move global_transf to asr_trainer.lua

1 files changed, 19 insertions, 4 deletions

diff --git a/nerv/examples/asr_trainer.lua b/nerv/examples/asr_trainer.lua
index dcadfa3..5a50542 100644
--- a/nerv/examples/asr_trainer.lua
+++ b/nerv/examples/asr_trainer.lua
@@ -3,6 +3,7 @@ function build_trainer(ifname)
     param_repo:import(ifname, nil, gconf)
     local layer_repo = make_layer_repo(param_repo)
     local network = get_network(layer_repo)
+    local global_transf = get_global_transf(layer_repo)
     local input_order = get_input_order()
     local iterative_trainer = function (prefix, scp_file, bp)
         gconf.randomize = bp
@@ -24,15 +25,29 @@ function build_trainer(ifname)
                 -- break
             end
             local input = {}
---            if gconf.cnt == 100 then break end
-            for i, id in ipairs(input_order) do
+--            if gconf.cnt == 1000 then break end
+            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
-                table.insert(input, data[id])
+                local transformed
+                if e.global_transf then
+                    transformed = nerv.speech_utils.global_transf(data[id],
+                                        global_transf,
+                                        gconf.frm_ext or 0,
+                                        gconf.frm_trim or 0,
+                                        gconf)
+                else
+                    transformed = data[id]
+                end
+                table.insert(input, transformed)
             end
             local output = {nerv.CuMatrixFloat(gconf.batch_size, 1)}
-            err_output = {input[1]:create()}
+            err_output = {}
+            for i = 1, #input do
+                table.insert(err_output, input[i]:create())
+            end
             network:propagate(input, output)
             if bp then
                 network:back_propagate(err_input, err_output, input, output)