From 211a20a47a0c8c1e9f4be9af26c77bb7ad9f9b3f Mon Sep 17 00:00:00 2001 From: Eren Golge Date: Tue, 11 Dec 2018 16:04:10 +0100 Subject: [PATCH] README update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e102ece0..8d6fae6e 100644 --- a/README.md +++ b/README.md @@ -106,7 +106,7 @@ Please feel free to offer new changes and pull things off. We are happy to discu ## Problems waiting to be solved. - Punctuations at the end of a sentence sometimes affect the pronounciation of the last word. Because punctuation sign is attended by the attention module , that forces network to create a voice signal or at least modify the voice signal being generated for neighboring frames. -- ~~Simpler stop-token prediction. Right now we use RNN to keep the history of the previous frames. However, we never tested, if something simpler would work as well.~~ +- ~~Simpler stop-token prediction. Right now we use RNN to keep the history of the previous frames. However, we never tested, if something simpler would work as well.~~ Yet RNN based model gives more stable predictions. - Train for better mel-specs. Mel-spectrograms are not good enough to be fed Neural Vocoder. Easy solution to this problem is to train the model with r=1. However,in this case model struggles to align the attention. - irregular words: "minute", "focus", "aren't" etc. Even though, ~~it might be solved~~ (Nancy dataset give much better results compared to LJSpeech) it is solved by a larger or better dataset, some of irregular words cause network to mis-pronounce. Irregular means in this context is that written form and pronounciation of a word have a unique disparity.