require 'kaldi_io'
gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9, frm_ext = 5,
tr_scp = "ark:/speechlab/tools/KALDI/kaldi-master/src/featbin/copy-feats " ..
"scp:/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/train.scp ark:- |",
cv_scp = "ark:/speechlab/tools/KALDI/kaldi-master/src/featbin/copy-feats " ..
"scp:/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/cv.scp ark:- |",
initialized_param = {"/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/nnet_init.nerv",
"/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/nnet_output.nerv",
"/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/nnet_trans.nerv"},
-- params in nnet_trans.nerv are included in the trained model
decode_param = {"/speechlab/users/mfy43/timit/s5/nerv_20160311205342/nnet_init_20160311211609_iter_13_lr0.013437_tr72.572_cv58.709.nerv"},
chunk_size = 1}
function make_layer_repo(param_repo)
local layer_repo = nerv.LayerRepo(
{
-- global transf
["nerv.BiasLayer"] =
{
blayer1 = {dim_in = {440}, dim_out = {440}, params = {bias = "bias0"}}
},
["nerv.WindowLayer"] =
{
wlayer1 = {dim_in = {440}, dim_out = {440}, params = {window = "window0"}}
},
-- biased linearity
["nerv.AffineLayer"] =
{
affine0 = {dim_in = {440}, dim_out = {1024},
params = {ltp = "affine0_ltp", bp = "affine0_bp"}},
affine1 = {dim_in = {1024}, dim_out = {1024},
params = {ltp = "affine1_ltp", bp = "affine1_bp"}},
affine2 = {dim_in = {1024}, dim_out = {1024},
params = {ltp = "affine2_ltp", bp = "affine2_bp"}},
affine3 = {dim_in = {1024}, dim_out = {1024},
params = {ltp = "affine3_ltp", bp = "affine3_bp"}},
affine4 = {dim_in = {1024}, dim_out = {1024},
params = {ltp = "affine4_ltp", bp = "affine4_bp"}},
affine5 = {dim_in = {1024}, dim_out = {1024},
params = {ltp = "affine5_ltp", bp = "affine5_bp"}},
affine6 = {dim_in = {1024}, dim_out = {1959},
params = {ltp = "affine6_ltp", bp = "affine6_bp"}}
},
["nerv.SigmoidLayer"] =
{
sigmoid0 = {dim_in = {1024}, dim_out = {1024}},
sigmoid1 = {dim_in = {1024}, dim_out = {1024}},
sigmoid2 = {dim_in = {1024}, dim_out = {1024}},
sigmoid3 = {dim_in = {1024}, dim_out = {1024}},
sigmoid4 = {dim_in = {1024}, dim_out = {1024}},
sigmoid5 = {dim_in = {1024}, dim_out = {1024}}
},
["nerv.SoftmaxCELayer"] = -- softmax + ce criterion layer for finetune output
{
ce_crit = {dim_in = {1959, 1}, dim_out = {1}, compressed = true}
},
["nerv.SoftmaxLayer"] = -- softmax for decode output
{
softmax = {dim_in = {1959}, dim_out = {1959}}
}
}, param_repo, gconf)
layer_repo:add_layers(
{
["nerv.GraphLayer"] =
{
global_transf = {
dim_in = {440}, dim_out = {440},
layer_repo = layer_repo,
connections = {
{"<input>[1]", "blayer1[1]", 0},
{"blayer1[1]", "wlayer1[1]", 0},
{"wlayer1[1]", "<output>[1]", 0}
}
},
main = {
dim_in = {440}, dim_out = {1959},
layer_repo = layer_repo,
connections = {
{"<input>[1]", "affine0[1]", 0},
{"affine0[1]", "sigmoid0[1]", 0},
{"sigmoid0[1]", "affine1[1]", 0},
{"affine1[1]", "sigmoid1[1]", 0},
{"sigmoid1[1]", "affine2[1]", 0},
{"affine2[1]", "sigmoid2[1]", 0},
{"sigmoid2[1]", "affine3[1]", 0},
{"affine3[1]", "sigmoid3[1]", 0},
{"sigmoid3[1]", "affine4[1]", 0},
{"affine4[1]", "sigmoid4[1]", 0},
{"sigmoid4[1]", "affine5[1]", 0},
{"affine5[1]", "sigmoid5[1]", 0},
{"sigmoid5[1]", "affine6[1]", 0},
{"affine6[1]", "<output>[1]", 0}
}
}
}
}, param_repo, gconf)
layer_repo:add_layers(
{
["nerv.GraphLayer"] =
{
ce_output = {
dim_in = {440, 1}, dim_out = {1},
layer_repo = layer_repo,
connections = {
{"<input>[1]", "main[1]", 0},
{"main[1]", "ce_crit[1]", 0},
{"<input>[2]", "ce_crit[2]", 0},
{"ce_crit[1]", "<output>[1]", 0}
}
},
softmax_output = {
dim_in = {440}, dim_out = {1959},
layer_repo = layer_repo,
connections = {
{"<input>[1]", "main[1]", 0},
{"main[1]", "softmax[1]", 0},
{"softmax[1]", "<output>[1]", 0}
}
}
}
}, param_repo, gconf)
return layer_repo
end
function get_network(layer_repo)
return layer_repo:get_layer("ce_output")
end
function get_decode_network(layer_repo)
return layer_repo:get_layer("softmax_output")