------------------------------------------------------------------------------------------------------------------------------------------------------

孤立词参考的例子就是yes/no脚本。

------------------------------------------------------------------------------------------------------------------------------------------------------

这里我们做10个词识别实验，熟悉整条链路。

后续尝试一些新的模型提高识别率；

再尝试模型语速、语调、平稳噪声的鲁棒性，尝试已有去噪处理算法的优化前端；

扩大孤立词的数量，裁剪模型，效率优化，熟悉FST解码器，将嵌入式硬件的孤立词识别能做到实用层面。

最后做连续语音识别、熟悉语言模型。

------------------------------------------------------------------------------------------------------------------------------------------------------

一、录制的语料说明

在egs/创建自己的项目coffee，再创建s5。

拷贝coffee语音库以及yes/no以下文件夹和文件。

.
├── conf
├── data
├── exp
├── input
├── local
├── mfcc
├── Nestle
├── path.sh
├── run.sh
├── steps -> ../../wsj/s5/steps
└── utils -> ../../wsj/s5/utils

Nestle语音库目录结构如下：

.
├── TestData
│   ├── 001_001.wav
│   ├── 001_002.wav
│   ├── 002_001.wav
│   ├── 002_002.wav
│   ├── 003_001.wav
│   ├── 003_002.wav
│   ├── 004_001.wav
│   ├── 004_002.wav
│   ├── 005_001.wav
│   ├── 005_002.wav
│   ├── 006_001.wav
│   ├── 006_002.wav
│   ├── 007_001.wav
│   ├── 007_02.wav
│   ├── 008_001.wav
│   ├── 008_002.wav
│   ├── 009_001.wav
│   ├── 009_002.wav
│   ├── 010_001.wav
│   └── 010_002.wav
└── TrainData
├── FastWord
│   ├── Model2_1
│   ├── Model2_10
│   ├── Model2_2
│   ├── Model2_3
│   ├── Model2_4
│   ├── Model2_5
│   ├── Model2_6
│   ├── Model2_7
│   ├── Model2_8
│   └── Model2_9
├── MiddWord
│   ├── Model1_1
│   ├── Model1_10
│   ├── Model1_2
│   ├── Model1_3
│   ├── Model1_4
│   ├── Model1_5
│   ├── Model1_6
│   ├── Model1_7
│   ├── Model1_8
│   └── Model1_9
└── SlowWord
├── Model3_1
├── Model3_10
├── Model3_2
├── Model3_3
├── Model3_4
├── Model3_5
├── Model3_6
├── Model3_7
├── Model3_8
└── Model3_9

首先，录制的语料是关于咖啡的品种，分为快速、中速、慢速

1)文件夹命名规则:  Modelx_x

Model1表示中速

Model2表示快速

Model3表示慢速

10个词，后缀表示表示发音id号

2)文件说明

CF_speaker-id_speed_utter-id.wav

CF  coffee，语库名缩写

二、脚本修改

由于现有的语音库，词汇表、目录结构、语音文件命名、词典、语言模型，还有对perl不是很熟悉等，所以很多脚本都得重新书写，顺便下python正则表达式。

run.sh

内容如下：

#!/bin/bash

train_cmd="utils/run.pl"

decode_cmd="utils/run.pl"

#have data, not need to download

#if [ ! -d waves_yesno ]; then

#  wget http://www.openslr.org/resources/1/waves_yesno.tar.gz || exit 1;

  # was:

  # wget http://sourceforge.net/projects/kaldi/files/waves_yesno.tar.gz || exit 1;

#  tar -xvzf waves_yesno.tar.gz || exit ;

#fi

train_yesno=waves_train

test_base_name=waves_test

#clear  data exp  mfcc filefolder

rm -rf data exp mfcc

# Data preparation

# we need to rewrite scripts below

local/prepare_data.sh Nestle   #structure of dir and file name is  different

local/prepare_dict.sh               #dict contains  words, not .

utils/prepare_lang.sh --position-dependent-phones false data/local/dict "<SIL>" data/local/lang data/lang

local/prepare_lm.sh

echo "Data Prepraration finish!"

# Feature extraction

for x in  waves_test waves_train; do

     steps/make_mfcc.sh --nj  data/$x exp/make_mfcc/$x mfcc

     steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x mfcc

     utils/fix_data_dir.sh data/$x

done

echo "Feature extraction finish!"

# Mono training

steps/train_mono.sh --nj  --cmd "$train_cmd" \

  --totgauss  \

  data/waves_train data/lang exp/mono0

echo "Mono training finish!"

