require 'htk_io' gconf = {lrate = 0.2, wcost = 1e-6, momentum = 0.9, cumat_type = nerv.CuMatrixFloat, mmat_type = nerv.MMatrixFloat, frm_ext = 5, frm_trim = 5, batch_size = 256, buffer_size = 81920, rearrange = true, tr_scp = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/train.scp", cv_scp = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/train_cv.scp", htk_conf = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/fbank_d_a_z.conf", initialized_param = {"/sgfs/users/wd007/src/nerv/tools/nerv.global.transf", "/sgfs/users/wd007/src/nerv/tools/nerv.svd0.55_3000h_iter1.init"}, debug = false} function make_layer_repo(param_repo) local layer_repo = nerv.LayerRepo( { -- global transf ["nerv.BiasLayer"] = { blayer1 = {{bias = "bias1"}, {dim_in = {1320}, dim_out = {1320}}}, }, ["nerv.WindowLayer"] = { wlayer1 = {{window = "window1"}, {dim_in = {1320}, dim_out = {1320}}}, }, -- biased linearity ["nerv.AffineLayer"] = { affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"}, {dim_in = {1320}, dim_out = {2048}}}, affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"}, {dim_in = {2048}, dim_out = {367}}}, affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"}, {dim_in = {367}, dim_out = {2048}}}, affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"}, {dim_in = {2048}, dim_out = {408}}}, affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"}, {dim_in = {408}, dim_out = {2048}}}, affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"}, {dim_in = {2048}, dim_out = {368}}}, affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"}, {dim_in = {368}, dim_out = {2048}}}, affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"}, {dim_in = {2048}, dim_out = {303}}}, affine8 = {{ltp = "affine8_ltp", bp = "affine8_bp"}, {dim_in = {303}, dim_out = {2048}}}, affine9 = {{ltp = "affine9_ltp", bp = "affine9_bp"}, {dim_in = {2048}, dim_out = {277}}}, affine10 = {{ltp = "affine10_ltp", bp = "affine10_bp"}, {dim_in = {277}, dim_out = {2048}}}, affine11 = {{ltp = "affine11_ltp", bp = "affine11_bp"}, {dim_in = {2048}, dim_out = {361}}}, affine12 = {{ltp = "affine12_ltp", bp = "affine12_bp"}, {dim_in = {361}, dim_out = {2048}}}, affine13 = {{ltp = "affine13_ltp", bp = "affine13_bp"}, {dim_in = {2048}, dim_out = {441}}}, affine14 = {{ltp = "affine14_ltp", bp = "affine14_bp"}, {dim_in = {441}, dim_out = {10092}}}, }, ["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 = {10092, 1}, dim_out = {1}, compressed = true}} }, ["nerv.SoftmaxLayer"] = -- softmax for decode output { softmax = {{}, {dim_in = {10092}, dim_out = {10092}}} } }, param_repo, gconf) layer_repo:add_layers( { ["nerv.DAGLayer"] = { global_transf = {{}, { dim_in = {1320}, dim_out = {1320}, sub_layers = layer_repo, connections = { ["[1]"] = "blayer1[1]", ["blayer1[1]"] = "wlayer1[1]", ["wlayer1[1]"] = "[1]" } }}, main = {{}, { dim_in = {1320}, dim_out = {10092}, sub_layers = layer_repo, connections = { ["[1]"] = "affine0[1]", ["affine0[1]"] = "sigmoid0[1]", ["sigmoid0[1]"] = "affine1[1]", ["affine1[1]"] = "affine2[1]", ["affine2[1]"] = "sigmoid1[1]", ["sigmoid1[1]"] = "affine3[1]", ["affine3[1]"] = "affine4[1]", ["affine4[1]"] = "sigmoid2[1]", ["sigmoid2[1]"] = "affine5[1]", ["affine5[1]"] = "affine6[1]", ["affine6[1]"] = "sigmoid3[1]", ["sigmoid3[1]"] = "affine7[1]", ["affine7[1]"] = "affine8[1]", ["affine8[1]"] = "sigmoid4[1]", ["sigmoid4[1]"] = "affine9[1]", ["affine9[1]"] = "affine10[1]", ["affine10[1]"] = "sigmoid5[1]", ["sigmoid5[1]"] = "affine11[1]", ["affine11[1]"] = "affine12[1]", ["affine12[1]"] = "sigmoid6[1]", ["sigmoid6[1]"] = "affine13[1]", ["affine13[1]"] = "affine14[1]", ["affine14[1]"] = "[1]", } }} } }, param_repo, gconf) layer_repo:add_layers( { ["nerv.DAGLayer"] = { ce_output = {{}, { dim_in = {1320, 1}, dim_out = {1}, sub_layers = layer_repo, connections = { ["[1]"] = "main[1]", ["main[1]"] = "ce_crit[1]", ["[2]"] = "ce_crit[2]", ["ce_crit[1]"] = "[1]" } }}, softmax_output = {{}, { dim_in = {1320}, dim_out = {10092}, sub_layers = layer_repo, connections = { ["[1]"] = "main[1]", ["main[1]"] = "softmax[1]", ["softmax[1]"] = "[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, feat_repo_shareid, data_mutex_shareid) 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 = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/ref.mlf", format = "map", format_arg = "/sgfs/users/wd007/asr/baseline_chn_50h/finetune/finetune_baseline/dict", dir = "*/", ext = "lab" } }, global_transf = layer_repo:get_layer("global_transf") }, feat_repo_shareid, data_mutex_shareid), data = {main_scp = 1320, phone_state = 1}} } end function get_feat_id() return {main_scp = true} end function make_buffer(readers) return nerv.SGDBuffer(gconf, { buffer_size = gconf.buffer_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_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 function print_xent(xent) local totalframes = xent:totalframes() local loss = xent:loss() local correct = xent:correct() nerv.info_stderr("*** training statistics info begin ***") nerv.info_stderr("total frames:\t\t%d", totalframes) nerv.info_stderr("cross entropy:\t%.8f", loss/totalframes) nerv.info_stderr("frame accuracy:\t%.3f%%", 100*correct/totalframes) nerv.info_stderr("*** training statistics info end ***") end function frame_acc(xent) local correct = xent:correct() local totalframes = xent:totalframes() return string.format("%.3f", 100*correct/totalframes) end function print_gconf() nerv.info_stderr("%s \t:= %s", "network", gconf.initialized_param[1]) nerv.info_stderr("%s \t:= %s", "transf", gconf.initialized_param[2]) nerv.info_stderr("%s \t:= %s", "batch_size", gconf.batch_size) nerv.info_stderr("%s \t:= %s", "buffer_size", gconf.buffer_size) nerv.info_stderr("%s \t:= %s", "lrate", gconf.lrate) nerv.info_stderr("%s \t:= %s", "tr_scp", gconf.tr_scp) nerv.info_stderr("%s \t:= %s", "cv_scp", gconf.cv_scp) end