CA_Recog *CA_AllocateRecognition()
{
CA_Recog *hRecog = NULL;
#ifdef SREC_ENGINE_VERBOSE_LOGGING
PLogMessage("in CA_AllocateRecognition\n");
#endif
TRY_CA_EXCEPT
/* CREC_SET_SIGNATURE must be 'tied' to the
* actual creation of the recog_info structure.
* Any methods which take 'recog_info' as an argument,
* even 'destroy_recognition()' will test the signature!
*/
hRecog = (CA_Recog *) CALLOC_CLR(1, sizeof(CA_Recog), "ca.hRecog");
hRecog->setup_count = 0;
hRecog->is_running = False;
hRecog->is_configured = False;
hRecog->is_resultBlocked = False;
hRecog->ca_rtti = CA_RECOGNIZER_SIGNATURE;
hRecog->recm = (multi_srec*)CALLOC_CLR(1, sizeof(multi_srec), "ca.hRecog.srec");
return (hRecog);
BEG_CATCH_CA_EXCEPT
END_CATCH_CA_EXCEPT(hRecog)
}
srec_word_lattice *allocate_word_lattice(frameID max_frames)
{
srec_word_lattice *wl;
wl = (srec_word_lattice*) CALLOC_CLR(1, sizeof(srec_word_lattice), "search.word_lattice.base");
wl->max_frames = max_frames;
wl->words_for_frame = (wtokenID*) CALLOC_CLR(max_frames, sizeof(wtokenID), "search.word_lattice.words");
wl->whether_sorted = (asr_int16_t*)CALLOC_CLR(max_frames, sizeof(asr_int16_t), "search.word_lattice.sflag");
return wl;
}
norm_info *create_channel_normalization()
{
norm_info *channorm;
channorm = (norm_info *) CALLOC_CLR(1, sizeof(norm_info), "clib.channorm");
return (channorm);
}
static void create_cepstrum_offsets(preprocessed *prep)
{
ASSERT(prep);
prep->chan_offset = (imeldata *) CALLOC_CLR(prep->dim,
sizeof(imeldata), "srec.chan_offset");
return;
}
CA_Acoustic *CA_AllocateAcoustic(void)
{
CA_Acoustic *hAcoust = NULL;
TRY_CA_EXCEPT;
hAcoust = (CA_Acoustic *) CALLOC_CLR(1,
sizeof(CA_Acoustic), "ca.hAcoust");
hAcoust->is_loaded = False;
hAcoust->pattern_setup_count = 0;
hAcoust->ca_rtti = CA_ACOUSTIC_SIGNATURE;
BEG_CATCH_CA_EXCEPT;
END_CATCH_CA_EXCEPT(hAcoust);
return (hAcoust);
}
static void allocate_recognition1(srec *rec,
int viterbi_prune_thresh, /*score-based pruning threshold - only keep paths within this delta of best cost*/
int max_hmm_tokens,
int max_fsmnode_tokens,
int max_word_tokens,
int max_altword_tokens,
int num_wordends_per_frame,
int max_frames,
int max_model_states)
{
#ifdef SREC_ENGINE_VERBOSE_LOGGING
PLogMessage("allocating recognition arrays2 prune %d max_hmm_tokens %d max_fsmnode_tokens %d max_word_tokens %d max_altword_tokens %d max_wordends_per_frame %d\n",
viterbi_prune_thresh,
max_hmm_tokens,
max_fsmnode_tokens,
max_word_tokens,
max_altword_tokens,
num_wordends_per_frame);
#endif
rec->current_model_scores = (costdata*) CALLOC_CLR(max_model_states, sizeof(costdata), "search.srec.current_model_scores"); /*FIX - either get NUM_MODELS from acoustic models, or check this someplace to make sure we have enough room*/
rec->num_model_slots_allocated = (modelID)max_model_states;
rec->fsmarc_token_array_size = (stokenID)max_hmm_tokens;
rec->fsmarc_token_array = (fsmarc_token*) CALLOC_CLR(rec->fsmarc_token_array_size , sizeof(fsmarc_token), "search.srec.fsmarc_token_array");
rec->max_new_states = (stokenID)max_hmm_tokens;
rec->word_token_array = (word_token*) CALLOC_CLR(max_word_tokens, sizeof(word_token), "search.srec.word_token_array");
