diff --git a/data/am/final.raw b/data/am/final.raw
old mode 100644
new mode 100755
diff --git a/data/am/phones.txt b/data/am/phones.txt
old mode 100644
new mode 100755
diff --git a/data/am/trans.txt b/data/am/trans.txt
old mode 100644
new mode 100755
diff --git a/data/am/tree b/data/am/tree
old mode 100644
new mode 100755
diff --git a/data/feats/final.mat b/data/feats/final.mat
old mode 100644
new mode 100755
diff --git a/data/feats/reduce_dim.mat b/data/feats/reduce_dim.mat
old mode 100644
new mode 100755
diff --git a/data/feats/splice_opts b/data/feats/splice_opts
old mode 100644
new mode 100755
diff --git a/data/filelists/lxc_clb_train_filelist.txt b/data/filelists/lxc_clb_train_filelist.txt
deleted file mode 100644
index 322b232..0000000
--- a/data/filelists/lxc_clb_train_filelist.txt
+++ /dev/null
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\ No newline at end of file
diff --git a/data/filelists/lxc_slt_train_filelist.txt b/data/filelists/lxc_slt_train_filelist.txt
deleted file mode 100644
index 2d55285..0000000
--- a/data/filelists/lxc_slt_train_filelist.txt
+++ /dev/null
@@ -1,1031 +0,0 @@
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0956.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0956.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0957.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0957.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0958.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0958.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0959.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0959.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0960.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0960.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0961.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0961.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0962.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0962.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0963.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0963.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0964.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0964.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0965.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0965.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0966.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0966.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0967.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0967.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0968.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0968.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0969.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0969.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0970.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0970.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0971.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0971.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0972.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0972.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0973.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0973.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0974.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0974.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0975.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0975.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0976.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0976.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0977.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0977.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0978.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0978.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0979.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0979.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0980.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0980.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0981.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0981.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0982.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0982.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0983.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0983.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0984.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0984.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0985.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0985.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0986.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0986.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0987.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0987.proto
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0989.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0989.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0990.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0990.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0991.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0991.proto
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0998.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0998.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/0999.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/0999.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1000.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1000.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1001.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1001.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1002.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1002.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1003.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1003.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1004.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1004.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1005.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1005.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1006.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1006.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1007.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1007.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1008.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1008.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1009.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1009.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1010.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1010.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1011.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1011.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1012.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1012.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1013.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1013.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1014.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1014.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1015.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1015.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1016.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1016.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1017.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1017.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1018.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1018.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1019.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1019.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1020.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1020.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1021.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1021.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1022.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1022.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1023.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1023.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1024.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1024.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1025.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1025.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1026.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1026.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1027.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1027.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1028.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1028.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1029.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1029.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1030.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1030.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1031.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1031.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1032.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1032.proto
\ No newline at end of file
diff --git a/data/filelists/lxc_slt_val_filelist.txt b/data/filelists/lxc_slt_val_filelist.txt
deleted file mode 100644
index e3e3233..0000000
--- a/data/filelists/lxc_slt_val_filelist.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1033.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1033.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1034.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1034.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1035.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1035.proto
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1038.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1038.proto
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1053.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1053.proto
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1055.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1055.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1056.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1056.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1057.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1057.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1058.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1058.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1059.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1059.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1060.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1060.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1061.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1061.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1062.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1062.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1063.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1063.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1064.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1064.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1065.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1065.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1066.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1066.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1067.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1067.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1068.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1068.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1069.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1069.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1070.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1070.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1071.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1071.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1072.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1072.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1073.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1073.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1074.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1074.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1075.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1075.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1076.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1076.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1077.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1077.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1078.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1078.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1079.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1079.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1080.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1080.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1081.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1081.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1082.proto|/media/guanlong/DATA1/exp/ppg-speech/data/slt/cache/1082.proto
\ No newline at end of file
diff --git a/data/filelists/lxc_train_filelist.txt b/data/filelists/lxc_train_filelist.txt
deleted file mode 100644
index 23dc12f..0000000
--- a/data/filelists/lxc_train_filelist.txt
+++ /dev/null
@@ -1,1031 +0,0 @@
-/home/guanlong/data/arctic/lxc/cache/0001.proto
-/home/guanlong/data/arctic/lxc/cache/0002.proto
-/home/guanlong/data/arctic/lxc/cache/0003.proto
-/home/guanlong/data/arctic/lxc/cache/0004.proto
-/home/guanlong/data/arctic/lxc/cache/0005.proto
-/home/guanlong/data/arctic/lxc/cache/0006.proto
-/home/guanlong/data/arctic/lxc/cache/0007.proto
-/home/guanlong/data/arctic/lxc/cache/0008.proto
-/home/guanlong/data/arctic/lxc/cache/0009.proto
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-/home/guanlong/data/arctic/lxc/cache/1032.proto
\ No newline at end of file
diff --git a/data/filelists/lxc_train_filelist_fc_ppg.txt b/data/filelists/lxc_train_filelist_fc_ppg.txt
deleted file mode 100644
index eb3eba3..0000000
--- a/data/filelists/lxc_train_filelist_fc_ppg.txt
+++ /dev/null
@@ -1,1031 +0,0 @@
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\ No newline at end of file
diff --git a/data/filelists/lxc_train_filelist_waveglow.txt b/data/filelists/lxc_train_filelist_waveglow.txt
deleted file mode 100644
index 0c23720..0000000
--- a/data/filelists/lxc_train_filelist_waveglow.txt
+++ /dev/null
@@ -1,1031 +0,0 @@
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/wav/1028.wav
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/wav/1030.wav
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/wav/1032.wav
\ No newline at end of file
diff --git a/data/filelists/lxc_val_filelist.txt b/data/filelists/lxc_val_filelist.txt
deleted file mode 100644
index 5a3768d..0000000
--- a/data/filelists/lxc_val_filelist.txt
+++ /dev/null
@@ -1,50 +0,0 @@
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-/home/guanlong/data/arctic/lxc/cache/1034.proto
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-/home/guanlong/data/arctic/lxc/cache/1042.proto
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-/home/guanlong/data/arctic/lxc/cache/1044.proto
-/home/guanlong/data/arctic/lxc/cache/1045.proto
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-/home/guanlong/data/arctic/lxc/cache/1047.proto
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-/home/guanlong/data/arctic/lxc/cache/1049.proto
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-/home/guanlong/data/arctic/lxc/cache/1052.proto
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-/home/guanlong/data/arctic/lxc/cache/1059.proto
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-/home/guanlong/data/arctic/lxc/cache/1062.proto
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-/home/guanlong/data/arctic/lxc/cache/1064.proto
-/home/guanlong/data/arctic/lxc/cache/1065.proto
-/home/guanlong/data/arctic/lxc/cache/1066.proto
-/home/guanlong/data/arctic/lxc/cache/1067.proto
-/home/guanlong/data/arctic/lxc/cache/1068.proto
-/home/guanlong/data/arctic/lxc/cache/1069.proto
-/home/guanlong/data/arctic/lxc/cache/1070.proto
-/home/guanlong/data/arctic/lxc/cache/1071.proto
-/home/guanlong/data/arctic/lxc/cache/1072.proto
-/home/guanlong/data/arctic/lxc/cache/1073.proto
-/home/guanlong/data/arctic/lxc/cache/1074.proto
-/home/guanlong/data/arctic/lxc/cache/1075.proto
-/home/guanlong/data/arctic/lxc/cache/1076.proto
-/home/guanlong/data/arctic/lxc/cache/1077.proto
-/home/guanlong/data/arctic/lxc/cache/1078.proto
-/home/guanlong/data/arctic/lxc/cache/1079.proto
-/home/guanlong/data/arctic/lxc/cache/1080.proto
-/home/guanlong/data/arctic/lxc/cache/1081.proto
-/home/guanlong/data/arctic/lxc/cache/1082.proto
\ No newline at end of file
diff --git a/data/filelists/lxc_val_filelist_fc_ppg.txt b/data/filelists/lxc_val_filelist_fc_ppg.txt
deleted file mode 100644
index 448f44f..0000000
--- a/data/filelists/lxc_val_filelist_fc_ppg.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1033.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1034.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1035.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1036.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1037.proto
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1040.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1041.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1042.proto
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1051.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1052.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1053.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1054.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1055.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1056.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1057.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1058.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1059.proto
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-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1062.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1063.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1064.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1065.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1066.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1067.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1068.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1069.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1070.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1071.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1072.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1073.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1074.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1075.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1076.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1077.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1078.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1079.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1080.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1081.proto
-/media/guanlong/DATA1/exp/ppg-speech/data/lxc/cache/1082.proto
\ No newline at end of file
diff --git a/data/filelists/njs_train_filelist_noise_reduced.txt b/data/filelists/njs_train_filelist_noise_reduced.txt
deleted file mode 100644
index 63e3190..0000000
--- a/data/filelists/njs_train_filelist_noise_reduced.txt
+++ /dev/null
@@ -1,1031 +0,0 @@
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0023.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0027.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0028.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0029.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0030.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0034.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0035.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0036.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0037.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0038.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0039.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0040.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0041.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0045.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0046.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0047.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0049.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0056.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0060.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0061.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0064.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/njs/recordings_noise_reduced/gsb_0065.wav
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diff --git a/data/filelists/svbi_train_filelist.txt b/data/filelists/svbi_train_filelist.txt
deleted file mode 100644
index 3a5fa01..0000000
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index 7d377ef..0000000
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diff --git a/data/filelists/ykwk_all_filelist.txt b/data/filelists/ykwk_all_filelist.txt
deleted file mode 100644
index 4ac82a0..0000000
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\ No newline at end of file
diff --git a/data/filelists/ykwk_mini_filelist.txt b/data/filelists/ykwk_mini_filelist.txt
deleted file mode 100644
index 0c918b8..0000000
--- a/data/filelists/ykwk_mini_filelist.txt
+++ /dev/null
@@ -1,10 +0,0 @@
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\ No newline at end of file
diff --git a/data/filelists/ykwk_test_filelist.txt b/data/filelists/ykwk_test_filelist.txt
deleted file mode 100644
index b1557a6..0000000
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+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/data/filelists/ykwk_train_filelist.txt b/data/filelists/ykwk_train_filelist.txt
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diff --git a/data/filelists/ykwk_train_filelist_noise_reduced.txt b/data/filelists/ykwk_train_filelist_noise_reduced.txt
deleted file mode 100644
index 5f78c33..0000000
--- a/data/filelists/ykwk_train_filelist_noise_reduced.txt
+++ /dev/null
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diff --git a/data/filelists/ykwk_train_filelist_vocoder_monophone.txt b/data/filelists/ykwk_train_filelist_vocoder_monophone.txt
deleted file mode 100644
index 3ae297c..0000000
--- a/data/filelists/ykwk_train_filelist_vocoder_monophone.txt
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diff --git a/data/filelists/ykwk_train_filelist_waveglow.txt b/data/filelists/ykwk_train_filelist_waveglow.txt
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-/data_repo/arctic//ykwk/wav/1009.wav
-/data_repo/arctic//ykwk/wav/1010.wav
-/data_repo/arctic//ykwk/wav/1011.wav
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-/data_repo/arctic//ykwk/wav/1014.wav
-/data_repo/arctic//ykwk/wav/1015.wav
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-/data_repo/arctic//ykwk/wav/1023.wav
-/data_repo/arctic//ykwk/wav/1024.wav
-/data_repo/arctic//ykwk/wav/1025.wav
-/data_repo/arctic//ykwk/wav/1026.wav
-/data_repo/arctic//ykwk/wav/1027.wav
-/data_repo/arctic//ykwk/wav/1028.wav
-/data_repo/arctic//ykwk/wav/1029.wav
-/data_repo/arctic//ykwk/wav/1030.wav
-/data_repo/arctic//ykwk/wav/1031.wav
-/data_repo/arctic//ykwk/wav/1032.wav
\ No newline at end of file
diff --git a/data/filelists/ykwk_val_filelist.txt b/data/filelists/ykwk_val_filelist.txt
deleted file mode 100644
index a75eec5..0000000
--- a/data/filelists/ykwk_val_filelist.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-/data_repo/arctic/ykwk/cache/1033.proto
-/data_repo/arctic/ykwk/cache/1034.proto
-/data_repo/arctic/ykwk/cache/1035.proto
-/data_repo/arctic/ykwk/cache/1036.proto
-/data_repo/arctic/ykwk/cache/1037.proto
-/data_repo/arctic/ykwk/cache/1038.proto
-/data_repo/arctic/ykwk/cache/1039.proto
-/data_repo/arctic/ykwk/cache/1040.proto
-/data_repo/arctic/ykwk/cache/1041.proto
-/data_repo/arctic/ykwk/cache/1042.proto
-/data_repo/arctic/ykwk/cache/1043.proto
-/data_repo/arctic/ykwk/cache/1044.proto
-/data_repo/arctic/ykwk/cache/1045.proto
-/data_repo/arctic/ykwk/cache/1046.proto
-/data_repo/arctic/ykwk/cache/1047.proto
-/data_repo/arctic/ykwk/cache/1048.proto
-/data_repo/arctic/ykwk/cache/1049.proto
-/data_repo/arctic/ykwk/cache/1050.proto
-/data_repo/arctic/ykwk/cache/1051.proto
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-/data_repo/arctic/ykwk/cache/1053.proto
-/data_repo/arctic/ykwk/cache/1054.proto
-/data_repo/arctic/ykwk/cache/1055.proto
-/data_repo/arctic/ykwk/cache/1056.proto
-/data_repo/arctic/ykwk/cache/1057.proto
-/data_repo/arctic/ykwk/cache/1058.proto
-/data_repo/arctic/ykwk/cache/1059.proto
-/data_repo/arctic/ykwk/cache/1060.proto
-/data_repo/arctic/ykwk/cache/1061.proto
-/data_repo/arctic/ykwk/cache/1062.proto
-/data_repo/arctic/ykwk/cache/1063.proto
-/data_repo/arctic/ykwk/cache/1064.proto
-/data_repo/arctic/ykwk/cache/1065.proto
-/data_repo/arctic/ykwk/cache/1066.proto
-/data_repo/arctic/ykwk/cache/1067.proto
-/data_repo/arctic/ykwk/cache/1068.proto
-/data_repo/arctic/ykwk/cache/1069.proto
-/data_repo/arctic/ykwk/cache/1070.proto
-/data_repo/arctic/ykwk/cache/1071.proto
-/data_repo/arctic/ykwk/cache/1072.proto
-/data_repo/arctic/ykwk/cache/1073.proto
-/data_repo/arctic/ykwk/cache/1074.proto
-/data_repo/arctic/ykwk/cache/1075.proto
-/data_repo/arctic/ykwk/cache/1076.proto
-/data_repo/arctic/ykwk/cache/1077.proto
-/data_repo/arctic/ykwk/cache/1078.proto
-/data_repo/arctic/ykwk/cache/1079.proto
-/data_repo/arctic/ykwk/cache/1080.proto
-/data_repo/arctic/ykwk/cache/1081.proto
-/data_repo/arctic/ykwk/cache/1082.proto
\ No newline at end of file
diff --git a/data/filelists/ykwk_val_filelist_noise_reduced.txt b/data/filelists/ykwk_val_filelist_noise_reduced.txt
deleted file mode 100644
index f9491d8..0000000
--- a/data/filelists/ykwk_val_filelist_noise_reduced.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1033.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1051.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1052.wav
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-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1057.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1058.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1059.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1060.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1061.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1062.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1063.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1064.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1065.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1066.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1067.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1068.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1069.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1070.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1071.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1072.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1073.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1074.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1075.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1076.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1077.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1078.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1079.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1080.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1081.wav
-/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/gsb16k/ykwk/recordings_noise_reduced/gsb_1082.wav
\ No newline at end of file
diff --git a/data/filelists/ykwk_val_filelist_vocoder_monophone.txt b/data/filelists/ykwk_val_filelist_vocoder_monophone.txt
deleted file mode 100644
index a0731fe..0000000
--- a/data/filelists/ykwk_val_filelist_vocoder_monophone.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-/data_repo/arctic/ykwk/cache_vocoder/1033.proto
-/data_repo/arctic/ykwk/cache_vocoder/1034.proto
-/data_repo/arctic/ykwk/cache_vocoder/1035.proto
-/data_repo/arctic/ykwk/cache_vocoder/1036.proto
-/data_repo/arctic/ykwk/cache_vocoder/1037.proto
-/data_repo/arctic/ykwk/cache_vocoder/1038.proto
-/data_repo/arctic/ykwk/cache_vocoder/1039.proto
-/data_repo/arctic/ykwk/cache_vocoder/1040.proto
-/data_repo/arctic/ykwk/cache_vocoder/1041.proto
-/data_repo/arctic/ykwk/cache_vocoder/1042.proto
-/data_repo/arctic/ykwk/cache_vocoder/1043.proto
-/data_repo/arctic/ykwk/cache_vocoder/1044.proto
-/data_repo/arctic/ykwk/cache_vocoder/1045.proto
-/data_repo/arctic/ykwk/cache_vocoder/1046.proto
-/data_repo/arctic/ykwk/cache_vocoder/1047.proto
-/data_repo/arctic/ykwk/cache_vocoder/1048.proto
-/data_repo/arctic/ykwk/cache_vocoder/1049.proto
-/data_repo/arctic/ykwk/cache_vocoder/1050.proto
-/data_repo/arctic/ykwk/cache_vocoder/1051.proto
-/data_repo/arctic/ykwk/cache_vocoder/1052.proto
-/data_repo/arctic/ykwk/cache_vocoder/1053.proto
-/data_repo/arctic/ykwk/cache_vocoder/1054.proto
-/data_repo/arctic/ykwk/cache_vocoder/1055.proto
-/data_repo/arctic/ykwk/cache_vocoder/1056.proto
-/data_repo/arctic/ykwk/cache_vocoder/1057.proto
-/data_repo/arctic/ykwk/cache_vocoder/1058.proto
-/data_repo/arctic/ykwk/cache_vocoder/1059.proto
-/data_repo/arctic/ykwk/cache_vocoder/1060.proto
-/data_repo/arctic/ykwk/cache_vocoder/1061.proto
-/data_repo/arctic/ykwk/cache_vocoder/1062.proto
-/data_repo/arctic/ykwk/cache_vocoder/1063.proto
-/data_repo/arctic/ykwk/cache_vocoder/1064.proto
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-/data_repo/arctic/ykwk/cache_vocoder/1072.proto
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-/data_repo/arctic/ykwk/cache_vocoder/1075.proto
-/data_repo/arctic/ykwk/cache_vocoder/1076.proto
-/data_repo/arctic/ykwk/cache_vocoder/1077.proto
-/data_repo/arctic/ykwk/cache_vocoder/1078.proto
-/data_repo/arctic/ykwk/cache_vocoder/1079.proto
-/data_repo/arctic/ykwk/cache_vocoder/1080.proto
-/data_repo/arctic/ykwk/cache_vocoder/1081.proto
-/data_repo/arctic/ykwk/cache_vocoder/1082.proto
\ No newline at end of file
diff --git a/data/filelists/zhaa_train_filelist.txt b/data/filelists/zhaa_train_filelist.txt
deleted file mode 100644
index cee78c4..0000000
--- a/data/filelists/zhaa_train_filelist.txt
+++ /dev/null
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diff --git a/data/filelists/zhaa_val_filelist.txt b/data/filelists/zhaa_val_filelist.txt
deleted file mode 100644
index 47edae0..0000000
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\ No newline at end of file
diff --git a/data/filelists/zhaa_val_filelist_noise_reduced.txt b/data/filelists/zhaa_val_filelist_noise_reduced.txt
deleted file mode 100644
index 6b11787..0000000
--- a/data/filelists/zhaa_val_filelist_noise_reduced.txt
+++ /dev/null
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\ No newline at end of file
diff --git a/environment.yml b/environment.yml
index 10b1ea5..fb77f98 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,154 +1,104 @@
-name: ppg-speech
+name: ppg-speech-lite
 channels:
   - pytorch
   - pykaldi
+  - anaconda
   - conda-forge
   - defaults
 dependencies:
+  - ca-certificates=2019.5.15=1
+  - certifi=2019.6.16=py37_1
+  - libtiff=4.0.10=h2733197_2
+  - olefile=0.46=py37_0
+  - openssl=1.1.1=h7b6447c_0
+  - pillow=6.1.0=py37h34e0f95_0
+  - zstd=1.3.7=h0b5b093_0
   - audioread=2.1.6=py37_0
-  - bzip2=1.0.6=h14c3975_1002
-  - decorator=4.3.2=py_0
-  - ffmpeg=4.1=h6dce934_1000
+  - blas=2.12=openblas
+  - bzip2=1.0.8=h516909a_0
+  - cffi=1.12.3=py37h8022711_0
+  - cycler=0.10.0=py_1
+  - dbus=1.13.6=he372182_0
+  - decorator=4.4.0=py_0
+  - expat=2.2.5=he1b5a44_1003
+  - ffmpeg=4.2=h167e202_0
+  - fontconfig=2.13.1=he4413a7_1000
+  - freetype=2.10.0=he983fc9_1
+  - gettext=0.19.8.1=hc5be6a0_1002
+  - glib=2.58.3=h6f030ca_1002
   - gmp=6.1.2=hf484d3e_1000
-  - gnutls=3.6.5=hd3a4fd2_1001
-  - joblib=0.13.1=py_0
-  - libiconv=1.15=h14c3975_1004
+  - gnutls=3.6.5=hd3a4fd2_1002
+  - gst-plugins-base=1.14.5=h0935bb2_0
+  - gstreamer=1.14.5=h36ae1b5_0
+  - icu=58.2=hf484d3e_1000
+  - joblib=0.13.2=py_0
+  - jpeg=9c=h14c3975_1001
+  - kiwisolver=1.1.0=py37hc9558a2_0
+  - lame=3.100=h14c3975_1001
+  - libblas=3.8.0=12_openblas
+  - libcblas=3.8.0=12_openblas
+  - libffi=3.2.1=he1b5a44_1006
+  - libiconv=1.15=h516909a_1005
+  - liblapack=3.8.0=12_openblas
+  - liblapacke=3.8.0=12_openblas
+  - libopenblas=0.3.7=h6e990d7_1
+  - libpng=1.6.37=hed695b0_0
   - librosa=0.6.2=py_0
-  - llvmlite=0.26.0=py37hdbcaa40_1000
-  - nettle=3.4.1=h14c3975_1002
-  - numba=0.41.0=py37h637b7d7_1000
+  - libuuid=2.32.1=h14c3975_1000
+  - libxcb=1.13=h14c3975_1002
+  - libxml2=2.9.9=h13577e0_2
+  - llvmlite=0.29.0=py37hfd453ef_1
+  - ncurses=6.1=hf484d3e_1002
+  - nettle=3.4.1=h1bed415_1002
+  - ninja=1.9.0=h6bb024c_0
+  - nose=1.3.7=py37_2
+  - numba=0.45.1=py37hb3f55d8_0
+  - openblas=0.3.7=h6e990d7_1
   - openh264=1.8.0=hdbcaa40_1000
-  - resampy=0.2.1=py_1
+  - pcre=8.41=hf484d3e_1003
+  - pip=19.2.3=py37_0
+  - pthread-stubs=0.4=h14c3975_1001
+  - pycparser=2.19=py37_1
+  - pyparsing=2.4.2=py_0
+  - pyqt=5.9.2=py37hcca6a23_3
+  - python-dateutil=2.8.0=py_0
+  - pytz=2019.2=py_0
+  - qt=5.9.7=h52cfd70_2
+  - readline=7.0=hf8c457e_1001
+  - resampy=0.2.2=py_0
+  - scikit-learn=0.21.3=py37hcdab131_0
+  - setuptools=41.2.0=py37_0
+  - sip=4.19.8=py37hf484d3e_1000
+  - six=1.12.0=py37_1000
+  - sqlite=3.28.0=h8b20d00_0
   - tensorboardx=1.2=py_0
-  - x264=1!152.20180717=h14c3975_1001
-  - backcall=0.1.0=py37_0
-  - binutils_impl_linux-64=2.28.1=had2808c_3
-  - binutils_linux-64=7.2.0=had2808c_27
-  - blas=1.0=openblas
-  - bleach=3.1.0=py37_0
-  - ca-certificates=2019.1.23=0
-  - certifi=2018.11.29=py37_0
-  - cffi=1.11.5=py37he75722e_1
+  - tk=8.6.9=hed695b0_1002
+  - tornado=6.0.3=py37h516909a_0
+  - wheel=0.33.6=py37_0
+  - x264=1!152.20180806=h14c3975_0
+  - xorg-libxau=1.0.9=h14c3975_0
+  - xorg-libxdmcp=1.1.3=h516909a_0
+  - xz=5.2.4=h14c3975_1001
+  - zlib=1.2.11=h516909a_1005
+  - _libgcc_mutex=0.1=main
   - coverage=4.5.2=py37h7b6447c_0
-  - cycler=0.10.0=py37_0
-  - dbus=1.13.6=h746ee38_0
-  - entrypoints=0.3=py37_0
-  - expat=2.2.6=he6710b0_0
-  - fontconfig=2.13.0=h9420a91_0
-  - freetype=2.9.1=h8a8886c_1
-  - gcc_impl_linux-64=7.2.0=habb00fd_3
-  - gcc_linux-64=7.2.0=h550dcbe_27
-  - glib=2.56.2=hd408876_0
-  - gst-plugins-base=1.14.0=hbbd80ab_1
-  - gstreamer=1.14.0=hb453b48_1
-  - gxx_impl_linux-64=7.2.0=hdf63c60_3
-  - gxx_linux-64=7.2.0=h550dcbe_27
-  - icu=58.2=h9c2bf20_1
-  - intel-openmp=2019.1=144
-  - ipykernel=5.1.0=py37h39e3cac_0
-  - ipython=7.2.0=py37h39e3cac_0
-  - ipython_genutils=0.2.0=py37_0
-  - ipywidgets=7.4.2=py37_0
-  - jedi=0.13.2=py37_0
-  - jinja2=2.10=py37_0
-  - jpeg=9b=h024ee3a_2
-  - jsonschema=2.6.0=py37_0
-  - jupyter=1.0.0=py37_7
-  - jupyter_client=5.2.4=py37_0
-  - jupyter_console=6.0.0=py37_0
-  - jupyter_core=4.4.0=py37_0
-  - kiwisolver=1.0.1=py37hf484d3e_0
-  - libedit=3.1.20181209=hc058e9b_0
-  - libffi=3.2.1=hd88cf55_4
-  - libgcc-ng=8.2.0=hdf63c60_1
+  - intel-openmp=2019.4=243
+  - libgcc-ng=9.1.0=hdf63c60_0
   - libgfortran-ng=7.3.0=hdf63c60_0
-  - libgpuarray=0.7.6=h14c3975_0
-  - libopenblas=0.3.3=h5a2b251_3
-  - libpng=1.6.36=hbc83047_0
   - libprotobuf=3.6.1=hd408876_0
-  - libsodium=1.0.16=h1bed415_0
-  - libstdcxx-ng=8.2.0=hdf63c60_1
-  - libtiff=4.0.10=h2733197_2
-  - libuuid=1.0.3=h1bed415_2
-  - libxcb=1.13=h1bed415_1
-  - libxml2=2.9.9=he19cac6_0
-  - mako=1.0.7=py37_0
-  - markupsafe=1.1.0=py37h7b6447c_0
+  - libstdcxx-ng=9.1.0=hdf63c60_0
   - matplotlib=3.0.2=py37h5429711_0
-  - mistune=0.8.4=py37h7b6447c_0
-  - mkl=2018.0.3=1
-  - mkl-service=1.1.2=py37h651fb7a_4
-  - nbconvert=5.3.1=py37_0
-  - nbformat=4.4.0=py37_0
-  - ncurses=6.1=he6710b0_1
-  - ninja=1.8.2=py37h6bb024c_1
-  - nomkl=3.0=0
-  - nose=1.3.7=py37_2
-  - notebook=5.7.4=py37_0
+  - mkl=2019.4=243
   - numpy=1.15.4=py37h99e49ec_0
   - numpy-base=1.15.4=py37h2f8d375_0
-  - olefile=0.46=py37_0
-  - openblas=0.3.3=3
-  - openblas-devel=0.3.3=3
-  - openssl=1.1.1a=h7b6447c_0
-  - pandoc=2.2.3.2=0
-  - pandocfilters=1.4.2=py37_1
-  - parso=0.3.2=py37_0
-  - pcre=8.42=h439df22_0
-  - pexpect=4.6.0=py37_0
-  - pickleshare=0.7.5=py37_0
-  - pillow=5.4.1=py37h34e0f95_0
-  - pip=18.1=py37_0
-  - prometheus_client=0.5.0=py37_0
-  - prompt_toolkit=2.0.8=py_0
   - protobuf=3.6.1=py37he6710b0_0
-  - ptyprocess=0.6.0=py37_0
-  - pycparser=2.19=py37_0
-  - pygments=2.3.1=py37_0
-  - pygpu=0.7.6=py37h035aef0_0
-  - pyparsing=2.3.1=py37_0
-  - pyqt=5.9.2=py37h05f1152_2
   - python=3.7.2=h0371630_0
-  - python-dateutil=2.7.5=py37_0
-  - pytz=2018.9=py37_0
-  - pyzmq=17.1.2=py37he6710b0_2
-  - qt=5.9.7=h5867ecd_1
-  - qtconsole=4.4.3=py37_0
-  - readline=7.0=h7b6447c_5
-  - scikit-learn=0.20.2=py37h22eb022_0
   - scipy=1.1.0=py37he2b7bc3_2
-  - send2trash=1.5.0=py37_0
-  - setuptools=40.6.3=py37_0
-  - sip=4.19.8=py37hf484d3e_0
-  - six=1.12.0=py37_0
-  - sqlite=3.26.0=h7b6447c_0
-  - terminado=0.8.1=py37_1
-  - testpath=0.4.2=py37_0
-  - theano=1.0.2=py37h6bb024c_0
-  - tk=8.6.8=hbc83047_0
-  - tornado=5.1.1=py37h7b6447c_0
-  - traitlets=4.3.2=py37_0
-  - wcwidth=0.1.7=py37_0
-  - webencodings=0.5.1=py37_1
-  - wheel=0.32.3=py37_0
-  - widgetsnbextension=3.4.2=py37_0
-  - xz=5.2.4=h14c3975_4
-  - zeromq=4.3.1=he6710b0_3
-  - zlib=1.2.11=h7b6447c_3
-  - zstd=1.3.7=h0b5b093_0
-  - pykaldi=0.1.0=py37ha23f8d6_3
+  - pykaldi=0.1.0=py37h82fe3e7_1
   - cuda80=1.0=h205658b_0
   - pytorch=1.0.0=py3.7_cuda8.0.61_cudnn7.1.2_1
   - pip:
-    - cython==0.29.3
-    - dill==0.2.9
-    - multiprocess==0.70.7
-    - pathos==0.2.3
-    - pox==0.2.5
-    - ppft==1.6.4.9
-    - pysptk==0.1.15
-    - pyworld==0.2.8
     - textgrid==1.4
     - torch==1.0.0
-prefix: /home/guanlong/anaconda2/envs/ppg-speech
+prefix: /home/guanlong/anaconda2/envs/ppg-speech-lite
 
