# willendzw.github.io

**Repository Path**: willenduan/willendzw.github.io

## Basic Information

- **Project Name**: willendzw.github.io
- **Description**: No description available
- **Primary Language**: JavaScript
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-30
- **Last Updated**: 2022-01-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# willen.github.io

山高水长不及三尺案头大部头，一杯清茶，一本书，可赏千古风流史。

​																													-----------记于2020-7-24凌晨. 中美决裂日。

jekyll --help

jekyll 4.1.1 -- Jekyll is a blog-aware, static site generator in Ruby

Usage:

  jekyll <subcommand> [options]

Options:
        -s, --source [DIR]  Source directory (defaults to ./)
        -d, --destination [DIR]  Destination directory (defaults to ./_site)
            --safe         Safe mode (defaults to false)
        -p, --plugins PLUGINS_DIR1[,PLUGINS_DIR2[,...]]  Plugins directory (defaults to ./_plugins)
            --layouts DIR  Layouts directory (defaults to ./_layouts)
            --profile      Generate a Liquid rendering profile
        -h, --help         Show this message
        -v, --version      Print the name and version
        -t, --trace        Show the full backtrace when an error occurs

Subcommands:
  compose
  docs
  import
  build, b              Build your site
  clean                 Clean the site (removes site output and metadata file) without building.
  doctor, hyde          Search site and print specific deprecation warnings
  help                  Show the help message, optionally for a given subcommand.
  new                   Creates a new Jekyll site scaffold in PATH
  new-theme             Creates a new Jekyll theme scaffold
  serve, server, s      Serve your site locally



Blog 编写步骤：

1. \_posts\ 下面创建一个年月日-日记名字.md，参考如下：

   2020-07-26-Kaldi study 1.md

2. 编写完成后，jekyll clean; jekyll build;

3. 推送到github；

   git add -A;git commit -a -m ""; git push

   



An example in Kaldi.

1. Task
Go to kaldi/egs/digits directory and create digits_audio folder. In kaldi/egs/digits/digits_audio create two folders: train and test. Select one speaker of your choice to represent testing dataset. Use this speaker's 'speakerID' as a name for an another new folder in kaldi/egs/digits/digits_audio/test directory. Then put there all the audio files related to that person. Put the rest (9 speakers) into train folder - this will be your training dataset. Also create subfolders for each speaker.

mkdir digits;cd digits;mkdir digits_audio
cd digits_audio;mkdir train test

Acoustic data
Now you have to create some text files that will allow Kaldi to communicate with your audio data. Consider these files as 'must be done'. Each file that you will create in this section (and in Language data section as well) can be considered as a text file with some number of strings (each string in a new line). These strings need to be sorted. If you will encounter any sorting issues you can use Kaldi scripts for checking (utils/validate_data_dir.sh) and fixing (utils/fix_data_dir.sh) data order. And for your information - utils directory will be attached to your project in Tools attachment section.

2. Task
In kaldi/egs/digits directory, create a folder data. Then create test and train subfolders inside. Create in each subfolder following files (so you have files named in the same way in test and train subfolders but they relate to two different datasets that you created before):

cd kaldi/egs/digits;mkdir data;cd data;mkdir test train

a.) spk2gender
This file informs about speakers gender. As we assumed, 'speakerID' is a unique name of each speaker (in this case it is also a 'recordingID' - every speaker has only one audio data folder from one recording session). In my example there are 5 female and 5 male speakers (f = female, m = male).

Pattern: <speakerID> <gender>

cristine f
dad m
josh m
july f
# and so on...
b.) wav.scp
This file connects every utterance (sentence said by one person during particular recording session) with an audio file related to this utterance. If you stick to my naming approach, 'utteranceID' is nothing more than 'speakerID' (speaker's folder name) glued with *.wav file name without '.wav' ending (look for examples below).

Pattern: <uterranceID> <full_path_to_audio_file>

dad_4_4_2 /home/{user}/kaldi/egs/digits/digits_audio/train/dad/4_4_2.wav
july_1_2_5 /home/{user}/kaldi/egs/digits/digits_audio/train/july/1_2_5.wav
july_6_8_3 /home/{user}/kaldi/egs/digits/digits_audio/train/july/6_8_3.wav
# and so on...
c.) text
This file contains every utterance matched with its text transcription.

Pattern: <uterranceID> <text_transcription>

dad_4_4_2 four four two
july_1_2_5 one two five
july_6_8_3 six eight three
# and so on...
d.) utt2spk
This file tells the ASR system which utterance belongs to particular speaker.

Pattern: <uterranceID> <speakerID>

dad_4_4_2 dad
july_1_2_5 july
july_6_8_3 july
# and so on...
e.) corpus.txt
This file has a slightly different directory. In kaldi/egs/digits/data create another folder local. In kaldi/egs/digits/data/local create a file corpus.txt which should contain every single utterance transcription that can occur in your ASR system (in our case it will be 100 lines from 100 audio files).

