TIMIT语音库是IT和MIT合作音素级别标注的语音库，用于自动语音识别系统的发展和评估，包括来自美式英语，8个地区方言，630个人。

每个人读10个句子，每个发音都是音素级别、词级别文本标注，16kHz,16bit。

注意：不用使用TIMIT配置作为运行Kaldi的一个通用型例子，因为它不是一个非常标准的结构。

其它的一些配置也是非常好用的。

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

librispeech/s5是非常好的，因为它是免费的。

yesno是非常轻量级、快速运行，而且也是免费的。

wsj/s5有一个不普遍的例子脚本，这些脚本可能让人感到疑惑的。

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

s5: 单音素、3音素 GMM/HMM系统，用ML训练。接着是SGMM和DNN配置。

基于48个音素完成训练，《Speaker-Independent Phone Recognition Using Hidden Markov Models》。

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

执行，修改run.sh里面的timit语音库路径，修改cmd.sh运行的脚本，queue.pl改成本地跑的run.pl，下载安装tools/extra/install_srilm.sh，拷贝irstlm文件夹

到tools目录下，最后运行run.sh.

运行完成后，屏幕打印的内容如下：

============================================================================

Data & Lexicon & Language Preparation   数据、词典、语言准备

============================================================================

wav-to-duration --read-entire-file=true scp:train_wav.scp ark,t:train_dur.ark

LOG (wav-to-duration[5.2.124~1396-70748]:main():wav-to-duration.cc:92) Printed duration for 3696 audio files.

LOG (wav-to-duration[5.2.124~1396-70748]:main():wav-to-duration.cc:94) Mean duration was 3.06336, min and max durations were 0.91525, 7.78881

wav-to-duration --read-entire-file=true scp:dev_wav.scp ark,t:dev_dur.ark

LOG (wav-to-duration[5.2.124~1396-70748]:main():wav-to-duration.cc:92) Printed duration for 400 audio files.

LOG (wav-to-duration[5.2.124~1396-70748]:main():wav-to-duration.cc:94) Mean duration was 3.08212, min and max durations were 1.09444, 7.43681

wav-to-duration --read-entire-file=true scp:test_wav.scp ark,t:test_dur.ark

LOG (wav-to-duration[5.2.124~1396-70748]:main():wav-to-duration.cc:92) Printed duration for 192 audio files.

LOG (wav-to-duration[5.2.124~1396-70748]:main():wav-to-duration.cc:94) Mean duration was 3.03646, min and max durations were 1.30562, 6.21444

Data preparation succeeded
LOGFILE:/dev/null

$bin/ngt -i="$inpfile" -n=$order -gooout=y -o="$gzip -c > $tmpdir/ngram.${sdict}.gz" -fd="$tmpdir/$sdict" $dictionary $additional_parameters >> $logfile 2>&1

$scr/build-sublm.pl $verbose $prune $prune_thr_str $smoothing "$additional_smoothing_parameters" --size $order --ngrams "$gunzip -c $tmpdir/ngram.${sdict}.gz" -sublm $tmpdir/lm.$sdict $additional_parameters >> $logfile 2>&1

inpfile: data/local/lm_tmp/lm_phone_bg.ilm.gz

outfile: /dev/stdout

loading up to the LM level 1000 (if any)

dub: 10000000

OOV code is 50

OOV code is 50

Saving in txt format to /dev/stdout

Dictionary & language model preparation succeeded
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 ...

--> reading data/local/dict/extra_questions.txt

--> data/local/dict/extra_questions.txt 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} ...

--> 47 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} ...

--> 48 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} ...

--> 48 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} ...

--> 2 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 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 train, dev and test data

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

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

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

Preparing language models for test

arpa2fst --disambig-symbol=#0 --read-symbol-table=data/lang_test_bg/words.txt - data/lang_test_bg/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]:Read():arpa-file-parser.cc:153) Reading \2-grams: section.

WARNING (arpa2fst[5.2.124~1396-70748]:ConsumeNGram():arpa-lm-compiler.cc:313) line 60 [-3.26717<s> <s>] skipped: n-gram has invalid BOS/EOS placement

LOG (arpa2fst[5.2.124~1396-70748]:RemoveRedundantStates():arpa-lm-compiler.cc:359) Reduced num-states from 50 to 50

fstisstochastic data/lang_test_bg/G.fst

0.000510126 -0.0763018

utils/validate_lang.pl data/lang_test_bg

Checking data/lang_test_bg/phones.txt ...

--> data/lang_test_bg/phones.txt is OK

Checking words.txt: #0 ...

--> data/lang_test_bg/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_test_bg/phones/context_indep.{txt, int, csl} ...

