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require 'speech.init'
gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
cumat_type = nerv.CuMatrixFloat,
mmat_type = nerv.MMatrixFloat,
batch_size = 256}
param_repo = nerv.ParamRepo({"converted.nerv", "global_transf.nerv"})
sublayer_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"] =
{
softmax_ce0 = {{}, {dim_in = {3001, 1}, dim_out = {}, compressed = true}}
}
}, param_repo, gconf)
layer_repo = nerv.LayerRepo(
{
["nerv.DAGLayer"] =
{
global_transf = {{}, {
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]"
}
}},
main = {{}, {
dim_in = {429, 1}, dim_out = {},
sub_layers = sublayer_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]"] = "softmax_ce0[1]",
["<input>[2]"] = "softmax_ce0[2]"
}
}}
}
}, param_repo, gconf)
tnet_reader = nerv.TNetReader(gconf,
{
id = "main_scp",
scp_file = "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
-- scp_file = "t.scp",
conf_file = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
frm_ext = 5,
mlfs = {
ref = {
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")
})
buffer = nerv.SGDBuffer(gconf,
{
buffer_size = 81920,
-- randomize = true,
readers = {
{ reader = tnet_reader,
data = {main_scp = 429, ref = 1}}
}
})
sm = sublayer_repo:get_layer("softmax_ce0")
main = layer_repo:get_layer("main")
main:init(gconf.batch_size)
gconf.cnt = 0
for data in buffer.get_data, buffer do
if gconf.cnt == 1000 then break end
gconf.cnt = gconf.cnt + 1
input = {data.main_scp, data.ref}
output = {}
err_input = {}
err_output = {input[1]:create()}
main:propagate(input, output)
main:back_propagate(err_output, err_input, input, output)
main:update(err_input, input, output)
nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
nerv.utils.printf("correct: %d\n", sm.total_correct)
nerv.utils.printf("frames: %d\n", sm.total_frames)
nerv.utils.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
nerv.utils.printf("accuracy: %.8f\n", sm.total_correct / sm.total_frames)
collectgarbage("collect")
end
nerv.Matrix.print_profile()
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