Pattern: <text_transcription>

one two five
six eight three
four four two
# and so on...
Language data
This section relates to language modeling files that also need to be considered as 'must be done'. Look for the syntax details here: Data preparation (each file is precisely described). Also feel free to read some examples in other egs scripts. Now is the perfect time.

Task
In kaldi/egs/digits/data/local directory, create a folder dict. In kaldi/egs/digits/data/local/dict create following files:

a.) lexicon.txt
This file contains every word from your dictionary with its 'phone transcriptions' (taken from /egs/voxforge).

Pattern: <word> <phone 1> <phone 2> ...

!SIL sil
<UNK> spn
eight ey t
five f ay v
four f ao r
nine n ay n
one hh w ah n
one w ah n
seven s eh v ah n
six s ih k s
three th r iy
two t uw
zero z ih r ow
zero z iy r ow
b.) nonsilence_phones.txt
This file lists nonsilence phones that are present in your project.

Pattern: <phone>

ah
ao
ay
eh
ey
f
hh
ih
iy
k
n
ow
r
s
t
th
uw
w
v
z
c.) silence_phones.txt
This file lists silence phones.

Pattern: <phone>

sil
spn
d.) optional_silence.txt
This file lists optional silence phones.

Pattern: <phone>

sil
Project finalization
Last chapter before runnig scripts creation. Your project structure will become complete.

Tools attachment
You need to add necessary Kaldi tools that are widely used in exemplary scripts.

Task
From kaldi/egs/wsj/s5 copy two folders (with the whole content) - utils and steps - and put them in your kaldi/egs/digits directory. You can also create links to these directories. You may find such links in, for example, kaldi/egs/voxforge/s5.

Scoring script
This script will help you to get decoding results.

Task
From kaldi/egs/voxforge/s5/local copy the script score.sh into similar location in your project (kaldi/egs/digits/local).

SRILM installation
You also need to install language modelling toolkit that is used in my example - SRI Language Modeling Toolkit (SRILM).

Task
For detailed installation instructions go to kaldi/tools/install_srilm.sh (read all comments inside).

Configuration files
It is not necessary to create configuration files but it can be a good habit for future.

Task
In kaldi/egs/digits create a folder conf. Inside kaldi/egs/digits/conf create two files (for some configuration modifications in decoding and mfcc feature extraction processes - taken from /egs/voxforge):

a.) decode.config
first_beam=10.0
beam=13.0
lattice_beam=6.0
b.) mfcc.conf
--use-energy=false
Running scripts creation
Your first ASR system written in Kaldi environment is almost ready. Your last job is to prepare running scripts to create ASR system of your choice. I put some comments in prepared scripts for ease of understanding.

These scripts are based on solution used in /egs/voxforge directory. I decided to use two different training methods:

MONO - monophone training,
TRI1 - simple triphone training (first triphone pass).
These two methods are enough to show noticable differences in decoding results using only digits lexicon and small training dataset.

Task
In kaldi/egs/digits directory create 3 scripts:

a.) cmd.sh
# Setting local system jobs (local CPU - no external clusters)
export train_cmd=run.pl
export decode_cmd=run.pl
b.) path.sh
# Defining Kaldi root directory
export KALDI_ROOT=`pwd`/../..
# Setting paths to useful tools
export PATH=$PWD/utils/:$KALDI_ROOT/src/bin:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/src/fstbin/:$KALDI_ROOT/src/gmmbin/:$KALDI_ROOT/src/featbin/:$KALDI_ROOT/src/lmbin/:$KALDI_ROOT/src/sgmm2bin/:$KALDI_ROOT/src/fgmmbin/:$KALDI_ROOT/src/latbin/:$PWD:$PATH
# Defining audio data directory (modify it for your installation directory!)
export DATA_ROOT="/home/{user}/kaldi/egs/digits/digits_audio"
# Enable SRILM
. $KALDI_ROOT/tools/env.sh
# Variable needed for proper data sorting
export LC_ALL=C
c.) run.sh

