Issue training using the Aspire recipe

Hello,

I'm trying to train a model using the Aspire recipe, using the latest code from the master branch, but am encountering the following error when running `local/chain/run_tdnn_lstm.sh`. When I trained using `local/chain/run_tdnn.sh`, it worked fine.

```

steps/nnet3/chain/get_egs.sh --frames-overlap-per-eg 0 --generate-egs-scp true --cmd "run.pl" --cmvn-opts "--norm-means=false --norm-vars=false" --online-ivector-dir "exp/nnet3/ivectors_train_rvb" --left-context 58 --right-context 28 --left-context-initial 18 --right-context-final 28 --left-tolerance '5' --right-tolerance '5' --frame-subsampling-factor 3 --alignment-subsampling-factor 3 --stage -10 --frames-per-iter 1500000 --frames-per-eg 160,140,110,80 --srand 0 data/train_rvb_hires exp/chain/tdnn_lstm_1a exp/chain/tri5a_train_rvb_lats exp/chain/tdnn_lstm_1a/egs
steps/nnet3/chain/get_egs.sh --frames-overlap-per-eg 0 --generate-egs-scp true --cmd run.pl --cmvn-opts --norm-means=false --norm-vars=false --online-ivector-dir exp/nnet3/ivectors_train_rvb --left-context 58 --right-context 28 --left-context-initial 18 --right-context-final 28 --left-tolerance 5 --right-tolerance 5 --frame-subsampling-factor 3 --alignment-subsampling-factor 3 --stage -10 --frames-per-iter 1500000 --frames-per-eg 160,140,110,80 --srand 0 data/train_rvb_hires exp/chain/tdnn_lstm_1a exp/chain/tri5a_train_rvb_lats exp/chain/tdnn_lstm_1a/egs
steps/nnet3/chain/get_egs.sh: File data/train_rvb_hires/utt2uniq exists, so ensuring the hold-out set includes all perturbed versions of the same source utterance.
steps/nnet3/chain/get_egs.sh: Holding out 300 utterances in validation set and 300 in training diagnostic set, out of total 5614836.
steps/nnet3/chain/get_egs.sh: creating egs. To ensure they are not deleted later you can do: touch exp/chain/tdnn_lstm_1a/egs/.nodelete
steps/nnet3/chain/get_egs.sh: feature type is raw, with 'apply-cmvn'
tree-info exp/chain/tdnn_lstm_1a/tree
feat-to-dim scp:exp/nnet3/ivectors_train_rvb/ivector_online.scp -
steps/nnet3/chain/get_egs.sh: working out number of frames of training data
steps/nnet3/chain/get_egs.sh: working out feature dim
steps/nnet3/chain/get_egs.sh: creating 1374 archives, each with 18749 egs, with
steps/nnet3/chain/get_egs.sh: 160,140,110,80 labels per example, and (left,right) context = (58,28)
steps/nnet3/chain/get_egs.sh: ... and (left-context-initial,right-context-final) = (18,28)
steps/nnet3/chain/get_egs.sh: Getting validation and training subset examples in background.
steps/nnet3/chain/get_egs.sh: Generating training examples on disk
run.pl: job failed, log is in exp/chain/tdnn_lstm_1a/egs/log/create_valid_subset.log
```
When I inspect the aforementioned log file, I see this:
```

# utils/filter_scp.pl exp/chain/tdnn_lstm_1a/egs/valid_uttlist exp/chain/tdnn_lstm_1a/egs/lat_special.scp | lattice-align-phones --replace-output-symb
ols=true exp/chain/tri5a_train_rvb_lats/final.mdl scp:- ark:- | chain-get-supervision --lattice-input=true --frame-subsampling-factor=3 --right-tolera
nce=5 --left-tolerance=5 exp/chain/tdnn_lstm_1a/tree exp/chain/tdnn_lstm_1a/0.trans_mdl ark:- ark:- | nnet3-chain-get-egs --online-ivectors=scp:exp/nn
