From 6e7396455a52c431cce8bf79368a1c59780cb857 Mon Sep 17 00:00:00 2001
From: MasterPhooey <hatted-limited0r@icloud.com>
Date: Sat, 18 Apr 2026 09:01:40 -0500
Subject: [PATCH] Automatic Calibration

---
 README.md                                  |   2 +-
 __pycache__/trainer_server.cpython-311.pyc | Bin 0 -> 61444 bytes
 cli/calibrate_detector.py                  | 405 +++++++++++++++++++++
 cli/setup_audioset                         | 137 +++++--
 cli/setup_fma                              |  63 +++-
 cli/setup_python_venv                      |  24 +-
 cli/wake_word_sample_trainer               |  58 ++-
 dockerfile                                 |   4 +-
 run_recorder.sh => run.sh                  |   2 +-
 trainer_server.py                          |  39 +-
 10 files changed, 656 insertions(+), 78 deletions(-)
 create mode 100644 __pycache__/trainer_server.cpython-311.pyc
 create mode 100644 cli/calibrate_detector.py
 rename run_recorder.sh => run.sh (97%)

diff --git a/README.md b/README.md
index db3472e..5cc8344 100644
--- a/README.md
+++ b/README.md
@@ -162,7 +162,7 @@ That will remove:
 - browser microphone recording has been removed
 - personal samples are optional
 - the server module is now `trainer_server.py`
-- the launcher script is still named `run_recorder.sh` for compatibility
+- the launcher script is now `run.sh`
 
 ---
 
diff --git a/__pycache__/trainer_server.cpython-311.pyc b/__pycache__/trainer_server.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3893d6fdccc8f50a78ced1b8388274b8a8abc6bc
GIT binary patch
literal 61444
zcmeFac~l%{mM@rlMs6gK>>x-G0ul+3gjS3eHtj+ZLb8RIG$IlSiOrFT7M$4F&s|0u
z>KVJ`7Iw+cS@mj>yHrz5PoHU3ot`e)<?8b3>8^_4g`1q5M;}e~@x1msXXfGK@{FJ7
z&Aj>DFLH@UK!vNi|C=EBBI1j0U+#DB{qDWr4_q$04%Z95-weDwqtpE(y(BJo{NeMz
z&(P^^>(1zS9j_nM^(c3JkDk37dJODt>@l*tsmH|b<{mS<TY4<)ZtbzMJJ-XpyRFBD
zyJ65iWbd)-DNW;`W60U#WY4BS*HA`J#*n+mJ(Ss#Ih56tHI&_x&AyojJwrJ?IYYTU
zx$NCCm^YN)lRs3@Q!rH6Q#e%AQ^fqOgT+H7Jtafyde*UbZm@Kytfx$`Gw1@&fcN`)
zo$iPDSFT8T;z@n~K3;y9^te=!c<fov($HV3O!!1LB%aiF-p1R%Z$KLOSFW$0Yfsf0
z^Ku|ubznX39Czq++I;@tGSsZG3@%oNDn8?1pbWk>mcbowhs=M0GSmjD_$*47&nCBL
zV_+liVNaU^zk4*~@VVNa=&4&ntUNwHEml2h*sz9}1$?14rgl{HY+hrF7R6h%nAN<5
zTy08^%4IDxt7pp^YO{_n)#ek~nwVAUyLQg<Wmj}PjcZ8V`+KLpZ4IfH^A&-rZ|V5;
z%&mOKtgT7U_BEujfv-x7wPTIrpqj7I#?<y+&(1Zb<Kt@|na(bLW6y4WQ%@6L*YnsK
z)2!zk9+_tI8q=Y%T+MIM*6fif??H@we(SwPZJeIHfttuZ^@vJ5^V|4s_qJ=()awF!
zBl{B})OVhGr;JJ!sL|$dZ>RR1ci!9eeIr&0{3{pMtKFzgQ=q!v#6R|msi%e4^ZLM!
zz^=efUUxyqH}iV9@Pn)4_uR4ad+%^Pt$_^W=I%EXCKA#G+W39Vctbg?m)aBF+BwqG
z@yM?S9{KekzaQmjxnt-#v|{e9lsn({imvA{>XR9$PL-|Sz_+7rM^^Cffd5hc!15I1
zZE_IbkMW22P5j|I2L1>Ox2*2!cHobqJ&)o4IQ}~W4gGfh#4DDb<47-Sc`lkVo<v@q
zYY2A=;ZE>f%Tq%=PvhIkHKfsvaHkM1b9pM7GMz!VF0@ZIuOGLfeU*`N7QUzX$KgvU
zWMeE;tJ^@8f{A|uVY~Tr2-_2IVw63J_cQUh)RGPAnE3t_zMo|^(DvZd@P8b&eu8~l
z)(4tApF!Ajy#LWHj&ypE-?Iq&B;U*GLbKpJ-k*xUFUtkr`|$l~Rt_c36a5yRhu<^&
zTS?yla#ocdIW13#<NIGR_xSk>7_rat7pVpL0i@H*UrLtcGTzVIbT%FTHb1Bx)BI4t
z$q(ayBrUJec&%6I5tOB&-^o9Ry!%-ECSH$P3CM|8y$A6gVDF(o1LA1slZ79{_kRA0
z)}Ozc_P#vcHN?BXUq`u{FmAE}&Q!l?-_l0KIO1L8pAWeCFj{AVwUE1?<6nUP0RJN1
zsV%537Jd?bmzdv6tcPF5`(;4Sl`-dk6)3s)iWb)LfAoqj4RQ@4{!jv{sqg$Z(qQbX
zEA+L2pL)fVmg4ZLCAyK8;>~5H8CkV7|BW^-AZigmy(YiH^l<$~{+sCge~tgw0tIN-
znODqdbr@Z>9Ir3Sb9wrDmac(+V_CY-t(xv1YtzM?Te-b&ElX2awY}d=OY={bwRdpU
z^4v~~JG(4yh}WOi`M&ij4Vq4$KF0CAp+IP0D1c|v$==XKpE+h~ALt9kj4i|0W2R#R
z!BC7lF&Y{e8SWj7nY+hE2LnDs%-R)rZY+Rr)-hplWHd1BGsUb2dV`^slZRvWGoyne
zz5IcJ!9dIucSnRFL~|bMK7On#5F8yD4hCY*Bd1SvCf-m0$LUaSXrPZ`1Y=x#OLvRE
z{czW~xdDajjoD9kw{#zFBd?ex$U?@PCp)^3M$0kgn>iW~f>cg_uy+XM492W&#}2bk
z#=gOUm^m~uG8i1s8y!HvIwefqg}`t?=nahsG0T;afj&gaJ9!w%`p=#?+}3g0f4H-~
z;|c$nu4Ci-FNQ*+!KQ|Wi(?lq3=CiB@9hiJ_l-0N7kh)j(d!LLvPvQiLhsdvq27Vv
z2Ial}5(*Z})uh~Y;zTz~c-PlTI1n5eyb@rE$I6rh+t5_UP8{^NwX_}T@OO6~^Plc$
zJJH#GI%eFxd2_5_rBDYBAL|&m)c5uFT?~vDva<V?vikeb+=C+*lrooeb)e0W!Rd}}
zf7`K+md-OL{ViQ>hYp|ZI2|+h_YMXF=zRT_m?<QT1!BhQXhQw<Sk+4L4tI8UbRF+#
zKityY;inRuj@i1qS`K&m+m5%7bHR}@p)cUA9^ckbe<d({rQtjpu(vNXa0R2Z+FMoS
z4GCyd|JB~h0sqwzf%n$c(b%Zt2Lx~RSNiIh^Z4VB`;WDB9z4@>up?#&pv|cq-7QDa
zmDH=JPj=wBD`wcTIhIYI;vT2{-6zn9!g@@Mfyuo(-KWGP`II;qu>zI^D<-vacgx{p
z{$qzbQF!AvG;hB4^YNA^{H^D@J5CSibewLybZDSY7<n8eew+$?GCoATr<Ea!VQ&r%
zga!lM?_pMr_6mXF5C(tX8YZ59<np++K^f`@XG1LmeHf|N-@{}b?j1s7W0=J(LLf9I
z4EH6uIw-Rl|Abe6{ts|&>%zKGV9gOcFTb=<G@?!=8CV-;LLZ{^<JYAm{>~eCV_2`n
z=~oSF;`M6|YhEdASdThc0L`qOG2K_jr-B<}djGiN$!B`&#y8jP_CLLG-1>}vW7xm3
zGRDz}p#g)DY7T~kfzg<GaO7%0h#3U5VsPxd&mcU83dQunAoaP|`_=J=GeLA#L+{1W
ziz6d}>kX&-5HA#L&~#{BH;qVulg=vx{6KGA`$*sAfY89kX+ZEJg)0G}e)M|G7VPa0
z_^H=~L&)F+{)7E+CUsGpbE<I0u)r10bA=LDEOW&RT=_g#E^+H+ZvAA-B4?krOkJ4?
