// doc/tutorial_code.dox
  
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  /**
   \page tutorial_code Reading and modifying the code (1/2 hour)
  
    \ref tutorial "Up: Kaldi tutorial" <BR>
    \ref tutorial_running "Previous: Running the example scripts" <BR>
  
  
   While the triphone system build is running, we will take a little while to
   glance at some parts of the code.  The main thing you will get out of this
   section of the tutorial is some idea of how the code is organized and what the
   dependency structure is; and some experience with modifying and debugging the
   code.  If you want to understand it code in more depth, we advise you to follow the links
   on the \ref index "main documentation page", where we have more detailed documentation
   organized by topic.
  
   \section tutorial_code_utils Common utilities
  
  
   Go to the top-level directory (we called it kaldi-1) and then into
   src/.
   First look at the file base/kaldi-common.h (don't follow the links within
   this document; view it from the shell or from an editor).  This \#includes a number of
   things from the base/ directory that are used by almost every Kaldi program.  You
   can mostly guess from the filenames the types of things that are provided: things
   like error-logging macros, typedefs, math utility functions such as random number
   generation, and miscellaneous \#defines.  But this is a stripped-down set of
   utilities; in util/common-utils.h there is a more complete set, including
   command-line parsing and I/O functions that handle extended filenames such as
   pipes.  Take a few seconds to glance over util/common-utils.h and see what it
   \#includes.  The reason why
   we segregated a subset of utilities into the base/ directory is so that we could
   minimize the dependencies of the matrix/ directory (which is useful in
   itself);
   the matrix/ directory only depends on the base/ directory.  Look at matrix/Makefile
   and search for base/ to see how this is specified.  Looking at this type of rule
   in the Makefiles can give you some insight into the structure of the toolkit.
  
  
   \section tutorial_code_matrix Matrix library (and modifying and debugging code)
  
   Now look at the file matrix/matrix-lib.h.  See what files it includes.  This provides
   an overview of the kinds of things that are in the matrix library.  This library
   is basically a C++ wrapper for BLAS and LAPACK, if that means anything to you (if not,
   don't worry).  The files sp-matrix.h and tp-matrix.h relate to symmetric packed matrices and
   triangular packed matrices, respectively.  Quickly scan the file matrix/kaldi-matrix.h.
   This will give you some idea what the matrix code looks like.  It consists of
   a C++ class representing a matrix.  We provide a mini-tutorial on the matrix
   library \ref matrix "here", if you are interested.  You might notice what seems like
   a strange comment style in the code, with comments started by three slashes (///).
   These types of commends, and block comments that begin with \verbatim /**\endverbatim, are interpreted by the
   Doxygen software that automatically generates documentation.  It also generates the
   page you are reading right now (the source for this type of documentation
   is in src/doc/).
  
   At this point we would like you to modify the code and compile it.  We will be
   adding a test function to the file matrix/matrix-lib-test.cc.  As mentioned
   before, the test programs are designed to abort or exit with nonzero status if
   something is wrong.
  
