tensorflow-wavenet | Implementation of WaveNet network based on Tensorflow | Speech library

by WindQAQ Python Version: Current License: BSD-3-Clause

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

kandi X-RAY | tensorflow-wavenet Summary

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

The repository contains the source code of end-to-end automatic speech recognition system based on WaveNet and Connectionist Temporal Classification (CTC), which is implemented by Tensorflow. Moreover, we use TIMIT for training and evaluation.

Support

Quality

Security

License

Reuse

Support

tensorflow-wavenet has a low active ecosystem.

It has 9 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 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 tensorflow-wavenet is current.

Quality

tensorflow-wavenet has 0 bugs and 0 code smells.

Security

tensorflow-wavenet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-wavenet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-wavenet is licensed under the BSD-3-Clause License. This license is Permissive.

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

Reuse

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

tensorflow-wavenet 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.

Top functions reviewed by kandi - BETA

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

Create logits
Dilation layer
1D convolutional layer
Apply temporal padding
Write matrix to file
Opens a file - like object
Write vector data to file
Train the model
Convert a list of lists to a sparse matrix
Yields the next batch
Get inputs from start to end
Write array v to file
Read a matcher from a file - like object
Read a key from a file
Read a MATLAB matrix from a file
Read matrix from fd
Generator that yields tuples from a file
Read the cntime from a file
Read the key - value pairs from a file - like object
Read data from a vector file
Parse command line arguments
Read key value pairs from a file - like object
Build the graph
Creates tensors
Read a feat feat file
Merge two utterances into a dataset

Get all kandi verified functions for this library.

tensorflow-wavenet Key Features

No Key Features are available at this moment for tensorflow-wavenet.

tensorflow-wavenet Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow-wavenet.

Community Discussions

Trending Discussions on tensorflow-wavenet

reading files in google cloud machine learning

QUESTION

reading files in google cloud machine learning

Asked 2017-Mar-13 at 14:06

I tried to run tensorflow-wavenet on the google cloud ml-engine with gcloud ml-engine jobs submit training but the cloud job crashed when it was trying to read the json configuration file:

...

ANSWER

Answered 2017-Mar-13 at 14:06

Python's open function cannot read files from GCS. You will need to use a library capable of doing so. TensorFlow includes one such library:

Source https://stackoverflow.com/questions/42761075

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-wavenet

You can download it from GitHub.
You can use tensorflow-wavenet 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.