int
main(int argc, char *argv[])
{
lexicon_t *lex;
model_def_t *omdef;
model_def_t *dmdef;
uint32 n_stream;
const uint32 *veclen;
uint32 ts_off;
uint32 ts_cnt;
FILE *fp;
timing_t *all_timer= NULL;
timing_t *km_timer= NULL;
timing_t *var_timer= NULL;
timing_t *em_timer= NULL;
if (main_initialize(argc, argv, &lex, &omdef, &dmdef) != S3_SUCCESS) {
return -1;
}
km_timer = timing_get("km");
var_timer = timing_get("var");
em_timer = timing_get("em");
all_timer = timing_get("all");
n_stream = feat_n_stream();
veclen = feat_vecsize();
if (strcmp((const char *)cmd_ln_access("-gthobj"), "state") == 0) {
ts_off = *(uint32 *)cmd_ln_access("-tsoff");
if (cmd_ln_access("-tscnt") == NULL) {
ts_cnt = omdef->n_tied_state - ts_off;
}
else {
ts_cnt = *(uint32 *)cmd_ln_access("-tscnt");
}
if (ts_off + ts_cnt > omdef->n_tied_state) {
E_FATAL("Too many tied states specified\n");
}
n_tot_frame = 0;
if (all_timer)
timing_reset(all_timer);
if (km_timer)
timing_reset(km_timer);
if (var_timer)
timing_reset(var_timer);
if (em_timer)
timing_reset(em_timer);
if (all_timer)
timing_start(all_timer);
if (init_state((const char *)cmd_ln_access("-segdmpfn"),
(const char *)cmd_ln_access("-segidxfn"),
*(int32 *)cmd_ln_access("-ndensity"),
n_stream,
veclen,
*(int32 *)cmd_ln_access("-reest"),
(const char *)cmd_ln_access("-mixwfn"),
(const char *)cmd_ln_access("-meanfn"),
(const char *)cmd_ln_access("-varfn"),
ts_off,
ts_cnt,
omdef->n_tied_state,
(dmdef != NULL ? dmdef->n_tied_state : omdef->n_tied_state))
!= S3_SUCCESS) {
E_ERROR("Unable to train [%u %u]\n", ts_off, ts_off+ts_cnt-1);
}
if (all_timer)
timing_stop(all_timer);
if (n_tot_frame > 0) {
E_INFO("TOTALS:");
if (km_timer) {
E_INFOCONT(" km %4.3fx %4.3e",
km_timer->t_cpu / (n_tot_frame * 0.01),
(km_timer->t_cpu > 0 ?
km_timer->t_elapsed / km_timer->t_cpu : 0.0));
}
if (var_timer) {
E_INFOCONT(" var %4.3fx %4.3e",
var_timer->t_cpu / (n_tot_frame * 0.01),
(var_timer->t_cpu > 0 ?
var_timer->t_elapsed / var_timer->t_cpu : 0.0));
}
if (em_timer) {
E_INFOCONT(" em %4.3fx %4.3e",
em_timer->t_cpu / (n_tot_frame * 0.01),
(em_timer->t_cpu > 0 ?
em_timer->t_elapsed / em_timer->t_cpu : 0.0));
}
if (all_timer) {
E_INFOCONT(" all %4.3fx %4.3e",
all_timer->t_cpu / (n_tot_frame * 0.01),
(all_timer->t_cpu > 0 ?
all_timer->t_elapsed / all_timer->t_cpu : 0.0));
}
E_INFOCONT("\n");
}
if (cmd_ln_access("-tsrngfn") != NULL) {
fp = fopen((const char *)cmd_ln_access("-tsrngfn"),
"w");
if (fp == NULL) {
E_FATAL_SYSTEM("Unable to open %s for reading",
(const char *)cmd_ln_access("-tsrngfn"));
}
fprintf(fp, "%d %d\n", ts_off, ts_cnt);
}
else if (ts_cnt != omdef->n_tied_state) {
E_WARN("Subset of tied states specified, but no -tsrngfn arg");
}
}
else if (strcmp((const char *)cmd_ln_access("-gthobj"), "single") == 0) {
n_tot_frame = 0;
if (all_timer)
timing_reset(all_timer);
if (km_timer)
timing_reset(km_timer);
if (var_timer)
timing_reset(var_timer);
if (em_timer)
timing_reset(em_timer);
if (all_timer)
timing_start(all_timer);
if (init_state((const char *)cmd_ln_access("-segdmpfn"),
NULL, /* No index -> single class dump file */
*(int32 *)cmd_ln_access("-ndensity"),
n_stream,
veclen,
*(int32 *)cmd_ln_access("-reest"),
(const char *)cmd_ln_access("-mixwfn"),
(const char *)cmd_ln_access("-meanfn"),
(const char *)cmd_ln_access("-varfn"),
0,
1,
1,
1) != S3_SUCCESS) {
E_ERROR("Unable to train\n");
}
if (all_timer)
timing_stop(all_timer);
if (n_tot_frame > 0) {
E_INFO("TOTALS:");
if (km_timer) {
E_INFOCONT(" km %4.3fx %4.3e",
km_timer->t_cpu / (n_tot_frame * 0.01),
(km_timer->t_cpu > 0 ?