   We will be adding a test routine for the function Vector::AddVec.  This function
   adds some constant times one vector, to another vector.  Read through the code
   below
   and try to understand as much of it as you can (be careful: we have deliberately
   inserted two errors into the code).  If you are not familiar with templates,
   understanding it may be difficult.  We have tried to avoid the use of templates as much as possible,
   so large parts of Kaldi are still understandable without knowing template progamming.
  \verbatim
  template<class Real>
  void UnitTestAddVec() {
    // note: Real will be float or double when instantiated.
    int32 dim = 1 + Rand() % 10;
    Vector<Real> v(dim); w(dim); // two vectors the same size.
    v.SetRandn();
    w.SetRandn();
    Vector<Real> w2(w); // w2 is a copy of w.
    Real f = RandGauss();
    w.AddVec(f, v); // w <-- w + f v
    for (int32 i = 0; i < dim; i++) {
      Real a = w(i), b = f * w2(i) + v(i);
      AssertEqual(a, b); // will crash if not equal to within
      // a tolerance.
    }
  }
  \endverbatim
  Add this code to the file matrix-lib-test.cc, just above the function
  MatrixUnitTest().  Then, inside MatrixUnitTest(), add the line:
  \verbatim
    UnitTestAddVec<Real>();
  \endverbatim
  It doesn't matter where in the function you add this.
  Then type "make test".  There should be an error (a semicolon that should be
  a comma); fix it and try again.
  Now type "./matrix-lib-test".  This should crash with an assertion failure,
  because there was another mistake in the unit-test code.  Next we will debug it.
  Type
  \verbatim
   gdb ./matrix-lib-test
  \endverbatim
  (if you are on cygwin, you should now type into the gdb prompt, "break __assert_func").
  Type "r".  When it crashes, it calls abort(), which gets caught by the debugger.
  Type "bt" to see the stack trace.  Nagivate up the stack by typing "up" until you are inside the
  test function.  When you are at the right place you should see output like:
  \verbatim
  #5  0x080943cf in kaldi::UnitTestAddVec<float> () at matrix-lib-test.cc:2568
  2568	    AssertEqual(a, b); // will crash if not equal to within
  \endverbatim
  If you go too far you can type "down".  Then type "p a" and "p b" to see the
  values of a and b ("p" is short for "print").  Your screen should look someting like this:
  \verbatim
  (gdb) p a
  $5 = -0.931363404
  (gdb) p b
  $6 = -0.270584524
  (gdb)
  \endverbatim
  The exact values are, of course, random, and may be different for you.  Since
  the numbers are considerably different, it's clear that it's not just a question
  of the tolerances being wrong.  In general you can access any kind of expression
  from the debugger using the "print" expression, but the parenthesis operator
  (expressions like "v(i)") doesn't work, so to see the values inside the vectors
  you have to enter expressions like the following:
  \verbatim
  (gdb) p v.data_[0]
  $8 = 0.281656802
  (gdb) p w.data_[0]
  $9 = -0.931363404
  (gdb) p w2.data_[0]
  $10 = -1.07592916
  (gdb)
  \endverbatim
  This may help you work out that the expression for "b" is wrong.  Fix it in the code, recompile, and run
  again (you can just type "r" in the gdb prompt to rerun).  It should now run OK.  Force gdb to break into the
  code at the point where it was previously failing, so you can check the values of the expressions again
  and see that things are now working OK.  To get the debugger to break there you have to set a
  breakpoint.  Work out the line number that the assertion was failing (somewhere in UnitTestAddVec()),
  and type into gdb something like the following:
  \verbatim
  (gdb) b matrix-lib-test.cc:2568
  Breakpoint 1 at 0x80943b4: file matrix-lib-test.cc, line 2568. (4 locations)
  \endverbatim
  Then run the program (type "r"), and when it breaks there, look at the values of the expressions
  using "p" commands.  To continue, type "c".  It will keep stopping there since it was inside
  a loop.  Type "d 1" to delete the breakpoint (assuming it was breakpoint number one),
  and type "c" to continue.  The program should run to the end.  Type "q" to quit the debugger.
  If you need to debug a program that takes command-line arguments, you can do it like:
  \verbatim
   gdb --args kaldi-program arg1 arg2 ...
   (gdb) r
   ...
  \endverbatim
  or you can invoke gdb without arguments and then type "r arg1 arg2..." at the prompt.
  
  When you are done, and it compiles, type
  \verbatim
   git diff
  \endverbatim
  to see what changes you made.  If you are contributing to the Kaldi project and
  planning to send us code in the near future, you
  may want to commit them to a branch as described in the \tutorial_git, so that you
  can generate a clean <a href="https://help.github.com/articles/using-pull-requests/">
  GitHub pull request</a> later. We recommend that you familiarize
  yourself with Git branches even if you are not contributing your changes
  outright; Git is a powerful tool to maintain your local code changes as well as
  those you may contribute.
  
  \section tutorial_code_acoustic Acoustic modeling code
  
  Next look at gmm/diag-gmm.h (this class stores a Gaussian Mixture Model).
  The class DiagGmm may look a bit confusing as
  it has many different accessor functions.  Search for "private" and look
  at the class member variables (they always end with an underscore, as per
  the Kaldi style).  This should make it clear how we store the GMM.
  This is just a single GMM, not a whole collection of GMMs.
  Look at gmm/am-diag-gmm.h; this class stores a collection of GMMs.
  Notice that it does not inherit from anything.
  Search for "private" and you can see the member variables (there
  are only two of them).  You can understand from this how simple the
  class is (everything else consists of various accessors and convenience
  functions).  A natural question to ask is: where are the transitions,
  where is the decision tree, and where is the HMM topology?  All of these
  things are kept separate from the acoustic model, because it's likely
  that researchers might want to replace the acoustic likelihoods while
  keeping the rest of the system the same.  We'll come to this other stuff later.
  
