require 'kaldi_io' require 'kaldi_seq' gconf = {lrate = 0.00001, wcost = 0, momentum = 0.0, cumat_type = nerv.CuMatrixFloat, mmat_type = nerv.MMatrixFloat, frm_ext = 5, tr_scp = "ark,s,cs:/slfs6/users/ymz09/kaldi/src/featbin/copy-feats scp:/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_smbr/train.scp ark:- |", initialized_param = {"/slfs6/users/ymz09/nerv-project/nerv/nerv-speech/kaldi_seq/test/chime3_init.nerv", "/slfs6/users/ymz09/nerv-project/nerv/nerv-speech/kaldi_seq/test/chime3_global_transf.nerv"}, decode_param = {"/slfs6/users/ymz09/nerv-project/test_mpe/1.nerv", "/slfs6/users/ymz09/nerv-project/nerv/nerv-speech/kaldi_seq/test/chime3_global_transf.nerv"}, debug = false} function make_layer_repo(param_repo) local layer_repo = nerv.LayerRepo( { -- global transf ["nerv.BiasLayer"] = { blayer1 = {{bias = "bias1"}, {dim_in = {440}, dim_out = {440}}}, blayer2 = {{bias = "bias2"}, {dim_in = {440}, dim_out = {440}}} }, ["nerv.WindowLayer"] = { wlayer1 = {{window = "window1"}, {dim_in = {440}, dim_out = {440}}}, wlayer2 = {{window = "window2"}, {dim_in = {440}, dim_out = {440}}} }, -- biased linearity ["nerv.AffineLayer"] = { affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"}, {dim_in = {440}, dim_out = {2048}}}, affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"}, {dim_in = {2048}, dim_out = {2048}}}, affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"}, {dim_in = {2048}, dim_out = {2048}}}, affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"}, {dim_in = {2048}, dim_out = {2048}}}, affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"}, {dim_in = {2048}, dim_out = {2048}}}, affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"}, {dim_in = {2048}, dim_out = {2048}}}, affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"}, {dim_in = {2048}, dim_out = {2048}}}, affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"}, {dim_in = {2048}, dim_out = {2011}}} }, ["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.MPELayer"] = { mpe_crit = {{}, {dim_in = {2011, -1}, dim_out = {1}, cmd = { arg = "--class-frame-counts=/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced/ali_train_pdf.counts --acoustic-scale=0.1 --lm-scale=1.0 --learn-rate=0.00001 --do-smbr=true --verbose=1", mdl = "/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_ali/final.mdl", lat = "scp:/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_denlats/lat.scp", ali = "ark:gunzip -c /slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_ali/ali.*.gz |" } } } }, ["nerv.SoftmaxLayer"] = -- softmax for decode output { softmax = {{}, {dim_in = {2011}, dim_out = {2011}}} } }, param_repo, gconf) layer_repo:add_layers( { ["nerv.DAGLayer"] = { global_transf = {{}, { dim_in = {440}, dim_out = {440}, sub_layers = layer_repo, connections = { ["[1]"] = "blayer1[1]", ["blayer1[1]"] = "wlayer1[1]", ["wlayer1[1]"] = "blayer2[1]", ["blayer2[1]"] = "wlayer2[1]", ["wlayer2[1]"] = "[1]" } }}, main = {{}, { dim_in = {440}, dim_out = {2011}, sub_layers = layer_repo, connections = { ["[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]"] = "[1]" } }} } }, param_repo, gconf) layer_repo:add_layers( { ["nerv.DAGLayer"] = { mpe_output = {{}, { dim_in = {440, -1}, dim_out = {1}, sub_layers = layer_repo, connections = { ["[1]"] = "main[1]", ["main[1]"] = "mpe_crit[1]", ["[2]"] = "mpe_crit[2]", ["mpe_crit[1]"] = "[1]" } }}, decode_output = {{}, { dim_in = {440}, dim_out = {2011}, sub_layers = layer_repo, connections = { ["[1]"] = "main[1]", ["main[1]"] = "[1]" } }} } }, param_repo, gconf) return layer_repo end function get_network(layer_repo) return layer_repo:get_layer("mpe_output") end function get_decode_network(layer_repo) return layer_repo:get_layer("decode_output") end function get_global_transf(layer_repo) return layer_repo:get_layer("global_transf") end function make_readers(feature_rspecifier, layer_repo) return { {reader = nerv.KaldiReader(gconf, { id = "main_scp", feature_rspecifier = feature_rspecifier, frm_ext = gconf.frm_ext, global_transf = layer_repo:get_layer("global_transf"), need_key = true, mlfs = {} }) } } end function get_input_order() return {{id = "main_scp", global_transf = true}, {id = "key"}} end function get_accuracy(layer_repo) local mpe_crit = layer_repo:get_layer("mpe_crit") return mpe_crit.total_correct / mpe_crit.total_frames * 100 end function print_stat(layer_repo) local mpe_crit = layer_repo:get_layer("mpe_crit") nerv.info("*** training stat begin ***") nerv.printf("correct:\t\t%d\n", mpe_crit.total_correct) nerv.printf("frames:\t\t\t%d\n", mpe_crit.total_frames) nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(layer_repo)) nerv.info("*** training stat end ***") end