km_timer->t_elapsed / km_timer->t_cpu : 0.0));
}
if (var_timer) {
E_INFOCONT(" var %4.3fx %4.3e",
var_timer->t_cpu / (n_tot_frame * 0.01),
(var_timer->t_cpu > 0 ?
var_timer->t_elapsed / var_timer->t_cpu : 0.0));
}
if (em_timer) {
E_INFOCONT(" em %4.3fx %4.3e",
em_timer->t_cpu / (n_tot_frame * 0.01),
(em_timer->t_cpu > 0 ?
em_timer->t_elapsed / em_timer->t_cpu : 0.0));
}
if (all_timer) {
E_INFOCONT(" all %4.3fx %4.3e",
all_timer->t_cpu / (n_tot_frame * 0.01),
(all_timer->t_cpu > 0 ?
all_timer->t_elapsed / all_timer->t_cpu : 0.0));
}
E_INFOCONT("\n");
}
}
return 0;
}
static int
init_state(const char *obsdmp,
const char *obsidx,
uint32 n_density,
uint32 n_stream,
const uint32 *veclen,
int reest,
const char *mixwfn,
const char *meanfn,
const char *varfn,
uint32 ts_off,
uint32 ts_cnt,
uint32 n_ts,
uint32 n_d_ts)
{
uint32 blksz;
vector_t ***mean;
vector_t ***var = NULL;
vector_t ****fullvar = NULL;
float32 ***mixw = NULL;
uint32 n_frame;
uint32 ignore = 0;
codew_t *label;
uint32 n_corpus = 0;
float64 sqerr;
float64 tot_sqerr;
segdmp_type_t t;
uint32 i, j, ts, n;
timing_t *km_timer;
timing_t *var_timer;
timing_t *em_timer;
int32 full_covar;
km_timer = timing_get("km");
var_timer = timing_get("var");
em_timer = timing_get("em");
blksz = feat_blksize();
full_covar = cmd_ln_int32("-fullvar");
/* fully-continuous for now */
mean = gauden_alloc_param(ts_cnt, n_stream, n_density, veclen);
if (full_covar)
fullvar = gauden_alloc_param_full(ts_cnt, n_stream, n_density, veclen);
else
var = gauden_alloc_param(ts_cnt, n_stream, n_density, veclen);
if (mixwfn)
mixw = (float32 ***)ckd_calloc_3d(ts_cnt,
n_stream,
n_density,
sizeof(float32));
if ((const char *)cmd_ln_access("-segidxfn")) {
E_INFO("Multi-class dump\n");
if (segdmp_open_read((const char **)cmd_ln_access("-segdmpdirs"),
(const char *)cmd_ln_access("-segdmpfn"),
(const char *)cmd_ln_access("-segidxfn"),
&n,
&t) != S3_SUCCESS) {
E_FATAL("Unable to open dumps\n");
}
if (n != n_d_ts) {
E_FATAL("Expected %u tied-states in dump, but apparently %u\n",
n_d_ts, n);
}
if (t != SEGDMP_TYPE_FEAT) {
E_FATAL("Expected feature dump, but instead saw %u\n", t);
}
multiclass = TRUE;
}
else {
E_INFO("1-class dump file\n");
multiclass = FALSE;
dmp_fp = s3open((const char *)cmd_ln_access("-segdmpfn"), "rb",
&dmp_swp);
if (dmp_fp == NULL) {
E_ERROR_SYSTEM("Unable to open dump file %s for reading\n",
(const char *)cmd_ln_access("-segdmpfn"));
return S3_ERROR;
}
if (s3read(&n_frame, sizeof(uint32), 1, dmp_fp, dmp_swp, &ignore) != 1) {
E_ERROR_SYSTEM("Unable to open dump file %s for reading\n",
(const char *)cmd_ln_access("-segdmpfn"));
return S3_ERROR;
}
data_offset = ftell(dmp_fp);
}
tot_sqerr = 0;
for (i = 0; i < ts_cnt; i++) {
ts = ts_off + i;
/* stride not accounted for yet */
if (o2d == NULL) {
if (multiclass)
n_frame = segdmp_n_seg(ts);
}
else {
for (j = 0, n_frame = 0; j < n_o2d[ts]; j++) {
n_frame += segdmp_n_seg(o2d[ts][j]);
}
}
E_INFO("Corpus %u: sz==%u frames%s\n",
ts, n_frame,
(n_frame > *(uint32 *)cmd_ln_access("-vartiethr") ? "" : " tied var"));
if (n_frame == 0) {
continue;
}
E_INFO("Convergence ratios are abs(cur - prior) / abs(prior)\n");
/* Do some variety of k-means clustering */
if (km_timer)
timing_start(km_timer);
sqerr = cluster(ts, n_stream, n_frame, veclen, mean[i], n_density, &label);
if (km_timer)
timing_stop(km_timer);
if (sqerr < 0) {