et3/ivectors_train_rvb/ivector_online.scp --online-ivector-period=10 --srand=0 --left-context=58 --right-context=28 --num-frames=160,140,110,80 --fram
e-subsampling-factor=3 --compress=true --left-context-initial=18 --right-context-final=28 --normalization-fst-scale=1.0 exp/chain/tdnn_lstm_1a/normali
zation.fst "ark,s,cs:utils/filter_scp.pl exp/chain/tdnn_lstm_1a/egs/valid_uttlist data/train_rvb_hires/feats.scp | apply-cmvn --norm-means=false --nor
m-vars=false --utt2spk=ark:data/train_rvb_hires/utt2spk scp:data/train_rvb_hires/cmvn.scp scp:- ark:- |" ark,s,cs:- ark:exp/chain/tdnn_lstm_1a/egs/val
id_all.cegs
# Started at Mon Apr 13 22:01:36 PDT 2020
#
chain-get-supervision --lattice-input=true --frame-subsampling-factor=3 --right-tolerance=5 --left-tolerance=5 exp/chain/tdnn_lstm_1a/tree exp/chain/t
dnn_lstm_1a/0.trans_mdl ark:- ark:-
nnet3-chain-get-egs --online-ivectors=scp:exp/nnet3/ivectors_train_rvb/ivector_online.scp --online-ivector-period=10 --srand=0 --left-context=58 --rig
ht-context=28 --num-frames=160,140,110,80 --frame-subsampling-factor=3 --compress=true --left-context-initial=18 --right-context-final=28 --normalizat
ion-fst-scale=1.0 exp/chain/tdnn_lstm_1a/normalization.fst 'ark,s,cs:utils/filter_scp.pl exp/chain/tdnn_lstm_1a/egs/valid_uttlist data/train_rvb_hires
/feats.scp | apply-cmvn --norm-means=false --norm-vars=false --utt2spk=ark:data/train_rvb_hires/utt2spk scp:data/train_rvb_hires/cmvn.scp scp:- ark:-
|' ark,s,cs:- ark:exp/chain/tdnn_lstm_1a/egs/valid_all.cegs
LOG (nnet3-chain-get-egs[5.5.569~1-6f329]:ComputeDerived():nnet-example-utils.cc:335) Rounding up --num-frames=160,140,110,80 to multiples of --frame-
subsampling-factor=3, to: 162,141,111,81
lattice-align-phones --replace-output-symbols=true exp/chain/tri5a_train_rvb_lats/final.mdl scp:- ark:-
apply-cmvn --norm-means=false --norm-vars=false --utt2spk=ark:data/train_rvb_hires/utt2spk scp:data/train_rvb_hires/cmvn.scp scp:- ark:-
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:ProcessFile():nnet3-chain-get-egs.cc:134) Not producing egs for utterance rev1-fe_03_00123-A-041128-0411
78 because it is too short: 48 frames.
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:ProcessFile():nnet3-chain-get-egs.cc:134) Not producing egs for utterance rev1-fe_03_00325-A-034285-0343
60 because it is too short: 73 frames.
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:ProcessFile():nnet3-chain-get-egs.cc:134) Not producing egs for utterance rev1-fe_03_04633-B-000179-0002
59 because it is too short: 78 frames.
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:ProcessFile():nnet3-chain-get-egs.cc:134) Not producing egs for utterance rev1-fe_03_05509-B-049806-0498
81 because it is too short: 73 frames.
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:ProcessFile():nnet3-chain-get-egs.cc:134) Not producing egs for utterance rev1-fe_03_11038-B-022852-0229
26 because it is too short: 72 frames.
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:ProcessFile():nnet3-chain-get-egs.cc:134) Not producing egs for utterance rev1-fe_03_11661-A-052912-0529
41 because it is too short: 27 frames.
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:ProcessFile():nnet3-chain-get-egs.cc:134) Not producing egs for utterance rev2-fe_03_00123-A-041128-0411