diff --git a/spec-file.txt b/spec-file.txt
index 730373a..e6be4a2 100644
--- a/spec-file.txt
+++ b/spec-file.txt
@@ -2,137 +2,94 @@
 # $ conda create --name <env> --file <this file>
 # platform: linux-64
 @EXPLICIT
-https://repo.anaconda.com/pkgs/main/linux-64/binutils_impl_linux-64-2.28.1-had2808c_3.tar.bz2
-https://repo.anaconda.com/pkgs/free/linux-64/blas-1.0-openblas.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2019.1.23-0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.tar.bz2
+https://conda.anaconda.org/anaconda/linux-64/ca-certificates-2019.5.15-1.tar.bz2
 https://conda.anaconda.org/pytorch/linux-64/cuda80-1.0-h205658b_0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2019.1-144.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-8.2.0-hdf63c60_1.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2019.4-243.tar.bz2
 https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-7.3.0-hdf63c60_0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-8.2.0-hdf63c60_1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/binutils_linux-64-7.2.0-had2808c_27.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.6-h14c3975_1002.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/expat-2.2.6-he6710b0_0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/gcc_impl_linux-64-7.2.0-habb00fd_3.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-9.1.0-hdf63c60_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-9.1.0-hdf63c60_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/mkl-2019.4-243.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h516909a_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/expat-2.2.5-he1b5a44_1003.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/gmp-6.1.2-hf484d3e_1000.tar.bz2
-https://repo.continuum.io/pkgs/main/linux-64/icu-58.2-h9c2bf20_1.tar.bz2
-https://repo.continuum.io/pkgs/main/linux-64/jpeg-9b-h024ee3a_2.tar.bz2
-https://repo.continuum.io/pkgs/main/linux-64/libffi-3.2.1-hd88cf55_4.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libgpuarray-0.7.6-h14c3975_0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h14c3975_1004.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libopenblas-0.3.3-h5a2b251_3.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libsodium-1.0.16-h1bed415_0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.0.3-h1bed415_2.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.13-h1bed415_1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/mkl-2018.0.3-1.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.1-he6710b0_1.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/nettle-3.4.1-h14c3975_1002.tar.bz2
-https://repo.anaconda.com/pkgs/free/linux-64/nomkl-3.0-0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/openssl-1.1.1a-h7b6447c_0.tar.bz2
-https://repo.anaconda.com/pkgs/main/linux-64/pcre-8.42-h439df22_0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/x264-1!152.20180717-h14c3975_1001.tar.bz2
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diff --git a/src/common/align.py b/src/common/align.py
index 61aa506..f55de07 100644
--- a/src/common/align.py
+++ b/src/common/align.py
@@ -14,194 +14,12 @@
 
 from io import StringIO
 import logging
-import os
 import re
-import subprocess
-import tempfile
-from glob import glob
-from numpy import ndarray
-from scipy.io import wavfile
-from shutil import move
 from textgrid import TextGrid, PointTier, IntervalTier, Interval, Point
 
 DEFAULT_TEXTGRID_PRECISION = 5
 
 
-class MontrealAligner(object):
-    """Class to perform forced alignment using the montreal aligner.
-
-    See https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner for
-    details about the montreal aligner. This class is based on the v1.0.0
-    release and therefore might not work with other versions.
-    You will need to configure the default settings of the aligner, i.e., the
-    location of the tool, the acoustic model, and lexicon. You might also need
-    to compile the montreal aligner for your platform since the official site
-    only provides the compiled version for macOS, Windows, and Ubuntu 16.04.
-
-    Sample usage,
-        aligner = MontrealAligner()
-        # Single utterance
-        tg = aligner.align_single("test.wav", "test.lab")
-        # Batch processing
-        output_dir = aligner.align_batch("input/path", "output/path")
-    """
-    def __init__(self, aligner_path="/var/montreal-forced-aligner-1.0.0/bin/"
-                                    "mfa_align",
-                 am_path="/var/montreal-forced-aligner-1.0.0/pretrained_models/"
-                         "english.zip",
-                 dict_path="/var/montreal-forced-aligner-1.0.0/"
-                           "pretrained_models/dictionary"):
-        """Create an aligner instance.
-
-        Args:
-            aligner_path: The path to the aligner binary file (mfa_align).
-            am_path: The path to the acoustic model (language.zip).
-            dict_path: The path to the lexicon file (dictionary).
-        """
-        if not os.path.isfile(aligner_path):
-            logging.error("Aligner %s does not exist!", aligner_path)
-        self.aligner_path = aligner_path
-        if not os.path.isfile(am_path):
-            logging.error("Model %s does not exist!", am_path)
-        self.am_path = am_path
-        if not os.path.isfile(dict_path):
-            logging.error("Lexicon %s does not exist!", dict_path)
-        self.dict_path = dict_path
-        self.filter_transcript = re.compile(r"[^0-9a-zA-Z,.\s'-]")
-
-    def align_single_internal(self, wav: ndarray,
-                              fs: int, text: str) -> TextGrid:
-        """Internal wrapper for aligning a single utterance.
-
-        This method does not perform speaker adaptation since it does not make
-        sense to adapt on only one arbitrary sentence. The method will also
-        clean up the input text transcription before the alignment. All
-        intermediate files will be stored in a temp folder created on-the-fly in
-        the system "tmp" folder, and will be cleaned after the alignment
-        finishes.
-
-        Args:
-            wav: Speech data.
-            fs: Sampling frequency of the wave data.
-            text: The text transcription of the speech.
-
-        Returns:
-            text_grid: A TextGrid object.
-        """
-        # Save all intermediate files in a temp folder.
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            temp_wav_path = os.path.join(tmpdirname, "test.wav")
-            wav = wav.astype('int16')
-            wavfile.write(temp_wav_path, fs, wav)
-            temp_txt_path = os.path.join(tmpdirname, "test.lab")
-            # Get rid of invalid characters.
-            text = self.filter_transcript.sub("", text)
-            with open(temp_txt_path, 'w') as transcript:
-                transcript.write(text)
-            temp_align_dir = os.path.join(tmpdirname, "temp_align")
-            os.mkdir(temp_align_dir)
-            temp_output_dir = os.path.join(tmpdirname, "temp_output")
-            # Run in single utterance mode, no need to do adaptation (-n).
-            cmd = [self.aligner_path, "-t", temp_align_dir, "-j", "1", "-v",
-                   "-n", "-c", "-i", tmpdirname, self.dict_path, self.am_path,
-                   temp_output_dir]
-            # if the process exits with a non-zero exit code, a
-            # "CalledProcessError" exception will be raised.
-            run_status = subprocess.run(cmd, check=True)
-            temp_basename = os.path.basename(tmpdirname)
-            temp_tg_path = os.path.join(temp_output_dir, temp_basename,
-                                        "test.TextGrid")
-            text_grid = TextGrid()
-            text_grid.read(temp_tg_path)
-        return text_grid
-
-    def align_single(self, wav, text, fs=None, is_wav_path=True,
-                     is_text_path=True) -> TextGrid:
-        """ This is the I/O wrapper for "align_single_internal."
-
-        Sample usage,
-            # Default
-            tg = aligner.align_single("test.wav", "test.lab")
-            # Input a wave file and its transcription in a string
-            tg = aligner.align_single("test.wav", "test", is_text_path=False)
-            # Directly input numpy format wave data whose content is the word
-            # "test" and has a sampling frequency of 16KHz; the flags tell the
-            # method that the inputs are not file paths
-            tg = aligner.align_single(wave_data, "test", 16000, False, False)
-
-        Args:
-            wav: Path to a wav file or a numpy array containing the speech data.
-            text: Path to a text file or a string containing the transcript.
-            fs: [optional] Sampling frequency. Only set if "wav" is a np array.
-            is_wav_path: [optional] Set to False if "wav" is a np array.
-            is_text_path: [optional] Set to False if "text" is a normal string.
-
-        Returns:
-            text_grid: A TextGrid object.
-        """
-        # Prepare speech data.
-        if is_wav_path:
-            if not os.path.isfile(wav):
-                logging.error("Wave file %s does not exist!", wav)
-            fs, wav_val = wavfile.read(wav, False)
-        else:
-            if fs is None:
-                logging.error("Need to specify sampling frequency!")
-            wav_val = wav
-        assert fs is not None, "Sampling frequency not determined."
-        assert isinstance(wav_val, ndarray), "Check your input for wav."
-        # Prepare transcription data.
-        if is_text_path:
-            if not os.path.isfile(text):
-                logging.error("Text file %s does not exist!", text)
-            with open(text, 'r') as reader:
-                text_val = reader.readline()
-            if not text_val:
-                logging.error("Empty text file %s!", text)
-        else:
-            text_val = text
-        assert isinstance(text_val, str), "Check your input for text."
-        # Run alignment.
-        text_grid = self.align_single_internal(wav_val, fs, text_val)
-        return text_grid
-
-    def align_batch(self, input_dir, output_dir, num_jobs=4):
-        """Wrapper for the normal montreal aligner usage.
-
-        Processing a group of utterances in "input_dir," the files should be
-        organized following the normal montreal aligner convention, i.e., each
-        wave file will have an accompanying .lab file with the same name that
-        contains the text transcription.
-
-        Args:
-            input_dir: Path to the audio and transcription files.
-            output_dir: Path to the output dir that will contain all the TGs.
-            num_jobs: Number of parallel threads.
-
-        Returns:
-            output_dir: Path to the output dir.
-        """
-        if not os.path.isdir(input_dir):
-            logging.error("Input dir %s does not exist!", input_dir)
-        if not os.path.isdir(output_dir):
-            os.mkdir(output_dir)
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            cmd = [self.aligner_path, "-t", tmpdirname, "-j", str(num_jobs),
-                   "-v", "-c", input_dir, self.dict_path, self.am_path,
-                   output_dir]
-            # if the process exits with a non-zero exit code, a
-            # "CalledProcessError" exception will be raised.
-            run_status = subprocess.run(cmd, check=True)
-        # Move the files around, since the aligner will put the tg files in a
-        # sub-folder in "output_dir".
-        output_basename = os.path.basename(input_dir)
-        actual_output_dir = os.path.join(output_dir, output_basename)
-        for each_file in glob(os.path.join(actual_output_dir, "*.TextGrid")):
-            move(each_file, output_dir)
-        return output_dir
-
-
 def write_tg_to_str(tg, null=''):
     """Write the current TextGrid into a Praat-format TextGrid string.
 
diff --git a/src/common/data_utils.py b/src/common/data_utils.py
index 0726d7a..48735ce 100644
--- a/src/common/data_utils.py
+++ b/src/common/data_utils.py
@@ -31,18 +31,15 @@
 
 """Modified from https://github.com/NVIDIA/tacotron2"""
 
-import logging
-import os
 import pickle
 import random
 import numpy as np
 import torch
 import torch.utils.data
-from common.utils import load_filepaths, load_filepaths_and_text
-from common.utterance import Utterance, is_sil
+from common.utils import load_filepaths
+from common.utterance import Utterance
 from common import layers
-from ppg import DependenciesPPG, reduce_ppg_dim
-from kaldi.matrix import Matrix
+from ppg import DependenciesPPG
 from scipy.io import wavfile
 from common import feat
 from common import ppg
@@ -55,14 +52,10 @@
 ACC_WIN = [0.25, 0, -0.5, 0, 0.25]
 
 
-def get_ppg(wav_path, deps, is_fmllr=False, speaker_code=None):
+def get_ppg(wav_path, deps):
     fs, wav = wavfile.read(wav_path)
     wave_data = feat.read_wav_kaldi_internal(wav, fs)
-    if is_fmllr:
-        seq = ppg.compute_full_ppg_wrapper(wave_data, deps.nnet, deps.lda, 10,
-                                           fmllr=deps.fmllr_mats[speaker_code])
-    else:
-        seq = ppg.compute_full_ppg_wrapper(wave_data, deps.nnet, deps.lda, 10)
+    seq = ppg.compute_full_ppg_wrapper(wave_data, deps.nnet, deps.lda, 10)
     return seq
 
 
@@ -146,274 +139,6 @@ def compute_delta_acc_feat(matrix, is_delta=False, is_acc=False):
     return matrix
 