z&bCNgsmzroyk3|UB3mSGgUoG6c#Y3)oqbN?%4M#6vL)(pPVJkqi>9K_k(2I#&l<A|
zW5dG$UNIYnSjgWu!UtkDOm_buYJq`AkE2)z;^n0_i)9P~u%jx&A%CcMU@+!l?-VjP
zFdp!6F*}B#fApfzi|TeFBP7#1d||BjLLinA91WoDg`ht)0;m$p3gIp22LSfRzr->W
zFO2QLz?A@x`9MKp9BV@=s+k~v$A6$I(1mrx-X917Zt?zJtUxH2Fn|c26~J7d5=C*7
zli~xs-`0gx-&P*>L~7nMXwfmfHXaR@WfyPMF2wD+r>iG`IEQtS<YZAt(oQeEZn|K-
zF2tomRO$qyUUyD6U3pb^&3I0C6-;Ohd{bw)upObkGS}8$?Y-hN3wd~pnTIX|Qp8Lb
z21m}vEMvpy*~>9o$F;ryTgCxA^rJzV=3e0-g)jCCfnXp+eWF=C{YXU!AjCEN2d}`H
z)GeAa7fgBcro7qEU0yWhNv1~G)F_%77hQ!5uF`o|>Fia>RV}-!mvkm`7IUWbO9rDY
zE9!K;cIf7z8%L&(OyTd#MR&oHPH#h)gcEgUPPH!?@%rVLUoKe_@q<~&`lmT9Zo~U-
zdrOJ={bD^_P0v~of?D?TJ8=Gho>hT0-uQ~Pr&sJb-jv?c+P>6&MW31DeWrodG>96h
z_?2*u>hug^Kkb+RguDbF<vOdot;f)CQrEwy?-X{!8&knhzggh%7zbOdOTV%;6Iyd^
zRM<Q2!_3C)P4C?L=E0FZtlfL-lR;mhRHy|1guh9hn6(GL*;6y+x2lw9wJRJ(K;aoU
zJ`)>uf*;R7%tnMzfES*{%X>N{5F2E9D;Gl`GUjFc2M@q`)cA5_DcOH-(?Uh#d`08j
zL8+oyu4sm@l(k2O>)0bZ_K2oEEZ3EX$9IqkhKFj6({|T{E~J{!+tCwYUC4w;@r&0J
zUnd0KL*fdj@q{&@4U7v!yeeHf5E*u?*KN@SO;-)dgr@1OXZc}}7(0ct$Ury)2hB@u
zq@ceY{$nTFjtWnZhjB14{GOf#RL|xTfAIQn-?IMp6B-c^1jKw5|G^t@(A>@n$yq5o
zD<?Y^ZJs5a!Cc3jXkqC$%~Ka{WGq%}hy=b}``y}an#GJ#i7Q(&C&I9RpHY<GCgY`2
zNPD6Qy;3NP&Y3lNjLq;rsc117-#3|BIP3ckJzSv+Wz@`*BmlpJ9@O?DK&z@(5ix%8
zdhHYh&S22`L{vSVhy_kR9)Fm8T?^Qz*(w;qps&>9Yt?e4EfK$8xh4!@gHRtfL{wum
zS!f+^;?p?pyd}lqSWBIT(OL?IW$Eu78W_AT05jIbOoTQF`|&6=kV7p9!qEuKDrUS8
z2nns^+Xe^N*683s$Y*4oDbSKDjKK*KH1&E_E6@c%sephbJ%Kv-<2hbO$k!;!zk!1x
z>&l!dxLxu_$?Qcbt6I*g7PIQ^Zk1g1va5c<wQb(DO>*s!T{{+B&GW8i$+cH@?VUUj
zwYsp*nDe6U+$q<hBV+2?YvG&W*#nY$z3g5ux_uEra@5L>+6BkvdB<kSu~l|#ootV8
z+AKPDO>KLv>1NYR-|QC2u}*fZo9&ey-UUb1yrW8T)X0t+(NQyZUNr4e;2kv)05j0?
z3!s`~=Dv}!;Si9lI7$>$TA>M{gmH47hm#!U)R8v)Q~P}W5gZ`z`XDx&PZRcl_vKd@
z121gAHVf`>4eyx3K*Yf!H{D6={t0u~%$u(nuBlk%Vbiz^`5XHfunHT6>aY=^R0HLT
zUMSQ0tUT4iX+xT}pHC<Mfu@feyft6xeQdbAXJ+F7Xn61Wz@R{_5pyv56F4cgmsM%L
zpzsnt2`|GTybpK^AL#EL8w|xPy`$he@IH$WrVwmsX&W_WCe-kH(1Pz6Yq%7ICmRTl
zGTf4bP51`F+{S<KFW^k-mNIqDY#K4{D3>v1i8^x^oZfk-7jF-9ilPN2Vy+Lrh()=>
zjph_Yi%X*Ug-Z^L&Az0A^O@P^viw#DXR>1{M`yNA)rqD8{A5$XY=>kjSKcI3L40`D
z%v43pkpbDeS<I$ilyglD$Xvk!S2oX;MJy6mEpydk;s;d8TtkJdQG!2W;j38&Fx3kk
z@La=y{+?00NMSKl;X`4Jr+aDRX}b>OooX?>lmtUzeMA)(O&Y(_z$*33vWvH90krmQ
zf&?$V#)2A32y`HyupyM0%1&EbfNlf89pTm1&ado5`_;7oiwb>JxFW&Vu%&)8P-*MM
zKrdin@Kef$-ietuG8X#EJQnJ&+vT$fBx)j*kb@0mz)r>q0`=L;2V*7yv11lK!00gh
zKyY9f>-2CR_+~tqa6Tg_{J?V*EI0};usXDWGsn*eA-}@DiSuoQw~)^q{)4}S1BhwQ
zTI5QjS?k2ICNcA|C9~0C`Ai4rx8zK=eZlGMSu>v5^P;U>vX#rWavD=RBi)N`&-D0g
z<-5MOeQ#~NvvHwl(|pmUyN^pn+vTF|l6!~j-Z5pOLAaAeV9sY0^|$1t{8oBm2*L7D
zpLo{I^51KhJU-du6CI6H+orbN4S~L3D8ipMwQM%NzuDBX&HDaMJ={K2FnJ}hgMWh8
zZ9viAP2g#VqXK5tgFeOE(4>_d=E=PAz(_?*vy^mA+Q8L?XaK}7;FZqAGq4S;pebn?
