gauden_t *gauden_init (char *meanfile, char *varfile, float32 varfloor)
{
int32 i, m, f, d, *flen;
gauden_t *g;
assert (meanfile != NULL);
assert (varfile != NULL);
assert (varfloor > 0.0);
g = (gauden_t *) ckd_calloc (1, sizeof(gauden_t));
g->mean = g->var = NULL; /* To force them to be allocated */
/* Read means and (diagonal) variances for all mixture gaussians */
gauden_param_read (&(g->mean), &g->n_mgau, &g->n_feat, &g->n_density, &g->featlen,
meanfile);
gauden_param_read (&(g->var), &m, &f, &d, &flen, varfile);
/* Verify mean and variance parameter dimensions */
if ((m != g->n_mgau) || (f != g->n_feat) || (d != g->n_density))
E_FATAL("Mixture-gaussians dimensions for means and variances differ\n");
for (i = 0; i < g->n_feat; i++)
if (g->featlen[i] != flen[i])
E_FATAL("Feature lengths for means and variances differ\n");
ckd_free (flen);
/* Floor variances and precompute variance determinants */
gauden_dist_precompute (g, varfloor);
/* Floor for density values */
min_density = logs3_to_log (LOGPROB_ZERO);
return g;
}
int32
gauden_mllr_transform(gauden_t *g, ps_mllr_t *mllr, cmd_ln_t *config)
{
int32 i, m, f, d, *flen;
float32 ****fgau;
/* Reload means and variances (un-precomputed). */
fgau = NULL;
gauden_param_read(&fgau, &g->n_mgau, &g->n_feat, &g->n_density,
&g->featlen, cmd_ln_str_r(config, "-mean"));
g->mean = (mfcc_t ****)fgau;
fgau = NULL;
gauden_param_read(&fgau, &m, &f, &d, &flen, cmd_ln_str_r(config, "-var"));
g->var = (mfcc_t ****)fgau;
/* Verify mean and variance parameter dimensions */
if ((m != g->n_mgau) || (f != g->n_feat) || (d != g->n_density))
E_FATAL
("Mixture-gaussians dimensions for means and variances differ\n");
for (i = 0; i < g->n_feat; i++)
if (g->featlen[i] != flen[i])
E_FATAL("Feature lengths for means and variances differ\n");
ckd_free(flen);
/* Transform codebook for each stream s */
for (i = 0; i < g->n_mgau; ++i) {
for (f = 0; f < g->n_feat; ++f) {
float64 *temp;
temp = (float64 *) ckd_calloc(g->featlen[f], sizeof(float64));
/* Transform each density d in selected codebook */
for (d = 0; d < g->n_density; d++) {
int l;
for (l = 0; l < g->featlen[f]; l++) {
temp[l] = 0.0;
for (m = 0; m < g->featlen[f]; m++) {
/* FIXME: For now, only one class, hence the zeros below. */
temp[l] += mllr->A[f][0][l][m] * g->mean[i][f][d][m];
}
temp[l] += mllr->b[f][0][l];
}
for (l = 0; l < g->featlen[f]; l++) {
g->mean[i][f][d][l] = (float32) temp[l];
g->var[i][f][d][l] *= mllr->h[f][0][l];
}
}
ckd_free(temp);
}
}
/* Re-precompute (if we aren't adapting variances this isn't
* actually necessary...) */
gauden_dist_precompute(g, g->lmath, cmd_ln_float32_r(config, "-varfloor"));
return 0;
}
gauden_t *
gauden_init(char const *meanfile, char const *varfile, float32 varfloor, logmath_t *lmath)
{
int32 i, m, f, d, *flen;
float32 ****fgau;
gauden_t *g;
assert(meanfile != 0);
assert(varfile != 0);
assert(varfloor > 0.0);
g = (gauden_t *) ckd_calloc(1, sizeof(gauden_t));
g->lmath = lmath;
/* Read means and (diagonal) variances for all mixture gaussians */
fgau = 0;
gauden_param_read(&fgau, &g->n_mgau, &g->n_feat, &g->n_density,
&g->featlen, meanfile);
g->mean = (mfcc_t ****)fgau;
fgau = 0;
gauden_param_read(&fgau, &m, &f, &d, &flen, varfile);
g->var = (mfcc_t ****)fgau;
/* Verify mean and variance parameter dimensions */