 
-class MeanVarianceNorm(object):
-    """Adapted from https://github.com/CSTR-Edinburgh/merlin/blob/master/src
-    /frontend/mean_variance_norm.py"""
-    def __init__(self, acoustic_norm_file=''):
-        """Initialize a mean and variance normalizer.
-
-        Args:
-            acoustic_norm_file: If given, will load from this file.
-        """
-        if acoustic_norm_file is '':
-            self.mean_vector = None
-            self.std_vector = None
-        else:
-            self.load_mean_std_values(acoustic_norm_file)
-
-    def feature_normalization(self, features):
-        """Normalize the input features to zero mean and unit variance.
-
-        Args:
-            features: A T*D numpy array. Acoustic feature vectors.
-
-        Returns:
-            norm_features: Normalized features.
-        """
-        if self.mean_vector is None:
-            raise ValueError('The mean vector is not computed.')
-        if self.std_vector is None:
-            raise ValueError('The std vector is not computed.')
-        current_frame_number = features.shape[0]
-        mean_matrix = np.tile(self.mean_vector, (current_frame_number, 1))
-        std_matrix = np.tile(self.std_vector, (current_frame_number, 1))
-        norm_features = (features - mean_matrix) / std_matrix
-        return norm_features
-
-    def feature_denormalization(self, features):
-        """Re-scale to have the original mean and variance.
-
-        Args:
-            features: A T*D numpy array. Normalized features.
-
-        Returns:
-            denorm_features: Recovered feature vectors.
-        """
-        if self.mean_vector is None:
-            raise ValueError('The mean vector is not computed.')
-        if self.std_vector is None:
-            raise ValueError('The std vector is not computed.')
-        current_frame_number = features.shape[0]
-        mean_matrix = np.tile(self.mean_vector, (current_frame_number, 1))
-        std_matrix = np.tile(self.std_vector, (current_frame_number, 1))
-        denorm_features = features * std_matrix + mean_matrix
-        return denorm_features
-
-    def save_mean_std_values(self, acoutic_norm_file):
-        """Save the computed mean and std vectors to a pickle file.
-
-        Args:
-            acoutic_norm_file: The path to a pickle file.
-        """
-        if self.mean_vector is None:
-            raise ValueError('The mean vector is not computed.')
-        if self.std_vector is None:
-            raise ValueError('The std vector is not computed.')
-        with open(acoutic_norm_file, 'wb') as writer:
-            pickle.dump([self.mean_vector, self.std_vector], writer)
-
-    def load_mean_std_values(self, acoustic_norm_file):
-        """Load pre-computed mean and variance vectors.
-
-        Args:
-            acoustic_norm_file: A pickle file.
-
-        Returns:
-            The mean and std vectors.
-        """
-        with open(acoustic_norm_file, 'rb') as reader:
-            data = pickle.load(reader)
-        self.mean_vector = data[0]
-        self.std_vector = data[1]
-        return self.mean_vector, self.std_vector
-
-    def compute_mean(self, acoustic_sequences):
-        """Compute the mean of the acoustic features.
-
-        Args:
-            acoustic_sequences: An array of acoustic feature sequences.
-
-        Returns:
-            mean_vector: The mean vector.
-        """
-        num_dims = acoustic_sequences[0].shape[1]
-        mean_vector = np.zeros((1, num_dims))
-        all_frame_number = 0
-
-        for seq in acoustic_sequences:
-            current_frame_number = seq.shape[0]
-            mean_vector += np.reshape(np.sum(seq, axis=0), (1, num_dims))
-            all_frame_number += current_frame_number
-        mean_vector /= float(all_frame_number)
-        self.mean_vector = mean_vector
-        return mean_vector
-
-    def compute_std(self, acoustic_sequences):
-        """Compute the standard deviation of the acoustic features.
-
-        Args:
-            acoustic_sequences: An array of acoustic feature sequences.
-
-        Returns:
-            std_vector: The std vector.
-        """
-        num_dims = acoustic_sequences[0].shape[1]
-        std_vector = np.zeros((1, num_dims))
-        all_frame_number = 0
-        if self.mean_vector is None:
-            raise ValueError('The mean vector is not computed.')
-
-        for seq in acoustic_sequences:
-            current_frame_number = seq.shape[0]
-            mean_matrix = np.tile(self.mean_vector, (current_frame_number, 1))
-            std_vector += np.reshape(np.sum(
-                (seq - mean_matrix) ** 2, axis=0), (1, num_dims))
-            all_frame_number += current_frame_number
-        std_vector /= float(all_frame_number)
-        std_vector = std_vector ** 0.5
-        self.std_vector = std_vector
-        return std_vector
-
-
-class PPGSpeechLoader(torch.utils.data.Dataset):
-    """Loads [ppg,speech] pairs.
-    """
-    def __init__(self, data_utterance_paths, hparams):
-        """Data loader for the PPG->Speech task.
-
-        Args:
-            data_utterance_paths: A text file containing a list of file paths.
-            hparams: The hyper-parameters.
-        """
-        self.data_utterance_paths = load_filepaths(data_utterance_paths)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.sequence_level = hparams.sequence_level
-        self.is_skip_sil = hparams.is_skip_sil
-        random.seed(hparams.seed)
-        random.shuffle(self.data_utterance_paths)
-        self.ppg_sequences = []
-        self.acoustic_sequences = []
-        for utterance_path in self.data_utterance_paths:
-            feat_pairs = self.chop_utterance(utterance_path,
-                                             self.sequence_level,
-                                             self.is_skip_sil)
-            for each_pair in feat_pairs:
-                self.ppg_sequences.append(each_pair[0])
-                self.acoustic_sequences.append(each_pair[1])
-        self.mvn_normalizer = MeanVarianceNorm()
-        if os.path.isfile(hparams.mvn_stats_file):
-            self.mvn_normalizer.load_mean_std_values(hparams.mvn_stats_file)
-        else:
-            self.mvn_normalizer.compute_mean(self.acoustic_sequences)
-            self.mvn_normalizer.compute_std(self.acoustic_sequences)
-
-    def stack_acoustic_feats(self, utt):
-        """Stack the vocoder features together to form the acoustic features.
-
-        Stack in the form of [mcep, log_f0, band_ap].
-
-        Args:
-            utt: A data utterance object.
-
-        Returns:
-            acoustic_feat: Stacked features.
-        """
-        f0 = utt.f0
-        lf0 = np.log(f0 + np.finfo(float).eps)  # Log F0.
-        lf0 = lf0.reshape(lf0.shape[0], 1)  # Convert to 2-dim matrix.
-        mcep = utt.mcep
-        bap = utt.bap
-        acoustic_feat = np.concatenate((mcep, lf0, bap), axis=1)
-        return acoustic_feat
-
-    def chop_following_alignment(self, ppg, acoustics, align,
-                                 is_skip_sil=False):
-        """Chop the given features into segments according to the alignment.
-
-        Args:
-            ppg: A T*D1 matrix, the phonetic posteriorgram.
-            acoustics: A T*D2 matrix, the stacked acoustic features.
-            align: A IntervalTier. Contains the alignment.
-            is_skip_sil: If set to true, will skip the silences.
-
-        Returns:
-            res: A list of [ppg, acoustic_feature] pairs for each segment.
-        """
-        # First fix the mismatch between the ppg and acoustic frames.
-        num_ppg_frames = ppg.shape[0]
-        num_acoustic_frames = acoustics.shape[0]
-        final_num_frames = min([num_ppg_frames, num_acoustic_frames])
-        ppg = ppg[:final_num_frames, :]
-        acoustics = acoustics[:final_num_frames, :]
-        align.intervals[-1].maxTime = final_num_frames - 1
-        res = []
-        for interval in align.intervals:
-            start_idx = int(interval.minTime)
-            end_idx = int(interval.maxTime)
-            is_last_seg = end_idx == int(final_num_frames - 1)
-
-            if is_sil(interval.mark) and is_skip_sil:
-                continue
-            else:
-                # The last segment should include the last frame
-                if is_last_seg:
-                    res.append([ppg[start_idx:(end_idx + 1), :],
-                                acoustics[start_idx:(end_idx + 1), :]])
-                else:
-                    res.append([ppg[start_idx:end_idx, :],
-                                acoustics[start_idx:end_idx, :]])
-        return res
-
-    def chop_utterance(self, data_utterance_path, level='sentence',
-                       is_skip_sil=False):
-        """Chop an utterances into segments.
-
-        Args:
-            data_utterance_path: The path to the data utterance protocol buffer.
-            level: Level of the segmentation.
-                - 'sentence': Return the whole sentence.
-                - 'word': Return the word segments.
-                - 'phone': Return the phone segments.
-            is_skip_sil: If set to true, will skip the silences in the
-            utterance. It does not take any effect if the level is 'sentence'.
-
-        Returns:
-            feat_pairs: A list, each is a [pps, acoustic_feature] pair.
-        """
-        utt = Utterance()
-        utt.read(data_utterance_path)
-        acoustic_feats = self.stack_acoustic_feats(utt)
-        if level == 'sentence':
-            return [[utt.monophone_ppg, acoustic_feats]]
-        elif level == 'word':
-            feat_pairs = self.chop_following_alignment(
-                utt.monophone_ppg, acoustic_feats, utt.word, is_skip_sil)
-        elif level == 'phone':
-            feat_pairs = self.chop_following_alignment(
-                utt.monophone_ppg, acoustic_feats, utt.phone, is_skip_sil)
-        else:
-            raise ValueError('Level %s is not supported.' % level)
-        return feat_pairs
-
-    def __getitem__(self, index):
-        """Get a new data sample in torch.float32 format.
-
-        Args:
-            index: An int.
-
-        Returns:
-            T*D1 PPG sequence, T*D2 mvn-ed acoustic features
-        """
-        curr_acoustics = self.mvn_normalizer.feature_normalization(
-            self.acoustic_sequences[index])
-        return torch.from_numpy(self.ppg_sequences[index]).float(), \
-            torch.from_numpy(curr_acoustics).float()
-
-    def __len__(self):
-        return len(self.ppg_sequences)
-
-
 def append_ppg(feats, f0):
     """Append log F0 and its delta and acc
 
@@ -436,10 +161,9 @@ def append_ppg(feats, f0):
 
 
 class PPGMelLoader(torch.utils.data.Dataset):
-    """Loads [ppg, mel] pairs.
-    """
+    """Loads [ppg, mel] pairs."""
 
-    def __init__(self, data_utterance_paths, hparams, n_jobs=1):
+    def __init__(self, data_utterance_paths, hparams):
         """Data loader for the PPG->Mel task.
 
         Args:
@@ -449,15 +173,12 @@ def __init__(self, data_utterance_paths, hparams, n_jobs=1):
         self.data_utterance_paths = load_filepaths(data_utterance_paths)
         self.max_wav_value = hparams.max_wav_value
         self.sampling_rate = hparams.sampling_rate
-        self.sequence_level = hparams.sequence_level
-        self.is_skip_sil = hparams.is_skip_sil
         self.is_full_ppg = hparams.is_full_ppg
         self.is_append_f0 = hparams.is_append_f0
         self.is_cache_feats = hparams.is_cache_feats
         self.load_feats_from_disk = hparams.load_feats_from_disk
         self.feats_cache_path = hparams.feats_cache_path
         self.ppg_subsampling_factor = hparams.ppg_subsampling_factor
-        self.get_ppg_on_the_fly = hparams.get_ppg_on_the_fly
         self.ppg_deps = DependenciesPPG()
 
         if self.is_cache_feats and self.load_feats_from_disk:
@@ -481,90 +202,37 @@ def __init__(self, data_utterance_paths, hparams, n_jobs=1):
             self.acoustic_sequences = data[1]
         else:
             for utterance_path in self.data_utterance_paths:
-                feat_pairs = self.chop_utterance(utterance_path,
-                                                 self.sequence_level,
-                                                 self.is_skip_sil,
-                                                 self.is_full_ppg,
-                                                 self.get_ppg_on_the_fly)
-                for each_pair in feat_pairs:
-                    self.ppg_sequences.append(each_pair[0].astype(
-                        np.float32))
-                    self.acoustic_sequences.append(each_pair[1])
+                ppg_feat_pair = self.extract_utterance_feats(utterance_path,
+                                                   self.is_full_ppg)
+                self.ppg_sequences.append(ppg_feat_pair[0].astype(
+                    np.float32))
+                self.acoustic_sequences.append(ppg_feat_pair[1])
         if self.is_cache_feats:
             print('Caching data to %s.' % self.feats_cache_path)
             with open(self.feats_cache_path, 'wb') as f:
                 pickle.dump([self.ppg_sequences, self.acoustic_sequences], f)
 
-    def chop_following_alignment(self, ppg, acoustics, align,
-                                 is_skip_sil=False):
-        """Chop the given features into segments according to the alignment.
-
-        Args:
-            ppg: A T*D1 matrix, the phonetic posteriorgram.
-            acoustics: A T*D2 matrix, the stacked acoustic features.
-            align: A IntervalTier. Contains the alignment.
-            is_skip_sil: If set to true, will skip the silences.
-
-        Returns:
-            res: A list of [ppg, acoustic_feature] pairs for each segment.
-        """
-        # First fix the mismatch between the ppg and acoustic frames.
-        num_ppg_frames = ppg.shape[0]
-        num_acoustic_frames = acoustics.shape[0]
-        final_num_frames = min([num_ppg_frames, num_acoustic_frames])
-        ppg = ppg[:final_num_frames, :]
-        acoustics = acoustics[:final_num_frames, :]
-        align.intervals[-1].maxTime = final_num_frames - 1
-        res = []
-        for interval in align.intervals:
-            start_idx = int(interval.minTime)
-            end_idx = int(interval.maxTime)
-            is_last_seg = end_idx == int(final_num_frames - 1)
-
-            if is_sil(interval.mark) and is_skip_sil:
-                continue
-            else:
-                # The last segment should include the last frame
-                if is_last_seg:
-                    res.append([ppg[start_idx:(end_idx + 1), :],
-                                acoustics[start_idx:(end_idx + 1), :]])
-                else:
-                    res.append([ppg[start_idx:end_idx, :],
-                                acoustics[start_idx:end_idx, :]])
-        return res
-
-    def chop_utterance(self, data_utterance_path, level='sentence',
-                       is_skip_sil=False, is_full_ppg=False,
-                       get_ppg_on_the_fly=False):
-        """Chop an utterances into segments.
+    def extract_utterance_feats(self, data_utterance_path, is_full_ppg=False):
+        """Get PPG and Mel (+ optional F0) for an utterance.
 
         Args:
             data_utterance_path: The path to the data utterance protocol buffer.
-            level: Level of the segmentation.
-                - 'sentence': Return the whole sentence.
-                - 'word': Return the word segments.
-                - 'phone': Return the phone segments.
-            is_skip_sil: If set to true, will skip the silences in the
-            utterance. It does not take any effect if the level is 'sentence'.
             is_full_ppg: If True, will use the full PPGs.
 
         Returns:
             feat_pairs: A list, each is a [pps, mel] pair.
         """
         utt = Utterance()
-        if get_ppg_on_the_fly:
-            fs, wav = wavfile.read(data_utterance_path)
-            utt.fs = fs
-            utt.wav = wav
-            utt.ppg = get_ppg(data_utterance_path, self.ppg_deps)
-        else:
-            utt.read(data_utterance_path)
+        fs, wav = wavfile.read(data_utterance_path)
+        utt.fs = fs
+        utt.wav = wav
+        utt.ppg = get_ppg(data_utterance_path, self.ppg_deps)
 
         audio = torch.FloatTensor(utt.wav.astype(np.float32))
         fs = utt.fs
 
         if fs != self.stft.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
+            raise ValueError("{} SR doesn't match target {} SR".format(
                 fs, self.stft.sampling_rate))
         audio_norm = audio / self.max_wav_value
         audio_norm = audio_norm.unsqueeze(0)
@@ -576,36 +244,18 @@ def chop_utterance(self, data_utterance_path, level='sentence',
         # (T, n_mel_channels)
         acoustic_feats = acoustic_feats.transpose(0, 1)
 
-        if level == 'sentence':
-            if is_full_ppg:
-                if self.is_append_f0:
-                    ppg_f0 = append_ppg(utt.ppg, utt.f0)
-                    return [[ppg_f0, acoustic_feats]]
-                else:
-                    return [[utt.ppg, acoustic_feats]]
-            else:
-                if self.is_append_f0:
-                    ppg_f0 = append_ppg(utt.monophone_ppg, utt.f0)
-                    return [[ppg_f0, acoustic_feats]]
-                else:
-                    return [[utt.monophone_ppg, acoustic_feats]]
-        elif level == 'word':
-            if is_full_ppg:
-                feat_pairs = self.chop_following_alignment(
-                    utt.ppg, acoustic_feats, utt.word, is_skip_sil)
-            else:
-                feat_pairs = self.chop_following_alignment(
-                    utt.monophone_ppg, acoustic_feats, utt.word, is_skip_sil)
-        elif level == 'phone':
-            if is_full_ppg:
-                feat_pairs = self.chop_following_alignment(
-                    utt.ppg, acoustic_feats, utt.phone, is_skip_sil)
+        if is_full_ppg:
+            if self.is_append_f0:
+                ppg_f0 = append_ppg(utt.ppg, utt.f0)
+                return [ppg_f0, acoustic_feats]
             else:
-                feat_pairs = self.chop_following_alignment(
-                    utt.monophone_ppg, acoustic_feats, utt.phone, is_skip_sil)
+                return [utt.ppg, acoustic_feats]
         else:
-            raise ValueError('Level %s is not supported.' % level)
-        return feat_pairs
+            if self.is_append_f0:
+                ppg_f0 = append_ppg(utt.monophone_ppg, utt.f0)
+                return [ppg_f0, acoustic_feats]
+            else:
+                return [utt.monophone_ppg, acoustic_feats]
 
     def __getitem__(self, index):
         """Get a new data sample in torch.float32 format.
@@ -628,172 +278,6 @@ def __len__(self):
         return len(self.ppg_sequences)
 
 
-class PPGMelLoaderOnDemand(torch.utils.data.Dataset):
-    """Loads [ppg, mel] pairs.
-    """
-
-    def __init__(self, data_utterance_paths, hparams):
-        """Data loader for the PPG->Mel task.
-
-        Args:
-            data_utterance_paths: A text file containing a list of file paths.
-            hparams: The hyper-parameters.
-        """
-        self.data_utterance_paths = load_filepaths(data_utterance_paths)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.ppg_subsampling_factor = hparams.ppg_subsampling_factor
-
-        self.stft = layers.TacotronSTFT(
-            hparams.filter_length, hparams.hop_length, hparams.win_length,
-            hparams.n_acoustic_feat_dims, hparams.sampling_rate,
-            hparams.mel_fmin, hparams.mel_fmax)
-        random.seed(hparams.seed)
-        random.shuffle(self.data_utterance_paths)
-        self.cache_mel = {}
-        self.cache_ppg = {}
-
-    def load_one_utterance(self, data_utterance_path):
-        """Chop an utterances into segments.
-
-        Args:
-            data_utterance_path: The path to the data utterance protocol buffer.
-
-        Returns:
-            A [pps, mel] pair.
-        """
-
-        if data_utterance_path in self.cache_ppg:
-            ppg = self.cache_ppg[data_utterance_path]
-        else:
-            utt = Utterance()
-            utt.read(data_utterance_path)
-            ppg = utt.ppg
-            if len(self.cache_ppg) > 200:
-                self.cache_ppg.pop(random.choice(list(self.cache_ppg.keys())))
-            self.cache_ppg[data_utterance_path] = ppg
-
-        if data_utterance_path in self.cache_mel:
-            acoustic_feats = self.cache_mel[data_utterance_path]
-        else:
-            audio = torch.FloatTensor(utt.wav.astype(np.float32))
-            fs = utt.fs
-
-            if fs != self.stft.sampling_rate:
-                raise ValueError("{} {} SR doesn't match target {} SR".format(
-                    fs, self.stft.sampling_rate))
-            audio_norm = audio / self.max_wav_value
-            audio_norm = audio_norm.unsqueeze(0)
-            audio_norm = torch.autograd.Variable(audio_norm,
-                                                 requires_grad=False)
-            # (1, n_mel_channels, T)
-            acoustic_feats = self.stft.mel_spectrogram(audio_norm)
-            # (n_mel_channels, T)
-            acoustic_feats = torch.squeeze(acoustic_feats, 0)
-            # (T, n_mel_channels)
-            acoustic_feats = acoustic_feats.transpose(0, 1)
-            self.cache_mel[data_utterance_path] = acoustic_feats
-
-        return ppg, acoustic_feats
-
-    def __getitem__(self, index):
-        """Get a new data sample in torch.float32 format.
-
-        Args:
-            index: An int.
-
-        Returns:
-            T*D1 PPG sequence, T*D2 mels
-        """
-        curr_ppg, curr_acoustics = \
-            self.load_one_utterance(self.data_utterance_paths[index])
-        if self.ppg_subsampling_factor > 1:
-            curr_ppg = curr_ppg[0::self.ppg_subsampling_factor, :]
-        return torch.from_numpy(curr_ppg), curr_acoustics
-
-    def __len__(self):
-        return len(self.data_utterance_paths)
-
-
-class PPG2PPGLoaderOnDemand(torch.utils.data.Dataset):
-    """Loads [ppg, ppg] pairs.
-    """
-
-    def __init__(self, data_utterance_paths, hparams):
-        """Data loader for the PPG->Mel task.
-
-        Args:
-            data_utterance_paths: A text file containing a list of file paths.
-            hparams: The hyper-parameters.
-        """
-        self.data_utterance_paths = load_filepaths_and_text(
-            data_utterance_paths, '|')
-        self.ppg_subsampling_factor = hparams.ppg_subsampling_factor
-        random.seed(hparams.seed)
-        random.shuffle(self.data_utterance_paths)
-        self.cache_ppg = {}
-        self.is_full_ppg = hparams.is_full_ppg
-        self.nnet_deps = DependenciesPPG()
-        if not self.is_full_ppg:
-            for each_file_pair in self.data_utterance_paths:
-                self.load_one_utterance(each_file_pair)
-
-    def load_one_utterance(self, data_utterance_path):
-        """Chop an utterances into segments.
-
-        Args:
-            data_utterance_path: The path to the data utterance protocol buffer.
-
-        Returns:
-            A [pps, mel] pair.
-        """
-
-        ppg = [None, None]
-        if data_utterance_path in self.cache_ppg:
-            ppg = self.cache_ppg[data_utterance_path]
-        else:
-            utt0 = Utterance()
-            utt0.read(data_utterance_path[0])
-            if self.is_full_ppg:
-                ppg[0] = utt0.ppg
-            else:
-                ppg[0] = reduce_ppg_dim(Matrix(utt0.ppg),
-                                        self.nnet_deps.monophone_trans).numpy()
-            utt1 = Utterance()
-            utt1.read(data_utterance_path[1])
-            if self.is_full_ppg:
-                ppg[1] = utt1.ppg
-            else:
-                ppg[1] = reduce_ppg_dim(Matrix(utt1.ppg),
-                                        self.nnet_deps.monophone_trans).numpy()
-            if self.is_full_ppg:
-                if len(self.cache_ppg) > 200:
-                    self.cache_ppg.pop(
-                        random.choice(list(self.cache_ppg.keys())))
-            self.cache_ppg[data_utterance_path] = ppg
-
-        return ppg[0], ppg[1]
-
-    def __getitem__(self, index):
-        """Get a new data sample in torch.float32 format.
-
-        Args:
-            index: An int.
-
-        Returns:
-            T*D1 PPG sequence, T*D2 mels
-        """
-        curr_ppg0, curr_ppg1 = \
-            self.load_one_utterance(self.data_utterance_paths[index])
-        if self.ppg_subsampling_factor > 1:
-            curr_ppg0 = curr_ppg0[0::self.ppg_subsampling_factor, :]
-            curr_ppg1 = curr_ppg1[0::self.ppg_subsampling_factor, :]
-        return torch.from_numpy(curr_ppg0), torch.from_numpy(curr_ppg1)
-
-    def __len__(self):
-        return len(self.data_utterance_paths)
-
-
 def ppg_acoustics_collate(batch):
     """Zero-pad the PPG and acoustic sequences in a mini-batch.
 
