Speech:Summer 2011 Brian Log


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Week Ending July 6, 2011
Do this and that.
 * Task:

Wrote training guide. Will test it if I have time tomorrow, but wanted to write the guide tonight. Having trouble uploading files to foss due to the fact that it is locked down so file links currently remain broken links.
 * Results:

Copied the virtual machine that I have been running on the server at home to the laptop. Not sure how long it will last here due to the fact that my hard drive is almost full, but it's there so I can work on it.

I have now gotten sphinx3, sphinxbase, and sphinxtrain to compile. I have built a project to use for training and downloaded the scripts, but am having trouble running a train due to the fact that I no longer have the original sph files and the genTrans.pl script will not generate a transcript without being able to process the sph files. Not sure why. Theoretically it should work.

Copied over transcript, fileids, wavs, and the other necessary scripts over from the original train that we got to work on caesar and tried to run the train. I have run into a couple of isuses with this, some of which I have worked through. Right now I have reached the point where I have an error concerning utterance id mismatches on line 1.

Got the train working by redownloading all of the files from caesar (audio as well as transcript, phonemes, filler, etc). Ran the train. It says that there is a 12% currnet likelihood per frame. Not sure what that means.

Next will do this. None this week.
 * Plan:
 * Concerns:

Week ending August 12, 2011
Today, August 8, 2011. Started at around 10:30PM

I've only looked for a couple of minutes so far, but looking through Nick's logs, it appears like he has written three or four scripts for the language model, but is still unsure of what a language model is. He states in the last week of class that he has just realized that the language model does not rely on the dictionary. I have not yet found a script that appears like it will generate a language model for us in his wiki, but will continue looking and if needed write a script to generate a language model myself.

I can not remember at this point what a language model is or what it does, so I am looking it up on CMU's website. It looks like our system will need the cmulmtoolkit and this is not yet installed, so I will download and install it. CMULMTK installed pretty easily. No errors or anything. Just a few minutes of wating for autogen, make, and make install to run. While I'm at it I'm downloading sclite as well. Took a few minutes to find it, but eventually found it here: http://www.itl.nist.gov/iad/mig//tools/ under sctk. Seems to be compiling fine. Will probably read through Nick's wiki after work tomorrow to see if I can make any sense of it. Compiles still fail, but I will look into that more later.

According to wikipedia, it looks like the language model is where the probability of a specific word occurring in a specific situation is set up (a certain word is more likely to occur after a word than another word would be). This is where I found the language model information: http://en.wikipedia.org/wiki/Language_model. Might be interesting to look into more later.

Nick's wiki discusses id 3-gram, which according to cam.ac.uk (http://mi.eng.cam.ac.uk/~prc14/toolkit_documentation.html#idngram_file) is:

ASCII or binary (by default) file containing a numerically sorted list of n-tuples of numbers, corresponding to the mapping of the word n-grams relative to the vocabulary. Out of vocabulary (OOV) words are mapped to the number 0.

Can't understand the definition at the moment, but will see if I can understand it tomorrow when I am less tired. That document might also be useful because it appears to explain what each of the executables do.

I'm going to go to bed. Tired and planning on getting up at 7:30 or 7:45.

Ending at 12:27AM on August 9, 2011.

Star time: Wednesday August 9, 2011 10:50PM

use text to build vocab data use text to build id 3-gram

1.convert text to a word frequency file 	text2wfreq filename.txt 2.create a vocab file from word ount		wfreq2vocab wfreqfile.wfreq 3. create 3-gram file using vocab or text	text2idngram -vocab vocab file > filename.idngram 4. build language model 			idngram2lm -idngram idngramfile.idngram -vocab vocabfile.vocab -binary filename.binlm

Use Nick's strip script (April 5) on unedited transcript. Won't remove and or

created directory languageModel in etc. then copied stripped transcript into this new directory.

