VGGish | An implementation of vggish in keras with tf backend | Machine Learning library

by DTaoo Python Version: Current License: No License

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

kandi X-RAY | VGGish Summary

VGGish is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow, Keras applications. VGGish has no bugs, it has no vulnerabilities and it has low support. However VGGish build file is not available. You can download it from GitHub.

An implementation of vggish in keras with tf backend

Support

Quality

Security

License

Reuse

Support

VGGish has a low active ecosystem.

It has 112 star(s) with 38 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 3 have been closed. On average issues are closed in 88 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of VGGish is current.

Quality

VGGish has 0 bugs and 0 code smells.

Security

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

VGGish code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

VGGish does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

VGGish has no build file. You will be need to create the build yourself to build the component from source.

It has 247 lines of code, 9 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Preprocess a sound
Convert a spectrogram to a mel matrix
Calculate the log - likelihood spectrum
Generate a frame of data
Compute the STFT magnitude
Calculate periodic Hann
Convert frequency to mel coefficient

Get all kandi verified functions for this library.

VGGish Key Features

No Key Features are available at this moment for VGGish.

VGGish Examples and Code Snippets

No Code Snippets are available at this moment for VGGish.

Community Discussions

Trending Discussions on VGGish

How to generate predictions from new data using trained tensorflow network?

How to get prediction scores between 0 and 1 (or -1 and 1)?

Why is 32768 used as a constant to normalize the wav data in VGGish?

QUESTION

How to generate predictions from new data using trained tensorflow network?

Asked 2022-Mar-10 at 12:05

I want to train Googles VGGish network (Hershey et al 2017) from scratch to predict classes specific to my own audio files.

For this I am using the vggish_train_demo.py script available on their github repo which uses tensorflow. I've been able to modify the script to extract melspec features from my own audio by changing the _get_examples_batch() function, and, then train the model on the output of this function. This runs to completetion and prints the loss at each epoch.

However, I've been unable to figure out how to get this trained model to generate predictions from new data. Can this be done with changes to the vggish_train_demo.py script?

...

ANSWER

Answered 2022-Mar-10 at 12:05

For anyone who stumbles across this in the future, I wrote this script which does the job. You must save logmel specs for train and test data in the arrays: X_train, y_train, X_test, y_test. The X_train/test are arrays of the (n, 96,64) features and the y_train/test are arrays of shape (n, _NUM_CLASSES) for two classes, where n = the number of 0.96s audio segments and _NUM_CLASSES = the number of classes used.

See the function definition statement for more info and the vggish github in my original post:

Source https://stackoverflow.com/questions/71037635

QUESTION

How to get prediction scores between 0 and 1 (or -1 and 1)?

Asked 2022-Feb-10 at 17:26

I am training a model that adds a couple of layers to the predifined VGGish network (see github repo), so that it can predict the class of input logmel spectrograms extracted from audio files (full code at bottom).

I generate X_train, X_test, y_train, y_test sets from a previous function first and then run the main() codeblock. This predicts the classes of the X_test at line 78 and prints these:

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ANSWER

Answered 2022-Feb-10 at 17:26

You are outputing the linear-layer before the sigmoid. Change the code as following:

Source https://stackoverflow.com/questions/71055668

QUESTION

Why is 32768 used as a constant to normalize the wav data in VGGish?

Asked 2021-Mar-23 at 12:16

I'm trying to follow along with what the code is doing for VGGish and I came across a piece that I don't really understand. In vggish_input.py there is this:

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ANSWER

Answered 2021-Mar-23 at 12:16

32768 is 2^15. int16 has a range of -32768 to +32767. If you have int16 as input and divide it by 2^15, you get a number between -1 and +1.

Source https://stackoverflow.com/questions/66762815

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

Vulnerabilities

No vulnerabilities reported

Install VGGish

You can download it from GitHub.
You can use VGGish 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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