@@ -852,8 +336,12 @@ def ppg_acoustics_collate(batch):
 
 def utt_to_sequence(utt: Utterance, is_full_ppg=False, is_append_f0=False):
     """Get PPG tensor for inference.
+
     Args:
         utt: A data utterance object.
+        is_full_ppg: If True, will use the full PPGs.
+        is_append_f0: If True, will append F0 features.
+
     Returns:
         A 1*D*T tensor.
     """
diff --git a/src/common/decode.py b/src/common/decode.py
index d38aba9..a180a28 100644
--- a/src/common/decode.py
+++ b/src/common/decode.py
@@ -12,8 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""This module provides handy functions for Kaldi-decoding related tasks
-"""
+"""This module provides handy functions for Kaldi-decoding related tasks."""
 
 from kaldi import nnet3
 from kaldi.util.io import xopen
diff --git a/src/common/feat.py b/src/common/feat.py
index 91bc0a2..8f3e555 100644
--- a/src/common/feat.py
+++ b/src/common/feat.py
@@ -23,7 +23,6 @@
 from kaldi.matrix.common import MatrixTransposeType
 from kaldi.matrix.sparse import SparseMatrix
 from kaldi.util.io import xopen
-from kaldi import online2
 from scipy.io import wavfile
 
 
@@ -187,52 +186,3 @@ def parse_config(config_path: str):
         options = dict([tuple(line.split('=')) for
                         line in f.read().splitlines(False)])
     return options
-
-
-def compute_mfcc_plus_ivector(wav_data: WaveData, config_path: str):
-    """Compute MFCCs and ivector using the OnlineNnetFeaturePipeline.
-    Inspired by https://github.com/kaldi-asr/kaldi/blob/master/src/online2bin
-    /online2-wav-nnet3-latgen-faster.cc
-
-    Args:
-        wav_data: WaveData.
-        config_path: Path to the online config file.
-
-    Returns:
-        mfccs: T*D1 matrix.
-        ivectors: T*D2 matrix.
-    """
-    options = parse_config(config_path)
-    feature_opts = online2.OnlineNnetFeaturePipelineConfig()
-    feature_opts.ivector_extraction_config = \
-        options['--ivector-extraction-config']
-    feature_opts.mfcc_config = options['--mfcc-config']
-    feature_opts.silence_weighting_config.silence_phones_str = \
-        options['--endpoint.silence-phones']
-    feature_info = \
-        online2.OnlineNnetFeaturePipelineInfo().from_config(feature_opts)
-    feature_info.ivector_extractor_info.use_most_recent_ivector = True
-    feature_info.ivector_extractor_info.greedy_ivector_extractor = True
-    feature_info.mfcc_opts.frame_opts.allow_downsample = True
-    feature_info.mfcc_opts.frame_opts.frame_shift_ms = \
-        int(options['--frame-shift'])
-    feature_info.ivector_extractor_info.ivector_period = \
-        int(options['--frame-shift'])
-    feature_pipeline = online2.OnlineNnetFeaturePipeline(feature_info)
-
-    wav = \
-        Vector(wav_data.data().shape[1]).copy_row_from_mat_(wav_data.data(), 0)
-
-    feature_pipeline.accept_waveform(wav_data.samp_freq, wav)
-    feature_pipeline.input_finished()
-    num_frames = feature_pipeline.num_frames_ready()
-    num_ceps = feature_info.mfcc_opts.num_ceps
-    mfccs = Matrix(num_frames, num_ceps)
-    feature_pipeline.input_feature().get_frames(list(range(num_frames)), mfccs)
-
-    num_ivecs = feature_info.ivector_extractor_info.extractor.ivector_dim()
-    ivectors = Matrix(num_frames, num_ivecs)
-    feature_pipeline.ivector_feature().get_frames(list(range(num_frames)),
-                                                  ivectors)
-
-    return mfccs, ivectors
diff --git a/src/common/hparams.py b/src/common/hparams.py
index 7e28cf0..bb79ab3 100644
--- a/src/common/hparams.py
+++ b/src/common/hparams.py
@@ -56,9 +56,7 @@ def create_hparams(**kwargs):
         "cudnn_benchmark": False,
         "output_directory": None,  # Directory to save checkpoints.
         "log_directory": 'log',  # Directory to save tensorboard logs.
-        "checkpoint_path": "/media/guanlong/DATA1/exp/ppg-speech/tacotron"
-                           "/trial_20190323-103343/output/checkpoint_10400",
-        # Path to a checkpoint file.
+        "checkpoint_path": '',  # Path to a checkpoint file.
         "warm_start": False,  # Load the model only (warm start)
         "n_gpus": 1,  # Number of GPUs
         "rank": 0,  # Rank of current gpu
@@ -67,13 +65,8 @@ def create_hparams(**kwargs):
         ################################
         # Data Parameters             #
         ################################
-        "training_files": '/home/guanlong/PycharmProjects/ppg-speech/data'
-                          '/filelists/zhaa_train_filelist_noise_reduced.txt',
-        "validation_files": '/home/guanlong/PycharmProjects/ppg-speech/data'
-                            '/filelists/zhaa_val_filelist_noise_reduced.txt',
-        "mvn_stats_file": None,  # Mean and var stats for the training data.
-        "sequence_level": 'sentence',  # How to segment the utterances.
-        "is_skip_sil": False,  # Whether to skip the silences or not.
+        "training_files": '/home/guanlong/PycharmProjects/fac-via-ppg/data/filelists/ykwk_train_filelist_noise_reduced_lite.txt',
+        "validation_files": '/home/guanlong/PycharmProjects/fac-via-ppg/data/filelists/ykwk_val_filelist_noise_reduced.txt',
         "is_full_ppg": True,  # Whether to use the full PPG or not.
         "is_append_f0": False,  # Currently only effective at sentence level
         "ppg_subsampling_factor": 1,  # Sub-sample the ppg & acoustic sequence.
@@ -87,10 +80,8 @@ def create_hparams(**kwargs):
         # Mutually exclusive with 'load_feats_from_disk', will overwrite
         # 'feats_cache_path' if set.
         "is_cache_feats": False,
-        "feats_cache_path": '/data_repo/arctic/cache/'
-                            'zhaa_feat_cache_noise_reduced.pkl',
-        "is_large_set": False,  # Chain model
-        "get_ppg_on_the_fly": True,  # Use wav as input.
+        "feats_cache_path":
+            '/data_repo/arctic/cache/ykwk_feat_cache_noise_reduced_lite.pkl',
 
         ################################
         # Audio Parameters             #
@@ -167,160 +158,71 @@ def create_hparams(**kwargs):
 def create_hparams_stage(**kwargs):
     """Create model hyperparameters. Parse nondefault from given string."""
 
-    hparams = {'attention_dim': 150,
- 'attention_location_kernel_size': 31,
- 'attention_location_n_filters': 32,
- 'attention_rnn_dim': 300,
- 'attention_window_size': 20,
- 'batch_size': 6,
- 'checkpoint_path': None,
- 'cudnn_benchmark': False,
- 'cudnn_enabled': True,
- 'decoder_rnn_dim': 300,
- 'dist_backend': 'nccl',
- 'dist_url': 'tcp://localhost:54321',
- 'distributed_run': False,
- 'dynamic_loss_scaling': True,
- 'encoder_embedding_dim': 600,
- 'encoder_kernel_size': 5,
- 'encoder_n_convolutions': 3,
- 'epochs': 1000,
- 'feats_cache_path': '/data_repo/arctic/ykwk/cache//ykwk_feat_cache.pkl',
- 'filter_length': 1024,
- 'fp16_run': False,
- 'gate_threshold': 0.5,
- 'gate_weight': 0.005,
- 'grad_clip_thresh': 1.0,
- 'group_name': 'group_name',
- 'hop_length': 160,
- 'is_append_f0': False,
- 'is_cache_feats': False,
- 'is_full_ppg': True,
- 'is_large_set': False,
- 'is_skip_sil': False,
- 'iters_per_checkpoint': 100,
- 'learning_rate': 0.0001,
- 'load_feats_from_disk': True,
- 'log_directory': 'log',
- 'mask_padding': True,
- 'max_decoder_steps': 1000,
- 'max_wav_value': 32768.0,
- 'mel_fmax': 8000.0,
- 'mel_fmin': 0.0,
- 'mel_weight': 1,
- 'mvn_stats_file': '/media/guanlong/DATA1/exp/ppg-speech/tacotron/trial_20190314-185345/train_set_mvn.pkl',
- 'n_acoustic_feat_dims': 80,
- 'n_gpus': 1,
- 'n_symbols': 5816,
- 'output_directory': '/media/guanlong/DATA1/exp/ppg-speech/tacotron/trial_20190314-185345/output',
- 'p_attention_dropout': 0.1,
- 'p_decoder_dropout': 0.1,
- 'postnet_embedding_dim': 512,
- 'postnet_kernel_size': 5,
- 'postnet_n_convolutions': 5,
- 'ppg_subsampling_factor': 1,
- 'prenet_dim': 300,
- 'rank': 0,
- 'sampling_rate': 16000,
- 'seed': 16807,
- 'sequence_level': 'sentence',
- 'symbols_embedding_dim': 600,
- 'training_files': '/home/guanlong/PycharmProjects/ppg-speech/data/filelists/ykwk_train_filelist.txt',
- 'use_saved_learning_rate': False,
- 'validation_files': '/home/guanlong/PycharmProjects/ppg-speech/data/filelists/ykwk_val_filelist.txt',
- 'warm_start': False,
- 'weight_decay': 1e-06,
- 'win_length': 1024}
-
-    for key, val in kwargs.items():
-        if key in hparams:
-            hparams[key] = val
-        else:
-            raise ValueError('The hyper-parameter %s is not supported.' % key)
-
-    hparams_view = HParamsView(hparams)
-
-    return hparams_view
-
-
-def create_hparams_ppg2ppg(**kwargs):
-    """Create model hyperparameters. Parse nondefault from given string."""
-
     hparams = {
-        ################################
-        # Experiment Parameters        #
-        ################################
-        "epochs": 1000,
-        "iters_per_checkpoint": 100,
-        "seed": 16807,
-        "dynamic_loss_scaling": True,
-        "fp16_run": False,
-        "distributed_run": False,
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "cudnn_enabled": True,
-        "cudnn_benchmark": False,
-        "output_directory": None,  # Directory to save checkpoints.
-        "log_directory": 'log',  # Directory to save tensorboard logs.
-        "checkpoint_path": None,  # Path to a checkpoint file.
-        "warm_start": False,  # Load the model only (warm start)
-        "n_gpus": 1,  # Number of GPUs
-        "rank": 0,  # Rank of current gpu
-        "group_name": 'group_name',  # Distributed group name
-
-        ################################
-        # Data Parameters             #
-        ################################
-        "training_files": '/home/guanlong/PycharmProjects/ppg-speech/data'
-                          '/filelists/lxc_slt_train_filelist.txt',
-        "validation_files": '/home/guanlong/PycharmProjects/ppg-speech/data'
-                            '/filelists/lxc_slt_val_filelist.txt',
-        "ppg_subsampling_factor": 1,  # Sub-sample the ppg sequence.
-        "n_acoustic_feat_dims": 40,
-        "is_full_ppg": False,
-
-        ################################
-        # Model Parameters             #
-        ################################
-        "n_symbols": 40,  # For chain 8629, for fc 5816
-        "symbols_embedding_dim": 600,
-
-        # Encoder parameters
-        "encoder_kernel_size": 5,
-        "encoder_n_convolutions": 3,
-        "encoder_embedding_dim": 600,
-
-        # Decoder parameters
-        "decoder_rnn_dim": 300,
-        "prenet_dim": 300,
-        "max_decoder_steps": 1000,
-        "gate_threshold": 0.5,
-        "p_attention_dropout": 0.1,
-        "p_decoder_dropout": 0.1,
-
-        # Attention parameters
-        "attention_rnn_dim": 300,
-        "attention_dim": 150,
-        # +- time steps to look at when computing the attention. Set to None
-        # to block it.
-        "attention_window_size": 20,
-
-        # Location Layer parameters
-        "attention_location_n_filters": 32,
-        "attention_location_kernel_size": 31,
-
-        ################################
-        # Optimization Hyperparameters #
-        ################################
-        "use_saved_learning_rate": False,
-        "learning_rate": 1e-4,
-        "weight_decay": 1e-6,
-        "grad_clip_thresh": 1.0,
-        "batch_size": 8,
-        "mask_padding": True,  # set model's padded outputs to padded values
-        "ppg_weight": 1,
-        "gate_weight": 0.005
-    }
+        'attention_dim': 150,
+        'attention_location_kernel_size': 31,
+        'attention_location_n_filters': 32,
+        'attention_rnn_dim': 300,
+        'attention_window_size': 20,
+        'batch_size': 6,
+        'checkpoint_path': None,
+        'cudnn_benchmark': False,
+        'cudnn_enabled': True,
+        'decoder_rnn_dim': 300,
+        'dist_backend': 'nccl',
+        'dist_url': 'tcp://localhost:54321',
+        'distributed_run': False,
+        'dynamic_loss_scaling': True,
+        'encoder_embedding_dim': 600,
+        'encoder_kernel_size': 5,
+        'encoder_n_convolutions': 3,
+        'epochs': 1000,
+        'feats_cache_path': '',
+        'filter_length': 1024,
+        'fp16_run': False,
+        'gate_threshold': 0.5,
+        'gate_weight': 0.005,
+        'grad_clip_thresh': 1.0,
+        'group_name': 'group_name',
+        'hop_length': 160,
+        'is_append_f0': False,
+        'is_cache_feats': False,
+        'is_full_ppg': True,
+        'is_large_set': False,
+        'is_skip_sil': False,
+        'iters_per_checkpoint': 100,
+        'learning_rate': 0.0001,
+        'load_feats_from_disk': True,
+        'log_directory': 'log',
+        'mask_padding': True,
+        'max_decoder_steps': 1000,
+        'max_wav_value': 32768.0,
+        'mel_fmax': 8000.0,
+        'mel_fmin': 0.0,
+        'mel_weight': 1,
+        'mvn_stats_file': '',
+        'n_acoustic_feat_dims': 80,
+        'n_gpus': 1,
+        'n_symbols': 5816,
+        'output_directory': '',
+        'p_attention_dropout': 0.1,
+        'p_decoder_dropout': 0.1,
+        'postnet_embedding_dim': 512,
+        'postnet_kernel_size': 5,
+        'postnet_n_convolutions': 5,
+        'ppg_subsampling_factor': 1,
+        'prenet_dim': 300,
+        'rank': 0,
+        'sampling_rate': 16000,
+        'seed': 16807,
+        'sequence_level': 'sentence',
+        'symbols_embedding_dim': 600,
+        'training_files': '',
+        'use_saved_learning_rate': False,
+        'validation_files': '',
+        'warm_start': False,
+        'weight_decay': 1e-06,
+        'win_length': 1024}
 
     for key, val in kwargs.items():
         if key in hparams:
diff --git a/src/common/layers.py b/src/common/layers.py
index 7e6874c..8d798ac 100644
--- a/src/common/layers.py
+++ b/src/common/layers.py
@@ -28,7 +28,7 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-"""From https://github.com/NVIDIA/tacotron2"""
+"""Modified from https://github.com/NVIDIA/tacotron2"""
 
 import torch
 from librosa.filters import mel as librosa_mel_fn
diff --git a/src/common/logger.py b/src/common/logger.py
index 12f2344..ca16947 100644
--- a/src/common/logger.py
+++ b/src/common/logger.py
@@ -34,10 +34,8 @@
 import torch.nn.functional as F
 from tensorboardX import SummaryWriter
 from common.plotting_utils import plot_alignment_to_numpy, \
-    plot_spectrogram_to_numpy, plot_ppg_to_numpy
+    plot_spectrogram_to_numpy
 from common.plotting_utils import plot_gate_outputs_to_numpy
-from ppg import DependenciesPPG, reduce_ppg_dim
-from kaldi.matrix import Matrix
 
 
 class Tacotron2Logger(SummaryWriter):
@@ -46,10 +44,10 @@ def __init__(self, logdir):
 
     def log_training(self, reduced_loss, grad_norm, learning_rate, duration,
                      iteration):
-            self.add_scalar("training.loss", reduced_loss, iteration)
-            self.add_scalar("grad.norm", grad_norm, iteration)
-            self.add_scalar("learning.rate", learning_rate, iteration)
-            self.add_scalar("duration", duration, iteration)
+        self.add_scalar("training.loss", reduced_loss, iteration)
+        self.add_scalar("grad.norm", grad_norm, iteration)
+        self.add_scalar("learning.rate", learning_rate, iteration)
+        self.add_scalar("duration", duration, iteration)
 
     def log_validation(self, reduced_loss, model, y, y_pred, iteration):
         self.add_scalar("validation.loss", reduced_loss, iteration)
@@ -94,65 +92,3 @@ def __init__(self, logdir):
 
     def log_training(self, reduced_loss, iteration):
             self.add_scalar("training.loss", reduced_loss, iteration)
-
-
-class PPG2PPGLogger(SummaryWriter):
-    def __init__(self, logdir, is_full_ppg=True):
-        super(PPG2PPGLogger, self).__init__(logdir, is_full_ppg)
-        self.nnet_deps = DependenciesPPG()
-        self.is_full_ppg = is_full_ppg
-
-    def log_training(self, reduced_loss, grad_norm, learning_rate, duration,
-                     iteration):
-            self.add_scalar("training.loss", reduced_loss, iteration)
-            self.add_scalar("grad.norm", grad_norm, iteration)
-            self.add_scalar("learning.rate", learning_rate, iteration)
-            self.add_scalar("duration", duration, iteration)
-
-    def log_validation(self, reduced_loss, model, y, y_pred, iteration,
-                       lengths):
-        self.add_scalar("validation.loss", reduced_loss, iteration)
-        mel_outputs, gate_outputs, alignments = y_pred
-        mel_targets, gate_targets = y
-
-        # plot distribution of parameters
-        for tag, value in model.named_parameters():
-            tag = tag.replace('.', '/')
-            self.add_histogram(tag, value.data.cpu().numpy(), iteration)
-
-        # plot alignment, mel target and predicted, gate target and predicted
-        idx = random.randint(0, alignments.size(0) - 1)
-        t = lengths[idx]
-        self.add_image(
-            "alignment",
-            plot_alignment_to_numpy(alignments[idx].data.cpu().numpy().T),
-            iteration)
-        self.add_image(
-            "gate",
-            plot_gate_outputs_to_numpy(
-                gate_targets[idx].data.cpu().numpy(),
-                F.sigmoid(gate_outputs[idx]).data.cpu().numpy()),
-            iteration)
-        # (B, D, T) -> (D, T) -> (T, D)
-        if self.is_full_ppg:
-            ppg_tgt_full = Matrix(mel_targets[idx, :, 0:t].data.cpu().numpy().T)
-            ppg_tgt = reduce_ppg_dim(ppg_tgt_full,
-                                     self.nnet_deps.monophone_trans)
-            ppg_tgt = ppg_tgt.numpy()
-        else:
-            ppg_tgt = mel_targets[idx, :, 0:t].data.cpu().numpy().T
-        self.add_image(
-            "mel_target",
-            plot_ppg_to_numpy(ppg_tgt), iteration)
-        # (B, D, T) -> (D, T) -> (T, D)
-        if self.is_full_ppg:
-            ppg_pred_full = Matrix(
-                mel_outputs[idx, :, 0:t].exp().data.cpu().numpy().T)
-            ppg_pred = reduce_ppg_dim(ppg_pred_full,
-                                      self.nnet_deps.monophone_trans)
-            ppg_pred = ppg_pred.numpy()
-        else:
-            ppg_pred = mel_outputs[idx, :, 0:t].exp().data.cpu().numpy().T
-        self.add_image(
-            "mel_predicted",
-            plot_ppg_to_numpy(ppg_pred), iteration)
diff --git a/src/common/loss_function.py b/src/common/loss_function.py
index e8b237e..bdd57d9 100644
--- a/src/common/loss_function.py
+++ b/src/common/loss_function.py
@@ -28,7 +28,7 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-"""From https://github.com/NVIDIA/tacotron2"""
+"""Modified from https://github.com/NVIDIA/tacotron2"""
 
 from torch import nn
 
@@ -51,23 +51,3 @@ def forward(self, model_output, targets):
             nn.MSELoss()(mel_out_postnet, mel_target)
         gate_loss = nn.BCEWithLogitsLoss()(gate_out, gate_target)
         return self.w_mel * mel_loss + self.w_gate * gate_loss
-
-
-class PPG2PPGLoss(nn.Module):
-    def __init__(self, mel_weight=1, gate_weight=0.005):
-        super(PPG2PPGLoss, self).__init__()
-        self.w_mel = mel_weight
-        self.w_gate = gate_weight
-
-    def forward(self, model_output, targets):
-        mel_target, gate_target = targets[0], targets[1]
-        mel_target.requires_grad = False
-        gate_target.requires_grad = False
-        gate_target = gate_target.view(-1, 1)
-
-        mel_out, gate_out, _ = model_output
-        gate_out = gate_out.view(-1, 1)
-        # mel_loss = nn.KLDivLoss(reduction='batchmean')(mel_out, mel_target)
-        mel_loss = nn.MSELoss()(mel_out, mel_target)
-        gate_loss = nn.BCEWithLogitsLoss()(gate_out, gate_target)
-        return self.w_mel * mel_loss + self.w_gate * gate_loss
diff --git a/src/common/loss_scaler.py b/src/common/loss_scaler.py
index 03d4b6d..f5d3961 100644
--- a/src/common/loss_scaler.py
+++ b/src/common/loss_scaler.py
@@ -28,9 +28,8 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-"""From https://github.com/NVIDIA/tacotron2"""
+"""Modified from https://github.com/NVIDIA/tacotron2"""
 