  \section tutorial_code_feat Feature extraction code
  
  Next look at feat/feature-mfcc.h.  Focus on the MfccOptions struct.
  The struct members give you some idea what kind of options are supported
  in MFCC feature extraction.
  Notice that some struct members are options structs themselves.
  Look at the Register function.  This is standard in Kaldi options classes.
  Then look at featbin/compute-mfcc-feats.cc (this is a command-line
  program) and search for Register.
  You can see where the Register function of the options struct is called.
  To see a complete list of the options supported for MFCC feature extraction,
  execute the program featbin/compute-mfcc-feats with no arguments.
  Recall that you saw some of these options being registered in
  the MfccOptions class, and others being registered in
  featbin/compute-mfcc-feats.cc.  The way to specify options is --option=value.
  Type
  \verbatim
  featbin/compute-mfcc-feats ark:/dev/null ark:/dev/null
  \endverbatim
  This should run successfuly, as it interprets /dev/null as an empty archive.
  You can try setting the options using this example.  Try, for example,
  \verbatim
  featbin/compute-mfcc-feats --raw-energy=false ark:/dev/null ark:/dev/null
  \endverbatim
  The only useful information you get from this is that it doesn't crash; try
  removing the "=" sign or abbreviating the option name or changing the
  number of arguments, and see that it fails and prints a usage message.
  
  \section tutorial_code_tree Acoustic decision-tree and HMM topology code
  
  Next look at tree/build-tree.h.  Find the BuildTree function.  This is the main
  top-level function for building the decision tree.  Notice that it returns a
  pointer the type EventMap.  This is a type that stores a function from a set of
  (key, value) pairs to an integer.  It's defined in tree/event-map.h.  The keys
  and values are both integers, but the keys represent phonetic-context positions
  (typically 0, 1 or 2) and the values represent phones.  There is also a special
  key, -1, that roughly represents the position in the HMM.  Go to the experimental
  directory (../egs/rm/s5), and we are going to look at how the tree is built.
  The main input to the BuildTree function is of type BuildTreeStatsType,
  which is a typedef as follows:
  \verbatim
  typedef vector<pair<EventType, Clusterable*> > BuildTreeStatsType;
  \endverbatim
  Here, EvenType is the following typedef:
  \verbatim
  typedef vector<pair<EventKeyType, EventValueType> > EventType;
  \endverbatim
  The EventType represents a set of (key,value) pairs, e.g. a typical one
  would be { {-1, 1}, {0, 15}, {1, 21}, {2, 38} } which represents phone
  21 with a left-context of phone 15, a right-context of phone 38, and
  "pdf-class" 1 (which in the normal case means it's in state number 1, which
  is the middle of three states).  The Clusterable* pointer is a pointer to
  a virtual class which has a generic interface that supports operations like
  adding statistics together and evaluating some kind of objective function
  (e.g. a likelihood).  In the normal recipe, it actually points to a class
  that contains sufficient statistics for estimating a diagonal Gaussian p.d.f..
  
  Do
  \verbatim
  less exp/tri1/log/acc_tree.log
  \endverbatim
  There won't be much information in this file, but you can see the command
  line.  This program accumulates the single-Gaussian statistics for each HMM-state
  (actually, pdf-class) of each seen triphone context.
  The <DFN>--ci-phones</DFN> options is so that it knows to avoid accumulating separate
  statistics for distinct context of phones like silence that we don't want to be
  context dependent (this is an optimization; it would work without this option).
  The output of this program can be thought of as being of the type BuildTreeStatsType
  discussed above, although in order to read it we have to know what concrete type it is.
  
  Do
  \verbatim
  less exp/tri1/log/train_tree.log
  \endverbatim
  This program does the decision-tree clustering; it reads in the statistics
  that were output by.  It is basically a wrapper for the BuildTree function discussed above.
  The questions that it asks in the decision-tree clustering are automatically generated,
  as you can see in the script steps/train_tri1.sh (look for the programs cluster-phones
  and compile-questions).
  
  
  
  
  
  Next look at hmm/hmm-topology.h.  The class HmmTopology defines a set of HMM
  topologies for a number of phones.  In general each phone can have a different
  topology.  The topology includes "default" transitions, used for initialization.
  Look at the example topology in the extended comment at the top of the header.
  There is a tag <PdfClass> (note: as with HTK text formats,
  this file looks vaguely XML-like, but it is not really XML).
  The <PdfClass> is always the same as the HMM-state (<State>) here; in
  general, it doesn't have to be.  This is a mechanism to enforce tying of
  distributions between distinct HMM states; it's possibly useful if you want to
  5~create more interesting transition models.
  
    \ref tutorial "Up: Kaldi tutorial" <BR>
    \ref tutorial_running "Previous: Running the example scripts" <BR>
  <P>
  */