原文链接 https://hlthu.github.io/2017/02/22/kaldi-numbers-asr.html
注：以下为加速网络访问所做的原文缓存，经过重新格式化，可能存在格式方面的问题，或偶有遗漏信息，请以原文为准。
本文主要参考的是 kaldi-asr.org，主要讲述的是用自己的录音来构建一个数字串识别系统。
本文将主要分为以下几个部分：
录制语音
这里是英文数字串识别，因此需要一些用英语朗读数字的语音。我录制了 128 个语音文件，分别是两个人朗读，其中每个文件只包含三个数字。这 128 文件中 80 个用于训练， 48 个用于测试。并且训练数据和测试数据都被分成了 8 部分（可以假装成 8 个人），每部分分别 10 个 和 6 个。读者可以到我的 GitHub 上下载这些语音数据。训练集和测试集的前五个目录是我(男)朗读，后面三个是女士朗读的。
我的录音是 16 kHz 采样，16 位量化的。如果是 8 kHz 采样，需要在提特征时指明采样频率。即将 steps/make_mfcc.sh 里面 compute-mfcc-feats 命令加上 --sample-frequency=8000 的选项。
目录结构如下：
├── test
│ ├── 1
│ ├── 2
│ ├── 3
│ ├── 4
│ ├── 5
│ ├── 6
│ ├── 7
│ └── 8
└── train
├── 1
├── 2
├── 3
├── 4
├── 5
├── 6
├── 7
└── 8
当然您也可以选择自己录音，也欢迎你把你的录音共享。
从数据准备到编写脚本一直都是在为最后做准备，如果都完成了的话，你的目录应该长这样：
.
├── cmd.sh
├── conf
│ ├── decode.config
│ └── mfcc.conf
├── data
│ ├── local
│ │ ├── corpus.txt
│ │ └── dict
│ │ ├── lexicon.txt
│ │ ├── nonsilence_phones.txt
│ │ ├── optional_silence.txt
│ │ └── silence_phones.txt
│ ├── test
│ │ ├── spk2gender
│ │ ├── text
│ │ ├── utt2spk
│ │ └── wav.scp
│ └── train
│ ├── spk2gender
│ ├── text
│ ├── utt2spk
│ └── wav.scp
├── local
│ ├── make_mfcc.sh
│ └── score.sh
├── path.sh
├── run.sh
├── steps -> ../../timit/s5/steps
└── utils -> ../../timit/s5/utils
数据准备
数据的准备包括声学数据和语言数据。首先建立一个目录 data/。
声学数据
声学数据主要包括训练集和测试集的数据，以及一个语料库，在 data/ 目录下新建两个目录：train/ 、test/ 和 local。
声学训练数据准备
这些数据主要是四个文件：spk2gender、wav.scp、text 和 utt2spk，都保存在 data/train/ 目录下。
spk2gender
这个文件主要描述说话人编号和性别的对应关系，具有这样的形式：<speakerID> <gender>，比如我这里训练集是“8”个人，前 5 个男(Male)，后 3 个女(Female)。比如
1 m
2 m
3 m
4 m
5 m
6 f
7 f
8 f
wav.scp
这个文件主要是保存了语音编号和语音文件的对应关系，具有这样的形式：<uterranceID> <full_path_to_audio_file>。比如
1_040 /mnt/b/huanglu/speech/numbers-en/train/1/1-040.wav
1_089 /mnt/b/huanglu/speech/numbers-en/train/1/1-089.wav
1_104 /mnt/b/huanglu/speech/numbers-en/train/1/1-104.wav
1_231 /mnt/b/huanglu/speech/numbers-en/train/1/1-231.wav
1_327 /mnt/b/huanglu/speech/numbers-en/train/1/1-327.wav
1_413 /mnt/b/huanglu/speech/numbers-en/train/1/1-413.wav
