I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
$ sphinxtrain run
Sphinxtrain path: /usr/local/lib/sphinxtrain
Sphinxtrain binaries path: /usr/local/libexec/sphinxtrain
Running the training
MODULE: 000 Computing feature from audio files
Extracting features from segments starting at (part 1 of 1)
Extracting features from segments starting at (part 1 of 1)
ERROR: This step had 3 ERROR messages and 0 WARNING messages. Please check the log file for details.
Feature extraction is done
MODULE: 00 verify training files
Phase 1: Checking to see if the dict and filler dict agrees with the phonelist file.
WARNING: The phonelist (/home/I3asta/cmusphinx/sphinxtrain/an4/etc/an4.phone) has duplicated phones
Found 35 words using 23 phones
Phase 2: Checking to make sure there are not duplicate entries in the dictionary
Phase 3: Check general format for the fileids file; utterance length (must be positive); files exist
Phase 4: Checking number of lines in the transcript file should match lines in fileids file
Phase 5: Determine amount of training data, see if n_tied_states seems reasonable.
Estimated Total Hours Training: 1.19120833333333
This is a small amount of data, no comment at this time
Phase 6: Checking that all the words in the transcript are in the dictionary
Words in dictionary: 32
Words in filler dictionary: 3
Phase 7: Checking that all the phones in the transcript are in the phonelist, and all phones in the phonelist appear at least once
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
$ sphinxtrain run
Sphinxtrain path: /usr/local/lib/sphinxtrain
Sphinxtrain binaries path: /usr/local/libexec/sphinxtrain
Running the training
MODULE: 000 Computing feature from audio files
Extracting features from segments starting at (part 1 of 1)
Extracting features from segments starting at (part 1 of 1)
Feature extraction is done
MODULE: 00 verify training files
Phase 1: Checking to see if the dict and filler dict agrees with the phonelist file.
WARNING: The phonelist (/home/I3asta/cmusphinx/sphinxtrain/an4/etc/an4.phone) has duplicated phones
Found 35 words using 23 phones
Phase 2: Checking to make sure there are not duplicate entries in the dictionary
Phase 3: Check general format for the fileids file; utterance length (must be positive); files exist
Phase 4: Checking number of lines in the transcript file should match lines in fileids file
Phase 5: Determine amount of training data, see if n_tied_states seems reasonable.
Estimated Total Hours Training: 1.19120833333333
This is a small amount of data, no comment at this time
Phase 6: Checking that all the words in the transcript are in the dictionary
Words in dictionary: 32
Words in filler dictionary: 3
Phase 7: Checking that all the phones in the transcript are in the phonelist, and all phones in the phonelist appear at least once
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
$ sphinxtrain run
Sphinxtrain path: /usr/local/lib/sphinxtrain
Sphinxtrain binaries path: /usr/local/libexec/sphinxtrain
Running the training
MODULE: 000 Computing feature from audio files
Extracting features from segments starting at (part 1 of 1)
Extracting features from segments starting at (part 1 of 1)
ERROR: This step had 3 ERROR messages and 0 WARNING messages. Please check the log file for details.
Feature extraction is done
MODULE: 00 verify training files
Phase 1: Checking to see if the dict and filler dict agrees with the phonelist file.
WARNING: The phonelist (/home/I3asta/cmusphinx/sphinxtrain/an4/etc/an4.phone) has duplicated phones
Found 35 words using 23 phones
Phase 2: Checking to make sure there are not duplicate entries in the dictionary
Phase 3: Check general format for the fileids file; utterance length (must be positive); files exist
Phase 4: Checking number of lines in the transcript file should match lines in fileids file
Phase 5: Determine amount of training data, see if n_tied_states seems reasonable.
Estimated Total Hours Training: 1.19120833333333
This is a small amount of data, no comment at this time
Phase 6: Checking that all the words in the transcript are in the dictionary
Words in dictionary: 32
Words in filler dictionary: 3
Phase 7: Checking that all the phones in the transcript are in the phonelist, and all phones in the phonelist appear at least once
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
Now I can debug error but not happen when plase:7
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
$ sphinxtrain run
Sphinxtrain path: /usr/local/lib/sphinxtrain
Sphinxtrain binaries path: /usr/local/libexec/sphinxtrain
Running the training
MODULE: 000 Computing feature from audio files
Extracting features from segments starting at (part 1 of 1)
Extracting features from segments starting at (part 1 of 1)
Feature extraction is done
MODULE: 00 verify training files
Phase 1: Checking to see if the dict and filler dict agrees with the phonelist file.
