let HTK I/O implementation be a single package

1 files changed, 75 insertions, 0 deletions

diff --git a/htk_io/examples/tnet_preprocessing_example.lua b/htk_io/examples/tnet_preprocessing_example.lua
new file mode 100644
index 0000000..9e1c0ce
--- /dev/null
+++ b/htk_io/examples/tnet_preprocessing_example.lua
@@ -0,0 +1,75 @@
+require 'libspeech'
+frm_ext = 5
+gconf = {cumat_type = nerv.CuMatrixFloat,
+        batch_size = 158}
+param_repo = nerv.ParamRepo({"global_transf.nerv"})
+sublayer_repo = nerv.LayerRepo(
+    {
+        ["nerv.BiasLayer"] =
+        {
+            blayer1 = {{bias = "bias1"}, {dim_in = {429}, dim_out = {429}}},
+            blayer2 = {{bias = "bias2"}, {dim_in = {429}, dim_out = {429}}}
+        },
+        ["nerv.WindowLayer"] =
+        {
+            wlayer1 = {{window = "window1"}, {dim_in = {429}, dim_out = {429}}},
+            wlayer2 = {{window = "window2"}, {dim_in = {429}, dim_out = {429}}}
+        }
+    }, param_repo, gconf)
+
+layer_repo = nerv.LayerRepo(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            main = {{}, {
+                dim_in = {429}, dim_out = {429},
+                sub_layers = sublayer_repo,
+                connections = {
+                    ["<input>[1]"] = "blayer1[1]",
+                    ["blayer1[1]"] = "wlayer1[1]",
+                    ["wlayer1[1]"] = "blayer2[1]",
+                    ["blayer2[1]"] = "wlayer2[1]",
+                    ["wlayer2[1]"] = "<output>[1]"
+                }
+            }}
+        }
+    }, param_repo, gconf)
+
+feat_repo = nerv.TNetFeatureRepo(
+                                "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
+                                "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
+                                frm_ext)
+lab_repo = nerv.TNetLabelRepo(
+                                "/slfs1/users/mfy43/swb_ivec/ref.mlf",
+                                "map",
+                                "/slfs1/users/mfy43/swb_ivec/dict",
+                                "*/",
+                                "lab")
+feat_utter = feat_repo:cur_utter()
+
+-- print(feat_utter)
+-- lab_utter = lab_repo:get_utter(feat_repo, feat_utter:nrow() - frm_ext * 2)
+-- print(lab_utter)
+
+cf2 = nerv.ChunkFile("feat_256", "r")
+input = cf2:read_chunk("input", gconf)
+
+step = frm_ext * 2 + 1
+expanded = nerv.CuMatrixFloat(feat_utter:nrow(), feat_utter:ncol() * step)
+expanded:expand_frm(nerv.CuMatrixFloat.new_from_host(feat_utter), frm_ext)
+
+rearranged = expanded:create()
+rearranged:rearrange_frm(expanded, step)
+
+output = {expanded:create()}
+main = layer_repo:get_layer("main")
+main:init()
+main:propagate({rearranged}, output)
+
+for i = 0, 157 - 10 do
+    row_diff = input.trans[i] - output[1][i + 5]
+    for j = 0, row_diff:ncol() - 1 do
+        nerv.printf("%.8f ", row_diff[j])
+    end
+    nerv.printf("\n")
+end