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diff --git a/tutorial/howto_pretrain_from_kaldi.rst b/tutorial/howto_pretrain_from_kaldi.rst new file mode 100644 index 0000000..95b5f36 --- /dev/null +++ b/tutorial/howto_pretrain_from_kaldi.rst @@ -0,0 +1,60 @@ +How to Use a Pretrained nnet Model from Kaldi +============================================= + +:author: Ted Yin (mfy43) <ted.sybil@gmail.com> +:abstract: Instruct on how to pretrain a basic dnn with timit dataset using + Kaldi and then convert the pretrained model to nerv format to let + NERV finetune. Finally it shows two possible ways to decode the + finetuned model in Kaldi framework. + +- Locate the egs/timit inside Kaldi trunk directory. + +- Configure ``cmd.sh`` and ``path.sh`` according to your machine setting. + +- 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. + +- Run ``./run.sh`` 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). + +- 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: + + - 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. + + - The previous conversion commands will automatically give identifiers to the + parameters read from the Kaldi network file. The identifiers are like, for + 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``. + +- 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 ``install/bin/nerv nerv/examples/asr_trainer.lua timit_mybaseline.lua``. + + - ``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 ``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. |