z3`LzX6hQ_;3Mp}c3^Wm*NP{#%2okPHt0RUU7Z}Dc0>SL#1TVZ|7RE&i3J`J939pln
zjmDcl6u1@=ZjtAk<eY~CL~<54H6mFCD2c#~LC9-In7<z^S|Nf6Kcz?ne*rQL7IV&`
zyL!Q0Kku%W+?!?hX7s$RAnM7V*~Z4w$3<Hgikjz(njc&ci<+gPPPwR4a-WdhC#Fn`
z`Q?$yADN{5Iyt{ibnl!BO@-#RiQLXF7ach?f>ch_?V-;!^l~teg83-G%Y`lLb?>y8
z;l97#-m(SbL=Tr$E4jKNoQ_})TV4Ma59z~%5KHA+87*7`M}X2^c7<q7k6)LPkP$F-
z8Pr`4WtDXlLqRoQRvZeJIBn_E$DD!T;Fu8b_Xhh02G{@*zJ=_4*7(R!7>x?eX1^*7
zgaXQ_prH~o^J7D!K{hUgcj2KP6<)s*di7yJbLvBi^{?R2u&|n4i<}n=*8*2J&(+QG
z54uIJPU4Qp+!2vGf^Q4l#(8ezqBC>)$V}*NrRZ#soDH(GA(~sVWY*a>0b1GYfL64w
zYMd_tyPUZ+Iv9GVI8n|yWl{i>i$+H_%JFhbR!gq#{aky?dgJ>Qdbk?UksK4<=shiA
zks`pgz+yS6s3!d)D~^J6Vg#$FYFw-#ej4clq9G0E0zsgEsU(b*ZXD}Qh}NF?b(!6T
zjIh3Ml@*k5wXe0Z0&h%QX>DT!@Kvq5X<tpl*%-~Ll^CPhOrw0lbWS&HN`N*shQdg3
z(@R^zCUq+*<aip1&hXffCs8u2`e{5G5cQ_j+d!TE+IsP&s;?hs_58!emny<Wb&9Lk
zG7PMJ<vu&D8(4vHv0_W97PGb0clA71W@#{-%G4=5i7pX($T<hcXUB#SaHUwbmTk)z
z98XcGt8lQv1WYL;l#)k;971js&_tt(sLtUL?13ooh$SC$@B_ii@r4Ts0C~C?B$%rr
z0X%*}z_hEH@MDDeHU5L&!E#J^Q^vIK#>VN5lWmK(Vq#?$F=sKWav`f`KC9+#p_J7q
zXEjb)7ITX}F0NlF-a22rb*@<|-Y*yL7mE)*Xp?de$+?H7>`}0<9P1*DcZ;O;jUVqk
zJ6~~DtT-F>6fW5k$)+;+jI#P|GQZS^)YBS*u)qw>TAsrfPMv3~l-($2H%=a1G8xUe
zi<y;DW|f>-HD#VMCzf0WSrsRmnL}Tu%(Up{BH1r2I(x~IF2lT)IZ;QZ=qQQSZT;9;
z7O_dr+UVv+dUQ(8x<%)vyJsY4qwH)HosCgX!IC2pEtSA$l+<sN>9G$ern79pQ8DkR
zi0qaeb+V&QbnIUAR4sUF=RLJ|cT1k_vS<5}du_=PQI{_|Dx!6}Rw!lEQ@WJD#)JxV
zwVOWXif0c?Tvc>a13ey-INzeXV!>TC@2-kmm)u)q_m-t~YfMmEZ&rEMb<3ltS}aKW
zNZ)G7ZM7Lcu$fw2)(<lE<gU+X-KzUwtFujS{JBm~uA}<EI^)mxHNyRHUq0MltgnOn
z*Xv9N%B_E0sfVjs4r%Lw7O>Bwm^Qb8q!59~8+j9$3g$cJ0X-H!d`?^|vjen1e?~8y
zQe)Pd_$x1#k0b-zbu;>>YbNwqXjDt63boQlEQ#`|@4WRMr^U;)Zxe>$%w<@&k!`;0
zPAp8|w^0i%yLg*6tx^4eZo;Tpiqe;BQ`jU}!q^MF43GoBrk@@R<)#u28&}E6o|aS1
zsv#X|Aw6MJL`8?B^3wK~HdaK1daf2@!n~%EJGCi~=B-+CS6WCtpP}_xQ>iSW;#7%&
z7@5N6umwoA`}-K%KTM+HdW@&(+R(aGSZ#Z-zO;s|5fxgqT39wVO%q(GJQX6$g(^~S
zT6!<2pDqk@tMv3g$Cx^_rk?r##}qo~4;WLKD~>4(5qX8luD_t4u!U_AqDhurYWT=!
zy+RCM6&jlgt{s`}X?qoX_A9!39_<Lz&Kn@WHn6#JI)#l$blll7d;xoe7riwQ<BD11
zZwhDsMGD}ktsfa4zQ*K%QnV!F=*OHS(clj#Da0J?Z5UEqR{}9NW8@0g6JHf>H{*_*
zR$|vOzOaT=r*)wb|DSJyliU||KLuDhp(j#@*Ij07$B!LhLwtWUlS#N4Sk*d(K}5vn
z!vJoK-z@wQo-{l(fe5_uYJcw)h&J?|9}FaU!od~xNDILq2-YG-e2>(5NOT^0{rQ>D
z?3w%99&Gz*^Zn+jGxy=rA3ouyaAlKx1;nKyQ^$UO01gS~s0T;{e#<Xymm`LXgWpeE
zG++ZU<~MDw`1EY=1vge(%39%kf5kNq_70urd-sl4{f-L9!$GG^7c4^ple*cl$k{s+
z3uRm9%eG2o+vKusbAkH@{(ML*ZJ$(jdclJ-UJhLM<-{@-oIWl>2Z=r59i$-;4a&BL
zQ!FWgc6Ahdp74F)Iyoe35HpW~zb`0TN#7t}LeC+j*E<No!xa09xLhIZ1H{Z=Vhja+
zcD5Z5vke78y-bpYae!3YPsu%km^HyLrDlp*A?iXKP(dP^8L6P67jjl=F5v@&{7?7~
z{%4HHN!>%vNxJ~K>!UgAl6wJe+6#bV<ThC*4}X!P13TYZI&UqNtX|pbCA9Y`{bFI+
z$KK5g-tF_=?Q=ttw_WzOONAYBVaJlrWqV5h8FQx&Ly*E%xR_sjYdqpxsMtPVvHkv4
zv0}SaaZs)}DCHlL^AF9KW=vl$dVreiU4<zpnprTz0e5#55?*Q16_nh*_Qth{UCQ4m
z=Wi7Ax7-z`4lU+xo-2@Yo8;W4sRKluP#UvmBcZpSnSBN+eB#NQxpq7JMmW+b<u}Ut
zjbc7OckM62e-?glT-w<s@9ctyc=oA<v*+i}o|n$@@>yQ;1Y}QO!E<@ub6N5X$)2I9
z_Ql+S*@AaV-YyZVc1nf2<icHI;S=`{{p{FJk9|}iHFwF)UGNakJ|X3vlXK54<of4x
z{ZekPoZCBfV9{ARdwnkNKL61U@m!DSd{S~gDLbE39zMw^monDN8SBLiZ0SuNjAj+w
zE`6g^$|{$$z}B{zpFmrZJ7r#U7tU_@UXJ9hlHFCPsVz6^%mHz7d-of=XL}@1jqIrr
zJ@t26r?|zeO?RJ=vUbQ>JFpkzCW^?GJDV5T_IBxPDPk?UvuAeQZhoUVk|B8-WKV<W
zd3J8sUo`(&^8=UE*eN%5!b9wOVxjAq`L1WAu4m=0XC-&9?CxD~Uzm4aklX{ZdjLGp
ztelxAZa?+LQ({G<l)FvN-6rOC-{*ej{HgQ76Vk3uc~>Vq#I9~B>x`UrW+Cgz`K%|U
ztf%Fyr$K{qMYFr_p1oiB;EH(WtjIksagWQ~<I2NBcfoAV^owFf8QR3;c(wE8&P6UG
z$^cYk;0Gh$8Ih`X%2hi7uO#Oo*?B0M*)C?bW3K?)SV`{NXGXIby9RKUm;>_6Gug=)
zZ0~2*wCpy#zuVqwF@Ipz!(9f+3G99TH*lCdqD4aisKFybD3=!Kt;~?sh8fkQIsv)`
zLOiMsDsAZWm6KMKu;B{!v)L|wq8PX{U#q&rV3sLhS5r;mr7*}&{EFMUi-7G<+et8h
z*QHQ^Hw@DbKy1CTAh8w7z*eZ3iGeV{62PB?AP;st`$mRF31SaJh`M(WB6rCpO0_~I
z!c`E&rtKz3@Z=p-es@h+pbp#$Cq0ynYFhmv5SiKDaiHbQvF@bYY-;<SLAdzlJ#8K^
znQw^bxl^kp?OX*lD{9QMLP-VccrTTWDh#2=tU_$uf4r`4A#>Y&<~AvFhn%@%A+vct
zvsudAD`)N%GrK-+?V3CsWtI4NbGx+pfV}wtsvzYZl=BWwo`_mAXHJOL_4q~I1tM3l
zXw9G9Ct7RqTXdI+Tv^oNp4!Ih8gnPM-{WEhnr@>J!bay4XnPu+pvkZ($7wRd1?;zg
zlGCf@I<zB+Q3u#SjI3cCLm2SyK@}z`4~#Bj$bu5YFD>Q<+Moaw5)G_nSGpt%D}_n3
z%aq3YBmsbM9A^Tp*|BQMX>=+}*%G#_OjB#y0`&__ST*&ts&}zgsGoHe+9$2_!937k
zD^uQPEryF$YYVAxY=}iz!dAFe2uJ8O)R0vh^HP#FZbo}g_fIc`QZpB#7!eiIJJliD
z7T`Hx;5KbNG2!hBzOCTRX4*mver0Bu^|(ESV1K1=7|+<pk6axl4&@$_sn?8Ef({JC
zQ1#N0N91DYWO%fK`RavVAe3tVM)(W71C+!uA4TGyZRe@6-8k~MkESbOLzB8W{{EIf
zzc3k>=(`U;Uj{?Sguldd3L9fgTZY8NOh5&L3Mz-4Nj`XWAaqe6`dXmDtYE7QA^a;!