WARNING: The phonelist (/home/I3asta/cmusphinx/sphinxtrain/an4/etc/an4.phone) has duplicated phones
Found 35 words using 23 phones
Phase 2: Checking to make sure there are not duplicate entries in the dictionary
Phase 3: Check general format for the fileids file; utterance length (must be positive); files exist
Phase 4: Checking number of lines in the transcript file should match lines in fileids file
Phase 5: Determine amount of training data, see if n_tied_states seems reasonable.
Estimated Total Hours Training: 1.19120833333333
This is a small amount of data, no comment at this time
Phase 6: Checking that all the words in the transcript are in the dictionary
Words in dictionary: 32
Words in filler dictionary: 3
Phase 7: Checking that all the phones in the transcript are in the phonelist, and all phones in the phonelist appear at least once
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
You need to fix this warning first: WARNING: The phonelist (/home/I3asta/cmusphinx/sphinxtrain/an4/etc/an4.phone) has duplicated phones
Now I can create acoustic model but when I can,t use my acoustic model
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
$ pocketsphinx_continuous -hmm model_parameters/an4.cd_cont_200 -lm an4_lm -dict an4.dic -inmic yes
INFO: pocketsphinx.c(152): Parsed model-specific feature parameters from model_parameters/an4.cd_cont_200/feat.params
Current configuration:
[NAME] [DEFLT] [VALUE]
-agc none none
-agcthresh 2.0 2.000000e+00
-allphone
-allphone_ci no no
-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
-debug 0
-dict an4.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/an4.cd_cont_200
-input_endian little little
-jsgf
-keyphrase
-kws
-kws_delay 10 10
-kws_plp 1e-1 1.000000e-01
-kws_threshold 1 1.000000e+00
-latsize 5000 5000
-lda
-ldadim 0 0
-lifter 0 22
-lm an4_lm
-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 2.0 2.000000e+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/an4.cd_cont_200/mdef
INFO: bin_mdef.c(181): Allocating 1055 * 8 bytes (8 KiB) for CD tree
INFO: tmat.c(149): Reading HMM transition probability matrices: model_parameters/an4.cd_cont_200/transition_matrices
INFO: acmod.c(113): Attempting to use PTM computation module
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/means
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/variances
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(304): 45485 variance values floored
INFO: ptm_mgau.c(804): Number of codebooks exceeds 256: 269
INFO: acmod.c(115): Attempting to use semi-continuous computation module
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/means
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/variances
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(304): 45485 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/an4.cd_cont_200/means
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/variances
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(304): 45485 variance values floored
INFO: ms_senone.c(149): Reading senone mixture weights: model_parameters/an4.cd_cont_200/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 269 senones: 1 features x 8 codewords
INFO: ms_senone.c(320): Mapping senones to individual codebooks
INFO: ms_mgau.c(144): The value of topn: 4
INFO: phone_loop_search.c(114): State beam -225 Phone exit beam -225 Insertion penalty 0
INFO: dict.c(320): Allocating 4131 * 32 bytes (129 KiB) for word entries
INFO: dict.c(333): Reading main dictionary: an4.dic
INFO: dict.c(213): Dictionary size 32, allocated 0 KiB for strings, 0 KiB for phones
INFO: dict.c(336): 32 words read
INFO: dict.c(358): Reading filler dictionary: model_parameters/an4.cd_cont_200/noisedict
INFO: dict.c(213): Dictionary size 35, 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 23^3 * 2 bytes (23 KiB) for word-initial triphones
INFO: dict2pid.c(132): Allocated 12880 bytes (12 KiB) for word-final triphones
INFO: dict2pid.c(196): Allocated 12880 bytes (12 KiB) for single-phone word triphones
INFO: ngram_model_trie.c(354): Trying to read LM in trie binary format
ERROR: "ngram_model_trie.c", line 356: File an4_lm not found
INFO: ngram_model_trie.c(177): Trying to read LM in arpa format
ERROR: "ngram_model_trie.c", line 179: File an4_lm not found
