int main( int argc , char *argv[] )
{
CmdLine cmd ;
cmd.setBOption("write log",false);
DiskXFile::setBigEndianMode() ;
processCmdLine( &cmd , argc , argv ) ;
LexiconInfo lex_info( am_models_fname , am_sil_phone , am_pause_phone , lex_dict_fname ,
lex_sent_start_word , lex_sent_end_word , lex_sil_word ) ;
PhoneModels phone_models ( lex_info.phone_info , am_models_fname ,
true , am_phone_del_pen ,
am_apply_pause_del_pen , "" , "" ,
9 , "" , false ,
(real)0.005 , (real)0.005 ) ;
LinearLexicon lexicon( &lex_info , &phone_models ) ;
LanguageModel *lang_model ;
if ( lm_ngram_order <= 0 )
lang_model = NULL ;
else
{
lang_model = new LanguageModel( 2 , lex_info.vocabulary ,
lm_fname, lm_scaling_factor ) ;
}
BeamSearchDecoder bs_decoder( &lexicon , lang_model , dec_word_entr_pen ,
dec_int_prune_window , dec_end_prune_window ,
dec_delayed_lm , dec_verbose ) ;
MlDecoderPage batch_tester( input_fname , wrdtrns_fname , &bs_decoder ,
true , true , output_fname ) ;
batch_tester.setOutputOptions( print_by_page, print_by_part, print_by_book );
batch_tester.run() ;
printf( SUCCESS );
return(0) ;
}
int main(int argc, char **argv)
{
int n_gaussians;
int n_states;
bool is_symbol;
char* file_states;
char* model_name1=NULL;
char* model_name2=NULL;
char* model_name3=NULL;
char* spacing_model;
char* lex_name1=NULL;
char* lex_name2=NULL;
char* lex_name3=NULL;
char *sent_start_symbol=NULL;
char *sent_end_symbol=NULL;
char *spacing_symbol=NULL;
char *model_file_in;
char *model_file_out;
char *dir_name;
real accuracy;
real threshold;
int max_iter;
real prior;
int max_load;
bool add_space_to_targets;
bool viterbi;
bool train_separate;
bool disk;
bool no_learn_means;
bool learn_var;
bool learn_weights;
real map_factor;
bool adapt_separate;
bool adapt_separate_set_data;
Allocator *allocator = new Allocator;
FileListCmdOption input_file_list("file name", "the list of inputs files or one data file");
input_file_list.isArgument(true);
FileListCmdOption target_file_list("file name", "the list of target files or one target file");
target_file_list.isArgument(true);
//===============================================================
//=================== The command-line ==========================
//===============================================================
// Construct the command line
CmdLine cmd;
cmd.setBOption("write log", false );
// Put the help line at the beginning
cmd.info(help);
// Adapt mode
cmd.addText("\nArguments:");
cmd.addSCmdArg("model_name1", &model_name1, "the list of the model models file");
cmd.addSCmdArg("lex_name1", &lex_name1, "the model lexicon file");
cmd.addSCmdArg("model_name2", &model_name2, "the list of the data models file");
cmd.addSCmdArg("lex_name2", &lex_name2, "the data lexicon file");
cmd.addSCmdArg("model_name3", &model_name3, "the list of the output models file");
cmd.addSCmdArg("lex_name3", &lex_name3, "the output lexicon file");
cmd.addCmdOption(&input_file_list);
cmd.addCmdOption(&target_file_list);
cmd.addSCmdArg("input model file", &model_file_in, "the input model file to be adapted");
cmd.addSCmdArg("output model file", &model_file_out, "the adapted output model file");
// Data Initialization
cmd.addText("\nInitialization Options:") ;
cmd.addICmdOption("-n_gaussians", &n_gaussians, 10, "number of Gaussians");
cmd.addICmdOption("-n_states", &n_states, 5, "number of states");
cmd.addSCmdOption("-file_states", &file_states, "", "file containing n_states per model");
cmd.addBCmdOption("-train_separate", &train_separate, false, "first train separate models");
// Data and Model Parameters
cmd.addText("\nData and Model Options:") ;
cmd.addBCmdOption("-symbol", &is_symbol, false, "targets are in symbol format");
cmd.addBCmdOption("-disk", &disk, false, "keep data on disk");
cmd.addICmdOption("-load", &max_load, -1, "max number of examples to load for train");
cmd.addSCmdOption("-spacing_model", &spacing_model,"", "name of silence phone");