E_ERROR("Unable to do k-means for state %u; skipping...\n", ts);
continue;
}
/* Given the k-means and assuming equal prior liklihoods
* compute the variances */
if (var_timer)
timing_start(var_timer);
if (full_covar)
full_variances(ts, mean[i], fullvar[i], n_density, veclen,
n_frame, n_stream, label);
else
variances(ts, mean[i], var[i], n_density, veclen, n_frame, n_stream, label);
if (var_timer)
timing_stop(var_timer);
if (mixwfn) {
/* initialize the mixing weights by counting # of occurrances
* of the top codeword over the corpus and normalizing */
init_mixw(mixw[i], mean[i], n_density, veclen, n_frame, n_stream, label);
ckd_free(label);
if (reest == TRUE && full_covar)
E_ERROR("EM re-estimation is not yet supported for full covariances\n");
else if (reest == TRUE) {
if (em_timer)
timing_start(em_timer);
/* Do iterations of EM to estimate the mixture densities */
reest_sum(ts, mean[i], var[i], mixw[i], n_density, n_stream,
n_frame, veclen,
*(uint32 *)cmd_ln_access("-niter"),
FALSE,
*(uint32 *)cmd_ln_access("-vartiethr"));
if (em_timer)
timing_stop(em_timer);
}
}
++n_corpus;
tot_sqerr += sqerr;
E_INFO("sqerr [%u] == %e\n", ts, sqerr);
}
if (n_corpus > 0) {
E_INFO("sqerr = %e tot %e rms\n", tot_sqerr, sqrt(tot_sqerr/n_corpus));
}
if (!multiclass)
s3close(dmp_fp);
if (meanfn) {
if (s3gau_write(meanfn,
(const vector_t ***)mean,
ts_cnt,
n_stream,
n_density,
veclen) != S3_SUCCESS) {
return S3_ERROR;
}
}
else {
E_INFO("No mean file given; none written\n");
}
if (varfn) {
if (full_covar) {
if (s3gau_write_full(varfn,
(const vector_t ****)fullvar,
ts_cnt,
n_stream,
n_density,
veclen) != S3_SUCCESS)
return S3_ERROR;
}
else {
if (s3gau_write(varfn,
(const vector_t ***)var,
ts_cnt,
n_stream,
n_density,
veclen) != S3_SUCCESS)
return S3_ERROR;
}
}
else {
E_INFO("No variance file given; none written\n");
}
if (mixwfn) {
if (s3mixw_write(mixwfn,
mixw,
ts_cnt,
n_stream,
n_density) != S3_SUCCESS) {
return S3_ERROR;
}
}
else {
E_INFO("No mixing weight file given; none written\n");
}
return S3_SUCCESS;
}
int32
viterbi_update(float64 *log_forw_prob,
vector_t **feature,
uint32 n_obs,
state_t *state_seq,
uint32 n_state,
model_inventory_t *inv,
float64 a_beam,
float32 spthresh,
s3phseg_t *phseg,
int32 mixw_reest,
int32 tmat_reest,
int32 mean_reest,
int32 var_reest,
int32 pass2var,
int32 var_is_full,
FILE *pdumpfh,
feat_t *fcb)
{
float64 *scale = NULL;
float64 **dscale = NULL;
float64 **active_alpha;
uint32 **active_astate;
uint32 **bp;
uint32 *n_active_astate;
gauden_t *g; /* Gaussian density parameters and
reestimation sums */
float32 ***mixw; /* all mixing weights */
float64 ***now_den = NULL; /* Short for den[t] */
uint32 ***now_den_idx = NULL;/* Short for den_idx[t] */
uint32 *active_cb;
uint32 n_active_cb;
float32 **tacc; /* Transition matrix reestimation sum accumulators
for the utterance. */
float32 ***wacc; /* mixing weight reestimation sum accumulators
for the utterance. */
float32 ***denacc = NULL; /* mean/var reestimation accumulators for time t */
size_t denacc_size; /* Total size of data references in denacc. Allows
for quick clears between time frames */
uint32 n_lcl_cb;
uint32 *cb_inv;
uint32 i, j, q;
int32 t;
uint32 n_feat;
uint32 n_density;
uint32 n_top;
int ret;
timing_t *fwd_timer = NULL;
timing_t *rstu_timer = NULL;
timing_t *gau_timer = NULL;
timing_t *rsts_timer = NULL;
