adapt to new network architecture

5 files changed, 46 insertions, 20 deletions

diff --git a/htk_io/tools/nerv_to_tnet.lua b/htk_io/tools/nerv_to_tnet.lua
index c0ac76b..17ff3db 100644
--- a/htk_io/tools/nerv_to_tnet.lua
+++ b/htk_io/tools/nerv_to_tnet.lua
@@ -1,8 +1,9 @@
--- usage: nerv config_file nerv_param_input tnet_output
+-- usage: nerv nerv_to_tnet.lua config_file nerv_param_input tnet_param_output
 
 dofile(arg[1])
+gconf.mmat_type = nerv.MMatrixFloat
 param_repo = nerv.ParamRepo()
-param_repo:import({arg[2], gconf.initialized_param[2]}, nil, gconf)
+param_repo:import({arg[2]}, nil, gconf)
 layer_repo = make_layer_repo(param_repo)
 f = assert(io.open(arg[3], "w"))
 
diff --git a/htk_io/tools/tnet_to_nerv.cpp b/htk_io/tools/tnet_to_nerv.cpp
index a779a25..63a104d 100644
--- a/htk_io/tools/tnet_to_nerv.cpp
+++ b/htk_io/tools/tnet_to_nerv.cpp
@@ -2,34 +2,54 @@
 #include <fstream>
 #include <string>
 #include <cstring>
+#include <cassert>
 #include <cstdlib>
+
 char token[1024];
 char output[1024];
-double **mat;
+
+double **new_matrix(int nrow, int ncol) {
+    double **mat = new double *[nrow];
+    int i;
+    for (i = 0; i < nrow; i++)
+        mat[i] = new double[ncol];
+    return mat;
+}
+
+void free_matrix(double **mat, int nrow, int ncol) {
+    int i;
+    for (i = 0; i < nrow; i++)
+        delete [] mat[i];
+    delete [] mat;
+}
+
 int main(int argc, char **argv) {
+    FILE *fin;
     std::ofstream fout;
-    fout.open(argv[1]);
-    int cnt = 0;
-    while (scanf("%s", token) != EOF)
+    assert(argc >= 3);
+    fin = fopen(argv[1], "r");
+    fout.open(argv[2]);
+    assert(fin != NULL);
+    int cnt = argc > 3 ? atoi(argv[3]) : 0;
+    while (fscanf(fin, "%s", token) != EOF)
     {
         int nrow, ncol;
         int i, j;
+        double **mat;
         if (strcmp(token, "<biasedlinearity>") == 0)
         {
-            scanf("%d %d", &ncol, &nrow);
-            scanf("%s %d %d", token, &ncol, &nrow);
+            fscanf(fin, "%d %d", &ncol, &nrow);
+            fscanf(fin, "%s %d %d", token, &ncol, &nrow);
             printf("%d %d\n", nrow, ncol);
-            mat = (double **)malloc(nrow * sizeof(double *));
-            for (i = 0; i < nrow; i++)
-                mat[i] = (double *)malloc(ncol * sizeof(double));
+            mat = new_matrix(nrow, ncol);
             for (j = 0; j < ncol; j++)
                 for (i = 0; i < nrow; i++)
-                    scanf("%lf", mat[i] + j);
+                    fscanf(fin, "%lf", mat[i] + j);
             long base = fout.tellp();
             sprintf(output, "%16d", 0);
             fout << output;
             sprintf(output, "{type=\"nerv.LinearTransParam\",id=\"affine%d_ltp\"}\n",
-                            cnt);
+                    cnt);
             fout << output;
             sprintf(output, "%d %d\n", nrow, ncol);
             fout << output;
@@ -38,19 +58,17 @@ int main(int argc, char **argv) {
                 for (j = 0; j < ncol; j++)
                     fout << mat[i][j] << " ";
                 fout << std::endl;
-                free(mat[i]);
             }
-            free(mat);
             long length = fout.tellp() - base;
             fout.seekp(base);
             sprintf(output, "[%13lu]\n", length);
             fout << output;
             fout.seekp(0, std::ios_base::end);
-            if (scanf("%s %d", token, &ncol) == 2 && *token == 'v')
+            if (fscanf(fin, "%s %d", token, &ncol) == 2 && *token == 'v')
             {
                 base = fout.tellp();
                 for (j = 0; j < ncol; j++)
-                    scanf("%lf", mat[0] + j);
+                    fscanf(fin, "%lf", mat[0] + j);
                 sprintf(output, "%16d", 0);
                 fout << output;
                 sprintf(output, "{type=\"nerv.BiasParam\",id=\"affine%d_bp\"}\n",
@@ -68,6 +86,7 @@ int main(int argc, char **argv) {
                 fout.seekp(0, std::ios_base::end);
                 cnt++;
             }
+            free_matrix(mat, nrow, ncol);
         }
     }
     return 0;
diff --git a/kaldi_io/tools/kaldi_to_nerv b/kaldi_io/tools/kaldi_to_nerv
index 78469f8..08a3d76 100755
--- a/kaldi_io/tools/kaldi_to_nerv
+++ b/kaldi_io/tools/kaldi_to_nerv
diff --git a/kaldi_io/tools/nerv_to_kaldi.lua b/kaldi_io/tools/nerv_to_kaldi.lua
index fba6a6c..0f0cb2a 100644
--- a/kaldi_io/tools/nerv_to_kaldi.lua
+++ b/kaldi_io/tools/nerv_to_kaldi.lua
@@ -1,6 +1,7 @@
 -- usage: nerv nerv_to_kaldi.lua config_file nerv_param_input kaldi_param_output
 
 dofile(arg[1])
+gconf.mmat_type = nerv.MMatrixFloat
 param_repo = nerv.ParamRepo()
 param_repo:import({arg[2]}, nil, gconf)
 layer_repo = make_layer_repo(param_repo)
diff --git a/speech_utils/init.lua b/speech_utils/init.lua
index 9e8adba..0b65edc 100644
--- a/speech_utils/init.lua
+++ b/speech_utils/init.lua
@@ -1,13 +1,18 @@
 nerv.speech_utils = {}
 
-function nerv.speech_utils.global_transf(feat_utter, global_transf,
+function nerv.speech_utils.global_transf(feat_utter, network,
                                         frm_ext, frm_trim, gconf)
     -- prepare for transf
     local input = {feat_utter}
     local output = {feat_utter:create()}
     -- do transf
-    global_transf:init(input[1]:nrow())
-    global_transf:propagate(input, output)
+    local batch_size = input[1]:nrow()
+    network:mini_batch_init({seq_length = table.vector(batch_size, 1),
+                            new_seq = {},
+                            do_train = false,
+                            input = {input},
+                            output = {output}})
+    network:propagate()
     -- trim frames
     if gconf.use_cpu then
         mat_type = gconf.mmat_type