auraloss | Collection of audio-focused loss functions in PyTorch | Machine Learning library

by csteinmetz1 Python Version: 0.4.0 License: Apache-2.0

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

kandi X-RAY | auraloss Summary

auraloss is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. auraloss 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 auraloss' or download it from GitHub, PyPI.

A collection of audio-focused loss functions in PyTorch.

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Security

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Support

auraloss has a low active ecosystem.

It has 498 star(s) with 54 fork(s). There are 15 watchers for this library.

It had no major release in the last 12 months.

There are 11 open issues and 17 have been closed. On average issues are closed in 74 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of auraloss is 0.4.0

Quality

auraloss has 0 bugs and 0 code smells.

Security

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

auraloss code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

auraloss is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

auraloss releases are available to install and integrate.

Deployable package is available in PyPI.

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

Perform the forward computation
Computes the Fourier transform of a time series
Apply reduction to losses
Performs a training step
Crop x to the center of the image
Forward computation
Randomize the STFTLoss
Add command line arguments to the parser
Forward convolution
Compute the loss function
Computes the sum of two stereo channels
Compute the loss
Calculate loss
Calculate the loss

Get all kandi verified functions for this library.

auraloss Key Features

No Key Features are available at this moment for auraloss.

auraloss Examples and Code Snippets

No Code Snippets are available at this moment for auraloss.

Community Discussions

Trending Discussions on auraloss

Noise To Signal Ratio custom loss function in Keras not working

QUESTION

Noise To Signal Ratio custom loss function in Keras not working

Asked 2021-Jul-31 at 15:16

I'm trying to implement a Noise To Signal Ratio loss function in Keras. I translated the code from this repo that works with torch. Not sure if the tf.math operations that I used do the same stuff as torch's functions. When training, I keep getting loss: nan - val_loss: nan. What am I doing wrong? Any help is appreciated. Thank you

...

ANSWER

Answered 2021-Jul-31 at 03:52

I tried applying your code to some inputs and found that it did work. I think it is the output of the network (not the loss function) that the cause of nan is. (For example, the output is nan or y_true is so close to zero that dividing by it results in inf.)

Source https://stackoverflow.com/questions/68598751

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

Vulnerabilities

No vulnerabilities reported

Install auraloss

You can install using 'pip install auraloss' or download it from GitHub, PyPI.
You can use auraloss 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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