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pocketsphinx-getstarted

  1. Get started to train new model with small data

  2. This repository only contain small data to train new model. However you needs more data to perform well

Init

I assume you have done the initial installation

  1. pocketsphinx
  2. sphinxtrain
  3. sphinxbase

Data structure

├─ etc
    ├── sphinx_train.cfg
    ├── feat.params
    ├── corpus.txt
    ├── feat.params
    ├── other.dic
    ├── other.filler
    ├── other.idngram
    ├── other.lm
    ├── other.lm.bin
    ├── other.lm.DMP
    ├── other.phone
    ├── other_test.fileids
    ├── other_test.transcription
    ├── other_train.fileids
    ├── other_train.transcription
    ├── other.vocab
    └── sphinx_train.cfg
└─ wav
    ├── one.wav
    ├── one2.wav
    ├── one3.wav
    ├── one4.wav
    ├── two.wav
    ├── two2.wav
    ├── two3.wav
    ├── two4.wav
    ├── three.wav
    ├── three2.wav
    ├── three3.wav
    ├── three4.wav
    ├── four.wav
    ├── four2.wav
    ├── four3.wav
    └── four4.wav

Data corpus

<s> sentences on english one </s>
<s> sentences on english two </s>
<s> sentences on english three </s>
<s> sentences on english four </s>
<s> sentences on english one </s>
<s> sentences on english two </s>
<s> sentences on english three </s>
<s> sentences on english four </s>
<s> sentences on english one </s>
<s> sentences on english two </s>
<s> sentences on english three </s>
<s> sentences on english four </s>
<s> sentences on english one </s>
<s> sentences on english two </s>
<s> sentences on english three </s>
<s> sentences on english four </s>

How to run

  1. clone this repo:
cd <your-path>
git clone https://github.com/kirralabs/pocketsphinx-getstarted.git other

example: (on Linux)

cd /home/kirra/sphinx
git clone https://github.com/kirralabs/pocketsphinx-getstarted.git other
  1. setting etc/sphinx_train.cfg
# These are filled in at configuration time
$CFG_DB_NAME = "other";
# Experiment name, will be used to name model files and log files
$CFG_EXPTNAME = "$CFG_DB_NAME";

# Directory containing SphinxTrain binaries
$CFG_BASE_DIR = "<your-path>/other";

example: (on Linux)

# These are filled in at configuration time
$CFG_DB_NAME = "other";
# Experiment name, will be used to name model files and log files
$CFG_EXPTNAME = "$CFG_DB_NAME";

# Directory containing SphinxTrain binaries
$CFG_BASE_DIR = "/home/kirra/sphinx/other";

  1. go to root folder
cd /home/kirra/sphinx/other
  1. train model (on Linux)
sphinxtrain run

Change config from default (sphinx_train.cfg)

  1. number of senons (optional)
# Number of tied states (senones) to create in decision-tree clustering
$CFG_N_TIED_STATES = 8;
  1. model to use
# Models to use.
# $DEC_CFG_MODEL_NAME = "$CFG_EXPTNAME.cd_${CFG_DIRLABEL}_${CFG_N_TIED_STATES}";
$DEC_CFG_MODEL_NAME = "$CFG_EXPTNAME.ci_cont";

Testing model

  1. go to your root folder
cd /home/kirra/sphinx/other
  1. run testing
sphinxtrain -s decode run

output:

Sphinxtrain binaries path: /usr/local/libexec/sphinxtrain
MODULE: DECODE Decoding using models previously trained
        Decoding 4 segments starting at 0 (part 1 of 1) 
        0% 
        Aligning results to find error rate
        SENTENCE ERROR: 0.0% (0/4)   WORD ERROR RATE: 0.0% (0/16)mmandline

Transcript wav with new model

  1. run command
pocketsphinx_continuous -hmm model_parameters/other.ci_cont/ -lm etc/other.lm.bin -dict etc/other.dic -infile wav/one.wav

output:

