操作系統 ： Ubutu18.04_x64
gcc版本 ：7.4.0
數據准備及訓練
數據地址： http://www.openslr.org/18/
在 egs/thchs30/s5 建立 thchs30-openslr 文件夾，然后把三個文件解壓在了該文件夾下：
[mike@local thchs30-openslr]$ pwd /home/mike/src/kaldi/egs/thchs30/s5/thchs30-openslr [mike@local thchs30-openslr]$ tree -L 2 . ├── data_thchs30 │ ├── data │ ├── dev │ ├── lm_phone │ ├── lm_word │ ├── README.TXT │ ├── test │ └── train ├── resource │ ├── dict │ ├── noise │ └── README └── test-noise ├── 0db ├── noise ├── noise.scp ├── README ├── test.scp └── utils 14 directories, 5 files [mike@local thchs30-openslr]$
進入 s5 目錄，修改腳本：
修改cmd.sh腳本，把原腳本注釋掉，修改為本地運行：
export train_cmd=run.pl export decode_cmd="run.pl --mem 4G" export mkgraph_cmd="run.pl --mem 8G" export cuda_cmd="run.pl --gpu 1"
修改run.sh腳本，需要修改兩個地方（cpu並行數及thchs30數據目錄）：
n=8 #parallel jobs #corpus and trans directory #thchs=/nfs/public/materials/data/thchs30-openslr thchs=/home/mike/src/kaldi/egs/thchs30/s5/thchs30-openslr
執行 run.sh 即可開始訓練。
查看訓練效果(比如看tri1的效果) :
$ cat tri1/decode_test_word/scoring_kaldi/best_wer %WER 36.06 [ 29255 / 81139, 545 ins, 1059 del, 27651 sub ] exp/tri1/decode_test_word/wer_10_0.0
在線識別
1、安裝 portaudio
cd tools ./install_portaudio.sh
2、編譯相關工具
cd .. cd src make ext
3、建立目錄結構並同步識別模型
從voxforge把online_demo拷貝到thchs30下，和s5同級，online_demo建online-data和work兩個文件夾。 online-data下建audio和models，audio放要識別的wav，models建tri1，將s5下/exp/下的tri1下的 final.mdl 和 35.mdl 拷貝過去，把s5下的exp下的tri1下的graph_word里面的 words.txt 和 HCLG.fst 也拷過去。
模型同步腳本：
mike@local:online_demo$ cat 1_tri1.sh #! /bin/bash set -v srcDir="/home/mike/src/kaldi/egs/thchs30/s5/exp/tri1" dst="/home/mike/src/kaldi/egs/thchs30/online_demo/online-data/models/tri1/" echo $dst rm -rf $dst/*.* cp $srcDir/final.mdl $dst cp $srcDir/35.mdl $dst cp $srcDir/graph_word/words.txt $dst cp $srcDir/graph_word/HCLG.fst $dst echo "done" mike@local:online_demo$
4、修改腳本
注釋如下代碼：
#if [ ! -s ${data_file}.tar.bz2 ]; then # echo "Downloading test models and data ..." # wget -T 10 -t 3 $data_url; # if [ ! -s ${data_file}.tar.bz2 ]; then # echo "Download of $data_file has failed!" # exit 1 # fi #fi
修改模型：
ac_model_type=tri1
修改識別代碼：
simulated) echo echo -e " SIMULATED ONLINE DECODING - pre-recorded audio is used\n" echo " The (bigram) language model used to build the decoding graph was" echo " estimated on an audio book's text. The text in question is" echo " \"King Solomon's Mines\" (http://www.gutenberg.org/ebooks/2166)." echo " The audio chunks to be decoded were taken from the audio book read" echo " by John Nicholson(http://librivox.org/king-solomons-mines-by-haggard/)" echo echo " NOTE: Using utterances from the book, on which the LM was estimated" echo " is considered to be \"cheating\" and we are doing this only for" echo " the purposes of the demo." echo echo " You can type \"./run.sh --test-mode live\" to try it using your" echo " own voice!" echo mkdir -p $decode_dir # make an input .scp file > $decode_dir/input.scp for f in $audio/*.wav; do bf=`basename $f` bf=${bf%.wav} echo $bf $f >> $decode_dir/input.scp done # online-wav-gmm-decode-faster --verbose=1 --rt-min=0.8 --rt-max=0.85\ # --max-active=4000 --beam=12.0 --acoustic-scale=0.0769 \ # scp:$decode_dir/input.scp $ac_model/model $ac_model/HCLG.fst \ # $ac_model/words.txt '1:2:3:4:5' ark,t:$decode_dir/trans.txt \ # ark,t:$decode_dir/ali.txt $trans_matrix;; online-wav-gmm-decode-faster --verbose=1 --rt-min=0.8 --rt-max=0.85 --max-active=4000 \ --beam=12.0 --acoustic-scale=0.0769 --left-context=3 --right-context=3 \ scp:$decode_dir/input.scp $ac_model/final.mdl $ac_model/HCLG.fst \ $ac_model/words.txt '1:2:3:4:5' ark,t:$decode_dir/trans.txt \ ark,t:$decode_dir/ali.txt $trans_matrix;;
5、執行語音識別
將聲音文件復制到 online-data/audio/ 目錄，然后運行 run.sh 執行識別操作。
測試文本：
自然語言理解和生成是一個多方面問題，我們對它可能也只是部分理解。
識別效果如下：
mike@local:online_demo$ ls online-data/audio/ A1_00.wav mike@local:online_demo$ ./run.sh SIMULATED ONLINE DECODING - pre-recorded audio is used The (bigram) language model used to build the decoding graph was estimated on an audio book's text. The text in question is "King Solomon's Mines" (http://www.gutenberg.org/ebooks/2166). The audio chunks to be decoded were taken from the audio book read by John Nicholson(http://librivox.org/king-solomons-mines-by-haggard/) NOTE: Using utterances from the book, on which the LM was estimated is considered to be "cheating" and we are doing this only for the purposes of the demo. You can type "./run.sh --test-mode live" to try it using your own voice! online-wav-gmm-decode-faster --verbose=1 --rt-min=0.8 --rt-max=0.85 --max-active=4000 --beam=12.0 --acoustic-scale=0.0769 --left-context=3 --right-context=3 scp:./work/input.scp online-data/models/tri1/final.mdl online-data/models/tri1/HCLG.fst online-data/models/tri1/words.txt 1:2:3:4:5 ark,t:./work/trans.txt ark,t:./work/ali.txt File: A1_00 自然 語言 理解 和 生產 是 一個 多方面 挽 起 我們 對 它 可能 也 只是 部分 禮節 ./run.sh: 行 116: online-data/audio/trans.txt: 沒有那個文件或目錄 ./run.sh: 行 121: gawk: 未找到命令 compute-wer --mode=present ark,t:./work/ref.txt ark,t:./work/hyp.txt WARNING (compute-wer[5.5.421~1453-85d1a]:Open():util/kaldi-table-inl.h:513) Failed to open stream ./work/ref.txt ERROR (compute-wer[5.5.421~1453-85d1a]:SequentialTableReader():util/kaldi-table-inl.h:860) Error constructing TableReader: rspecifier is ark,t:./work/ref.txt [ Stack-Trace: ] /data/asr/kaldi_full/src/lib/libkaldi-base.so(kaldi::MessageLogger::LogMessage() const+0xb42) [0x7fce5ef61692] compute-wer(kaldi::MessageLogger::LogAndThrow::operator=(kaldi::MessageLogger const&)+0x21) [0x55bb3a782299] compute-wer(kaldi::SequentialTableReader<kaldi::TokenVectorHolder>::SequentialTableReader(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)+0xc2) [0x55bb3a787c0c] compute-wer(main+0x226) [0x55bb3a780f60] /lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xe7) [0x7fce5e3cbb97] compute-wer(_start+0x2a) [0x55bb3a780c5a] kaldi::KaldiFatalError mike@local:online_demo$
可以看到，識別效果比較差。
本文中涉及訓練數據及測試示例地址： https://pan.baidu.com/s/1OdLkcoDPl1Hkd06m2Xt7wA
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