refactor kaldi_decode

1 files changed, 0 insertions, 86 deletions

diff --git a/kaldi_decode/src/nerv4decode.lua b/kaldi_decode/src/nerv4decode.lua
deleted file mode 100644
index 898b5a8..0000000
--- a/kaldi_decode/src/nerv4decode.lua
+++ /dev/null
@@ -1,86 +0,0 @@
-print = function(...) io.write(table.concat({...}, "\t")) end
-io.output('/dev/null')
--- path and cpath are correctly set by `path.sh`
-local k,l,_=pcall(require,"luarocks.loader") _=k and l.add_context("nerv","scm-1")
-require 'nerv'
-nerv.printf("*** NERV: A Lua-based toolkit for high-performance deep learning (alpha) ***\n")
-nerv.info("automatically initialize a default MContext...")
-nerv.MMatrix._default_context = nerv.MContext()
-nerv.info("the default MContext is ok")
--- only for backward compatibilty, will be removed in the future
-local function _add_profile_method(cls)
-    local c = cls._default_context
-    cls.print_profile = function () c:print_profile() end
-    cls.clear_profile = function () c:clear_profile() end
-end
-_add_profile_method(nerv.MMatrix)
-
-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
-                transformed = nerv.speech_utils.global_transf(data[id],
-                global_transf,
-                gconf.frm_ext or 0, 0,
-                gconf)
-            else
-                transformed = data[id]
-            end
-            table.insert(input, transformed)
-        end
-        local output = {nerv.MMatrixFloat(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)
-    dofile(config)
-    gconf.use_cpu = true -- use CPU to decode
-    trainer = build_trainer(gconf.decode_param, feature)
-end
-
-function feed()
-    local utt, mat = trainer()
-    return utt, mat
-end