1_462 /mnt/b/huanglu/speech/numbers-en/train/1/1-462.wav
1_468 /mnt/b/huanglu/speech/numbers-en/train/1/1-468.wav
1_470 /mnt/b/huanglu/speech/numbers-en/train/1/1-470.wav
1_560 /mnt/b/huanglu/speech/numbers-en/train/1/1-560.wav
··············
text
这个文件主要是标注，描述了语音编号和标注之间的对应关系，具有这样的形式：<utterranceID> <text_transcription>。比如
1_040 zero four zero
1_089 zero eight nine
1_104 one zero four
1_231 two three one
1_327 three two seven
1_413 four one three
1_462 four six two
1_468 four six eight
1_470 four seven zero
1_560 five six zero
utt2spk
这个文件是联系说话人编号和语音编号，具有这样的形式：<uterranceID> <speakerID>。比如
1_040 1
1_089 1
1_104 1
1_231 1
1_327 1
1_413 1
1_462 1
1_468 1
1_470 1
1_560 1
这里我们建议把说话人的编号放在语音编号的前缀。至此，训练集的声学数据准备好了。
声学测试数据准备
和训练集数据一样，这些数据也主要是四个文件：spk2gender、wav.scp、text 和 utt2spk，都保存在 data/test/ 目录下。这些目录的形式和之前一样，只是 wav.scp 需要映射到测试集的数据，重新修改语音编号。
语料库
我们还需要一个语料库，其包含了所有训练集数据的标注，请命名为 corpus.txt，并保存在 data/local/ 目录下。比如
zero four zero
zero eight nine
one zero four
two three one
three two seven
four one three
four six two
four six eight
four seven zero
five six zero
语言数据
语言数据主要包括 lexicon.txt、optional_silence.txt、nonsilence_phones.txt、silence_phones.txt，并在 data/local/ 目录下新建文件夹 dict，将这四个文件保存到那里。
lexicon.txt
这个文件应该包括你标注里所有出现的词的发音，即音素表达，由于这里只有十个单词，再加上静音，因此不管是词还是音素，数量都比较少。
!SIL sil
<UNK> spn
eight ey t
five f ay v
four f ao r
nine n ay n
one hh w ah n
one w ah n
seven s eh v ah n
six s ih k s
three th r iy
two t uw
zero z ih r ow
zero z iy r ow
nonsilence_phones.txt
这个文件列出了上面出现的所有的非静音音素。
ah
ao
ay
eh
ey
f
hh
ih
iy
k
n
ow
r
s
t
th
uw
w
v
z
silence_phones.txt
这里面包含了静音音素。
sil
spn
optional_silence.txt
只有可选的静音音素。
sil
环境准备
回到项目根目录。首先我们需要定义文件 cmd.sh 和 path.sh，其中前者主要包括运行的形式，而后者主要包括 kaldi 依赖的路径。
cmd.sh：
export train_cmd="run.pl"
export decode_cmd="run.pl"
export mkgraph_cmd="run.pl"
path.sh：
export train_cmd="run.pl"
export decode_cmd="run.pl"
export cuda_cmd="run.pl"
export mkgraph_cmd="run.pl"
Huang-Lus-MacBook-Air:en huanglu$ cat path.sh
export KALDI_ROOT=`pwd`/../../..
[ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/tools/irstlm/bin/:$PWD:$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
Huang-Lus-MacBoo
此外我们还需要一些 kaldi 已经写好的脚本，比如 steps/decode.sh 和 utils/mkgraph.sh，简单起见，我们直接这两个文件夹链接过来。
$ ln -s ../../timit/s5/steps steps
$ ln -s ../../timit/s5/utils utils
然后就是为了打分以及处理提特征时采样率非 16 kHz 时，需要在根目录下建立 local 文件夹，然后从别的那里拷贝来 make_mfcc.sh 和 score.sh。
最后就是建立目录 conf，存放一些配置文件。包括：
decode.config：
first_beam=10.0
beam=13.0
lattice_beam=6.0
mfcc.conf：
--use-energy=false
编写运行脚本
在根目录下建立一个 run.sh 脚本，输入以下内容（有注释，我不解释了）：
#!/bin/bash
. ./path.sh || exit 1
. ./cmd.sh || exit 1
nj=4
lm_order=1
. utils/parse_options.sh || exit 1
[[ $# -ge 1 ]] && { echo "Wrong arguments!"; exit 1; }
# Removing previously created data (from last run.sh execution)
rm -rf exp mfcc data/train/spk2utt data/train/cmvn.scp data/train/feats.scp \
data/train/split1 data/test/spk2utt data/test/cmvn.scp data/test/feats.scp \
data/test/split1 data/local/lang data/lang data/local/tmp \
data/local/dict/lexiconp.txt
echo
echo "===== PREPARING ACOUSTIC DATA ====="
echo
# Making spk2utt files
utils/utt2spk_to_spk2utt.pl data/train/utt2spk > data/train/spk2utt
utils/utt2spk_to_spk2utt.pl data/test/utt2spk > data/test/spk2utt
echo
echo "===== FEATURES EXTRACTION ====="
echo
# Making feats.scp files
mfccdir=mfcc
# Uncomment and modify arguments in scripts below if you have any problems with data sorting
# utils/validate_data_dir.sh data/train # script for checking prepared data - here: for data/train directory
# utils/validate_data_dir.sh data/test
# utils/fix_data_dir.sh data/train # tool for data proper sorting if needed - here: for data/train directory
# utils/fix_data_dir.sh data/test
steps/make_mfcc.sh --nj $nj --cmd "$train_cmd" data/train \
exp/make_mfcc/train $mfccdir
steps/make_mfcc.sh --nj $nj --cmd "$train_cmd" data/test \
exp/make_mfcc/test $mfccdir
# Making cmvn.scp files
steps/compute_cmvn_stats.sh data/train exp/make_mfcc/train $mfccdir
steps/compute_cmvn_stats.sh data/test exp/make_mfcc/test $mfccdir
echo
echo "===== PREPARING LANGUAGE DATA ====="
echo
# Preparing language data
utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang data/lang
echo
echo "===== LANGUAGE MODEL CREATION ====="
echo "===== MAKING lm.arpa ====="
echo
loc=`which ngram-count`;
if [ -z $loc ]; then
if uname -a | grep 64 >/dev/null; then
sdir=$KALDI_ROOT/tools/srilm/bin/i686-m64
else
sdir=$KALDI_ROOT/tools/srilm/bin/i686
fi
if [ -f $sdir/ngram-count ]; then
echo "Using SRILM language modelling tool from $sdir"
export PATH=$PATH:$sdir
else
echo "SRILM toolkit is probably not installed. Instructions: tools/install_srilm.sh"
exit 1
fi
fi
local=data/local
mkdir $local/tmp
ngram-count -order $lm_order -write-vocab $local/tmp/vocab-full.txt \
-wbdiscount -text $local/corpus.txt -lm $local/tmp/lm.arpa
echo
echo "===== MAKING G.fst ====="
echo
lang=data/lang
arpa2fst --disambig-symbol=#0 --read-symbol-table=$lang/words.txt \
$local/tmp/lm.arpa $lang/G.fst
echo
echo "===== MONO TRAINING ====="
echo
steps/train_mono.sh --nj $nj --cmd "$train_cmd" data/train data/lang exp/mono || exit 1
echo
echo "===== MONO DECODING ====="
echo
utils/mkgraph.sh --mono data/lang exp/mono exp/mono/graph || exit 1
steps/decode.sh --config conf/decode.config --nj $nj --cmd "$decode_cmd" \
exp/mono/graph data/test exp/mono/decode
local/score.sh data/test data/lang exp/mono/decode/
echo
echo "===== MONO ALIGNMENT ====="
echo
steps/align_si.sh --nj $nj --cmd "$train_cmd" data/train data/lang exp/mono exp/mono_ali || exit 1
echo
echo "===== TRI1 (first triphone pass) TRAINING ====="
echo
steps/train_deltas.sh --cmd "$train_cmd" 2000 11000 data/train data/lang exp/mono_ali exp/tri1 || exit 1
echo
echo "===== TRI1 (first triphone pass) DECODING ====="
echo
utils/mkgraph.sh data/lang exp/tri1 exp/tri1/graph || exit 1
steps/decode.sh --config conf/decode.config --nj $nj --cmd "$decode_cmd" \
exp/tri1/graph data/test exp/tri1/decode
local/score.sh data/test data/lang exp/tri1/decode/
echo
echo "===== run.sh script is finished ====="
echo
运行脚本
最后在运行 run.sh 之前先进行下面的命令：
$ chmod u+x local/*.sh *.sh
运行结果（省去了部分重复的）：
$ ./run.sh
===== PREPARING ACOUSTIC DATA =====
===== FEATURES EXTRACTION =====
steps/make_mfcc.sh --nj 4 --cmd run.pl data/train exp/make_mfcc/train mfcc
utils/validate_data_dir.sh: Successfully validated data-directory data/train
steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.
