[egs] Speaker recognition scripts for FAME! Speech Corpus (#1897)

[processor-sdk/kaldi.git] / egs / fame / v1 / local / dnn / train_dnn.sh

diff --git a/egs/fame/v1/local/dnn/train_dnn.sh b/egs/fame/v1/local/dnn/train_dnn.sh
new file mode 100755 (executable)
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+++ b/egs/fame/v1/local/dnn/train_dnn.sh
@@ -0,0 +1,89 @@
+#!/bin/bash
+# Copyright 2017  Radboud University (Author: Emre Yilmaz)
+
+. ./cmd.sh
+. ./path.sh
+
+stage=0
+feat_nj=10
+train_nj=10
+decode_nj=10
+famecorpus=./corpus/ASR
+. ./utils/parse_options.sh
+
+numLeavesTri1=5000
+numGaussTri1=25000
+numLeavesMLLT=5000
+numGaussMLLT=25000
+numLeavesSAT=5000
+numGaussSAT=25000
+numGaussUBM=800
+numLeavesSGMM=10000
+numGaussSGMM=20000
+
+if [ -d $famecorpus ] ; then
+  echo "Fame corpus present. OK."
+elif [ -f ./fame.tar.gz ] ; then
+  echo "Unpacking..."
+  tar xzf fame.tar.gz
+elif [ ! -d $famecorpus ] && [ ! -f ./fame.tar.gz ] ; then
+  echo "The Fame! corpus is not present. Please register here: http://www.ru.nl/clst/datasets/ "
+  echo " and download the corpus and put it at $famecorpus" && exit 1
+fi
+
+if [ $stage -le 1 ]; then
+  local/fame_data_prep.sh $famecorpus || exit 1;
+  local/fame_dict_prep.sh $famecorpus || exit 1;
+  utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang data/lang || exit 1;
+  utils/format_lm.sh data/lang data/local/LM.gz data/local/dict/lexicon.txt data/lang_test || exit 1;
+fi
+
+if [ $stage -le 2 ]; then
+  # Feature extraction
+  for x in train_asr devel_asr test_asr; do
+      steps/make_mfcc.sh --nj $feat_nj --mfcc-config conf/mfcc_asr.conf --cmd "$train_cmd" data/$x exp/make_mfcc/$x mfcc || exit 1;
+      steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x mfcc || exit 1;
+  done
+fi
+
+if [ $stage -le 3 ]; then
+  ### Monophone
+  echo "Starting monophone training."
+  steps/train_mono.sh --nj $train_nj --cmd "$train_cmd" data/train_asr data/lang exp/mono || exit 1;
+  echo "Mono training done."
+
+fi
+
+if [ $stage -le 4 ]; then
+  ### Triphone
+  echo "Starting triphone training."
+  steps/align_si.sh --nj $train_nj --cmd "$train_cmd" data/train_asr data/lang exp/mono exp/mono_ali || exit 1;
+  steps/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd"  $numLeavesTri1 $numGaussTri1 data/train_asr data/lang exp/mono_ali exp/tri1 || exit 1;
+  echo "Triphone training done."
+
+fi
+
+if [ $stage -le 5 ]; then
+  ### Triphone + LDA and MLLT
+  echo "Starting LDA+MLLT training."
+  steps/align_si.sh  --nj $train_nj --cmd "$train_cmd"  data/train_asr data/lang exp/tri1 exp/tri1_ali || exit 1;
+  steps/train_lda_mllt.sh  --cmd "$train_cmd"  --splice-opts "--left-context=3 --right-context=3" $numLeavesMLLT $numGaussMLLT data/train_asr data/lang  exp/tri1_ali exp/tri2 || exit 1;
+  echo "LDA+MLLT training done."
+
+fi
+
+if [ $stage -le 6 ]; then
+  ### Triphone + LDA and MLLT + SAT and FMLLR
+  echo "Starting SAT+FMLLR training."
+  steps/align_si.sh  --nj $train_nj --cmd "$train_cmd" --use-graphs true data/train_asr data/lang exp/tri2 exp/tri2_ali || exit 1;
+  steps/train_sat.sh --cmd "$train_cmd" $numLeavesSAT $numGaussSAT data/train_asr data/lang exp/tri2_ali exp/tri3 || exit 1;
+  echo "SAT+FMLLR training done."
+
+  echo "Decoding the development and test sets using SAT+FMLLR models."
+  utils/mkgraph.sh data/lang_test exp/tri3 exp/tri3/graph || exit 1;
+  steps/decode_fmllr.sh --nj $decode_nj --cmd "$decode_cmd" --skip-scoring true exp/tri3/graph data/devel_asr exp/tri3/decode_devel || exit 1;
+  steps/decode_fmllr.sh --nj $decode_nj --cmd "$decode_cmd" --skip-scoring true exp/tri3/graph data/test_asr exp/tri3/decode_test || exit 1;
+  echo "SAT+FMLLR decoding done."
+fi
+
+local/dnn/run_nnet2_multisplice.sh