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run_xvector_1a.sh
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run_xvector_1a.sh
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#!/usr/bin/env bash
# Copyright 2017 David Snyder
# 2017 Johns Hopkins University (Author: Daniel Garcia-Romero)
# 2017 Johns Hopkins University (Author: Daniel Povey)
#
# Copied from egs/sre16/v1/local/nnet3/xvector/tuning/run_xvector_1a.sh (commit e082c17d4a8f8a791428ae4d9f7ceb776aef3f0b).
#
# Apache 2.0.
# This script trains a DNN similar to the recipe described in
# http://www.danielpovey.com/files/2018_icassp_xvectors.pdf
. ./cmd.sh
set -e
stage=1
train_stage=0
use_gpu=true
remove_egs=false
data=data/train
nnet_dir=exp/xvector_nnet_1a/
egs_dir=exp/xvector_nnet_1a/egs
. ./path.sh
. ./cmd.sh
. ./utils/parse_options.sh
num_pdfs=$(awk '{print $2}' $data/utt2spk | sort | uniq -c | wc -l)
# Now we create the nnet examples using sid/nnet3/xvector/get_egs.sh.
# The argument --num-repeats is related to the number of times a speaker
# repeats per archive. If it seems like you're getting too many archives
# (e.g., more than 200) try increasing the --frames-per-iter option. The
# arguments --min-frames-per-chunk and --max-frames-per-chunk specify the
# minimum and maximum length (in terms of number of frames) of the features
# in the examples.
#
# To make sense of the egs script, it may be necessary to put an "exit 1"
# command immediately after stage 3. Then, inspect
# exp/<your-dir>/egs/temp/ranges.* . The ranges files specify the examples that
# will be created, and which archives they will be stored in. Each line of
# ranges.* has the following form:
# <utt-id> <local-ark-indx> <global-ark-indx> <start-frame> <end-frame> <spk-id>
# For example:
# 100304-f-sre2006-kacg-A 1 2 4079 881 23
# If you're satisfied with the number of archives (e.g., 50-150 archives is
# reasonable) and with the number of examples per speaker (e.g., 1000-5000
# is reasonable) then you can let the script continue to the later stages.
# Otherwise, try increasing or decreasing the --num-repeats option. You might
# need to fiddle with --frames-per-iter. Increasing this value decreases the
# the number of archives and increases the number of examples per archive.
# Decreasing this value increases the number of archives, while decreasing the
# number of examples per archive.
if [ $stage -le 6 ]; then
echo "$0: Getting neural network training egs";
# dump egs.
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $egs_dir/storage ]; then
utils/create_split_dir.pl \
/export/b{03,04,05,06}/$USER/kaldi-data/egs/voxceleb2/v2/xvector-$(date +'%m_%d_%H_%M')/$egs_dir/storage $egs_dir/storage
fi
sid/nnet3/xvector/get_egs.sh --cmd "$train_cmd" \
--nj 8 \
--stage 0 \
--frames-per-iter 1000000000 \
--frames-per-iter-diagnostic 100000 \
--min-frames-per-chunk 200 \
--max-frames-per-chunk 400 \
--num-diagnostic-archives 3 \
--num-repeats 50 \
"$data" $egs_dir
fi
if [ $stage -le 7 ]; then
echo "$0: creating neural net configs using the xconfig parser";
num_targets=$(wc -w $egs_dir/pdf2num | awk '{print $1}')
feat_dim=$(cat $egs_dir/info/feat_dim)
# This chunk-size corresponds to the maximum number of frames the
# stats layer is able to pool over. In this script, it corresponds
# to 100 seconds. If the input recording is greater than 100 seconds,
# we will compute multiple xvectors from the same recording and average
# to produce the final xvector.
max_chunk_size=10000
# The smallest number of frames we're comfortable computing an xvector from.
# Note that the hard minimum is given by the left and right context of the
# frame-level layers.
min_chunk_size=25
mkdir -p $nnet_dir/configs
cat <<EOF > $nnet_dir/configs/network.xconfig
# please note that it is important to have input layer with the name=input
# The frame-level layers
input dim=${feat_dim} name=input
relu-batchnorm-layer name=tdnn1 input=Append(-2,-1,0,1,2) dim=512
relu-batchnorm-layer name=tdnn2 input=Append(-2,0,2) dim=512
relu-batchnorm-layer name=tdnn3 input=Append(-3,0,3) dim=512
relu-batchnorm-layer name=tdnn4 dim=512
relu-batchnorm-layer name=tdnn5 dim=1500
# The stats pooling layer. Layers after this are segment-level.
# In the config below, the first and last argument (0, and ${max_chunk_size})
# means that we pool over an input segment starting at frame 0
# and ending at frame ${max_chunk_size} or earlier. The other arguments (1:1)
# mean that no subsampling is performed.
stats-layer name=stats config=mean+stddev(0:1:1:${max_chunk_size})
# This is where we usually extract the embedding (aka xvector) from.
relu-batchnorm-layer name=tdnn6 dim=512 input=stats
# This is where another layer the embedding could be extracted
# from, but usually the previous one works better.
relu-batchnorm-layer name=tdnn7 dim=512
output-layer name=output include-log-softmax=true dim=${num_targets}
EOF
steps/nnet3/xconfig_to_configs.py \
--xconfig-file $nnet_dir/configs/network.xconfig \
--config-dir $nnet_dir/configs/
cp $nnet_dir/configs/final.config $nnet_dir/nnet.config
# These three files will be used by sid/nnet3/xvector/extract_xvectors.sh
echo "output-node name=output input=tdnn6.affine" > $nnet_dir/extract.config
echo "$max_chunk_size" > $nnet_dir/max_chunk_size
echo "$min_chunk_size" > $nnet_dir/min_chunk_size
fi
dropout_schedule='0,0@0.20,0.1@0.50,0'
srand=123
if [ $stage -le 8 ]; then
steps/nnet3/train_raw_dnn.py --stage=$train_stage \
--cmd="$train_cmd" \
--trainer.optimization.proportional-shrink 10 \
--trainer.optimization.momentum=0.5 \
--trainer.optimization.num-jobs-initial=3 \
--trainer.optimization.num-jobs-final=8 \
--trainer.optimization.initial-effective-lrate=0.001 \
--trainer.optimization.final-effective-lrate=0.0001 \
--trainer.optimization.minibatch-size=64 \
--trainer.srand=$srand \
--trainer.max-param-change=2 \
--trainer.num-epochs=3 \
--trainer.dropout-schedule="$dropout_schedule" \
--trainer.shuffle-buffer-size=1000 \
--egs.frames-per-eg=1 \
--egs.dir="$egs_dir" \
--cleanup.remove-egs $remove_egs \
--cleanup.preserve-model-interval=10 \
--use-gpu=true \
--dir=$nnet_dir || exit 1;
fi
exit 0;