Pocketsphinx installation Windows 10

[Thomas Pilarski]

I am trying to install pocketsphinx on Windows 10. I have downloaded pocketsphinx and sphinxbase, built them both, and put sphinxbase.dll into the pocketsphinx bin. However when I run the command:

bin\Debug\Win32\pocketsphinx_continuous.exe -inmic yes -hmm model\en-us\en-us -lm model\en-us\en-us.lm.bin -dict model\en-us\cmudict-en-us.dict

the last few lines of output look like this:

INFO: feat.c(715): Initializing feature stream to type: '1s_c_d_dd', ceplen=13, CMN='batch', VARNORM='no', AGC='none'
INFO: acmod.c(162): Using subvector specification 0-12/13-25/26-38
INFO: mdef.c(518): Reading model definition: model/en-us/en-us/mdef
INFO: mdef.c(531): Found byte-order mark BMDF, assuming this is a binary mdef file
INFO: bin_mdef.c(336): Reading binary model definition: model/en-us/en-us/mdef
INFO: bin_mdef.c(516): 42 CI-phone, 137053 CD-phone, 3 emitstate/phone, 126 CI-sen, 5126 Sen, 29324 Sen-Seq
INFO: tmat.c(149): Reading HMM transition probability matrices: model/en-us/en-us/transition_matrices

However I do not get the Ready... or Listening... prompt that I have seen other people get to test out the software. I seem to get much less output than others as well (nothing about dict.c or ms_gauden.c etc.) Any advice?

[Nickolay V. Shmyrev]

it is a crash due to runtime mismatch as described in:

https://cmusphinx.github.io/wiki/faq/#q-pocketsphinx-crashes-on-windows-in-_lock_file

you need to check how your visual studio updated the project files. It most likely screwed things.

[Thomas Pilarski]

Thanks for the quick response! I'm still a little unclear when looking over that link. Could you explain how to check how that in visual studio? Sorry I don't have a ton of experience with visual studio or how it works

[Thomas Pilarski]

I was able to figure it out. I redownloaded the files and changed the Runtime Library property to Multi-threaded DLL (/MD).

For anyone else who ran into this problem I went to Project -> Properties -> C/C++ -> Code Generation -> Runtime Library.

Works great now. Thanks!

[Nickolay V. Shmyrev]

Good. Try vosk-api between.

[prathna]

hello can someone help me with this please

Configuration script for sphinx trainer --mode:Perl--

$CFG_VERBOSE = 1; # Determines how much goes to the screen.

These are filled in at configuration time

$CFG_DB_NAME = "kreol";

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/prathna/speech_recognition/other";
$CFG_SPHINXTRAIN_DIR = "/home/prathna/speech_recognition/sphinxtrain";
$CFG_BIN_DIR = "/home/prathna/speech_recognition/sphinxtrain/build";
$CFG_SCRIPT_DIR = "/home/prathna/speech_recognition/sphinxtrain/scripts";

Audio waveform and feature file information

$CFG_WAVFILES_DIR = "$CFG_BASE_DIR/wav";
$CFG_WAVFILE_EXTENSION = 'wav';
$CFG_WAVFILE_TYPE = 'mswav'; # one of nist, mswav, raw
$CFG_FEATFILES_DIR = "$CFG_BASE_DIR/feat";
$CFG_FEATFILE_EXTENSION = 'mfc';

Feature extraction parameters

$CFG_WAVFILE_SRATE = 8000.0;
$CFG_NUM_FILT = 31; # For wideband speech it's 25, for telephone 8khz reasonable value is 15
$CFG_LO_FILT = 200; # For telephone 8kHz speech value is 200
$CFG_HI_FILT = 3500; # For telephone 8kHz speech value is 3500
$CFG_TRANSFORM = "dct"; # Previously legacy transform is used, but dct is more accurate
$CFG_LIFTER = "22"; # Cepstrum lifter is smoothing to improve recognition
$CFG_VECTOR_LENGTH = 13; # 13 is usually enough

$CFG_MIN_ITERATIONS = 1; # BW Iterate at least this many times
$CFG_MAX_ITERATIONS = 10; # BW Don't iterate more than this, somethings likely wrong.

