require 'kaldi_io'
require 'kaldi_seq'
gconf = {lrate = 0.00001, wcost = 0, momentum = 0.0,
cumat_type = nerv.CuMatrixFloat,
mmat_type = nerv.MMatrixFloat,
frm_ext = 5,
tr_scp = "ark,o:/slfs6/users/ymz09/kaldi/src/featbin/copy-feats scp:/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_mmi/train.scp ark:- |",
initialized_param = {"/slfs6/users/ymz09/nerv-project/nerv/nerv-speech/kaldi_seq/test/chime3_init_mmi.nerv",
"/slfs6/users/ymz09/nerv-project/nerv/nerv-speech/kaldi_seq/test/chime3_global_transf_mmi.nerv"},
debug = false}
function make_layer_repo(param_repo)
local layer_repo = nerv.LayerRepo(
{
-- global transf
["nerv.BiasLayer"] =
{
blayer1 = {{bias = "bias1"}, {dim_in = {440}, dim_out = {440}}},
blayer2 = {{bias = "bias2"}, {dim_in = {440}, dim_out = {440}}}
},
["nerv.WindowLayer"] =
{
wlayer1 = {{window = "window1"}, {dim_in = {440}, dim_out = {440}}},
wlayer2 = {{window = "window2"}, {dim_in = {440}, dim_out = {440}}}
},
-- biased linearity
["nerv.AffineLayer"] =
{
affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
{dim_in = {440}, dim_out = {2048}}},
affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"},
{dim_in = {2048}, dim_out = {2048}}},
affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"},
{dim_in = {2048}, dim_out = {2048}}},
affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"},
{dim_in = {2048}, dim_out = {2048}}},
affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"},
{dim_in = {2048}, dim_out = {2048}}},
affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"},
{dim_in = {2048}, dim_out = {2048}}},
affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"},
{dim_in = {2048}, dim_out = {2048}}},
affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"},
{dim_in = {2048}, dim_out = {2011}}}
},
["nerv.SigmoidLayer"] =
{
sigmoid0 = {{}, {dim_in = {2048}, dim_out = {2048}}},
sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}},
sigmoid2 = {{}, {dim_in = {2048}, dim_out = {2048}}},
sigmoid3 = {{}, {dim_in = {2048}, dim_out = {2048}}},
sigmoid4 = {{}, {dim_in = {2048}, dim_out = {2048}}},
sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}},
sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}}
},
["nerv.MMILayer"] =
{
mmi_crit = {{}, {dim_in = {2011, -1}, dim_out = {1},
cmd = {
arg = "--class-frame-counts=/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced/ali_train_pdf.counts --acoustic-scale=0.1 --lm-scale=1.0 --learn-rate=0.00001 --drop-frames=true --verbose=1",
mdl = "/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_ali/final.mdl",
lat = "scp:/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_denlats/lat.scp",
ali = "ark:gunzip -c /slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_ali/ali.*.gz |"
}
}
}
},
["nerv.SoftmaxLayer"] = -- softmax for decode output
{
softmax = {{}, {dim_in = {2011}, dim_out = {2011}}}
}
}, param_repo, gconf)
layer_repo:add_layers(
{
["nerv.DAGLayer"] =
{
global_transf = {{}, {
dim_in = {440}, dim_out = {440},
sub_layers = layer_repo,
connections = {
["<input>[1]"] = "blayer1[1]",
["blayer1[1]"] = "wlayer1[1]",
["wlayer1[1]"] = "blayer2[1]",
["blayer2[1]"] = "wlayer2[1]",
["wlayer2[1]"] = "<output>[1]"
}
}},
main = {{}, {
dim_in = {440}, dim_out = {2011},
sub_layers = layer_repo,
connections = {
["<input>[1]"] = "affine0[1]",
["affine0[1]"] = "sigmoid0[1]",
["sigmoid0[1]"] = "affine1[1]",
["affine1[1]"] = "sigmoid1[1]",
["sigmoid1[1]"] = "affine2[1]",
["affine2[1]"] = "sigmoid2[1]",
["sigmoid2[1]"] = "affine3[1]",
["affine3[1]"] = "sigmoid3[1]",
["sigmoid3[1]"] = "affine4[1]",
["affine4[1]"] = "sigmoid4[1]",
["sigmoid4[1]"] = "affine5[1]",
["affine5[1]"] = "sigmoid5[1]",
["sigmoid5[1]"] = "affine6[1]",
["affine6[1]"] = "sigmoid6[1]",
["sigmoid6[1]"] = "affine7[1]",
["affine7[1]"] = "<output>[1]"
}
}}
}
}, param_repo, gconf)
layer_repo:add_layers(
{
["nerv.DAGLayer"] =
{
mmi_output = {{}, {
dim_in = {440, -1}, dim_out = {1},
sub_layers = layer_repo,
connections = {
["<input>[1]"] = "main[1]",
["main[1]"] = "mmi_crit[1]",
["<input>[2]"] = "mmi_crit[2]",
["mmi_crit[1]"] = "<output>[1]"
}
}},
softmax_output = {{}, {
dim_in = {440}, dim_out = {2011},
sub_layers = layer_repo,
connections = {
["<input>[1]"] = "main[1]",
["main[1]"] = "softmax[1]",
["softmax[1]"] = "<output>[1]"
}
}}
}
}, param_repo, gconf)
return layer_repo
end
function get_network(layer_repo)
return layer_repo:get_layer("mmi_output")
end
function get_decode_network(layer_repo)
return layer_repo:get_layer("softmax_output")
end
function get_global_transf(layer_repo)
return layer_repo:get_layer("global_transf")
end
function make_readers(feature_rspecifier, layer_repo)
return {
{reader = nerv.KaldiReader(gconf,
{
id = "main_scp",
feature_rspecifier = feature_rspecifier,
frm_ext = gconf.frm_ext,
global_transf = layer_repo:get_layer("global_transf"),
need_key = true,
mlfs = {}
})
}
}
end
function get_input_order()
return {{id = "main_scp", global_transf = true},
{id = "key"}}
end
function get_accuracy(layer_repo)
return 0
end
function print_stat(layer_repo)
local mmi_crit = layer_repo:get_layer("mmi_crit")
nerv.info("*** training stat begin ***")
nerv.printf("frames:\t\t\t%d\n", mmi_crit.total_frames)
nerv.info("*** training stat end ***")
end