INFO: pocketsphinx.c(152): Parsed model-specific feature parameters from model_parameters/other.ci_cont//feat.params
Current configuration:
[NAME]			[DEFLT]		[VALUE]
-agc			none		none
-agcthresh		2.0		2.000000e+00
-allphone				
-allphone_ci		yes		yes
-alpha			0.97		9.700000e-01
-ascale			20.0		2.000000e+01
-aw			1		1
-backtrace		no		no
-beam			1e-48		1.000000e-48
-bestpath		yes		yes
-bestpathlw		9.5		9.500000e+00
-ceplen			13		13
-cmn			live		batch
-cmninit		40,3,-1		40,3,-1
-compallsen		no		no
-dict					etc/other.dic
-dictcase		no		no
-dither			no		no
-doublebw		no		no
-ds			1		1
-fdict					
-feat			1s_c_d_dd	1s_c_d_dd
-featparams				
-fillprob		1e-8		1.000000e-08
-frate			100		100
-fsg					
-fsgusealtpron		yes		yes
-fsgusefiller		yes		yes
-fwdflat		yes		yes
-fwdflatbeam		1e-64		1.000000e-64
-fwdflatefwid		4		4
-fwdflatlw		8.5		8.500000e+00
-fwdflatsfwin		25		25
-fwdflatwbeam		7e-29		7.000000e-29
-fwdtree		yes		yes
-hmm					model_parameters/other.ci_cont/
-input_endian		little		little
-jsgf					
-keyphrase				
-kws					
-kws_delay		10		10
-kws_plp		1e-1		1.000000e-01
-kws_threshold		1e-30		1.000000e-30
-latsize		5000		5000
-lda					
-ldadim			0		0
-lifter			0		22
-lm					etc/other.lm.bin
-lmctl					
-lmname					
-logbase		1.0001		1.000100e+00
-logfn					
-logspec		no		no
-lowerf			133.33334	1.300000e+02
-lpbeam			1e-40		1.000000e-40
-lponlybeam		7e-29		7.000000e-29
-lw			6.5		6.500000e+00
-maxhmmpf		30000		30000
-maxwpf			-1		-1
-mdef					
-mean					
-mfclogdir				
-min_endfr		0		0
-mixw					
-mixwfloor		0.0000001	1.000000e-07
-mllr					
-mmap			yes		yes
-ncep			13		13
-nfft			512		512
-nfilt			40		25
-nwpen			1.0		1.000000e+00
-pbeam			1e-48		1.000000e-48
-pip			1.0		1.000000e+00
-pl_beam		1e-10		1.000000e-10
-pl_pbeam		1e-10		1.000000e-10
-pl_pip			1.0		1.000000e+00
-pl_weight		3.0		3.000000e+00
-pl_window		5		5
-rawlogdir				
-remove_dc		no		no
-remove_noise		yes		yes
-remove_silence		yes		yes
-round_filters		yes		yes
-samprate		16000		1.600000e+04
-seed			-1		-1
-sendump				
-senlogdir				
-senmgau				
-silprob		0.005		5.000000e-03
-smoothspec		no		no
-svspec					
-tmat					
-tmatfloor		0.0001		1.000000e-04
-topn			4		4
-topn_beam		0		0
-toprule				
-transform		legacy		dct
-unit_area		yes		yes
-upperf			6855.4976	6.800000e+03
-uw			1.0		1.000000e+00
-vad_postspeech		50		50
-vad_prespeech		20		20
-vad_startspeech	10		10
-vad_threshold		3.0		3.000000e+00
-var					
-varfloor		0.0001		1.000000e-04
-varnorm		no		no
-verbose		no		no
-warp_params				
-warp_type		inverse_linear	inverse_linear
-wbeam			7e-29		7.000000e-29
-wip			0.65		6.500000e-01
-wlen			0.025625	2.562500e-02