zjT8PS+#ur~uXs>+K%odTQ{irZM`1W2Fvu{wAdQ6trlyE?<g8RP;bVlWrOKRw1K8sL
zBUf+Eo*kLXnJ?WgmTr%_v!-khy%m#(7axs4xXitJpk#3W00hF3HRGRmREmyD!Z<Q>
zZ`<Cm%~ng96>?^Um{}7!KV^;<tb5n~w*4*V9VeDr8)Zr^P&90iU;L3#@@$eln;3ou
zyeyhivXHZ3K4(MZf|OG)=hU+gb2STF+vc~nJvb(9Jtc2Fl?c7KxkIdb7Qc@;<qmi1
z@SFVXuGfce4a<2xaBoFt?UI9X{2~K_Ff+c}4R16^+3V%(^`dVN{TzEG$6nd7S2XQa
z0IU&;K<GZ`1hKy9qj8$JlJNBw+8<+9CDpnz#9Vos6|+yBY9e(?kDxcZc;h(k+BrR8
zQ1b`@*&!DPaTkkrh5{xh=RUz?#wkR8seO5fyO=_%`42Ixv{{Z2X7$F`r6jZ>zLPdT
z+VJ{1+ihPjG}a;&Uo_r0xIyOveUOq-P&StVn-o#<aZ`P0XjGU&NZ|%KH{pCrpiuZG
zoS5P2c~DsYmfo4XuY+c_KRDLkKX47gzwE9^yii-lOr#nH|BGWG=nXjfMn<m_+#5N6
ziR8OR2cba)`K`aF<Q6DAhs+hC?N7+>Hu)JLO|wjdE6bk%6woVBV*Dclx6&kUS0=g1
zC7W^<OhxmiqR6(nN+>}{rk%2Br)b&<bYa0=Iq$BFoRc?fzi*K??3LX6WcR+wLlBmk
zSs!tV^@qi>BclDNWIrm~kJ1iM<}wkPsz~>bw#{+(E2WM5<c<3Xrc^y>1YRuz72g)|
zD4H1}w}JLdh{T9HBqLKrGBPT^)YsG#+X48Uq5i<s9-5pBrkr_G&dfQ<R3@9s#KbSS
z11<V~X;y2E@%@Ip)-2%lPCS2*Wopf}eUNLU=K?)F7qaIXQ|l({2Mv0-n%Pg|kml#-
zG)uLc>*><dn5n86zVd9>uC+p~8o$@Xre$W?71oiU<U45_>DoCl4Eq8T#;_sS2XV+w
z1Lh15i5$c;zLSO*kyRah)#6-fVGs{E7hDVMU!>1Hs}|;Hzd!05&)qRX(pUwJcpJ;b
zo?gb6^_o^uQA4T5;T^o=o^u&|vQC(ID<GOG4(|eA(vVI$Y0EufSwov;Xlj?X*Mrt_
zuhwRi`_luA2WSP4T{T|9R<hB;_^T@4R4vm+&U)}+lkhneDqZ&bQWB2x*2`>6fjThr
znItwlVV*v~>Y7E^W%{Uss*rjwhYP2z79@xePscQUiKUaxYK=V_v_MYS3^(_kGz^tu
zh0Tv@J!Ne8@;b+@euZ^Ap0kH8?@7{p^`2#VuHKq)tIx~)#|X*$%{Sigo`C@31@Cx;
zm&qXx4hDEHlgX_K`n>%kf;T}#d8-8<q9}sj##m+o7(;xU;kB_0CVGa%ly`ylV8(r&
zBk>ZP=<Iw#NjZpg$A<Z|)W?geQ3P-3V!%5#3aRoy?~pg4m8vXM8l0GVh4G1x;33H;
zE=}=?Bk*5-K`TQuAAejF*2lTXWpOU@uMi7*(!xCM2|hAoAnu2UhU!)L$UDI7W`bA4
zBf|;4camC<>({D?_Jo#>pVqCNNHWe=E;uC?oceZkr1Gx*e&x^Vep>gDRcbmZH=Ts{
zYz~~XA49>$2pCW=c2E&mM;5HweGLdZMj|*Ap*@atQ=Bp49sK6bAIE!2Q3>{vDByM0
zN!8=VY7CuRFLgAHi&Wol@{VVDdk2-Uqoh<e9P;@b4E+{<ie%Hdz@zYsIlCb44MC6&
zK^PGftinXfDlsc59fI2-(8><1gAoWus2vLbj>TgNOz}1qKB46QH}a25k<(5@FlM7t
zDq5FGEMr9m@A@@#oIt$ZqtsP2@F5_QSsC$5ncL;e?I0XZ^)8&cIDhJ*bn23P>XPUY
z-rVtVQSCxe!+cT0+&*#hQROEU9g~ZWNuJ}f=eUU9s4#gfN)nC^Go(cFmvmMe_;T*4
zHZgNw<V85jPjc>;o%==S{`fckozsyLX<fa%u3pUK>DSd8f5_w|XFzraL}%b(<p$E=
z8I&p-<%-6|qII)_Qqe}aXd|@iaxx$x!W>t|7j~VyIO^=X?-reX%I`Cy!3~)^I8fZN
z*&%3W&RrRsdYBKH_MB*L-el)uc7ZZ7@1Db5DmW#>%|0b(pPD@S&}5x#gFNT#i*S^m
zWZEU0c0q9>s(kBy$2fabDyWeQYD5k~g53`qlsjC>bVfFv5lv?v7Oh7knM;{-BV)=o
z)3}&Z35koG%~LkC<btDk-chV*YEaR0BfcNhf2Urm*ecVn>HewtioIgR-l(%^wpVhN
zO?46##yO^pAbMu6!@*DDHp<*a5kiSv;q3a^>k#gcIiJY+qFe!K;jRO_2|re+C4=5}
zMjv?*h#T&T8+Dcd=}Wyfwy0l~FrOKX#7%_*sYW<<-MYGHQO#m@?#yK=yF$*cKrcD*
zQE|)`Hp}}k29uWsPe7`4pT7Y|i&~{5S<|Hs6;TczIvy7+6JT~>IrTB+r41UUO&XyP
zp?sEIX-g`YD}4q%*k#kTaY_1^rN4h@G;m?u-7ppum_mQtBbRxwNv!rRXc!p{HC!AS
z3Y-@LSJhuOXunV}C8_;8)xp-U7EBcgN_GE$y2Q*^FAnrwR2UMpLq#ekeZ9l{0GY)A
z^hiAuLXprPabi&$#!AO3CVYV}*9a&K;9*kt370YLoOwb~)Q)n6QLrJoiYV6*<?5DP
z)@;k<5y&Csc}X2^9dp>Ir6IBIot)W$x61C6EfhA)7dA+RTjatm>_gO3B0A&0FH>Ai
z%_2wy7aI%rP#7lHjC<lrjRtier^x1XL>yxVHP(>6;9^G@`jbdAao3IkVxq4__X7yF
zQ80%MVIv`Jq-X|Y6Ml$Iqk7q;%>|)UNNFmtHWY6f2b&dP*MR0hH-y+~@e7Dq8oAC}
z?trXWnFv3vTV3)I!bCZVRCT%)<+fqKz#yDwQjswu6uCd8;(khf`JNSHaAX)-_b@RK
zb0{WD{4iG%h;e-vdxwVugTYu<-23VP4_f^E0PIJA(gqa|zC0<QS^5RG7TWnS#DtGO
z=ySwO$S5EzB9oY5U?gU3y^axo_(aSO{k`FM3@4u1Bg>KAhLgF{8)eLLjXCf}d4{f!