Ran this next to generate wfreq. Wouldn't quite work the way Nick had it written, but a quick reference to the help (-help) fixed that text2wfreq  transcript.wfreq Appears to be a count of the number of times that each word appears in the text.

Ran this next to generate vocab. Wouldn't quite work the way Nick had it written, but a quick reference to the help (-help) fixed that

wfreq2vocab  transcript.vocab

Appears to be a dictionary of the words in the text\

Used this command to generate idngram: text2idngram -vocab transcript.vocab -idngram transcript.idngram

Took a while to figure out how to use text2idngram. Nick's script/wiki seems to be way off here. But here's what I found. I think it's right.

text2idngram -vocab transcript.vocab -idngram transcript.idngram < transcript.text

text2idngram Vocab                 : transcript.vocab Output idngram        : transcript.idngram N-gram buffer size    : 100 Hash table size       : 2000000 Temp directory        : cmuclmtk-voJvlt Max open files        : 20 FOF size              : 10 n                     : 3 Initialising hash table... Reading vocabulary... Allocating memory for the n-gram buffer... Reading text into the n-gram buffer... 20,000 n-grams processed for each ".", 1,000,000 for each line.

Sorting n-grams... Writing sorted n-grams to temporary file cmuclmtk-voJvlt/1 Merging 1 temporary files...

2-grams occurring:	N times		> N times	Sug. -spec_num value 0						  1272		   1294      1				   1053		    219		    231      2				    160		     59		     69      3				     37		     22		     32      4				     12		     10		     20      5				      4		      6		     16      6				      2		      4		     14      7				      1		      3		     13      8				      1		      2		     12      9				      0		      2		     12     10				      0		      2		     12

3-grams occurring:	N times		> N times	Sug. -spec_num value 0						  1474		   1498      1				   1340		    134		    145      2				    127		      7		     17      3				      7		      0		     10      4				      0		      0		     10      5				      0		      0		     10      6				      0		      0		     10      7				      0		      0		     10      8				      0		      0		     10      9				      0		      0		     10     10				      0		      0		     10 text2idngram : Done.

No idea what any of that means.

Next up: langauge model.

idngram2lm -idngram transcript.idngram -vocab transcript.vocab -binary transcript.binlm

n : 3 Input file : transcript.idngram    (binary format) Output files : Binary format : transcript.binlm Vocabulary file : transcript.vocab Cutoffs : 2-gram : 0    3-gram : 0 Vocabulary type : Open - type 1 Minimum unigram count : 0 Zeroton fraction : 1 Counts will be stored in two bytes. Count table size : 65535 Discounting method : Good-Turing Discounting ranges : 1-gram : 1    2-gram : 7     3-gram : 7 Memory allocation for tree structure : Allocate 100 MB of memory, shared equally between all n-gram tables. Back-off weight storage : Back-off weights will be stored in four bytes. Reading vocabulary.

read_wlist_into_siht: a list of 520 words was read from "transcript.vocab". read_wlist_into_array: a list of 520 words was read from "transcript.vocab". Allocated space for 3571428 2-grams. Allocated space for 8333333 3-grams. table_size 521 Allocated 57142848 bytes to table for 2-grams. Allocated (2+33333332) bytes to table for 3-grams. Processing id n-gram file. 20,000 n-grams processed for each ".", 1,000,000 for each line.

Calculating discounted counts. Warning : 1-gram : Discounting range is 1; setting P(zeroton)=P(singleton). Discounted value : 1.00 Warning : 2-gram : Some discount values are out of range; lowering discounting range to 6. Warning : 3-gram : GT statistics are out of range; lowering cutoff to 6. Warning : 3-gram : GT statistics are out of range; lowering cutoff to 5. Warning : 3-gram : GT statistics are out of range; lowering cutoff to 4. Warning : 3-gram : GT statistics are out of range; lowering cutoff to 3. Warning : 3-gram : GT statistics are out of range; lowering cutoff to 2. Unigrams's discount mass is 0.000617209 (n1/N = 0.19257) 1 zerotons, P(zeroton) = 0.000617209 P(singleton) = 0.00061721 prob[UNK] = 0.000617209 Incrementing contexts... Calculating back-off weights... Writing out language model... Binary 3-gram language model will be written to transcript.binlm idngram2lm : Done.

