support kaldi decoder

1 files changed, 79 insertions, 0 deletions

diff --git a/kaldi_decode/src/nerv4decode.lua b/kaldi_decode/src/nerv4decode.lua
new file mode 100644
index 0000000..b2ff344
--- /dev/null
+++ b/kaldi_decode/src/nerv4decode.lua
@@ -0,0 +1,79 @@
+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