z24ZG`P&&4$3!QY*D*)f1U(jPbLY0LTK2el(xy)=oFwap~xqyw2EB^CTCc%w(ui`&Q
zTN@-$?uMR$D{K1Pn*#PhE2aEuIluaDj-mp%q;uLb<|?AO`L~~b<LTG^xBRq|nR|QR
z8~Y?rrR=GkYLC{|i}^ceEs>nJT(hpZwy8r8*HuKz)<;W9-#zj638|z`E~$%dsQ!Wf
zJAP@y4tc|l`&rV4rf6CDyDz-`f>hQZmo-F-yo;slBZuzxN~N3S(#=b5j+Rn#Y$yOL
zMaub%3iVrZQhqBvF@pb5p}zd0C@$~*2yKY%Xz-V-+swN6S`Dqcj2~=t;`xJJrq*Wb
z2hB!$-lxa&&vo?tbF;n8WBhrx9xnEuhWke}K!v6v11bXAUc11Abt89I)ox8&Ja~h$
zmooC&sxi{2X0-rxJnnO=#!cHNQ17P1<DOnMZW>LlHoL%E2z<m-*t%*8Y4jP-#bXq%
z8e^G#T;8rwmRfoQ+<Zr;@L$jc!k6UyKji$s<os81{xdl`^h?atd7`s}%`b%_gAJ$i
zFm<FzP-l0ybRC54p3^PIPaf;=cfpiK%pLb@JJizI*>UW2tWX>1@x$%ihZHL|Abv7w
z^5H)&MH0*K55Sy9uYfsE(^SaEx3?+G&kzP^u>(qzU|U;kRj}wlHXmlNM_(>G%Ohvz
z!B;lo!^8N8rzPil*}4AX>fQ6s-AiVCfoy^+i$&^@oL<@K{oaXrXXBEUK5_9+Vs4G(
z^vO=&$8`rj%984i%sY=P*(iiP5jmc4<jTCWjx;i>mYj)CPryKmQk<4~=Z4SRoD*0W
z98BLhjumr_2m>(j1WQsxMrdY4lACn^HP+4u?czhb&$Ji<y*dWovI>0ysUN?<S4vm9
zB#F>QHOB=DQ<4LL#muzw;+l@+X-5+5B_c=dl0IRWKCb2;srlB-UQpVMO!2a{Y15vf
z-~mU(q{w0kF7Ug^3by5sR6<I`9l;`Rov`p6@y)+w3R}S4u)kwcMmf==;~DXltmY~H
z`E3(EFpim#B%uW#h5c|o_0rQ`IG_&MH-F5yc^m(gi5Wn#F;fXbJAF3~^}<9<%sAA0
zEoK~mLb3S*mZec<e1{=?F;JO-A!tMupd%p*GzMcQ2xf=fj1X2KK5$Lhqk`#dov@yq
ze~)y7gm%RDoRlr6B0r0fW?)01IjazjS}XWIVqs=vR7x3Daz+&ireseZiDrV{Gn!jL
zG>{7ckj(5`oLE?Uw_VEIB4=*FMvx<uFu+!b2$jtC{K#|n%#WY^(<kp|z4y$$XCz;<
z>}$UNoaEat6}HHQEwHNci6iID?%6uAsPXPDMTA7oh3I^f>}Y~IySGwwwcX8`E1LH`
zCi)(m^3UYV^vwt}eYXmy{Pg!A8@lq2Ei*l$V+($fv&x-Z(-wvPKy{KAAu4QnxP=Gp
zWKc{_X@^P*3x(#=Dw{dlNwhN6k-qUw)C=iCDcD)uHQljdXqh|NaQRAK4^wr+m!T^%
zEd1XnEGPgIZmdrgO9hreW*7>FplCoSovp_Di~Qh#VNm%mVl?^tg^?k~+adrVY($Lz
z9{)kw6rq03p4lUMsv@mYMzu`8dNE_$T*ZRxv3b{Hl53Cb+OyzlpLexCI4!vj%dW$d
z2Oj1Y1FX$nkLDMX5ZStNddSX;<`n_AV8BTYp)?G3H!@|t;DoE~1#N2r92tUcH7H64
z8(<b=$xB?URnu7IDVl*=9Dq#)oKoWVOp6f>lG<oun`i2@(-v)P&>IOzFcz*^Q;PJE
z3Y!;9*p(@TbXo=sQv=WOHVuGFTlRT7?@)hNYo}?)Fz?iaQwbTQg>&&4>X6|y7WV}w
zgd|k#;gsM(*vh+k_r1(CLuy=@yW~vt44<U{+h7iaGE|!YiSOx5?BH?4PO1`*Qd%@>
z*oA(~j;OTp68`CFOxV;TCu|dnz(6P4Ov^6qsN+2;2?{I$+1hq`S&tg1S}~QFHkIFp
z_JWA>_*sQa_YMTX#C&cHCiHl3Xv7Pu3gk}+>}95j4_0$;$P4Rx-r*74<2=kQJG_?<
zjeCbihDW?xcGQ8i@}6uv?tQ%FY(0Z2u%FmF#)Eot3|+nISCaBW`_=;5OW6=*1`WqE
zl9F3ItyANMy6gBK7&q2k8HUr_M~*OVs_Sd&joC)~hWx=TJ75wJ?1+rM-ceZh3;3}M
zIyx4D&KxOk6I>cEh}TT13Xyu=e#kHf_)q^UE}w^SCWL=N&=jb{4r^d&l$fHi>~1`b
z2))Af_Jm!*m_t$SRp_oO6cdL{=!6_Nu)Q&MepDENh0GwV5{wPQ>YHMBkZi#a^90K^
zjB^Muq=`F<%f!eKjSRpj*5wcbN_Q-E@N<bURAB9A^hB#`%z_PIGG4|71=uhmJ9Hc$
zmneuC`-b?KiHZ<2BAXT2xTPx5GuAb<J*+}iX>a%e{)2xD2Y}jDc;lJrXK2mKqBYpQ
zm{AmU^2>RjZqIUn-ZWP;d0^3&CEF_R9v5v*lC4R$H7)51%p30aFM9H4Ew4A<YKAE1
z-5qd9q|?&~S<=*pt&(T!6c^<(7r2snu4J}b;wogWLgXq43HAVa#&6N-p5bmBpFSRS
z!h~8DUf*;ph_UkaaVAVS5e^-7W=Ea*$>2+cF1LNk_(h2>vuOH-*}l2T``x0uRdTn=
z?$#+2m`Kz6W_#xyWul`ja_t8%e&<E0dY4?i3$IYvzx9~tIU1?_?nd0iEk_r!yXLdI
zr0i}vyL+l_F}rjwD3<li22%v_oIx&Y6!F{NBV|7+XOlecLwEjyyJFs5A-OAMcjeL+
zv<z#Wl=B(2)^Ev4`K|QCDC0+k3KH|?eOG%~$8O^<cAGl(SpQn5ha2NC5)~|7g@Nk`
zi(^aX|A}!xQdC$L@szk$SQb^W(-)Z049#+tM*CukRI#ekjpR%azEAcZ-0FoCdP->w
zIPAizOOUpFO;|K}q%AdRbxT{&vE<YCU;H9PYqg<)mDy}SwT$;nT6%((S+s;|K}eT1
z!Gf1uz(C0;%Q%*zRU}o?>iBi#Wy035$%%Fx)#J$+hL#=ddzi5RS$OLmvKz{Ej+b;0
z%gT$L&4FPk77XxS1~k~-R{eN~V#gQ@hT;o-mO-8^5a(%~hzsMg<>V17MO?6^x=mSe
zl;vmj@Z-B$;;T=51GEpjVql120a|S_qJ23aralY6CYXA#ib<^NM0N^&<K_b)q(U3C
