add network config for timit baseline

3 files changed, 417 insertions, 1 deletions

diff --git a/nerv/examples/swb_baseline.lua b/nerv/examples/swb_baseline.lua
index 51052ba..cacc401 100644
--- a/nerv/examples/swb_baseline.lua
+++ b/nerv/examples/swb_baseline.lua
@@ -9,7 +9,7 @@ gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
         cv_scp = "/slfs1/users/mfy43/swb_ivec/train_cv.scp",
         htk_conf = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
         initialized_param = {"/slfs1/users/mfy43/swb_init.nerv",
-                "/slfs1/users/mfy43/swb_global_transf.nerv"},
+                            "/slfs1/users/mfy43/swb_global_transf.nerv"},
         debug = false}
 
 function make_layer_repo(param_repo)
diff --git a/nerv/examples/swb_baseline2.lua b/nerv/examples/swb_baseline2.lua
new file mode 100644
index 0000000..0e2a6e0
--- /dev/null
+++ b/nerv/examples/swb_baseline2.lua
@@ -0,0 +1,201 @@
+require 'htk_io'
+gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
+        cumat_type = nerv.CuMatrixFloat,
+        mmat_type = nerv.MMatrixFloat,
+        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"},
+        debug = false}
+
+function make_layer_repo(param_repo)
+    local layer_repo = nerv.LayerRepo(
+    {
+        -- global transf
+        ["nerv.BiasLayer"] =
+        {
+            blayer1 = {{bias = "bias1"}, {dim_in = {429}, dim_out = {429}}},
+            blayer2 = {{bias = "bias2"}, {dim_in = {429}, dim_out = {429}}}
+        },
+        ["nerv.WindowLayer"] =
+        {
+            wlayer1 = {{window = "window1"}, {dim_in = {429}, dim_out = {429}}},
+            wlayer2 = {{window = "window2"}, {dim_in = {429}, dim_out = {429}}}
+        },
+        -- biased linearity
+        ["nerv.AffineLayer"] =
+        {
+            affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
+            {dim_in = {429}, 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 = {3001}}}
+        },
+        ["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,
+            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
diff --git a/nerv/examples/timit_baseline2.lua b/nerv/examples/timit_baseline2.lua
new file mode 100644
index 0000000..174b9e7
--- /dev/null
+++ b/nerv/examples/timit_baseline2.lua
@@ -0,0 +1,215 @@
+require 'kaldi_io'
+gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
+        cumat_type = nerv.CuMatrixFloat,
+        mmat_type = nerv.MMatrixFloat,
+        frm_ext = 5,
+        tr_scp = "ark:/speechlab/tools/KALDI/kaldi-master/src/featbin/copy-feats " ..
+                    "scp:/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_prepare/train.scp ark:- |",
+        cv_scp = "ark:/speechlab/tools/KALDI/kaldi-master/src/featbin/copy-feats " ..
+                    "scp:/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_prepare/cv.scp ark:- |",
+        initialized_param = {"/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_prepare/nnet_init.nerv",
+                            "/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_prepare/nnet_output.nerv",
+                            "/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_prepare/nnet_trans.nerv"},
+        decode_param = {"/speechlab/users/mfy43/timit/nnet_init_20160229015745_iter_13_lr0.013437_tr72.434_cv58.729.nerv",
+                        "/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_prepare/nnet_trans.nerv"},
+        debug = false}
+
+function make_layer_repo(param_repo)
+    local layer_repo = nerv.LayerRepo(
+    {
+        -- global transf
+        ["nerv.BiasLayer"] =
+        {
+            blayer1 = {{bias = "bias0"}, {dim_in = {440}, dim_out = {440}}}
+        },
+        ["nerv.WindowLayer"] =
+        {
+            wlayer1 = {{window = "window0"}, {dim_in = {440}, dim_out = {440}}}
+        },
+        -- biased linearity
+        ["nerv.AffineLayer"] =
+        {
+            affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
+            {dim_in = {440}, dim_out = {1024}}},
+            affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"},
+            {dim_in = {1024}, dim_out = {1024}}},
+            affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"},
+            {dim_in = {1024}, dim_out = {1024}}},
+            affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"},
+            {dim_in = {1024}, dim_out = {1024}}},
+            affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"},
+            {dim_in = {1024}, dim_out = {1024}}},
+            affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"},
+            {dim_in = {1024}, dim_out = {1024}}},
+            affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"},
+            {dim_in = {1024}, dim_out = {1959}}}
+        },
+        ["nerv.SigmoidLayer"] =
+        {
+            sigmoid0 = {{}, {dim_in = {1024}, dim_out = {1024}}},
+            sigmoid1 = {{}, {dim_in = {1024}, dim_out = {1024}}},
+            sigmoid2 = {{}, {dim_in = {1024}, dim_out = {1024}}},
+            sigmoid3 = {{}, {dim_in = {1024}, dim_out = {1024}}},
+            sigmoid4 = {{}, {dim_in = {1024}, dim_out = {1024}}},
+            sigmoid5 = {{}, {dim_in = {1024}, dim_out = {1024}}}
+        },
+        ["nerv.SoftmaxCELayer"] = -- softmax + ce criterion layer for finetune output
+        {
+            ce_crit = {{}, {dim_in = {1959, 1}, dim_out = {1}, compressed = true}}
+        },
+        ["nerv.SoftmaxLayer"] = -- softmax for decode output
+        {
+            softmax = {{}, {dim_in = {1959}, dim_out = {1959}}}
+        }
+    }, param_repo, gconf)
+
+    layer_repo:add_layers(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            global_transf = {{}, {
+                dim_in = {440}, dim_out = {440},
+                sub_layers = layer_repo,
+                connections = {
+                    ["<input>[1]"] = "blayer1[1]",
+                    ["blayer1[1]"] = "wlayer1[1]",
+                    ["wlayer1[1]"] = "<output>[1]"
+                }
+            }},
+            main = {{}, {
+                dim_in = {440}, dim_out = {1959},
+                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]"] = "<output>[1]"
+                }
+            }}
+        }
+    }, param_repo, gconf)
+
+    layer_repo:add_layers(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            ce_output = {{}, {
+                dim_in = {440, 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 = {440}, dim_out = {1959},
+                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.KaldiReader(gconf,
+                    {
+                        id = "main_scp",
+                        feature_rspecifier = scp_file,
+                        conf_file = gconf.htk_conf,
+                        frm_ext = gconf.frm_ext,
+                        mlfs = {
+                            phone_state = {
+                                targets_rspecifier = "ark:/speechlab/tools/KALDI/kaldi-master/src/bin/ali-to-pdf " ..
+                                                        "/speechlab/users/mfy43/timit/s5/exp/tri3_ali/final.mdl " ..
+                                                        "\"ark:gunzip -c /speechlab/users/mfy43/timit/s5/exp/tri3_ali/ali.*.gz |\" " ..
+                                                        "ark:- | " ..
+                                                    "/speechlab/tools/KALDI/kaldi-master/src/bin/ali-to-post " ..
+                                                        "ark:- ark:- |",
+                                format = "map"
+                            }
+                        }
+                    }),
+                data = {main_scp = 440, phone_state = 1}}
+            }
+end
+
+function make_decode_readers(scp_file, layer_repo)
+    return {
+                {reader = nerv.KaldiReader(gconf,
+                    {
+                        id = "main_scp",
+                        feature_rspecifier = scp_file,
+                        conf_file = gconf.htk_conf,
+                        frm_ext = gconf.frm_ext,
+                        mlfs = {},
+                        need_key = true
+                    }),
+                data = {main_scp = 440, phone_state = 1}}
+            }
+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_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