let HTK I/O implementation be a single package

1 files changed, 0 insertions, 75 deletions

diff --git a/examples/tnet_preprocessing_example.lua b/examples/tnet_preprocessing_example.lua
deleted file mode 100644
index 9e1c0ce..0000000
--- a/examples/tnet_preprocessing_example.lua
+++ /dev/null
@@ -1,75 +0,0 @@
-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