From 5d8f596e8fc5be4538f033405079584e4be00f38 Mon Sep 17 00:00:00 2001
From: Determinant <ted.sybil@gmail.com>
Date: Sun, 13 Mar 2016 17:02:58 +0800
Subject: correct a typo

---
 tutorial/howto_pretrain_from_tnet.rst | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

(limited to 'tutorial/howto_pretrain_from_tnet.rst')

diff --git a/tutorial/howto_pretrain_from_tnet.rst b/tutorial/howto_pretrain_from_tnet.rst
index 7636478..b37a1a7 100644
--- a/tutorial/howto_pretrain_from_tnet.rst
+++ b/tutorial/howto_pretrain_from_tnet.rst
@@ -17,6 +17,7 @@ How to Use a Pre-trained Model from TNet
 - To convert a TNet DNN model file:
 
   ::
+
     # compile the tool written in C++:
     g++ -o tnet_to_nerv <nerv_home>/speech/htk_io/tools/tnet_to_nerv.cpp
     # conver the model (the third argument indicates the initial number used in naming the parameters)
@@ -31,18 +32,18 @@ How to Use a Pre-trained Model from TNet
 
 - Create a copy of ``<nerv_home>/speech/htk_io/tools/nerv_to_tnet.lua``.
 
-    - Modify the list named ``lnames`` to list the name of layers you want to
-      put into the output TNet parameter file in order. You may ask why the
-      NERV-to-TNet converstion is so cumbersome. This is because TNet nnet is a
-      special case of more general NERV toolkit -- it only allows stacked DNNs
-      and therefore TNet-to-NERV conversion is lossless but the other direction
-      is not. Your future NERV network may have multiple branches and that's
-      why you need to specify how to select and "stack" your layers in the TNet
-      parameter output.
+  - Modify the list named ``lnames`` to list the name of layers you want to
+    put into the output TNet parameter file in order. You may ask why the
+    NERV-to-TNet converstion is so cumbersome. This is because TNet nnet is a
+    special case of more general NERV toolkit -- it only allows stacked DNNs
+    and therefore TNet-to-NERV conversion is lossless but the other direction
+    is not. Your future NERV network may have multiple branches and that's
+    why you need to specify how to select and "stack" your layers in the TNet
+    parameter output.
 
-    - Do the conversion by:
+  - Do the conversion by:
 
-      ::
+    ::
 
-         <nerv_home>/install/bin/nerv --use-cpu nerv_to_tnet.lua <your_network_config>.lua <your_trained_params>.nerv <path_to_converted>.nnet
+       <nerv_home>/install/bin/nerv --use-cpu nerv_to_tnet.lua <your_network_config>.lua <your_trained_params>.nerv <path_to_converted>.nnet
 
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