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This file explains how to compile and install the CLAM libraries and
applications from the source tarballs or a subversion checkous.
Alternatively, CLAM website provides already compiled packages for several
platforms.
http://clam-project.org/download.html
Checkout the subversion repository
$ svn co http://clam-project.org/clam/trunk clam
1. Install CLAM dependencies
This step is different for each architecture (Linux, Windows, MacOSX).
Such platform dependant steps at detailed at the end of this file.
2. Compile CLAM libraries from the source
Change the directory to CLAM/ and execute:
$ scons configure
$ scons
$ sudo scons install
This will compile all the libraries with default options, and install
them to the default dirs ( /usr/local/lib/ and /usr/local/include/ )
Troubleshooting: in some scenarios, the first scons command ends with error
and is necessary to re-run scons a second time.
Tip: use "scons -j3" to take advantadge of multi-threading if you machine is a dual-core.
To install clam in a non system-wide place (usefull when developing) use
scons configure with these options: (you must create the destination dir before)
$ scons configure prefix=~/local
Then export LD_LIBRARY_PATH=~/local/lib before running the apps with these libs.
(Probably you also want to add this export line into your .bashrc)
To see all available build options run:
$ scons --help
Note that scons saves the last used options in the options.cache file.
These options will be used by default in further "scons" commands.
If you plan to use your CLAM build for performance-sensitive applications consult the
tips below (4. Tips for increasing performance).
MacOSX specific: the equivalent to LD_LIBRARY_PATH in mac is DYLD_LIBRARY_PATH.
** Warning about compiler Errors: **
Because scons copies all sources in CLAM/scons/libs/ you must be aware that the
paths given by the g++ error log are NOT the original sources. For example, if scons
returns: "scons/libs/core/src/Processing.cxx:56: error: ..." you should not edit this
file but the one in src/ (searchable by $ find src/ -name "Processing.cxx")
3. Compile CLAM applications.
Applications can be download from the CLAM website in separate tarballs.
If you are using svn they are folders at the clam trunk.
Go to the root of the application source tree (e.g. NetworkEditor)
and issue: (maybe changing the prefix)
$ scons prefix=/usr/local clam_prefix=/usr/local
For further building options do :
$ scons --help
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2.1 Install CLAM dependencies on Linux
Debian/Ubuntu
(do "sudo apt-get install <packages>")
* Packages to compile clam libs:
build-essential scons ladspa-sdk libfftw3-dev libjack-dev libmad0-dev libogg-dev libsndfile1-dev libvorbis-dev libid3-3.8.3-dev libasound2-dev portaudio19-dev libxerces-c2-dev
NOTE about libxerces: devel package to be installed for versions of Ubuntu < 8.10 is libxerces27-dev
libxerces27-dev
** to pass the tests: libcppunit-dev
* Qt4 packages to compile NetworkEditor and Annotator:
qt4-dev-tools libqt4-dev libqt4-opengl-dev (the last, from revision 11755)
NOTE about Qt version:
from CLAM svn revision 11755 support to qt4.3 is discontinued and qt4.4 is required.
If you are a Ubuntu 8.04 (Hardy) user you can enable the hardy-backports repository
* Qt3 packages to compile SMSTools and Voice2MIDI:
libqt3-headers libqt3-mt-dev
Redhat / Fedora Core packages list (tested with FC5)
gcc-c++ scons fftw2-devel fltk-devel libvorbis-devel libogg-devel qt-devel
qt-designer libxml++-devel alsa-lib-devel libsndfile-devel ladspa-devel
id3lib-devel libXi-devel
Unofficial Pakages:
libmad-devel (lvn5) jack-audio-connection-kit-devel (rhfc5.ccrma)
qt4-devel, qt4
2.2 Install CLAM dependencies on Mac OSX
Build instructions for CLAM in Mac OSX are currently in this wiki:
http://clam-project.org/wiki/Devel/Mac_OSX_build
2.3 Install CLAM dependencies on Windows
See instructions on the wiki for native compilation with MinGW
or cross compilation from Linux
http://www.clam-project.org/wiki/Devel/Windows_MinGW_build
http://www.clam-project.org/wiki/Devel/Windows_MinGW_cross_compile
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
3. Tips for increasing performance
If you plan to use CLAM in performance-sensitive applications it's a good idea to
compile with the release flag set, as this tells the compiler to use optimizations:
$ scons configure release=1
Also if you use the fourier transform a lot in CLAM, try using the fast fftw3
implementation:
$ scons configure with-fftw3=1
Of course you can do both:
$ scons configure release=1 with-fftw3=1
If you decide to use the fftw3 library you can further improve the fft performance
by precomputing wisdom. The fftw3 tries to optimize the algorithms it uses for your
system, this takes a long time at first but allows the transforms to be later
computed much faster. To calculate the wisdom for the transforms most often used in
CLAM issue these commands (you need the fftw3 package):
$ fftw-wisdom -v -x -o wisdom rof32768 cif32768
And then move the resulting wisdom file to the default system wisdom location (you
need root access for this):
$ sudo mv wisdom /etc/fftw/
Don't worry if the file /etc/fftw/wisdom already exists - you do not lose any
information previously stored in it, because fftw-wisdom reads this file first and
includes it in the new wisdom file it generates.
Generating fftw wisdom has been tested under linux, for using wisdom on other
operating systems consult your fftw3 user documentation.
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