WaveNet | unconditional speech generation - Theano Implementation | Machine Learning library

I am making use of the Google TTS API and would like to use timepoints in order to show words of a sentence at the right time. (like subtitles). Unfortunately, I can not get this to work.

HTTP request

my json is as given below. i need to convert it into c# class. Please note all values will be different in actual scenario.

Dear Stack,

I'm being charged on google cloud for using wavenet, despite the fact that the code im using is not using wavenet "i think", if I'm using wavenet, is there a way to disable it?

Here is my code:

I am trying to understand how a nn.conv1d processes an input for a specific example related to audio processing in a WaveNet model.

I have input data of shape (1,1,8820), which passes through an input layer (1,16,1), to output a shape of (1,16,8820).

That part I understand, because you can just multiply the two matrices. The next layer is a conv1d, kernel size=3, input channels=16, output channels=16, so the state dict shows a matrix with shape (16,16,3) for the weights. When the input of (1,16,8820) goes through that layer, the result is another (1,16,8820).

What multiplication steps occur within the layer to apply the weights to the audio data? In other words, if I wanted to apply the layer(forward calculations only) using only the input matrix, the state_dict matrix, and numpy, how would I do that?

This example is using the nn.conv1d layer from Pytorch. Also, if the same layer had a dilation=2, how would that change the operations?

In WaveNet, dilated convolution is used to increase receptive field of the layers above.

From the illustration, you can see that layers of dilated convolution with kernel size 2 and dilation rate of powers of 2 create a tree like structure of receptive fields. I tried to (very simply) replicate the above in Keras.

I was earlier able to browse the github repo at https://github.com/r9y9/Tacotron-2/blob/master/wavenet_vocoder/models/wavenet.py easily in browser, so that when I put cursor on top of jResidualConv1dGLU at Line84, it'd highlight and let me click on "Definition" and "References" of class ResidualConv1dGLU.

But I used the same repo in the same browser today, and it doesn't do anything. It doesn't highlight ResidualConv1dGLU or show links for Definition/References of it. It's as if it doesn't know that it's a class.

Is there some default setting needed to enable that? What am I missing?

PS: (It was working a few days ago, so I am not sure what changed in just a few days)

I'm trying to implement Google's Text-to-Speech api in python/Django, but I'm not able to set the correct values for Seed and Pict: According to my understanding of API's documentation, we have values for Speed between the range 0.25 -to- 4.0 AND Pitch between -20 -to- 20. But when I passed values between these ranges it returns an error, which says:

• pitch
  • Select a valid choice. -1.0 is not one of the available choices.
• speed
  • Select a valid choice. 0.5 is not one of the available choices.

Here's my model code:

I'm a beginner in coding. I would like to make a simple web application using Google Cloud Text to Speech API.

1. a web site with a text box
2. you input a sentence in the text box and click a button "submit"
3. you can download a mp3 file which is made by Google Cloud Text to Speech API

I'm an English teacher in Japan, so I would like my students to use this website to improve their English pronunciation.

Firstly, I'd like to tell you my problem.

I have almost completed my web app. However, one problem happened. Dependent drop down list doesn't work. When a user use the app, she choose country and voiceId.

If you choose US--> you choose from en-US-Wavenet-A or en-US-Wavenet-B or en-US-Wavenet-C.

If you choose GB--> you choose from en-GB-Wavenet-A or en-GB-Wavenet-B or en-GB-Wavenet-C.

If you choose US, it work perfectly. However, if you choose GB, a problem happens.

Even if choose GB--> en-GB-Wavenet-B, you download a mp3 file which sounds voice of en-GB-Wavenet-A.

Also, even if choose GB--> en-GB-Wavenet-C, you download a mp3 file which sounds voice of en-GB-Wavenet-A.

Secondly, I'd like to show you my code. I use Flask on Google App Engine standard environment Python3.7.

This is directory structure.

I am trying to build a Django app that would use Keras models to make recommendations. Right now I'm trying to use one custom container that would hold both Django and Keras. Here's the Dockerfile I've written.