#!/bin/bash
. ./path.sh || exit 1
. ./cmd.sh || exit 1
nj=1       # number of parallel jobs - 1 is perfect for such a small dataset
lm_order=1 # language model order (n-gram quantity) - 1 is enough for digits grammar
# Safety mechanism (possible running this script with modified arguments)
. utils/parse_options.sh || exit 1
[[ $# -ge 1 ]] && { echo "Wrong arguments!"; exit 1; }
# Removing previously created data (from last run.sh execution)
rm -rf exp mfcc data/train/spk2utt data/train/cmvn.scp data/train/feats.scp data/train/split1 data/test/spk2utt data/test/cmvn.scp data/test/feats.scp data/test/split1 data/local/lang data/lang data/local/tmp data/local/dict/lexiconp.txt
echo
echo "===== PREPARING ACOUSTIC DATA ====="
echo
# Needs to be prepared by hand (or using self written scripts):
#
# spk2gender  [<speaker-id> <gender>]
# wav.scp     [<uterranceID> <full_path_to_audio_file>]
# text        [<uterranceID> <text_transcription>]
# utt2spk     [<uterranceID> <speakerID>]
# corpus.txt  [<text_transcription>]
# Making spk2utt files
utils/utt2spk_to_spk2utt.pl data/train/utt2spk > data/train/spk2utt
utils/utt2spk_to_spk2utt.pl data/test/utt2spk > data/test/spk2utt
echo
echo "===== FEATURES EXTRACTION ====="
echo
# Making feats.scp files
mfccdir=mfcc
# Uncomment and modify arguments in scripts below if you have any problems with data sorting
# utils/validate_data_dir.sh data/train     # script for checking prepared data - here: for data/train directory
# utils/fix_data_dir.sh data/train          # tool for data proper sorting if needed - here: for data/train directory
steps/make_mfcc.sh --nj $nj --cmd "$train_cmd" data/train exp/make_mfcc/train $mfccdir
steps/make_mfcc.sh --nj $nj --cmd "$train_cmd" data/test exp/make_mfcc/test $mfccdir
# Making cmvn.scp files
steps/compute_cmvn_stats.sh data/train exp/make_mfcc/train $mfccdir
steps/compute_cmvn_stats.sh data/test exp/make_mfcc/test $mfccdir
echo
echo "===== PREPARING LANGUAGE DATA ====="
echo
# Needs to be prepared by hand (or using self written scripts):
#
# lexicon.txt           [<word> <phone 1> <phone 2> ...]
# nonsilence_phones.txt [<phone>]
# silence_phones.txt    [<phone>]
# optional_silence.txt  [<phone>]
# Preparing language data
utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang data/lang
echo
echo "===== LANGUAGE MODEL CREATION ====="
echo "===== MAKING lm.arpa ====="
echo
loc=`which ngram-count`;
if [ -z $loc ]; then
        if uname -a | grep 64 >/dev/null; then
                sdir=$KALDI_ROOT/tools/srilm/bin/i686-m64
        else
                        sdir=$KALDI_ROOT/tools/srilm/bin/i686
        fi
        if [ -f $sdir/ngram-count ]; then
                        echo "Using SRILM language modelling tool from $sdir"
                        export PATH=$PATH:$sdir
        else
                        echo "SRILM toolkit is probably not installed.
                                Instructions: tools/install_srilm.sh"
                        exit 1
        fi
fi
local=data/local
mkdir $local/tmp
ngram-count -order $lm_order -write-vocab $local/tmp/vocab-full.txt -wbdiscount -text $local/corpus.txt -lm $local/tmp/lm.arpa
echo
echo "===== MAKING G.fst ====="
echo
lang=data/lang
arpa2fst --disambig-symbol=#0 --read-symbol-table=$lang/words.txt $local/tmp/lm.arpa $lang/G.fst
echo
echo "===== MONO TRAINING ====="
echo
steps/train_mono.sh --nj $nj --cmd "$train_cmd" data/train data/lang exp/mono  || exit 1
echo
echo "===== MONO DECODING ====="
echo
utils/mkgraph.sh --mono data/lang exp/mono exp/mono/graph || exit 1
steps/decode.sh --config conf/decode.config --nj $nj --cmd "$decode_cmd" exp/mono/graph data/test exp/mono/decode
echo
echo "===== MONO ALIGNMENT ====="
echo
steps/align_si.sh --nj $nj --cmd "$train_cmd" data/train data/lang exp/mono exp/mono_ali || exit 1
echo
echo "===== TRI1 (first triphone pass) TRAINING ====="
echo
steps/train_deltas.sh --cmd "$train_cmd" 2000 11000 data/train data/lang exp/mono_ali exp/tri1 || exit 1
echo
echo "===== TRI1 (first triphone pass) DECODING ====="
echo
utils/mkgraph.sh data/lang exp/tri1 exp/tri1/graph || exit 1
steps/decode.sh --config conf/decode.config --nj $nj --cmd "$decode_cmd" exp/tri1/graph data/test exp/tri1/decode
echo
echo "===== run.sh script is finished ====="
echo
Getting results
Now all you have to do is to run run.sh script. If I have made any mistakes in this tutorial, logs from the terminal should guide you how to deal with it.

Besides the fact that you will notice some decoding results in the terminal window, go to newly made kaldi/egs/digits/exp. You may notice there folders with mono and tri1 results as well - directories structure are the same. Go to mono/decode directory. Here you may find result files (named in a wer_{number} way). Logs for decoding process may be found in log folder (same directory).