rec->word_token_array_size = (wtokenID)max_word_tokens;
/* todo: change this to a bit array later */
rec->word_token_array_flags = (asr_int16_t*) CALLOC_CLR(max_word_tokens, sizeof(asr_int16_t), "search.srec.word_token_array_flags");
rec->fsmnode_token_array = (fsmnode_token*) CALLOC_CLR(max_fsmnode_tokens, sizeof(fsmnode_token), "search.srec.fsmnode_token_array");
rec->fsmnode_token_array_size = (ftokenID)max_fsmnode_tokens;
rec->altword_token_array = (altword_token*) CALLOC_CLR(max_altword_tokens, sizeof(altword_token), "search.srec.altword_token_array");
rec->altword_token_array_size = (wtokenID)max_altword_tokens;
rec->prune_delta = (costdata)viterbi_prune_thresh;
rec->max_frames = (frameID)max_frames;
rec->best_model_cost_for_frame = (costdata*)CALLOC_CLR(max_frames, sizeof(costdata), "search.srec.best_model_cost_for_frame");
rec->word_lattice = allocate_word_lattice((frameID)max_frames);
rec->word_priority_q = allocate_priority_q(num_wordends_per_frame);
rec->best_fsmarc_token = MAXstokenID;
#define ASTAR_NBEST_LEN 10
rec->astar_stack = astar_stack_make(rec, ASTAR_NBEST_LEN);
rec->context = NULL;
}
CA_Pattern *CA_AllocatePattern(void)
{
TRY_CA_EXCEPT
CA_Pattern *hPattern = NULL;
hPattern = (CA_Pattern *) CALLOC_CLR(1,
sizeof(CA_Pattern), "ca.hPattern");
hPattern->is_loaded = False;
//hPattern->setup_whole = NULL;
//hPattern->setup_sub = NULL;
hPattern->ca_rtti = CA_PATTERN_SIGNATURE;
return (hPattern);
BEG_CATCH_CA_EXCEPT
END_CATCH_CA_EXCEPT(hPattern)
}
CA_ConfidenceScorer* CA_AllocateConfidenceScorer(void)
{
CA_ConfidenceScorer *hConfidenceScorer;
hConfidenceScorer = NULL;
TRY_CA_EXCEPT
hConfidenceScorer = (CA_ConfidenceScorer *) CALLOC_CLR(1,
sizeof(CA_ConfidenceScorer), "ca.hConfidenceScorer");
return (hConfidenceScorer);
BEG_CATCH_CA_EXCEPT
END_CATCH_CA_EXCEPT(hConfidenceScorer)
}
CA_RecInputParams *CA_AllocateRecognitionParameters(void)
{
CA_RecInputParams *hRecInp = NULL;
TRY_CA_EXCEPT
hRecInp = (CA_RecInputParams *) CALLOC_CLR(1,
sizeof(CA_RecInputParams), "ca.hRecInp");
if ( hRecInp != NULL )
{
hRecInp->is_loaded = False;
hRecInp->ca_rtti = CA_RECOGNIZER_PARAMETERS_SIGNATURE;
}
return (hRecInp);
BEG_CATCH_CA_EXCEPT;
END_CATCH_CA_EXCEPT(hRecInp);
}
CA_PatInputParams *CA_AllocatePatternParameters(void)
{
CA_PatInputParams *hPatInp = NULL;
TRY_CA_EXCEPT
hPatInp = (CA_PatInputParams *) CALLOC_CLR(1,
sizeof(CA_PatInputParams), "ca.hPatInp");
hPatInp->is_loaded = False;
hPatInp->ca_rtti = CA_PATTERN_PARAMETERS_SIGNATURE;
return (hPatInp);
BEG_CATCH_CA_EXCEPT;
END_CATCH_CA_EXCEPT(hPatInp);
}
CA_AcoustInputParams *CA_AllocateAcousticParameters(void)
{
CA_AcoustInputParams *hAcoustInp = NULL;
TRY_CA_EXCEPT
hAcoustInp = (CA_AcoustInputParams *) CALLOC_CLR(1,
sizeof(CA_AcoustInputParams), "ca.hAcoustInp");
hAcoustInp->is_loaded = False;
hAcoustInp->ca_rtti = CA_ACOUSTIC_PARAMETERS_SIGNATURE;
BEG_CATCH_CA_EXCEPT;
END_CATCH_CA_EXCEPT(hAcoustInp);
return (hAcoustInp);
}
int CA_LoadPattern(CA_Pattern *hPattern, CA_PatInputParams *hPatInput,
int dimen , char *multable , char *imelda)
{
TRY_CA_EXCEPT
#ifndef _RTT
int ii, ret_code;
ASSERT(hPattern);
ASSERT(hPatInput);
if (hPattern->is_loaded == True)
SERVICE_ERROR(PATTERN_ALREADY_LOADED);