A couple of warnings, but I will ignore them for now.

Copied the language model up to etc.

Attempted a decode. This was my command:

sphinx3_decode -hmm model_parameters/train1.cd_cont_1000/ -lm etc/transcript.binlm -dict etc/train1.dic -fdict etc/train1.filler -ctl etc/train1_train.fileids -cepdir wav -cepext .sph > ~/decodeOutput.txt

INFO: info.c(65): Host: 'alphaSphinx' INFO: info.c(69): Directory: '/home/briansvgs/sphinxSetup/SphinxTrain/train1' INFO: info.c(73): sphinx3_decode Compiled on: Jul 21 2011, AT: 10:44:17

INFO: cmd_ln.c(691): Parsing command line: sphinx3_decode \ -hmm model_parameters/train1.cd_cont_1000/ \ -lm etc/transcript.binlm \ -dict etc/train1.dic \ -fdict etc/train1.filler \ -ctl etc/train1_train.fileids \ -cepdir wav \ -cepext .sph

Current configuration: [NAME]			[DEFLT]		[VALUE] -adchdr			0		0 -adcin			no		no -agc			none		none -agcthresh		2.0		2.000000e+00 -alpha			0.97		9.700000e-01 -backtrace		yes		yes -beam			1.0e-55		1.000000e-55 -bestpath		no		no -bestpathlw				0.000000e+00 -bestscoredir -bestsenscrdir -bghist			no		no -bgonly			no		no -bptbldir -bptblsize		32768		32768 -build_outdirs		yes		yes -cb2mllr		.1cls. .1cls. -cepdir			. wav -cepext			.mfc		.sph -ceplen			13		13 -ci_pbeam		1e-80		1.000000e-80 -cmn			current		current -cmninit		8.0		8.0 -cond_ds		no		no -ctl					etc/train1_train.fileids -ctlcount		1000000000	1000000000 -ctloffset		0		0 -ctl_lm -ctl_mllr -dagfudge		2		2 -debug					0 -dict					etc/train1.dic -dist_ds		no		no -dither			no		no -doublebw		no		no -ds			1		1 -epl			3		3 -fdict					etc/train1.filler -feat			1s_c_d_dd	1s_c_d_dd -featparams -fillpen -fillprob		0.1		1.000000e-01 -frate			100		100 -fsg -fsgusealtpron		yes		yes -fsgusefiller		yes		yes -gs -gs4gs			yes		yes -hmm					model_parameters/train1.cd_cont_1000/ -hmmdump		no		no -hmmdumpef		200000000	200000000 -hmmdumpsf		200000000	200000000 -hmmhistbinsize		5000		5000 -hyp -hypseg -hypsegscore_unscale	yes		yes -inlatdir -inlatwin		50		50 -input_endian		little		little -kdmaxbbi		-1		-1 -kdmaxdepth		0		0 -kdtree -latcompress		yes		yes -latext			lat.gz		lat.gz -lda -ldadim			0		0 -lextreedump		0		0 -lifter			0		0 -lm					etc/transcript.binlm -lmctlfn -lmdumpdir -lmname -log3table		yes		yes -logbase		1.0003		1.000300e+00 -logfn -logspec		no		no -lowerf			133.33334	1.333333e+02 -lts_mismatch		no		no -lw			9.5		9.500000e+00 -maxcdsenpf		100000		100000 -maxedge		2000000		2000000 -maxhistpf		100		100 -maxhmmpf		20000		20000 -maxlmop		100000000	100000000 -maxlpf			40000		40000 -maxppath		1000000		1000000 -maxwpf			20		20 -mdef -mdef_fillers		no		no -mean -min_endfr		3		3 -mixw -mixwfloor		0.0000001	1.000000e-07 -mllr -mode			fwdtree		fwdtree -nbest			200		200 -nbestdir -nbestext		nbest.gz	nbest.gz -ncep			13		13 -nfft			512		512 -nfilt			40		40 -Nlextree		3		3 -Nstalextree		25		25 -op_mode		-1		-1 -outlatdir -outlatfmt		s3		s3 -pbeam			1.0e-50		1.000000e-50 -pheurtype		0		0 -phonepen		1.0		1.000000e+00 -phsegdir -pl_beam		1.0e-80		1.000000e-80 -pl_window		1		1 -ppathdebug		no		no -ptranskip		0		0 -remove_dc		no		no -round_filters		yes		yes -samprate		16000		1.600000e+04 -seed			-1		-1 -sendump -senmgau		.cont. .cont. -silprob		0.1		1.000000e-01 -smoothspec		no		no -subvq -subvqbeam		3.0e-3		3.000000e-03 -svq4svq		no		no -svspec -tighten_factor		0.5		5.000000e-01 -tmat -tmatfloor		0.0001		1.000000e-04 -topn			4		4 -topn_beam		0		0 -tracewhmm -transform		legacy		legacy -treeugprob		yes		yes -ugonly			no		no -unit_area		yes		yes -upperf			6855.4976	6.855498e+03 -utt -uw			0.7		7.000000e-01 -var -varfloor		0.0001		1.000000e-04 -varnorm		no		no -verbose		no		no -vqeval			3		3 -warp_params -warp_type		inverse_linear	inverse_linear -wbeam			1.0e-35		1.000000e-35 -wend_beam		1.0e-80		1.000000e-80 -wip			0.7		7.000000e-01 -wlen			0.025625	2.562500e-02 -worddumpef		200000000	200000000 -worddumpsf		200000000	200000000

