voice-conversion | Voice conversion investigation using three variants | Speech library

by vsimkus Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | voice-conversion Summary

voice-conversion is a Python library typically used in Artificial Intelligence, Speech, Deep Learning, Pytorch, Tensorflow applications. voice-conversion has no bugs, it has no vulnerabilities and it has low support. However voice-conversion build file is not available. You can download it from GitHub.

Voice Conversion (VC) is widely desirable across many industries and applications, including speaker anonymisation, film dubbing, gaming, and voice restoration for people who have lost their ability to speak. In this work we compare standard VAE, VQ-VAE and Gumbel VAE models as approaches to VC on the Voice Conversion Challenge 2016 dataset. We assess speech reconstruction and VC performance on both spectral frames as obtained from a WORLD vocoder and on the raw waveform data.

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voice-conversion has a low active ecosystem.

It has 35 star(s) with 6 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 187 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of voice-conversion is current.

Quality

voice-conversion has 0 bugs and 0 code smells.

Security

voice-conversion has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

voice-conversion code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

voice-conversion 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

voice-conversion releases are not available. You will need to build from source code and install.

voice-conversion 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.

voice-conversion saves you 1362 person hours of effort in developing the same functionality from scratch.

It has 3050 lines of code, 195 functions and 25 files.

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

Top functions reviewed by kandi - BETA

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

Copy the zip file to the local folder
Extract the min and speaker profiles
Write the info to a file
Extract the mean and variance of a speaker
Creates the zip of the zip file
Save a single chunk to training folder
Write the info txt file
Build the joint VAE module
Sample from gumbel distribution
Reparameterize the latent distribution
Encodes latent parameters from hidden representation
Argument parser
Extract arguments from a json file
Convert nested dict to AttributeAccessableDict
Run training
Saves the stats_log to a file
Saves the model to disk
Run the evaluation loop
Copies the zip file
Write vctk_info txt file
Build the VAE module
Train the model
Evaluate the model
Load data at given index
Calculate the latent space
Compute the latent distribution

Get all kandi verified functions for this library.

voice-conversion Key Features

No Key Features are available at this moment for voice-conversion.

voice-conversion Examples and Code Snippets

No Code Snippets are available at this moment for voice-conversion.

Community Discussions

Trending Discussions on voice-conversion

I have just completed fast.ai course, any suggestion on how to get this machine learning project work on Colab?

pycharm and tensorflow-gpu; DLL not found

QUESTION

I have just completed fast.ai course, any suggestion on how to get this machine learning project work on Colab?

Asked 2019-Jun-02 at 16:47

I am new to Machine Learning, but I have basic knowledge in programming.

I have just completed fast.ai course and I know how to use Google Colab.

But I still have no idea on how to get this project work on Colab.

I have just created a new notebook, and set up the environment,

I run

...

ANSWER

Answered 2019-Jun-02 at 16:47

Based on the Requirements in GitHub you should install and import following libraries not only Keras (actually it hasn;t mentioned you need Keras! it's mentioned Tensorflow clearly nevertheless you can use Keras if you modify the codes):

Tensorflow >= 1.1
Numpy >= 1.11.1
Librosa == 0.5.1

You can follow these videos tp learn how you install libraries in colab less than 2 mins:

Install TensorFlow 2.0 with GPU in Google CoLab

Getting Started with TensorFlow in Google Colaboratory (Coding TensorFlow)

Source https://stackoverflow.com/questions/56416664

QUESTION

pycharm and tensorflow-gpu; DLL not found

Asked 2018-Dec-28 at 17:06

I am trying to get a github repository to work on my pc. This repository is based on tensorflow-gpu. Currently, when I try to run the code, I get the following error:

...

ANSWER

Answered 2018-Dec-28 at 17:06

protobuf seems to be a conda package. Try installing it with

Source https://stackoverflow.com/questions/53952370

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

Vulnerabilities

No vulnerabilities reported

Install voice-conversion

You can download it from GitHub.
You can use voice-conversion 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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