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const char *help = "\
GMM (c) Samy Bengio & Co 2001\n\
\n\
This program is used to do decode simple speech recognition \n";
#include <torch/LexiconInfo.h>
#include <torch/EMTrainer.h>
#include <torch/HMM.h>
#include <torch/CmdLine.h>
#include <torch/Random.h>
#include <torch/DiskHTKDataSet.h>
#include <torch/HTKDataSet.h>
#include <torch/SimpleDecoderSpeechHMM.h>
#include <torch/WordSegMeasurer.h>
#include <torch/FrameSegMeasurer.h>
#include <torch/Grammar.h>
#include <torch/DiagonalGMM.h>
#include <torch/string_utils.h>
#include <torch/FileListCmdOption.h>
using namespace Torch;
#include "speech_include.cc"
int main(int argc, char **argv)
{
char* saved_model;
char* phoneme_name;
char* lex_name;
char* silence_name;
char* silence_phone;
char* sp_name;
char* sp_phone;
real log_word_entrance_penalty;
bool add_sil_to_targets;
bool no_self_transitions;
bool force;
int max_load;
char *dir_name;
bool htk_model;
bool disk;
bool isolated;
int min_duration;
bool confusion;
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;
// Put the help line at the beginning
cmd.info(help);
// Train mode
cmd.addText("\nArguments:");
cmd.addSCmdArg("saved_model", &saved_model, "the saved model");
cmd.addSCmdArg("phoneme_name", &phoneme_name, "the list of phonemes file");
cmd.addSCmdArg("lex_name", &lex_name, "the lexicon file");
cmd.addCmdOption(&input_file_list);
cmd.addCmdOption(&target_file_list);
cmd.addText("\nModel Options:");
cmd.addSCmdOption("-silence_word", &silence_name,"sil", "name of silence word");
cmd.addSCmdOption("-silence_phone", &silence_phone,"h#", "name of silence phone");
cmd.addSCmdOption("-sp_word", &sp_name,"", "name of short pause word");
cmd.addSCmdOption("-sp_phone", &sp_phone,"", "name of short pause phoneme");
cmd.addRCmdOption("-log_word_entrance_penalty", &log_word_entrance_penalty, -15., "log of the word entrance penalty");
cmd.addBCmdOption("-add_sil_to_targets", &add_sil_to_targets, false, "add silence at begining of targets");
cmd.addBCmdOption("-no_self_transitions", &no_self_transitions, false, "do not admit grammar self transitions");
cmd.addBCmdOption("-isolated", &isolated, false, "isolated word recognition");
cmd.addBCmdOption("-force", &force, false, "do a forced alignment decoding");
cmd.addICmdOption("-min_duration", &min_duration, -1, "use minimum duration models");
cmd.addText("\nMisc Options:");
cmd.addICmdOption("-load", &max_load, -1, "max number of examples to load for train");
cmd.addSCmdOption("-dir", &dir_name, ".", "directory to save measures");
cmd.addBCmdOption("-confusion", &confusion, false, "print confusion matrix");
cmd.addBCmdOption("-htk_model", &htk_model, false,"model file is in HTK format");
cmd.addBCmdOption("-disk", &disk, false, "keep data on disk");
// Read the command line
cmd.read(argc, argv);
cmd.setWorkingDirectory(dir_name);
DiskXFile::setBigEndianMode();
//====================================================================
//=================== Testing Mode =================================
//====================================================================
Random::seed();
// read lexicon
if (strlen(sp_phone) == 0) sp_phone = NULL;
if (strlen(silence_phone) == 0)
silence_phone = NULL;
LexiconInfo lex(phoneme_name,silence_phone,sp_phone,lex_name,"<s>","</s>",silence_name);
// create grammar
int n_words = lex.vocabulary->n_words;
int silence_word = lex.vocabulary->sil_index;
Grammar grammar(n_words+3);
grammar.words[0] = -1; // initial state
grammar.words[1] = silence_word; // initial silence
grammar.words[n_words+1] = silence_word; // final silence
grammar.words[n_words+2] = -1; // final state
int* gw = &grammar.words[2];
for (int i=0;i<n_words;i++) {
if (i != silence_word)
*gw++ = i;
}
grammar.transitions[1][0] = true;
for (int i=0;i<n_words-1;i++) {
grammar.transitions[i+2][1] = true;
grammar.transitions[n_words+1][i+2] = true;
if (!isolated) {
for (int j=0;j<n_words-1;j++)
if (!no_self_transitions || i!=j)
grammar.transitions[j+2][i+2] = true;
}
}
grammar.transitions[n_words+2][n_words+1] = true;
//====================================================================
//=================== Create the DataSet ... =========================
//====================================================================
// some basic tests on the files
if (input_file_list.n_files != target_file_list.n_files) {
error("the input and target files should have the same number of files (%d != %d)\n",input_file_list.n_files,target_file_list.n_files);
}
for (int i=0;i<input_file_list.n_files;i++) {
char* si = strRemoveSuffix(input_file_list.file_names[i]);
char* st = strRemoveSuffix(target_file_list.file_names[i]);
if (strcmp(strBaseName(si),strBaseName(st)))
warning("input file (%s) do not correspond to target file (%s)",input_file_list.file_names[i],target_file_list.file_names[i]);
free(si);
free(st);
}
DataSet* data;
int n_per_frame;
if (disk) {
data = (DataSet*)new(allocator) DiskHTKDataSet(input_file_list.file_names,target_file_list.file_names, input_file_list.n_files, true, max_load, &lex);
n_per_frame = ((DiskHTKDataSet*)data)->n_per_frame;
} else {
data = (DataSet*)new(allocator) HTKDataSet(input_file_list.file_names,target_file_list.file_names, input_file_list.n_files, true, max_load, &lex);
n_per_frame = ((HTKDataSet*)data)->n_per_frame;
}
//=================== Create the HMM... =========================
SpeechHMM* shmm = NULL;
if (htk_model)
shmm = newSpeechHMMFromHTK(saved_model,&lex,NULL,0,allocator);
else
shmm = newSpeechHMMFromTorch(saved_model,&lex,NULL,0,allocator,&cmd);
if (min_duration > 0) {
int silence_model = silence_phone ? lex.phone_info->getIndex(silence_phone) : -1;
shmm = extend_SpeechHMM_to_min_duration(shmm,min_duration,allocator,silence_model);
}
SimpleDecoderSpeechHMM dhmm(shmm,&grammar);
dhmm.setROption("log word entrance penalty",log_word_entrance_penalty);
dhmm.setBOption("forced alignment",force);
//=================== Measurers and Trainer ===============================
// Measurers on the training dataset
MeasurerList measurers;
WordSegMeasurer word_m(dhmm.wordseg,data,cmd.getXFile("decoder_word"),new(allocator)EditDistance(confusion));
word_m.setIOption("n per frame",n_per_frame);
word_m.setBOption("print timing",true);
measurers.addNode(&word_m);
FrameSegMeasurer frame_m(dhmm.frameseg,data,cmd.getXFile("decoder_frame"));
frame_m.setIOption("n per frame",n_per_frame);
frame_m.setBOption("print timing",true);
frame_m.setBOption("print desired timing",true);
frame_m.setBOption("print frame err",true);
measurers.addNode(&frame_m);
// The Gradient Machine Trainer
EMTrainer decoder(&dhmm);
//=================== Let's go... ===============================
decoder.decode(&measurers);
delete allocator;
return(0);
}
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