run_nnet2_common.sh
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#!/bin/bash
# this script contains some common (shared) parts of the run_nnet*.sh scripts.
. ./cmd.sh
stage=0
set -e
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if [ $stage -le 1 ]; then
# Create high-resolution MFCC features (with 40 cepstra instead of 13).
# this shows how you can split across multiple file-systems. we'll split the
# MFCC dir across multiple locations. You might want to be careful here, if you
# have multiple copies of Kaldi checked out and run the same recipe, not to let
# them overwrite each other.
mfccdir=mfcc
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $mfccdir/storage ]; then
utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/tedlium-$(date +'%m_%d_%H_%M')/s5/$mfccdir/storage $mfccdir/storage
fi
for datadir in train dev test; do
utils/copy_data_dir.sh data/$datadir data/${datadir}_hires
if [ "$datadir" == "train" ]; then
dir=data/train_hires
cat $dir/wav.scp | python -c "
import sys, os, subprocess, re, random
scale_low = 1.0/8
scale_high = 2.0
for line in sys.stdin.readlines():
if len(line.strip()) == 0:
continue
print '{0} sox --vol {1} -t wav - -t wav - |'.format(line.strip(), random.uniform(scale_low, scale_high))
"| sort -k1,1 -u > $dir/wav.scp_scaled || exit 1;
mv $dir/wav.scp $dir/wav.scp_nonorm
mv $dir/wav.scp_scaled $dir/wav.scp
fi
steps/make_mfcc.sh --nj 70 --mfcc-config conf/mfcc_hires.conf \
--cmd "$train_cmd" data/${datadir}_hires exp/make_hires/$datadir $mfccdir || exit 1;
steps/compute_cmvn_stats.sh data/${datadir}_hires exp/make_hires/$datadir $mfccdir || exit 1;
done
fi
if [ $stage -le 2 ]; then
# Train a system just for its LDA+MLLT transform. We use --num-iters 13
# because after we get the transform (12th iter is the last), any further
# training is pointless.
steps/train_lda_mllt.sh --cmd "$train_cmd" --num-iters 13 \
--realign-iters "" \
--splice-opts "--left-context=3 --right-context=3" \
5000 10000 data/train_hires data/lang \
exp/tri3_ali exp/nnet2_online/tri4
fi
if [ $stage -le 3 ]; then
mkdir -p exp/nnet2_online
steps/online/nnet2/train_diag_ubm.sh --cmd "$train_cmd" --nj 30 --num-frames 700000 \
data/train_hires 512 exp/nnet2_online/tri4 exp/nnet2_online/diag_ubm
fi
if [ $stage -le 4 ]; then
# iVector extractors can in general be sensitive to the amount of data, but
# this one has a fairly small dim (defaults to 100)
steps/online/nnet2/train_ivector_extractor.sh --cmd "$train_cmd" --nj 10 \
data/train_hires exp/nnet2_online/diag_ubm exp/nnet2_online/extractor || exit 1;
fi
if [ $stage -le 5 ]; then
ivectordir=exp/nnet2_online/ivectors_train_hires
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $ivectordir/storage ]; then
utils/create_split_dir.pl /export/b0{1,2,3,4}/$USER/kaldi-data/egs/tedlium-$(date +'%m_%d_%H_%M')/s5/$ivectordir/storage $ivectordir/storage
fi
# We extract iVectors on all the train data, which will be what we train the
# system on. With --utts-per-spk-max 2, the script. pairs the utterances
# into twos, and treats each of these pairs as one speaker. Note that these
# are extracted 'online'.
# having a larger number of speakers is helpful for generalization, and to
# handle per-utterance decoding well (iVector starts at zero).
steps/online/nnet2/copy_data_dir.sh --utts-per-spk-max 2 data/train_hires data/train_hires_max2
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 30 \
data/train_hires_max2 exp/nnet2_online/extractor exp/nnet2_online/ivectors_train_hires || exit 1;
fi
exit 0;