zEO5*T9yaLP_?s1*WkJ~MgjTlwENNIMyK%&NW5_L|@dbjOF{i>vIIWnX%fq-~n|4AK
zh1~%MHg8g(y@R5p$Z29Qfy7F-UCf~rgRKcL>&eQb1X7*}y;kNhtp-6tHsjP+f*YR;
zYtL5#=?3qFZe;wA_zzxyLo$DE&{5{yix~xC;Z7-Imz=Rn<aT|MQxyqGIh*91O=8Zu
zk2fFrXpgk{oV@wm<nhI<vWQE{s+Y6sL8%p0irG)kRL)d>TzlxFoR21?+Nb5(rzejt
zX04ALm9jR=S(_)1eBvmat(6=bWCy^R-h5uqa(h<F=#?{iMXvV~8&n5O=33?~aybiJ
z(LAR}Cvim*w^L>tW6|7oV(yWT%OAUcTq-{zmmit3FM7%%wUGH!uNF@x_oPUNTuGB9
zOuFH<q&}|lOJ$pSVlZ`uP#L=1vS{J@nP;MfRbt_`yS35cnrH#7NjarUMuY2^UUY89
z?_t)_C69xs62&oF7!dxz+Tv>S8h`FJwN+YwUag1Qmw+yGx8R?F-YQ@*pjK4hz@{Jp
z4uInnrMR*X5caSP>GrAsxYU4_iTOdu4|%1I7$J*Q*bgAmZ`sACG4cS=RtuS?Li~u3
zE2XKTf>MRhV!Hr#Hu_5kv8$PlR0CE9!jsrN0BxF>gPOMC?J3-xcQm6o7c`bRG~b=S
z<GYK#gX&Sy5}NpK_I<^A@lZ%yWe<f^6*I^8*lF5#1Mh?!NC2ojz#@=Mf5J#`J>&wI
zd_u368GP^tmEDhe7VoRv6(cC1Ii|mw(rWzb6qAVgXLRi2K9-udGjKIo?nLptkaQq}
zsfrv9B*Y&_6AH)4IY!P2atPZOPQn3XB{U}`f2r)>m>JeirZUEY7ljcD!EpB{D2jol
z7BdX?t1ZMrN26~r(3@&M*u%IWJOxl`KyJZrFqnxw|I7lhXbWr^Odg7woUfc%Fcr?5
z3L}+sIijghGVPE}J4DltPi(HKi?hba1<_V7+3ICmJpmaHc40XCYursv%nC?czs&WE
zTz}GQkHqbjxxFH{H|e!3)D8QzeacSY&BIb;4uLlhfj5<3>TBvL4&Ks3!KQ>An<pl7
z19KK@Hr_q-p6i|q_D(O!*zCvF|Awe<W8!rqS$L@59DfyyNHQQ=SD*OYAaS)+J|!`h
zB9*U!%BS*6<(KvpFXZY$gB#F^KWWKnEi`^mXlh+&{lKdycVkONyT$mS#nf)Me(2J}
zjhTqZ((tO2pq2<Y!g3kg4GAv4ma){9S;AHC$nZuL*f6D@8E{qMsi}O@fe?VTigA>V
zDe~sHq+KqlPCYs#89MqS!=6>7sYwiB6~KZoK1+f<Xjf()O}bCNf&;@c7J)GYl?g?2
zNqB{vQTl2So<@_#jOWL+(<;laSfy0p5}Q=iBY8BbNPY>32#j1GnQ2{cl+HU!XG4;s
zQg&2Mwlnj)CNk#;XR)w!c3cJ<_%XR~=adn`DX-aY+Gn;&Tmh^>lBN?Zm<Dg`n%*_j
zcXRL5-e{&r%&B@aINSL8wOiLB>tBBXuVQ93jZ7s)mMo3T8XB1@KlWiICq6P)56aa3
z-i2MQJ;wL<m|FGL4@`Qv!YNc!GYV<BV+;PD;X%uuOfB<dVI;ompjoh2UjDU<rB*Xb
zm8J=4E<29^R(f-=u&+2uwdsOZfQ&6z$m1F8RDqVd9vm#-eR|_pdN1Su2>VbYaFW|`
z6FSIgC1)G?8dyUJ2k@)`yxD$s+=IeEj@!)d#}L7pY0ytWXmBOS-oNPfM%G8h#p<1N
zT@trT=5{S`d*`{m61QLG_9x8!YYqj778Ht(@~JJa?Yz13#_s9eQ@dvc(NxaZ_CgCA
zuYFf}Wj(Mcb}PY28NSk|Mo5SjWWDtdC=g(VQP5Dv>GUh+F%nK()Qb_-d%}M?62kVQ
z5HiIeMoOx3fMpANGcY)+84aXg5jL;fcj;zBfs`4Ag4IHke#c5>SS|e5tJ$h)n^G;3
zdeV+Mv;&Fq#IH-1#1o8nsgb%gE`yqePwmuce3S5&nBig5J^S~8F8?rzGEYFi?^2Qz
zFljiVBi3xn2m6>!6V}z*C%hcdHLr|jFRQI;yev;;`Z2JFRqc^zjdbSp@5LT=bdFJn
zkO-_0Krv$aUKJ+TW&dZy0$EaafdqZKnQ4vpECNxc!eit#k+T&}%t|W?3B3;B&F2&d
zlEP-}$iOhe^@MX2vWJ|V^u>IVG;9<tI};gQh*?f|x1TuE&C(?y9~j@}*yLBTW2ogt
zicc8`Pm)7gGi*IuL7~p&2g(iO@g#x`yZ$uUHxQ)yHXb00lcD1v)1o(5kep<da_)%8
zm49N&o#_XA@Y>tg?i$~kxHBQ;ZIVoNvIz!|>sCS+^UcL7bkPT_O6F=su9op-^)@KV
zWiXk5v$Oo`@vVIkEKdc$oXMMA|8C9OHIc@*>h9FRD)`+>(Cre}Aajr(h2R~-LKS54
z6L+EPE`t#5Vy0)N?RD-J7_#eb?Sli^plDWZG$$9UDYo1)q4y34k^z}^7?aG=W#&vj
zFS@IgAGYXj9R<hOR>>R)6~znKeWP=_lSE2cd6mO17M(C^t1xL%X#5_!J+EEAdHu%o
z)6Xw8pz2FHjq@43{#NUk7KFXBC0Y|REtJ9(wY57tDvZCVFm+T}f8o=^)hy36xfsCz
zZ_%YI(c?VZlY?R%LNWvo;g(<8X)R=_fqD8V8H&=GT1<yGv((q9LLJOjqlDNwg!S`u
z<qNPiRbYd+vK>HGu}S!VSxtASIS}x5zGJ;$RQo|;&~-24`+yWbOoDHcUgX_zfd`MK
zG?}!I$!9HB!vWH<hL#IzQ_E&H-95`wWAe4w-!ik4vVe&W49B>{Sy^L*g8b&rAAw+o
z=pc#p({Wm$k4e>{CU|*Zbd)?{Ghht*cGp7}Arwd<d=Mr~_Zu%gJ^(?&m4bVFLrq@D
z8ubP*er2p1?EA{p$KxC>3p=W-`m2m;)%jd|6qS+jdW9HH>L{FKxhv(XVJE#QXMe?2
zQ{FSC!!1A`-FW4)V#fErV8keKuF)Rh1q!;nmSFD=!`@MV-{d_%79ynuYz_vYp*ZLr
zQmP82L`G=SwiW}yLIFLoy)*)W!1)*;y^3cXaX?K55zf9KjSsLX6ypuC+=Na+f=R*J
zSa=DMjuH$dxhi0L?tCc!y2~RSlG}Ip2<&Qu*tWV~eevZNC2N6f#VI)kvm5YLEIKK1