-import torch
 
 class LossScaler:
 
@@ -60,6 +59,7 @@ def backward(self, loss):
         scaled_loss = loss*self.loss_scale
         scaled_loss.backward()
 
+
 class DynamicLossScaler:
 
     def __init__(self,
@@ -74,7 +74,6 @@ def __init__(self,
 
     # `params` is a list / generator of torch.Variable
     def has_overflow(self, params):
-#        return False
         for p in params:
             if p.grad is not None and DynamicLossScaler._has_inf_or_nan(p.grad.data):
                 return True
@@ -91,7 +90,6 @@ def _has_inf_or_nan(x):
     # `overflow` is boolean indicating whether we overflowed in gradient
     def update_scale(self, overflow):
         if overflow:
-            #self.cur_scale /= self.scale_factor
             self.cur_scale = max(self.cur_scale/self.scale_factor, 1)
             self.last_overflow_iter = self.cur_iter
         else:
@@ -117,7 +115,6 @@ def backward(self, loss):
 if __name__ == "__main__":
     import torch
     from torch.autograd import Variable
-    from dynamic_loss_scaler import DynamicLossScaler
 
     # N is batch size; D_in is input dimension;
     # H is hidden dimension; D_out is output dimension.
diff --git a/src/common/model.py b/src/common/model.py
index e3f81ba..e3b0a86 100644
--- a/src/common/model.py
+++ b/src/common/model.py
@@ -278,10 +278,6 @@ def __init__(self, hparams):
             hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
             hparams.decoder_rnn_dim, 1)
 
-        # self.decoder_rnn2 = nn.LSTMCell(
-        #     hparams.decoder_rnn_dim,
-        #     hparams.decoder_rnn_dim, 1)
-
         self.linear_projection = LinearNorm(
             hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
             hparams.n_acoustic_feat_dims)
@@ -612,362 +608,3 @@ def inference(self, inputs):
                                      gate_outputs, alignments])
 
         return outputs
-
-
-class DecoderPPG2PPG(nn.Module):
-    def __init__(self, hparams):
-        super(DecoderPPG2PPG, self).__init__()
-        self.n_acoustic_feat_dims = hparams.n_acoustic_feat_dims
-        self.encoder_embedding_dim = hparams.encoder_embedding_dim
-        self.attention_rnn_dim = hparams.attention_rnn_dim
-        self.decoder_rnn_dim = hparams.decoder_rnn_dim
-        self.prenet_dim = hparams.prenet_dim
-        self.max_decoder_steps = hparams.max_decoder_steps
-        self.gate_threshold = hparams.gate_threshold
-        self.p_attention_dropout = hparams.p_attention_dropout
-        self.p_decoder_dropout = hparams.p_decoder_dropout
-        self.attention_window_size = hparams.attention_window_size
-        self.prenet = Prenet(hparams.n_acoustic_feat_dims,
-                             [hparams.prenet_dim, hparams.prenet_dim])
-
-        self.attention_rnn = nn.LSTMCell(
-            hparams.prenet_dim + hparams.encoder_embedding_dim,
-            hparams.attention_rnn_dim)
-
-        self.attention_layer = Attention(
-            hparams.attention_rnn_dim, hparams.encoder_embedding_dim,
-            hparams.attention_dim, hparams.attention_location_n_filters,
-            hparams.attention_location_kernel_size)
-
-        self.decoder_rnn = nn.LSTMCell(
-            hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
-            hparams.decoder_rnn_dim, 1)
-
-        self.linear_projection = Prenet(
-            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
-            [hparams.symbols_embedding_dim, hparams.n_acoustic_feat_dims])
-
-        self.gate_layer = LinearNorm(
-            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, 1,
-            bias=True, w_init_gain='sigmoid')
-
-    def get_go_frame(self, memory):
-        """ Gets all zeros frames to use as first decoder input
-        PARAMS
-        ------
-        memory: decoder outputs
-
-        RETURNS
-        -------
-        decoder_input: all zeros frames
-        """
-        B = memory.size(0)
-        decoder_input = Variable(memory.data.new(
-            B, self.n_acoustic_feat_dims).zero_())
-        return decoder_input
-
-    def initialize_decoder_states(self, memory, mask):
-        """ Initializes attention rnn states, decoder rnn states, attention
-        weights, attention cumulative weights, attention context, stores memory
-        and stores processed memory
-        PARAMS
-        ------
-        memory: Encoder outputs
-        mask: Mask for padded data if training, expects None for inference
-        """
-        B = memory.size(0)
-        MAX_TIME = memory.size(1)
-
-        self.attention_hidden = Variable(memory.data.new(
-            B, self.attention_rnn_dim).zero_())
-        self.attention_cell = Variable(memory.data.new(
-            B, self.attention_rnn_dim).zero_())
-
-        self.decoder_hidden = Variable(memory.data.new(
-            B, self.decoder_rnn_dim).zero_())
-        self.decoder_cell = Variable(memory.data.new(
-            B, self.decoder_rnn_dim).zero_())
-
-        self.attention_weights = Variable(memory.data.new(
-            B, MAX_TIME).zero_())
-        self.attention_weights_cum = Variable(memory.data.new(
-            B, MAX_TIME).zero_())
-        self.attention_context = Variable(memory.data.new(
-            B, self.encoder_embedding_dim).zero_())
-
-        self.memory = memory
-        self.processed_memory = self.attention_layer.memory_layer(memory)
-        self.mask = mask
-
-    def parse_decoder_inputs(self, decoder_inputs):
-        """ Prepares decoder inputs, i.e. acoustic outputs
-        PARAMS
-        ------
-        decoder_inputs: inputs used for teacher-forced training,
-        i.e. acoustic-feats.
-
-        RETURNS
-        -------
-        inputs: processed decoder inputs
-
-        """
-        # (B, n_acoustic_feat_dims, T_out) -> (B, T_out, n_acoustic_feat_dims)
-        decoder_inputs = decoder_inputs.transpose(1, 2)
-        decoder_inputs = decoder_inputs.view(
-            decoder_inputs.size(0),
-            int(decoder_inputs.size(1)), -1)
-        # (B, T_out, n_acoustic_feat_dims) -> (T_out, B, n_acoustic_feat_dims)
-        decoder_inputs = decoder_inputs.transpose(0, 1)
-        return decoder_inputs
-
-    def parse_decoder_outputs(self, acoustic_outputs, gate_outputs, alignments):
-        """ Prepares decoder outputs for output
-        PARAMS
-        ------
-        acoustic_outputs:
-        gate_outputs: gate output energies
-        alignments:
-
-        RETURNS
-        -------
-        acoustic_outputs:
-        gate_outpust: gate output energies
-        alignments:
-        """
-        # (T_out, B) -> (B, T_out)
-        alignments = torch.stack(alignments).transpose(0, 1)
-        # (T_out, B) -> (B, T_out)
-        gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
-        gate_outputs = gate_outputs.contiguous()
-        # (T_out, B, n_acoustic_feat_dims) -> (B, T_out, n_acoustic_feat_dims)
-        acoustic_outputs = torch.stack(acoustic_outputs).transpose(0, 1).contiguous()
-        # decouple frames per step
-        acoustic_outputs = acoustic_outputs.view(
-            acoustic_outputs.size(0), -1, self.n_acoustic_feat_dims)
-        # (B, T_out, n_acoustic_feat_dims) -> (B, n_acoustic_feat_dims, T_out)
-        acoustic_outputs = acoustic_outputs.transpose(1, 2)
-
-        return acoustic_outputs, gate_outputs, alignments
-
-    def decode(self, decoder_input, attention_windowed_mask=None):
-        """ Decoder step using stored states, attention and memory
-        PARAMS
-        ------
-        decoder_input: previous acoustic output
-        attention_window: B*T mask.
-
-        RETURNS
-        -------
-        decoder_output:
-        gate_output: gate output energies
-        attention_weights:
-        """
-        cell_input = torch.cat((decoder_input, self.attention_context), -1)
-        self.attention_hidden, self.attention_cell = self.attention_rnn(
-            cell_input, (self.attention_hidden, self.attention_cell))
-        self.attention_hidden = F.dropout(
-            self.attention_hidden, self.p_attention_dropout, self.training)
-        self.attention_cell = F.dropout(
-            self.attention_cell, self.p_attention_dropout, self.training)
-
-        attention_weights_cat = torch.cat(
-            (self.attention_weights.unsqueeze(1),
-             self.attention_weights_cum.unsqueeze(1)), dim=1)
-
-        if attention_windowed_mask is None:
-            self.attention_context, self.attention_weights = \
-                self.attention_layer(self.attention_hidden, self.memory,
-                                     self.processed_memory,
-                                     attention_weights_cat, self.mask)
-        else:
-            self.attention_context, self.attention_weights = \
-                self.attention_layer(self.attention_hidden, self.memory,
-                                     self.processed_memory,
-                                     attention_weights_cat,
-                                     attention_windowed_mask)
-
-        self.attention_weights_cum += self.attention_weights
-        decoder_input = torch.cat(
-            (self.attention_hidden, self.attention_context), -1)
-        self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
-            decoder_input, (self.decoder_hidden, self.decoder_cell))
-        self.decoder_hidden = F.dropout(
-            self.decoder_hidden, self.p_decoder_dropout, self.training)
-        self.decoder_cell = F.dropout(
-            self.decoder_cell, self.p_decoder_dropout, self.training)
-
-        decoder_hidden_attention_context = torch.cat(
-            (self.decoder_hidden, self.attention_context), dim=1)
-        decoder_output = self.linear_projection(
-            decoder_hidden_attention_context)
-
-        gate_prediction = self.gate_layer(decoder_hidden_attention_context)
-        return decoder_output, gate_prediction, self.attention_weights
-
-    def forward(self, memory, decoder_inputs, memory_lengths):
-        """ Decoder forward pass for training
-        PARAMS
-        ------
-        memory: Encoder outputs
-        decoder_inputs: Decoder inputs for teacher forcing. i.e. acoustic-feats.
-        memory_lengths: Encoder output lengths for attention masking.
-
-        RETURNS
-        -------
-        acoustic_outputs: acoustic outputs from the decoder
-        gate_outputs: gate outputs from the decoder
-        alignments: sequence of attention weights from the decoder
-        """
-
-        decoder_input = self.get_go_frame(memory).unsqueeze(0)
-        decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
-        decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
-        decoder_inputs = self.prenet(decoder_inputs)
-
-        self.initialize_decoder_states(
-            memory, mask=~get_mask_from_lengths(memory_lengths))
-
-        acoustic_outputs, gate_outputs, alignments = [], [], []
-        while len(acoustic_outputs) < decoder_inputs.size(0) - 1:
-            decoder_input = decoder_inputs[len(acoustic_outputs)]
-
-            if self.attention_window_size is not None:
-                time_step = len(acoustic_outputs)
-                attention_windowed_mask = \
-                    get_mask_from_lengths_window_and_time_step(
-                        memory_lengths, self.attention_window_size, time_step)
-            else:
-                attention_windowed_mask = None
-            acoustic_output, gate_output, attention_weights = self.decode(
-                decoder_input, attention_windowed_mask)
-            acoustic_outputs += [acoustic_output.squeeze(1)]
-            gate_outputs += [gate_output.squeeze()]
-            alignments += [attention_weights]
-
-        acoustic_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
-            acoustic_outputs, gate_outputs, alignments)
-
-        return acoustic_outputs, gate_outputs, alignments
-
-    def inference(self, memory, memory_lengths):
-        """ Decoder inference
-        PARAMS
-        ------
-        memory: Encoder outputs
-
-        RETURNS
-        -------
-        acoustic_outputs: acoustic outputs from the decoder
-        gate_outputs: gate outputs from the decoder
-        alignments: sequence of attention weights from the decoder
-        """
-        decoder_input = self.get_go_frame(memory)
-
-        self.initialize_decoder_states(memory, mask=None)
-
-        acoustic_outputs, gate_outputs, alignments = [], [], []
-        while True:
-            decoder_input = self.prenet(decoder_input)
-
-            if self.attention_window_size is not None:
-                time_step = len(acoustic_outputs)
-                attention_windowed_mask = \
-                    get_mask_from_lengths_window_and_time_step(
-                        memory_lengths, self.attention_window_size, time_step)
-            else:
-                attention_windowed_mask = None
-
-            acoustic_output, gate_output, alignment = self.decode(
-                decoder_input, attention_windowed_mask)
-
-            acoustic_outputs += [acoustic_output.squeeze(1)]
-            gate_outputs += [gate_output]
-            alignments += [alignment]
-
-            if torch.sigmoid(gate_output.data) > self.gate_threshold:
-                break
-            elif len(acoustic_outputs) == self.max_decoder_steps:
-                print("Warning! Reached max decoder steps")
-                break
-
-            decoder_input = acoustic_output
-
-        acoustic_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
-            acoustic_outputs, gate_outputs, alignments)
-
-        return acoustic_outputs, gate_outputs, alignments
-
-
-class PPG2PPG(nn.Module):
-    def __init__(self, hparams):
-        super(PPG2PPG, self).__init__()
-        self.mask_padding = hparams.mask_padding
-        self.fp16_run = hparams.fp16_run
-        self.n_acoustic_feat_dims = hparams.n_acoustic_feat_dims
-        self.encoder = Encoder(hparams)
-        self.decoder = DecoderPPG2PPG(hparams)
-
-    def parse_batch(self, batch):
-        ppg_padded, input_lengths, acoustic_padded, gate_padded,\
-            output_lengths = batch
-        ppg_padded = to_gpu(ppg_padded).float()
-        input_lengths = to_gpu(input_lengths).long()
-        max_len = torch.max(input_lengths.data).item()
-        acoustic_padded = to_gpu(acoustic_padded).float()
-        gate_padded = to_gpu(gate_padded).float()
-        output_lengths = to_gpu(output_lengths).long()
-
-        return ((ppg_padded, input_lengths, acoustic_padded, max_len,
-                 output_lengths),
-                (acoustic_padded, gate_padded))
-
-    def parse_input(self, inputs):
-        inputs = fp32_to_fp16(inputs) if self.fp16_run else inputs
-        return inputs
-
-    def parse_output(self, outputs, output_lengths=None):
-        if self.mask_padding and output_lengths is not None:
-            mask = ~get_mask_from_lengths(output_lengths)
-            mask = mask.expand(self.n_acoustic_feat_dims, mask.size(0),
-                               mask.size(1))
-            mask = mask.permute(1, 0, 2)
-
-            outputs[0].data.masked_fill_(mask, 0.0)
-            outputs[1].data.masked_fill_(mask[:, 0, :], 1e3)  # gate energies
-
-        outputs = fp16_to_fp32(outputs) if self.fp16_run else outputs
-        return outputs
-
-    def forward(self, inputs):
-        inputs, input_lengths, targets, max_len, \
-            output_lengths = self.parse_input(inputs)
-        input_lengths, output_lengths = input_lengths.data, output_lengths.data
-
-        # inputs: (B, D, T)
-        encoder_outputs = self.encoder(inputs, input_lengths)
-
-        acoustic_outputs, gate_outputs, alignments = self.decoder(
-            encoder_outputs, targets, memory_lengths=input_lengths)
-
-        # (B, D, T)
-        # acoustic_outputs = F.log_softmax(acoustic_outputs, dim=1)
-        acoustic_outputs = F.softmax(acoustic_outputs, dim=1)
-
-        return self.parse_output([acoustic_outputs, gate_outputs,
-                                  alignments], output_lengths)
-
-    def inference(self, inputs):
-        inputs = self.parse_input(inputs)
-        input_lengths = torch.cuda.LongTensor([t.shape[1] for t in inputs])
-        encoder_outputs = self.encoder.inference(inputs)
-        acoustic_outputs, gate_outputs, alignments = self.decoder.inference(
-            encoder_outputs, input_lengths)
-
-        # (B, D, T)
-        # acoustic_outputs = F.log_softmax(acoustic_outputs, dim=1)
-        acoustic_outputs = F.softmax(acoustic_outputs, dim=1)
-
-        outputs = self.parse_output([acoustic_outputs, gate_outputs,
-                                     alignments])
-
-        return outputs
diff --git a/src/common/plotting_utils.py b/src/common/plotting_utils.py
index eb9c445..23737ad 100644
--- a/src/common/plotting_utils.py
+++ b/src/common/plotting_utils.py
@@ -28,7 +28,7 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-"""From https://github.com/NVIDIA/tacotron2"""
+"""Modified from https://github.com/NVIDIA/tacotron2"""
 
 import matplotlib
 matplotlib.use("Agg")
diff --git a/src/common/stft.py b/src/common/stft.py
index 0a19d42..1db4636 100644
--- a/src/common/stft.py
+++ b/src/common/stft.py
@@ -30,7 +30,7 @@
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 """
 
-"""From https://github.com/NVIDIA/tacotron2"""
+"""Modified from https://github.com/NVIDIA/tacotron2"""
 
 import torch
 import numpy as np
diff --git a/src/common/utils.py b/src/common/utils.py
index 80b489a..80039f6 100644
--- a/src/common/utils.py
+++ b/src/common/utils.py
@@ -28,13 +28,11 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-"""From https://github.com/NVIDIA/tacotron2"""
+"""Modified from https://github.com/NVIDIA/tacotron2"""
 
 import numpy as np
 from scipy.io.wavfile import read
 import torch
-from kaldi.util.table import RandomAccessMatrixReaderMapped
-from kaldi.util.io import write_matrix
 from scipy import signal
 
 
@@ -114,26 +112,6 @@ def to_gpu(x):
     return torch.autograd.Variable(x)
 
 
-def dump_fmllr_mat(transform_path, utt2spk_path, utt_id, output_path):
-    """Dump the fmllr matrix for a specific utterance/speaker.
-
-    To use this, you have to fix a bug in pykaldi's "table" module. In
-    kaldi.util.table.py:_RandomAccessReaderMappedBase, around line 568,
-    change the uppercase "Open" to lowercase "open".
-
-    Args:
-        transform_path: Path to a RandomAccessMatrix file.
-        utt2spk_path: A utt2spk file.
-        utt_id: An id that can give us the fMLLR transform of a speaker.
-        output_path: A path to save the transform matrix.
-    """
-    trans_rspec = 'ark:' + transform_path
-    utt2spk_rspec = 'ark:' + utt2spk_path
-    trans_map = RandomAccessMatrixReaderMapped(trans_rspec, utt2spk_rspec)
-    mat = trans_map.value(utt_id)
-    write_matrix(mat, output_path, True)
-
-
 def notch_filtering(wav, fs, w0, Q):
     """ Apply a notch (band-stop) filter to the audio signal.
 
diff --git a/src/common/utterance.py b/src/common/utterance.py
index 41995d2..4f68633 100644
--- a/src/common/utterance.py
+++ b/src/common/utterance.py
@@ -14,17 +14,14 @@
 
 """This module provides a base data utterance class and helper functions."""
 
-import datetime
 import logging
 import numpy as np
 import ppg
-import pyworld as pw
 import re
-import time
 import math
 from common.data_utterance_pb2 import DataUtterance, Segment, MetaData,\
     VocoderFeature
-from common.align import write_tg_to_str, read_tg_from_str, MontrealAligner
+from common.align import write_tg_to_str, read_tg_from_str
 from common.feat import read_wav_kaldi_internal
 from numpy import ndarray
 from scipy.io import wavfile
@@ -380,24 +377,6 @@ def write(self, pb_path):
         with open(pb_path, 'wb') as writer:
             writer.write(self.write_internal())
 
-    def get_alignment(self) -> TextGrid:
-        """A wrapper function to initialize the alignment of this utterance.
-
-        Requires non-empty waveform, fs, and text data.
-
-        Returns:
-            tg: The alignment in TextGrid format.
-        """
-        if self.wav.size == 0 or self.fs < 0 or self.text == '':
-            raise ValueError('To perform alignment, the object must contain '
-                             'valid speech data, sampling frequency, and the '
-                             'text transcript.')
-
-        aligner = MontrealAligner()
-        tg = aligner.align_single_internal(self.wav, self.fs, self.text)
-        self.align = tg
-        return tg
-
     def get_phone_tier(self) -> IntervalTier:
         """A wrapper function to initialize the phone tier of this utterance.
 