INFO: feat.c(715): Initializing feature stream to type: '1s_c_d_dd', ceplen=13, CMN='batch', VARNORM='no', AGC='none'
INFO: mdef.c(518): Reading model definition: model_parameters/other.ci_cont//mdef
INFO: bin_mdef.c(181): Allocating 84 * 8 bytes (0 KiB) for CD tree
INFO: tmat.c(149): Reading HMM transition probability matrices: model_parameters/other.ci_cont//transition_matrices
INFO: acmod.c(113): Attempting to use PTM computation module
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/other.ci_cont//means
INFO: ms_gauden.c(242): 60 codebook, 1 feature, size: 
INFO: ms_gauden.c(244):  1x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/other.ci_cont//variances
INFO: ms_gauden.c(242): 60 codebook, 1 feature, size: 
INFO: ms_gauden.c(244):  1x39
INFO: ms_gauden.c(304): 39 variance values floored
INFO: ptm_mgau.c(807): Number of codebooks doesn't match number of ciphones, doesn't look like PTM: 60 != 20
INFO: acmod.c(115): Attempting to use semi-continuous computation module
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/other.ci_cont//means
INFO: ms_gauden.c(242): 60 codebook, 1 feature, size: 
INFO: ms_gauden.c(244):  1x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/other.ci_cont//variances
INFO: ms_gauden.c(242): 60 codebook, 1 feature, size: 
INFO: ms_gauden.c(244):  1x39
INFO: ms_gauden.c(304): 39 variance values floored
INFO: acmod.c(117): Falling back to general multi-stream GMM computation
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/other.ci_cont//means
INFO: ms_gauden.c(242): 60 codebook, 1 feature, size: 
INFO: ms_gauden.c(244):  1x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/other.ci_cont//variances
INFO: ms_gauden.c(242): 60 codebook, 1 feature, size: 
INFO: ms_gauden.c(244):  1x39
INFO: ms_gauden.c(304): 39 variance values floored
INFO: ms_senone.c(149): Reading senone mixture weights: model_parameters/other.ci_cont//mixture_weights
INFO: ms_senone.c(200): Truncating senone logs3(pdf) values by 10 bits
INFO: ms_senone.c(207): Not transposing mixture weights in memory
INFO: ms_senone.c(268): Read mixture weights for 60 senones: 1 features x 1 codewords
INFO: ms_senone.c(320): Mapping senones to individual codebooks
INFO: ms_mgau.c(144): The value of topn: 4
WARN: "ms_mgau.c", line 148: -topn argument (4) invalid or > #density codewords (1); set to latter
INFO: phone_loop_search.c(114): State beam -225 Phone exit beam -225 Insertion penalty 0
INFO: dict.c(320): Allocating 4106 * 32 bytes (128 KiB) for word entries
INFO: dict.c(333): Reading main dictionary: etc/other.dic
INFO: dict.c(213): Dictionary size 7, allocated 0 KiB for strings, 0 KiB for phones
INFO: dict.c(336): 7 words read
INFO: dict.c(358): Reading filler dictionary: model_parameters/other.ci_cont//noisedict
INFO: dict.c(213): Dictionary size 10, allocated 0 KiB for strings, 0 KiB for phones
INFO: dict.c(361): 3 words read
INFO: dict2pid.c(396): Building PID tables for dictionary
INFO: dict2pid.c(406): Allocating 20^3 * 2 bytes (15 KiB) for word-initial triphones
INFO: dict2pid.c(132): Allocated 9760 bytes (9 KiB) for word-final triphones
INFO: dict2pid.c(196): Allocated 9760 bytes (9 KiB) for single-phone word triphones
INFO: ngram_model_trie.c(354): Trying to read LM in trie binary format
INFO: ngram_search_fwdtree.c(74): Initializing search tree
INFO: ngram_search_fwdtree.c(101): 7 unique initial diphones
INFO: ngram_search_fwdtree.c(186): Creating search channels
INFO: ngram_search_fwdtree.c(323): Max nonroot chan increased to 142
INFO: ngram_search_fwdtree.c(333): Created 7 root, 14 non-root channels, 3 single-phone words
INFO: ngram_search_fwdflat.c(157): fwdflat: min_ef_width = 4, max_sf_win = 25
INFO: continuous.c(307): pocketsphinx_continuous COMPILED ON: Dec 13 2017, AT: 14:49:57

