WaveRNN | Based on https : //github.com/fatchord/WaveRNN

by hhguo Python Version: Current License: MIT

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, it has a Permissive License and it has low support. However WaveRNN build file is not available. You can download it from GitHub.

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Support

WaveRNN has a low active ecosystem.

It has 18 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

WaveRNN has no issues reported. 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 is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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.

WaveRNN saves you 740 person hours of effort in developing the same functionality from scratch.

It has 1707 lines of code, 136 functions and 15 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 a model
Discretized mixture logistic loss
Logarithm of x
Power loss
Generate audio from a file
Fold the model
Sample from discretized mixture
Decode a logarithmic likelihood
Collate a batch of labels
Process utterance file
Convert a melspectrogram
Encodes a mu - law into a logarithm
Calculate a melpectrogram
Parse the values from the given values
Cast hparam to a given type
Set the value of a hyperparameter
Override parameters from a dictionary
Get the model
Given a checkpoint path return the base path
Fast inverse of a spectrogram
Fast approximation of the grill algorithm
Denormalize S
Inspect a spectrogram
Maximum likelihood distribution
Compute the spectrogram
Inverse of mel 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 .

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