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-rw-r--r--kaldi_decode/src/asr_propagator.lua41
1 files changed, 13 insertions, 28 deletions
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