(none/max) Type of AGC to apply to input files

$CFG_AGC = 'none';

(current/none) Type of cepstral mean subtraction/normalization

to apply to input files

$CFG_CMN = 'batch';

(yes/no) Normalize variance of input files to 1.0

$CFG_VARNORM = 'no';

(yes/no) Train full covariance matrices

$CFG_FULLVAR = 'no';

(yes/no) Use diagonals only of full covariance matrices for

Forward-Backward evaluation (recommended if CFG_FULLVAR is yes)

$CFG_DIAGFULL = 'no';

(yes/no) Perform vocal tract length normalization in training. This

will result in a "normalized" model which requires VTLN to be done

during decoding as well.

$CFG_VTLN = 'no';

Starting warp factor for VTLN

$CFG_VTLN_START = 0.80;

Ending warp factor for VTLN

$CFG_VTLN_END = 1.40;

Step size of warping factors

$CFG_VTLN_STEP = 0.05;

Directory to write queue manager logs to

$CFG_QMGR_DIR = "$CFG_BASE_DIR/qmanager";

Directory to write training logs to

$CFG_LOG_DIR = "$CFG_BASE_DIR/logdir";

Directory for re-estimation counts

$CFG_BWACCUM_DIR = "$CFG_BASE_DIR/bwaccumdir";

Directory to write model parameter files to

$CFG_MODEL_DIR = "$CFG_BASE_DIR/model_parameters";

Directory containing transcripts and control files for

speaker-adaptive training

$CFG_LIST_DIR = "$CFG_BASE_DIR/etc";

Decoding variables for MMIE training

$CFG_LANGUAGEWEIGHT = "11.5";
$CFG_BEAMWIDTH = "1e-100";
$CFG_WORDBEAM = "1e-80";
$CFG_LANGUAGEMODEL = "$CFG_LIST_DIR/$CFG_DB_NAME.lm.DMP";
$CFG_WORDPENALTY = "0.2";

Lattice pruning variables

$CFG_ABEAM = "1e-50";
$CFG_NBEAM = "1e-10";
$CFG_PRUNED_DENLAT_DIR = "$CFG_BASE_DIR/pruned_denlat";

MMIE training related variables

$CFG_MMIE = "no";
$CFG_MMIE_MAX_ITERATIONS = 5;
$CFG_LATTICE_DIR = "$CFG_BASE_DIR/lattice";
$CFG_MMIE_TYPE = "rand"; # Valid values are "rand", "best" or "ci"
$CFG_MMIE_CONSTE = "3.0";
$CFG_NUMLAT_DIR = "$CFG_BASE_DIR/numlat";
$CFG_DENLAT_DIR = "$CFG_BASE_DIR/denlat";

Variables used in main training of models

$CFG_DICTIONARY = "$CFG_LIST_DIR/$CFG_DB_NAME.dic";
$CFG_RAWPHONEFILE = "$CFG_LIST_DIR/$CFG_DB_NAME.phone";
$CFG_FILLERDICT = "$CFG_LIST_DIR/$CFG_DB_NAME.filler";
$CFG_LISTOFFILES = "$CFG_LIST_DIR/${CFG_DB_NAME}_train.fileids";
$CFG_TRANSCRIPTFILE = "$CFG_LIST_DIR/${CFG_DB_NAME}_train.transcription";
$CFG_FEATPARAMS = "$CFG_LIST_DIR/feat.params";

(yes/no) Allow cased phone and word names; otherwise, forces upper case

$CFG_CASEDSYMBOLS = 'no';

Variables used in characterizing models

$CFG_HMM_TYPE = '.cont.'; # Sphinx 4, PocketSphinx
$CFG_HMM_TYPE = '.semi.'; # PocketSphinx

$CFG_HMM_TYPE = '.ptm.'; # PocketSphinx (larger data sets)

if (($CFG_HMM_TYPE ne ".semi.")
and ($CFG_HMM_TYPE ne ".ptm.")
and ($CFG_HMM_TYPE ne ".cont.")) {
die "Please choose one CFG_HMM_TYPE out of '.cont.', '.ptm.', or '.semi.', " .
"currently $CFG_HMM_TYPE\n";
}

This configuration is fastest and best for most acoustic models in

PocketSphinx and Sphinx-III. See below for Sphinx-II.