Succeeded creating MFCC features for train
steps/make_mfcc.sh --nj 4 --cmd run.pl data/test exp/make_mfcc/test mfcc
utils/validate_data_dir.sh: Successfully validated data-directory data/test
steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.
Succeeded creating MFCC features for test
steps/compute_cmvn_stats.sh data/train exp/make_mfcc/train mfcc
Succeeded creating CMVN stats for train
steps/compute_cmvn_stats.sh data/test exp/make_mfcc/test mfcc
Succeeded creating CMVN stats for test
===== PREPARING LANGUAGE DATA =====
utils/prepare_lang.sh data/local/dict <UNK> data/local/lang data/lang
Checking data/local/dict/silence_phones.txt ...
--> reading data/local/dict/silence_phones.txt
--> data/local/dict/silence_phones.txt is OK
Checking data/local/dict/optional_silence.txt ...
--> reading data/local/dict/optional_silence.txt
--> data/local/dict/optional_silence.txt is OK
Checking data/local/dict/nonsilence_phones.txt ...
--> reading data/local/dict/nonsilence_phones.txt
--> data/local/dict/nonsilence_phones.txt is OK
Checking disjoint: silence_phones.txt, nonsilence_phones.txt
--> disjoint property is OK.
Checking data/local/dict/lexicon.txt
--> reading data/local/dict/lexicon.txt
--> data/local/dict/lexicon.txt is OK
Checking data/local/dict/extra_questions.txt ...
--> data/local/dict/extra_questions.txt is empty (this is OK)
--> SUCCESS [validating dictionary directory data/local/dict]
**Creating data/local/dict/lexiconp.txt from data/local/dict/lexicon.txt
fstaddselfloops data/lang/phones/wdisambig_phones.int data/lang/phones/wdisambig_words.int
prepare_lang.sh: validating output directory
utils/validate_lang.pl data/lang
Checking data/lang/phones.txt ...
--> data/lang/phones.txt is OK
Checking words.txt: #0 ...
--> data/lang/words.txt is OK
Checking disjoint: silence.txt, nonsilence.txt, disambig.txt ...
--> silence.txt and nonsilence.txt are disjoint
--> silence.txt and disambig.txt are disjoint
--> disambig.txt and nonsilence.txt are disjoint
--> disjoint property is OK
Checking sumation: silence.txt, nonsilence.txt, disambig.txt ...
--> summation property is OK
Checking data/lang/phones/context_indep.{txt, int, csl} ...
--> 10 entry/entries in data/lang/phones/context_indep.txt
--> data/lang/phones/context_indep.int corresponds to data/lang/phones/context_indep.txt
--> data/lang/phones/context_indep.csl corresponds to data/lang/phones/context_indep.txt
--> data/lang/phones/context_indep.{txt, int, csl} are OK
Checking data/lang/phones/nonsilence.{txt, int, csl} ...
--> 80 entry/entries in data/lang/phones/nonsilence.txt
--> data/lang/phones/nonsilence.int corresponds to data/lang/phones/nonsilence.txt
--> data/lang/phones/nonsilence.csl corresponds to data/lang/phones/nonsilence.txt
--> data/lang/phones/nonsilence.{txt, int, csl} are OK
Checking data/lang/phones/silence.{txt, int, csl} ...
