kaldi 三个脚本cmd.sh path.sh run.sh

2022-10-28 07:42:22

参考   kaldi 的全部资料_v0.4

cmd.sh 脚本为：

可以很清楚的看到有 3 个分类分别对应 a，b，c。a 和 b 都是集群上去运行这个样子， c

就是我们需要的。我们在虚拟机上运行的。你需要修改这个脚本

# "queue.pl" uses qsub. The options to it are

# options to qsub. If you have GridEngine installed,

# change this to a queue you have access to.

# Otherwise, use "run.pl", which will run jobs locally

# (make sure your --num-jobs options are no more than

# the number of cpus on your machine.

#a) JHU cluster options

#export train_cmd="queue.pl -l arch=*64"

#export decode_cmd="queue.pl -l arch=*64,mem_free=2G,ram_free=2G"

#export mkgraph_cmd="queue.pl -l arch=*64,ram_free=4G,mem_free=4G"

#export cuda_cmd=run.pl

#b) BUT cluster options

#export train_cmd="queue.pl -q all.q@@blade -l ram_free=1200M,mem_free=1200M"

#export decode_cmd="queue.pl -q all.q@@blade -l ram_free=1700M,mem_free=1700M"

#export decodebig_cmd="queue.pl -q all.q@@blade -l ram_free=4G,mem_free=4G"

#export cuda_cmd="queue.pl -q long.q@@pco203 -l gpu=1"

#export cuda_cmd="queue.pl -q long.q@pcspeech-gpu"

#export mkgraph_cmd="queue.pl -q all.q@@servers -l ram_free=4G,mem_free=4G"

#c) run it locally...

export train_cmd=run.pl

export decode_cmd=run.pl

export cuda_cmd=run.pl

export mkgraph_cmd=run.pl

Path.sh 的内容：　　

在这里一般只要修改 export KALDI_ROOT=`pwd`/../../..改为你安装 kaldi 的目录，有时候不

修改也可以，根据实际情况。

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 >& "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit

. $KALDI_ROOT/tools/config/common_path.sh

export LC_ALL=C

Run.sh

需要指定你的数据在什么路径下，你只需要修改：
如：

#timit=/export/corpora5/LDC/LDC93S1/timit/TIMIT # @JHU

timit=/mnt/matylda2/data/TIMIT/timit # @BUT

修改为你的 timit 所在的路径。
其他的数据库都一样。
此外，voxforge 或者 vystadial_cz 或者 vystadial_en 这些数据库都提供下载，没有数据库的可
以利用这些来做实验。
最后，来解释下 run.sh 脚本。我们就用 timit 里的 s5 来举例阐述：

位置： /home/dahu/myfile/my_git/kaldi/egs/timit/s5

#!/bin/bash

#

# Copyright  Bagher BabaAli,

#           - Brno University of Technology (Author: Karel Vesely)

#

# TIMIT, description of the database:

# http://perso.limsi.fr/lamel/TIMIT_NISTIR4930.pdf

#

# Hon and Lee paper on TIMIT, , introduces mapping to  training phonemes,

# then re-mapping to  phonemes for scoring:

# http://repository.cmu.edu/cgi/viewcontent.cgi?article=2768&context=compsci

#

. ./cmd.sh

[ -f path.sh ] && . ./path.sh           #最好看看path.sh 的路径是否有问题

set -e

# Acoustic model parameters  ,声学模型的参数,暂时先不改

numLeavesTri1=

numGaussTri1=

numLeavesMLLT=

numGaussMLLT=

numLeavesSAT=

numGaussSAT=

numGaussUBM=

numLeavesSGMM=

numGaussSGMM=

feats_nj=

train_nj=

decode_nj=         #nj是指需要运行jobs的数量，一般不超过cpu的数量

echo ============================================================================

echo "                Data & Lexicon & Language Preparation                     "

echo ============================================================================

#timit=/export/corpora5/LDC/LDC93S1/timit/TIMIT # @JHU

timit=/mnt/matylda2/data/TIMIT/timit # @BUT   #修改为自己的timit所在路径

local/timit_data_prep.sh $timit || exit 

local/timit_prepare_dict.sh

# Caution below: we remove optional silence by setting "--sil-prob 0.0",

# in TIMIT the silence appears also as a word in the dictionary and is scored.

utils/prepare_lang.sh --sil-prob 0.0 --position-dependent-phones false --num-sil-states  \

 data/local/dict "sil" data/local/lang_tmp data/lang

local/timit_format_data.sh

echo ============================================================================

echo "         MFCC Feature Extration & CMVN for Training and Test set          "

echo ============================================================================