timing_t *rstf_timer = NULL;
float64 log_fp; /* accumulator for the log of the probability
* of observing the input given the model */
uint32 max_n_next = 0;
uint32 n_cb;
static float64 *p_op = NULL;
static float64 *p_ci_op = NULL;
static float64 **d_term = NULL;
static float64 **d_term_ci = NULL;
/* caller must ensure that there is some non-zero amount
of work to be done here */
assert(n_obs > 0);
assert(n_state > 0);
/* Get the forward estimation CPU timer */
fwd_timer = timing_get("fwd");
/* Get the per utterance reestimation CPU timer */
rstu_timer = timing_get("rstu");
/* Get the Gaussian density evaluation CPU timer */
gau_timer = timing_get("gau");
/* Get the per state reestimation CPU timer */
rsts_timer = timing_get("rsts");
/* Get the per frame reestimation CPU timer */
rstf_timer = timing_get("rstf");
g = inv->gauden;
n_feat = gauden_n_feat(g);
n_density = gauden_n_density(g);
n_top = gauden_n_top(g);
n_cb = gauden_n_mgau(g);
if (p_op == NULL) {
p_op = ckd_calloc(n_feat, sizeof(float64));
p_ci_op = ckd_calloc(n_feat, sizeof(float64));
}
if (d_term == NULL) {
d_term = (float64 **)ckd_calloc_2d(n_feat, n_top, sizeof(float64));
d_term_ci = (float64 **)ckd_calloc_2d(n_feat, n_top, sizeof(float64));
}
scale = (float64 *)ckd_calloc(n_obs, sizeof(float64));
dscale = (float64 **)ckd_calloc(n_obs, sizeof(float64 *));
n_active_astate = (uint32 *)ckd_calloc(n_obs, sizeof(uint32));
active_alpha = (float64 **)ckd_calloc(n_obs, sizeof(float64 *));
active_astate = (uint32 **)ckd_calloc(n_obs, sizeof(uint32 *));
active_cb = ckd_calloc(2*n_state, sizeof(uint32));
bp = (uint32 **)ckd_calloc(n_obs, sizeof(uint32 *));
/* Run forward algorithm, which has embedded Viterbi. */
if (fwd_timer)
timing_start(fwd_timer);
ret = forward(active_alpha, active_astate, n_active_astate, bp,
scale, dscale,
feature, n_obs, state_seq, n_state,
inv, a_beam, phseg, 0);
/* Dump a phoneme segmentation if requested */
if (cmd_ln_str("-outphsegdir")) {
const char *phsegdir;
char *segfn, *uttid;
phsegdir = cmd_ln_str("-outphsegdir");
uttid = (cmd_ln_int32("-outputfullpath")
? corpus_utt_full_name() : corpus_utt());
segfn = ckd_calloc(strlen(phsegdir) + 1
+ strlen(uttid)
+ strlen(".phseg") + 1, 1);
strcpy(segfn, phsegdir);
strcat(segfn, "/");
strcat(segfn, uttid);
strcat(segfn, ".phseg");
write_phseg(segfn, inv, state_seq, active_astate, n_active_astate,
n_state, n_obs, active_alpha, scale, bp);
ckd_free(segfn);
}
if (fwd_timer)
timing_stop(fwd_timer);
if (ret != S3_SUCCESS) {
/* Some problem with the utterance, release per utterance storage and
* forget about adding the utterance accumulators to the global accumulators */
goto all_done;
}
mixw = inv->mixw;
if (mixw_reest) {
/* Need to reallocate mixing accumulators for utt */
if (inv->l_mixw_acc) {
ckd_free_3d((void ***)inv->l_mixw_acc);
inv->l_mixw_acc = NULL;
}
inv->l_mixw_acc = (float32 ***)ckd_calloc_3d(inv->n_mixw_inverse,
n_feat,
n_density,
sizeof(float32));
}
wacc = inv->l_mixw_acc;
n_lcl_cb = inv->n_cb_inverse;
cb_inv = inv->cb_inverse;
/* Allocate local accumulators for mean, variance reestimation
sums if necessary */
gauden_alloc_l_acc(g, n_lcl_cb,
mean_reest, var_reest,
var_is_full);
if (tmat_reest) {
if (inv->l_tmat_acc) {
ckd_free_2d((void **)inv->l_tmat_acc);
inv->l_tmat_acc = NULL;
}
for (i = 0; i < n_state; i++) {
if (state_seq[i].n_next > max_n_next)
max_n_next = state_seq[i].n_next;
}
inv->l_tmat_acc = (float32 **)ckd_calloc_2d(n_state,
max_n_next,
sizeof(float32));
}
/* transition matrix reestimation sum accumulators
for the utterance */
tacc = inv->l_tmat_acc;
n_active_cb = 0;
now_den = (float64 ***)ckd_calloc_3d(n_lcl_cb,
n_feat,
n_top,
sizeof(float64));
now_den_idx = (uint32 ***)ckd_calloc_3d(n_lcl_cb,
n_feat,
n_top,
sizeof(uint32));
if (mean_reest || var_reest) {
/* allocate space for the per frame density counts */
denacc = (float32 ***)ckd_calloc_3d(n_lcl_cb,
n_feat,
n_density,
sizeof(float32));
/* # of bytes required to store all weighted vectors */
denacc_size = n_lcl_cb * n_feat * n_density * sizeof(float32);
}
else {
denacc = NULL;
denacc_size = 0;
}
/* Okay now run through the backtrace and accumulate counts. */
/* Find the non-emitting ending state */
for (q = 0; q < n_active_astate[n_obs-1]; ++q) {
if (active_astate[n_obs-1][q] == n_state-1)
break;
}
if (q == n_active_astate[n_obs-1]) {
E_ERROR("Failed to align audio to trancript: final state of the search is not reached\n");
ret = S3_ERROR;
goto all_done;
}
for (t = n_obs-1; t >= 0; --t) {
uint32 l_cb;
uint32 l_ci_cb;
float64 op, p_reest_term;
uint32 prev;
j = active_astate[t][q];
/* Follow any non-emitting states at time t first. */
while (state_seq[j].mixw == TYING_NON_EMITTING) {
prev = active_astate[t][bp[t][q]];
#if VITERBI_DEBUG
printf("Following non-emitting state at time %d, %u => %u\n",
t, j, prev);
#endif
/* Backtrace and accumulate transition counts. */
if (tmat_reest) {
assert(tacc != NULL);
tacc[prev][j - prev] += 1.0;
}
q = bp[t][q];
j = prev;
}
/* Now accumulate statistics for the real state. */
l_cb = state_seq[j].l_cb;
l_ci_cb = state_seq[j].l_ci_cb;
n_active_cb = 0;
if (gau_timer)
timing_start(gau_timer);
gauden_compute_log(now_den[l_cb],
now_den_idx[l_cb],
feature[t],
g,
state_seq[j].cb,
NULL);
active_cb[n_active_cb++] = l_cb;
if (l_cb != l_ci_cb) {
gauden_compute_log(now_den[l_ci_cb],
now_den_idx[l_ci_cb],
feature[t],
g,
state_seq[j].ci_cb,
NULL);
active_cb[n_active_cb++] = l_ci_cb;
}
gauden_scale_densities_bwd(now_den, now_den_idx,
&dscale[t],
active_cb, n_active_cb, g);
assert(state_seq[j].mixw != TYING_NON_EMITTING);
/* Now calculate mixture densities. */
/* This is the normalizer sum_m c_{jm} p(o_t|\lambda_{jm}) */
op = gauden_mixture(now_den[l_cb], now_den_idx[l_cb],
mixw[state_seq[j].mixw], g);
if (gau_timer)
timing_stop(gau_timer);
if (rsts_timer)
timing_start(rsts_timer);
/* Make up this bogus value to be consistent with backward.c */
p_reest_term = 1.0 / op;
/* Compute the output probability excluding the contribution
* of each feature stream. i.e. p_op[0] is the output
* probability excluding feature stream 0 */
partial_op(p_op,
op,
now_den[l_cb],
now_den_idx[l_cb],
mixw[state_seq[j].mixw],
n_feat,
n_top);
/* compute the probability of each (of possibly topn) density */
den_terms(d_term,
p_reest_term,
p_op,
now_den[l_cb],
now_den_idx[l_cb],
mixw[state_seq[j].mixw],
n_feat,
n_top);
if (l_cb != l_ci_cb) {
/* For each feature stream f, compute:
* sum_k(mixw[f][k] den[f][k])
* and store the results in p_ci_op */
partial_ci_op(p_ci_op,
now_den[l_ci_cb],
now_den_idx[l_ci_cb],
mixw[state_seq[j].ci_mixw],
n_feat,
n_top);
/* For each feature stream and density compute the terms:
* w[f][k] den[f][k] / sum_k(w[f][k] den[f][k]) * post_j
