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https://github.com/cmusphinx/sphinxtrain

Acoustic model trainer for CMU Sphinx
https://github.com/cmusphinx/sphinxtrain

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Acoustic model trainer for CMU Sphinx

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README 

        
SphinxTrain 5.0.0

=================



This is SphinxTrain, Carnegie Mellon University's open source acoustic

model trainer. This directory contains the scripts and instructions

necessary for building models for the CMU Sphinx Recognizer.



This distribution is free software, see LICENSE for licence.



For up-to-date information, please see the web site at



   https://cmusphinx.github.io



Among the interesting resources there, you will find a link to

"Resources to build a recognition system", with pointers to a

dictionary, audio data, acoustic model etc.



For introduction in training the acoustic model see the tutorial



https://cmusphinx.github.io/wiki/tutorialam



Installation Guide:

-------------------



This sections contain installation guide for various platforms. 



All Platforms:

--------------



You will unfortunately need both Perl and Python to use the scripts

provided. Linux usually comes with some version of Perl and Python. If

you do not have Perl installed, please check:



http://www.perl.org



where you can download it for free. For Windows, if you insist on not

using Windows Subsystem for Linux, a popular version, ActivePerl, is

available from ActiveState at:



https://www.activestate.com/products/perl/



Python for Windows can be obtained from:



http://www.python.org/download/



For some advanced techniques (which are not enabled by default) you

will need NumPy and SciPy.  Packages for NumPy and SciPy can be

obtained from:



http://scipy.org/Download



Or you can use Anaconda which makes all of this somewhat easier:



https://www.anaconda.com/products/distribution



If you wish to use the grapheme-to-phoneme support, you will need

rather specific versions of

[OpenFST](https://www.openfst.org/twiki/bin/view/FST/WebHome) and

[OpenGRM

NGram](https://www.opengrm.org/twiki/bin/view/GRM/NGramLibrary).  It

is known to work with OpenFST 1.6.3, and known *not* to work with

1.8.2. There is probably nothing you want in the latest version

anyway, and compiling it will consume several hours of your life and

several gigabytes of your disk for no good reason, so best to just use

what Ubuntu 20.04 LTS or 22.04 LTS will install for you with:



    apt install libfst-dev libngram-dev



See the note about `-DBUILD_G2P=ON` below to enable G2P support.



Linux/Unix Installation:

------------------------



This distribution uses CMake to find out basic information about your

system, and should compile on most Unix and Unix-like systems, and

certainly on Linux.  On reasonable Linux distributions, a suitable

version of CMake (at least 3.14) can be installed with your package

manager, or may already be there if you have installed development

tools.



On certain unreasonable distributions that are far too often installed

on "enterprise" or "cloud" or HPC systems, the version of CMake is

incredibly ancient, and the package manager will not help you, so you

will have to install it manually, following the instructions at

https://cmake.org/download/



To build, simply run:



    cmake -S . -B build

    cmake --build build



This should configure everything automatically. The code has been

tested with gcc.



To enable G2P, you need to add a magic incantation to the first

command above, namely:



    cmake -S . -B build -DBUILD_G2P=ON

    

You can also enable shared libraries with `-DBUILD_SHARED_LIBS=ON`,

but I suggest that you *not* do that unless you have a very good

reason.



You do not need to install SphinxTrain to run it, simply run

`scripts/sphinxtrain` from the source directory when initializing a

training directory.  Note that you do need to build and install

PocketSphinx for evaluation to work properly, however.



You can also install SphinxTrain system-wide if you so desire:



    sudo cmake --build build --target install



This will put various files in `/usr/local/lib`,

`/usr/local/libexec/sphinxbase` and `/usr/local/share/sphinxbase` and

create `/usr/local/bin/sphinxbase`.



Also, check the section title "All Platforms" above.



Windows Installation:

---------------------



You can build with Visual Studio Code using the C++ and CMake

extensions.  This will create all the binaries in `build\Debug` or

`build\Release` depending on the configuration you select.  As above,

you can run `python ..\sphinxtrain\scripts\sphinxtrain` (or whatever

the path is to `scripts\sphinxtrain` in your source directory) to set

up and run training.



Note that you will need to have Perl on your path, among other things,

and also, note that none of this has been tested, so we suggest you just

use [Windows Subsystem for

Linux](https://learn.microsoft.com/en-us/windows/wsl/install), which

is really a lot faster and easier to use than the native Windows

command-line.



If you are using Windows Subsystem for Linux, the installation

procedure is identical to the Unix installation.



Also, check the section title "All Platforms" above.



Acknowldegments

---------------



The development of this code has included support at different times

by various United States Government agencies, under different programs,

including the Defence Advanced Projects Agency (DARPA) and the

National Science Foundation (NSF). We are grateful for their support.



This work was built over a large number of years at CMU by most of the

people in the Sphinx Group. Some code goes back to 1986. The most

recent work in tidying this up for release includes the following,

listed alphabetically (at least these are the people who are most

likely able to help you).



- Alan W Black ([email protected])

- Arthur Chan ([email protected])

- Evandro Gouvea ([email protected])

- Ricky Houghton ([email protected])

- David Huggins-Daines ([email protected])

- Kevin Lenzo ([email protected])

- Ravi Mosur

- Long Qin ([email protected])

- Rita Singh ([email protected])

- Eric Thayer