@@ -461,88 +440,6 @@ def get_monophone_ppg(self) -> ndarray:
                                                        self.kaldi_shift)
         return self.monophone_ppg
 
-    def get_vocoder_feat(self, **kwargs):
-        """Extract vocoder features. Currently does not save the raw spec and ap
-
-        Args:
-            See 'options'.
-        """
-        options = {'f0_floor': DEFAULT_F0_FLOOR,
-                   'f0_ceil': DEFAULT_F0_CEIL,
-                   'shift': DEFAULT_SHIFT,
-                   'pitch_tracker': DEFAULT_PITCH_TRACKER,
-                   'fft_size': DEFAULT_FFT_SIZE,
-                   'mcep_dim': DEFAULT_MCEP_DIM}
-        for key, val in kwargs.items():
-            if key in options:
-                options[key] = val
-            else:
-                raise ValueError("Option %s not allowed!" % key)
-
-        if self.wav.size == 0 or self.fs < 0:
-            raise ValueError('To perform analysis, the object must contain '
-                             'valid speech data and sampling frequency.')
-
-        if self.wav.ndim >= 2:
-            x = self.wav[:, 0]
-        else:
-            x = self.wav
-        if x.max() > 1:  # If it is in the int16 format, convert to float.
-            x = x / 32767  # Required by vocoder. [-1, 1].
-
-        fs = self.fs
-
-        # Extracting raw F0.
-        if options['pitch_tracker'] == 'dio':
-            _f0, t = pw.dio(x, fs, f0_floor=options['f0_floor'],
-                            f0_ceil=options['f0_ceil'],
-                            frame_period=options['shift'])
-        elif options['pitch_tracker'] == 'harvest':
-            _f0, t = pw.harvest(x, fs, f0_floor=options['f0_floor'],
-                                f0_ceil=options['f0_ceil'],
-                                frame_period=options['shift'])
-        else:
-            raise ValueError('F0 tracker %s not supported!' % options[
-                'pitch_tracker'])
-        # Refine F0.
-        f0 = pw.stonemask(x, _f0, t, fs)
-        # Spectrogram and mel-cepstrum.
-        sp = pw.cheaptrick(x, f0, t, fs, f0_floor=options['f0_floor'],
-                           fft_size=options['fft_size'])
-        mcep = pw.code_spectral_envelope(sp, fs, options['mcep_dim'])
-        # Aperiodicity; band-ap, 16KHz: 1, 48KHz: 5.
-        ap = pw.d4c(x, f0, t, fs, fft_size=options['fft_size'])
-        bap = pw.code_aperiodicity(ap, fs)
-
-        # Save to the object.
-        self.vocoder = 'WORLD'
-        self.mcep = mcep
-        self.f0 = f0
-        self.bap = bap
-        self.temporal_position = t
-        self.vocoder_shift = options['shift']
-        self.fft_size = options['fft_size']
-        self.f0_floor = options['f0_floor']
-        self.f0_ceil = options['f0_ceil']
-        timestamp = datetime.datetime.fromtimestamp(time.time())
-        self.timestamp = '%04d%02d%02d-%02d%02d%02d' % (
-            timestamp.year, timestamp.month, timestamp.day, timestamp.hour,
-            timestamp.minute, timestamp.second)
-        self.pitch_tracker = options['pitch_tracker']
-
-    def resynthesize(self) -> ndarray:
-        """Re-synthesize the audio from f0, mcep, and bap.
-
-        This re-synthesis process does not update the "wav" field.
-
-        Returns:
-            The re-synthesized waveform ranging from -1 to 1.
-        """
-        sp = pw.decode_spectral_envelope(self.mcep, self.fs, self.fft_size)
-        ap = pw.decode_aperiodicity(self.bap, self.fs, self.fft_size)
-        wav = pw.synthesize(self.f0, sp, ap, self.fs, self.vocoder_shift)
-        return wav
-
     def write_audio(self, path):
         """Save the audio to the given path.
 
diff --git a/src/ppg/compute_ppg.py b/src/ppg/compute_ppg.py
index 60a0892..6ebcbfc 100644
--- a/src/ppg/compute_ppg.py
+++ b/src/ppg/compute_ppg.py
@@ -12,13 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""This module contains functions related to PPGs.
-"""
+"""This module contains functions related to PPGs."""
 
 import os
 import re
 import logging
-from kaldi import nnet3, chain
+from kaldi import nnet3
 from kaldi.nnet3 import Nnet
 from kaldi.util.io import read_matrix
 from kaldi.feat.functions import splice_frames
@@ -27,9 +26,6 @@
 from kaldi.matrix.sparse import SparseMatrix
 from kaldi.matrix import Matrix, Vector
 from kaldi.matrix.common import MatrixTransposeType
-from kaldi.cudamatrix import CuMatrix
-from kaldi.fstext import StdVectorFst
-from math import isnan
 from common import feat
 from common import decode
 from numpy import ndarray
@@ -41,10 +37,6 @@
 LDA_PATH = os.path.join(DATA_DIR, 'feats', 'final.mat')
 REDUCE_DIM_PATH = os.path.join(DATA_DIR, 'feats', 'reduce_dim.mat')
 SPLICE_OPTS_PATH = os.path.join(DATA_DIR, 'feats', 'splice_opts')
-# Speaker dependent fmllr transforms. key: speaker-id, value: path to fmllr
-# matrix file.
-FMLLR_PATHS = {'rms': os.path.join(DATA_DIR, 'feats', 'rms.fmllr'),
-               'clb': os.path.join(DATA_DIR, 'feats', 'clb.fmllr')}
 
 
 def compute_full_ppg(nnet: Nnet, feats: Matrix) -> Matrix:
@@ -78,73 +70,6 @@ def compute_full_ppg(nnet: Nnet, feats: Matrix) -> Matrix:
     return raw_ppgs
 
 
-def compute_chain_full_ppg(nnet: Nnet, den_fst: StdVectorFst, mfccs: Matrix,
-                           ivecs: Matrix, is_hidden_feats=False) -> Matrix:
-    """Compute full PPG features given appropriate input features using a
-    chain model. It can be a BiLSTM or TDNN model.
-
-    It is a close reimplementation of
-    https://github.com/kaldi-asr/kaldi/blob/master/src/chainbin/nnet3-chain
-    -compute-post.cc
-
-    Args:
-        nnet: An neural network AM.
-        den_fst: A dense FST.
-        mfccs: Suitable T*D input feature matrix.
-        ivecs: T*D' ivector matrix.
-        is_hidden_feats: If set to True, it means that the given nnet model
-        will output the hidden bottleneck features -- the nnet output prior
-        to the softmax (or other operations). And we will skip the chain
-        operations.
-
-    Returns:
-        posteriors_cpu: T*K raw PPGs, K is the number of senones.
-    """
-    # Obtain the nnet computer, for some unknown reason, the computer must be
-    # constructed within this function.
-    nnet3.set_batchnorm_test_mode(True, nnet)
-    nnet3.set_dropout_test_mode(True, nnet)
-    nnet3.collapse_model(nnet3.CollapseModelConfig(), nnet)
-    opts = nnet3.NnetSimpleComputationOptions()
-    opts.acoustic_scale = 1.0
-    compiler = nnet3.CachingOptimizingCompiler. \
-        new_with_optimize_opts(nnet, opts.optimize_config)
-    priors = Vector()  # We do not need prior
-    nnet_computer = nnet3.DecodableNnetSimple(opts, nnet, priors, mfccs,
-                                              compiler, None, ivecs, 1)
-    # Obtain frame-level raw nnet outputs -- they are not PPGs yet.
-    raw_ppgs = Matrix(nnet_computer.num_frames(), nnet_computer.output_dim())
-    for i in range(nnet_computer.num_frames()):
-        temp = Vector(nnet_computer.output_dim())
-        nnet_computer.get_output_for_frame(i, temp)
-        raw_ppgs.copy_row_from_vec_(temp, i)
-
-    if is_hidden_feats:
-        return raw_ppgs
-
-    # Chain operations.
-    chain_opts = chain.ChainTrainingOptions()
-    chain_opts.leaky_hmm_coefficient = 0.1
-    num_pdfs = nnet.output_dim('output')
-    den_graph = chain.DenominatorGraph(den_fst, num_pdfs)
-    gpu_raw_ppgs = CuMatrix.from_matrix(raw_ppgs)
-    den_computation = chain.DenominatorComputation(chain_opts, den_graph, 1,
-                                                   gpu_raw_ppgs)
-    forward_prob = den_computation.forward()
-    num_frames = gpu_raw_ppgs.num_rows()
-    posteriors = CuMatrix.from_size(num_frames, num_pdfs)
-    ok = den_computation.backward(1, posteriors)
-
-    if not ok or isnan(forward_prob):
-        raise ValueError('Something went wrong for this utterance;'
-                         'forward-prob = %f, num-frames = %d.', forward_prob,
-                         num_frames)
-
-    posteriors_cpu = Matrix(num_frames, num_pdfs)
-    posteriors.swap_with_matrix(posteriors_cpu)
-    return posteriors_cpu.numpy()
-
-
 def reduce_ppg_dim(ppgs: Matrix, transform: SparseMatrix) -> Matrix:
     """Reduce full PPGs to monophone PPGs.
 
@@ -177,14 +102,13 @@ def compute_feat_for_nnet_internal(wav: WaveData, lda: Matrix,
     Args:
         wav: A Kaldi WaveData object.
         lda: A D*D LDA transform matrix.
-        **kwargs: See "options." "fmllr" should be a D*(D+1) matrix.
+        **kwargs: See "options"
 
     Returns:
         feats: A T*D feature matrix.
     """
     options = {"is_use_energy": False, "is_downsample": True, "frame_shift": 10,
-               "is_snip_edges": False, "left_context": 3, "right_context": 3,
-               "fmllr": None}
+               "is_snip_edges": False, "left_context": 3, "right_context": 3}
     for key, val in kwargs.items():
         if key in options:
             options[key] = val
@@ -209,10 +133,6 @@ def compute_feat_for_nnet_internal(wav: WaveData, lda: Matrix,
     # Apply LDA
     feats = feat.apply_feat_transform(feats, lda)
 
-    # Apply fMLLR
-    if options['fmllr']:
-        feats = feat.apply_feat_transform(feats, options['fmllr'])
-
     return feats
 
 
@@ -243,8 +163,7 @@ def compute_feat_for_nnet(wav_path: str, lda_path: str) -> Matrix:
 
 
 def compute_monophone_ppg(wav: WaveData, nnet: Nnet, lda: Matrix,
-                          transform: SparseMatrix, shift=10,
-                          fmllr=None) -> ndarray:
+                          transform: SparseMatrix, shift=10) -> ndarray:
     """A convenient one-stop interface for computing monophone PPGs.
 
     Args:
@@ -253,13 +172,11 @@ def compute_monophone_ppg(wav: WaveData, nnet: Nnet, lda: Matrix,
         lda: LDA transform.
         transform: Pdf-to-Monophone transformation.
         shift: [optional] Frame shift in ms.
-        fmllr: [optional] fMLLR transform matrix.
 
     Returns:
         monophone_ppgs: Monophone PPGs in numpy array.
     """
-    feats = compute_feat_for_nnet_internal(wav, lda, frame_shift=shift,
-                                           fmllr=fmllr)
+    feats = compute_feat_for_nnet_internal(wav, lda, frame_shift=shift)
     raw_ppgs = compute_full_ppg(nnet, feats)
     monophone_ppgs = reduce_ppg_dim(raw_ppgs, transform)
     monophone_ppgs = monophone_ppgs.numpy()
@@ -267,7 +184,7 @@ def compute_monophone_ppg(wav: WaveData, nnet: Nnet, lda: Matrix,
 
 
 def compute_full_ppg_wrapper(wav: WaveData, nnet: Nnet, lda: Matrix,
-                             shift=10, fmllr=None) -> ndarray:
+                             shift=10) -> ndarray:
     """A convenient one-stop interface for computing the full PPGs.
 
     Args:
@@ -275,13 +192,11 @@ def compute_full_ppg_wrapper(wav: WaveData, nnet: Nnet, lda: Matrix,
         nnet: Acoustic model.
         lda: LDA transform.
         shift: [optional] Frame shift in ms.
-        fmllr: [optional] fMLLR transform matrix.
 
     Returns:
         raw_ppgs: Full PPGs in numpy array.
     """
-    feats = compute_feat_for_nnet_internal(wav, lda, frame_shift=shift,
-                                           fmllr=fmllr)
+    feats = compute_feat_for_nnet_internal(wav, lda, frame_shift=shift)
     raw_ppgs = compute_full_ppg(nnet, feats)
     raw_ppgs = raw_ppgs.numpy()
     return raw_ppgs
@@ -299,8 +214,7 @@ def __init__(self,
                  nnet_path=NNET_PATH,
                  lda_path=LDA_PATH,
                  reduce_dim_path=REDUCE_DIM_PATH,
-                 splice_opts_path=SPLICE_OPTS_PATH,
-                 fmllr_paths=FMLLR_PATHS):
+                 splice_opts_path=SPLICE_OPTS_PATH):
         """Load the given resources.
 
         Args:
@@ -308,7 +222,6 @@ def __init__(self,
             lda_path: Path to LDA.
             reduce_dim_path: Path to pdf-to-Monophone transformation.
             splice_opts_path: Path to splice options.
-            fmllr_paths: See FMLLR_PATHS.
         """
         # Check inputs
         if not os.path.isfile(nnet_path):
@@ -341,8 +254,3 @@ def __init__(self,
             logging.warning("Splice options are empty.")
         self.left_context = context[0]
         self.right_context = context[1]
-        self.fmllr_mats = {}
-        for key, val in fmllr_paths.items():
-            if not os.path.isfile(val):
-                logging.error("File %s does not exist!", val)
-            self.fmllr_mats[key] = read_matrix(val)
diff --git a/src/script/cache_data.py b/src/script/cache_data.py
deleted file mode 100644
index 2c63aea..0000000
--- a/src/script/cache_data.py
+++ /dev/null
@@ -1,266 +0,0 @@
-# Copyright 2019 Guanlong Zhao
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#    http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""This script processes data utterances for the accent conversion task."""
-
-import logging
-import os
-import time
-from common.feat import read_wav_kaldi_internal, compute_mfcc_plus_ivector
-from common.utterance import Utterance
-from glob import glob
-from ppg import DependenciesPPG, compute_monophone_ppg, \
-    compute_full_ppg_wrapper, compute_chain_full_ppg
-from scipy.io import wavfile
-from textgrid import TextGrid
-from common import decode
-from kaldi.matrix import Vector
-
-
-CHAIN_AM_PATH = '/home/guanlong/PycharmProjects/ppg-speech/data/am/lstm/' \
-                'final.mdl'
-CHAIN_DEN_FST_PATH = '/home/guanlong/PycharmProjects/ppg-speech/data/am/lstm' \
-                     '/den.fst'
-CHAIN_FEAT_CONFIG_PATH = '/home/guanlong/PycharmProjects/ppg-speech/data/' \
-                         'feats/chain_configs/online.conf'
-
-
-def process_utt(wav, fs, text, tg, ppg_deps, is_vocoder=False,
-                is_mono_ppg=False, is_full_ppg=True, speaker_code=None):
-    """Generate data utterance for one sentence.
-
-    Args:
-        wav: Speech data in ndarray.
-        fs: Sampling frequency.
-        text: Transcript.
-        tg: Textgrid object.
-        ppg_deps: Dependencies for computing the PPGs.
-        is_vocoder: If True, will compute vocoder features.
-        is_mono_ppg: If True, will compute monophone PPGs.
-        is_full_ppg: If True, will compute the full PPGs.
-        speaker_code: If we have fmllr for the speaker, will use that transform.
-
-    Returns:
-        utt: The data utterance.
-    """
-    utt = Utterance(wav, fs, text)
-    utt.align = tg
-    utt.kaldi_shift = 10  # ms
-    wav_kaldi = read_wav_kaldi_internal(wav, fs)
-
-    fmllr = None
-    if speaker_code in ppg_deps.fmllr_mats:
-        print('Using %s fMLLR.' % speaker_code)
-        fmllr = ppg_deps.fmllr_mats[speaker_code]
-
-    if is_mono_ppg:
-        utt.monophone_ppg = compute_monophone_ppg(wav_kaldi, ppg_deps.nnet,
-                                                  ppg_deps.lda,
-                                                  ppg_deps.monophone_trans,
-                                                  utt.kaldi_shift, fmllr=fmllr)
-    utt.get_phone_tier()
-    utt.get_word_tier()
-
-    if is_vocoder:
-        utt.get_vocoder_feat()
-
-    if is_full_ppg:
-        utt.ppg = compute_full_ppg_wrapper(wav_kaldi, ppg_deps.nnet,
-                                           ppg_deps.lda, utt.kaldi_shift,
-                                           fmllr=fmllr)
-    return utt
-
-
-def process_utt_chain_model_with_ref(
-        wav, wav_resample, fs, fs_resample,  text, tg, nnet, den_fst,
-        feat_config, ref_wav, is_vocoder=False, is_full_ppg=True):
-    """Generate data utterance for one sentence.
-
-    Args:
-        wav: Speech data in ndarray.
-        fs: Sampling frequency.
-        text: Transcript.
-        tg: Textgrid object.
-        is_vocoder: If True, will compute vocoder features.
-        is_full_ppg: If True, will compute the full PPGs.
-
-    Returns:
-        utt: The data utterance.
-    """
-    utt = Utterance(wav, fs, text)
-    utt.align = tg
-    utt.kaldi_shift = 10  # ms
-    wav_kaldi = read_wav_kaldi_internal(wav_resample, fs_resample)
-    ref_wav_kaldi = read_wav_kaldi_internal(ref_wav, fs_resample)
-
-    utt.get_phone_tier()
-    utt.get_word_tier()
-
-    if is_vocoder:
-        utt.get_vocoder_feat()
-
-    if is_full_ppg:
-        _, ref_ivec = compute_mfcc_plus_ivector(ref_wav_kaldi, feat_config)
-        # Copy ivector from the reference.
-        ref_ivec_single = Vector(ref_ivec.num_cols).copy_row_from_mat_(
-            ref_ivec, 0)
-        mfcc, ivec = compute_mfcc_plus_ivector(wav_kaldi, feat_config)
-        print('Copy the ref ivector...')
-        for ii in range(ref_ivec.num_rows):
-            ivec.copy_row_from_vec_(ref_ivec_single, ii)
-        last_ivec_single = Vector(ref_ivec.num_cols).copy_row_from_mat_(
-            ref_ivec, ref_ivec.num_rows - 1)
-        ivec.copy_row_from_vec_(last_ivec_single, ref_ivec.num_rows - 1)
-        utt.ppg = compute_chain_full_ppg(nnet, den_fst, mfcc, ivec)
-    return utt
-
-
-def process_utt_chain_model(wav, wav_resample, fs, fs_resample, text, tg,
-                            nnet, den_fst, feat_config, is_vocoder=False,
-                            is_full_ppg=True):
-    """Generate data utterance for one sentence.
-
-    Args:
-        wav: Speech data in ndarray.
-        fs: Sampling frequency.
-        text: Transcript.
-        tg: Textgrid object.
-        is_vocoder: If True, will compute vocoder features.
-        is_full_ppg: If True, will compute the full PPGs.
-
-    Returns:
-        utt: The data utterance.
-    """
-    utt = Utterance(wav, fs, text)
-    utt.align = tg
-    utt.kaldi_shift = 10  # ms
-    wav_kaldi = read_wav_kaldi_internal(wav_resample, fs_resample)
-
-    utt.get_phone_tier()
-    utt.get_word_tier()
-
-    if is_vocoder:
-        utt.get_vocoder_feat()
-
-    if is_full_ppg:
-        mfcc, ivec = compute_mfcc_plus_ivector(wav_kaldi, feat_config)
-        utt.ppg = compute_chain_full_ppg(nnet, den_fst, mfcc, ivec)
-    return utt
-
-
-def process_speaker(path, gender='', dialect='', skip_exist=True,
-                    is_chain=False, ref_speaker_wav_path=None):
-    """Process data for one speaker.
-
-    Args:
-        path: Root dir to that speaker.
-        gender: [optional] Gender.
-        dialect: [optional] Dialect.
-        skip_exist: [optional] If True then skip files that have already cached.
-        ref_speaker_wav_path: should be the downsampled wav path to the ref.
-    """
-    if not os.path.isdir(path):
-        logging.error('Path %s does not exist.', path)
-    speaker_id = os.path.basename(path)
-    wav_dir = os.path.join(path, 'wav')
-    wav_dir_8k = os.path.join(path, 'wav_8k')
-    if not os.path.isdir(wav_dir):
-        logging.error('Path %s does not exist.', wav_dir)
-    tg_dir = os.path.join(path, 'tg')
-    if not os.path.isdir(tg_dir):
-        logging.error('Path %s does not exist.', tg_dir)
-    text_dir = os.path.join(path, 'text')
-    if not os.path.isdir(text_dir):
-        logging.error('Path %s does not exist.', text_dir)
-    cache_dir = os.path.join(path, 'cache')
-    if not os.path.isdir(cache_dir):
-        os.mkdir(cache_dir)
-
-    ppg_deps = DependenciesPPG()
-    print(ppg_deps.nnet_path)
-    if is_chain:
-        nnet = decode.read_nnet3_model(CHAIN_AM_PATH)
-        den_fst = decode.read_den_fst(CHAIN_DEN_FST_PATH)
-    for wav_file in glob(os.path.join(wav_dir, '*.wav')):
-        basename = os.path.basename(wav_file).split('.')[0]
-        cache_file = os.path.join(cache_dir, basename + '.proto')
-        if skip_exist and os.path.isfile(cache_file):
-            logging.info('Skip existing cache.')
-            continue
-        utt_id = '%s_%s' % (speaker_id, basename)
-        logging.info('Processing utterance %s', utt_id)
-        fs, wav = wavfile.read(wav_file, False)
-        tg_file = os.path.join(tg_dir, basename + '.TextGrid')
-        tg = TextGrid()
-        tg.read(tg_file)
-        text_file = os.path.join(text_dir, basename + '.lab')
-        with open(text_file, 'r') as reader:
-            text = reader.readline()
-        start = time.time()
-        if is_chain:
-            wav_file_8k = os.path.join(wav_dir_8k, basename + '.wav')
-            fs_8k, wav_8k = wavfile.read(wav_file_8k, False)
-
-            if ref_speaker_wav_path is None:
-                utt = process_utt_chain_model(
-                    wav, wav_8k, fs, fs_8k, text, tg,
-                    nnet, den_fst, CHAIN_FEAT_CONFIG_PATH)
-            else:
-                ref_wav_file_8k = os.path.join(ref_speaker_wav_path,
-                                               basename + '.wav')
-                ref_fs_8k, ref_wav_8k = wavfile.read(ref_wav_file_8k, False)
-                assert ref_fs_8k == fs_8k
-                utt = process_utt_chain_model_with_ref(
-                    wav, wav_8k, fs, fs_8k, text, tg,
-                    nnet, den_fst, CHAIN_FEAT_CONFIG_PATH, ref_wav_8k)
-        else:
-            utt = process_utt(wav, fs, text, tg, ppg_deps,
-                              speaker_code=speaker_id)
-        end = time.time()
-        logging.info('Took %1.2f second(s).', end - start)
-        utt.speaker_id = speaker_id
-        utt.utterance_id = utt_id
-        utt.original_file = wav_file
-        if len(dialect) > 0:
-            utt.dialect = dialect
-        if len(gender) > 0:
-            utt.gender = gender
-        utt.write(cache_file)
-
-
-def main(path, skip_exist=True):
-    """Process all speakers.
-
-    Args:
-        path: Root dir to all speaker data and the metadata file.
-        skip_exist: [optional] If True then skip files that have already cached.
-    """
-    meta_data_file = os.path.join(path, 'metadata')
-    with open(meta_data_file, 'r') as reader:
-        for ii, line in enumerate(reader):
-            if ii == 0:
-                # Skip the header
-                continue
-            name, gender, dialect = line.split()
-            curr_path = os.path.join(path, name)
-            process_speaker(curr_path, gender, dialect, skip_exist)
-
-
-if __name__ == '__main__':
-    logging.getLogger().setLevel(logging.INFO)
-    root_dir = '/media/guanlong/DATA1/exp/ppg-speech/data'
-    start = time.time()
-    main(root_dir, True)
-    end = time.time()
-    logging.info('All the steps took %f seconds.', end - start)
diff --git a/src/script/fix_vocoder_feats.py b/src/script/fix_vocoder_feats.py
deleted file mode 100644
index 850e4c5..0000000
--- a/src/script/fix_vocoder_feats.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright 2019 Guanlong Zhao
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#    http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""This script re-processes data utterances to fix the vocoder features."""
-
-import logging
-import os
-import time
-from common.utterance import Utterance
-from glob import glob
-
-
-def process_speaker(path):
-    """Process data for one speaker.
-
-    Args:
-        path: Root dir to that speaker.
-    """
-    if not os.path.isdir(path):
-        logging.error('Path %s does not exist.', path)
-    speaker_id = os.path.basename(path)
-    cache_dir = os.path.join(path, 'cache')
-    if not os.path.isdir(cache_dir):
-        os.mkdir(cache_dir)
-
-    for cache_file in glob(os.path.join(cache_dir, '*.proto')):
-        basename = os.path.basename(cache_file).split('.')[0]
-        utt_id = '%s_%s' % (speaker_id, basename)
-        logging.info('Processing utterance %s', utt_id)
-        curr_start = time.time()
-        utt = Utterance()
-        utt.read(cache_file)
-        utt.get_vocoder_feat()
-        curr_end = time.time()
-        logging.info('Took %1.2f second(s).', curr_end - curr_start)
-        utt.write(cache_file)
-
-
-def main(path):
-    """Process all speakers.
-
-    Args:
-        path: Root dir to all speaker data and the metadata file.
-    """
-    meta_data_file = os.path.join(path, 'metadata')
-    with open(meta_data_file, 'r') as reader:
-        for ii, line in enumerate(reader):
-            if ii == 0:
-                # Skip the header
-                continue
-            name, gender, dialect = line.split()
-            curr_path = os.path.join(path, name)
-            process_speaker(curr_path)
-
-
-if __name__ == '__main__':
-    logging.getLogger().setLevel(logging.INFO)
-    root_dir = '/data_repo/arctic'
-    start = time.time()
-    main(root_dir)
-    end = time.time()
-    logging.info('All the steps took %f seconds.', end - start)
diff --git a/src/script/generate_synthesis.py b/src/script/generate_synthesis.py
index 23b2f2d..f60e8df 100644
--- a/src/script/generate_synthesis.py
+++ b/src/script/generate_synthesis.py
@@ -13,22 +13,23 @@
 # limitations under the License.
 