cmd.addBCmdOption("-add_sil_to_targets", &add_space_to_targets, false, "add silence at begining of targets");
cmd.addSCmdOption("-sent_start_symbol" , &sent_start_symbol , SP_START , "symbol that will start every sentence" ) ;
cmd.addSCmdOption("-sent_end_symbol" , &sent_end_symbol , SP_END , "symbol that will end every sentence" ) ;
cmd.addSCmdOption("-spacing_symbol" , &spacing_symbol , SP_WORD ,"the silence dictionary symbol" ) ;
// Learning Options
cmd.addText("\nLearning Options:");
cmd.addBCmdOption("-viterbi", &viterbi, false, "viterbi training (instead of EM)");
cmd.addRCmdOption("-threshold", &threshold, 0.001, "variance threshold");
cmd.addRCmdOption("-prior", &prior, 0.001, "prior on the weights");
cmd.addICmdOption("-iter", &max_iter, 25, "max number of iterations of HMM");
cmd.addRCmdOption("-e", &accuracy, 0.00001, "end accuracy");
cmd.addBCmdOption("-adapt_separate", &adapt_separate, false, "adapt models separately");
cmd.addBCmdOption("-adapt_separate_set_data", &adapt_separate_set_data, true, "set data to non represented models when adapting models separately");
// MAP Options
cmd.addText("\nMAP Options:");
cmd.addRCmdOption("-map", &map_factor, 0.5, "adaptation factor [0-1]");
cmd.addBCmdOption("-no_means", &no_learn_means, false, "no enroll means");
cmd.addBCmdOption("-learn_var", &learn_var, false, "enroll var");
cmd.addBCmdOption("-learn_weights", &learn_weights, false, "enroll weights");
// Misc Options
cmd.addText("\nMisc Options:");
cmd.addSCmdOption("-dir", &dir_name, ".", "directory to save measures");
// Read the command line
cmd.read(argc, argv);
cmd.setWorkingDirectory(dir_name);
DiskXFile::setBigEndianMode();
//====================================================================
//=================== Data preparation ===============================
//====================================================================
Random::seed();
clock_t start_time , end_time ;
real total_time = 0.0 ;
start_time = clock() ;
MlHMM hmm;
//====================================================================
//=================== Set Options ====================================
//====================================================================
// initialization
hmm.setIOption("number of gaussians", n_gaussians );
hmm.setIOption("number of states", n_states );
hmm.setBOption("train separate", train_separate );
// data
hmm.setBOption("is symbol", is_symbol );
hmm.setBOption("add spacing", add_space_to_targets );
hmm.setIOption("max load", max_load );
hmm.setBOption("disk", disk );
// training
hmm.setROption("accuracy", accuracy );
hmm.setROption("threshold", threshold );
hmm.setIOption("iterations", max_iter );
hmm.setROption("prior", prior );
hmm.setBOption("viterbi", viterbi );
// map
hmm.setROption("map factor", map_factor );
hmm.setBOption("no means", no_learn_means );
hmm.setBOption("learn var", learn_var );
hmm.setBOption("learn weights", learn_weights );
hmm.setBOption("adapt separate", adapt_separate );
hmm.setBOption("adapt separate set data", adapt_separate_set_data );
hmm.setLexicon( model_name1, spacing_model, lex_name1, add_space_to_targets ? sent_start_symbol : NULL );
hmm.mapSetDataLexicon( model_name2, lex_name2, model_name3, lex_name3 );
//====================================================================
//=================== Create the DataSet ... =========================
//====================================================================
hmm.setData( input_file_list.file_names, input_file_list.n_files, target_file_list.file_names, target_file_list.n_files );
hmm.mapInit( file_states );
char* n_nll = strConcat(2,"hmm_train_val_",viterbi ? "viterbi" : "em");
allocator->retain(n_nll);
hmm.mapAdapt( model_file_in, model_file_out, cmd.getXFile(n_nll) );
end_time = clock() ;
total_time = (real)(end_time-start_time) / CLOCKS_PER_SEC ;
printf("\nTotal time spent adapting = %.2f secs\n", total_time) ;
delete allocator;
printf( SUCCESS );
return(0);
}
