[egs] add v2

egs/cnceleb/README.txt

@@ -6,4 +6,5 @@ CSLT@Tsinghua http://cslt.riit.tsinghua.edu.cn/~data/CN-Celeb/
 
 The subdirectories "v1" and so on are different speaker recognition 
 recipes. The recipe in v1 demonstrates a standard approach using a 
-full-covariance GMM-UBM, iVectors, and a PLDA backend. 
+full-covariance GMM-UBM, iVectors, and a PLDA backend. The example 
+in v2 demonstrates DNN speaker embeddings with a PLDA backend.

egs/cnceleb/v2/README.txt

@@ -0,0 +1,14 @@
+ This recipe replaces i-vectors used in the v1 recipe with embeddings extracted
+ from a deep neural network.  In the scripts, we refer to these embeddings as
+ "x-vectors." The recipe in local/nnet3/xvector/tuning/run_xvector_1a.sh is
+ closesly based on the following paper:
+
+ @inproceedings{snyder2018xvector,
+ title={X-vectors: Robust DNN Embeddings for Speaker Recognition},
+ author={Snyder, D. and Garcia-Romero, D. and Sell, G. and Povey, D. and Khudanpur, S.},
+ booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+ year={2018},
+ organization={IEEE},
+ url={http://www.danielpovey.com/files/2018_icassp_xvectors.pdf}
+ }
+

egs/cnceleb/v2/cmd.sh

@@ -0,0 +1,15 @@
+# you can change cmd.sh depending on what type of queue you are using.
+# If you have no queueing system and want to run on a local machine, you
+# can change all instances 'queue.pl' to run.pl (but be careful and run
+# commands one by one: most recipes will exhaust the memory on your
+# machine).  queue.pl works with GridEngine (qsub).  slurm.pl works
+# with slurm.  Different queues are configured differently, with different
+# queue names and different ways of specifying things like memory;
+# to account for these differences you can create and edit the file
+# conf/queue.conf to match your queue's configuration.  Search for
+# conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information,
+# or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl.
+
+export train_cmd="queue.pl --mem 4G"
+
+

egs/cnceleb/v2/conf/mfcc.conf

@@ -0,0 +1,7 @@
+--sample-frequency=16000
+--frame-length=25 # the default is 25
+--low-freq=20 # the default.
+--high-freq=7600 # the default is zero meaning use the Nyquist (8k in this case).
+--num-mel-bins=30
+--num-ceps=30
+--snip-edges=false

egs/cnceleb/v2/conf/vad.conf

@@ -0,0 +1,4 @@
+--vad-energy-threshold=5.5
+--vad-energy-mean-scale=0.5
+--vad-proportion-threshold=0.12
+--vad-frames-context=2

egs/cnceleb/v2/local

@@ -0,0 +1 @@
+../v1/local

egs/cnceleb/v2/path.sh

@@ -0,0 +1,5 @@
+export KALDI_ROOT=`pwd`/../../..
+export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/tools/sph2pipe_v2.5:$PWD:$PATH
+[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1
+. $KALDI_ROOT/tools/config/common_path.sh
+export LC_ALL=C