78 because it is too short: 48 frames.
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:ProcessFile():nnet3-chain-get-egs.cc:134) Not producing egs for utterance rev2-fe_03_00325-A-034285-0343
60 because it is too short: 73 frames.
WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:main():nnet3-chain-get-egs.cc:386) No pdf-level posterior for key rev2-fe_03_03392-A-000991-001172
ERROR (nnet3-chain-get-egs[5.5.569~1-6f329]:FindKeyInternal():util/kaldi-table-inl.h:2149) You provided the "s" option (sorted order), but keys are o
ut of order or duplicated: rev2-fe_03_03635-B-013708-013832 is followed by rev2-fe_03_03392-A-000991-001172: rspecifier is ark,s,cs:-

[ Stack-Trace: ]
nnet3-chain-get-egs(kaldi::MessageLogger::LogMessage() const+0xb42) [0x56104d2e1960]
nnet3-chain-get-egs(kaldi::MessageLogger::LogAndThrow::operator=(kaldi::MessageLogger const&)+0x21) [0x56104cf7a447]
nnet3-chain-get-egs(kaldi::RandomAccessTableReaderDSortedArchiveImpl >::FindKeyInternal(std::__cxx
11::basic_string, std::allocator > const&)+0x469) [0x56104cf8d909]
nnet3-chain-get-egs(kaldi::RandomAccessTableReaderDSortedArchiveImpl >::HasKey(std::__cxx11::basic
_string, std::allocator > const&)+0x9) [0x56104cf8db61]
nnet3-chain-get-egs(kaldi::RandomAccessTableReader >::HasKey(std::__cxx11::basic_string, std::allocator > const&)+0x40) [0x56104cf81a62]
nnet3-chain-get-egs(main+0xe24) [0x56104cf76bfe]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xe7) [0x7f9ad2216b97]
nnet3-chain-get-egs(_start+0x2a) [0x56104cf75cfa]

WARNING (nnet3-chain-get-egs[5.5.569~1-6f329]:Close():kaldi-io.cc:515) Pipe utils/filter_scp.pl exp/chain/tdnn_lstm_1a/egs/valid_uttlist data/train_rv
b_hires/feats.scp | apply-cmvn --norm-means=false --norm-vars=false --utt2spk=ark:data/train_rvb_hires/utt2spk scp:data/train_rvb_hires/cmvn.scp scp:-
ark:- | had nonzero return status 36096
LOG (nnet3-chain-get-egs[5.5.569~1-6f329]:~UtteranceSplitter():nnet-example-utils.cc:357) Split 127 utts, with total length 46459 frames (0.129053 hou
rs assuming 100 frames per second)
LOG (nnet3-chain-get-egs[5.5.569~1-6f329]:~UtteranceSplitter():nnet-example-utils.cc:366) Average chunk length was 132.473 frames; overlap between adj
acent chunks was 1.12357% of input length; length of output was 99.5135% of input length (minus overlap = 98.39%).
LOG (nnet3-chain-get-egs[5.5.569~1-6f329]:~UtteranceSplitter():nnet-example-utils.cc:382) Output frames are distributed among chunk-sizes as follows:
81 = 14.89%, 111 = 12.24%, 141 = 11.89%, 162 = 60.97%
kaldi::KaldiFatalError
# Accounting: time=10 threads=1
# Ended (code 255) at Mon Apr 13 22:01:46 PDT 2020, elapsed time 10 seconds
```

So is the recipe updated and currently working with master or should I just use fisher_english?

Thanks

Start by reproducing local/chain/run_tdnn_lstm.sh and compare it with the working local/chain/run_tdnn.sh. Inspect steps/nnet3/chain/get_egs.sh and exp/chain/tdnn_lstm_1a/egs/log/create_valid_subset.log, focusing on nnet3-chain-get-egs and the out-of-order supervision keys. Done means determining whether the Aspire recipe works with master and documenting or resolving the failure.

Written by the indexing model from the issue text.