zr)2Jw$em(V=|6FLMAFvvO?E&`<hACT%{TT=?<H{D0nTHQSlo8M0}g&tM!THRE^_Uk
z*z%$__e`!>aN?b7aPYf3rrdvVNV${Com_-Wd{$x9y>H28w!>UF92owvTP9lx=G&)E
zy?k}@D$C^Hg9@0sRDM#%Avxoa$Q^p<@VvI~=03?$B0I<wjrmEXMCa~b6pPO(KPjU}
z&gc=j9#W5dk_6}BE|)Q(-E9V+XnkA>KCKld0cxlaOd<_dgc?bN3mpz56hwdtYKbK2
z62FqZMv|fDt!g9-GoNEK*2Z9qoxuV}S_tPmjtj;#RbrDm%!&|)gsM^yM_rRlrYN=o
z#9=TZyEB%r%$DRZWQOD+zB^v2o!ZL+un8_yqT)gYIoHtz!Z<n4lM{wBo=3Bsm`TH9
zqu9mny9nD~!9CUEUbR0&W&#2bhygI~>X?cc;K2l?;X}IP`_q6zq9EQxNddGNTuOZK
zCqBf%gzy5T#Xv*tDj`<`5F`Ocbz^g#W_YY%)#DCJ(!Zz=G*mza0}mDjJiLfP#hmfB
z@l)HxT!}Z<A_`(QiD1h#oC}u$k3^M)DTF5hpx}Q&X#qe843Nd_Wnkc_GU*OK*dP`k
z#!t#PB4?1z?N8Qi7d@>rJ7#v=-#K|~v9w9dIXH7*=D-8%<Z<x%Z4lseEtyQN>Nj_R
zH^2__a#h1p!IClY@^F2_k~#5$DD{s)StTA2e8ZAT@E+o?N(wf+gU%W#(Ao3B&q#om
zQ@<0u9RJ8579UrBQbwno(J6ABOGX{%p1jI{1x$VQUQ>>*(?~_pJZ5m?|3GsQ^D6?6
z<(C$32{L0Z?4&ltT7GFmrvy0xA(Dg?9R&cPO%=o-7L}HhpJr?T#)P!rNm>e`o+?<B
zD$8;b93fS?r$TB=J7K16TUZRo(D@-v6?W-$tL-U()Kk?V5gxUJZp1R<31l25NnobJ
zrjuz}nWuDeFRZ`JS{)nWkln+!t=<8pYqc0DG4n#S=NP{f%i$L=9nOKBmoU)kvyKal
z@81P^6Q)H<Cl-1;y-W-Rt3KgDV7i~b?p017rDHvl=XNSv23>$R!f+5#5ObtoiR!I^
zBoM(MBy_<$9JtC7^eHhEKK=iNk|{?r2oLc{(m)Va84-dY<`{_i6u!cLB*%-&#yl&O
zKw;~ff`Pzg;SCD;$K*64IO9GlniZS^V{T9)CL$T>WJ_!&#Pt2j{wt#=&8&ng5O$f$
z@eXvLqcVAfH;~fH1Xi};0koZ6=giSA;@Qkj%96=x##8=l6E`Pryg2<L%%_p$s%NHU
zCiHRX7Ab#=l(|*T+zJaRuB@4w*>=fQF1yM}7q298ijDz1aPz>-c9LnCIv(}p+}`oV
zj@NhH+J&?K%m~85F$af>*-D^-sP;>JO+7I>XoXM~&}^?gfAjg-op*OnJukVp%kJ%v
ze96qY?S8{8WtPjC<tQUqsbe>qrkiF9VP=}FLwgFQjz!%$GXpa{Gf&PuDZ9%-(I!k{
zvqOOthc(8OZOTUYOEwA-+=FhKZPB-F(7j(++v+xcV07a71GlNwWBtHmq~|<6J?FO=
z@bdE_JGmR2ZQG1LZ`8wu@Rty{qMWkHVR*5pm7$u5yqMq$@EGlpV;Vt`^u0fVi190(
z>8^zffM_Zl1ME1k_O#46FoZUM4g#AkeU^f05Bf+wZxxjY!3hIvfI#Xmaecums)f%q
z{t}zF2Z2Kjd#No3M#h3(Xyke0^4VS*u-@Qs?`ZJiNGP}oj2oJ#-jU(K>ujdeq`Y3&
z%hUX3V_%t^IDGeV&>Ns+2l`n+me5rQdXHT71}_fuhl1*9+=w~QN$2kjf%8Hve`qz~
z052SMEc`J(3Af;ojQKVH<(U3*8seXgGpvRntq09x^dT#SKnfCX(d_yL5>sXuv#ef#
znKb2wHFa0!o5q>LuV>uK_?|Ix=-aOEx+G`4?5xMpe6H-7?X%^xU6RWyyO`#n;!NqG
z?>f^v(=~H?=JfR5*(W0BBhN+7-+5}@fx~lrQBUz~%WUgxE2PkzWy%jLCsZ(SYDUiF
z31#`TE0TqD+NXvuK)uh_&u)}_?dvBg&TB{D$}If!4JSHpb2-D8aC?aDAWe)Z)tO`@
z$sAFQ<@k`sEqxcCDD_C{v+7B^Itn;7Eo>xPs%t8JngjrETe0+%rb*zSO{gk6C<;5X
z+3FZyWTX861|b`gLnByg=p5YP5JvM54)f_Bz%Z^4^+SX*z@`DT`@E3#0hWygh*{(X
zY7Ew#7esh~gk^jzNyrYRy0zYWpm%UEh#-BJN%j?k5@wp>u`wHZhrNMoU>zt4L!mzq
z9Ig(*4@;kRF|1Ka!Xz^{dFdo8_UI!Cs+!J`;lL&@x$y<@Jws>bI9D4lzGiR{`au--
z=ftR3>)=Z5?3~sK7ZLG2gFve(#GXR_A+jML^x;*ds-&1Zt5x)M2SIxvV7y&Mdj|xS
zC5E`kv;To+D2cF5LQM3RmJclNb02c@{!^2R<ugd+3O}J7VZ8|A!#E?1DgrBR)D+{E
zT6X^zsHpHR!JI#|az9H|?taV94*c}MPY(U%A-VZ@ymFm@Z45)Xj5-O=lJmd7fePoh
z2-sPbzK8HH9%6<|!I+t99ZXl@ELg3zg}X@RM^uIfNERRv=0o!q93}IP63J02JD}08
zH<v`+#Z%!=GIC~mZu{TxM|z~(&2sK$amOk8J?R%SAWrqHobfDFXdG-ER$@Ga-KO&C
z+Q~MYru|yk&9d2i<tVes)~MC?>h+hezx4bo&r=44VDHX!O<l(>>L;*l)<l}2kzI;C
zHF4{4`aKTe)u$xqQ?m0ZkW^0h>=`j@(@gHnbGHg+3htUjoI+u$Q-G(DOj#L626-Gq
z61du@hDat<lHNbhLA77iziQBKePTO@#!mdwuDwDYs1?=ML@1r^hW<txrJ!B=k*XyX
zIxSWAlwnHW4`})dM_T_{__r(_74DywPI){X1EsUFI+`}^<#~l^^W8XpB95hFaSvk2
z2hfXK1@n&dDGNTt%?bNP3zI>Z-r?(D)G^y8iXCV#*&ic-robu9)FYCJ!I-UhcoWTk
z0Yu=_9_?;%SbxKaCPhc3gv4GqOy>Z?3?dp#TIc%tKwuQuGBa03TOk0wLClo_e2?j`
z2|uK{`KNH=Sit0@V!PGeOtgbyyw$g2B5L@P2>LfPL8_4SmoVV!-mm=Tp8Gg+L!|ev