if ((m != g->n_mgau) || (f != g->n_feat) || (d != g->n_density))
E_FATAL
("Mixture-gaussians dimensions for means and variances differ\n");
for (i = 0; i < g->n_feat; i++)
if (g->featlen[i] != flen[i])
E_FATAL("Feature lengths for means and variances differ\n");
ckd_free(flen);
/* Floor variances and precompute variance determinants */
gauden_dist_precompute(g, lmath, varfloor);
return g;
}
int32 gauden_mean_reload (gauden_t *g, char *meanfile)
{
int32 i, m, f, d, *flen;
assert (g->mean != NULL);
gauden_param_read (&(g->mean), &m, &f, &d, &flen, meanfile);
/* Verify original and new mean parameter dimensions */
if ((m != g->n_mgau) || (f != g->n_feat) || (d != g->n_density))
E_FATAL("Mixture-gaussians dimensions for original and new means differ\n");
for (i = 0; i < g->n_feat; i++)
if (g->featlen[i] != flen[i])
E_FATAL("Feature lengths for original and new means differ\n");
ckd_free (flen);
return 0;
}
/*
* Read mean and variance S3-format files and cluster them as follows:
* assume single feature vector
* for each dimension of feature vector {
* create list of <mean,var> pairs in the entire codebook set (for that dimension);
* // HACK!! 0 vectors omitted from the above list
* VQ cluster this list into the given number of vqsize points;
* replace codebook entries with nearest clustered values (for that dimension);
* // HACK!! 0 vectors remain untouched
* }
* write remapped codebooks to files "mean-vq" and "var-vq";
*/
main (int32 argc, char *argv[])
{
float32 ****mean, ****var;
int32 n_cb, n_feat, n_den, *featlen;
int32 i, j, c, w, dim, p, n_pt, vqsize;
float32 **pt, **cb;
float64 err;
FILE *fp;
if ((argc < 4) || (sscanf (argv[3], "%d", &vqsize) != 1))
E_FATAL("Usage: %s meanfile varfile vqsize\n", argv[0]);
if (argc > 4) {
if ((fp = fopen(argv[4], "w")) != NULL) {
*stdout = *fp;
*stderr = *fp;
} else
E_ERROR("fopen(%s,w) failed\n", argv[4]);
}
gauden_param_read (&mean, &n_cb, &n_feat, &n_den, &featlen, argv[1]);
E_INFO("%s: %d x %d x %d ", argv[1], n_cb, n_feat, n_den);
for (i = 0; i < n_feat; i++)
printf (" %d", featlen[i]);
printf ("\n");
gauden_param_read (&var, &n_cb, &n_feat, &n_den, &featlen, argv[2]);
E_INFO("%s: %d x %d x %d ", argv[2], n_cb, n_feat, n_den);
for (i = 0; i < n_feat; i++)
printf (" %d", featlen[i]);
printf ("\n");
assert (n_feat == 1);
n_pt = n_cb * n_den;
pt = (float32 **) ckd_calloc_2d (n_pt, 2, sizeof(float32));
cb = (float32 **) ckd_calloc_2d (vqsize, 2, sizeof(float32));
tmg = timing_new ();
/* VQ each column of mean,var pair (assuming just one feature vector) */
for (dim = 0; dim < featlen[0]; dim++) {
j = 0;
for (c = 0; c < n_cb; c++) {
for (w = 0; w < n_den; w++) {
if ((mean[c][0][w][dim] != 0.0) || (var[c][0][w][dim] != 0.0)) {
pt[j][0] = mean[c][0][w][dim];
pt[j][1] = var[c][0][w][dim];
j++;
}
}
}
err = vq (pt, cb, j, vqsize, 2);
vq_dump (cb, vqsize, 2);
j = 0;
for (c = 0; c < n_cb; c++) {
for (w = 0; w < n_den; w++) {
if ((mean[c][0][w][dim] != 0.0) || (var[c][0][w][dim] != 0.0)) {
mean[c][0][w][dim] = pt[j][0];
var[c][0][w][dim] = pt[j][1];
j++;
}
}
}
E_INFO("%d values quantized for dimension %d; error = %e\n", j, dim, err);
}
fflush (fp);
gauden_param_write (mean, n_cb, n_feat, n_den, featlen, "mean-vq");
gauden_param_write (var, n_cb, n_feat, n_den, featlen, "var-vq");
fflush (fp);
exit(0);
}
