1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
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
|