WaveRNN | This is a Pytorch implementation of WaveRNN

by yanggeng1995 Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | WaveRNN Summary

WaveRNN is a Python library. WaveRNN has no bugs, it has no vulnerabilities and it has low support. However WaveRNN build file is not available. You can download it from GitHub.

This is a Pytorch implementation of WaveRNN provided:.

Support

Quality

Security

License

Reuse

Support

WaveRNN has a low active ecosystem.

It has 33 star(s) with 5 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 357 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of WaveRNN is current.

Quality

WaveRNN has 0 bugs and 0 code smells.

Security

WaveRNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

WaveRNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

WaveRNN does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

WaveRNN releases are not available. You will need to build from source code and install.

WaveRNN has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

WaveRNN saves you 311 person hours of effort in developing the same functionality from scratch.

It has 748 lines of code, 72 functions and 11 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed WaveRNN and discovered the below as its top functions. This is intended to give you an instant insight into WaveRNN implemented functionality, and help decide if they suit your requirements.

Train the model
Try to restore a checkpoint from checkpoint_dir
Update learning rate
Gets the learning rate based on the current learning rate
Generate a tensor
Forward f function
Forward computation
Generate an audio signal
Preprocess the files
Process audio files
Write metadata to out_dir
Finds all files in a given directory
Prepare audio data
Converts a WAV file into a numpy array
Convert a linear spectrogram to a melogram
Calculate the melspectrogram of a matrix
Compute the spectrogram

Get all kandi verified functions for this library.

WaveRNN Key Features

No Key Features are available at this moment for WaveRNN.

WaveRNN Examples and Code Snippets

No Code Snippets are available at this moment for WaveRNN.

Community Discussions

Trending Discussions on WaveRNN

How to reproduce RNN results on several runs?

QUESTION

How to reproduce RNN results on several runs?

Asked 2019-May-20 at 03:44

I call same model on same input twice in a row and I don't get the same result, this model have nn.GRU layers so I suspect that it have some internal state that should be release before second run?

How to reset RNN hidden state to make it the same as if model was initially loaded?

UPDATE:

Some context:

I'm trying to run model from here:

https://github.com/erogol/WaveRNN/blob/master/models/wavernn.py#L93

I'm calling generate:

https://github.com/erogol/WaveRNN/blob/master/models/wavernn.py#L148

Here it's actually have some code using random generator in pytorch:

https://github.com/erogol/WaveRNN/blob/master/models/wavernn.py#L200

https://github.com/erogol/WaveRNN/blob/master/utils/distribution.py#L110

https://github.com/erogol/WaveRNN/blob/master/utils/distribution.py#L129

I have placed (I'm running code on CPU):

...

ANSWER

Answered 2019-May-18 at 00:09

I believe this may be highly related to Random Seeding. To ensure reproducible results (as stated by them) you have to seed torch as in this:

Source https://stackoverflow.com/questions/56190274

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install WaveRNN

You can download it from GitHub.
You can use WaveRNN like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: