static int
acmod_init_feat(acmod_t *acmod)
{
acmod->fcb =
feat_init(cmd_ln_str_r(acmod->config, "-feat"),
cmn_type_from_str(cmd_ln_str_r(acmod->config,"-cmn")),
cmd_ln_boolean_r(acmod->config, "-varnorm"),
agc_type_from_str(cmd_ln_str_r(acmod->config, "-agc")),
1, cmd_ln_int32_r(acmod->config, "-ceplen"));
if (acmod->fcb == NULL)
return -1;
if (cmd_ln_str_r(acmod->config, "-lda")) {
E_INFO("Reading linear feature transformation from %s\n",
cmd_ln_str_r(acmod->config, "-lda"));
if (feat_read_lda(acmod->fcb,
cmd_ln_str_r(acmod->config, "-lda"),
cmd_ln_int32_r(acmod->config, "-ldadim")) < 0)
return -1;
}
if (cmd_ln_str_r(acmod->config, "-svspec")) {
int32 **subvecs;
E_INFO("Using subvector specification %s\n",
cmd_ln_str_r(acmod->config, "-svspec"));
if ((subvecs = parse_subvecs(cmd_ln_str_r(acmod->config, "-svspec"))) == NULL)
return -1;
if ((feat_set_subvecs(acmod->fcb, subvecs)) < 0)
return -1;
}
if (cmd_ln_exists_r(acmod->config, "-agcthresh")
&& 0 != strcmp(cmd_ln_str_r(acmod->config, "-agc"), "none")) {
agc_set_threshold(acmod->fcb->agc_struct,
cmd_ln_float32_r(acmod->config, "-agcthresh"));
}
if (acmod->fcb->cmn_struct
&& cmd_ln_exists_r(acmod->config, "-cmninit")) {
char *c, *cc, *vallist;
int32 nvals;
vallist = ckd_salloc(cmd_ln_str_r(acmod->config, "-cmninit"));
c = vallist;
nvals = 0;
while (nvals < acmod->fcb->cmn_struct->veclen
&& (cc = strchr(c, ',')) != NULL) {
*cc = '\0';
acmod->fcb->cmn_struct->cmn_mean[nvals] = FLOAT2MFCC(atof_c(c));
c = cc + 1;
++nvals;
}
if (nvals < acmod->fcb->cmn_struct->veclen && *c != '\0') {
acmod->fcb->cmn_struct->cmn_mean[nvals] = FLOAT2MFCC(atof_c(c));
}
ckd_free(vallist);
}
return 0;
}
static void
cmn_prior_shiftwin(cmn_t *cmn)
{
mfcc_t sf;
int32 i;
E_INFO("cmn_prior_update: from < ");
for (i = 0; i < cmn->veclen; i++)
E_INFOCONT("%5.2f ", MFCC2FLOAT(cmn->cmn_mean[i]));
E_INFOCONT(">\n");
sf = FLOAT2MFCC(1.0) / cmn->nframe;
for (i = 0; i < cmn->veclen; i++)
cmn->cmn_mean[i] = cmn->sum[i] / cmn->nframe; /* sum[i] * sf */
/* Make the accumulation decay exponentially */
if (cmn->nframe >= CMN_WIN_HWM) {
sf = CMN_WIN * sf;
for (i = 0; i < cmn->veclen; i++)
cmn->sum[i] = MFCCMUL(cmn->sum[i], sf);
cmn->nframe = CMN_WIN;
}
E_INFO("cmn_prior_update: to < ");
for (i = 0; i < cmn->veclen; i++)
E_INFOCONT("%5.2f ", MFCC2FLOAT(cmn->cmn_mean[i]));
E_INFOCONT(">\n");
}
agc_t *agc_init(void)
{
agc_t *agc;
agc = ckd_calloc(1, sizeof(*agc));
agc->noise_thresh = FLOAT2MFCC(2.0);
return agc;
}
static int32
read_kd_nodes(FILE * fp, kd_tree_t * tree, uint32 maxdepth, int32 maxbbi)
{
uint32 i, j, in, out;
int32 ilevel, olevel;
/* Balanced binary trees, so we have 2^nlevels-1 nodes. */
if (maxdepth == 0 || maxdepth > tree->n_level)
maxdepth = tree->n_level;
in = (1 << tree->n_level) - 1;
out = (1 << maxdepth) - 1;
tree->nodes = ckd_calloc(out, sizeof(kd_tree_node_t));
/* Nodes are read in depth-first ordering. */
for (j = i = 0; i < in; ++i) {
float32 split_plane;
int32 split_comp;
if (read_tree_int(fp, "NODE", &ilevel, FALSE) < 0)
break;
if (read_tree_int(fp, "split_comp", &split_comp, FALSE) < 0)
return -1;
if (read_tree_float(fp, "split_plane", &split_plane, FALSE) < 0)
return -1;
olevel = ilevel - (tree->n_level - maxdepth);
if (olevel > 0) {
/* Only create a node if we are above maxdepth */
assert(j < out);
tree->nodes[j].split_comp = split_comp;
tree->nodes[j].split_plane = FLOAT2MFCC(split_plane);
/* We only need the BBI list for leafnodes now. */
if (olevel == 1) {
if (read_bbi_list(fp, tree->nodes + j, maxbbi) < 0)
return -1;
}
else {
if (read_bbi_list(fp, NULL, 0) < 0)
return -1;
}
/* They are also full trees, hence: */
if (olevel > 1) {
tree->nodes[j].left = j + 1;
tree->nodes[j].right = j + (1 << (olevel - 1));
}
++j;
}
else { /* Have to read the BBI list anyway. */
if (read_bbi_list(fp, NULL, 0) < 0)
return -1;
}
}
E_INFO("Read %d nodes\n", j);
return 0;
}
/**
* Convert a block of float32 to mfcc_t (can be done in-place)
**/
int32
fe_float_to_mfcc(fe_t * FE,
float32 ** input, mfcc_t ** output, int32 nframes)
{
int32 i;
#ifndef FIXED_POINT
if ((void *) input == (void *) output)
return nframes * FE->FEATURE_DIMENSION;
#endif
for (i = 0; i < nframes * FE->FEATURE_DIMENSION; ++i)
output[0][i] = FLOAT2MFCC(input[0][i]);
return i;
}
/**
* Convert a block of float32 to mfcc_t (can be done in-place)
**/
int32
fe_float_to_mfcc(fe_t * fe,
float32 ** input, mfcc_t ** output, int32 nframes)
{
int32 i;
#ifndef FIXED_POINT
if ((void *) input == (void *) output)
return nframes * fe->feature_dimension;
#endif
for (i = 0; i < nframes * fe->feature_dimension; ++i)
output[0][i] = FLOAT2MFCC(input[0][i]);
return i;
}
cmn_t *
cmn_init(int32 veclen)
{
cmn_t *cmn;
cmn = (cmn_t *) ckd_calloc(1, sizeof(cmn_t));
cmn->veclen = veclen;
cmn->cmn_mean = (mfcc_t *) ckd_calloc(veclen, sizeof(mfcc_t));
cmn->cmn_var = (mfcc_t *) ckd_calloc(veclen, sizeof(mfcc_t));
cmn->sum = (mfcc_t *) ckd_calloc(veclen, sizeof(mfcc_t));
/* A front-end dependent magic number */
cmn->cmn_mean[0] = FLOAT2MFCC(12.0);
cmn->nframe = 0;
E_INFO("mean[0]= %.2f, mean[1..%d]= 0.0\n",
MFCC2FLOAT(cmn->cmn_mean[0]), veclen - 1);
return cmn;
}
/*
* Some of the gaussian density computation can be carried out in advance:
* log(determinant) calculation,
* 1/(2*var) in the exponent,
* NOTE; The density computation is performed in log domain.
*/
static int32
gauden_dist_precompute(gauden_t * g, logmath_t *lmath, float32 varfloor)
{
int32 i, m, f, d, flen;
mfcc_t *meanp;
mfcc_t *varp;
mfcc_t *detp;
int32 floored;
floored = 0;
/* Allocate space for determinants */
g->det = (mfcc_t***)ckd_calloc_3d(g->n_mgau, g->n_feat, g->n_density, sizeof(***g->det));
for (m = 0; m < g->n_mgau; m++) {
for (f = 0; f < g->n_feat; f++) {
flen = g->featlen[f];
/* Determinants for all variance vectors in g->[m][f] */
for (d = 0, detp = g->det[m][f]; d < g->n_density; d++, detp++) {
*detp = 0;
for (i = 0, varp = g->var[m][f][d], meanp = g->mean[m][f][d];
i < flen; i++, varp++, meanp++) {
float32 *fvarp = (float32 *)varp;
#ifdef FIXED_POINT
float32 *fmp = (float32 *)meanp;
*meanp = FLOAT2MFCC(*fmp);
#endif
if (*fvarp < varfloor) {
*fvarp = varfloor;
++floored;
}
*detp += (mfcc_t)logmath_log(lmath,
1.0 / sqrt(*fvarp * 2.0 * M_PI));
/* Precompute this part of the exponential */
*varp = (mfcc_t)logmath_ln_to_log(lmath,
(1.0 / (*fvarp * 2.0)));
}
}
}
}
E_INFO("%d variance values floored\n", floored);
return 0;
}
/* Update estimated max for next utterance */
void
agc_emax_update(agc_t *agc)
{
if (agc->obs_frame) { /* Update only if some data observed */
agc->obs_max_sum += agc->obs_max;
agc->obs_utt++;
/* Re-estimate max over past history; decay the history */
agc->max = agc->obs_max_sum / agc->obs_utt;
if (agc->obs_utt == 8) {
agc->obs_max_sum /= 2;
agc->obs_utt = 4;
}
}
E_INFO("AGCEMax: obs= %.2f, new= %.2f\n", agc->obs_max, agc->max);
/* Reset the accumulators for the next utterance. */
agc->obs_frame = 0;
agc->obs_max = FLOAT2MFCC(-1000.0); /* Less than any real C0 value (hopefully!!) */
}
static int32
s3_precomp(s2_semi_mgau_t *s, logmath_t *lmath, float32 vFloor)
{
int feat;
for (feat = 0; feat < s->n_feat; ++feat) {
float32 *fmp;
mfcc_t *mp;
mfcc_t *vp, *dp;
int32 vecLen, i;
vecLen = s->veclen[feat];
fmp = (float32 *) s->means[feat];
mp = s->means[feat];
vp = s->vars[feat];
dp = s->dets[feat];
for (i = 0; i < s->n_density; ++i) {
mfcc_t d;
int32 j;
d = 0;
for (j = 0; j < vecLen; ++j, ++vp, ++mp, ++fmp) {
float64 fvar;
*mp = FLOAT2MFCC(*fmp);
/* Always do these pre-calculations in floating point */
fvar = *(float32 *) vp;
if (fvar < vFloor)
fvar = vFloor;
d += (mfcc_t)logmath_log(lmath, 1 / sqrt(fvar * 2.0 * M_PI));
*vp = (mfcc_t)logmath_ln_to_log(lmath, 1.0 / (2.0 * fvar));
}
*dp++ = d;
}
}
return 0;
}
#ifdef HAVE_CONFIG_H
#include <config.h>
#endif
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "feat.h"
#include "ckd_alloc.h"
#include "test_macros.h"
const mfcc_t data[6][13] = {
{ FLOAT2MFCC(15.114), FLOAT2MFCC(-1.424), FLOAT2MFCC(-0.953),
FLOAT2MFCC(0.186), FLOAT2MFCC(-0.656), FLOAT2MFCC(-0.226),
FLOAT2MFCC(-0.105), FLOAT2MFCC(-0.412), FLOAT2MFCC(-0.024),
FLOAT2MFCC(-0.091), FLOAT2MFCC(-0.124), FLOAT2MFCC(-0.158), FLOAT2MFCC(-0.197)},
{ FLOAT2MFCC(14.729), FLOAT2MFCC(-1.313), FLOAT2MFCC(-0.892),
FLOAT2MFCC(0.140), FLOAT2MFCC(-0.676), FLOAT2MFCC(-0.089),
FLOAT2MFCC(-0.313), FLOAT2MFCC(-0.422), FLOAT2MFCC(-0.058),
FLOAT2MFCC(-0.101), FLOAT2MFCC(-0.100), FLOAT2MFCC(-0.128), FLOAT2MFCC(-0.123)},
{ FLOAT2MFCC(14.502), FLOAT2MFCC(-1.351), FLOAT2MFCC(-1.028),
FLOAT2MFCC(-0.189), FLOAT2MFCC(-0.718), FLOAT2MFCC(-0.139),
FLOAT2MFCC(-0.121), FLOAT2MFCC(-0.365), FLOAT2MFCC(-0.139),
FLOAT2MFCC(-0.154), FLOAT2MFCC(0.041), FLOAT2MFCC(0.009), FLOAT2MFCC(-0.073)},
{ FLOAT2MFCC(14.557), FLOAT2MFCC(-1.676), FLOAT2MFCC(-0.864),
FLOAT2MFCC(0.118), FLOAT2MFCC(-0.445), FLOAT2MFCC(-0.168),
FLOAT2MFCC(-0.069), FLOAT2MFCC(-0.503), FLOAT2MFCC(-0.013),
FLOAT2MFCC(0.007), FLOAT2MFCC(-0.056), FLOAT2MFCC(-0.075), FLOAT2MFCC(-0.237)},
{ FLOAT2MFCC(14.665), FLOAT2MFCC(-1.498), FLOAT2MFCC(-0.582),
FLOAT2MFCC(0.209), FLOAT2MFCC(-0.487), FLOAT2MFCC(-0.247),
#include <pocketsphinx.h>
#include <stdio.h>
#include <string.h>
#include "pocketsphinx_internal.h"
#include "test_macros.h"
static const mfcc_t prior[13] = {
FLOAT2MFCC(33.89),
FLOAT2MFCC(-1.13),
FLOAT2MFCC(0.83),
FLOAT2MFCC(0.49),
FLOAT2MFCC(-0.65),
FLOAT2MFCC(0.12),
FLOAT2MFCC(-0.03),
FLOAT2MFCC(0.28),
FLOAT2MFCC(0.41),
FLOAT2MFCC(0.59),
FLOAT2MFCC(0.11),
FLOAT2MFCC(-0.20),
FLOAT2MFCC(0.17)
};
int
ps_decoder_test(cmd_ln_t *config, char const *sname, char const *expected)
{
ps_decoder_t *ps;
mfcc_t **cepbuf;
FILE *rawfh;
int16 *buf;
float32
agc_get_threshold(agc_t *agc)
{
return FLOAT2MFCC(agc->noise_thresh);
}
void
agc_set_threshold(agc_t *agc, float32 threshold)
{
agc->noise_thresh = FLOAT2MFCC(threshold);
}
void
agc_emax_set(agc_t *agc, float32 m)
{
agc->max = FLOAT2MFCC(m);
E_INFO("AGCEMax: max= %.2f\n", m);
}
static int
initialize(int argc,
char *argv[])
{
const char *fdictfn;
const char *dictfn;
const char *ts2cbfn;
uint32 n_ts;
uint32 n_cb;
/* define, parse and (partially) validate the command line */
parse_cmd_ln(argc, argv);
feat =
feat_init(cmd_ln_str("-feat"),
cmn_type_from_str(cmd_ln_str("-cmn")),
cmd_ln_boolean("-varnorm"),
agc_type_from_str(cmd_ln_str("-agc")),
1, cmd_ln_int32("-ceplen"));
if (cmd_ln_str("-lda")) {
E_INFO("Reading linear feature transformation from %s\n",
cmd_ln_str("-lda"));
if (feat_read_lda(feat,
cmd_ln_str("-lda"),
cmd_ln_int32("-ldadim")) < 0)
return -1;
}
if (cmd_ln_str("-svspec")) {
int32 **subvecs;
E_INFO("Using subvector specification %s\n",
cmd_ln_str("-svspec"));
if ((subvecs = parse_subvecs(cmd_ln_str("-svspec"))) == NULL)
return -1;
if ((feat_set_subvecs(feat, subvecs)) < 0)
return -1;
}
if (cmd_ln_exists("-agcthresh")
&& 0 != strcmp(cmd_ln_str("-agc"), "none")) {
agc_set_threshold(feat->agc_struct,
cmd_ln_float32("-agcthresh"));
}
if (feat->cmn_struct
&& cmd_ln_exists("-cmninit")) {
char *c, *cc, *vallist;
int32 nvals;
vallist = ckd_salloc(cmd_ln_str("-cmninit"));
c = vallist;
nvals = 0;
while (nvals < feat->cmn_struct->veclen
&& (cc = strchr(c, ',')) != NULL) {
*cc = '\0';
feat->cmn_struct->cmn_mean[nvals] = FLOAT2MFCC(atof(c));
c = cc + 1;
++nvals;
}
if (nvals < feat->cmn_struct->veclen && *c != '\0') {
feat->cmn_struct->cmn_mean[nvals] = FLOAT2MFCC(atof(c));
}
ckd_free(vallist);
}
if (cmd_ln_str("-segdir"))
corpus_set_seg_dir(cmd_ln_str("-segdir"));
if (cmd_ln_str("-segext"))
corpus_set_seg_ext(cmd_ln_str("-segext"));
corpus_set_mfcc_dir(cmd_ln_str("-cepdir"));
corpus_set_mfcc_ext(cmd_ln_str("-cepext"));
if (cmd_ln_str("-lsnfn"))
corpus_set_lsn_filename(cmd_ln_str("-lsnfn"));
corpus_set_ctl_filename(cmd_ln_str("-ctlfn"));
if (cmd_ln_int32("-nskip") && cmd_ln_int32("-runlen")) {
corpus_set_interval(cmd_ln_int32("-nskip"),
cmd_ln_int32("-runlen"));
} else if (cmd_ln_int32("-part") && cmd_ln_int32("-npart")) {
corpus_set_partition(cmd_ln_int32("-part"),
cmd_ln_int32("-npart"));
}
if (corpus_init() != S3_SUCCESS) {
return S3_ERROR;
}
if (cmd_ln_str("-moddeffn")) {
E_INFO("Reading %s\n", cmd_ln_str("-moddeffn"));
/* Read in the model definitions. Defines the set of
CI phones and context dependent phones. Defines the
transition matrix tying and state level tying. */
if (model_def_read(&mdef,
cmd_ln_str("-moddeffn")) != S3_SUCCESS) {
return S3_ERROR;
}
ts2cbfn = cmd_ln_str("-ts2cbfn");
if (strcmp(SEMI_LABEL, ts2cbfn) == 0) {
mdef->cb = semi_ts2cb(mdef->n_tied_state);
n_ts = mdef->n_tied_state;
n_cb = 1;
}
else if (strcmp(CONT_LABEL, ts2cbfn) == 0) {
mdef->cb = cont_ts2cb(mdef->n_tied_state);
n_ts = mdef->n_tied_state;
n_cb = mdef->n_tied_state;
}
else if (strcmp(PTM_LABEL, ts2cbfn) == 0) {
mdef->cb = ptm_ts2cb(mdef);
n_ts = mdef->n_tied_state;
n_cb = mdef->acmod_set->n_ci;
}
else if (s3ts2cb_read(ts2cbfn,
&mdef->cb,
&n_ts,
&n_cb) != S3_SUCCESS) {
return S3_ERROR;
}
dictfn = cmd_ln_str("-dictfn");
if (dictfn == NULL) {
E_FATAL("You must specify a content dictionary using -dictfn\n");
}
E_INFO("Reading %s\n", dictfn);
lex = lexicon_read(NULL, /* no lexicon to start */
dictfn,
mdef->acmod_set);
if (lex == NULL)
return S3_ERROR;
fdictfn = cmd_ln_str("-fdictfn");
if (fdictfn) {
E_INFO("Reading %s\n", fdictfn);
(void)lexicon_read(lex, /* add filler words content lexicon */
fdictfn,
mdef->acmod_set);
}
}
return S3_SUCCESS;
}
/**
* Read Sphinx-II format mfc file (s2mfc = Sphinx-II format MFC data).
* If out_mfc is 0, no actual reading will be done, and the number of
* frames (plus padding) that would be read is returned.
*
* It's important that normalization is done before padding because
* frames outside the data we are interested in shouldn't be taken
* into normalization stats.
*
* @return # frames read (plus padding) if successful, -1 if
* error (e.g., mfc array too small).
*/
static int32
feat_s2mfc_read_norm_pad(feat_t *fcb, char *file, int32 win,
int32 sf, int32 ef,
mfcc_t ***out_mfc,
int32 maxfr,
int32 cepsize)
{
FILE *fp;
int32 n_float32;
float32 *float_feat;
struct stat statbuf;
int32 i, n, byterev;
int32 start_pad, end_pad;
mfcc_t **mfc;
/* Initialize the output pointer to 0, so that any attempts to
free() it if we fail before allocating it will not segfault! */
if (out_mfc)
*out_mfc = 0;
E_INFO("Reading mfc file: '%s'[%d..%d]\n", file, sf, ef);
if (ef >= 0 && ef <= sf) {
E_ERROR("%s: End frame (%d) <= Start frame (%d)\n", file, ef, sf);
return -1;
}
/* Find filesize; HACK!! To get around intermittent NFS failures, use stat_retry */
if ((stat_retry(file, &statbuf) < 0)
|| ((fp = fopen(file, "rb")) == 0)) {
#ifndef POCKETSPHINX_NET
E_ERROR("Failed to open file '%s' for reading: %s\n", file, strerror(errno));
#endif
return -1;
}
/* Read #floats in header */
if (fread_retry(&n_float32, sizeof(int32), 1, fp) != 1) {
E_ERROR("%s: fread(#floats) failed\n", file);
fclose(fp);
return -1;
}
/* Check if n_float32 matches file size */
byterev = 0;
if ((int32) (n_float32 * sizeof(float32) + 4) != (int32) statbuf.st_size) { /* RAH, typecast both sides to remove compile warning */
n = n_float32;
SWAP_INT32(&n);
if ((int32) (n * sizeof(float32) + 4) != (int32) (statbuf.st_size)) { /* RAH, typecast both sides to remove compile warning */
E_ERROR
("%s: Header size field: %d(%08x); filesize: %d(%08x)\n",
file, n_float32, n_float32, statbuf.st_size,
statbuf.st_size);
fclose(fp);
return -1;
}
n_float32 = n;
byterev = 1;
}
if (n_float32 <= 0) {
E_ERROR("%s: Header size field (#floats) = %d\n", file, n_float32);
fclose(fp);
return -1;
}
/* Convert n to #frames of input */
n = n_float32 / cepsize;
if (n * cepsize != n_float32) {
E_ERROR("Header size field: %d; not multiple of %d\n", n_float32,
cepsize);
fclose(fp);
return -1;
}
/* Check start and end frames */
if (sf > 0) {
if (sf >= n) {
E_ERROR("%s: Start frame (%d) beyond file size (%d)\n", file,
sf, n);
fclose(fp);
return -1;
}
}
if (ef < 0)
ef = n-1;
else if (ef >= n) {
E_WARN("%s: End frame (%d) beyond file size (%d), will truncate\n",
file, ef, n);
ef = n-1;
}
/* Add window to start and end frames */
sf -= win;
ef += win;
if (sf < 0) {
start_pad = -sf;
sf = 0;
}
else
start_pad = 0;
if (ef >= n) {
end_pad = ef - n + 1;
ef = n - 1;
}
else
end_pad = 0;
/* Limit n if indicated by [sf..ef] */
if ((ef - sf + 1) < n)
n = (ef - sf + 1);
if (maxfr > 0 && n + start_pad + end_pad > maxfr) {
E_ERROR("%s: Maximum output size(%d frames) < actual #frames(%d)\n",
file, maxfr, n + start_pad + end_pad);
fclose(fp);
return -1;
}
/* If no output buffer was supplied, then skip the actual data reading. */
if (out_mfc != 0) {
/* Position at desired start frame and read actual MFC data */
mfc = (mfcc_t **)ckd_calloc_2d(n + start_pad + end_pad, cepsize, sizeof(mfcc_t));
if (sf > 0)
fseek(fp, sf * cepsize * sizeof(float32), SEEK_CUR);
n_float32 = n * cepsize;
#ifdef FIXED_POINT
float_feat = ckd_calloc(n_float32, sizeof(float32));
#else
float_feat = mfc[start_pad];
#endif
if (fread_retry(float_feat, sizeof(float32), n_float32, fp) != n_float32) {
E_ERROR("%s: fread(%dx%d) (MFC data) failed\n", file, n, cepsize);
ckd_free_2d(mfc);
fclose(fp);
return -1;
}
if (byterev) {
for (i = 0; i < n_float32; i++) {
SWAP_FLOAT32(&float_feat[i]);
}
}
#ifdef FIXED_POINT
for (i = 0; i < n_float32; ++i) {
mfc[start_pad][i] = FLOAT2MFCC(float_feat[i]);
}
ckd_free(float_feat);
#endif
/* Normalize */
feat_cmn(fcb, mfc + start_pad, n, 1, 1);
feat_agc(fcb, mfc + start_pad, n, 1, 1);
/* Replicate start and end frames if necessary. */
for (i = 0; i < start_pad; ++i)
memcpy(mfc[i], mfc[start_pad], cepsize * sizeof(mfcc_t));
for (i = 0; i < end_pad; ++i)
memcpy(mfc[start_pad + n + i], mfc[start_pad + n - 1],
cepsize * sizeof(mfcc_t));
*out_mfc = mfc;
}
fclose(fp);
return n + start_pad + end_pad;
}
void
cmn(cmn_t *cmn, mfcc_t ** mfc, int32 varnorm, int32 n_frame)
{
mfcc_t *mfcp;
mfcc_t t;
int32 i, f;
oe_assert(mfc != NULL);
if (n_frame <= 0)
return;
/* If cmn->cmn_mean wasn't NULL, we need to zero the contents */
memset(cmn->cmn_mean, 0, cmn->veclen * sizeof(mfcc_t));
/* Find mean cep vector for this utterance */
for (f = 0; f < n_frame; f++) {
mfcp = mfc[f];
for (i = 0; i < cmn->veclen; i++) {
cmn->cmn_mean[i] += mfcp[i];
}
}
for (i = 0; i < cmn->veclen; i++)
cmn->cmn_mean[i] /= n_frame;
E_INFO("CMN: ");
for (i = 0; i < cmn->veclen; i++)
E_INFOCONT("%5.2f ", MFCC2FLOAT(cmn->cmn_mean[i]));
E_INFOCONT("\n");
if (!varnorm) {
/* Subtract mean from each cep vector */
for (f = 0; f < n_frame; f++) {
mfcp = mfc[f];
for (i = 0; i < cmn->veclen; i++)
mfcp[i] -= cmn->cmn_mean[i];
}
}
else {
/* Scale cep vectors to have unit variance along each dimension, and subtract means */
/* If cmn->cmn_var wasn't NULL, we need to zero the contents */
memset(cmn->cmn_var, 0, cmn->veclen * sizeof(mfcc_t));
for (f = 0; f < n_frame; f++) {
mfcp = mfc[f];
for (i = 0; i < cmn->veclen; i++) {
t = mfcp[i] - cmn->cmn_mean[i];
cmn->cmn_var[i] += MFCCMUL(t, t);
}
}
for (i = 0; i < cmn->veclen; i++)
/* Inverse Std. Dev, RAH added type case from sqrt */
cmn->cmn_var[i] = FLOAT2MFCC(sqrt((float64)n_frame / MFCC2FLOAT(cmn->cmn_var[i])));
for (f = 0; f < n_frame; f++) {
mfcp = mfc[f];
for (i = 0; i < cmn->veclen; i++)
mfcp[i] = MFCCMUL((mfcp[i] - cmn->cmn_mean[i]), cmn->cmn_var[i]);
}
}
}
#include <stdio.h>
#include <string.h>
#include <pocketsphinx.h>
#include <sphinxbase/logmath.h>
#include "acmod.h"
#include "test_macros.h"
static const mfcc_t cmninit[13] = {
FLOAT2MFCC(41.00),
FLOAT2MFCC(-5.29),
FLOAT2MFCC(-0.12),
FLOAT2MFCC(5.09),
FLOAT2MFCC(2.48),
FLOAT2MFCC(-4.07),
FLOAT2MFCC(-1.37),
FLOAT2MFCC(-1.78),
FLOAT2MFCC(-5.08),
FLOAT2MFCC(-2.05),
FLOAT2MFCC(-6.45),
FLOAT2MFCC(-1.42),
FLOAT2MFCC(1.17)
};
int
main(int argc, char *argv[])
{
acmod_t *acmod;
logmath_t *lmath;
cmd_ln_t *config;
int32
feat_read_lda(feat_t *feat, const char *ldafile, int32 dim)
{
FILE *fh;
int32 byteswap, chksum_present;
uint32 chksum, i, m, n;
char **argname, **argval;
assert(feat);
if (feat->n_stream != 1) {
E_ERROR("LDA incompatible with multi-stream features (n_stream = %d)\n",
feat->n_stream);
return -1;
}
if ((fh = fopen(ldafile, "rb")) == NULL) {
E_ERROR_SYSTEM("Failed to open transform file '%s' for reading", ldafile);
return -1;
}
if (bio_readhdr(fh, &argname, &argval, &byteswap) < 0) {
E_ERROR("Failed to read header from transform file '%s'\n", ldafile);
fclose(fh);
return -1;
}
chksum_present = 0;
for (i = 0; argname[i]; i++) {
if (strcmp(argname[i], "version") == 0) {
if (strcmp(argval[i], MATRIX_FILE_VERSION) != 0)
E_WARN("%s: Version mismatch: %s, expecting %s\n",
ldafile, argval[i], MATRIX_FILE_VERSION);
}
else if (strcmp(argname[i], "chksum0") == 0) {
chksum_present = 1; /* Ignore the associated value */
}
}
bio_hdrarg_free(argname, argval);
argname = argval = NULL;
chksum = 0;
if (feat->lda)
ckd_free_3d((void ***)feat->lda);
{
/* Use a temporary variable to avoid strict-aliasing problems. */
void ***outlda;
if (bio_fread_3d(&outlda, sizeof(float32),
&feat->n_lda, &m, &n,
fh, byteswap, &chksum) < 0) {
E_ERROR_SYSTEM("%s: bio_fread_3d(lda) failed\n", ldafile);
fclose(fh);
return -1;
}
feat->lda = (void *)outlda;
}
fclose(fh);
#ifdef FIXED_POINT
/* FIXME: This is a fragile hack that depends on mfcc_t and
* float32 being the same size (which they are, but...) */
for (i = 0; i < feat->n_lda * m * n; ++i) {
feat->lda[0][0][i] = FLOAT2MFCC(((float *)feat->lda[0][0])[i]);
}
#endif
/* Note that SphinxTrain stores the eigenvectors as row vectors. */
if (n != feat->stream_len[0])
E_FATAL("LDA matrix dimension %d doesn't match feature stream size %d\n", n, feat->stream_len[0]);
/* Override dim from file if it is 0 or greater than m. */
if (dim > m || dim <= 0) {
dim = m;
}
feat->out_dim = dim;
return 0;
}
int
main_initialize(int argc,
char *argv[],
lexicon_t **out_lex,
model_def_t **out_omdef,
model_def_t **out_dmdef,
feat_t** out_feat)
{
model_def_t *dmdef = NULL;
model_def_t *omdef = NULL;
lexicon_t *lex = NULL;
feat_t *feat;
const char *fn;
uint32 n_ts;
uint32 n_cb;
const char *ts2cbfn;
parse_cmd_ln(argc, argv);
feat =
feat_init(cmd_ln_str("-feat"),
cmn_type_from_str(cmd_ln_str("-cmn")),
cmd_ln_boolean("-varnorm"),
agc_type_from_str(cmd_ln_str("-agc")),
1, cmd_ln_int32("-ceplen"));
if (cmd_ln_str("-lda")) {
E_INFO("Reading linear feature transformation from %s\n",
cmd_ln_str("-lda"));
if (feat_read_lda(feat,
cmd_ln_str("-lda"),
cmd_ln_int32("-ldadim")) < 0)
return -1;
}
if (cmd_ln_str("-svspec")) {
int32 **subvecs;
E_INFO("Using subvector specification %s\n",
cmd_ln_str("-svspec"));
if ((subvecs = parse_subvecs(cmd_ln_str("-svspec"))) == NULL)
return -1;
if ((feat_set_subvecs(feat, subvecs)) < 0)
return -1;
}
if (cmd_ln_exists("-agcthresh")
&& 0 != strcmp(cmd_ln_str("-agc"), "none")) {
agc_set_threshold(feat->agc_struct,
cmd_ln_float32("-agcthresh"));
}
if (feat->cmn_struct
&& cmd_ln_exists("-cmninit")) {
char *c, *cc, *vallist;
int32 nvals;
vallist = ckd_salloc(cmd_ln_str("-cmninit"));
c = vallist;
nvals = 0;
while (nvals < feat->cmn_struct->veclen
&& (cc = strchr(c, ',')) != NULL) {
*cc = '\0';
feat->cmn_struct->cmn_mean[nvals] = FLOAT2MFCC(atof(c));
c = cc + 1;
++nvals;
}
if (nvals < feat->cmn_struct->veclen && *c != '\0') {
feat->cmn_struct->cmn_mean[nvals] = FLOAT2MFCC(atof(c));
}
ckd_free(vallist);
}
*out_feat = feat;
if (cmd_ln_str("-omoddeffn")) {
E_INFO("Reading output model definitions: %s\n", cmd_ln_str("-omoddeffn"));
/* Read in the model definitions. Defines the set of
CI phones and context dependent phones. Defines the
transition matrix tying and state level tying. */
if (model_def_read(&omdef,
cmd_ln_str("-omoddeffn")) != S3_SUCCESS) {
return S3_ERROR;
}
if (cmd_ln_str("-dmoddeffn")) {
E_INFO("Reading dump model definitions: %s\n", cmd_ln_str("-dmoddeffn"));
if (model_def_read(&dmdef,
cmd_ln_str("-dmoddeffn")) != S3_SUCCESS) {
return S3_ERROR;
}
setup_d2o_map(dmdef, omdef);
}
else {
E_INFO("Assuming dump and output model definitions are identical\n");
}
ts2cbfn = cmd_ln_str("-ts2cbfn");
if (ts2cbfn) {
if (strcmp(SEMI_LABEL, ts2cbfn) == 0) {
omdef->cb = semi_ts2cb(omdef->n_tied_state);
n_ts = omdef->n_tied_state;
n_cb = 1;
}
else if (strcmp(CONT_LABEL, ts2cbfn) == 0) {
omdef->cb = cont_ts2cb(omdef->n_tied_state);
n_ts = omdef->n_tied_state;
n_cb = omdef->n_tied_state;
}
else if (strcmp(PTM_LABEL, ts2cbfn) == 0) {
omdef->cb = ptm_ts2cb(omdef);
n_ts = omdef->n_tied_state;
n_cb = omdef->acmod_set->n_ci;
}
else if (s3ts2cb_read(cmd_ln_str("-ts2cbfn"),
&omdef->cb,
&n_ts,
&n_cb) != S3_SUCCESS) {
return S3_ERROR;
}
if (omdef->n_tied_state != n_ts) {
E_FATAL("Model definition file n_tied_state = %u, but %u mappings in ts2cb\n",
omdef->n_tied_state, n_ts);
}
}
}
else {
E_INFO("No mdef files. Assuming 1-class init\n");
}
*out_omdef = omdef;
*out_dmdef = dmdef;
fn = cmd_ln_str("-dictfn");
if (fn) {
E_INFO("Reading main lexicon: %s\n", fn);
lex = lexicon_read(NULL,
fn,
omdef->acmod_set);
if (lex == NULL)
return S3_ERROR;
}
fn = cmd_ln_str("-fdictfn");
if (fn) {
E_INFO("Reading filler lexicon: %s\n", fn);
(void)lexicon_read(lex,
fn,
omdef->acmod_set);
}
*out_lex = lex;
stride = cmd_ln_int32("-stride");
return S3_SUCCESS;
}
#include <stdio.h>
#include <string.h>
#include <pocketsphinx.h>
#include <sphinxbase/logmath.h>
#include "acmod.h"
#include "test_macros.h"
static const mfcc_t prior[13] = {
FLOAT2MFCC(37.03),
FLOAT2MFCC(-1.01),
FLOAT2MFCC(0.53),
FLOAT2MFCC(0.49),
FLOAT2MFCC(-0.60),
FLOAT2MFCC(0.14),
FLOAT2MFCC(-0.05),
FLOAT2MFCC(0.25),
FLOAT2MFCC(0.37),
FLOAT2MFCC(0.58),
FLOAT2MFCC(0.13),
FLOAT2MFCC(-0.16),
FLOAT2MFCC(0.17)
};
int
main(int argc, char *argv[])
{
acmod_t *acmod;
logmath_t *lmath;
cmd_ln_t *config;
/**
* For fixed point we are doing the computation in a fixlog domain,
* so we have to add many processing cases.