INFO: ngram_model_trie.c(445): Trying to read LM in dmp format
ERROR: "ngram_model_trie.c", line 447: Dump file an4_lm not found
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
The language model file is probably
an4.lm, notan4_lm.I speak word in my dic but not show text
I3asta@I3asta-PC ~/cmusphinx/sphinxtrain/an4
$ pocketsphinx_continuous -hmm model_parameters/an4.cd_cont_200 -lm an4.lm -dict an4.dic -inmic yes
INFO: pocketsphinx.c(152): Parsed model-specific feature parameters from model_parameters/an4.cd_cont_200/feat.params
Current configuration:
[NAME] [DEFLT] [VALUE]
-agc none none
-agcthresh 2.0 2.000000e+00
-allphone
-allphone_ci no no
-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
-debug 0
-dict an4.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/an4.cd_cont_200
-input_endian little little
-jsgf
-keyphrase
-kws
-kws_delay 10 10
-kws_plp 1e-1 1.000000e-01
-kws_threshold 1 1.000000e+00
-latsize 5000 5000
-lda
-ldadim 0 0
-lifter 0 22
-lm an4.lm
-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 2.0 2.000000e+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/an4.cd_cont_200/mdef
INFO: bin_mdef.c(181): Allocating 1055 * 8 bytes (8 KiB) for CD tree
INFO: tmat.c(149): Reading HMM transition probability matrices: model_parameters/an4.cd_cont_200/transition_matrices
INFO: acmod.c(113): Attempting to use PTM computation module
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/means
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/variances
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(304): 45485 variance values floored
INFO: ptm_mgau.c(804): Number of codebooks exceeds 256: 269
INFO: acmod.c(115): Attempting to use semi-continuous computation module
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/means
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/variances
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(304): 45485 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/an4.cd_cont_200/means
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(127): Reading mixture gaussian parameter: model_parameters/an4.cd_cont_200/variances
INFO: ms_gauden.c(242): 269 codebook, 1 feature, size:
INFO: ms_gauden.c(244): 8x39
INFO: ms_gauden.c(304): 45485 variance values floored
INFO: ms_senone.c(149): Reading senone mixture weights: model_parameters/an4.cd_cont_200/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 269 senones: 1 features x 8 codewords
INFO: ms_senone.c(320): Mapping senones to individual codebooks
INFO: ms_mgau.c(144): The value of topn: 4
INFO: phone_loop_search.c(114): State beam -225 Phone exit beam -225 Insertion penalty 0
INFO: dict.c(320): Allocating 4131 * 32 bytes (129 KiB) for word entries
INFO: dict.c(333): Reading main dictionary: an4.dic
INFO: dict.c(213): Dictionary size 32, allocated 0 KiB for strings, 0 KiB for phones
INFO: dict.c(336): 32 words read
INFO: dict.c(358): Reading filler dictionary: model_parameters/an4.cd_cont_200/noisedict
INFO: dict.c(213): Dictionary size 35, 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 23^3 * 2 bytes (23 KiB) for word-initial triphones
INFO: dict2pid.c(132): Allocated 12880 bytes (12 KiB) for word-final triphones
INFO: dict2pid.c(196): Allocated 12880 bytes (12 KiB) for single-phone word triphones
INFO: ngram_model_trie.c(354): Trying to read LM in trie binary format
INFO: ngram_model_trie.c(365): Header doesn't match
INFO: ngram_model_trie.c(177): Trying to read LM in arpa format
INFO: ngram_model_trie.c(193): LM of order 3
INFO: ngram_model_trie.c(195): #1-grams: 34
INFO: ngram_model_trie.c(195): #2-grams: 46
INFO: ngram_model_trie.c(195): #3-grams: 56
INFO: lm_trie.c(474): Training quantizer
INFO: lm_trie.c(482): Building LM trie
INFO: ngram_search_fwdtree.c(74): Initializing search tree
INFO: ngram_search_fwdtree.c(101): 27 unique initial diphones
INFO: ngram_search_fwdtree.c(186): Creating search channels
INFO: ngram_search_fwdtree.c(323): Max nonroot chan increased to 150
INFO: ngram_search_fwdtree.c(333): Created 27 root, 22 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: Oct 13 2016, AT: 13:31:48
Allocating 32 buffers of 2500 samples each
INFO: continuous.c(252): Ready....
INFO: continuous.c(261): Listening...
