04_train_mono_LIA.sh
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#!/bin/sh
EXPE_DIR=$1
. ../LIA_kaldiUtils/path.sh
. ../LIA_kaldiUtils/cmd.sh
#echo $PATH
LM_DIR=$EXPE_DIR/LANGUAGE_MODEL/
AM_DIR=$EXPE_DIR/ACOUSTIC_MODEL/
AM_DATA=$EXPE_DIR/ac_Data/
LM_DATA=$EXPE_DIR/ling_Data/
FORK=4
cp $AM_DATA/* $AM_DIR
cp $LM_DATA/text $AM_DIR
cp -R $LM_DATA/* $LM_DIR
# Flat start and monophone training, with delta-delta features.
# This script applies cepstral mean normalization (per speaker).
echo "steps/train_mono.sh --nj $FORK --cmd "$train_cmd" $AM_DIR $LM_DATA $AM_DIR/mono"
##___a remettre___## steps/train_mono.sh --nj $FORK --cmd "$train_cmd" $AM_DIR $LM_DATA $AM_DIR/mono || exit 1;
# This script creates a fully expanded decoding graph (HCLG) that represents
# all the language-model, pronunciation dictionary (lexicon), context-dependency,
# and HMM structure in our model. The output is a Finite State Transducer
# that has word-ids on the output, and pdf-ids on the input (these are indexes
# that resolve to Gaussian Mixture Models).
# option mono for monophone ( default is contextual 3-grams)
echo "=====> utils/mkgraph.sh --mono $LM_DATA $AM_DIR/mono $AM_DIR/mono/graph"
##___ a remettre ____ #utils/mkgraph.sh --mono $LM_DIR $AM_DIR/mono $AM_DIR/mono/graph
#utils/mkgraph.sh --mono $LM_DATA $AM_DIR/mono $AM_DIR/mono/graph
echo "=====> steps/decode.sh --nj $FORK --cmd "$decode_cmd" --config $CONF_DIR/decode.config $AM_DIR/mono/graph $EXPE_DIR/TEST/ $AM_DIR/mono/decode"
decode.sh --nj $FORK --cmd "$decode_cmd" --config $CONF_DIR/decode.config $AM_DIR/mono/graph $EXPE_DIR/TEST/ac_Data $AM_DIR/mono/decode