add TNet tutorial; support converting global transf from TNet format

1 files changed, 45 insertions, 2 deletions

diff --git a/htk_io/tools/tnet_to_nerv.cpp b/htk_io/tools/tnet_to_nerv.cpp
index 63a104d..067097e 100644
--- a/htk_io/tools/tnet_to_nerv.cpp
+++ b/htk_io/tools/tnet_to_nerv.cpp
@@ -4,6 +4,7 @@
 #include <cstring>
 #include <cassert>
 #include <cstdlib>
+#include <map>
 
 char token[1024];
 char output[1024];
@@ -23,6 +24,18 @@ void free_matrix(double **mat, int nrow, int ncol) {
     delete [] mat;
 }
 
+int cnt0;
+std::map<std::string, int> param_cnt;
+int get_param_cnt(const std::string &key) {
+    std::map<std::string, int>::iterator it = param_cnt.find(key);
+    if (it == param_cnt.end())
+    {
+        param_cnt[key] = cnt0 + 1;
+        return cnt0;
+    }
+    return it->second++;
+}
+
 int main(int argc, char **argv) {
     FILE *fin;
     std::ofstream fout;
@@ -30,12 +43,14 @@ int main(int argc, char **argv) {
     fin = fopen(argv[1], "r");
     fout.open(argv[2]);
     assert(fin != NULL);
-    int cnt = argc > 3 ? atoi(argv[3]) : 0;
+    cnt0 = argc > 3 ? atoi(argv[3]) : 0;
+    bool shift;
     while (fscanf(fin, "%s", token) != EOF)
     {
         int nrow, ncol;
         int i, j;
         double **mat;
+        int cnt = get_param_cnt(token);
         if (strcmp(token, "<biasedlinearity>") == 0)
         {
             fscanf(fin, "%d %d", &ncol, &nrow);
@@ -84,10 +99,38 @@ int main(int argc, char **argv) {
                 sprintf(output, "[%13lu]\n", length);
                 fout << output;
                 fout.seekp(0, std::ios_base::end);
-                cnt++;
             }
             free_matrix(mat, nrow, ncol);
         }
+        else if ((shift = (strcmp(token, "<bias>") == 0)) ||
+                strcmp(token, "<window>") == 0)
+        {
+            fscanf(fin, "%d %d", &ncol, &nrow);
+            printf("%d %d\n", nrow, ncol);
+            assert(nrow == ncol);
+            mat = new_matrix(1, ncol);
+            assert(fscanf(fin, "%s %d", token, &ncol) == 2 && *token == 'v');
+            for (j = 0; j < ncol; j++)
+                    fscanf(fin, "%lf", mat[0] + j);
+            long base = fout.tellp();
+            sprintf(output, "%16d", 0);
+            fout << output;
+            sprintf(output, "{type=\"nerv.BiasParam\",id=\"%s%d\"}\n",
+                    shift ? "bias" : "window",
+                    cnt);
+            fout << output;
+            sprintf(output, "%d %d\n", 1, ncol);
+            fout << output;
+            for (j = 0; j < ncol; j++)
+                fout << mat[0][j] << " ";
+            fout << std::endl;
+            long length = fout.tellp() - base;
+            fout.seekp(base);
+            sprintf(output, "[%13lu]\n", length);
+            fout << output;
+            fout.seekp(0, std::ios_base::end);
+            free_matrix(mat, 1, ncol);
+        }
     }
     return 0;
 }