* and store results in d_term_ci */
den_terms_ci(d_term_ci,
1.0, /* post_j = 1.0 */
p_ci_op,
now_den[l_ci_cb],
now_den_idx[l_ci_cb],
mixw[state_seq[j].ci_mixw],
n_feat,
n_top);
}
/* accumulate the probability for each density in the mixing
* weight reestimation accumulators */
if (mixw_reest) {
accum_den_terms(wacc[state_seq[j].l_mixw], d_term,
now_den_idx[l_cb], n_feat, n_top);
/* check if mixw and ci_mixw are different to avoid
* doubling the EM counts in a CI run. */
if (state_seq[j].mixw != state_seq[j].ci_mixw) {
if (n_cb < inv->n_mixw) {
/* semi-continuous, tied mixture, and discrete case */
accum_den_terms(wacc[state_seq[j].l_ci_mixw], d_term,
now_den_idx[l_cb], n_feat, n_top);
}
else {
/* continuous case */
accum_den_terms(wacc[state_seq[j].l_ci_mixw], d_term_ci,
now_den_idx[l_ci_cb], n_feat, n_top);
}
}
}
/* accumulate the probability for each density in the
* density reestimation accumulators */
if (mean_reest || var_reest) {
accum_den_terms(denacc[l_cb], d_term,
now_den_idx[l_cb], n_feat, n_top);
if (l_cb != l_ci_cb) {
accum_den_terms(denacc[l_ci_cb], d_term_ci,
now_den_idx[l_ci_cb], n_feat, n_top);
}
}
if (rsts_timer)
timing_stop(rsts_timer);
/* Note that there is only one state/frame so this is kind of
redundant */
if (rstf_timer)
timing_start(rstf_timer);
if (mean_reest || var_reest) {
/* Update the mean and variance reestimation accumulators */
if (pdumpfh)
fprintf(pdumpfh, "time %d:\n", t);
accum_gauden(denacc,
cb_inv,
n_lcl_cb,
feature[t],
now_den_idx,
g,
mean_reest,
var_reest,
pass2var,
inv->l_mixw_acc,
var_is_full,
pdumpfh,
fcb);
memset(&denacc[0][0][0], 0, denacc_size);
}
if (rstf_timer)
timing_stop(rstf_timer);
if (t > 0) {
prev = active_astate[t-1][bp[t][q]];
#if VITERBI_DEBUG
printf("Backtrace at time %d, %u => %u\n",
t, j, prev);
#endif
/* Backtrace and accumulate transition counts. */
if (tmat_reest) {
assert(tacc != NULL);
tacc[prev][j-prev] += 1.0;
}
q = bp[t][q];
j = prev;
}
}
/* If no error was found, add the resulting utterance reestimation
* accumulators to the global reestimation accumulators */
if (rstu_timer)
timing_start(rstu_timer);
accum_global(inv, state_seq, n_state,
mixw_reest, tmat_reest, mean_reest, var_reest,
var_is_full);
if (rstu_timer)
timing_stop(rstu_timer);
/* Find the final state */
for (i = 0; i < n_active_astate[n_obs-1]; ++i) {
if (active_astate[n_obs-1][i] == n_state-1)
break;
}
/* Calculate log[ p( O | \lambda ) ] */
assert(active_alpha[n_obs-1][i] > 0);
log_fp = log(active_alpha[n_obs-1][i]);
for (t = 0; t < n_obs; t++) {
assert(scale[t] > 0);
log_fp -= log(scale[t]);
for (j = 0; j < inv->gauden->n_feat; j++) {
log_fp += dscale[t][j];
}
}
*log_forw_prob = log_fp;
all_done:
ckd_free((void *)scale);
for (i = 0; i < n_obs; i++) {
if (dscale[i])
ckd_free((void *)dscale[i]);
}
ckd_free((void **)dscale);
ckd_free(n_active_astate);
for (i = 0; i < n_obs; i++) {
ckd_free((void *)active_alpha[i]);
ckd_free((void *)active_astate[i]);
ckd_free((void *)bp[i]);
}
ckd_free((void *)active_alpha);
ckd_free((void *)active_astate);
ckd_free((void *)active_cb);
if (denacc)
ckd_free_3d((void ***)denacc);
if (now_den)
ckd_free_3d((void ***)now_den);
if (now_den_idx)
ckd_free_3d((void ***)now_den_idx);
if (ret != S3_SUCCESS)
E_ERROR("%s ignored\n", corpus_utt_brief_name());
return ret;
}
int main( int argc, char *argv[] )
{
int32 k;
char *tsuf;
timing_t start;
