PLDA | PLDA estimator using KALDI in python for speaker | Speech library

by RicherMans Python Version: 0.1 License: No License

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

kandi X-RAY | PLDA Summary

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

An LDA/PLDA estimator using KALDI in python for speaker verification tasks.

Support

Quality

Security

License

Reuse

Support

PLDA has a low active ecosystem.

It has 88 star(s) with 23 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 5 have been closed. On average issues are closed in 68 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of PLDA is 0.1

Quality

PLDA has 0 bugs and 0 code smells.

Security

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

PLDA code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PLDA 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

PLDA releases are available to install and integrate.

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

Installation instructions, examples and code snippets are available.

PLDA saves you 108 person hours of effort in developing the same functionality from scratch.

It has 274 lines of code, 24 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Estimate the LDA
Solve the eigenvectors
Compute the covariance matrix
Calculate the empirical covariance matrix
Check if files are binary
Check if a file is marshaled
Check cPickle file
Parses the input file and returns a dictionary of speakers
Return a list of features
Return a delimited string from a filename
Predict probability for the decision function
Safe dot product
Compute the decision function for the model
Extracts feature vectors from the data dictionary
Extract the vectors and labels from a dictionary
Parse command line arguments
Parse paths
Parse test_ref
Extract the variance of a feature matrix
Get a spk model from a filename
Compute the log probability of the given sample
Apply the transform
Return the norm of vectors
Calculate the score of the target
Parse the MLF file
Plots the detection curve

Get all kandi verified functions for this library.

PLDA Key Features

No Key Features are available at this moment for PLDA.

PLDA Examples and Code Snippets

No Code Snippets are available at this moment for PLDA.

Community Discussions

Trending Discussions on PLDA

PCA-LDA analysis - R

Can I classify ivectors with neural networks for language recognition?

How plot a discriminant analysis resualts with three LD

QUESTION

PCA-LDA analysis - R

Asked 2020-Jun-25 at 17:27

In this example (https://gist.github.com/thigm85/8424654) LDA was examined vs. PCA on iris dataset. How can I also do LDA on the PCA results (PCA-LDA) ?

Code:

...

ANSWER

Answered 2020-Jun-25 at 11:04

This is very simple, apply lda to the principal components coordinates returned by princomp in the question's code.

Source https://stackoverflow.com/questions/62573178

QUESTION

Can I classify ivectors with neural networks for language recognition?

Asked 2019-Apr-13 at 19:58

I'm doing a language recognizer, I had planned to classify my i-vectors with neural networks, but I've read a lot of papers and they always use other methods like SVM or PLDA, can someone explain to me why? or it's fine to do it with neural networks?

...

ANSWER

Answered 2019-Apr-13 at 19:58

Neural networks are good for complex non-linear multifeature input. I-vectors by design map speaker space to very simple space where speakers are easily separated with logistic regression or SVM.

If you want to try with neural networks, try something end-to-end like https://github.com/FlashTek/vggvox-pytorch

Source https://stackoverflow.com/questions/55466111

QUESTION

How plot a discriminant analysis resualts with three LD

Asked 2017-Oct-30 at 11:02

I have to plot the resultas of discriminant analysis function. But the discriminant function give me three LD , LD1,LD2,LD3 .

I just know how plot in 2D.(X=LD2 and Y=LD1) using this code:

...

ANSWER

Answered 2017-Oct-30 at 11:02

I get the solution using plotly packages:

Source https://stackoverflow.com/questions/47013331

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

Vulnerabilities

No vulnerabilities reported

Install PLDA

Make sure that you have KALDI compiled and installed. Further make sure that KALDI was compiled using the option --shared, during ./configure (e.g. ./configure --shared). Moreover the included ATLAS within KALDI is sufficient that PLDA works. If any compilation errors happen it's most likely that not all of the ATLAS libraries was installed successfully.

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 .

Find more information at: