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Scripts/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 |