# Graph compilation

utils/mkgraph.sh data/lang_test_tg exp/mono0 exp/mono0/graph_tgpr

echo "Graph compilation finish!"

# Decoding

steps/decode.sh --nj  --cmd "$decode_cmd" \

    exp/mono0/graph_tgpr data/waves_test exp/mono0/decode_waves_test

for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done
prepare_data.sh

修改如下：

#!/bin/bash



mkdir -p data/local
local=`pwd`/local
scripts=`pwd`/scripts




export PATH=$PATH:`pwd`/../../../tools/irstlm/bin




echo "Preparing train and test data"




train_base_name=waves_train
test_base_name=waves_test
root_dir=$1
train_dir=$root_dir/TrainData/MiddWord
test_dir=$root_dir/TestData




echo fix up uncorrect file name
#fix file name
for subdir in $(ls $train_dir)
    do
    moddir=$train_dir/$subdir
    if [ -d $moddir ];then
    	for wav_file in $(ls $moddir)
            do
	    #filename=$moddir/$wav_file   
	    python ./local/FixUpName.py $moddir $wav_file 
        done
    fi
done
echo "fix up files finish!"




#遍历各个词的文件夹
#文件列表
#注意空格
#utter_id 文件路径
for subdir in $(ls $train_dir)
    do
    moddir=$train_dir/$subdir    
    if [ -d $moddir ];then   
       echo "$moddir make file list and labels"    
       #ls -l $moddir >  ${moddir}.list     
       #ls -l  $moddir | grep -P 'CF.+\.wav' -o > ${moddir}.list
       ls -l $moddir | grep -P 'CF(_[0-9]{1,3}){3}\.wav' -o  >${moddir}.list
       if [ -f ${moddir}_waves_train.scp ];then
          rm ${moddir}_waves_train.scp
       fi
       python ./local/create_coffee_labels.py   ${moddir}.list  ${moddir}  ${moddir}_waves_train.scp
    fi
done




cat $train_dir/*.list > data/local/waves_train
#sort -u data/local/waves_train -o data/local/waves_train
cat $train_dir/*.scp > data/local/waves_train_wav.scp
#sort -u data/local/waves_train_wav.scp -o data/local/waves_train_wav.scp




ls -l $test_dir | grep -P '[0-9]{1,3}_[0-9]{1,3}\.wav' -o >data/local/waves_test
python ./local/create_coffee_test_labels.py  data/local/waves_test  $test_dir  data/local/waves_test_wav.scp




#sort -u data/local/waves_test -o data/local/waves_test
#sort -u data/local/waves_test_wav.scp -o data/local/waves_test_wav.scp
echo "$moddir make file list and labels finish!"




echo "$moddir make file utter"
cd data/local
#测试数据的命名与训练数据的命名不一致
python ../../local/create_coffee_test_txt.py waves_test   ../../input/trans.txt   ${test_base_name}.txt
python ../../local/create_coffee_txt.py waves_train  ../../input/trans.txt   ${train_base_name}.txt   
echo "make make file utter finish"




#sort -u ${test_base_name}.txt -o ${test_base_name}.txt
#sort -u ${train_base_name}.txt -o ${train_base_name}.txt




cp ../../input/task.arpabo lm_tg.arpa




cd ../..




#暂时不区分说话人
# This stage was copied from WSJ example
for x in waves_train waves_test; do 
  mkdir -p data/$x
  cp data/local/${x}_wav.scp data/$x/wav.scp
  echo  "data/$x/wav.scp sort!"
  sort -u data/$x/wav.scp -o data/$x/wav.scp
  cp data/local/$x.txt data/$x/text
  echo  "data/$x/text sort!"
  sort -u data/$x/text -o data/$x/text
  cat data/$x/text | awk '{printf("%s global\n", $1);}' >data/$x/utt2spk  
  echo  "data/$x/utt2spk sort!"
  sort -u data/$x/utt2spk -o data/$x/utt2spk
  #create_coffee_speaker.py
  utils/utt2spk_to_spk2utt.pl <data/$x/utt2spk >data/$x/spk2utt
done


 






FixUpName.py

#coding=utf-8

import re

import sys

import os

def addzeros(matched):

    digits = matched.group('digits')

    n = len(digits)

    zeros = ''*(3-n)

    return (zeros+digits)

moddir=sys.argv[1]

wav_file=sys.argv[2]

filename=os.path.join(moddir,wav_file)

out_file = re.sub('(?P<digits>\d+)',addzeros,wav_file)

