LipNet | Keras implementation of 'LipNet : End-to-End Sentence | Machine Learning library

by rizkiarm Python Version: Current License: MIT

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kandi X-RAY | LipNet Summary

LipNet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. LipNet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Keras implementation of the method described in the paper 'LipNet: End-to-End Sentence-level Lipreading' by Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, and Nando de Freitas (

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LipNet has a low active ecosystem.

It has 505 star(s) with 207 fork(s). There are 26 watchers for this library.

It had no major release in the last 6 months.

There are 62 open issues and 51 have been closed. On average issues are closed in 91 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of LipNet is current.

Quality

LipNet has no bugs reported.

Security

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

License

LipNet 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

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

Build file is available. You can 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 LipNet and discovered the below as its top functions. This is intended to give you an instant insight into LipNet implemented functionality, and help decide if they suit your requirements.

Train a neural network
Connects the mesh
Show video subtitle
Set the data
Remove temporal distortion from a video
Compute statistics for a given dataset
Get statistics from the decoder
Write the csv output to CSV
Calculate the mean of each individual
Run the decoder
Wrapper around ctc_decode
Decode a prediction
Pad a video
Flip a video
Build from file
Build the sentence
Load images from path
Detect frames from a list of frames
Process video type
Process frames from a frame
Pick a random subsequence from a video
Merge two video alignments
Split the words in align
Read from a video file
Read frames from a video file

Get all kandi verified functions for this library.

LipNet Key Features

No Key Features are available at this moment for LipNet.

LipNet Examples and Code Snippets

No Code Snippets are available at this moment for LipNet.

Community Discussions

Trending Discussions on LipNet

Convert a python list of strings into bytecode to write for writing into a file

QUESTION

Convert a python list of strings into bytecode to write for writing into a file

Asked 2019-Jan-28 at 02:28

I am trying to train a Keras model (LipNet) and on begin training, it opens a file with wb and tries to write the header contained in a list. However, I get this error:

...

ANSWER

Answered 2019-Jan-28 at 02:28

csv always wants text files, not binary files, but you can try to emulate that by doing this:

Source https://stackoverflow.com/questions/54394777

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

Vulnerabilities

No vulnerabilities reported

Install LipNet

You can download it from GitHub.
You can use LipNet 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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