INFO: kbcore.c(442): Begin Initialization of Core Models: INFO: cmd_ln.c(691): Parsing command line: \	-alpha 0.97 \ -doublebw no \ -nfilt 40 \ -ncep 13 \ -lowerf 133.33334 \ -upperf 6855.4976 \ -nfft 512 \ -wlen 0.0256 \ -transform legacy \ -feat 1s_c_d_dd \ -agc none \ -cmn current \ -varnorm no

Current configuration: [NAME]		[DEFLT]		[VALUE] -agc		none		none -agcthresh	2.0		2.000000e+00 -alpha		0.97		9.700000e-01 -ceplen		13		13 -cmn		current		current -cmninit	8.0		8.0 -dither		no		no -doublebw	no		no -feat		1s_c_d_dd	1s_c_d_dd -frate		100		100 -input_endian	little		little -lda -ldadim		0		0 -lifter		0		0 -logspec	no		no -lowerf		133.33334	1.333333e+02 -ncep		13		13 -nfft		512		512 -nfilt		40		40 -remove_dc	no		no -round_filters	yes		yes -samprate	16000		1.600000e+04 -seed		-1		-1 -smoothspec	no		no -svspec -transform	legacy		legacy -unit_area	yes		yes -upperf		6855.4976	6.855498e+03 -varnorm	no		no -verbose	no		no -warp_params -warp_type	inverse_linear	inverse_linear -wlen		0.025625	2.560000e-02

