I just played with DeepSpeech (v0.6.1) and I found significant improvements by using a custom language model.

The language model is built from sentences and is rather quick to build (~seconds), at least for the small number of sentences I used. This can then be combined with the pre-trained neural net.

Though I hear DeepSpeech is currently fairly US-influenced when it comes to recognizing accents. So if you're not a native speaker, consider contributing to the open-source dataset over at https://voice.mozilla.org/

it requires hundreds of hours of speech data to make any difference, at least for a context-free asr. and gpu-powered high-end hardware, and several days for training.

training an asr model is totally different requirement than using it as a client

I tested the text-to-speech and it was acceptable, on pair with the robotic voice many screen readers use. For me it's not that important that it sounds like a real human, it's more important that it's accurate, and that you can hear what it say. Also pre-processed examples is not really any useful, as the author will likely pick examples that turned out good. This API testing page was very useful though! As it was very easy for me to test myself.

when using right ssml formatting, output quality is not so bad. but it cannot compete commercial engines, thats right.

i guess thats for the same reason that google is dominating the speech recogniction world: they have tons of training data available. not smarter algorithms, just more data.