timit實例訓(xùn)練

TIMIT S5實例：

首先，將TIMIT.ISO中的TIMIT復(fù)制到主文件夾。

1.進入對應(yīng)的目錄，進行如下操作：

zhangju@ubuntu :~$ cd kaldi-trunk/egs/timit/s5/

zhangju@ubuntu :~/kaldi-trunk/egs/timit/s5$

sudo local/timit_data_prep.sh /home/zhangju/TIMIT

會看到如下顯示：

Creating coretest set.

MDAB0? MWBT0? FELC0? MTAS1? MWEW0? FPAS0? MJMP0? MLNT0? FPKT0? MLLL0? MTLS0? FJLM0? MBPM0? MKLT0? FNLP0? MCMJ0? MJDH0? FMGD0? MGRT0? MNJM0? FDHC0? MJLN0? MPAM0? FMLD0

# of utterances in coretest set = 192

Creating dev set.

FAKS0? FDAC1? FJEM0? MGWT0? MJAR0? MMDB1? MMDM2? MPDF0? FCMH0? FKMS0? MBDG0? MBWM0? MCSH0? FADG0? FDMS0? FEDW0? MGJF0? MGLB0? MRTK0? MTAA0? MTDT0? MTHC0? MWJG0? FNMR0? FREW0? FSEM0? MBNS0? MMJR0? MDLS0? MDLF0? MDVC0? MERS0? FMAH0? FDRW0? MRCS0? MRJM4? FCAL1? MMWH0? FJSJ0? MAJC0? MJSW0? MREB0? FGJD0? FJMG0? MROA0? MTEB0? MJFC0? MRJR0? FMML0? MRWS1

# of utterances in dev set = 400

Finalizing test

Finalizing dev

timit_data_prep succeeded.

于是在/home/zhangju/kaldi-trunk/egs/timit/s5文件夾下新生成data文件夾，其內(nèi)包含local文件夾以及相關(guān)內(nèi)容。

2 在終端輸入：

local/timit_train_lms.sh data/local（下載、計算文本，用以建立語言模型）

local/timit_format_data.sh（處理與fst有關(guān)的東西）

3創(chuàng)建train的mfcc：

sudo steps/make_mfcc.sh data/train exp/make_mfcc/train mfccs 4

（要對train，dev，test創(chuàng)建）

會看到：

Succeeded creating MFCC features for train

sudo steps/make_mfcc.sh data/test exp/make_mfcc/test mfccs 4

會看到：

Succeeded creating MFCC features for test

sudo steps/make_mfcc.sh data/dev exp/make_mfcc/dev mfccs 4

會看到：

Succeeded creating MFCC features for dev

4訓(xùn)練單音素系統(tǒng)（monophone systom）

sudo steps/train_mono.sh data/train data/lang exp/mono

會顯示：

Computing cepstral mean and variance statistics

Initializing monophone system.

Compiling training graphs

Pass 0

Pass 1

Aligning data

Pass 2

Aligning data

Pass 3

Aligning data

Pass 4

Aligning data

Pass 5

Aligning data

Pass 6

Aligning data

Pass 7

Aligning data

Pass 8

Aligning data

Pass 9

Aligning data

Pass 10

Aligning data

Pass 11

Pass 12

Aligning data

Pass 13

Pass 14

Pass 15

Aligning data

Pass 16

Pass 17

Pass 18

Pass 19

Pass 20

Aligning data

Pass 21

Pass 22

Pass 23

Pass 24

Pass 25

Aligning data

Pass 26

Pass 27

Pass 28

Pass 29

于是，新建了exp/mono文件夾

scripts/mkgraph.sh --mono data/lang exp/mono exp/mono/graph（制圖）

會顯示：

fsttablecompose data/lang/L.fst data/lang/G.fst

fstdeterminizestar --use-log=true

fstminimizeencoded

fstisstochastic data/lang/tmp/LG.fst

-0.000244359 -0.0912761

warning: LG not stochastic.

fstcomposecontext --context-size=1 --central-position=0 --read-disambig-syms=data/lang/tmp/disambig_phones.list --write-disambig-syms=data/lang/tmp/disambig_ilabels_1_0.list data/lang/tmp/ilabels_1_0

fstisstochastic data/lang/tmp/CLG_1_0.fst

-0.000244359 -0.0912761

warning: CLG not stochastic.