--> 1 entry/entries in data/lang_test_bg/phones/context_indep.txt

--> data/lang_test_bg/phones/context_indep.int corresponds to data/lang_test_bg/phones/context_indep.txt

--> data/lang_test_bg/phones/context_indep.csl corresponds to data/lang_test_bg/phones/context_indep.txt

--> data/lang_test_bg/phones/context_indep.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/nonsilence.{txt, int, csl} ...

--> 47 entry/entries in data/lang_test_bg/phones/nonsilence.txt

--> data/lang_test_bg/phones/nonsilence.int corresponds to data/lang_test_bg/phones/nonsilence.txt

--> data/lang_test_bg/phones/nonsilence.csl corresponds to data/lang_test_bg/phones/nonsilence.txt

--> data/lang_test_bg/phones/nonsilence.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/silence.{txt, int, csl} ...

--> 1 entry/entries in data/lang_test_bg/phones/silence.txt

--> data/lang_test_bg/phones/silence.int corresponds to data/lang_test_bg/phones/silence.txt

--> data/lang_test_bg/phones/silence.csl corresponds to data/lang_test_bg/phones/silence.txt

--> data/lang_test_bg/phones/silence.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/optional_silence.{txt, int, csl} ...

--> 1 entry/entries in data/lang_test_bg/phones/optional_silence.txt

--> data/lang_test_bg/phones/optional_silence.int corresponds to data/lang_test_bg/phones/optional_silence.txt

--> data/lang_test_bg/phones/optional_silence.csl corresponds to data/lang_test_bg/phones/optional_silence.txt

--> data/lang_test_bg/phones/optional_silence.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/disambig.{txt, int, csl} ...

--> 2 entry/entries in data/lang_test_bg/phones/disambig.txt

--> data/lang_test_bg/phones/disambig.int corresponds to data/lang_test_bg/phones/disambig.txt

--> data/lang_test_bg/phones/disambig.csl corresponds to data/lang_test_bg/phones/disambig.txt

--> data/lang_test_bg/phones/disambig.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/roots.{txt, int} ...

--> 48 entry/entries in data/lang_test_bg/phones/roots.txt

--> data/lang_test_bg/phones/roots.int corresponds to data/lang_test_bg/phones/roots.txt

--> data/lang_test_bg/phones/roots.{txt, int} are OK

Checking data/lang_test_bg/phones/sets.{txt, int} ...

--> 48 entry/entries in data/lang_test_bg/phones/sets.txt

--> data/lang_test_bg/phones/sets.int corresponds to data/lang_test_bg/phones/sets.txt

--> data/lang_test_bg/phones/sets.{txt, int} are OK

Checking data/lang_test_bg/phones/extra_questions.{txt, int} ...

--> 2 entry/entries in data/lang_test_bg/phones/extra_questions.txt

--> data/lang_test_bg/phones/extra_questions.int corresponds to data/lang_test_bg/phones/extra_questions.txt

--> data/lang_test_bg/phones/extra_questions.{txt, int} are OK

Checking optional_silence.txt ...

--> reading data/lang_test_bg/phones/optional_silence.txt

--> data/lang_test_bg/phones/optional_silence.txt is OK

Checking disambiguation symbols: #0 and #1

--> data/lang_test_bg/phones/disambig.txt has "#0" and "#1"

--> data/lang_test_bg/phones/disambig.txt is OK

Checking topo ...

Checking word-level disambiguation symbols...

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

Checking data/lang_test_bg/oov.{txt, int} ...

--> 1 entry/entries in data/lang_test_bg/oov.txt

--> data/lang_test_bg/oov.int corresponds to data/lang_test_bg/oov.txt

--> data/lang_test_bg/oov.{txt, int} are OK

--> data/lang_test_bg/L.fst is olabel sorted

--> data/lang_test_bg/L_disambig.fst is olabel sorted

--> data/lang_test_bg/G.fst is ilabel sorted

--> data/lang_test_bg/G.fst has 50 states

fstdeterminizestar data/lang_test_bg/G.fst /dev/null

--> data/lang_test_bg/G.fst is determinizable

--> utils/lang/check_g_properties.pl successfully validated data/lang_test_bg/G.fst

--> utils/lang/check_g_properties.pl succeeded.

--> Testing determinizability of L_disambig . G

fstdeterminizestar

fsttablecompose data/lang_test_bg/L_disambig.fst data/lang_test_bg/G.fst

--> L_disambig . G is determinizable

--> SUCCESS [validating lang directory data/lang_test_bg]

Succeeded in formatting data.

