refactor kaldi_decode

1 files changed, 84 insertions, 0 deletions

diff --git a/kaldi_decode/src/asr_propagator.lua b/kaldi_decode/src/asr_propagator.lua
new file mode 100644
index 0000000..5d0ad7c
--- /dev/null
+++ b/kaldi_decode/src/asr_propagator.lua
@@ -0,0 +1,84 @@
+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_propagator(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_decode_input_order()
+    local readers = make_decode_readers(feature, layer_repo)
+
+    local batch_propagator = function()
+        local data = nil
+        for ri = 1, #readers do
+            data = readers[ri].reader:get_data()
+            if data ~= nil then
+                break
+            end
+        end
+
+        if data == nil then
+            return "", nil
+        end
+
+        gconf.batch_size = data[input_order[1].id]:nrow()
+        network:init(gconf.batch_size)
+
+        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:propagate(input, output)
+        
+        local utt = data["key"]
+        if utt == nil then
+            nerv.error("no key found.")
+        end
+
+        collectgarbage("collect")
+        return utt, output[1]
+    end
+
+    return batch_propagator
+end
+
+function init(config, feature)
+    dofile(config)
+    gconf.use_cpu = true -- use CPU to decode
+    trainer = build_propagator(gconf.decode_param, feature)
+end
+
+function feed()
+    local utt, mat = trainer()
+    return utt, mat
+end