$CFG_STATESPERHMM = 3;
$CFG_SKIPSTATE = 'no';

if ($CFG_HMM_TYPE eq '.semi.') {
$CFG_DIRLABEL = 'semi';

Four stream features for PocketSphinx

$CFG_FEATURE = "s2_4x";
$CFG_NUM_STREAMS = 4;
$CFG_INITIAL_NUM_DENSITIES = 256;
$CFG_FINAL_NUM_DENSITIES = 256;
die "For semi continuous models, the initial and final models have the same density"
if ($CFG_INITIAL_NUM_DENSITIES != $CFG_FINAL_NUM_DENSITIES);
} elsif ($CFG_HMM_TYPE eq '.ptm.') {
$CFG_DIRLABEL = 'ptm';

Four stream features for PocketSphinx

$CFG_FEATURE = "s2_4x";
$CFG_NUM_STREAMS = 4;
$CFG_INITIAL_NUM_DENSITIES = 64;
$CFG_FINAL_NUM_DENSITIES = 8;
die "For phonetically tied models, the initial and final models have the same density"
if ($CFG_INITIAL_NUM_DENSITIES != $CFG_FINAL_NUM_DENSITIES);
} elsif ($CFG_HMM_TYPE eq '.cont.') {
$CFG_DIRLABEL = 'cont';

Single stream features - Sphinx 3

$CFG_FEATURE = "1s_c_d_dd";
$CFG_NUM_STREAMS = 1;
$CFG_INITIAL_NUM_DENSITIES = 1;
$CFG_FINAL_NUM_DENSITIES = 8;
die "The initial has to be less than the final number of densities"
if ($CFG_INITIAL_NUM_DENSITIES > $CFG_FINAL_NUM_DENSITIES);
}

Number of top gaussians to score a frame. A little bit less accurate computations

make training significantly faster. Uncomment to apply this during the training

For good accuracy make sure you are using the same setting in decoder

In theory this can be different for various training stages. For example 4 for

CI stage and 16 for CD stage

$CFG_CI_TOPN = 4;

$CFG_CD_TOPN = 16;

(yes/no) Train multiple-gaussian context-independent models (useful

for alignment, use 'no' otherwise) in the models created

specifically for forced alignment

$CFG_FALIGN_CI_MGAU = 'no';

(yes/no) Train multiple-gaussian context-independent models (useful

for alignment, use 'no' otherwise)

$CFG_CI_MGAU = 'no';

(yes/no) Train context-dependent models

$CFG_CD_TRAIN = 'yes';

Number of tied states (senones) to create in decision-tree clustering

$CFG_N_TIED_STATES = 1000;

How many parts to run Forward-Backward estimatinon in

$CFG_NPART = 1;

(yes/no) Train a single decision tree for all phones (actually one

per state) (useful for grapheme-based models, use 'no' otherwise)

$CFG_CROSS_PHONE_TREES = 'no';

Use force-aligned transcripts (if available) as input to training

$CFG_FORCEDALIGN = 'no';

Use a specific set of models for force alignment. If not defined,

context-independent models for the current experiment will be used.

$CFG_FORCE_ALIGN_MODELDIR = "$CFG_MODEL_DIR/$CFG_EXPTNAME.falign_ci_$CFG_DIRLABEL";

Use a specific dictionary and filler dictionary for force alignment.

If these are not defined, a dictionary and filler dictionary will be

created from $CFG_DICTIONARY and $CFG_FILLERDICT, with noise words

removed from the filler dictionary and added to the dictionary (this

is because the force alignment is not very good at inserting them)

$CFG_FORCE_ALIGN_DICTIONARY = "$ST::CFG_BASE_DIR/falignout$ST::CFG_EXPTNAME.falign.dict";;

$CFG_FORCE_ALIGN_FILLERDICT = "$ST::CFG_BASE_DIR/falignout/$ST::CFG_EXPTNAME.falign.fdict";;

Use a particular beam width for force alignment. The wider

(i.e. smaller numerically) the beam, the fewer sentences will be

rejected for bad alignment.

$CFG_FORCE_ALIGN_BEAM = 1e-60;

Calculate an LDA/MLLT transform?