*/
void
fe_track_snr(fe_t * fe, int32 *in_speech)
{
powspec_t *signal;
powspec_t *gain;
noise_stats_t *noise_stats;
powspec_t *mfspec;
int32 i, num_filts;
powspec_t lrt, snr;
if (!(fe->remove_noise || fe->remove_silence)) {
*in_speech = TRUE;
return;
}
noise_stats = fe->noise_stats;
mfspec = fe->mfspec;
num_filts = noise_stats->num_filters;
signal = (powspec_t *) ckd_calloc(num_filts, sizeof(powspec_t));
if (noise_stats->undefined) {
for (i = 0; i < num_filts; i++) {
noise_stats->power[i] = mfspec[i];
noise_stats->noise[i] = mfspec[i];
#ifndef FIXED_POINT
noise_stats->floor[i] = mfspec[i] / noise_stats->max_gain;
noise_stats->peak[i] = 0.0;
#else
noise_stats->floor[i] = mfspec[i] - noise_stats->max_gain;
noise_stats->peak[i] = MIN_FIXLOG;
#endif
}
noise_stats->undefined = FALSE;
}
/* Calculate smoothed power */
for (i = 0; i < num_filts; i++) {
#ifndef FIXED_POINT
noise_stats->power[i] =
noise_stats->lambda_power * noise_stats->power[i] + noise_stats->comp_lambda_power * mfspec[i];
#else
noise_stats->power[i] = fe_log_add(noise_stats->lambda_power + noise_stats->power[i],
noise_stats->comp_lambda_power + mfspec[i]);
#endif
}
/* Noise estimation and vad decision */
fe_lower_envelope(noise_stats, noise_stats->power, noise_stats->noise, num_filts);
lrt = FLOAT2MFCC(-10.0);
for (i = 0; i < num_filts; i++) {
#ifndef FIXED_POINT
signal[i] = noise_stats->power[i] - noise_stats->noise[i];
if (signal[i] < 0)
signal[i] = 0;
snr = log(noise_stats->power[i] / noise_stats->noise[i]);
#else
signal[i] = fe_log_sub(noise_stats->power[i], noise_stats->noise[i]);
snr = MFCC2FLOAT(noise_stats->power[i] - noise_stats->noise[i]);
#endif
if (snr > lrt)
lrt = snr;
}
if (fe->remove_silence && (lrt < fe->vad_threshold))
*in_speech = FALSE;
else
*in_speech = TRUE;
fe_lower_envelope(noise_stats, signal, noise_stats->floor, num_filts);
fe_temp_masking(noise_stats, signal, noise_stats->peak, num_filts);
if (!fe->remove_noise) {
//no need for further calculations if noise cancellation disabled
ckd_free(signal);
return;
}
for (i = 0; i < num_filts; i++) {
if (signal[i] < noise_stats->floor[i])
signal[i] = noise_stats->floor[i];
}
gain = (powspec_t *) ckd_calloc(num_filts, sizeof(powspec_t));
#ifndef FIXED_POINT
for (i = 0; i < num_filts; i++) {
if (signal[i] < noise_stats->max_gain * noise_stats->power[i])
gain[i] = signal[i] / noise_stats->power[i];
else
gain[i] = noise_stats->max_gain;
if (gain[i] < noise_stats->inv_max_gain)
gain[i] = noise_stats->inv_max_gain;
}
#else
for (i = 0; i < num_filts; i++) {
gain[i] = signal[i] - noise_stats->power[i];
if (gain[i] > noise_stats->max_gain)
gain[i] = noise_stats->max_gain;
if (gain[i] < noise_stats->inv_max_gain)
gain[i] = noise_stats->inv_max_gain;
}
#endif
/* Weight smoothing and time frequency normalization */
fe_weight_smooth(noise_stats, mfspec, gain, num_filts);
ckd_free(gain);
ckd_free(signal);
}