Kaldi_NL | Code related to the Dutch instance | Speech library

by opensource-spraakherkenning-nl Shell Version: v0.4.1 License: Apache-2.0

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

kandi X-RAY | Kaldi_NL Summary

Kaldi_NL is a Shell library typically used in Artificial Intelligence, Speech applications. Kaldi_NL has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

These scripts may be used to convert speech contained in audio files into text using the Kaldi open-source speech recognition system.

Support

Quality

Security

License

Reuse

Support

Kaldi_NL has a low active ecosystem.

It has 47 star(s) with 12 fork(s). There are 14 watchers for this library.

It had no major release in the last 12 months.

There are 4 open issues and 16 have been closed. On average issues are closed in 293 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Kaldi_NL is v0.4.1

Quality

Kaldi_NL has 0 bugs and 2 code smells.

Security

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

Kaldi_NL code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Kaldi_NL 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

Kaldi_NL releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 148 lines of code, 0 functions and 3 files.

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

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Kaldi_NL Key Features

No Key Features are available at this moment for Kaldi_NL.

Kaldi_NL Examples and Code Snippets

Container without Web Interface

Shell

Lines of Code : 5

License : Permissive (Apache-2.0)

Copy

$ docker pull proycon/kaldi_nl

$ docker build -t proycon/kaldi -f kaldi.Dockerfile
$ docker build -t proycon/kaldi_nl --build-arg MODELS="utwente radboud_OH radboud_PR radboud_GN" .

$ docker run -t -i -v /your/data/path:/data proycon/kaldi_nl

$ do

Community Discussions

Trending Discussions on Kaldi_NL

Exclamation mark and hash within curly braces in bash

QUESTION

Exclamation mark and hash within curly braces in bash

Asked 2020-Jan-23 at 15:37

I'm trying to understand a bash script and I'm having trouble with the following line:

...

ANSWER

Answered 2020-Jan-23 at 15:37

${#} is the number of arguments in the current shell/function:

Source https://stackoverflow.com/questions/59882018

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

Vulnerabilities

No vulnerabilities reported

Install Kaldi_NL

The software is run from the directory where you cloned this repository. Kaldi NL depends on a working installation of Kaldi (http://kaldi-asr.org/), Java, Perl, Python 3, and SoX (http://sox.sourceforge.net/). They have been tested on Ubuntu Linux 16.10, 18.04 LTS and 20.04 LTS but are expected to run on other Linux distributions as well. A container image is provided that contains all of the dependencies and models. Before running the decoder for the first time, or when you need to change its configuration, please run configure.sh. The configure.sh script will ask for the location of your Kaldi installation, and for the location to put the models. A decode.sh script is dynamically generated based on the selected models, as are the decoding graphs needed for Kaldi. This last step may take a while (but on a 16GB machine usually no more than an hour or so).
utwente - Starter Pack - These are the dutch models and decoder graphs originally provided with Kaldi_NL
radboud_OH - Oral History - These are dutch models and decoder graphs trained on oral history interviews
radboud_PR - Parliamentary Talks - These are dutch models and decoder graphs trained on parliamentary talks
radboud_GN - Daily Conversation - These are dutch models and decoder graphs trained on daily conversations