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egs/tedlium/s5_r3/local/run_cleanup_segmentation.sh
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#!/bin/bash
# Copyright 2016 Vimal Manohar
# 2016 Johns Hopkins University (author: Daniel Povey)
# Apache 2.0
# This script demonstrates how to re-segment training data selecting only the
# "good" audio that matches the transcripts.
# The basic idea is to decode with an existing in-domain acoustic model, and a
# biased language model built from the reference, and then work out the
# segmentation from a ctm like file.
# For nnet3 and chain results after cleanup, see the scripts in
# local/nnet3/run_tdnn.sh and local/chain/run_tdnn.sh
# GMM Results for speaker-independent (SI) and speaker adaptive training (SAT) systems on dev and test sets
# [will add these later].
set -e
set -o pipefail
set -u
stage=0
cleanup_stage=0
data=data/train
cleanup_affix=cleaned
srcdir=exp/tri3
nj=100
decode_nj=16
decode_num_threads=4
. ./path.sh
. ./cmd.sh
. utils/parse_options.sh
cleaned_data=${data}_${cleanup_affix}
dir=${srcdir}_${cleanup_affix}_work
cleaned_dir=${srcdir}_${cleanup_affix}
if [ $stage -le 1 ]; then
# This does the actual data cleanup.
steps/cleanup/clean_and_segment_data.sh --stage $cleanup_stage --nj $nj --cmd "$train_cmd" \
$data data/lang $srcdir $dir $cleaned_data
fi
if [ $stage -le 2 ]; then
steps/align_fmllr.sh --nj $nj --cmd "$train_cmd" \
$cleaned_data data/lang $srcdir ${srcdir}_ali_${cleanup_affix}
fi
if [ $stage -le 3 ]; then
steps/train_sat.sh --cmd "$train_cmd" \
5000 100000 $cleaned_data data/lang ${srcdir}_ali_${cleanup_affix} ${cleaned_dir}
fi
if [ $stage -le 4 ]; then
# Test with the models trained on cleaned-up data.
utils/mkgraph.sh data/lang ${cleaned_dir} ${cleaned_dir}/graph
for dset in dev test; do
steps/decode_fmllr.sh --nj $decode_nj --num-threads $decode_num_threads \
--cmd "$decode_cmd" --num-threads 4 \
${cleaned_dir}/graph data/${dset} ${cleaned_dir}/decode_${dset}
steps/lmrescore_const_arpa.sh --cmd "$decode_cmd" data/lang data/lang_rescore \
data/${dset} ${cleaned_dir}/decode_${dset} ${cleaned_dir}/decode_${dset}_rescore
done
fi
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