timing_t end;
timing_t diff;
real32 time;
real32 secs;
carray_t haystack;
hthread_t *threads;
arg_t *args;
// Allocate the threads structure
threads = (hthread_t*)malloc( tarsize * sizeof(hthread_t) );
// Allocate the argument structures
args = (arg_t*)malloc( tarsize * sizeof(arg_t) );
// Create arrays for the search string
carray_fromstr( &haystack, beowulf );
carray_concatstr( &haystack, britannica1 );
carray_concatstr( &haystack, britannica2 );
carray_concatstr( &haystack, britannica3 );
carray_concatstr( &haystack, caesar );
carray_concatstr( &haystack, hamlet );
carray_concatstr( &haystack, huckfinn );
carray_concatstr( &haystack, illiad );
carray_concatstr( &haystack, macbeth );
carray_concatstr( &haystack, pride );
carray_concatstr( &haystack, sense );
carray_concatstr( &haystack, tomsawyer );
carray_concatstr( &haystack, twist );
carray_concatstr( &haystack, ulysses );
carray_concatstr( &haystack, venice );
// Create all of the thread arguments
for( k = 0; k < tarsize; k++ )
{
args[k].search = &haystack;
args[k].found = 0;
carray_fromstr( &args[k].target, tar[k] );
boyermoore_init( &args[k].bm, &args[k].target );
}
// Create worker threads for all of the search strings
timing_get( &start );
for( k = 0; k < tarsize; k++ )
{
hthread_create( &threads[k], NULL, search, &args[k] );
}
// Wait for all of the worker threads to complete
for( k = 0; k < tarsize; k++ )
{
hthread_join( threads[k], (void*)&args[k].found );
}
timing_get( &end );
timing_diff(diff,end,start);
secs = timing_sec(diff);
calculate_time( &tsuf, &time, secs );
printf( "Test Finished: %.2f %s\n", time, tsuf );
// Show the results and destroy the thread arguments
for( k = 0; k < tarsize; k++ )
{
printf( "Thread %d found %d matches of the string '%s'\n", k, args[k].found, tar[k] );
boyermoore_destroy( &args[k].bm );
carray_destroy( &args[k].target );
}
// Exit the program
return 0;
}
int32
baum_welch_update(float64 *log_forw_prob,
vector_t **feature,
uint32 n_obs,
state_t *state,
uint32 n_state,
model_inventory_t *inv,
float64 a_beam,
float64 b_beam,
float32 spthresh,
s3phseg_t *phseg,
int32 mixw_reest,
int32 tmat_reest,
int32 mean_reest,
int32 var_reest,
int32 pass2var,
int32 var_is_full,
FILE *pdumpfh,
float32 ***lda)
{
float64 *scale = NULL;
float64 **dscale = NULL;
float64 **active_alpha;
uint32 **active_astate;
uint32 **bp;
uint32 *n_active_astate;
float64 log_fp; /* accumulator for the log of the probability
* of observing the input given the model */
uint32 t; /* time */
int ret;
timing_t *fwd_timer = NULL;
timing_t *bwd_timer = NULL;
timing_t *rstu_timer = NULL;
uint32 i,j;
/* caller must ensure that there is some non-zero amount
of work to be done here */
assert(n_obs > 0);
assert(n_state > 0);
fwd_timer = timing_get("fwd");
bwd_timer = timing_get("bwd");
rstu_timer = timing_get("rstu");
scale = (float64 *)ckd_calloc(n_obs, sizeof(float64));
dscale = (float64 **)ckd_calloc(n_obs, sizeof(float64 *));
n_active_astate = (uint32 *)ckd_calloc(n_obs, sizeof(uint32));
active_alpha = (float64 **)ckd_calloc(n_obs, sizeof(float64 *));
active_astate = (uint32 **)ckd_calloc(n_obs, sizeof(uint32 *));
bp = (uint32 **)ckd_calloc(n_obs, sizeof(uint32 *));
/* Compute the scaled alpha variable and scale factors
* for all states and time subject to the pruning constraints */
if (fwd_timer)
timing_start(fwd_timer);
/*
* Debug?
