1 files changed, 0 insertions, 79 deletions
diff --git a/kaldi_decode/src/nerv4decode.lua b/kaldi_decode/src/nerv4decode.lua
deleted file mode 100644
index b2ff344..0000000
--- a/kaldi_decode/src/nerv4decode.lua
+++ /dev/null
@@ -1,79 +0,0 @@
-package.path="/home/slhome/ymz09/.luarocks/share/lua/5.1/?.lua;/home/slhome/ymz09/.luarocks/share/lua/5.1/?/init.lua;/slfs6/users/ymz09/nerv-project/nerv/install/share/lua/5.1/?.lua;/slfs6/users/ymz09/nerv-project/nerv/install/share/lua/5.1/?/init.lua;"..package.path; 
-package.cpath="/home/slhome/ymz09/.luarocks/lib/lua/5.1/?.so;/slfs6/users/ymz09/nerv-project/nerv/install/lib/lua/5.1/?.so;"..package.cpath;
-local k,l,_=pcall(require,"luarocks.loader") _=k and l.add_context("nerv","scm-1")
-require 'nerv'
-
-function build_trainer(ifname, feature)
-    local param_repo = nerv.ParamRepo()
-    param_repo:import(ifname, nil, gconf)
-    local layer_repo = make_layer_repo(param_repo)
-    local network = get_decode_network(layer_repo)
-    local global_transf = get_global_transf(layer_repo)
-    local input_order = get_input_order()
-    local readers = make_readers(feature, layer_repo)
-    network:init(1)
-
-    local iterative_trainer = function()
-        local data = nil
-        for ri = 1, #readers, 1 do
-            data = readers[ri].reader:get_data()
-            if data ~= nil then
-                break
-            end
-        end
-
-        if data == nil then
-            return "", nil
-        end
-
-        local input = {}
-        for i, e in ipairs(input_order) do
-            local id = e.id
-            if data[id] == nil then
-                nerv.error("input data %s not found", id)
-            end
-            local transformed
-            if e.global_transf then
-                local batch = gconf.cumat_type(data[id]:nrow(), data[id]:ncol())
-                batch:copy_fromh(data[id])
-                transformed = nerv.speech_utils.global_transf(batch,
-                global_transf,
-                gconf.frm_ext or 0, 0,
-                gconf)
-            else
-                transformed = data[id]
-            end
-            table.insert(input, transformed)
-        end
-        local output = {nerv.CuMatrixFloat(input[1]:nrow(), network.dim_out[1])}
-        network:batch_resize(input[1]:nrow())
-        network:propagate(input, output)
-        
-        local utt = data["key"]
-        if utt == nil then
-            nerv.error("no key found.")
-        end
-
-        local mat = nerv.MMatrixFloat(output[1]:nrow(), output[1]:ncol())
-        output[1]:copy_toh(mat)
-
-        collectgarbage("collect")
-        return utt, mat
-    end
-
-    return iterative_trainer
-end
-
-function init(config, feature)
-    local tmp = io.write
-    io.write = function(...)
-    end
-    dofile(config)
-    trainer = build_trainer(gconf.decode_param, feature)
-    io.write = tmp
-end
-
-function feed()
-    local utt, mat = trainer()
-    return utt, mat
-end