--> 10 entry/entries in data/lang/phones/silence.txt
--> data/lang/phones/silence.int corresponds to data/lang/phones/silence.txt
--> data/lang/phones/silence.csl corresponds to data/lang/phones/silence.txt
--> data/lang/phones/silence.{txt, int, csl} are OK
Checking data/lang/phones/optional_silence.{txt, int, csl} ...
--> 1 entry/entries in data/lang/phones/optional_silence.txt
--> data/lang/phones/optional_silence.int corresponds to data/lang/phones/optional_silence.txt
--> data/lang/phones/optional_silence.csl corresponds to data/lang/phones/optional_silence.txt
--> data/lang/phones/optional_silence.{txt, int, csl} are OK
Checking data/lang/phones/disambig.{txt, int, csl} ...
--> 2 entry/entries in data/lang/phones/disambig.txt
--> data/lang/phones/disambig.int corresponds to data/lang/phones/disambig.txt
--> data/lang/phones/disambig.csl corresponds to data/lang/phones/disambig.txt
--> data/lang/phones/disambig.{txt, int, csl} are OK
Checking data/lang/phones/roots.{txt, int} ...
--> 22 entry/entries in data/lang/phones/roots.txt
--> data/lang/phones/roots.int corresponds to data/lang/phones/roots.txt
--> data/lang/phones/roots.{txt, int} are OK
Checking data/lang/phones/sets.{txt, int} ...
--> 22 entry/entries in data/lang/phones/sets.txt
--> data/lang/phones/sets.int corresponds to data/lang/phones/sets.txt
--> data/lang/phones/sets.{txt, int} are OK
Checking data/lang/phones/extra_questions.{txt, int} ...
--> 9 entry/entries in data/lang/phones/extra_questions.txt
--> data/lang/phones/extra_questions.int corresponds to data/lang/phones/extra_questions.txt
--> data/lang/phones/extra_questions.{txt, int} are OK
Checking data/lang/phones/word_boundary.{txt, int} ...
--> 90 entry/entries in data/lang/phones/word_boundary.txt
--> data/lang/phones/word_boundary.int corresponds to data/lang/phones/word_boundary.txt
--> data/lang/phones/word_boundary.{txt, int} are OK
Checking optional_silence.txt ...
--> reading data/lang/phones/optional_silence.txt
--> data/lang/phones/optional_silence.txt is OK
Checking disambiguation symbols: #0 and #1
--> data/lang/phones/disambig.txt has "#0" and "#1"
--> data/lang/phones/disambig.txt is OK
Checking topo ...
Checking word_boundary.txt: silence.txt, nonsilence.txt, disambig.txt ...
--> data/lang/phones/word_boundary.txt doesn't include disambiguation symbols
--> data/lang/phones/word_boundary.txt is the union of nonsilence.txt and silence.txt
--> data/lang/phones/word_boundary.txt is OK
Checking word-level disambiguation symbols...
--> data/lang/phones/wdisambig.txt exists (newer prepare_lang.sh)
Checking word_boundary.int and disambig.int
--> generating a 67 word sequence
--> resulting phone sequence from L.fst corresponds to the word sequence
--> L.fst is OK
--> generating a 16 word sequence
--> resulting phone sequence from L_disambig.fst corresponds to the word sequence
--> L_disambig.fst is OK
Checking data/lang/oov.{txt, int} ...
--> 1 entry/entries in data/lang/oov.txt
--> data/lang/oov.int corresponds to data/lang/oov.txt
--> data/lang/oov.{txt, int} are OK
--> data/lang/L.fst is olabel sorted
--> data/lang/L_disambig.fst is olabel sorted
--> SUCCESS [validating lang directory data/lang]
===== LANGUAGE MODEL CREATION =====
===== MAKING lm.arpa =====
===== MAKING G.fst =====
arpa2fst --disambig-symbol=#0 --read-symbol-table=data/lang/words.txt data/local/tmp/lm.arpa data/lang/G.fst
LOG (arpa2fst:Read():arpa-file-parser.cc:96) Reading \data\ section.