make-h-transducer --disambig-syms-out=exp/mono/graph/disambig_tid.list --transition-scale=1.0 data/lang/tmp/ilabels_1_0 exp/mono/tree exp/mono/final.mdl

fstminimizeencoded

fstdeterminizestar --use-log=true

fsttablecompose exp/mono/graph/Ha.fst data/lang/tmp/CLG_1_0.fst

fstrmsymbols exp/mono/graph/disambig_tid.list

fstrmepslocal

fstisstochastic exp/mono/graph/HCLGa.fst

0.000331581 -0.091291

HCLGa is not stochastic

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

5.for test in dev test ; do

? steps/decode_deltas.sh exp/mono data/$test data/lang exp/mono/decode_$test &

done（解碼test數(shù)據(jù)集（test是*/s5/data中dev、test文件夾中的test文件夾））

終端輸出結(jié)果是：[1] 2307

? ? ? ? ? ? ? ? ? ? ? [2] 2308

6.scripts/average_wer.sh exp/mono/decode_*/wer > exp/mono/wer

會顯示：

[1]-? 完成? ? ? ? ? ? ? ? ? steps/decode_deltas.sh exp/mono data/$test data/lang exp/mono/decode_$test

[2]+? 完成? ? ? ? ? ? ? ? ? steps/decode_deltas.sh exp/mono data/$test data/lang exp/mono/decode_$test

7從單音素系統(tǒng)中獲得alignments：（分別從mono文件夾中的train,dev,test中獲得）（用以訓(xùn)練其他系統(tǒng)）

steps/align_deltas.sh data/train data/lang exp/mono exp/mono_ali_train

會顯示：

Computing cepstral mean and variance statistics

Aligning all training data

Done.

方法二：修改run.sh中的timit路徑，但后直接運行run.sh

TIMIT S3實例

1 數(shù)據(jù)準(zhǔn)備，輸入：

local/timit_data_prep.sh? /home/zhangju/TIMIT

終端顯示：

Creating coretest set.

MDAB0? MWBT0? FELC0? MTAS1? MWEW0? FPAS0? MJMP0? MLNT0? FPKT0? MLLL0? MTLS0? FJLM0? MBPM0? MKLT0? FNLP0? MCMJ0? MJDH0? FMGD0? MGRT0? MNJM0? FDHC0? MJLN0? MPAM0? FMLD0? （這是說話人的名字，前面加M，F(xiàn)分別表示男性和女性）

# of utterances in coretest set = 192 （核心測試集中有192句話）

Creating dev set.

FAKS0? FDAC1? FJEM0? MGWT0? MJAR0? MMDB1? MMDM2? MPDF0? FCMH0? FKMS0? MBDG0? MBWM0? MCSH0? FADG0? FDMS0? FEDW0? MGJF0? MGLB0? MRTK0? MTAA0? MTDT0? MTHC0? MWJG0? FNMR0? FREW0? FSEM0? MBNS0? MMJR0? MDLS0? MDLF0? MDVC0? MERS0? FMAH0? FDRW0? MRCS0? MRJM4? FCAL1? MMWH0? FJSJ0? MAJC0? MJSW0? MREB0? FGJD0? FJMG0? MROA0? MTEB0? MJFC0? MRJR0? FMML0? MRWS1

# of utterances in dev set = 400 （設(shè)備集中有400句話）

Finalizing test （完成test）

Finalizing dev （完成dev）

timit_data_prep succeeded.

輸入：

local/timit_train_lms.sh data/local

終端顯示為

Not installing the kaldi_lm toolkit since it is already there.

