QPPWG | Quasi-Periodic Parallel WaveGAN Pytorch implementation | Speech library

by bigpon Python Version: 0.1.2a0 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | QPPWG Summary

QPPWG is a Python library typically used in Artificial Intelligence, Speech, Deep Learning, Pytorch applications. QPPWG has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install QPPWG' or download it from GitHub, PyPI.

This is official QPPWG [1, 2] PyTorch implementation. QPPWG is a non-autoregressive neural speech generation model developed based on PWG and QP structure. In this repo, we provide an example to train and test QPPWG as a vocoder for WORLD acoustic features. More details can be found on our Demo page.

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Quality

Security

License

Reuse

Support

QPPWG has a low active ecosystem.

It has 35 star(s) with 5 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of QPPWG is 0.1.2a0

Quality

QPPWG has 0 bugs and 0 code smells.

Security

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

QPPWG code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

QPPWG 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

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

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

QPPWG saves you 986 person hours of effort in developing the same functionality from scratch.

It has 2242 lines of code, 99 functions and 27 files.

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

Top functions reviewed by kandi - BETA

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

Core feature extraction
Convert continuous f0 to binary
Low - pass filter
Low cut filter
Extract f0 and uv features
Compute the log - likelihood of an audio filter
Synthesize audio
Read data from hdf5 file
Check to see if an hdf5 file exists
Parse command line options
Divide a list of files
Perform the forward computation
Perform padded indexing
Argument parser
Parse command line arguments
Save checkpoint to file
Loads a checkpoint
Calculate the spectral loss
Compute the Fourier Transform
Calculate stats for all of the features
Creates a list of auxiliary features
Read a text file
Start training
Save checkpoint to disk
Train one epoch
Read files from a text file
Check that the given pathlist exists

Get all kandi verified functions for this library.

QPPWG Key Features

No Key Features are available at this moment for QPPWG.

QPPWG Examples and Code Snippets

Quasi-Periodic Parallel WaveGAN (QPPWG),Usage of pre-trained models,Analysis-synthesis

Python

Lines of Code : 46

License : Permissive (MIT)

Copy

# Make sure you have installed `qppwg`
# If not, install it via pip
$ pip install qppwg
# Take "vcc18" corpus as an example
# Download the whole folder of "vcc18"
$ ls vcc18
  data    exp
# Change directory to `vcc18` folder
$ cd vcc18
# Put audio

Quasi-Periodic Parallel WaveGAN (QPPWG),Citation

Python

Lines of Code : 18

License : Permissive (MIT)

Copy

@inproceedings{qppwg_2020,
author={Yi-Chiao Wu and Tomoki Hayashi and Takuma Okamoto and Hisashi Kawai and Tomoki Toda},
title={{Quasi-Periodic Parallel WaveGAN Vocoder: A Non-Autoregressive Pitch-Dependent Dilated Convolution Model for Parametric Sp

Quasi-Periodic Parallel WaveGAN (QPPWG),Inference speed (RTF)

Python

Lines of Code : 18

License : Permissive (MIT)

Copy

# On CPU (Intel(R) Xeon(R) Gold 6142 CPU @ 2.60GHz 32 threads)
[decode]: 100%|███████████| 140/140 [04:50<00:00,  2.08s/it, RTF=0.771]
2020-05-26 12:30:27,273 (decode:156) INFO: Finished generation of 140 utterances (RTF = 0.579).
# On GPU (TITAN

Community Discussions

Trending Discussions on QPPWG

How to parse JSON array string by using mssql?

QUESTION

How to parse JSON array string by using mssql?

Asked 2019-Mar-06 at 06:51

I've had created the function ParseJson below :

...

ANSWER

Answered 2019-Mar-06 at 06:51

If I understand your question correctly, next statement is one possible approach to get your results. It's an example that shows how to get array keys and values (I've added additional Something key). What you need is one additional CROSS APPLY.

Source https://stackoverflow.com/questions/54981465

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

Vulnerabilities

No vulnerabilities reported

Install QPPWG

The code works with both anaconda and virtualenv. The following example uses anaconda. Please refer to the PWG repo for more details.

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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