ELF>@@RQ !"#$%&'()*,-012GHIJATUSH@HdH%(H$81D$ D$ D$H$H$H5D$ D$?D$H$H$HDŽ$Ƅ$H$ H$HDŽ$Ƅ$ H$@H$0HDŽ$8Ƅ$@H$`H5H$H$H5H$H$H5H$HT$ H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$HT$ H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$H$H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$HT$ H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$H$H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$H$H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$H$0H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$HT$H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$HT$H$H$`H$H$H9tH$H$H9tH$H$H5H$H$H5H$HT$H$H$`H$H$H9tH$H$H9tH$`HH$`uH$PH$`H$`H$pH$`HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$ HDŽ$(HDŽ$0HDŽ$8HDŽ$@HDŽ$HHDŽ$PHDŽ$XHDŽ$`HDŽ$hHDŽ$pHDŽ$xHDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$DŽ$HDŽ$DŽ$DŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$DŽ$HT$H$PH|$ \$H|$ H$H\$H|$ H$HH|$ H$uUH$3|$ hD$.gH$jH$8HD$ HD$(HD$0HD$8HD$@HD$HHD$PHD$XHD$`HD$hHD$pHD$xHT$H$H|$\$H|$H|$ HH|$Ht$ H$HHH|$H|$ +H|$ H$H|$ H|$ H$HH|$ H$H$H$H|$ H|$ H$HH|$ VHH9$8vHH$0:;u H$0H$H$H$H$H$D$.z7u5|$u=|$ \$ H$pH$^H$H$H$D$ D$!D$"D$#H$H|$ tH$H$HH$HAH HHHHH$L$H$LH$IHH$pH$H9tLH$PH$`H9H$H5H$H$XH$PH$H5H$H$xH$pH$H$H$H$HH$H$pH$H9tH$PH$`H9tH$`H$0H$@H9tH$H$ H9tH$H$H9tH$H$H9tH$8dH3%(H@[]A\T$ H$pH$\$ H$H$H\$ H$H$HH$AH HH$H5H$H$XH$PH$H5H$H$xH$pH$H$H$HHH$H$H9tH$H$H9tH$`H$0H$@H9tH$H$ H9tH$H$H9tH$H$H9tHHHHHQHHH$H$H9tH$H$H92(HHHHH$H$H9tH$H$H9HHHHH$H$H9tH$H$H9HHHHH$H$H9tH$H$H9QGHHHHH$H$H9tH$H$H9HHHHH$H$H9tH$H$H9HHHHH$H$H9tH$H$H9pfHHHHH$H$H9tH$H$H9%HHHHH$H$H9tH$H$H9HHHHH|$ wHH2HHH|$ WHHH|$ BHHH$*HHH$HHH$H$HHHHHHHHHHHhHHRHH=Ƅ$ H$H,HHHH=HH5H=HATUSHHEPH]HH9tVHS0H)HH HHH!Ld@II H0L9tH{H{HtH}HHtLe8H]0I9uEH}0HtH}HtLeH]I9u8H}Ht[]A\H I9tH;HCH9tH I9tH;HCH9tAVAUATUSIHIIHtI}xHtI}`HtI}HHtIE8Me0L9trIT$H)HH HHH!HD@M40H H9tH{HtI<$HtIM9tIl$I$H9uI}0HtI}HtI}Ht[]A\A]A^ATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu[]A\HHHtHsLHkH{ HC0H9tATUSIHHu9[]A\H{@HCPH9tH{ HC0H9tHHHtHsLHkH{`HCpH9tATUSHHH@HHHH9tLHI9HHtH`HpH9tH8H(HHHHHHHuxH}hHuHH}8HuH}[]A\H I9RH;HCH9tATUSHHH@HHHH9tLHI9HHtH`HpH9tH8H(HHHHHHHuxH}hHuHH}8HuH}H[]A\H I9JH;HCH9tbasic_string::_M_construct null not validATUSHHIdH%(HD$1H9tHtHHL)H$HwDH}HtXHu\H$HEHUHD$dH3%(uIH[]A\H=HHHEH$HEA$HLconfigprint-argsPrint out usage messagehelpverboseConfiguration file to read (this option may be repeated)Print the command line arguments (to stderr)Verbose level (higher->more logging)UHAWAVAUATSH`HdH%(HE1HH@HGHGHGHG HG(HG0G@HGHHG@HGPHGXHG`GpHGxHGpHHHLJLJHLJHHHHLJLJHLJHHHHLJLJHLJHHHHLJ LJ0HLJ8H0H@HHHLJPƇXƇYHpIH`HLJhƇpHLJHLJHLJHLJHLJHLLjHHL`HHǃHH8H}HGHEHjHHrH}HGHE$HHrHHMHuLHH}HEH9tH}HEH9tH}HGHEHjHHrLXH}HGHE$HHrHHMHuLHH}HEH9tH}HEH9tH}HGHEHjHHrLYH}HGHE$HHrHHMHuLHH}HEH9tH}HEH9tH}HGHEHjHHrH}HGHE$HHrHHMHuHHH}HEH9tH}HEH9tHEdH3%(He[A\A]A^A_]IH}HEH9tH}HEH9tHL9tLLM9I<$ID$H9tI IIH}HEH9tH}HEH9tIIH}HEH9tH}HEH9f\IIH}HEH9tH}HEH91'III#HHtH`I9tH8H(HHHHHHHsxH{hHsHH{8HsH{LS            Copy nnet3 neural-net acoustic model file; supports conversion to raw model (--raw=true). Also supports setting all learning rates to a supplied value (the --learning-rate option), and supports replacing the raw nnet in the model (the Nnet) with a provided raw nnet (the --set-raw-nnet option) Usage: nnet3-am-copy [options] e.g.: nnet3-am-copy --binary=false 1.mdl text.mdl nnet3-am-copy --raw=true 1.mdl 1.raw If true, write only 'raw' neural net without transition model and priors.Set the raw nnet inside the model to the one provided in the option string (interpreted as an rxfilename). Done before the learning-rate is changed.Convert all RepeatedAffineComponents and NaturalGradientRepeatedAffineComponents to BlockAffineComponents in the model. Done after set-raw-nnet.Name of nnet3 config file that can be used to add or replace components or nodes of the neural network (the same as you would give to nnet3-init).Name of edits-config file that can be used to modify the network (applied after nnet-config). See comments for ReadEditConfig()in nnet3/nnet-utils.h to see currently supported commands.Can be used as an inline alternative to --edits-config; semicolons will be converted to newlines before parsing. E.g. '--edits=remove-orphans'.If supplied, all the learning rates of updatable components are set to this value.The parameter matrices are scaled by the specified value.If true, prepares the model for test time (may reduce model size slightly. Involves setting test mode in dropout and batch-norm components, and calling CollapseModel() which may remove some components.Write output in binary modebinaryrawset-raw-nnetconvert-repeated-to-blocknnet-configedits-configeditslearning-ratescaleprepare-for-testnnet3-am-copy.ccCopied neural net from to raw format as to L''' '! 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