#print wav_file

#print out_file

outname=os.path.join(moddir,out_file)

if os.path.exists(filename):

   os.rename(filename, outname);






create_coffee_labels.py

#coding=utf-8

import re

import codecs

import sys

import os

list_file = sys.argv[1]#os.path.join(os.getcwd(),sys.argv[1])

wav_dir = sys.argv[2]

label_file = sys.argv[3]#os.path.join(os.getcwd(),sys.argv[3])

#print list_file, label_file

file_object = codecs.open(list_file, 'r', 'utf-8')

try:

    all_the_text = file_object.readlines()

finally:

    file_object.close()

pattern = r"CF_(.+)\.wav"

try:

    utf_file = codecs.open(label_file, 'w', 'utf-8')

    for each_text in all_the_text:

            print each_text

        ret = re.findall(pattern, each_text)

        #print ret[0]

        if len(ret) > 0:

        content = ret[0] + "\t" + wav_dir  +"/"+each_text

        #print content

        utf_file.write(content)

finally:

    utf_file.close()






create_coffee_test_labels.py

#coding=utf-8

import re

import codecs

import sys

import os

list_file = sys.argv[1]#os.path.join(os.getcwd(),sys.argv[1])

wav_dir = sys.argv[2]

label_file = sys.argv[3]#os.path.join(os.getcwd(),sys.argv[3])

#print list_file, label_file

file_object = codecs.open(list_file, 'r', 'utf-8')

try:

    all_the_text = file_object.readlines()

finally:

    file_object.close()

pattern = r"(.+)\.wav"

try:

    utf_file = codecs.open(label_file, 'w', 'utf-8')

    for each_text in all_the_text:

            print each_text

        ret = re.findall(pattern, each_text)

        #print ret[0]

        if len(ret) > 0:

        content = ret[0] + "\t" + wav_dir  +"/"+each_text

        #print content

        utf_file.write(content)

finally:

    utf_file.close()






create_coffee_txt.py

#coding=utf-8

import re

import codecs

import sys

import os

filename = sys.argv[1]

#print filename

wordfile = sys.argv[2]

#print wordfile

outname = sys.argv[3]

#print wordfile

word_object = codecs.open(wordfile, 'r', 'utf-8')

try:

    all_the_words = word_object.readlines()

        #print all_the_words[0]

finally:

    word_object.close()

#print filename

file_object = codecs.open(filename, 'r', 'utf-8')

try:

    all_the_text = file_object.readlines()

finally:

    file_object.close()

pattern = r"_(\d+)\.wav"

try:

    out_file = codecs.open(outname, 'w', 'utf-8')

    for each_text in all_the_text:

            #print each_text

        ret = re.findall(pattern, each_text)

        #print ret[0]

        if len(ret) > 0:

                index = int(ret[0])

                #print all_the_words[index-1]

        content = each_text[3:-5]+ "\t" + all_the_words[index-1]

        #print content

        out_file.write(content)

finally:

    out_file.close()






create_coffee_test_txt.py

#coding=utf-8

import re

import codecs

import sys

import os

filename = sys.argv[1]

print filename

wordfile = sys.argv[2]

print wordfile

outname = sys.argv[3]

#print wordfile

word_object = codecs.open(wordfile, 'r', 'utf-8')

try:

    all_the_words = word_object.readlines()

        print all_the_words[0]

finally:

    word_object.close()

#print filename

file_object = codecs.open(filename, 'r', 'utf-8')

try:

    all_the_text = file_object.readlines()

finally:

    file_object.close()

pattern = r"(\d+)_.+\.wav"

try:

    out_file = codecs.open(outname, 'w', 'utf-8')

    for each_text in all_the_text:

            print each_text

        ret = re.findall(pattern, each_text)

        print ret[0]

        if len(ret) > 0:

                index = int(ret[0])

                print "test_data"

                print all_the_words[index-1]

        content = each_text[:-5]+ "\t" + all_the_words[index-1]

        #print content

        out_file.write(content)

finally:

    out_file.close()