INFO:	Initialization of the log add table INFO:	Log-Add table size = 29356 x 2 >> 0 INFO: INFO: feat.c(684): Initializing feature stream to type: '1s_c_d_dd', ceplen=13, CMN='current', VARNORM='no', AGC='none' INFO: cmn.c(142): mean[0]= 12.00, mean[1..12]= 0.0 INFO: kbcore.c(489): .cont. INFO:	Initialization of feat_t, report: INFO:	Feature type        = 1s_c_d_dd INFO:	Cepstral size       = 13 INFO:	Number of streams   = 1 INFO:	Vector size of stream[0]: 39 INFO:	Number of subvectors = 0 INFO:	Whether CMN is used = 1 INFO:	Whether AGC is used = 0 INFO:	Whether variance is normalized = 0 INFO: INFO:	Reading HMM in Sphinx 3 Model format INFO:	Model Definition File: model_parameters/train1.cd_cont_1000//mdef INFO:	Mean File: model_parameters/train1.cd_cont_1000//means INFO:	Variance File: model_parameters/train1.cd_cont_1000//variances INFO:	Mixture Weight File: model_parameters/train1.cd_cont_1000//mixture_weights INFO:	Transition Matrices File: model_parameters/train1.cd_cont_1000//transition_matrices INFO: mdef.c(683): Reading model definition: model_parameters/train1.cd_cont_1000//mdef INFO:	Initialization of mdef_t, report: INFO:	40 CI-phone, 20389 CD-phone, 3 emitstate/phone, 120 CI-sen, 1120 Sen, 1291 Sen-Seq INFO: INFO: kbcore.c(299): Using optimized GMM computation for Continuous HMM, -topn will be ignored INFO: cont_mgau.c(164): Reading mixture gaussian file 'model_parameters/train1.cd_cont_1000//means' INFO: cont_mgau.c(423): 1120 mixture Gaussians, 8 components, 1 streams, veclen 39 INFO: cont_mgau.c(164): Reading mixture gaussian file 'model_parameters/train1.cd_cont_1000//variances' INFO: cont_mgau.c(423): 1120 mixture Gaussians, 8 components, 1 streams, veclen 39 INFO: cont_mgau.c(524): Reading mixture weights file 'model_parameters/train1.cd_cont_1000//mixture_weights' INFO: cont_mgau.c(679): Read 1120 x 8 mixture weights INFO: cont_mgau.c(707): Removing uninitialized Gaussian densities 123 124 125 126 128 130 131 132 134 135 137 138 139 140 141 142 143 144 145 147 149 150 151 152 153 154 155 156 157 158 159 160 161 163 164 166 167 168 169 170 172 173 174 175 176 177 179 180 181 182 183 184 186 187 188 190 191 192 193 194 195 196 198 199 200 203 206 207 208 209 210 212 213 214 215 217 218 219 220 221 222 223 224 225 227 228 229 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cont_mgau.c(815): 689 variance values floored INFO: cont_mgau.c(863): Precomputing Mahalanobis distance invariants INFO: tmat.c(119): Reading HMM transition probability matrices: model_parameters/train1.cd_cont_1000//transition_matrices INFO:	Initialization of tmat_t, report: INFO:	Read 40 transition matrices of size 3x4 INFO: INFO: dict.c(385): Reading main dictionary: etc/train1.dic INFO: dict.c(388): 519 words read INFO: dict.c(393): Reading filler dictionary: etc/train1.filler INFO: dict.c(396): 3 words read INFO: dict.c(429): Added 0 fillers from mdef file INFO:	Initialization of dict_t, report: INFO:	No of CI phone: 0 INFO:	Max word: 4618 INFO:	No of word: 522 INFO: INFO: lm.c(612): LM read('etc/transcript.binlm', lw= 9.50, wip= 0.70, uw= 0.70) INFO: lm.c(614): Reading LM file etc/transcript.binlm (LM name "default") INFO: lm_3g_dmp.c(472): Bad magic number: 39911424(02610000), not an LM dumpfile?? INFO: lm.c(622): In lm_read, LM is not a DMP file. Trying to read it as a txt file INFO: lm_3g.c(832): Reading LM file etc/transcript.binlm WARNING: "lm_3g.c", line 229: No \data\ mark in LM file WARNING: "lm_3g.c", line 843: Couldnt' read the ngram count INFO: lm.c(641): Lm is both not DMP and TXT format FATAL_ERROR: "lmset.c", line 295: lm_read_advance(etc/transcript.binlm, 9.500000e+00, 7.000000e-01, 7.000000e-01 522 [Arbitrary Fmt], Weighted Apply) failed

