diff options
Diffstat (limited to 'nerv/examples/swb_baseline2.lua')
-rw-r--r-- | nerv/examples/swb_baseline2.lua | 203 |
1 files changed, 203 insertions, 0 deletions
diff --git a/nerv/examples/swb_baseline2.lua b/nerv/examples/swb_baseline2.lua new file mode 100644 index 0000000..8b5ebb1 --- /dev/null +++ b/nerv/examples/swb_baseline2.lua @@ -0,0 +1,203 @@ +require 'htk_io' +gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9, + rearrange = true, -- just to make the context order consistent with old results, deprecated + frm_ext = 5, + frm_trim = 5, -- trim the first and last 5 frames, TNet just does this, deprecated + tr_scp = "/speechlab/users/mfy43/swb50/train_bp.scp", + cv_scp = "/speechlab/users/mfy43/swb50/train_cv.scp", + htk_conf = "/speechlab/users/mfy43/swb50/plp_0_d_a.conf", + initialized_param = {"/speechlab/users/mfy43/swb50/swb_init.nerv", + "/speechlab/users/mfy43/swb50/swb_global_transf.nerv"}} + +function make_layer_repo(param_repo) + local layer_repo = nerv.LayerRepo( + { + -- global transf + ["nerv.BiasLayer"] = + { + blayer1 = {dim_in = {429}, dim_out = {429}, params = {bias = "bias1"}}, + blayer2 = {dim_in = {429}, dim_out = {429}, params = {bias = "bias2"}} + }, + ["nerv.WindowLayer"] = + { + wlayer1 = {dim_in = {429}, dim_out = {429}, params = {window = "window1"}}, + wlayer2 = {dim_in = {429}, dim_out = {429}, params = {window = "window2"}} + }, + -- biased linearity + ["nerv.AffineLayer"] = + { + affine0 = {dim_in = {429}, dim_out = {2048}, + params = {ltp = "affine0_ltp", bp = "affine0_bp"}}, + affine1 = {dim_in = {2048}, dim_out = {2048}, + params = {ltp = "affine1_ltp", bp = "affine1_bp"}}, + affine2 = {dim_in = {2048}, dim_out = {2048}, + params = {ltp = "affine2_ltp", bp = "affine2_bp"}}, + affine3 = {dim_in = {2048}, dim_out = {2048}, + params = {ltp = "affine3_ltp", bp = "affine3_bp"}}, + affine4 = {dim_in = {2048}, dim_out = {2048}, + params = {ltp = "affine4_ltp", bp = "affine4_bp"}}, + affine5 = {dim_in = {2048}, dim_out = {2048}, + params = {ltp = "affine5_ltp", bp = "affine5_bp"}}, + affine6 = {dim_in = {2048}, dim_out = {2048}, + params = {ltp = "affine6_ltp", bp = "affine6_bp"}}, + affine7 = {dim_in = {2048}, dim_out = {3001}, + params = {ltp = "affine7_ltp", bp = "affine7_bp"}} + }, + ["nerv.SigmoidLayer"] = + { + sigmoid0 = {dim_in = {2048}, dim_out = {2048}}, + sigmoid1 = {dim_in = {2048}, dim_out = {2048}}, + sigmoid2 = {dim_in = {2048}, dim_out = {2048}}, + sigmoid3 = {dim_in = {2048}, dim_out = {2048}}, + sigmoid4 = {dim_in = {2048}, dim_out = {2048}}, + sigmoid5 = {dim_in = {2048}, dim_out = {2048}}, + sigmoid6 = {dim_in = {2048}, dim_out = {2048}} + }, + ["nerv.SoftmaxCELayer"] = -- softmax + ce criterion layer for finetune output + { + ce_crit = {dim_in = {3001, 1}, dim_out = {1}, compressed = true} + }, + ["nerv.SoftmaxLayer"] = -- softmax for decode output + { + softmax = {dim_in = {3001}, dim_out = {3001}} + } + }, param_repo, gconf) + + layer_repo:add_layers( + { + ["nerv.DAGLayer"] = + { + global_transf = { + dim_in = {429}, dim_out = {429}, + sub_layers = layer_repo, + connections = { + ["<input>[1]"] = "blayer1[1]", + ["blayer1[1]"] = "wlayer1[1]", + ["wlayer1[1]"] = "blayer2[1]", + ["blayer2[1]"] = "wlayer2[1]", + ["wlayer2[1]"] = "<output>[1]" + } + }, + main = { + dim_in = {429}, dim_out = {3001}, + sub_layers = layer_repo, + connections = { + ["<input>[1]"] = "affine0[1]", + ["affine0[1]"] = "sigmoid0[1]", + ["sigmoid0[1]"] = "affine1[1]", + ["affine1[1]"] = "sigmoid1[1]", + ["sigmoid1[1]"] = "affine2[1]", + ["affine2[1]"] = "sigmoid2[1]", + ["sigmoid2[1]"] = "affine3[1]", + ["affine3[1]"] = "sigmoid3[1]", + ["sigmoid3[1]"] = "affine4[1]", + ["affine4[1]"] = "sigmoid4[1]", + ["sigmoid4[1]"] = "affine5[1]", + ["affine5[1]"] = "sigmoid5[1]", + ["sigmoid5[1]"] = "affine6[1]", + ["affine6[1]"] = "sigmoid6[1]", + ["sigmoid6[1]"] = "affine7[1]", + ["affine7[1]"] = "<output>[1]" + } + } + } + }, param_repo, gconf) + + layer_repo:add_layers( + { + ["nerv.DAGLayer"] = + { + ce_output = { + dim_in = {429, 1}, dim_out = {1}, + sub_layers = layer_repo, + connections = { + ["<input>[1]"] = "main[1]", + ["main[1]"] = "ce_crit[1]", + ["<input>[2]"] = "ce_crit[2]", + ["ce_crit[1]"] = "<output>[1]" + } + }, + softmax_output = { + dim_in = {429}, dim_out = {3001}, + sub_layers = layer_repo, + connections = { + ["<input>[1]"] = "main[1]", + ["main[1]"] = "softmax[1]", + ["softmax[1]"] = "<output>[1]" + } + } + } + }, param_repo, gconf) + + return layer_repo +end + +function get_network(layer_repo) + return layer_repo:get_layer("ce_output") +end + +function get_decode_network(layer_repo) + return layer_repo:get_layer("softmax_output") +end + +function get_global_transf(layer_repo) + return layer_repo:get_layer("global_transf") +end + +function make_readers(scp_file, layer_repo) + return { + {reader = nerv.TNetReader(gconf, + { + id = "main_scp", + scp_file = scp_file, + conf_file = gconf.htk_conf, + frm_ext = gconf.frm_ext, + mlfs = { + phone_state = { + file = "/speechlab/users/mfy43/swb50/ref.mlf", + format = "map", + format_arg = "/speechlab/users/mfy43/swb50/dict", + dir = "*/", + ext = "lab" + } + } + }), + data = {main_scp = 429, phone_state = 1}} + } +end + +function make_buffer(readers) + return nerv.SGDBuffer(gconf, + { + buffer_size = gconf.buffer_size, + batch_size = gconf.batch_size, + randomize = gconf.randomize, + readers = readers, + use_gpu = true + }) +end + +function get_input_order() + return {{id = "main_scp", global_transf = true}, + {id = "phone_state"}} +end + +function get_decode_input_order() + return {{id = "main_scp", global_transf = true}} +end + +function get_accuracy(layer_repo) + local ce_crit = layer_repo:get_layer("ce_crit") + return ce_crit.total_correct / ce_crit.total_frames * 100 +end + +function print_stat(layer_repo) + local ce_crit = layer_repo:get_layer("ce_crit") + nerv.info("*** training stat begin ***") + nerv.printf("cross entropy:\t\t%.8f\n", ce_crit.total_ce) + nerv.printf("correct:\t\t%d\n", ce_crit.total_correct) + nerv.printf("frames:\t\t\t%d\n", ce_crit.total_frames) + nerv.printf("err/frm:\t\t%.8f\n", ce_crit.total_ce / ce_crit.total_frames) + nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(layer_repo)) + nerv.info("*** training stat end ***") +end |