LOG (arpa2fst:Read():arpa-file-parser.cc:151) Reading \1-grams: section.
LOG (arpa2fst:RemoveRedundantStates():arpa-lm-compiler.cc:355) Reduced num-states from 1 to 1
===== MONO TRAINING =====
steps/train_mono.sh --nj 4 --cmd run.pl data/train data/lang exp/mono
steps/train_mono.sh: Initializing monophone system.
steps/train_mono.sh: Compiling training graphs
steps/train_mono.sh: Aligning data equally (pass 0)
steps/train_mono.sh: Pass 1
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 2
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 3
···············
steps/train_mono.sh: Pass 38
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 39
steps/diagnostic/analyze_alignments.sh --cmd run.pl data/lang exp/mono
steps/diagnostic/analyze_alignments.sh: see stats in exp/mono/log/analyze_alignments.log
311 warnings in exp/mono/log/update.*.log
84 warnings in exp/mono/log/align.*.*.log
exp/mono: nj=4 align prob=-79.54 over 0.06h [retry=0.0%, fail=0.0%] states=70 gauss=1007
steps/train_mono.sh: Done training monophone system in exp/mono
===== MONO DECODING =====
WARNING: the --mono, --left-biphone and --quinphone options are now deprecated and ignored.
tree-info exp/mono/tree
tree-info exp/mono/tree
fstdeterminizestar --use-log=true
fsttablecompose data/lang/L_disambig.fst data/lang/G.fst
fstminimizeencoded
fstpushspecial
fstisstochastic data/lang/tmp/LG.fst
-0.0421835 -0.0423222
[info]: LG not stochastic.
fstcomposecontext --context-size=1 --central-position=0 --read-disambig-syms=data/lang/phones/disambig.int --write-disambig-syms=data/lang/tmp/disambig_ilabels_1_0.int data/lang/tmp/ilabels_1_0
fstisstochastic data/lang/tmp/CLG_1_0.fst
-0.0421835 -0.0423222
[info]: CLG not stochastic.
make-h-transducer --disambig-syms-out=exp/mono/graph/disambig_tid.int --transition-scale=1.0 data/lang/tmp/ilabels_1_0 exp/mono/tree exp/mono/final.mdl
fsttablecompose exp/mono/graph/Ha.fst data/lang/tmp/CLG_1_0.fst
fstminimizeencoded
fstrmepslocal
fstdeterminizestar --use-log=true
fstrmsymbols exp/mono/graph/disambig_tid.int
fstisstochastic exp/mono/graph/HCLGa.fst
0.000319138 -0.0427566
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/mono/final.mdl
steps/decode.sh --config conf/decode.config --nj 4 --cmd run.pl exp/mono/graph data/test exp/mono/decode
decode.sh: feature type is delta
steps/diagnostic/analyze_lats.sh --cmd run.pl exp/mono/graph exp/mono/decode
steps/diagnostic/analyze_lats.sh: see stats in exp/mono/decode/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(1,1,2) and mean=1.2
steps/diagnostic/analyze_lats.sh: see stats in exp/mono/decode/log/analyze_lattice_depth_stats.log
exp/mono/decode/wer_10
%WER 1.39 [ 2 / 144, 1 ins, 0 del, 1 sub ]
%SER 4.17 [ 2 / 48 ]
exp/mono/decode/wer_11
%WER 1.39 [ 2 / 144, 1 ins, 0 del, 1 sub ]
%SER 4.17 [ 2 / 48 ]
exp/mono/decode/wer_12
····················
%SER 4.17 [ 2 / 48 ]
exp/mono/decode//wer_8
%WER 1.39 [ 2 / 144, 1 ins, 0 del, 1 sub ]
%SER 4.17 [ 2 / 48 ]
exp/mono/decode//wer_9
%WER 1.39 [ 2 / 144, 1 ins, 0 del, 1 sub ]
%SER 4.17 [ 2 / 48 ]
===== MONO ALIGNMENT =====
steps/align_si.sh --nj 4 --cmd run.pl data/train data/lang exp/mono exp/mono_ali
steps/align_si.sh: feature type is delta
steps/align_si.sh: aligning data in data/train using model from exp/mono, putting alignments in exp/mono_ali
steps/diagnostic/analyze_alignments.sh --cmd run.pl data/lang exp/mono_ali
steps/diagnostic/analyze_alignments.sh: see stats in exp/mono_ali/log/analyze_alignments.log
steps/align_si.sh: done aligning data.