三、实验流程


1.准备好语音库


2.准备好脚本文件


local里面的所有脚本文件。


├── create_coffee_labels.py
├── create_coffee_speaker.py
├── create_coffee_test_labels.py
├── create_coffee_test_txt.py
├── create_coffee_txt.py
├── FixUpName.py
├── prepare_data.sh
├── prepare_dict.sh
├── prepare_lm.sh
└── score.sh


input里面的所有文件。


.
├── lexicon_nosil.txt
├── lexicon.txt
├── phones.txt
├── task.arpabo
└── trans.txt


conf 配置文件夹


conf
├── mfcc.conf
└── topo_orig.proto


---------------------------------------------------------------------------------------------------------------------------------------------------------------------


1.数据准备阶段


prepare_data.sh 运行完，得到 text，wav.scp，utt2spk， spk2utt文件，这几个文件需要保证已排序。






text : < uttid > < word >


wav.scp : < uttid > < utter_file_path >


utt2spk : < uttid > < speakid >


spk2utt : < speakid > < uttid >


word.txt : 同 text






2.准备好词典，生成语言模型文件


input文件夹下：


├── lexicon_nosil.txt
├── lexicon.txt
├── phones.txt
└── trans.txt


lexicon.txt： 词典，包括语料中涉及的词汇与发音。


lexicon_nosil.txt 不包含静音的词典。


phones.txt 所有发音音素


trans.txt 训练的所有词汇表


生成语言模型


task.arpabo 用 ngram-count  trans.txt生成。


四、碰到的问题


1.shell脚本输出文件名，不能有空格。


2.生成语言模型，生成arpa文件，对于孤立词来说，语言模型没有意义，这个可能是kaldi整个架构的原因，必须要有语言模型。


ngram-count


3.文件命名规范，需要进行排序，如果没有，可能报如下错误。


echo "$0: file $1 is not in sorted order or has duplicates" && exit 1;


有时加 sort -u 就可以保证有序 ，但是如果命名不规范，依然报错，所以自己写了个脚本，对语音库文件重新命名。


FixUpName.py


五、跑完的流程




make make file utter finish

data/waves_train/wav.scp sort!

data/waves_train/text sort!

data/waves_train/utt2spk sort!

data/waves_test/wav.scp sort!

data/waves_test/text sort!

data/waves_test/utt2spk sort!

Dictionary preparation succeeded

utils/prepare_lang.sh --position-dependent-phones false data/local/dict <SIL> 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} ...

--> 1 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} ...

--> 10 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} ...

--> 1 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} ...

--> 11 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} ...

--> 11 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} ...

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-level disambiguation symbols...

--> data/lang/phones/wdisambig.txt exists (newer prepare_lang.sh)

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]

Preparing language models for test

arpa2fst --disambig-symbol=#0 --read-symbol-table=data/lang_test_tg/words.txt input/task.arpabo data/lang_test_tg/G.fst

LOG (arpa2fst[5.2.124~1396-70748]:Read():arpa-file-parser.cc:98) Reading \data\ section.

LOG (arpa2fst[5.2.124~1396-70748]:Read():arpa-file-parser.cc:153) Reading \1-grams: section.

LOG (arpa2fst[5.2.124~1396-70748]:RemoveRedundantStates():arpa-lm-compiler.cc:359) Reduced num-states from 1 to 1

fstisstochastic data/lang_test_tg/G.fst

7.27531e-08 7.27531e-08

Succeeded in formatting data.

Data Prepraration finish!

steps/make_mfcc.sh --nj 1 data/waves_test exp/make_mfcc/waves_test mfcc

utils/validate_data_dir.sh: WARNING: you have only one speaker.  This probably a bad idea.

   Search for the word 'bold' in http://kaldi-asr.org/doc/data_prep.html

   for more information.

utils/validate_data_dir.sh: Successfully validated data-directory data/waves_test

steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.

Succeeded creating MFCC features for waves_test

steps/compute_cmvn_stats.sh data/waves_test exp/make_mfcc/waves_test mfcc

Succeeded creating CMVN stats for waves_test

fix_data_dir.sh: kept all 20 utterances.

fix_data_dir.sh: old files are kept in data/waves_test/.backup

steps/make_mfcc.sh --nj 1 data/waves_train exp/make_mfcc/waves_train mfcc

utils/validate_data_dir.sh: WARNING: you have only one speaker.  This probably a bad idea.

   Search for the word 'bold' in http://kaldi-asr.org/doc/data_prep.html

   for more information.

utils/validate_data_dir.sh: Successfully validated data-directory data/waves_train

steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.

