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Diffstat (limited to 'tutorial/howto_pretrain_from_kaldi.rst')
| -rw-r--r-- | tutorial/howto_pretrain_from_kaldi.rst | 61 |
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. |