of data from a single speaker for good results. See for example, how samples get progressively worse when increasing the number of speakers in training data[3].
[3]: http://research.baidu.com/deep-voice-3-2000-speaker-neural-t...
[4]: https://r9y9.github.io/deepvoice3_pytorch/
By high quality I mean something as good as Tacotron 2 [1]. However, I have no illusions of Google ever releasing code or trained models.
Someone will likely re-implement it from the paper, but it's unlikely to be anywhere near as good as those samples, due to worse tuning and lack of a good training set[2].
This effect can be seen if you compare Baidu's samples [3] for Deep Voice 3 with those from an implementation by a third party [4].
[1]: https://google.github.io/tacotron/publications/tacotron2/ind...
[2]: Common voice, for example, will not work well, since you want lots of data from a single speaker for good results. See for example, how samples get progressively worse when increasing the number of speakers in training data[3].
[3]: http://research.baidu.com/deep-voice-3-2000-speaker-neural-t...
[4]: https://r9y9.github.io/deepvoice3_pytorch/