From 3101d1f9c1b2e31fbde75c1c9de5f6872340f5f7 Mon Sep 17 00:00:00 2001
From: Determinant <ted.sybil@gmail.com>
Date: Sun, 8 May 2016 11:40:13 +0800
Subject: change decoder API (adapted to `trainer.lua`); remove redundant
 options in kaldi_io

---
 kaldi_decode/src/asr_propagator.lua | 41 ++++++++++++-------------------------
 1 file changed, 13 insertions(+), 28 deletions(-)

(limited to 'kaldi_decode')

diff --git a/kaldi_decode/src/asr_propagator.lua b/kaldi_decode/src/asr_propagator.lua
index a3c5eb1..ab18d6d 100644
--- a/kaldi_decode/src/asr_propagator.lua
+++ b/kaldi_decode/src/asr_propagator.lua
@@ -16,34 +16,33 @@ end
 _add_profile_method(nerv.MMatrix)
 
 function build_propagator(ifname, feature)
+    -- FIXME: this is still a hack
+    local trainer = nerv.Trainer
+    ----
     local param_repo = nerv.ParamRepo()
     param_repo:import(ifname, 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 layer_repo = trainer.make_layer_repo(nil, param_repo)
+    local network = trainer.get_decode_network(nil, layer_repo)
+    local input_order = trainer.get_decode_input_order(nil)
     local input_name = gconf.decode_input_name or "main_scp"
-    local readers = make_decode_readers(feature, layer_repo)
-    --nerv.info("prepare")
+    local readers = trainer.make_decode_readers(nil, feature)
+    -- nerv.info("prepare")
     local buffer = nerv.SeqBuffer(gconf, {
                                         buffer_size = gconf.buffer_size,
                                         batch_size = gconf.batch_size,
                                         chunk_size = gconf.chunk_size,
                                         randomize = gconf.randomize,
                                         readers = readers,
-                                        use_gpu = true
                                 })
 
     network = nerv.Network("nt", gconf, {network = network})
     network:init(gconf.batch_size, gconf.chunk_size)
-    global_transf = nerv.Network("gt", gconf, {network = global_transf})
-    global_transf:init(gconf.batch_size, gconf.chunk_size)
 
     local prev_data = buffer:get_data() or nerv.error("no data in buffer")
     local terminate = false
     local input_pos = nil
     for i, v in ipairs(input_order) do
-        if v.id == input_name then
+        if v == input_name then
             input_pos = i
         end
     end
@@ -54,7 +53,6 @@ function build_propagator(ifname, feature)
         if terminate then
             return "", nil
         end
-        global_transf:epoch_init()
         network:epoch_init()
         local accu_output = {}
         local utt_id = readers[input_pos].reader.key
@@ -79,24 +77,11 @@ function build_propagator(ifname, feature)
 
             local input = {}
             local output = {{}}
-            for i, e in ipairs(input_order) do
-                local id = e.id
+            for i, id in ipairs(input_order) do
                 if d.data[id] == nil then
                     nerv.error("input data %s not found", id)
                 end
-                local transformed = {}
-                if e.global_transf then
-                    for _, mini_batch in ipairs(d.data[id]) do
-                        table.insert(transformed,
-                                        nerv.speech_utils.global_transf(mini_batch,
-                                            global_transf,
-                                            gconf.frm_ext or 0, 0,
-                                            gconf))
-                    end
-                else
-                    transformed = d.data[id]
-                end
-                table.insert(input, transformed)
+                table.insert(input, d.data[id])
                 for i = 1, gconf.chunk_size do
                     table.insert(output[1], gconf.mmat_type(gconf.batch_size, network.dim_out[1]))
                 end
@@ -137,10 +122,10 @@ function init(config, feature)
     gconf.mmat_type = nerv.MMatrixFloat
     gconf.use_cpu = true -- use CPU to decode
     gconf.batch_size = 1
-    trainer = build_propagator(gconf.decode_param, feature)
+    propagator = build_propagator(gconf.decode_param, feature)
 end
 
 function feed()
-    local utt, mat = trainer()
+    local utt, mat = propagator()
     return utt, mat
 end
-- 
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