egs/cnceleb/v2/run.sh

@@ -0,0 +1,152 @@
+#!/bin/bash
+# Copyright    2017   Johns Hopkins University (Author: Daniel Povey)
+#              2017   Johns Hopkins University (Author: Daniel Garcia-Romero)
+#              2018   Ewald Enzinger
+#              2018   David Snyder
+#              2019   Tsinghua University (Author: Jiawen Kang and Lantian Li)
+# Apache 2.0.
+#
+# This is an x-vector-based recipe for CN-Celeb database.
+# It is based on "X-vectors: Robust DNN Embeddings for Speaker Recognition"
+# by Snyder et al. The recipe uses CN-Celeb/dev for training the UBM, T matrix 
+# and PLDA, and CN-Celeb/eval for evaluation. The results are reported in terms 
+# of EER and minDCF, and are inline in the comments below.
+
+. ./cmd.sh
+. ./path.sh
+set -e
+mfccdir=`pwd`/mfcc
+vaddir=`pwd`/mfcc
+
+cnceleb_root=/export/corpora/CN-Celeb
+nnet_dir=exp/xvector_nnet_1a
+eval_trials_core=data/eval_test/trials/trials.lst
+
+stage=0
+
+if [ $stage -le 0 ]; then
+  # Prepare the CN-Celeb dataset. The script is used to prepare the development
+  # dataset and evaluation dataset.
+  local/make_cnceleb.sh $cnceleb_root data
+fi
+
+if [ $stage -le 1 ]; then
+  # Make MFCCs and compute the energy-based VAD for each dataset
+  for name in train eval_enroll eval_test; do
+    steps/make_mfcc.sh --write-utt2num-frames true --mfcc-config conf/mfcc.conf --nj 20 --cmd "$train_cmd" \
+      data/${name} exp/make_mfcc $mfccdir
+    utils/fix_data_dir.sh data/${name}
+    sid/compute_vad_decision.sh --nj 20 --cmd "$train_cmd" \
+      data/${name} exp/make_vad $vaddir
+    utils/fix_data_dir.sh data/${name}
+  done
+fi
+
+if [ $stage -le 3 ]; then
+  # Note that there are over one-third of the utterances less than 2 seconds in our training set,
+  # and these short utterances are harmful for DNNs x-vector training. Therefore, to improve 
+  # performance of DNN training, we will combine the short utterances longer than 5 seconds.
+  utils/data/combine_short_segments.sh --speaker-only true \
+    data/train 5 data/train_comb
+  # Compute the energy-based VAD for train_comb
+  sid/compute_vad_decision.sh --nj 20 --cmd "$train_cmd" \
+    data/train_comb exp/make_vad $vaddir
+  utils/fix_data_dir.sh data/train_comb
+fi
+
+# Now we prepare the features to generate examples for xvector training.
+if [ $stage -le 4 ]; then
+  # This script applies CMVN and removes nonspeech frames. Note that this is somewhat
+  # wasteful, as it roughly doubles the amount of training data on disk. After
+  # creating training examples, this can be removed.
+  local/nnet3/xvector/prepare_feats_for_egs.sh --nj 20 --cmd "$train_cmd" \
+    data/train_comb data/train_comb_no_sil exp/train_comb_no_sil
+  utils/fix_data_dir.sh data/train_comb_no_sil
+fi
+
+if [ $stage -le 5 ]; then
+  # Now, we need to remove features that are too short after removing silence
+  # frames. We want atleast 5s (500 frames) per utterance.
+  min_len=400
+  mv data/train_comb_no_sil/utt2num_frames data/train_comb_no_sil/utt2num_frames.bak
+  awk -v min_len=${min_len} '$2 > min_len {print $1, $2}' data/train_comb_no_sil/utt2num_frames.bak > data/train_comb_no_sil/utt2num_frames
+  utils/filter_scp.pl data/train_comb_no_sil/utt2num_frames data/train_comb_no_sil/utt2spk > data/train_comb_no_sil/utt2spk.new
+  mv data/train_comb_no_sil/utt2spk.new data/train_comb_no_sil/utt2spk
+  utils/fix_data_dir.sh data/train_comb_no_sil
+
+  # We also want several utterances per speaker. Now we'll throw out speakers
+  # with fewer than 8 utterances.
+  min_num_utts=8
+  awk '{print $1, NF-1}' data/train_comb_no_sil/spk2utt > data/train_comb_no_sil/spk2num
+  awk -v min_num_utts=${min_num_utts} '$2 >= min_num_utts {print $1, $2}' data/train_comb_no_sil/spk2num | utils/filter_scp.pl - data/train_comb_no_sil/spk2utt > data/train_comb_no_sil/spk2utt.new
+  mv data/train_comb_no_sil/spk2utt.new data/train_comb_no_sil/spk2utt