 from common.hparams import create_hparams_stage
-from script.train import load_model
+from script.train_ppg2mel import load_model
 from common.utils import to_gpu
-from common.utils import notch_filtering
 from common.layers import TacotronSTFT
 from common import feat
 from scipy.io import wavfile
-import sys
 import numpy as np
+import sys
 import torch
 import ppg
 import os
 import logging
 import datetime
 import time
-sys.path.append('/home/guanlong/PycharmProjects/ppg-speech/src/waveglow')
+# sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..',
+#                              'src', 'waveglow'))
 from waveglow.denoiser import Denoiser
+from common.data_utils import get_ppg
 
 
 def get_mel(wav, stft):
@@ -83,17 +84,6 @@ def load_waveglow_model(path):
     return model
 
 
-def get_ppg(wav_path, deps, is_fmllr=False, speaker_code=None):
-    fs, wav = wavfile.read(wav_path)
-    wave_data = feat.read_wav_kaldi_internal(wav, fs)
-    if is_fmllr:
-        seq = ppg.compute_full_ppg_wrapper(wave_data, deps.nnet, deps.lda, 10,
-                                           fmllr=deps.fmllr_mats[speaker_code])
-    else:
-        seq = ppg.compute_full_ppg_wrapper(wave_data, deps.nnet, deps.lda, 10)
-    return seq
-
-
 if __name__ == '__main__':
     # Prepare dirs
     timestamp = datetime.datetime.fromtimestamp(time.time())
@@ -104,77 +94,32 @@ def get_ppg(wav_path, deps, is_fmllr=False, speaker_code=None):
            timestamp.minute, timestamp.second)
     if not os.path.isdir(output_dir):
         os.mkdir(output_dir)
-        os.mkdir(os.path.join(output_dir, 'ac'))
-        os.mkdir(os.path.join(output_dir, 'inference'))
     logging.basicConfig(filename=os.path.join(output_dir, 'debug.log'),
                         level=logging.DEBUG)
     logging.info('Output dir: %s', output_dir)
 
     # Parameters
-    exp_info = ''
     checkpoint_path = ''
-    # Use None for SI AM.
-    am_path = None
-    fmllr_speaker_name = None  # 'spk' | None (no fmllr, SI model)
-    teacher_utts_root = '/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/' \
-                        'arctic/XXX/recordings'
-    student_utts_root = '/media/guanlong/DATA1/GDriveTAMU/PSI-Lab/corpora/' \
-                        'gsb16k/YYY/recordings_noise_reduced'
-    teacher_file_prefix = 'arctic_'  # Change to '' if no prefix
-    student_file_prefix = 'gsb_'  # Change to '' if no prefix
-
+    teacher_utt_path = ''
     waveglow_path = ''
     is_clip = False  # Set to True to control the output length of AC.
     fs = 16000
     waveglow_sigma = 0.6
-    test_utt_indices = range(1083, 1133)
-    num_utts = len(test_utt_indices)
-    # Parameters for notch filtering.
-    # Set to None to disable. [2000, 4000, 6000] for 16kHz
-    filter_freqs = None
-    Q = 50
-    # Parameters for official waveglow denoising.
-    is_official_denoiser = True
-    if is_official_denoiser:
-        waveglow_for_denoiser = torch.load(waveglow_path)['model']
-        waveglow_for_denoiser.cuda()
-        denoiser_mode = 'zeros'
-        denoiser = Denoiser(waveglow_for_denoiser, mode=denoiser_mode)
-        denoiser_strength = 0.005
-    else:
-        denoiser = None
-        denoiser_strength = 0
-        denoiser_mode = ''
-    is_self_inference = False
+    waveglow_for_denoiser = torch.load(waveglow_path)['model']
+    waveglow_for_denoiser.cuda()
+    denoiser_mode = 'zeros'
+    denoiser = Denoiser(waveglow_for_denoiser, mode=denoiser_mode)
+    denoiser_strength = 0.005
     # End of parameters
 
-    logging.info('Info: %s', exp_info)
     logging.debug('Tacotron: %s', checkpoint_path)
     logging.debug('Waveglow: %s', waveglow_path)
-    if am_path:
-        logging.debug('AM: %s', am_path)
-    else:
-        logging.debug('AM: SI model')
+    logging.debug('AM: SI model')
     logging.debug('is_clip: %d', is_clip)
     logging.debug('Fs: %d', fs)
     logging.debug('Sigma: %f', waveglow_sigma)
-    if fmllr_speaker_name:
-        logging.debug('fMLLR name: %s', fmllr_speaker_name)
-    else:
-        logging.debug('fMLLR name: None')
-    logging.info('Number of test utts: %d', num_utts)
-    logging.info('Teacher root dir: %s', teacher_utts_root)
-    logging.info('Student root dir: %s', student_utts_root)
-    if filter_freqs and not is_official_denoiser:
-        logging.debug('Notch filtering freqs: [%s]',
-                      ', '.join([str(freq) for freq in filter_freqs]))
-        logging.debug('Notch filtering Q: %f', Q)
-    else:
-        logging.debug('Notch filtering freqs: None')
-    logging.debug('is_official_denoiser: %d', is_official_denoiser)
     logging.debug('Denoiser strength: %f', denoiser_strength)
     logging.debug('Denoiser mode: %s', denoiser_mode)
-    logging.debug('is_self_inference: %d', is_self_inference)
 
     hparams = create_hparams_stage()
     taco_stft = TacotronSTFT(
@@ -188,75 +133,20 @@ def get_ppg(wav_path, deps, is_fmllr=False, speaker_code=None):
     _ = tacotron_model.eval()
     waveglow_model = load_waveglow_model(waveglow_path)
 
-    if am_path:
-        deps = ppg.DependenciesPPG(nnet_path=am_path)
-        is_fmllr = True
-    else:
-        deps = ppg.DependenciesPPG()
-        is_fmllr = False
-    logging.debug('is_fmllr: %d', is_fmllr)
-
-    processed_utts = 0
-    for ii in test_utt_indices:
-        processed_utts += 1
-        logging.info('Processing %02d/%02d', processed_utts, num_utts)
-        teacher_utt_path = os.path.join(teacher_utts_root,
-                                        '%s%04d.wav' % (teacher_file_prefix,
-                                                        ii))
-        student_utt_path = os.path.join(student_utts_root, '%s%04d.wav' % (
-            student_file_prefix, ii))
-
-        if os.path.isfile(teacher_utt_path):
-            logging.info('Perform AC on %s', teacher_utt_path)
-            teacher_ppg = get_ppg(teacher_utt_path, deps, is_fmllr,
-                                  speaker_code=fmllr_speaker_name)
-            ac_mel = get_inference(teacher_ppg, tacotron_model, is_clip)
-
-            if is_official_denoiser:
-                ac_wav = waveglow_audio(ac_mel, waveglow_model,
-                                        waveglow_sigma, True)
-                ac_wav = denoiser(
-                    ac_wav, strength=denoiser_strength)[:, 0].cpu().numpy().T
-            else:
-                ac_wav = waveglow_audio(ac_mel, waveglow_model, waveglow_sigma)
+    deps = ppg.DependenciesPPG()
 
-            output_file = os.path.join(output_dir, 'ac', '%04d.wav' % ii)
-            wavfile.write(output_file, fs, ac_wav)
+    if os.path.isfile(teacher_utt_path):
+        logging.info('Perform AC on %s', teacher_utt_path)
+        teacher_ppg = get_ppg(teacher_utt_path, deps)
+        ac_mel = get_inference(teacher_ppg, tacotron_model, is_clip)
+        ac_wav = waveglow_audio(ac_mel, waveglow_model,
+                                waveglow_sigma, True)
+        ac_wav = denoiser(
+            ac_wav, strength=denoiser_strength)[:, 0].cpu().numpy().T
 
-            if filter_freqs and not is_official_denoiser:
-                fs, ac_wav = wavfile.read(output_file)
-                for w0 in filter_freqs:
-                    ac_wav = notch_filtering(ac_wav, fs, w0, Q)
-                wavfile.write(output_file, fs, ac_wav.astype(np.int16))
-        else:
-            logging.warning('Missing %s', teacher_utt_path)
-        if is_self_inference:
-            if os.path.isfile(student_utt_path):
-                logging.info('Perform self-inference on %s', student_utt_path)
-                student_ppg = get_ppg(student_utt_path, deps, is_fmllr,
-                                      speaker_code=fmllr_speaker_name)
-                infer_mel = get_inference(student_ppg, tacotron_model)
-
-                if is_official_denoiser:
-                    infer_wav = waveglow_audio(infer_mel, waveglow_model,
-                                               waveglow_sigma, True)
-                    infer_wav = denoiser(
-                        infer_wav,
-                        strength=denoiser_strength)[:, 0].cpu().numpy().T
-                else:
-                    infer_wav = waveglow_audio(infer_mel, waveglow_model,
-                                               waveglow_sigma)
-
-                output_file = os.path.join(output_dir,
-                                           'inference', '%04d.wav' % ii)
-                wavfile.write(output_file, fs, infer_wav)
-
-                if filter_freqs and not is_official_denoiser:
-                    fs, infer_wav = wavfile.read(output_file)
-                    for w0 in filter_freqs:
-                        infer_wav = notch_filtering(infer_wav, fs, w0, Q)
-                    wavfile.write(output_file, fs, infer_wav.astype(np.int16))
-            else:
-                logging.warning('Missing %s', student_utt_path)
+        output_file = os.path.join(output_dir, 'ac.wav')
+        wavfile.write(output_file, fs, ac_wav)
+    else:
+        logging.warning('Missing %s', teacher_utt_path)
 
     logging.info('Done!')
diff --git a/src/script/resample.py b/src/script/resample.py
deleted file mode 100644
index 79eeaf1..0000000
--- a/src/script/resample.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright 2019 Guanlong Zhao
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#    http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-
-
-def prep_wav(orig_wav, out_wav, sr, new_sr):
-    print("Resampling wav file from " + str(sr) + " to " + str(new_sr) + "...")
-    os.system("sox " + orig_wav + " -r " + str(new_sr) + " " + out_wav)
-    return True
-
-
-if __name__ == '__main__':
-    in_dir = ''
-    out_dir = ''
-    in_fs = 44100
-    out_fs = 16000
-    if not os.path.isdir(out_dir):
-        os.mkdir(out_dir)
-
-    for in_file in os.listdir(in_dir):
-        if in_file.endswith('.wav'):
-            prep_wav(os.path.join(in_dir, in_file),
-                     os.path.join(out_dir, in_file), in_fs, out_fs)
diff --git a/src/script/train.py b/src/script/train_ppg2mel.py
similarity index 94%
rename from src/script/train.py
rename to src/script/train_ppg2mel.py
index c1b20b4..96f354f 100644
--- a/src/script/train.py
+++ b/src/script/train_ppg2mel.py
@@ -42,13 +42,11 @@
 from torch.utils.data import DataLoader
 from common.fp16_optimizer import FP16_Optimizer
 from common.model import Tacotron2
-from common.data_utils import PPGMelLoader, ppg_acoustics_collate, \
-    PPGMelLoaderOnDemand
+from common.data_utils import PPGMelLoader, ppg_acoustics_collate
 from common.loss_function import Tacotron2Loss
 from common.logger import Tacotron2Logger
 from common.hparams import create_hparams
 from pprint import pprint
-from torch import autograd
 
 
 def batchnorm_to_float(module):
@@ -84,15 +82,12 @@ def init_distributed(hparams, n_gpus, rank, group_name):
 
 def prepare_dataloaders(hparams):
     # Get data, data loaders and collate function ready
-    if not hparams.is_large_set:
-        trainset = PPGMelLoader(hparams.training_files, hparams)
-        hparams.load_feats_from_disk = False
-        hparams.is_cache_feats = False
-        hparams.feats_cache_path = ''
-        valset = PPGMelLoader(hparams.validation_files, hparams)
-    else:
-        trainset = PPGMelLoaderOnDemand(hparams.training_files, hparams)
-        valset = PPGMelLoaderOnDemand(hparams.validation_files, hparams)
+    trainset = PPGMelLoader(hparams.training_files, hparams)
+    hparams.load_feats_from_disk = False
+    hparams.is_cache_feats = False
+    hparams.feats_cache_path = ''
+    valset = PPGMelLoader(hparams.validation_files, hparams)
+
     collate_fn = ppg_acoustics_collate
 
