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require 'htk_io'
gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
cumat_type = nerv.CuMatrixFloat,
mmat_type = nerv.MMatrixFloat,
frm_ext = 5,
tr_scp = "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
cv_scp = "/slfs1/users/mfy43/swb_ivec/train_cv.scp",
htk_conf = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
initialized_param = {"/slfs1/users/mfy43/swb_init.nerv",
"/slfs1/users/mfy43/swb_global_transf.nerv"},
debug = false}
function make_layer_repo(param_repo)
local layer_repo = nerv.LayerRepo(
{
-- global transf
["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}}}
},
-- biased linearity
["nerv.AffineLayer"] =
{
affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
{dim_in = {429}, 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 = {3001}}}
},
["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.SoftmaxCELayer"] =
{
ce_crit = {{}, {dim_in = {3001, 1}, dim_out = {1}, compressed = true}}
}
}, param_repo, gconf)
layer_repo:add_layers(
{
["nerv.DAGLayer"] =
{
global_transf = {{}, {
dim_in = {429}, dim_out = {429},
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 = {429, 1}, dim_out = {1},
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]"] = "ce_crit[1]",
["<input>[2]"] = "ce_crit[2]",
["ce_crit[1]"] = "<output>[1]"
}
}}
}
}, param_repo, gconf)
return layer_repo
end
function get_network(layer_repo)
return layer_repo:get_layer("main")
end
function make_readers(scp_file, layer_repo)
return {
{reader = nerv.TNetReader(gconf,
{
id = "main_scp",
scp_file = scp_file,
conf_file = gconf.htk_conf,
frm_ext = gconf.frm_ext,
mlfs = {
phone_state = {
file = "/slfs1/users/mfy43/swb_ivec/ref.mlf",
format = "map",
format_arg = "/slfs1/users/mfy43/swb_ivec/dict",
dir = "*/",
ext = "lab"
}
},
global_transf = layer_repo:get_layer("global_transf")
}),
data = {main_scp = 429, phone_state = 1}}
}
end
function make_buffer(readers)
return nerv.SGDBuffer(gconf,
{
buffer_size = gconf.buffer_size,
randomize = gconf.randomize,
readers = readers
})
end
function get_input_order()
return {"main_scp", "phone_state"}
end
function get_accuracy(layer_repo)
local ce_crit = layer_repo:get_layer("ce_crit")
return ce_crit.total_correct / ce_crit.total_frames * 100
end
function print_stat(layer_repo)
local ce_crit = layer_repo:get_layer("ce_crit")
nerv.info("*** training stat begin ***")
nerv.printf("cross entropy:\t\t%.8f\n", ce_crit.total_ce)
nerv.printf("correct:\t\t%d\n", ce_crit.total_correct)
nerv.printf("frames:\t\t\t%d\n", ce_crit.total_frames)
nerv.printf("err/frm:\t\t%.8f\n", ce_crit.total_ce / ce_crit.total_frames)
nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(layer_repo))
nerv.info("*** training stat end ***")
end
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