$CFG_LDA_MLLT = 'no';

Dimensionality of LDA/MLLT output

$CFG_LDA_DIMENSION = 29;

This is actually just a difference in log space (it doesn't make

sense otherwise, because different feature parameters have very

different likelihoods)

$CFG_CONVERGENCE_RATIO = 0.1;

Queue::POSIX for multiple CPUs on a local machine

Queue::PBS to use a PBS/TORQUE queue

$CFG_QUEUE_TYPE = "Queue::POSIX";

Name of queue to use for PBS/TORQUE

$CFG_QUEUE_NAME = "workq";

(yes/no) Build questions for decision tree clustering automatically

$CFG_MAKE_QUESTS = "yes";

If CFG_MAKE_QUESTS is yes, questions are written to this file.

If CFG_MAKE_QUESTS is no, questions are read from this file.

$CFG_QUESTION_SET = "${CFG_BASE_DIR}/model_architecture/${CFG_EXPTNAME}.tree_questions";

$CFG_QUESTION_SET = "${CFG_BASE_DIR}/linguistic_questions";

$CFG_CP_OPERATION = "${CFG_BASE_DIR}/model_architecture/${CFG_EXPTNAME}.cpmeanvar";

Configuration for grapheme-to-phoneme model

$CFG_G2P_MODEL= 'no';

Configuration script for sphinx decoder

Variables starting with $DEC_CFG_ refer to decoder specific

arguments, those starting with $CFG_ refer to trainer arguments,

some of them also used by the decoder.

$DEC_CFG_VERBOSE = 1; # Determines how much goes to the screen.

These are filled in at configuration time

Name of the decoding script to use (psdecode.pl or s3decode.pl, probably)

$DEC_CFG_SCRIPT = 'psdecode.pl';

$DEC_CFG_EXPTNAME = "$CFG_EXPTNAME";
$DEC_CFG_JOBNAME = "$CFG_EXPTNAME"."_job";

Models to use.

if ($CFG_CD_TRAIN eq 'yes') {
$DEC_CFG_MODEL_NAME = "$CFG_EXPTNAME.cd_${CFG_DIRLABEL}${CFG_N_TIED_STATES}";
} else {
$DEC_CFG_MODEL_NAME = "$CFG_EXPTNAME.ci${CFG_DIRLABEL}";
}

$DEC_CFG_FEATFILES_DIR = "$CFG_BASE_DIR/feat";
$DEC_CFG_FEATFILE_EXTENSION = '.mfc';
$DEC_CFG_AGC = $CFG_AGC;
$DEC_CFG_CMN = $CFG_CMN;
$DEC_CFG_VARNORM = $CFG_VARNORM;

$DEC_CFG_QMGR_DIR = "$CFG_BASE_DIR/qmanager";
$DEC_CFG_LOG_DIR = "$CFG_BASE_DIR/logdir";
$DEC_CFG_MODEL_DIR = "$CFG_MODEL_DIR";

$DEC_CFG_DICTIONARY = "$CFG_BASE_DIR/etc/$CFG_DB_NAME.dic";
$DEC_CFG_FILLERDICT = "$CFG_BASE_DIR/etc/$CFG_DB_NAME.filler";
$DEC_CFG_LISTOFFILES = "$CFG_BASE_DIR/etc/${CFG_DB_NAME}_test.fileids";
$DEC_CFG_TRANSCRIPTFILE = "$CFG_BASE_DIR/etc/${CFG_DB_NAME}_test.transcription";
$DEC_CFG_RESULT_DIR = "$CFG_BASE_DIR/result";
$DEC_CFG_PRESULT_DIR = "$CFG_BASE_DIR/presult";

This variables, used by the decoder, have to be user defined, and

may affect the decoder output

$DEC_CFG_LANGUAGEMODEL = "$CFG_BASE_DIR/etc/kreol.ug.lm.DMP";

Or can be JSGF or FSG too, used if uncommented

$DEC_CFG_GRAMMAR = "$CFG_BASE_DIR/etc/${CFG_DB_NAME}.jsgf";

$DEC_CFG_FSG = "$CFG_BASE_DIR/etc/${CFG_DB_NAME}.fsg";

$DEC_CFG_LANGUAGEWEIGHT = "10";
$DEC_CFG_BEAMWIDTH = "1e-80";
$DEC_CFG_WORDBEAM = "1e-40";
$DEC_CFG_WORDPENALTY = "0.2";

$DEC_CFG_ALIGN = "builtin";

$DEC_CFG_NPART = 1; # Define how many pieces to split decode in

This variable has to be defined, otherwise utils.pl will not load.

$CFG_DONE = 1;

return 1;