============================================================================
============================================================================

         MFCC Feature Extration & CMVN for Training and Test set

============================================================================

steps/make_mfcc.sh --cmd run.pl --mem 4G --nj 10 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/compute_cmvn_stats.sh data/train exp/make_mfcc/train mfcc

Succeeded creating CMVN stats for train

steps/make_mfcc.sh --cmd run.pl --mem 4G --nj 10 data/dev exp/make_mfcc/dev mfcc

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

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

Succeeded creating MFCC features for dev

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

Succeeded creating CMVN stats for dev

steps/make_mfcc.sh --cmd run.pl --mem 4G --nj 10 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/test exp/make_mfcc/test mfcc

Succeeded creating CMVN stats for test

============================================================================

kaldi的TIMIT实例一的更多相关文章

  1. kaldi的TIMIT实例三

    ============================================================================ MMI + SGMM2 Training &a ...

  2. kaldi的TIMIT实例二

    ============================================================================ MonoPhone Training & ...

  3. kaldi 三个脚本cmd.sh path.sh run.sh

    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 ...

  4. Ubuntu下kaldi安装

    该文章为博主原创,如若转载请注明出处:https://www.cnblogs.com/fengleixue/p/9482202.html 因公司业务需要需使用kaldi语音识别工具,现将kaldi环境 ...

  5. 最近学习工作流 推荐一个activiti 的教程文档

    全文地址:http://www.mossle.com/docs/activiti/ Activiti 5.15 用户手册 Table of Contents 1. 简介 协议 下载 源码 必要的软件 ...

  6. kaldi实例脚本运行

    Getting started, and prerequisites. rm/s5/run.sh Data preparation 如果有GridEngine, train_cmd="que ...

  7. [转]Kaldi语音识别

    转:http://ftli.farbox.com/post/kaldizhong-wen-shi-bie Kaldi语音识别 1.声学建模单元的选择 1.1对声学建模单元加入位置信息 2.输入特征 3 ...

  8. Kaldi的关键词搜索（Keyword Search，KWS）

    本文简单地介绍了KWS的原理--为Lattice中每个词生成索引并进行搜索:介绍了如何处理OOV--替补(Proxy,词典内对OOV的替补)关键词技术:介绍了KWS的语料库格式:介绍了KWS在Kald ...

  9. Kaldi语料的两种切分/组织方式及其处理

    text中每一个文本段由一个音频索引(indexed by utterance) 使用该方式的egs:librispeech.timit.thchs30.atc_en.atc_cn 语料的组织形式为: ...

随机推荐

  1. 深度学习原理与框架-Tfrecord数据集的制作 1.tf.train.Examples(数据转换为二进制) 3.tf.image.encode_jpeg(解码图片加码成jpeg) 4.tf.train.Coordinator(构建多线程通道) 5.threading.Thread(建立单线程) 6.tf.python_io.TFR(TFR读入器)

    1. 配套使用: tf.train.Examples将数据转换为二进制,提升IO效率和方便管理 对于int类型 : tf.train.Examples(features=tf.train.Featur ...

  2. A CLOSER LOOK AT CSS

    A CLOSER LOOK AT CSS css-review Congratulations! You worked hard and made it to the end of a challen ...

  3. python中并发编程基础1

    并发编程基础概念 1.进程. 什么是进程? 正在运行的程序就是进程.程序只是代码. 什么是多道? 多道技术: 1.空间上的复用(内存).将内存分为几个部分,每个部分放入一个程序,这样同一时间在内存中就 ...

  4. ExecutorService——shutdown方法和awaitTermination方法

    ExecutorService的关闭shutdown和awaitTermination为接口ExecutorService定义的两个方法,一般情况配合使用来关闭线程池. 方法简介shutdown方法: ...

  5. hdu2089-不要62-（数位dp）

    不要62 Time Limit: 1000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)Total Submis ...

  6. R语言-散点图进阶

    1.分组散点图 ①xyplot()函数 > library(lattice) > xyplot(mpg~disp, #定义Y~X轴 + data=mtcars, + groups=cyl, ...

  7. SVG绘制太极图

    思路:先画一整个圆,填充颜色为黑色,再用一个边框和填充颜色均为白色的长方形覆盖右半边的半圆,再以同一个圆心,相同半径绘制一整个圆,该圆的边线颜色为黑色,没有填充颜色,最后常规操作再画四个小圆 源代码: ...

  8. 十四、ChainOfResponsibility 责任链模式

    设计: 代码清单: Trouble: public class Trouble { private int number; public Trouble(int number){ this.numbe ...

  9. json11阅读

    概要:json11是一个基于c++11的json解析库,dropbox出品. 使用 直接举自带单元测试test.cpp中的例子: const string simple_test = R"( ...

  10. 使用 Composer 安装Laravel扩展包的几种方法

    使用 Composer 安装Laravel扩展包的几种方法 以下的三种方法都是需要你在项目的根目录运行 第一种:composer install 如有 composer.lock 文件,直接安装,否则 ...