Doesn't look like it was happy about something. Will look into it more tomorrow. No idea what cepstral is, but the guide that I have specified sph in their version as it's extension so I tried the wav directory and the extension sph. Don't know what an mfcc file is either

End time: Wednesday August 10, 12:19AM

Week ending August 24, 2011
Started 21:15 August 23, 2011

Plan: A couple of things planned tonight:
 * Add wiki entries that I gave to Professor Jonas last week to the wiki
 * Rewrite decode script
 * Get sclite to compile

Add wiki entries to wiki
Completed at 23:44

Rewrite decode script
Been working on this one for a couple of hours right now. Currently have this:


 * 1) !/usr/bin/perl

$hmmDir = "~/sphinxSetup/SphinxTrain/train1/model_parameters/train1.cd_cont_1000"; $languageModelFile = "~/sphinxSetup/SphinxTrain/train1/etc/languageModel/trans.arpa"; $dictFile = "~/sphinxSetup/SphinxTrain/train1/etc/train1.dic"; $fillerDictFile = "~/sphinxSetup/SphinxTrain/train1/etc/train1.filler"; $fileIdsFile = "~/sphinxSetup/SphinxTrain/train1/etc/train1_train.fileids"; $featuresDir = "~/sphinxSetup/SphinxTrain/train1/feat/"; $featuresExtension = ".mfc";

system("sphinx3_decode -hmm " . $hmmDir . " -lm " . $languageModelFile . " -dict " . $dictFile . " -fdict " . $fillerDictFile . " -ctl " . $fileIdsFile . " -  cepdir " . $featuresDir . " -cepext " . $featuresExtension  . " &>    decodeResults.txt");

That is a working script to decode. However, the second piece of this is to parse the decoded lines of text out of the decoders output so that it can be plugged into sclite later. Unfortunately, perl seems to ignore the pipe character (&>) in it's output and will not pipe the output to a file. Manually entering this command in the shell works however, so the command is right. This output will not end up in the file without &> rather than an normal > probably due to the way that sphinx3_decode outputs the data. My guess is that perl somehow handles this data itself and drops it rather than letting the command handle it. Trying to figure out how to get this data into the file now. After that, I will right a sed command to strip miscelaneous data off.

Here is what I have tried so far:
 * The original command did not work. I have tried several variations on it itself.
 * I have perused the documentation (via google) for the perl system command and tried to take it's output in various ways from within perl and output it to a file.
 * I have tried seperating the commands into two commands hoping that perl would see the first command (decode) as ending when it put itself in the background and would then return and allow the next command to be run.
 * I have also tried to emulate a bash script directly in the perl script by making everything run through /bin/sh to see if I could get that to work and haven't had any luck with that either.

This would be very easy to fix by using a simple bash script rather than a perl script but Professor Jonas likes perl more, so that might be what we have to use.

Get sclite to compile

 * Using sctk-2.4.0. Should have saved the link because it took me a while to find it a couple of weeks ago, but all I have is the tar.gz and the extracted archive. Oh well. Compared the versions that Professor Jonas and I have and they are the same.
 * Also saw an sclite archive as well as a methodmaker class on caesar, but looking through the readme for the sclite package, it looks like it is just a wrapper for sclite and needs sclite to be installed before it can be used.
 * Trying to compile sctk, I get what looks like a compiler error: recording.h:122:29: error: ‘Filter::Filter’ cannot appear in a constant-expression
 * Not seeing any results other than how to modify the source code yet and I don't really want to do that (basically change the way that the new operator is used).

I'm going to bed. Will work on it some more tomorrow if I have the time. No luck on the perl stuff yet either.

Ending at 01:03 August 24, 2011.