rnnlm2wfst

Conversion of recurrent neural network language models to weighted finite state transducers This directory contains all the code to run the conversion

Gwénolé Lecorvé Idiap Research Institute, Martigny, Switzerland 2011-2012

How to cite

@inproceedings{lecorve2012conversion,
  title={Conversion of recurrent neural network language models to weighted finite state transducers for automatic speech recognition},
  author={Lecorv{\'e}, Gw{\'e}nol{\'e} and Motlicek, Petr},
  booktitle={Thirteenth Annual Conference of the International Speech Communication Association (Interspeech)},
  year={2012}
}

Prerequisites

OpenFst: You can use your own version. In that case, edit the makefile in rnnlm-0.2b/src. BLAS: Better if you have it installed (much faster).

Configuration & compilation

Same as install.sh.

OpenFst

cd openfst-1.2.0
./configure --prefix=`pwd`
make
cd ..

K-means

cd kmeans
make
cd ..

RNNLM

cd rnnlm-0.2b
# Do not use USE_BLAS=1 if BLAS is not installed
make USE_BLAS=1
cd ..

Examples

RNNLM basic usage

See examples/example_rrnlm.sh

Train RNNLM

bin/rnnlm -train examples/rnn2wfst.train.txt -valid examples/rnn2wfst.dev.txt -rnnlm examples/rnn2wfst.model -hidden 2 -rand-seed 1 -bptt 3 -debug 2 -class 1

Write logs of continuous states

bin/trace-hidden-layer -rnnlm examples/rnn2wfst.model -text examples/rnn2wfst.train.txt >  examples/rnn2wfst.train.trace

Generate artificial data (if you think training data is too small or anything else)

bin/rnnlm -rnnlm examples/rnn2wfst.model -gen 10000 | tail -n +2 > examples/rnn2wfst.generated.txt
bin/trace-hidden-layer -rnnlm examples/rnn2wfst.model -text examples/rnn2wfst.generated.txt >  examples/rnn2wfst.generated.trace

Build K-means (flat or hiearchical)

perl bin/build-cluster-hierarchy.pl examples/rnn2wfst.train.trace 2 4 > examples/rnn2wfst.4.kmeans
perl bin/build-cluster-hierarchy.pl examples/rnn2wfst.train.trace 2 1 8 > examples/rnn2wfst.1+8.kmeans
perl bin/build-cluster-hierarchy.pl examples/rnn2wfst.train.trace 2 1 2 4 8 > examples/rnn2wfst.1+2+4+8.kmeans

Cluster-based convertion

time bin/rnn2fst -rnnlm examples/rnn2wfst.model -fst examples/rnn2wfst.k1+8.p1e-3.fst -discretize examples/rnn2wfst.1+8.kmeans -hcluster -prune 1e-3 -backoff 2

Remark: the value of the backoff option (2) is the depth of the cluster hieararchy.

See the resulting WFST

fstprint examples/rnn2wfst.k1+8.p1e-3.fst

Simulate perplexity with discretized RNNLM but without pruning

bin/rnnlm -rnnlm examples/rnn2wfst.model -test examples/rnn2wfst.test.txt -debug 2 -discretize examples/rnn2wfst.1+8.kmeans | less

Measure perplexity

bin/wfst-ppl -fst examples/rnn2wfst.k1+8.p1e-3.fst -text examples/rnn2wfst.test.txt | less

Describe WFST

../openfst-1.3.2/bin/fstinfo --info_type=long examples/rnn2wfst.k1+8.p1e-3.fst