path: root/examples/tnet_preprocessing_example2.lua

require 'speech.init'
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)

reader = nerv.TNetReader({},
    {
        id = "main_scp",
        scp_file = "/slfs1/users/mfy43/swb_ivec/train_bp.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("main")
    })

utter = reader:get_data()
-- print(utter.main_scp)
print(utter.ref)
-- cf2 = nerv.ChunkFile("feat_256", "r")
-- input = cf2:read_chunk("input", gconf)

-- for i = 0, 157 - 10 do
--     row_diff = input.trans[i] - utter.main_scp[i]
--     for j = 0, row_diff:ncol() - 1 do
--         nerv.printf("%.8f ", row_diff[j])
--     end
--     nerv.printf("\n")
-- end