To reproduce our Wall Street Journal (WSJ) experiments, please follow the instruction below:

1. Compile a Fuel-compatible dataset file in HDF5 format. This step requires 
   kaldi and kaldi-python.
   ```
   $LVSR/exp/wsj/write_hdf_dataset.sh
   ```
   The resulting file `wsj.h5` should be put to $FUEL_DATA_PATH folder. 

2. Compile language model FST's from ARPA-format language models provided with WSJ.
   This step requires kaldi.

   `$LVSR/exp/wsj/make_all_wsj_graphs.sh <lmfile> <lmsdir>`
    
   where `<lmfile>` is the arpa languge model which goes with WSJ dataset. 
   (we placed it to `$FUEL_DATA_PATH/WSJ/lm_bg.arpa.gz`) and `<lmsdir>` is a 
   directry to place FST language models (we use `data/lms`).

3. Train the model:

   `$LVSR/bin/run.py train $LVSR/lvsr/configs/wsj_paper6.yaml`

4. Decode the model on the validation and training datasets. 

   `$LVSR/exp/wsj/decode.sh wsj_paper6 <beam-size>`

    We typically use beam size 200 to get the best performance. However, with beam size 
    the scores are typically only 10\% worse and decoding is **much** faster.

5. Score the recognized transcripts:

    **TODO**

To reproduce our Wall Street Journal (WSJ) experiments, please follow the 
instructions below. All the steps should be done at the kaldi WSJ recipe 
directory. In order to perform steps 1, 2, 5 you should source `path.sh`
file from the recipe.

1. Compile a Fuel-compatible dataset file in HDF5 format. This step requires 
   kaldi and kaldi-python.
   ```
   $LVSR/exp/wsj/write_hdf_dataset.sh
   ```
   The resulting file `wsj.h5` should be put to $FUEL_DATA_PATH folder. 

2. Compile language model FST's from ARPA-format language models provided with WSJ.
   This step requires kaldi.

   `$LVSR/exp/wsj/make_all_wsj_graphs.sh <lmfile> <lmsdir>`
    
   where `<lmfile>` is the arpa languge model which goes with WSJ dataset. 
   (we placed it to `$FUEL_DATA_PATH/WSJ/lm_bg.arpa.gz`) and `<lmsdir>` is a 
   directry to place FST language models (we use `data/lms`).

3. Train the model:

   `$LVSR/bin/run.py train $LVSR/lvsr/configs/wsj_paper6.yaml`

4. Decode the model on the validation and training datasets. 

   `$LVSR/exp/wsj/decode.sh wsj_paper6 <beam-size>`

    We typically use beam size 200 to get the best performance. However, with beam size 
    the scores are typically only 10\% worse and decoding is **much** faster.

5. Score the recognized transcripts:

    **TODO**

Add recomendation where to run the scripts
Add recomendation where to run the scripts