（kaldi_lm工具箱里有：

compute_perplexity計算復(fù)雜度（用于對語言模型作評估，復(fù)雜度越低越好）

discount_ngrams給n階語法模型作平滑處理（留出頻率給實際會出現(xiàn)的但ngram中沒出現(xiàn)的詞語組合）

get_raw_ngrams（得到原始n階語法模型）

get_word_map.pl*（得到詞語的映射表）

interpolate_ngrams（補充（修改）n階語法模型）

finalize_arpa.pl（完成arpa（arpa是一種格式，協(xié)議），是interpolate_ngrams程序中調(diào)用的）

map_words_in_arpa.pl（得到arpa格式的詞語）

merge_ngrams（合并、融合n階語法模型）

merge_ngrams_online（在線合并、融合n階語法模型）

optimize_alpha.pl（使alpha最優(yōu)化）

prune_lm.sh（刪去出現(xiàn)頻率較低的數(shù)據(jù)）

prune_ngrams（刪去出現(xiàn)頻率較低的數(shù)據(jù)）

scale_configs.pl

train_lm.sh（訓(xùn)練語言模型）

uniq_to_ngrams

）

Creating phones file, and monophone lexicon (mapping phones to itself). （創(chuàng)建音子文件及單音素詞典）

Creating biphone model（創(chuàng)建雙音子模型）

Training biphone language model in folder data/local/lm （訓(xùn)練雙音子語言模型）

Creating directory data/local/lm/biphone (創(chuàng)建目錄data/local/lm/biphone )

Getting raw N-gram counts （）

Iteration 1/7 of optimizing discounting parameters

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.900000 phi=2.000000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.900000 phi=2.000000

discount_ngrams: for n-gram order 3, D=0.800000, tau=1.100000 phi=2.000000

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.675000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.675000 phi=2.000000

discount_ngrams: for n-gram order 3, D=0.800000, tau=0.825000 phi=2.000000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=1.215000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=1.215000 phi=2.000000

discount_ngrams: for n-gram order 3, D=0.800000, tau=1.485000 phi=2.000000

interpolate_ngrams: 60 words in wordslist

Perplexity over 11412.000000 words is 17.013357

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.460842

real? 0m0.021s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.016472

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.464985

real? 0m0.020s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.021475

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.471402

real? 0m0.025s

user? 0m0.012s

sys 0m0.000s

optimize_alpha.pl: alpha=-2.1628504673 is too negative, limiting it to -0.5

Projected perplexity change from setting alpha=-0.5 is 17.016472->17.0106241428571, reduction of 0.00584785714286085

Alpha value on iter 1 is -0.5

Iteration 2/7 of optimizing discounting parameters

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=0.600000, tau=0.550000 phi=2.000000

interpolate_ngrams: 60 words in wordslist

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=0.800000, tau=0.550000 phi=2.000000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.080000, tau=0.550000 phi=2.000000

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.018s

user? 0m0.004s

sys 0m0.008s

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.022s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.019s

user? 0m0.008s

sys 0m0.004s

optimize_alpha.pl: objective function is not convex; returning alpha=0.7

Projected perplexity change from setting alpha=0.7 is 17.011355->17.011355, reduction of 0

Alpha value on iter 2 is 0.7

Iteration 3/7 of optimizing discounting parameters

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.412500 phi=2.000000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.550000 phi=2.000000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.742500 phi=2.000000

interpolate_ngrams: 60 words in wordslist

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.020s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.019s

user? 0m0.008s

sys 0m0.004s

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.021s

user? 0m0.012s

sys 0m0.000s

optimize_alpha.pl: objective function is not convex; returning alpha=0.7

Projected perplexity change from setting alpha=0.7 is 17.011355->17.011355, reduction of 0

Alpha value on iter 3 is 0.7

Iteration 4/7 of optimizing discounting parameters

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=1.750000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.000000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.350000

interpolate_ngrams: 60 words in wordslist

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.018s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.018s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.023s

user? 0m0.012s

sys 0m0.000s

optimize_alpha.pl: objective function is not convex; returning alpha=0.7

Projected perplexity change from setting alpha=0.7 is 17.011355->17.011355, reduction of 0

Alpha value on iter 4 is 0.7

Iteration 5/7 of optimizing discounting parameters

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.450000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

interpolate_ngrams: 60 words in wordslist

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.600000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.810000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

Perplexity over 11412.000000 words is 17.008195

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.454326

real? 0m0.019s

user? 0m0.008s

sys 0m0.004s

Perplexity over 11412.000000 words is 17.011355

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.457880

real? 0m0.019s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.018212

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.465417

real? 0m0.021s

user? 0m0.012s

sys 0m0.000s

optimize_alpha.pl: alpha=-0.670499383475985 is too negative, limiting it to -0.5

Projected perplexity change from setting alpha=-0.5 is 17.011355->17.0064832142857, reduction of 0.00487178571427904