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 < 32.85 28.27 -27.36 -48.82 -32.73 16.70 47.55 12.71 -11.23 -13.10 -12.92 12.54 1.27 >
INFO: ngram_search_fwdtree.c(1550): 736 words recognized (2/fr)
INFO: ngram_search_fwdtree.c(1552): 10109 senones evaluated (29/fr)
INFO: ngram_search_fwdtree.c(1556): 5099 channels searched (14/fr), 4227 1st, 795 last
INFO: ngram_search_fwdtree.c(1559): 756 words for which last channels evaluated (2/fr)
INFO: ngram_search_fwdtree.c(1561): 15 candidate words for entering last phone (0/fr)
INFO: ngram_search_fwdtree.c(1564): fwdtree 0.00 CPU 0.000 xRT
INFO: ngram_search_fwdtree.c(1567): fwdtree 3.72 wall 1.080 xRT
INFO: ngram_search_fwdflat.c(302): Utterance vocabulary contains 2 words
INFO: ngram_search_fwdflat.c(948): 1017 words recognized (3/fr)
INFO: ngram_search_fwdflat.c(950): 1029 senones evaluated (3/fr)
INFO: ngram_search_fwdflat.c(952): 1023 channels searched (2/fr)
INFO: ngram_search_fwdflat.c(954): 1023 words searched (2/fr)
INFO: ngram_search_fwdflat.c(957): 76 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
.0 end node.327INFO: ngram_search.c(1276): Eliminated 0 nodes before end node
INFO: ngram_search.c(1381): Lattice has 58 nodes, 99 links
INFO: ps_lattice.c(1380): Bestpath score: -5664
INFO: ps_lattice.c(1384): Normalizer P(O) = alpha(:327:342) = -278388
INFO: ps_lattice.c(1441): Joint P(O,S) = -315703 P(S|O) = -37315
INFO: ngram_search.c(872): bestpath 0.00 CPU 0.000 xRT
INFO: ngram_search.c(875): bestpath 0.00 wall 0.000 xRT
INFO: continuous.c(275): Ready....
INFO: continuous.c(261): Listening...
INFO: cmn_live.c(120): Update from < 32.85 28.27 -27.36 -48.82 -32.73 16.70 47.55 12.71 -11.23 -13.10 -12.92 12.54 1.27 >
INFO: cmn_live.c(138): Update to < 34.59 27.21 -23.47 -37.33 -30.27 11.33 38.86 11.03 -6.47 -8.86 -8.94 12.62 0.63 >
INFO: ngram_search_fwdtree.c(1550): 246 words recognized (2/fr)
INFO: ngram_search_fwdtree.c(1552): 3008 senones evaluated (24/fr)
INFO: ngram_search_fwdtree.c(1556): 1527 channels searched (12/fr), 1238 1st, 270 last
INFO: ngram_search_fwdtree.c(1559): 261 words for which last channels evaluated (2/fr)
INFO: ngram_search_fwdtree.c(1561): 10 candidate words for entering last phone (0/fr)
INFO: ngram_search_fwdtree.c(1564): fwdtree 0.01 CPU 0.012 xRT
INFO: ngram_search_fwdtree.c(1567): fwdtree 2.77 wall 2.248 xRT
INFO: ngram_search_fwdflat.c(302): Utterance vocabulary contains 2 words
INFO: ngram_search_fwdflat.c(948): 345 words recognized (3/fr)
INFO: ngram_search_fwdflat.c(950): 366 senones evaluated (3/fr)
INFO: ngram_search_fwdflat.c(952): 360 channels searched (2/fr)
INFO: ngram_search_fwdflat.c(954): 360 words searched (2/fr)
INFO: ngram_search_fwdflat.c(957): 74 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
.0 end node.98INFO: ngram_search.c(1276): Eliminated 0 nodes before end node
INFO: ngram_search.c(1381): Lattice has 15 nodes, 21 links
INFO: ps_lattice.c(1380): Bestpath score: -2284
INFO: ps_lattice.c(1384): Normalizer P(O) = alpha(:98:121) = -134472
INFO: ps_lattice.c(1441): Joint P(O,S) = -144398 P(S|O) = -9926
INFO: ngram_search.c(872): bestpath 0.00 CPU 0.000 xRT
INFO: ngram_search.c(875): bestpath 0.00 wall 0.000 xRT
INFO: continuous.c(275): Ready....
Most likely your database is too small for accurate recognition. Or you did something else wrong in training. For example, sample rate was wrong.
You need to share acoustic model training folder to get help on this issue.
Thank you very much Nickolay . I will try it if i have question can I ask you again please?
Yes sure.