* E_INFO("Before Forward search\n");
*/
ret = forward(active_alpha, active_astate, n_active_astate, bp,
scale, dscale,
feature, n_obs, state, n_state,
inv, a_beam, phseg);
#if BW_DEBUG
for (i=0 ; i < n_obs;i++){
E_INFO("Number of active states %d at time %d\n",n_active_astate[i],i);
E_INFO("Scale of time %d is %e \n",i,scale[i]);
for(j=0 ; j < n_active_astate[i];j++){
E_INFO("Active state: %d Active alpha: %e\n",active_astate[i][j], active_alpha[i][j]);
}
}
i=0;
j=0;
#endif
/* Dump a phoneme segmentation if requested */
if (cmd_ln_str("-outphsegdir")) {
const char *phsegdir;
char *segfn, *uttid;
phsegdir = cmd_ln_str("-outphsegdir");
uttid = (cmd_ln_int32("-outputfullpath")
? corpus_utt_full_name() : corpus_utt());
segfn = ckd_calloc(strlen(phsegdir) + 1
+ strlen(uttid)
+ strlen(".phseg") + 1, 1);
strcpy(segfn, phsegdir);
strcat(segfn, "/");
strcat(segfn, uttid);
strcat(segfn, ".phseg");
write_phseg(segfn, inv, state, active_astate, n_active_astate,
n_state, n_obs, active_alpha, scale, bp);
ckd_free(segfn);
}
if (fwd_timer)
timing_stop(fwd_timer);
if (ret != S3_SUCCESS) {
/* Some problem with the utterance, release per utterance storage and
* forget about adding the utterance accumulators to the global accumulators */
goto error;
}
/* Compute the scaled beta variable and update the reestimation
* sums */
if (bwd_timer)
timing_start(bwd_timer);
#if BW_DEBUG
E_INFO("Before Backward search\n");
#endif
ret = backward_update(active_alpha, active_astate, n_active_astate, scale, dscale,
feature, n_obs,
state, n_state,
inv, b_beam, spthresh,
mixw_reest, tmat_reest, mean_reest, var_reest, pass2var,
var_is_full, pdumpfh, lda);
if (bwd_timer)
timing_stop(bwd_timer);
if (ret != S3_SUCCESS) {
/* Some problem with the utterance, release per utterance storage and
* forget about adding the utterance accumulators to the global accumulators */
goto error;
}
#if BW_DEBUG
E_INFO("Before Global Accumulation\n");
#endif
/* If no error was found in the forward or backward procedures,
* add the resulting utterance reestimation accumulators to the
* global reestimation accumulators */
if (rstu_timer)
timing_start(rstu_timer);
accum_global(inv, state, n_state,
mixw_reest, tmat_reest, mean_reest, var_reest,
var_is_full);
if (rstu_timer)
timing_stop(rstu_timer);
for (i = 0; i < n_active_astate[n_obs-1] && active_astate[n_obs-1][i] != (n_state-1); i++);
assert(i < n_active_astate[n_obs-1]);
/* Calculate log[ p( O | \lambda ) ] */
assert(active_alpha[n_obs-1][i] > 0);
log_fp = log(active_alpha[n_obs-1][i]);
for (t = 0; t < n_obs; t++) {
assert(scale[t] > 0);
log_fp -= log(scale[t]);
for (j = 0; j < inv->gauden->n_feat; j++) {
log_fp += dscale[t][j];
}
}
*log_forw_prob = log_fp;
ckd_free((void *)scale);
ckd_free(n_active_astate);
for (i = 0; i < n_obs; i++) {
ckd_free((void *)active_alpha[i]);
ckd_free((void *)active_astate[i]);
ckd_free((void *)dscale[i]);
ckd_free((void *)bp[i]);
}
ckd_free((void *)active_alpha);
ckd_free((void *)active_astate);
ckd_free((void **)dscale);
return S3_SUCCESS;
error:
ckd_free((void *)scale);
for (i = 0; i < n_obs; i++) {
if (dscale[i])
ckd_free((void *)dscale[i]);
}
ckd_free((void **)dscale);
ckd_free(n_active_astate);
for (i = 0; i < n_obs; i++) {
ckd_free((void *)active_alpha[i]);
ckd_free((void *)active_astate[i]);
ckd_free((void *)bp[i]);
}
ckd_free((void *)active_alpha);
ckd_free((void *)active_astate);
E_ERROR("%s ignored\n", corpus_utt_brief_name());
return S3_ERROR;
}