INFO: cmn_live.c(120): Update from < 40.00  3.00 -1.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00 >
INFO: cmn_live.c(138): Update to   < 66.04 -3.88  8.58  3.03 -8.41  1.44  3.32  0.09  1.85 -3.52  8.11 -3.18 -1.55 >
INFO: ngram_search_fwdtree.c(1550):      330 words recognized (1/fr)
INFO: ngram_search_fwdtree.c(1552):     3261 senones evaluated (9/fr)
INFO: ngram_search_fwdtree.c(1556):     1136 channels searched (3/fr), 400 1st, 410 last
INFO: ngram_search_fwdtree.c(1559):      410 words for which last channels evaluated (1/fr)
INFO: ngram_search_fwdtree.c(1561):       24 candidate words for entering last phone (0/fr)
INFO: ngram_search_fwdtree.c(1564): fwdtree 0.01 CPU 0.002 xRT
INFO: ngram_search_fwdtree.c(1567): fwdtree 0.01 wall 0.002 xRT
INFO: ngram_search_fwdflat.c(302): Utterance vocabulary contains 4 words
INFO: ngram_search_fwdflat.c(948):      329 words recognized (1/fr)
INFO: ngram_search_fwdflat.c(950):     2406 senones evaluated (7/fr)
INFO: ngram_search_fwdflat.c(952):     1229 channels searched (3/fr)
INFO: ngram_search_fwdflat.c(954):     1001 words searched (2/fr)
INFO: ngram_search_fwdflat.c(957):      136 word transitions (0/fr)
INFO: ngram_search_fwdflat.c(960): fwdflat 0.00 CPU 0.000 xRT
INFO: ngram_search_fwdflat.c(963): fwdflat 0.00 wall 0.000 xRT
INFO: ngram_search.c(1250): lattice start node <s>.0 end node </s>.346
INFO: ngram_search.c(1276): Eliminated 0 nodes before end node
INFO: ngram_search.c(1381): Lattice has 24 nodes, 16 links
INFO: ps_lattice.c(1374): Bestpath score: -12109
INFO: ps_lattice.c(1378): Normalizer P(O) = alpha(</s>:346:352) = -637945
INFO: ps_lattice.c(1435): Joint P(O,S) = -640502 P(S|O) = -2557
INFO: ngram_search.c(872): bestpath 0.00 CPU 0.000 xRT
INFO: ngram_search.c(875): bestpath 0.00 wall 0.000 xRT
sentences on
INFO: cmn_live.c(120): Update from < 66.04 -3.88  8.58  3.03 -8.41  1.44  3.32  0.09  1.85 -3.52  8.11 -3.18 -1.55 >
INFO: cmn_live.c(138): Update to   < 66.04 -3.88  8.58  3.03 -8.41  1.44  3.32  0.09  1.85 -3.52  8.11 -3.18 -1.55 >
INFO: ngram_search_fwdflat.c(302): Utterance vocabulary contains 0 words
INFO: ngram_search_fwdtree.c(429): TOTAL fwdtree 0.01 CPU 0.002 xRT
INFO: ngram_search_fwdtree.c(432): TOTAL fwdtree 0.01 wall 0.002 xRT
INFO: ngram_search_fwdflat.c(176): TOTAL fwdflat 0.00 CPU 0.000 xRT
INFO: ngram_search_fwdflat.c(179): TOTAL fwdflat 0.00 wall 0.000 xRT
INFO: ngram_search.c(303): TOTAL bestpath 0.00 CPU 0.000 xRT
INFO: ngram_search.c(306): TOTAL bestpath 0.00 wall 0.000 xRT
  1. the result is:
sentences on

this model still not accurate, you need put more data to get high accuracy

Models

You can use the pretain model that i have trained in "sample-output-trainnewmodel.zip".

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