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
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
This sections contain installation guide for various 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:
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:
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 and OpenGRM NGram. 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.
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.
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, 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.
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