+  utils/spk2utt_to_utt2spk.pl data/train_comb_no_sil/spk2utt > data/train_comb_no_sil/utt2spk
+
+  utils/filter_scp.pl data/train_comb_no_sil/utt2spk data/train_comb_no_sil/utt2num_frames > data/train_comb_no_sil/utt2num_frames.new
+  mv data/train_comb_no_sil/utt2num_frames.new data/train_comb_no_sil/utt2num_frames
+
+  # Now we're ready to create training examples.
+  utils/fix_data_dir.sh data/train_comb_no_sil
+fi
+
+# Stages 6 through 8 are handled in run_xvector.sh
+local/nnet3/xvector/run_xvector.sh --stage $stage --train-stage -1 \
+  --data data/train_comb_no_sil --nnet-dir $nnet_dir \
+  --egs-dir $nnet_dir/egs
+
+if [ $stage -le 9 ]; then
+  # These x-vectors will be used for mean-subtraction, LDA, and PLDA training.
+  sid/nnet3/xvector/extract_xvectors.sh --cmd "$train_cmd --mem 4G" --nj 20 \
+    $nnet_dir data/train_comb \
+    $nnet_dir/xvectors_train_comb
+
+  # Extract x-vector for eval sets.
+  for name in eval_enroll eval_test; do
+    sid/nnet3/xvector/extract_xvectors.sh --cmd "$train_cmd --mem 4G" --nj 20 \
+      $nnet_dir data/$name \
+      $nnet_dir/xvectors_$name
+  done
+fi
+
+if [ $stage -le 10 ]; then
+  # Compute the mean.vec used for centering.
+  $train_cmd $nnet_dir/xvectors_train_comb/log/compute_mean.log \
+    ivector-mean scp:$nnet_dir/xvectors_train_comb/xvector.scp \
+    $nnet_dir/xvectors_train_comb/mean.vec || exit 1;
+
+  # Use LDA to decrease the dimensionality prior to PLDA.
+  lda_dim=128
+  $train_cmd $nnet_dir/xvectors_train_comb/log/lda.log \
+    ivector-compute-lda --total-covariance-factor=0.0 --dim=$lda_dim \
+    "ark:ivector-subtract-global-mean scp:$nnet_dir/xvectors_train_comb/xvector.scp ark:- |" \
+    ark:data/train_comb/utt2spk $nnet_dir/xvectors_train_comb/transform.mat || exit 1;
+
+  # Train the PLDA model.
+  $train_cmd $nnet_dir/xvectors_train_comb/log/plda.log \
+    ivector-compute-plda ark:data/train_comb/spk2utt \
+    "ark:ivector-subtract-global-mean scp:$nnet_dir/xvectors_train_comb/xvector.scp ark:- | transform-vec $nnet_dir/xvectors_train_comb/transform.mat ark:- ark:- | ivector-normalize-length ark:- ark:- |" \
+    $nnet_dir/xvectors_train_comb/plda || exit 1;
+fi
+
+if [ $stage -le 11 ]; then
+  # Compute PLDA scores for CN-Celeb eval core trials
+  $train_cmd $nnet_dir/scores/log/cnceleb_eval_scoring.log \
+    ivector-plda-scoring --normalize-length=true \
+    --num-utts=ark:$nnet_dir/xvectors_eval_enroll/num_utts.ark \
+    "ivector-copy-plda --smoothing=0.0 $nnet_dir/xvectors_train_comb/plda - |" \
+    "ark:ivector-mean ark:data/eval_enroll/spk2utt scp:$nnet_dir/xvectors_eval_enroll/xvector.scp ark:- | ivector-subtract-global-mean $nnet_dir/xvectors_train_comb/mean.vec ark:- ark:- | transform-vec $nnet_dir/xvectors_train_comb/transform.mat ark:- ark:- | ivector-normalize-length ark:- ark:- |" \
+    "ark:ivector-subtract-global-mean $nnet_dir/xvectors_train_comb/mean.vec scp:$nnet_dir/xvectors_eval_test/xvector.scp ark:- | transform-vec $nnet_dir/xvectors_train_comb/transform.mat ark:- ark:- | ivector-normalize-length ark:- ark:- |" \
+    "cat '$eval_trials_core' | cut -d\  --fields=1,2 |" $nnet_dir/scores/cnceleb_eval_scores || exit 1;
+
+  # CN-Celeb Eval Core:
+  # EER: 14.70%
+  # minDCF(p-target=0.01): 0.6814
+  # minDCF(p-target=0.001): 0.7979
+  echo -e "\nCN-Celeb Eval Core:";
+  eer=$(paste $eval_trials_core $nnet_dir/scores/cnceleb_eval_scores | awk '{print $6, $3}' | compute-eer - 2>/dev/null)
+  mindcf1=`sid/compute_min_dcf.py --p-target 0.01 $nnet_dir/scores/cnceleb_eval_scores $eval_trials_core 2>/dev/null`
+  mindcf2=`sid/compute_min_dcf.py --p-target 0.001 $nnet_dir/scores/cnceleb_eval_scores $eval_trials_core 2>/dev/null`
+  echo "EER: $eer%"
+  echo "minDCF(p-target=0.01): $mindcf1"
+  echo "minDCF(p-target=0.001): $mindcf2"
+fi

egs/cnceleb/v2/sid

@@ -0,0 +1 @@
+../v1/sid

egs/cnceleb/v2/steps

@@ -0,0 +1 @@
+../v1/steps

egs/cnceleb/v2/utils

@@ -0,0 +1 @@
+../v1/utils