adapt kaldi_decode propagator to the new arch

2 files changed, 18 insertions, 4 deletions

diff --git a/kaldi_decode/README.timit b/kaldi_decode/README.timit
index 0a3e33a..4c4e310 100755
--- a/kaldi_decode/README.timit
+++ b/kaldi_decode/README.timit
@@ -5,8 +5,8 @@ source cmd.sh
 gmmdir=/speechlab/users/mfy43/timit/s5/exp/tri3/
 data_fmllr=/speechlab/users/mfy43/timit/s5/data-fmllr-tri3/
 dir=/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/
-nerv_config=/speechlab/users/mfy43/nerv/nerv/examples/timit_baseline2.lua
-decode=/speechlab/users/mfy43/nerv/install/bin/decode_with_nerv.sh
+nerv_config=/speechlab/users/mfy43/timit/s5/timit_baseline2.lua
+decode=/speechlab/users/mfy43/timit/s5/nerv/install/bin/decode_with_nerv.sh
 
 # Decode (reuse HCLG graph)
 $decode --nj 20 --cmd "$decode_cmd" --acwt 0.2 \
diff --git a/kaldi_decode/src/asr_propagator.lua b/kaldi_decode/src/asr_propagator.lua
index 5d0ad7c..4005875 100644
--- a/kaldi_decode/src/asr_propagator.lua
+++ b/kaldi_decode/src/asr_propagator.lua
@@ -24,6 +24,9 @@ function build_propagator(ifname, feature)
     local input_order = get_decode_input_order()
     local readers = make_decode_readers(feature, layer_repo)
 
+    network = nerv.Network("nt", gconf, {network = network})
+    global_transf = nerv.Network("gt", gconf, {network = global_transf})
+
     local batch_propagator = function()
         local data = nil
         for ri = 1, #readers do
@@ -38,7 +41,10 @@ function build_propagator(ifname, feature)
         end
 
         gconf.batch_size = data[input_order[1].id]:nrow()
-        network:init(gconf.batch_size)
+        global_transf:init(gconf.batch_size, 1)
+        global_transf:epoch_init()
+        network:init(gconf.batch_size, 1)
+        network:epoch_init()
 
         local input = {}
         for i, e in ipairs(input_order) do
@@ -58,7 +64,14 @@ function build_propagator(ifname, feature)
             table.insert(input, transformed)
         end
         local output = {nerv.MMatrixFloat(input[1]:nrow(), network.dim_out[1])}
-        network:propagate(input, output)
+        network:mini_batch_init({seq_length = table.vector(gconf.batch_size, 1),
+                                new_seq = {},
+                                do_train = false,
+                                input = {input},
+                                output = {output},
+                                err_input = {},
+                                err_output = {}})
+        network:propagate()
         
         local utt = data["key"]
         if utt == nil then
@@ -74,6 +87,7 @@ end
 
 function init(config, feature)
     dofile(config)
+    gconf.mmat_type = nerv.MMatrixFloat
     gconf.use_cpu = true -- use CPU to decode
     trainer = build_propagator(gconf.decode_param, feature)
 end