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authorDeterminant <[email protected]>2016-03-01 00:33:28 +0800
committerDeterminant <[email protected]>2016-03-01 00:33:28 +0800
commit18b0e3d993ec5ce8e97a6affb533c9ace940bfff (patch)
tree58e78530bf2d5e1cff03754404a1be2c11856fee /tutorial/howto_pretrain_from_kaldi.rst
parente7c9175e1059a15720025a99ccdb35d5bf6dc30f (diff)
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-rw-r--r--tutorial/howto_pretrain_from_kaldi.rst105
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diff --git a/tutorial/howto_pretrain_from_kaldi.rst b/tutorial/howto_pretrain_from_kaldi.rst
index ff6ef3d..6b8253a 100644
--- a/tutorial/howto_pretrain_from_kaldi.rst
+++ b/tutorial/howto_pretrain_from_kaldi.rst
@@ -7,38 +7,53 @@ How to Use a Pretrained nnet Model from Kaldi
NERV finetune. Finally it shows two possible ways to decode the
finetuned model in Kaldi framework.
-- Locate the egs/timit inside Kaldi trunk directory.
+- Note: in this tutorial, we use the following notations to denote the directory prefix:
-- Configure ``cmd.sh`` and ``path.sh`` according to your machine setting.
+ - ``<nerv_home>``: the path of NERV (the location of outer most directory ``nerv``)
-- Open the ``run.sh`` and locate the line saying ``exit 0 # From this point
- you can run Karel's DNN: local/nnet/run_dnn.sh``. Uncomment this line. This
- is because in this tutorial, we only want to train a basic tri-phone DNN,
- so we simply don't do MMI training, system combination or fancy things like
- these.
+ - ``<timit_home>``: the working directory of timit (the location of directory ``timit/s5``)
-- Run ``./run.sh`` to start the training stages. After that, we will get
+- Locate the ``egs/timit`` inside Kaldi trunk directory.
+
+- Configure ``<timit_home>/cmd.sh`` and ``<timit_home>/path.sh`` according to your machine setting.
+
+- Open the ``<timit_home>/run.sh`` and locate the line saying
+
+ ::
+
+ exit 0 # From this point you can run Karel's DNN: local/nnet/run_dnn.sh
+ .
+ Uncomment this line. This is because in this tutorial, we only want to train
+ a basic tri-phone DNN, so we simply don't do MMI training, system combination
+ or fancy things like these.
+
+- Run ``./run.sh`` (at ``<timit_home>``) to start the training stages. After that, we will get
tri-phone GMM-HMM trained and the aligned labels. Let's move forward to
pretrain a DNN.
-- Open ``local/nnet/run_dnn.sh``, there are again several stages. Note that
- the first stage is what we actually need (pretraining the DNN), since in
- this tutorial we want to demonstrate how to get the pretrained model from
- stage 1, replace stage 2 with NERV (finetune per-frame cross-entropy), and
- decode using the finetuned network. However, here we add a line ``exit 0``
- after stage 2 to preserve stage 2 in order to compare the NERV result
- against the standard one (the decode result using finetuned model produced
- by the original stage 2).
+- Open ``<timit_home>/local/nnet/run_dnn.sh``, there are again several stages.
+ Note that the first stage is what we actually need (pretraining the DNN),
+ since in this tutorial we want to demonstrate how to get the pretrained model
+ from stage 1, replace stage 2 with NERV (finetune per-frame cross-entropy),
+ and decode using the finetuned network. However, here we add a line ``exit
+ 0`` after stage 2 to preserve stage 2 in order to compare the NERV result
+ against the standard one (the decode result using finetuned model produced by
+ the original stage 2).
-- Run ``local/nnet/run_dnn.sh`` (first two stages).
+- Run ``local/nnet/run_dnn.sh`` (at ``<timit_home>``, for 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:
+ ``dnn4_pretrain-dbn_dnn`` inside the ``<timit_home>/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
+ - Check the file ``<timit_home>/exp/dnn4_pretrain-dbn/6.dbn`` exists.
+ (pretrained network)
+
+ - Check the file
+ ``<timit_home>/exp/dnn4_pretrain-dbn/tr_splice5_cmvn-g.nnet`` exists.
+ (global transformation)
+
+ - Run script from ``<nerv_home>/speech/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.
@@ -47,18 +62,25 @@ How to Use a Pretrained nnet Model from Kaldi
example, ``affine0_ltp`` and ``bias0``. These names should correspond to
the identifiers used in the declaration of the network. Luckily, this
tutorial comes with a written network declaration at
- ``nerv/examples/timit_baseline2.lua``.
+ ``<nerv_home>/nerv/examples/timit_baseline2.lua``.
+
+- Copy the file ``<nerv_home>/nerv/examples/timit_baseline2.lua`` to
+ ``<timit_home>/timit_mybaseline.lua``, and change the line containing
+ ``/speechlab`` to your own setting.
-- Copy the file ``nerv/examples/timit_baseline2.lua`` to
- ``timit_mybaseline.lua``, and change the line containing ``/speechlab`` to
- your own setting.
+- Start the NERV training by
+
+ ::
+
+ <nerv_home>/install/bin/nerv <nerv_home>/nerv/examples/asr_trainer.lua timit_mybaseline.lua
-- Start the NERV training by ``install/bin/nerv nerv/examples/asr_trainer.lua timit_mybaseline.lua``.
+ (at ``<timit_home>``).
- - ``install/bin/nerv`` is the program which sets up the NERV environment,
+ - ``<nerv_home>/install/bin/nerv`` is the program which sets up the NERV environment,
- - followed by an argument ``nerv/examples/asr_trainer.lua`` which is the script
- you actually want to run (the general DNN training scheduler),
+ - followed by an argument ``<nerv_home>/nerv/examples/asr_trainer.lua`` which
+ is the script you actually want to run (the general DNN training
+ scheduler),
- followed by an argument ``timit_mybaseline.lua`` to the scheduler,
specifying the network you want to train and some relevant settings, such
@@ -74,19 +96,17 @@ How to Use a Pretrained nnet Model from Kaldi
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.
+ - Copy the script ``<nerv_home>/nerv/speech/kaldi_io/README.timit`` to
+ ``<timit_home>`` and modify the paths listed in the script.
- - Run the modified ``README.timit`` in ``s5`` directory (where there is the
- ``path.sh``).
+ - Run the modified ``README.timit`` (at ``<timit_home>``).
- - After decoding, run ``bash RESULT exp/dnn4_nerv`` to see the results.
+ - After decoding, run ``bash RESULT exp/dnn4_nerv_dnn`` 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``.
+ - Create a copy of ``<nerv_home>/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
@@ -103,14 +123,15 @@ How to Use a Pretrained nnet Model from Kaldi
::
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
+ <nerv_home>/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:
+ - Finally, locate the directory of stage 2:
+ ``<timit_home>/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
+ cd <timit_home>/exp/dnn4_pretrain-dbn_dnn
mv final.nnet final.nnet.orig
ln -sv your_kaldi_output.nnet final.nnet