asr_diarization_clean: Adding run_train_sad.sh

egs/aspire/s5/local/segmentation/run_train_sad.sh

@@ -0,0 +1,150 @@
+#!/bin/bash
+
+# this is the standard "tdnn" system, built in nnet3; it's what we use to
+# call multi-splice.
+
+set -o pipefail
+set -e 
+set -u
+
+. cmd.sh
+
+# At this script level we don't support not running on GPU, as it would be painfully slow.
+# If you want to run without GPU you'd have to call train_tdnn.sh with --gpu false,
+# --num-threads 16 and --minibatch-size 128.
+
+stage=0
+train_stage=-10
+get_egs_stage=-10
+egs_opts=
+
+splice_indexes="-3,-2,-1,0,1,2,3 -6,0 -9,0,3 0"
+relu_dim=256
+
+# training options
+num_epochs=2
+initial_effective_lrate=0.0003
+final_effective_lrate=0.00003
+num_jobs_initial=3
+num_jobs_final=8
+remove_egs=false
+max_param_change=1
+extra_egs_copy_cmd=
+
+num_utts_subset_valid=40
+num_utts_subset_train=40
+add_idct=true
+
+# target options
+train_data_dir=data/train_azteec_whole_sp_corrupted_hires
+
+snr_scp=
+speech_feat_scp=
+
+deriv_weights_scp=
+deriv_weights_for_irm_scp=
+
+egs_dir=
+nj=40
+feat_type=raw
+config_dir=
+compute_objf_opts=
+
+dir=
+affix=a
+
+. cmd.sh
+. ./path.sh
+. ./utils/parse_options.sh
+
+num_hidden_layers=`echo $splice_indexes | perl -ane 'print scalar @F'` || exit 1
+if [ -z "$dir" ]; then
+  dir=exp/nnet3_sad_snr/nnet_tdnn
+fi
+
+dir=$dir${affix:+_$affix}_n${num_hidden_layers}
+
+if ! cuda-compiled; then
+  cat <<EOF && exit 1 
+This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA 
+If you want to use GPUs (and have them), go to src/, and configure and make on a machine
+where "nvcc" is installed.
+EOF
+fi
+
+mkdir -p $dir
+
+num_snr_bins=`feat-to-dim scp:$snr_scp -`
+
+if [ $stage -le 3 ]; then
+  local/segmentation/make_sad_tdnn_configs.py \
+    --feat-dir=$train_data_dir \
+    --splice-indexes="$splice_indexes" \
+    --relu-dim=$relu_dim \
+    --add-lda=false \
+    --output-node-parameters "--output-suffix=snr --dim=$num_snr_bins --add-final-sigmoid=false --include-log-softmax=false --objective-type=quadratic" \
+    --output-node-parameters "--output-suffix=speech --dim=2 --include-log-softmax=true --objective-type=linear" \
+    $dir/configs
+
+fi
+
+if [ -z "$egs_dir" ]; then
+  egs_dir=$dir/egs
+  if [ $stage -le 4 ]; then
+    if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
+      utils/create_split_dir.pl \
+        /export/b{05,06,11,12}/$USER/kaldi-data/egs/aspire-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
+    fi
+
+    . $dir/configs/vars
+
+    steps/nnet3/get_egs_multiple_targets.py --cmd="$decode_cmd" \
+      --feat.dir="$train_data_dir" \
+      --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+      --frames-per-eg=8 \
+      --left-context=$[model_left_context] \
+      --right-context=$[model_right_context] \
+      --num-utts-subset-train=$num_utts_subset_train \
+      --num-utts-subset-valid=$num_utts_subset_valid \
+      --samples-per-iter=400000 \
+      --stage=$get_egs_stage \
+      --targets-parameters="--output-name=output-snr --target-type=dense --targets-scp=$snr_scp --deriv-weights-scp=$deriv_weights_for_irm_scp --compress=true" \
+      --targets-parameters="--output-name=output-speech --target-type=sparse --dim=2 --targets-scp=$speech_feat_scp --deriv-weights-scp=$deriv_weights_scp --scp2ark-cmd=\"extract-column --column-index=0 scp:- ark,t:- | steps/segmentation/quantize_vector.pl | ali-to-post ark,t:- ark:- |\" --compress=true" \
+      --dir=$dir/egs
+  fi
+fi
+
+if [ $stage -le 5 ]; then
+  steps/nnet3/train_raw_dnn.py --stage=$train_stage \
+    --feat.cmvn-opts="--norm-means=false --norm-vars=false" \
+    --egs.frames-per-eg=8 \
+    --egs.dir="$egs_dir" --egs.stage=$get_egs_stage --egs.opts="$egs_opts" \
+    --trainer.num-epochs=$num_epochs \
+    --trainer.samples-per-iter=400000 \
+    --trainer.optimization.num-jobs-initial=$num_jobs_initial \
+    --trainer.optimization.num-jobs-final=$num_jobs_final \
+    --trainer.optimization.initial-effective-lrate=$initial_effective_lrate \
+    --trainer.optimization.final-effective-lrate=$final_effective_lrate \
+    --trainer.max-param-change=$max_param_change \
+    --cmd="$decode_cmd" --nj 40 \
+    --egs.extra-copy-cmd="$extra_egs_copy_cmd" \
+    --cleanup=true \
+    --cleanup.remove-egs=$remove_egs \
+    --cleanup.preserve-model-interval=10 \
+    --use-gpu=true \
+    --use-dense-targets=false \
+    --feat-dir=$train_data_dir \
+    --targets-scp="$speech_feat_scp" \
+    --dir=$dir || exit 1
+fi
+
+if [ $stage -le 6 ]; then
+  extract-column scp:$speech_feat_scp ark,t:- | \
+    steps/segmentation/quantize_vector.pl | \
+    ali-to-post ark,t:- ark:- | \
+    weight-post ark:- scp:$deriv_weights_scp ark:- | \
+    post-to-feats --post-dim=2 ark:- ark:- | \
+    matrix-sum-rows ark:- ark:- | vector-sum ark:- $dir/post_output-speech.vec
+fi
+
+