audio-super-res | Audio super resolution using neural networks | Computer Vision library

by kuleshov Python Version: Current License: MIT

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

kandi X-RAY | audio-super-res Summary

audio-super-res is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. audio-super-res has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However audio-super-res has 5 bugs. You can download it from GitHub.

The model is implemented in Python 3.7.10 and uses several additional libraries. A full list of the packages on our enviornment is in requirements.txt.

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Quality

Security

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audio-super-res has a medium active ecosystem.

It has 935 star(s) with 195 fork(s). There are 23 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 45 have been closed. On average issues are closed in 356 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of audio-super-res is current.

Quality

audio-super-res has 5 bugs (0 blocker, 0 critical, 1 major, 4 minor) and 159 code smells.

Security

audio-super-res has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

audio-super-res code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

audio-super-res 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

audio-super-res 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, examples and code snippets are available.

audio-super-res saves you 880 person hours of effort in developing the same functionality from scratch.

It has 2012 lines of code, 108 functions and 23 files.

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

Top functions reviewed by kandi - BETA

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

Create a LSTM model
Subpixel subpixel
Add data to HDF5
Upsamples x_lr
Create argument parser
Create a convolutional model
Train the model
Fit the model
Generate the next batch of data
Load training data from h5 file
1D convolution layer
Creates summaries for variance
Create a training op
Create objective function
Create an optimizer
Compute gradients
Run the spline
Compute the logDistribution of the log distribution
Call LSTM
Evaluate the model
Subpixel
Predict for the given data
Apply normalization
Predict for given data
Run the prediction on the input data
Bandpass filter

Get all kandi verified functions for this library.

audio-super-res Key Features

No Key Features are available at this moment for audio-super-res.

audio-super-res Examples and Code Snippets

No Code Snippets are available at this moment for audio-super-res.

Community Discussions

Trending Discussions on audio-super-res

upsampling convolution has no parameters

QUESTION

upsampling convolution has no parameters

Asked 2020-Apr-15 at 09:37

I have read many papers where convolutional neuronal networks are used for super-resolution or for image segmentation or autoencoder and so on. They use different kinds of upsampling aka deconvolutions and a discussion over here in a different question. Here in Tensorflow there is a function Here in Keras there are some

I implemented the Keras one:

...

ANSWER

Answered 2020-Apr-15 at 09:37

In Keras Upsampling simply copies your input to the size provided. you can find the documentation here, So there is no need to have parameters for these layers.

I think you have confused upsampling with Transposed Convolution/ Deconvolution.

Source https://stackoverflow.com/questions/61215316

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