     train_sampler = DistributedSampler(trainset) \
@@ -220,7 +215,7 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
     # Load checkpoint if one exists
     iteration = 0
     epoch_offset = 0
-    if checkpoint_path is not None:
+    if checkpoint_path:
         if warm_start:
             model = warm_start_model(checkpoint_path, model)
         else:
@@ -295,10 +290,8 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
     os.mkdir(exp_output_root_dir)
     if hparams.output_directory is None:
         hparams.output_directory = os.path.join(exp_output_root_dir, 'output')
-    if hparams.mvn_stats_file is None:
-        hparams.mvn_stats_file = os.path.join(exp_output_root_dir,
-                                              'train_set_mvn.pkl')
-    # Stage the hyper-parameters.
+
+    # Record the hyper-parameters.
     hparams_snapshot_file = os.path.join(exp_output_root_dir, 'hparams.txt')
     with open(hparams_snapshot_file, 'w') as writer:
         pprint(hparams.__dict__, writer)
diff --git a/src/script/train_ppg2ppg.py b/src/script/train_ppg2ppg.py
deleted file mode 100644
index f673982..0000000
--- a/src/script/train_ppg2ppg.py
+++ /dev/null
@@ -1,292 +0,0 @@
-# Copyright 2019 Guanlong Zhao
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#    http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import datetime
-import os
-import time
-import math
-from numpy import finfo
-import torch
-import torch.distributed as dist
-from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data import DataLoader
-from common.fp16_optimizer import FP16_Optimizer
-from common.model import PPG2PPG
-from common.data_utils import PPG2PPGLoaderOnDemand, ppg_acoustics_collate
-from common.loss_function import PPG2PPGLoss
-from common.logger import PPG2PPGLogger
-from common.hparams import create_hparams_ppg2ppg
-from pprint import pprint
-
-
-def batchnorm_to_float(module):
-    """Converts batch norm modules to FP32"""
-    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
-        module.float()
-    for child in module.children():
-        batchnorm_to_float(child)
-    return module
-
-
-def reduce_tensor(tensor, n_gpus):
-    rt = tensor.clone()
-    dist.all_reduce(rt, op=dist.reduce_op.SUM)
-    rt /= n_gpus
-    return rt
-
-
-def init_distributed(hparams, n_gpus, rank, group_name):
-    assert torch.cuda.is_available(), "Distributed mode requires CUDA."
-    print("Initializing Distributed")
-
-    # Set cuda device so everything is done on the right GPU.
-    torch.cuda.set_device(rank % torch.cuda.device_count())
-
-    # Initialize distributed communication
-    dist.init_process_group(
-        backend=hparams.dist_backend, init_method=hparams.dist_url,
-        world_size=n_gpus, rank=rank, group_name=group_name)
-
-    print("Done initializing distributed")
-
-
-def prepare_dataloaders(hparams):
-    # Get data, data loaders and collate function ready
-    trainset = PPG2PPGLoaderOnDemand(hparams.training_files, hparams)
-    valset = PPG2PPGLoaderOnDemand(hparams.validation_files, hparams)
-    collate_fn = ppg_acoustics_collate
-
-    train_sampler = DistributedSampler(trainset) \
-        if hparams.distributed_run else None
-
-    train_loader = DataLoader(trainset, num_workers=1, shuffle=True,
-                              sampler=train_sampler,
-                              batch_size=hparams.batch_size, pin_memory=False,
-                              drop_last=True, collate_fn=collate_fn)
-    return train_loader, valset, collate_fn
-
-
-def prepare_directories_and_logger(output_directory, log_directory, rank,
-                                   is_full_ppg):
-    if rank == 0:
-        if not os.path.isdir(output_directory):
-            os.makedirs(output_directory)
-            os.chmod(output_directory, 0o775)
-        logger = PPG2PPGLogger(os.path.join(output_directory, log_directory),
-                               is_full_ppg)
-    else:
-        logger = None
-    return logger
-
-
-def load_model(hparams):
-    model = PPG2PPG(hparams).cuda()
-    if hparams.fp16_run:
-        model = batchnorm_to_float(model.half())
-        model.decoder.attention_layer.score_mask_value = float(finfo('float16').min)
-
-    return model
-
-
-def warm_start_model(checkpoint_path, model):
-    assert os.path.isfile(checkpoint_path)
-    print("Warm starting model from checkpoint '{}'".format(checkpoint_path))
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    model.load_state_dict(checkpoint_dict['state_dict'])
-    return model
-
-
-def load_checkpoint(checkpoint_path, model, optimizer):
-    assert os.path.isfile(checkpoint_path)
-    print("Loading checkpoint '{}'".format(checkpoint_path))
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    model.load_state_dict(checkpoint_dict['state_dict'])
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    learning_rate = checkpoint_dict['learning_rate']
-    iteration = checkpoint_dict['iteration']
-    print("Loaded checkpoint '{}' from iteration {}" .format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, filepath):
-    print("Saving model and optimizer state at iteration {} to {}".format(
-        iteration, filepath))
-    torch.save({'iteration': iteration,
-                'state_dict': model.state_dict(),
-                'optimizer': optimizer.state_dict(),
-                'learning_rate': learning_rate}, filepath)
-
-
-def validate(model, criterion, valset, iteration, batch_size, n_gpus,
-             collate_fn, logger, distributed_run, rank):
-    """Handles all the validation scoring and printing"""
-    model.eval()
-    with torch.no_grad():
-        val_sampler = DistributedSampler(valset) if distributed_run else None
-        val_loader = DataLoader(valset, sampler=val_sampler, num_workers=1,
-                                shuffle=True, batch_size=batch_size,
-                                pin_memory=False, collate_fn=collate_fn)
-
-        val_loss = 0.0
-        for i, batch in enumerate(val_loader):
-            x, y = model.parse_batch(batch)
-            y_pred = model(x)
-            output_lengths = x[-1]
-            loss = criterion(y_pred, y)
-            if distributed_run:
-                reduced_val_loss = reduce_tensor(loss.data, n_gpus).item()
-            else:
-                reduced_val_loss = loss.item()
-            val_loss += reduced_val_loss
-        val_loss = val_loss / (i + 1)
-
-    model.train()
-    if rank == 0:
-        print("Validation loss {}: {:9f}  ".format(iteration, val_loss))
-        logger.log_validation(val_loss, model, y, y_pred, iteration,
-                              output_lengths)
-
-
-def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
-          rank, group_name, hparams):
-    """Training and validation logging results to tensorboard and stdout
-
-    Params
-    ------
-    output_directory (string): directory to save checkpoints
-    log_directory (string) directory to save tensorboard logs
-    checkpoint_path(string): checkpoint path
-    n_gpus (int): number of gpus
-    rank (int): rank of current gpu
-    hparams (object): comma separated list of "name=value" pairs.
-    """
-    if hparams.distributed_run:
-        init_distributed(hparams, n_gpus, rank, group_name)
-
-    torch.manual_seed(hparams.seed)
-    torch.cuda.manual_seed(hparams.seed)
-
-    model = load_model(hparams)
-    learning_rate = hparams.learning_rate
-    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,
-                                 weight_decay=hparams.weight_decay)
-    if hparams.fp16_run:
-        optimizer = FP16_Optimizer(
-            optimizer, dynamic_loss_scale=hparams.dynamic_loss_scaling)
-
-    criterion = PPG2PPGLoss(hparams.ppg_weight, hparams.gate_weight)
-
-    logger = prepare_directories_and_logger(
-        output_directory, log_directory, rank, hparams.is_full_ppg)
-
-    train_loader, valset, collate_fn = prepare_dataloaders(hparams)
-
-    # Load checkpoint if one exists
-    iteration = 0
-    epoch_offset = 0
-    if checkpoint_path is not None:
-        if warm_start:
-            model = warm_start_model(checkpoint_path, model)
-        else:
-            model, optimizer, _learning_rate, iteration = load_checkpoint(
-                checkpoint_path, model, optimizer)
-            if hparams.use_saved_learning_rate:
-                learning_rate = _learning_rate
-            iteration += 1  # next iteration is iteration + 1
-            epoch_offset = max(0, int(iteration / len(train_loader)))
-
-    model.train()
-    # ================ MAIN TRAINNIG LOOP! ===================
-    for epoch in range(epoch_offset, hparams.epochs):
-        print("Epoch: {}".format(epoch))
-        for i, batch in enumerate(train_loader):
-            start = time.perf_counter()
-            for param_group in optimizer.param_groups:
-                param_group['lr'] = learning_rate
-
-            model.zero_grad()
-            x, y = model.parse_batch(batch)
-            y_pred = model(x)
-
-            loss = criterion(y_pred, y)
-            if hparams.distributed_run:
-                reduced_loss = reduce_tensor(loss.data, n_gpus).item()
-            else:
-                reduced_loss = loss.item()
-
-            if hparams.fp16_run:
-                optimizer.backward(loss)
-                grad_norm = optimizer.clip_fp32_grads(hparams.grad_clip_thresh)
-            else:
-                loss.backward()
-                grad_norm = torch.nn.utils.clip_grad_norm_(
-                    model.parameters(), hparams.grad_clip_thresh)
-
-            optimizer.step()
-
-            overflow = optimizer.overflow if hparams.fp16_run else False
-
-            if not overflow and not math.isnan(reduced_loss) and rank == 0:
-                duration = time.perf_counter() - start
-                print("Train loss {} {:.6f} Grad Norm {:.6f} {:.2f}s/it".format(
-                    iteration, reduced_loss, grad_norm, duration))
-                logger.log_training(
-                    reduced_loss, grad_norm, learning_rate, duration, iteration)
-
-            if not overflow and (iteration % hparams.iters_per_checkpoint == 0):
-                validate(model, criterion, valset, iteration,
-                         hparams.batch_size, n_gpus, collate_fn, logger,
-                         hparams.distributed_run, rank)
-                if rank == 0:
-                    checkpoint_path = os.path.join(
-                        output_directory, "checkpoint_{}".format(iteration))
-                    save_checkpoint(model, optimizer, learning_rate, iteration,
-                                    checkpoint_path)
-
-            iteration += 1
-
-
-if __name__ == '__main__':
-    hparams = create_hparams_ppg2ppg()
-
-    # Prepare paths for this experiment, this helps avoid collisions.
-    timestamp = datetime.datetime.fromtimestamp(time.time())
-    exp_output_root_dir = \
-        '/media/guanlong/DATA1/exp/ppg-speech/ppg2ppg/trial_%04d%02d%02d' \
-        '-%02d%02d%02d' \
-        % (timestamp.year, timestamp.month, timestamp.day, timestamp.hour,
-           timestamp.minute, timestamp.second)
-    os.mkdir(exp_output_root_dir)
-    if hparams.output_directory is None:
-        hparams.output_directory = os.path.join(exp_output_root_dir, 'output')
-
-    # Stage the hyper-parameters.
-    hparams_snapshot_file = os.path.join(exp_output_root_dir, 'hparams.txt')
-    with open(hparams_snapshot_file, 'w') as writer:
-        pprint(hparams.__dict__, writer)
-
-    torch.backends.cudnn.enabled = hparams.cudnn_enabled
-    torch.backends.cudnn.benchmark = hparams.cudnn_benchmark
-
-    print("FP16 Run:", hparams.fp16_run)
-    print("Dynamic Loss Scaling:", hparams.dynamic_loss_scaling)
-    print("Distributed Run:", hparams.distributed_run)
-    print("cuDNN Enabled:", hparams.cudnn_enabled)
-    print("cuDNN Benchmark:", hparams.cudnn_benchmark)
-
-    train(hparams.output_directory, hparams.log_directory,
-          hparams.checkpoint_path, hparams.warm_start, hparams.n_gpus,
-          hparams.rank, hparams.group_name, hparams)
diff --git a/src/script/train_waveglow.py b/src/script/train_waveglow.py
index 9cbdeac..2323c45 100644
--- a/src/script/train_waveglow.py
+++ b/src/script/train_waveglow.py
@@ -24,7 +24,9 @@
 #  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #
 # *****************************************************************************
-import argparse
+
+"""Modified from https://github.com/NVIDIA/waveglow/blob/master/train.py"""
+
 import json
 import os
 import torch
diff --git a/src/waveglow/config.json b/src/waveglow/config.json
index 016ce85..ac36878 100644
--- a/src/waveglow/config.json
+++ b/src/waveglow/config.json
@@ -7,10 +7,10 @@
         "iters_per_checkpoint": 2500,
         "batch_size": 3,
         "seed": 16807,
-        "checkpoint_path": "/media/guanlong/DATA1/exp/ppg-speech/waveglow/waveglow_20190322-164430/waveglow_245000"
+        "checkpoint_path": ""
     },
     "data_config": {
-        "training_files":"/home/guanlong/PycharmProjects/ppg-speech/data/filelists/zhaa_train_filelist_noise_reduced.txt",
+        "training_files":"",
         "segment_length": 10000,
         "sampling_rate": 16000,
         "filter_length": 1024,
diff --git a/src/waveglow/mel2samp.py b/src/waveglow/mel2samp.py
index 354bdce..aa481fa 100644
--- a/src/waveglow/mel2samp.py
+++ b/src/waveglow/mel2samp.py
@@ -31,7 +31,6 @@
 import torch
 import torch.utils.data
 import sys
-from common.utterance import Utterance
 from scipy.io import wavfile
 
 # We're using the audio processing from TacoTron2 to make sure it matches
diff --git a/test/data/air.TextGrid b/test/data/air.TextGrid
deleted file mode 100644
index 3a89459..0000000
--- a/test/data/air.TextGrid
+++ /dev/null
@@ -1,44 +0,0 @@
-File type = "ooTextFile"
-Object class = "TextGrid"
-
-xmin = 0 
-xmax = 0.76625 
-tiers? <exists> 
-size = 2 
-item []: 
-    item [1]:
-        class = "IntervalTier" 
-        name = "words" 
-        xmin = 0 
-        xmax = 0.76625 
-        intervals: size = 2 
-        intervals [1]:
-            xmin = 0 
-            xmax = 0.65 
-            text = "air" 
-        intervals [2]:
-            xmin = 0.65 
-            xmax = 0.76625 
-            text = "" 
-    item [2]:
-        class = "IntervalTier" 
-        name = "phones" 
-        xmin = 0 
-        xmax = 0.76625 
-        intervals: size = 4 
-        intervals [1]:
-            xmin = 0 
-            xmax = 0.3 
-            text = "EH1, sil, d" 
-        intervals [2]:
-            xmin = 0.3 
-            xmax = 0.3043270184819381 
-            text = "R, ER, S" 
-        intervals [3]:
-            xmin = 0.3043270184819381 
-            xmax = 0.75 
-            text = "sil, R, A" 
-        intervals [4]:
-            xmin = 0.75 
-            xmax = 0.76625 
-            text = "" 
diff --git a/test/data/air.wav b/test/data/air.wav
deleted file mode 100644
index 5d6e94c..0000000
Binary files a/test/data/air.wav and /dev/null differ
diff --git a/test/data/air_8k.wav b/test/data/air_8k.wav
deleted file mode 100644
index a28e371..0000000
Binary files a/test/data/air_8k.wav and /dev/null differ
diff --git a/test/data/align/batch/test_01.lab b/test/data/align/batch/test_01.lab
deleted file mode 100644
index 9974266..0000000
--- a/test/data/align/batch/test_01.lab
+++ /dev/null
@@ -1 +0,0 @@
-Author of the danger trail, Philip Steels, etc.
diff --git a/test/data/align/batch/test_01.wav b/test/data/align/batch/test_01.wav
deleted file mode 100644
index f93d2fa..0000000
Binary files a/test/data/align/batch/test_01.wav and /dev/null differ
diff --git a/test/data/align/batch/test_02.lab b/test/data/align/batch/test_02.lab
deleted file mode 100644
index c555da9..0000000
--- a/test/data/align/batch/test_02.lab
+++ /dev/null
@@ -1 +0,0 @@
-Not at this particular case Tom apologized Whittemore
\ No newline at end of file
diff --git a/test/data/align/batch/test_02.wav b/test/data/align/batch/test_02.wav
deleted file mode 100644
index c38b7da..0000000
Binary files a/test/data/align/batch/test_02.wav and /dev/null differ
diff --git a/test/data/align/batch/test_03.lab b/test/data/align/batch/test_03.lab
deleted file mode 100644
index 3b79bb6..0000000
--- a/test/data/align/batch/test_03.lab
+++ /dev/null
@@ -1 +0,0 @@
-For the twentieth time that evening the two men shook hands
\ No newline at end of file
diff --git a/test/data/align/batch/test_03.wav b/test/data/align/batch/test_03.wav
deleted file mode 100644
index 9fd4bd1..0000000
Binary files a/test/data/align/batch/test_03.wav and /dev/null differ
diff --git a/test/data/align/test_dict b/test/data/align/test_dict
deleted file mode 100644
index 160634f..0000000
--- a/test/data/align/test_dict
+++ /dev/null
@@ -1,26 +0,0 @@
-AUTHOR  AO1 TH ER0
-OF  AH0 V
-THE  DH AH0
-DANGER  D EY1 N JH ER0
-TRAIL  T R EY1 L
-PHILIP  F IH1 L IH0 P
-ETC  EH2 T S EH1 T ER0 AH0
-NOT  N AA1 T
-AT  AE1 T
-THIS  DH IH0 S
-PARTICULAR  P AA2 T IH1 K Y AH0 L ER0
-CASE  K EY1 S
-TOM  T AA1 M
-APOLOGIZED  AH0 P AA1 L AH0 JH AY2 Z D
-FOR  F AO1 R
-TWENTIETH  T W EH1 N IY0 IH0 TH
-TIME  T AY1 M
-THAT  DH AE1 T
-EVENING  IY1 V N IH0 NG
-TWO  T UW1
-MEN  M EH1 N
-SHOOK  SH UH1 K
-HANDS  HH AE1 N D Z
-IS  IH0 Z
-A  AH0
-TESTING  T EH1 S T IH0 NG
diff --git a/test/data/align/test_english_am.zip b/test/data/align/test_english_am.zip
deleted file mode 100644
index 8d1bac8..0000000
Binary files a/test/data/align/test_english_am.zip and /dev/null differ
diff --git a/test/data/lda.mat b/test/data/lda.mat
deleted file mode 100644
index 74352cd..0000000
--- a/test/data/lda.mat
+++ /dev/null
@@ -1 +0,0 @@
-../../data/feats/final.mat
\ No newline at end of file
diff --git a/test/data/lda.mat b/test/data/lda.mat
new file mode 120000
index 0000000..74352cd
--- /dev/null
+++ b/test/data/lda.mat
@@ -0,0 +1 @@
+../../data/feats/final.mat
\ No newline at end of file
diff --git a/test/data/nnet3.raw b/test/data/nnet3.raw
deleted file mode 100644
index 7821543..0000000
--- a/test/data/nnet3.raw
+++ /dev/null
@@ -1 +0,0 @@
-../../data/am/final.raw
\ No newline at end of file
diff --git a/test/data/nnet3.raw b/test/data/nnet3.raw
new file mode 120000
index 0000000..7821543
--- /dev/null
+++ b/test/data/nnet3.raw
@@ -0,0 +1 @@
+../../data/am/final.raw
\ No newline at end of file
diff --git a/test/data/phoneme_table b/test/data/phoneme_table
deleted file mode 100644
index 355477a..0000000
--- a/test/data/phoneme_table
+++ /dev/null
@@ -1 +0,0 @@
-../../data/arpa_phonemes
\ No newline at end of file
diff --git a/test/data/phoneme_table b/test/data/phoneme_table
new file mode 120000
index 0000000..355477a
--- /dev/null
+++ b/test/data/phoneme_table
@@ -0,0 +1 @@
+../../data/arpa_phonemes
\ No newline at end of file
diff --git a/test/data/reduce_dim.mat b/test/data/reduce_dim.mat
deleted file mode 100644
index c13fdb8..0000000
--- a/test/data/reduce_dim.mat
+++ /dev/null
@@ -1 +0,0 @@
-../../data/feats/reduce_dim.mat
\ No newline at end of file
diff --git a/test/data/reduce_dim.mat b/test/data/reduce_dim.mat
new file mode 120000
index 0000000..c13fdb8
--- /dev/null
+++ b/test/data/reduce_dim.mat
@@ -0,0 +1 @@
+../../data/feats/reduce_dim.mat
\ No newline at end of file
diff --git a/test/run_coverage.sh b/test/run_coverage.sh
old mode 100644
new mode 100755
diff --git a/test/test_align.py b/test/test_align.py
index 227c51e..74d381c 100644
--- a/test/test_align.py
+++ b/test/test_align.py
@@ -12,88 +12,26 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-import tempfile
 import unittest
 from common import align
-from glob import glob
-from scipy.io import wavfile
 from textgrid import TextGrid
 
 
 class TestAligner(unittest.TestCase):
     def setUp(self):
-        self.wav_path = "data/align/batch/test_01.wav"
-        self.text_path = "data/align/batch/test_01.lab"
-        self.fs, self.wav = wavfile.read(self.wav_path, False)
-        with open(self.text_path, 'r') as reader:
-            self.text = reader.readline()
-        self.aligner = align.MontrealAligner(
-            am_path="data/align/test_english_am.zip",
-            dict_path="data/align/test_dict")
+        self.tg_file = "data/test.TextGrid"
 
     def tearDown(self):
         pass
 
-    def test_align_single_internal_mono(self):
-        tg = self.aligner.align_single_internal(self.wav, self.fs, self.text)
-        self.assertEqual(len(tg.tiers), 2)
-
-    def test_align_single_internal_dual(self):
-        fs, wav = wavfile.read("data/test_dual_channel.wav", False)
-        with open("data/test_dual_channel.txt", 'r') as reader:
-            text = reader.readline()
-        tg = self.aligner.align_single_internal(wav, fs, text)
-        self.assertEqual(len(tg.tiers), 2)
-
-    def test_align_single_wf_tf(self):
-        # wav is file, text is file
-        tg = self.aligner.align_single(self.wav_path, self.text_path)
-        self.assertEqual(len(tg.tiers), 2)
-
-    def test_align_single_wd_tf(self):
-        # wav is data, text is file
-        tg = self.aligner.align_single(self.wav, self.text_path, self.fs,
-                                       is_wav_path=False)
-        self.assertEqual(len(tg.tiers), 2)
-
-    def test_align_single_wf_td(self):
-        # wav is file, text is data
-        tg = self.aligner.align_single(self.wav_path, self.text,
-                                       is_text_path=False)
-        self.assertEqual(len(tg.tiers), 2)
-
-    def test_align_single_wd_td(self):
-        # wav is data, text is data
-        tg = self.aligner.align_single(self.wav, self.text, self.fs, False,
-                                       False)
-        self.assertEqual(len(tg.tiers), 2)
-
-    def test_align_batch(self):
-        input_dir = "data/align/batch"
-        with tempfile.TemporaryDirectory() as output_dir:
-            actual_output_dir = self.aligner.align_batch(input_dir, output_dir)
-            tgs = glob(os.path.join(actual_output_dir, "*.TextGrid"))
-            num_tgs = len(tgs)
-            num_wavs = len(glob(os.path.join(input_dir, "*.wav")))
-            # Check each TG file to make sure it's valid
-            for each_tg in tgs:
-                tg = TextGrid()
-                tg.read(each_tg)
-                self.assertEqual(len(tg.tiers), 2)
-            self.assertEqual(num_tgs, num_wavs)
-            self.assertEqual(actual_output_dir, output_dir)
-
     def test_write_tg_to_str(self):
-        tg_file = "data/test.TextGrid"
         tg = TextGrid()
-        tg.read(tg_file, 2)
+        tg.read(self.tg_file, 2)
         tg_str = align.write_tg_to_str(tg)
         self.assertTrue(tg_str.startswith('File type = "ooTextFile"'))
 
     def test_read_tg_from_str(self):
-        tg_file = "data/test.TextGrid"
-        with open(tg_file, 'r') as reader:
+        with open(self.tg_file, 'r') as reader:
             tg_str = reader.read()
         tg = align.read_tg_from_str(tg_str)
         self.assertTrue(isinstance(tg, TextGrid))
diff --git a/test/test_utterance.py b/test/test_utterance.py
index 4d0d2b5..ebaa4c3 100644
--- a/test/test_utterance.py
+++ b/test/test_utterance.py
@@ -483,30 +483,6 @@ def test_get_hardcoded_sym_table(self):
         self.assertTrue(
             set(sym_table.items()) == set(correct_sym_table.items()))
 
-    def test_get_alignment_valid(self):
-        fs, wav = wavfile.read('data/test_mono_channel.wav')
-        with open('data/test_mono_channel.txt', 'r') as reader:
-            text = reader.readline()
-        utt = utterance.Utterance(wav, fs, text)
-        tg = utt.get_alignment()
-        self.assertEqual(len(tg), 2)
-
-    def test_get_alignment_invalid(self):
-        try:
-            self.utt.get_alignment()
-        except ValueError:
-            pass
-
-    def test_get_phone_tier_valid(self):
-        fs, wav = wavfile.read('data/test_mono_channel.wav')
-        with open('data/test_mono_channel.txt', 'r') as reader:
-            text = reader.readline()
-        utt = utterance.Utterance(wav, fs, text)
-        utt.get_alignment()
-        utt.kaldi_shift = 5
-        utt.get_phone_tier()
-        self.assertTrue(len(utt.phone) > 0)
-
     def test_get_phone_tier_invalid_0(self):
         try:
             self.utt.get_phone_tier()
@@ -520,16 +496,6 @@ def test_get_phone_tier_invalid_1(self):
         except ValueError:
             pass
 
-    def test_get_word_tier_valid(self):
-        fs, wav = wavfile.read('data/test_mono_channel.wav')
-        with open('data/test_mono_channel.txt', 'r') as reader:
-            text = reader.readline()
-        utt = utterance.Utterance(wav, fs, text)
-        utt.get_alignment()
-        utt.kaldi_shift = 5
-        utt.get_word_tier()
-        self.assertTrue(len(utt.word) > 0)
-
     def test_get_word_tier_invalid_0(self):
         try:
             self.utt.get_word_tier()
@@ -562,44 +528,3 @@ def test_get_monophone_ppg_invalid_1(self):
             self.utt.get_monophone_ppg()
         except ValueError:
             pass
-
-    def test_get_vocoder_feat_valid_mono(self):
-        fs, wav = wavfile.read('data/test_mono_channel.wav')
-        utt = utterance.Utterance(wav, fs)
-        utt.get_vocoder_feat()
-        self.assertEqual(utt.mcep_dim, 60)
-        self.assertEqual(utt.bap_dim, 5)
-
-    def test_get_vocoder_feat_valid_dual(self):
-        fs, wav = wavfile.read('data/test_dual_channel.wav')
-        utt = utterance.Utterance(wav, fs)
-        utt.get_vocoder_feat()
-        self.assertEqual(utt.mcep_dim, 60)
-        self.assertEqual(utt.bap_dim, 5)
-
-    def test_get_vocoder_feat_invalid_0(self):
-        try:
-            self.utt.get_vocoder_feat()
-        except ValueError:
-            pass
-
-    def test_get_vocoder_feat_invalid_1(self):
-        try:
-            self.utt.get_vocoder_feat(window_size=25)
-        except ValueError:
-            pass
-
-    def test_get_vocoder_feat_invalid_3(self):
-        try:
-            fs, wav = wavfile.read('data/test_mono_channel.wav')
-            utt = utterance.Utterance(wav, fs)
-            utt.get_vocoder_feat(pitch_tracker='legacy_straight')
-        except ValueError:
-            pass
-
-    def test_resynthesize(self):
-        fs, wav = wavfile.read('data/test_dual_channel.wav')
-        utt = utterance.Utterance(wav, fs)
-        utt.get_vocoder_feat()
-        wav = utt.resynthesize()
-        self.assertTrue(isinstance(wav, np.ndarray))