Alpha value on iter 5 is -0.5

Iteration 6/7 of optimizing discounting parameters

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.300000, tau=0.337500 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 2, D=0.300000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.300000, tau=0.607500 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

Perplexity over 11412.000000 words is 17.008198

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.454134

real? 0m0.019s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.006972

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.452861

real? 0m0.020s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.006526

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.452349

real? 0m0.022s

user? 0m0.012s

sys 0m0.000s

Projected perplexity change from setting alpha=0.280321158690507 is 17.006972->17.0064966287094, reduction of 0.000475371290633575

Alpha value on iter 6 is 0.280321158690507

Iteration 7/7 of optimizing discounting parameters

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.300000, tau=0.576145 phi=1.750000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

interpolate_ngrams: 60 words in wordslist

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.300000, tau=0.576145 phi=2.350000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.300000, tau=0.576145 phi=2.000000

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

interpolate_ngrams: 60 words in wordslist

interpolate_ngrams: 60 words in wordslist

Perplexity over 11412.000000 words is 17.006845

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.452750

real? 0m0.019s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.006575

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.452414

real? 0m0.021s

user? 0m0.012s

sys 0m0.000s

Perplexity over 11412.000000 words is 17.006336

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.452127

real? 0m0.022s

user? 0m0.012s

sys 0m0.000s

Projected perplexity change from setting alpha=0.690827338145686 is 17.006575->17.0062591109755, reduction of 0.000315889024498972

Alpha value on iter 7 is 0.690827338145686

Final config is:

D=0.4 tau=0.45 phi=2.0

D=0.3 tau=0.576144521410728 phi=2.69082733814569

D=1.36 tau=0.935 phi=2.7

Discounting N-grams.

discount_ngrams: for n-gram order 1, D=0.400000, tau=0.450000 phi=2.000000

discount_ngrams: for n-gram order 2, D=0.300000, tau=0.576145 phi=2.690827

discount_ngrams: for n-gram order 3, D=1.360000, tau=0.935000 phi=2.700000

Computing final perplexity

Building ARPA LM (perplexity computation is in background)

interpolate_ngrams: 60 words in wordslist

interpolate_ngrams: 60 words in wordslist

Perplexity over 11412.000000 words is 17.006029

Perplexity over 10833.000000 words (excluding 579.000000 OOVs) is 17.451754

17.006029

輸入

local/timit_format_data.sh

終端顯示：

Creating L.fst

Done creating L.fst

Creating L_disambig.fst

Done creating L_disambig.fst

Creating G.fst

arpa2fst -

\data\

Processing 1-grams

Processing 2-grams

Connected 0 states without outgoing arcs.

remove_oovs.pl: removed 0 lines.

G.fst created. How stochastic is it ?

fstisstochastic data/lang_test/G.fst

0 -0.0900995

fsttablecompose data/lang_test/L_disambig.fst data/lang_test/G.fst

How stochastic is LG.fst.

fstisstochastic data/lang_test/G.fst

0 -0.0900995

fstisstochastic

fsttablecompose data/lang/L.fst data/lang_test/G.fst

0 -0.0900994

How stochastic is LG_disambig.fst.

fsttablecompose data/lang_test/L_disambig.fst data/lang_test/G.fst

fstisstochastic

0 -0.0900994

First few lines of lexicon FST:

0? 1? ? ? 0.356674939

0? 1? sil? 1.20397282

1? 2? aa? AA? 1.20397282

1? 1? aa? AA? 0.356674939

1? 1? ae? AE? 0.356674939

1? 2? ae? AE? 1.20397282

1? 1? ah? AH? 0.356674939

1? 2? ah? AH? 1.20397282

1? 1? ao? AO? 0.356674939

1? 2? ao? AO? 1.20397282

timit_format_data succeeded.