===== TRI1 (first triphone pass) TRAINING =====
steps/train_deltas.sh --cmd run.pl 2000 11000 data/train data/lang exp/mono_ali exp/tri1
steps/train_deltas.sh: accumulating tree stats
steps/train_deltas.sh: getting questions for tree-building, via clustering
steps/train_deltas.sh: building the tree
WARNING (gmm-init-model:InitAmGmm():gmm-init-model.cc:55) Tree has pdf-id 1 with no stats; corresponding phone list: 6 7 8 9 10
** The warnings above about 'no stats' generally mean you have phones **
** (or groups of phones) in your phone set that had no corresponding data. **
** You should probably figure out whether something went wrong, **
** or whether your data just doesn't happen to have examples of those **
** phones. **
steps/train_deltas.sh: converting alignments from exp/mono_ali to use current tree
steps/train_deltas.sh: compiling graphs of transcripts
steps/train_deltas.sh: training pass 1
steps/train_deltas.sh: training pass 2
··················
steps/train_deltas.sh: aligning data
steps/train_deltas.sh: training pass 31
steps/train_deltas.sh: training pass 32
steps/train_deltas.sh: training pass 33
steps/train_deltas.sh: training pass 34
steps/diagnostic/analyze_alignments.sh --cmd run.pl data/lang exp/tri1
steps/diagnostic/analyze_alignments.sh: see stats in exp/tri1/log/analyze_alignments.log
382 warnings in exp/tri1/log/update.*.log
1 warnings in exp/tri1/log/questions.log
1 warnings in exp/tri1/log/build_tree.log
12 warnings in exp/tri1/log/align.*.*.log
28 warnings in exp/tri1/log/init_model.log
1 warnings in exp/tri1/log/mixup.log
exp/tri1: nj=4 align prob=-79.41 over 0.06h [retry=0.0%, fail=0.0%] states=88 gauss=1003 tree-impr=5.48
steps/train_deltas.sh: Done training system with delta+delta-delta features in exp/tri1
===== TRI1 (first triphone pass) DECODING =====
tree-info exp/tri1/tree
tree-info exp/tri1/tree
fstcomposecontext --context-size=3 --central-position=1 --read-disambig-syms=data/lang/phones/disambig.int --write-disambig-syms=data/lang/tmp/disambig_ilabels_3_1.int data/lang/tmp/ilabels_3_1
fstisstochastic data/lang/tmp/CLG_3_1.fst
0 -0.0423222
[info]: CLG not stochastic.
make-h-transducer --disambig-syms-out=exp/tri1/graph/disambig_tid.int --transition-scale=1.0 data/lang/tmp/ilabels_3_1 exp/tri1/tree exp/tri1/final.mdl
fsttablecompose exp/tri1/graph/Ha.fst data/lang/tmp/CLG_3_1.fst
fstdeterminizestar --use-log=true
fstminimizeencoded
fstrmsymbols exp/tri1/graph/disambig_tid.int
fstrmepslocal
fstisstochastic exp/tri1/graph/HCLGa.fst
0.000384301 -0.0999308
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/tri1/final.mdl
steps/decode.sh --config conf/decode.config --nj 4 --cmd run.pl exp/tri1/graph data/test exp/tri1/decode
decode.sh: feature type is delta
steps/diagnostic/analyze_lats.sh --cmd run.pl exp/tri1/graph exp/tri1/decode
steps/diagnostic/analyze_lats.sh: see stats in exp/tri1/decode/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(1,1,1) and mean=1.1
steps/diagnostic/analyze_lats.sh: see stats in exp/tri1/decode/log/analyze_lattice_depth_stats.log
exp/tri1/decode/wer_10
···················
===== run.sh script is finished =====