if (hPatInput->is_loaded == False)
SERVICE_ERROR(PATTERN_INPUT_NOT_LOADED);
hPattern->data.prep = (preprocessed *) CALLOC_CLR(1,
sizeof(preprocessed), "ca.hPattern->data.prep");
/* Load the Imelda transform if specified */
if (imelda && strlen(imelda) > 0)
{
ret_code = init_newton_transform(hPattern->data.prep, 0, imelda, hPatInput->dimen);
if (ret_code > 0)
SERVICE_ERROR(PATTERN_NOT_LOADED);
}
else
{
hPattern->data.prep->use_dim = hPatInput->dimen;
hPattern->data.prep->use_from = hPatInput->feat_start;
}
if (hPatInput->whole_dimen == 0)
hPattern->data.prep->whole_dim = hPatInput->dimen;
else
hPattern->data.prep->whole_dim = hPatInput->whole_dimen;
if (hPattern->data.prep->whole_dim > hPattern->data.prep->use_dim)
SERVICE_ERROR(BAD_PARAMETER);
hPattern->data.prep->mix_score_scale = (prdata)(128 * hPatInput->mix_score_scale + 0.5)
- (prdata)0.5;
hPattern->data.prep->uni_score_scale = (prdata)(128 * hPatInput->uni_score_scale + 0.5)
- (prdata)0.5;
hPattern->data.prep->uni_score_offset = (prdata) hPatInput->uni_score_offset;
hPattern->data.prep->imelda_scale = (prdata)hPatInput->imelda_scale;
init_preprocessed(hPattern->data.prep, dimen, hPatInput->imelda_scale); /* TODO: move this to Setup */
/* Annotation parameters */
hPattern->data.prep->end.rel_low = hPatInput->rel_low;
hPattern->data.prep->end.rel_high = hPatInput->rel_high;
hPattern->data.prep->end.gap_period = hPatInput->gap_period;
hPattern->data.prep->end.click_period = hPatInput->click_period;
hPattern->data.prep->end.breath_period = hPatInput->breath_period;
hPattern->data.prep->end.extend_annotation = hPatInput->extend_annotation;
hPattern->data.prep->end.min_annotation_frames = hPatInput->min_annotation_frames;
hPattern->data.prep->end.max_annotation_frames = hPatInput->max_annotation_frames;
hPattern->data.prep->end.min_segment_rel_c0 = hPatInput->min_segment_rel_c0;
hPattern->data.prep->end.min_initial_quiet_frames = hPatInput->min_initial_quiet_frames;
hPattern->data.prep->end.delete_leading_segments = hPatInput->delete_leading_segments;
hPattern->data.prep->end.leading_segment_min_frames = hPatInput->leading_segment_min_frames;
hPattern->data.prep->end.leading_segment_max_frames = hPatInput->leading_segment_max_frames;
hPattern->data.prep->end.leading_segment_min_silence_gap_frames
= hPatInput->leading_segment_min_silence_gap_frames;
hPattern->data.prep->end.leading_segment_accept_if_not_found
= hPatInput->leading_segment_accept_if_not_found;
#if DO_SUBTRACTED_SEGMENTATION
hPattern->data.prep->end.snr_holdoff = hPatInput->snr_holdoff;
hPattern->data.prep->end.min_acceptable_snr = hPatInput->min_acceptable_snr;
#endif
hPattern->data.prep->end.param = hPatInput->param;
hPattern->data.prep->end.beep_size = hPatInput->beep_size;
hPattern->data.prep->end.beep_threshold = hPatInput->beep_threshold;
/* log-lookup table */
create_lookup_logadd(&hPattern->data.prep->add, (float)MUL_SCALE);
/* Build the weights conversion table */
for (ii = 0; ii < MAX_WTS; ii++)
hPattern->data.prep->exp_wt[ii] =
(prdata)(WEIGHT_SCALE * exp((double)(0x01 << WT_ADJUST) *
(double) - ii / (MUL_SCALE * hPattern->data.prep->add.scale)) +
0.5) - (prdata)0.5;
hPattern->data.prep->ref_count = 1;