輸入：mfccdir=mfccs

for test in train test dev ; do

>? steps/make_mfcc.sh data/$test exp/make_mfcc/$test $mfccdir 4

> done

終端顯示：

Succeeded creating MFCC features for train

Succeeded creating MFCC features for test

Succeeded creating MFCC features for dev

2 訓(xùn)練單音素系統(tǒng)，終端輸入：

steps/train_mono.sh data/train data/lang exp/mono

終端顯示：

Computing cepstral mean and variance statistics

Initializing monophone system.

Compiling training graphs

Pass 0

Pass 1

Aligning data

Pass 2

Aligning data

Pass 3

Aligning data

Pass 4

Aligning data

Pass 5

Aligning data

Pass 6

Aligning data

Pass 7

Aligning data

Pass 8

Aligning data

Pass 9

Aligning data

Pass 10

Aligning data

Pass 11

Pass 12

Aligning data

Pass 13

Pass 14

Pass 15

Aligning data

Pass 16

Pass 17

Pass 18

Pass 19

Pass 20

Aligning data

Pass 21

Pass 22

Pass 23

Pass 24

Pass 25

Aligning data

Pass 26

Pass 27

Pass 28

Pass 29

scripts/mkgraph.sh --mono data/lang_test exp/mono exp/mono/graph（制圖）

終端顯示：

fsttablecompose data/lang_test/L_disambig.fst data/lang_test/G.fst

fstminimizeencoded

fstdeterminizestar --use-log=true

fstisstochastic data/lang_test/tmp/LG.fst

0 -0.0901494

warning: LG not stochastic.

fstcomposecontext --context-size=1 --central-position=0 --read-disambig-syms=data/lang_test/tmp/disambig_phones.list --write-disambig-syms=data/lang_test/tmp/disambig_ilabels_1_0.list data/lang_test/tmp/ilabels_1_0

fstisstochastic data/lang_test/tmp/CLG_1_0.fst

0 -0.0901494

warning: CLG not stochastic.

make-h-transducer --disambig-syms-out=exp/mono/graph/disambig_tid.list --transition-scale=1.0 data/lang_test/tmp/ilabels_1_0 exp/mono/tree exp/mono/final.mdl

fsttablecompose exp/mono/graph/Ha.fst data/lang_test/tmp/CLG_1_0.fst

fstdeterminizestar --use-log=true

fstminimizeencoded

fstrmsymbols exp/mono/graph/disambig_tid.list

fstrmepslocal

fstisstochastic exp/mono/graph/HCLGa.fst

0 -0.0901494

HCLGa is not stochastic

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

3 解碼測試的數(shù)據(jù)集，輸入

for test in dev test ; do

? steps/decode_deltas.sh exp/mono data/$test data/lang exp/mono/decode_$test &

done

終端顯示：

[1] 16368

[2] 16369

3.1計算結(jié)果，輸入：

scripts/average_wer.sh exp/mono/decode_*/wer > exp/mono/wer

終端顯示：

[1]-? 完成? ? ? ? ? ? ? ? ? steps/decode_deltas.sh exp/mono data/$test data/lang exp/mono/decode_$test

[2]+? 完成? ? ? ? ? ? ? ? ? steps/decode_deltas.sh exp/mono data/$test data/lang exp/mono/decode_$test

4 從單音素系統(tǒng)中獲得排列

創(chuàng)建排列用以訓(xùn)練其他系統(tǒng)，如ANN-HMM。

輸入：

steps/align_deltas.sh data/train data/lang exp/mono exp/mono_ali_train

終端顯示：

Computing cepstral mean and variance statistics

Aligning all training data

Done.

steps/align_deltas.sh data/dev data/lang exp/mono exp/mono_ali_dev

方法二：修改相應(yīng)的TIMIT路徑之后，直接運行run.sh

TIMIT S4實例此腳本是用于構(gòu)建一個音位識別器

WORKDIR=/home/zhangju/ss4（自己找個有空間的路徑作為WORKDIR）

mkdir -p $WORKDIR

cp -r conf local utils steps path.sh $WORKDIR

cd $WORKDIR

. path.sh（此文件中的環(huán)境變量KALDIROOT要自己修改路徑，改到自己裝的kaldi文件中。KALDIROOT=/home/mayuan/kaldi-trunk（我用nano改的。））

local/timit_data_prep.sh --config-dir=$PWD/conf --corpus-dir=/home/zhangju/TIMIT --work-dir=$WORKDIR

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