Succeeded creating MFCC features for waves_train

steps/compute_cmvn_stats.sh data/waves_train exp/make_mfcc/waves_train mfcc

Succeeded creating CMVN stats for waves_train

fix_data_dir.sh: kept all 680 utterances.

fix_data_dir.sh: old files are kept in data/waves_train/.backup

Feature extraction finish!

steps/train_mono.sh --nj 1 --cmd utils/run.pl --totgauss 400 data/waves_train data/lang exp/mono0

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: Aligning data

steps/train_mono.sh: Pass 4

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 5

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 6

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 7

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 8

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 9

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 10

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 11

steps/train_mono.sh: Pass 12

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 13

steps/train_mono.sh: Pass 14

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 15

steps/train_mono.sh: Pass 16

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 17

steps/train_mono.sh: Pass 18

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 19

steps/train_mono.sh: Pass 20

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 21

steps/train_mono.sh: Pass 22

steps/train_mono.sh: Pass 23

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 24

steps/train_mono.sh: Pass 25

steps/train_mono.sh: Pass 26

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 27

steps/train_mono.sh: Pass 28

steps/train_mono.sh: Pass 29

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 30

steps/train_mono.sh: Pass 31

steps/train_mono.sh: Pass 32

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 33

steps/train_mono.sh: Pass 34

steps/train_mono.sh: Pass 35

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 36

steps/train_mono.sh: Pass 37

steps/train_mono.sh: Pass 38

steps/train_mono.sh: Aligning data

steps/train_mono.sh: Pass 39

steps/diagnostic/analyze_alignments.sh --cmd utils/run.pl data/lang exp/mono0

analyze_phone_length_stats.py: WARNING: optional-silence SIL is seen only 32.6470588235% of the time at utterance begin.  This may not be optimal.

steps/diagnostic/analyze_alignments.sh: see stats in exp/mono0/log/analyze_alignments.log

1 warnings in exp/mono0/log/analyze_alignments.log

exp/mono0: nj=1 align prob=-99.75 over 0.14h [retry=0.0%, fail=0.0%] states=35 gauss=396

steps/train_mono.sh: Done training monophone system in exp/mono0

Mono training finish!

tree-info exp/mono0/tree

tree-info exp/mono0/tree

fstpushspecial

fsttablecompose data/lang_test_tg/L_disambig.fst data/lang_test_tg/G.fst

fstminimizeencoded

fstdeterminizestar --use-log=true

fstisstochastic data/lang_test_tg/tmp/LG.fst

0.000133727 0.000118745

fstcomposecontext --context-size=1 --central-position=0 --read-disambig-syms=data/lang_test_tg/phones/disambig.int --write-disambig-syms=data/lang_test_tg/tmp/disambig_ilabels_1_0.int data/lang_test_tg/tmp/ilabels_1_0.30709

fstisstochastic data/lang_test_tg/tmp/CLG_1_0.fst

0.000133727 0.000118745

make-h-transducer --disambig-syms-out=exp/mono0/graph_tgpr/disambig_tid.int --transition-scale=1.0 data/lang_test_tg/tmp/ilabels_1_0 exp/mono0/tree exp/mono0/final.mdl

fsttablecompose exp/mono0/graph_tgpr/Ha.fst data/lang_test_tg/tmp/CLG_1_0.fst

fstrmepslocal

fstrmsymbols exp/mono0/graph_tgpr/disambig_tid.int

fstminimizeencoded

fstdeterminizestar --use-log=true

fstisstochastic exp/mono0/graph_tgpr/HCLGa.fst

0.000286827 -0.000292581

add-self-loops --self-loop-scale=0.1 --reorder=true exp/mono0/final.mdl

Graph compilation finish!

steps/decode.sh --nj 1 --cmd utils/run.pl exp/mono0/graph_tgpr data/waves_test exp/mono0/decode_waves_test

decode.sh: feature type is delta

steps/diagnostic/analyze_lats.sh --cmd utils/run.pl exp/mono0/graph_tgpr exp/mono0/decode_waves_test

analyze_phone_length_stats.py: WARNING: optional-silence SIL is seen only 55.0% of the time at utterance begin.  This may not be optimal.

steps/diagnostic/analyze_lats.sh: see stats in exp/mono0/decode_waves_test/log/analyze_alignments.log

Overall, lattice depth (10,50,90-percentile)=(3,8,31) and mean=13.9

steps/diagnostic/analyze_lats.sh: see stats in exp/mono0/decode_waves_test/log/analyze_lattice_depth_stats.log

%WER 65.00 [ 13 / 20, 11 ins, 0 del, 2 sub ] exp/mono0/decode_waves_test/wer_10


												


											
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