hPattern->is_loaded = True;
return (True);
#else
log_report("RTT not in module\n");
SERVICE_ERROR(FEATURE_NOT_SUPPORTED);
return (False);
#endif
BEG_CATCH_CA_EXCEPT
END_CATCH_CA_EXCEPT(hPattern)
}
int allocate_recognition(multi_srec *rec,
int viterbi_prune_thresh, /*score-based pruning threshold - only keep paths within this delta of best cost*/
int max_hmm_tokens,
int max_fsmnode_tokens,
int max_word_tokens,
int max_altword_tokens,
int num_wordends_per_frame,
int max_fsm_nodes,
int max_fsm_arcs,
int max_frames,
int max_model_states,
int max_searches)
{
int i;
if (check_parameter_range(max_fsm_nodes, 1, MAXnodeID, "max_fsm_nodes"))
return 1;
if (check_parameter_range(max_fsm_arcs, 1, MAXarcID, "max_fsm_arcs"))
return 1;
if (check_parameter_range(max_frames, 1, MAXframeID, "max_frames"))
return 1;
if (check_parameter_range(max_model_states, 1, MAXmodelID, "max_model_states"))
return 1;
if (check_parameter_range(max_hmm_tokens, 1, MAXstokenID, "max_hmm_tokens"))
return 1;
if (check_parameter_range(max_fsmnode_tokens, 1, MAXftokenID, "max_fsmnode_tokens"))
return 1;
if (check_parameter_range(viterbi_prune_thresh, 1, MAXcostdata, "viterbi_prune_thresh"))
return 1;
if (check_parameter_range(max_altword_tokens, 0, MAXftokenID, "max_altword_tokens"))
return 1;
if (check_parameter_range(max_searches, 1, 2, "max_searches"))
return 1;
rec->rec = (srec*)CALLOC_CLR(max_searches, sizeof(srec), "search.srec.base");
rec->num_allocated_recs = max_searches;
rec->num_swimodels = 0;
/* best_token_for_arc and best_token_for_node are shared across
multiple searches */
rec->best_token_for_arc = (stokenID*)CALLOC_CLR(max_fsm_arcs, sizeof(stokenID), "search.srec.best_token_for_arc");
rec->max_fsm_arcs = (arcID)max_fsm_arcs;
rec->best_token_for_node = (ftokenID*)CALLOC_CLR(max_fsm_nodes, sizeof(ftokenID), "search.srec.best_token_for_node");
rec->max_fsm_nodes = (nodeID)max_fsm_nodes;
/* cost offsets and accumulated cost offsets are pooled for all
different searches, this saves memory and enables each search
to know it's total scores */
rec->cost_offset_for_frame = (costdata*)CALLOC_CLR(max_frames, sizeof(costdata), "search.srec.current_best_costs");
rec->accumulated_cost_offset = (bigcostdata*)CALLOC_CLR(max_frames, sizeof(bigcostdata), "search.srec.accumulated_cost_offset");
rec->max_frames = (frameID)max_frames;
for (i = 0; i < max_frames; i++)
rec->accumulated_cost_offset[i] = 0;
/* now copy the shared data down to individual recogs */
for (i = 0; i < rec->num_allocated_recs; i++)
{
allocate_recognition1(&rec->rec[i], viterbi_prune_thresh, max_hmm_tokens, max_fsmnode_tokens, max_word_tokens, max_altword_tokens, num_wordends_per_frame, max_frames, max_model_states);
rec->rec[i].best_token_for_node = rec->best_token_for_node;
rec->rec[i].max_fsm_nodes = rec->max_fsm_nodes;
rec->rec[i].best_token_for_arc = rec->best_token_for_arc;
rec->rec[i].max_fsm_arcs = rec->max_fsm_arcs;
rec->rec[i].max_frames = rec->max_frames;
rec->rec[i].cost_offset_for_frame = rec->cost_offset_for_frame;
rec->rec[i].accumulated_cost_offset = rec->accumulated_cost_offset;
rec->rec[i].id = (asr_int16_t)i;
}
rec->eos_status = VALID_SPEECH_NOT_YET_DETECTED;
return 0;
}