# Now make MFCC features.   #这部分主要是特征提取部分，

mfccdir=mfcc

for x in train dev test; do

  steps/make_mfcc.sh --cmd "$train_cmd" --nj $feats_nj data/$x exp/make_mfcc/$x $mfccdir

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

done

echo ============================================================================

echo "                     MonoPhone Training & Decoding                        "

echo ============================================================================

#这里是单音素的训练和解码部分，语音识别最基础的部分!!要详细研究一下。

steps/train_mono.sh  --nj "$train_nj" --cmd "$train_cmd" data/train data/lang exp/mono

utils/mkgraph.sh data/lang_test_bg exp/mono exp/mono/graph

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

 exp/mono/graph data/dev exp/mono/decode_dev

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

 exp/mono/graph data/test exp/mono/decode_test

echo ============================================================================

echo "           tri1 : Deltas + Delta-Deltas Training & Decoding               "

echo ============================================================================

#这里是三音素的训练和解码部分

steps/align_si.sh --boost-silence 1.25 --nj "$train_nj" --cmd "$train_cmd" \

 data/train data/lang exp/mono exp/mono_ali

# Train tri1, which is deltas + delta-deltas, on train data.

steps/train_deltas.sh --cmd "$train_cmd" \

 $numLeavesTri1 $numGaussTri1 data/train data/lang exp/mono_ali exp/tri1

utils/mkgraph.sh data/lang_test_bg exp/tri1 exp/tri1/graph

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

 exp/tri1/graph data/dev exp/tri1/decode_dev

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

 exp/tri1/graph data/test exp/tri1/decode_test

echo ============================================================================

echo "                 tri2 : LDA + MLLT Training & Decoding                    "

echo ============================================================================

#这里在三音素模型的基础上做了 LDA + MLLT 变换

steps/align_si.sh --nj "$train_nj" --cmd "$train_cmd" \

  data/train data/lang exp/tri1 exp/tri1_ali

steps/train_lda_mllt.sh --cmd "$train_cmd" \

 --splice-opts "--left-context=3 --right-context=3" \

 $numLeavesMLLT $numGaussMLLT data/train data/lang exp/tri1_ali exp/tri2

utils/mkgraph.sh data/lang_test_bg exp/tri2 exp/tri2/graph

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

 exp/tri2/graph data/dev exp/tri2/decode_dev

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

 exp/tri2/graph data/test exp/tri2/decode_test

echo ============================================================================

echo "              tri3 : LDA + MLLT + SAT Training & Decoding                 "

echo ============================================================================

#这里是三音素模型的基础上做了 LDA + MLLT + SAT 变换

# Align tri2 system with train data.

steps/align_si.sh --nj "$train_nj" --cmd "$train_cmd" \

 --use-graphs true data/train data/lang exp/tri2 exp/tri2_ali

# From tri2 system, train tri3 which is LDA + MLLT + SAT.

steps/train_sat.sh --cmd "$train_cmd" \

 $numLeavesSAT $numGaussSAT data/train data/lang exp/tri2_ali exp/tri3

utils/mkgraph.sh data/lang_test_bg exp/tri3 exp/tri3/graph

steps/decode_fmllr.sh --nj "$decode_nj" --cmd "$decode_cmd" \

 exp/tri3/graph data/dev exp/tri3/decode_dev

steps/decode_fmllr.sh --nj "$decode_nj" --cmd "$decode_cmd" \

 exp/tri3/graph data/test exp/tri3/decode_test

echo ============================================================================

echo "                        SGMM2 Training & Decoding                         "

echo ============================================================================

#这里是三音素模型的基础上做了 sgmm2

steps/align_fmllr.sh --nj "$train_nj" --cmd "$train_cmd" \

 data/train data/lang exp/tri3 exp/tri3_ali

exit  # From this point you can run Karel's DNN : local/nnet/run_dnn.sh

steps/train_ubm.sh --cmd "$train_cmd" \

 $numGaussUBM data/train data/lang exp/tri3_ali exp/ubm4

steps/train_sgmm2.sh --cmd "$train_cmd" $numLeavesSGMM $numGaussSGMM \

 data/train data/lang exp/tri3_ali exp/ubm4/final.ubm exp/sgmm2_4

utils/mkgraph.sh data/lang_test_bg exp/sgmm2_4 exp/sgmm2_4/graph

steps/decode_sgmm2.sh --nj "$decode_nj" --cmd "$decode_cmd"\

 --transform-dir exp/tri3/decode_dev exp/sgmm2_4/graph data/dev \

 exp/sgmm2_4/decode_dev

steps/decode_sgmm2.sh --nj "$decode_nj" --cmd "$decode_cmd"\

 --transform-dir exp/tri3/decode_test exp/sgmm2_4/graph data/test \

 exp/sgmm2_4/decode_test

echo ============================================================================

echo "                    MMI + SGMM2 Training & Decoding                       "

echo ============================================================================

