add decode part to the nerv-kaldi tutorial

1 files changed, 59 insertions, 2 deletions

diff --git a/tutorial/howto_pretrain_from_kaldi.rst b/tutorial/howto_pretrain_from_kaldi.rst
index 95b5f36..ff6ef3d 100644
--- a/tutorial/howto_pretrain_from_kaldi.rst
+++ b/tutorial/howto_pretrain_from_kaldi.rst
@@ -31,11 +31,16 @@ How to Use a Pretrained nnet Model from Kaldi
   by the original stage 2).
 
 - Run ``local/nnet/run_dnn.sh`` (first two stages).
-- You'll find directory like ``dnn4_pretrain-dbn`` and ``dnn4_pretrain-dbn_dnn`` inside the ``exp/``. They correspond to stage 1 and stage 2 respectively. To use NERV to do stage 2 instead, we need the pretrained network and the global transformation from stage 1:
+- You'll find directory like ``dnn4_pretrain-dbn`` and
+  ``dnn4_pretrain-dbn_dnn`` inside the ``exp/``. They correspond to stage 1 and
+  stage 2 respectively. To use NERV to do stage 2 instead, we need the
+  pretrained network and the global transformation from stage 1:
   
   - Check the file ``exp/dnn4_pretrain-dbn/6.dbn`` exists. (pretrained network)
   - Check the file ``exp/dnn4_pretrain-dbn/tr_splice5_cmvn-g.nnet`` exists. (global transformation)
-  - Run script from ``kaldi_io/tools/convert_from_kaldi_pretrain.sh`` to generate the parameters for the output layer and the script files for training and cross-validation set.
+  - Run script from ``kaldi_io/tools/convert_from_kaldi_pretrain.sh`` to
+    generate the parameters for the output layer and the script files for
+    training and cross-validation set.
 
   - The previous conversion commands will automatically give identifiers to the
     parameters read from the Kaldi network file. The identifiers are like, for
@@ -58,3 +63,55 @@ How to Use a Pretrained nnet Model from Kaldi
   - followed by an argument ``timit_mybaseline.lua`` to the scheduler,
     specifying the network you want to train and some relevant settings, such
     as where to find the initialized parameters and learning rate, etc.
+
+- Finally, after about 13 iterations, the funetune ends. There are two ways to
+  decode your model:
+  
+  - Plan A:
+    
+    - Open your ``timit_mybaseline.lua`` again and modify ``decode_param`` to
+      your final chunk file (the file with an extension ``.nerv``) and your
+      global transformation chunk file once used in training. This part lets
+      the decoder know about the set of parameters for decoding.
+
+    - Copy the script ``nerv/speech/kaldi_io/README.timit`` to your Kaldi
+      working directory (``timit/s5``) and modify the paths listed in the
+      script.
+
+    - Run the modified ``README.timit`` in ``s5`` directory (where there is the
+      ``path.sh``).
+
+    - After decoding, run ``bash RESULT exp/dnn4_nerv`` to see the results.
+
+  - Plan B: In this plan, we manually convert the trained model back to Kaldi
+    nnet format, and use Kaldi to decode.
+
+    - Create a copy of ``nerv/speech/kaldi_io/tools/nerv_to_kaldi.lua``.
+
+    - Modify the list named ``lnames`` to list the name of layers you want to
+      put into the output Kaldi parameter file in order. (You don't actually
+      need to change for this tutorial) You may ask why the NERV-to-Kaldi
+      converstion is so cumbersome. This is because Kaldi nnet is a special
+      case of more general NERV toolkit --- it only allows stacked DNNs and
+      therefore Kaldi-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 Kaldi
+      parameter output.
+
+    - Do the conversion by:
+     
+      ::
+     
+         cat your_trained_params.nerv your_global_trans.nerv > all.nerv
+         install/bin/nerv nerv_to_kaldi.lua timit_mybaseline.lua all.nerv your_kaldi_output.nnet
+
+    - Finally, locate the directory of stage 2: ``exp/dnn4_pretrain-dbn_dnn``
+      and temporarily change the symbolic link for the final network file to the converted one:
+
+      ::
+        
+         cd exp/dnn4_pretrain-dbn_dnn
+         mv final.nnet final.nnet.orig
+         ln -sv your_kaldi_output.nnet final.nnet
+
+      Then proceed a normal Kaldi decoding.