#这里是三音素模型的基础上做了 MMI + SGMM2  

steps/align_sgmm2.sh --nj "$train_nj" --cmd "$train_cmd" \

 --transform-dir exp/tri3_ali --use-graphs true --use-gselect true \

 data/train data/lang exp/sgmm2_4 exp/sgmm2_4_ali

steps/make_denlats_sgmm2.sh --nj "$train_nj" --sub-split "$train_nj" \

 --acwt 0.2 --lattice-beam 10.0 --beam 18.0 \

 --cmd "$decode_cmd" --transform-dir exp/tri3_ali \

 data/train data/lang exp/sgmm2_4_ali exp/sgmm2_4_denlats

steps/train_mmi_sgmm2.sh --acwt 0.2 --cmd "$decode_cmd" \

 --transform-dir exp/tri3_ali --boost 0.1 --drop-frames true \

 data/train data/lang exp/sgmm2_4_ali exp/sgmm2_4_denlats exp/sgmm2_4_mmi_b0.

for iter in    ; do

  steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $iter \

   --transform-dir exp/tri3/decode_dev data/lang_test_bg data/dev \

   exp/sgmm2_4/decode_dev exp/sgmm2_4_mmi_b0./decode_dev_it$iter

  steps/decode_sgmm2_rescore.sh --cmd "$decode_cmd" --iter $iter \

   --transform-dir exp/tri3/decode_test data/lang_test_bg data/test \

   exp/sgmm2_4/decode_test exp/sgmm2_4_mmi_b0./decode_test_it$iter

done

echo ============================================================================

echo "                    DNN Hybrid Training & Decoding                        "

echo ============================================================================

#这里是povey版本的dnn模型，教程说不建议使用

# DNN hybrid system training parameters

dnn_mem_reqs="--mem 1G"

dnn_extra_opts="--num_epochs 20 --num-epochs-extra 10 --add-layers-period 1 --shrink-interval 3"

steps/nnet2/train_tanh.sh --mix-up  --initial-learning-rate 0.015 \

  --final-learning-rate 0.002 --num-hidden-layers   \

  --num-jobs-nnet "$train_nj" --cmd "$train_cmd" "${dnn_train_extra_opts[@]}" \

  data/train data/lang exp/tri3_ali exp/tri4_nnet

[ ! -d exp/tri4_nnet/decode_dev ] && mkdir -p exp/tri4_nnet/decode_dev

decode_extra_opts=(--num-threads )

steps/nnet2/decode.sh --cmd "$decode_cmd" --nj "$decode_nj" "${decode_extra_opts[@]}" \

  --transform-dir exp/tri3/decode_dev exp/tri3/graph data/dev \

  exp/tri4_nnet/decode_dev | tee exp/tri4_nnet/decode_dev/decode.log

[ ! -d exp/tri4_nnet/decode_test ] && mkdir -p exp/tri4_nnet/decode_test

steps/nnet2/decode.sh --cmd "$decode_cmd" --nj "$decode_nj" "${decode_extra_opts[@]}" \

  --transform-dir exp/tri3/decode_test exp/tri3/graph data/test \

  exp/tri4_nnet/decode_test | tee exp/tri4_nnet/decode_test/decode.log

echo ============================================================================

echo "                    System Combination (DNN+SGMM)                         "

echo ============================================================================

#这里是 dnn + sgmm 模型

for iter in    ; do

  local/score_combine.sh --cmd "$decode_cmd" \

   data/dev data/lang_test_bg exp/tri4_nnet/decode_dev \

   exp/sgmm2_4_mmi_b0./decode_dev_it$iter exp/combine_2/decode_dev_it$iter

  local/score_combine.sh --cmd "$decode_cmd" \

   data/test data/lang_test_bg exp/tri4_nnet/decode_test \

   exp/sgmm2_4_mmi_b0./decode_test_it$iter exp/combine_2/decode_test_it$iter

done

echo ============================================================================

echo "               DNN Hybrid Training & Decoding (Karel's recipe)            "

echo ============================================================================

#这里是 karel 的 dnn 模型，通用的深度学习模型框架！！

local/nnet/run_dnn.sh

#local/nnet/run_autoencoder.sh : an example, not used to build any system,

echo ============================================================================

echo "                    Getting Results [see RESULTS file]                    "

echo ============================================================================

#这里是得到上述模型的最后识别结果

bash RESULTS dev

bash RESULTS test

echo ============================================================================

echo "Finished successfully on" `date`

echo ============================================================================

exit

看完这3个基本的脚本，了解下大概都是做什么用的，正在下载 timit 的data，之后跑一下。

timit 数据集下载：   kaldi timit 实例运行全过程

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