z=Wd^ZTPMFP=a*5xv1&KrfvP<~)y_{D!wI3Ni7RZlVY*?|R{xI;(*^PB3$!Ny06<k0
z-okTCe??VaLJ`32OjVc;EnH)TlR^KMD!d6Pu?lyJZu<SC@_zaKQ#1OHJvDc;?zYW&
zl#^bRk8oMF!KJ-L1!KZa5Aj4IFzBB*$@8z^z+%sPC^YLFR&2#$&$G|64ibJ$-^lqn
z72z#J_!4CR_kzowS^s8GvKGr$?9J%S8=~%PMo2V9jEXF~$W<;{VJYs-kYp{9t&GLl
zqK_u}OX;5bZMaMB{W83{{ma6iK=^odm3lo`|4ZKozV|nNxDoG?yHmzzt~2V$m^{Mj
zD|DiW8ft)sID;`U%=%Y#uR30JuEZg|=}_Y|H`0y-VI#u;JjoEuQRDo);f7;5hZK7^
z5Q@0rxIre(OmTfJQ~ceW(BBFhQhH+8SXRA-O{p(&s$e5F>V08j%0FyQJ;%coYDxxS
z?o^1?%BbFzNol5OA=AnnmUJBQrjzH?CTyWgmWh^+U(g*h_FJYLQ@SZ<zma#m0?jUP
zWNeA=>bo{~VF$t`x7=3`2V8dzq2jwyP8bqH{@8QFs8;iNz|UDrnRcN!Y7uq~IRdj@
zLnuwTaQ^VJa;>R$Yip@^UIC1c<nG_fM|$v1-g(cJUKaIXF)4jHjZ#CJ`h@0Nnx^8i
z)KwHUpTQKM-Dx8^Ed>%{!O=;>Xz4Zd9YUVNfk<Vkk#s3JPLTrPwaYF(^In!#BTxG_
z!Lif&DpL^^o*Q&OIrV9MYa0t|8&~mO>|8@{q$#OudLvDN<dMCRt%g5<Y;H9S0)$!K
zFWgJt4ppaGCCrhENeU`y*VG9c?*YGL|Aal{Q^m1`YEy68@&O{+!?r6rVS_eCSS7Ko
zhC*qv?dr0GZDD&eGMa8d`>VL8sk}8~CO_dHw(&W9?!7#1UHE(woMPi94Z>((bb-1A
ztHEemOEj?xwWCj2ZG1)J|1ORx5{VlpZK19i8t4bz8YH5&IoR7D@Lz>g_}&D)+@SF!
zVud9_+$1QI^j%~$_mR^lI=#UWFLXPhw21>iaQZS5gylgzlFW-&p?m7>Eb<}5hT(4S
z)xdy&Pqe)bi;5JGSjCqEUOz6upC|%2%n4-|BG^Yk;MYN*M!{BN7KIiaBkPF2c{}o-
z?!N}|IZ5s-yY;|&VaG9^p70%DD`OidI5ziBFqP1F<_VqEM0Nqb$vf`aaBjoU2HwBn
z(1zn1P76FDDjM%J8Z?*$@COBJ{Ev8e)3{cWTzDGjGa0MY-yma^%ARUmmP3tmq;^Ww
z`y}Z?!J2Pra8^6Mwc^9zHtJF<JmDq4IFhh-rP)h6qilB+_5o_prm%Z_zW4H25i0+i
zZ~Q5BL@Ssj==`x!g%3i#_XWduNm>k;C!>`A0pVk&p%L%`7!peK2eTT10LjLV8O~qE
z=`v751dEN~a4|zDnAq@}jAPHSOk&SK9Alv0ubkUFzFnov8s}OhhkS~3NRRd@A+xWd
zU|*8flk>G*H+K<xo8**E>v0~+?0{I@gx_6k+tYonO1Vo}O;fFl&g|)<u)^2!mgSBG
ze8|>sTfb|SoHequMs(IdkVz~(b$`oW?!XPdj|_inRh}egm+b5k6Tc-ZWu|1wP*+9M
zVvOh#8o6EZsRb5SV6e$6{Dj)=HGE{J6ps;z(ged<KD7JUgoX#xl_JIL?8Zzm#4=LP
zG!vmk%ot`dhjLc}xm7O|O4y8C4nW1)LdHIZL&A3`e-h9eqXm@mKS@vmgMh&Zz?dX}
zX!4FVQkc}7CFl~DuR%ZsywtE_CE*CMsC~WyWt*6IA&O)Pk!b=U@xl+`5HCyUXS^(8
zASlC%#u~Alf+K?%Y5$h|$<PF|VJG~QQktRH*U9-e<eVpmkvEJ$Vg%85$?tn`d@eQ!
z1t!7rJ&H{*H)bVXHQFd<p$D*coFKZ%LosFYR}^p-(PLItI|u_3CrtQD^8X*<1W8ci
zS&)PAYyW=5K1%T;MzKI@*dh(sAEO1Zg#z*`LkEGF0x6?>^59}dRphFa(I96uymWA~
zZ7Tbr1JZ^GX_%J#R<WoBKZ$FVxmJ;DWl}K@GjeBliv@McPs*qRr^uBv>l5>9m7nCQ
zg~GQd@7Au#W7xUo9IsrP*?FsZ-dZMF%OYKH?zY}LICtgGpP%2<DsF03L~kBcf4B(}
zILn1_Y+2JKH%g~V7i`{nn>SJ>*|x~GEs&Ejnarp4@3fO}{b~J=V6>9%a}XG$``2(G
zZ{vRTg_mEDtogDP24nQ*y4iy%OJ#GOzbO8*;{R^lpRb!Q*(;VnorW|7X7eMEqTD2N
zn?!C?)arV5;^m2#UVMd&RGMoe2IbcOgW>NCOI17MsvY+$epdU_+MjIt%T4oD2gIra
z@l^66`6)F+YO7bDfBE^BCSIACwZ7|q+btFN<O1Kk)hAkE8Ph$Tp=h#4y5}HlR3dS^
zWp1~~?S@J8X~&ywv&PpC-8%G6U!?l2!8?O$6PseusW~Sc{2uK5uvvMMxGtIN61gtw
zBuHcCnNR7b&c8NrbKu71>C4JH&aSl;K&Adx)9m?o2j3o?I|vJ>rOk3_v*g($d-hDV
zPqlx!=!Ogx4x&#v&~T?O*l~oB&H~y$wCEtQ*>tn%M)P#@?2dQ$zP(o}-Y6GuoOf&#
z9XOg?0gJ6~S?^fC=ZT#CcJX(Mak8!KtP|;cTY$x`IpNQ*;)dUYvmZXGJW0;evh%d)
zJpIt&79GWlc^mJx&uw~O{m?1p9hdWtPaQ<Y1w$AO2G?fh%oyo}hYQ~eB~N(B+C1mE
zZ=EZhEB(chkFJW3_xx@6<ENjM+I!{p-nr7~`r7X{&m5d>d;Qp#51rWy&UN$7b?@+Z
zF5S()pY>6$Sb9P#Jt3E#5WCJwrDw(7i=uO#<Q$Nl1EO<)6}*#*BbIjJCpk~Z&J$wd
zx8%{m=%l0gi#(mJNGv)baVKT&B!t71A5#GN$W(KvPWMrr^H8(#qsR1cV_Dd27`cjB
zpVZ0KU>Za*GM>dhfm5aBRxr&)K!e1!G7mIeIUh<*hP4=uPd_UiheZ+A6Tc>mirt`e
zJYzNK2sL*hoQA7Fwkwe=?o=z7h*#B%H>XslcnjfTP;y%ByOq8}D|5B)9DUbvlQrqv
zen)&p8Nm9@YUwkrPqi$q#%Ng5rVQ%fA{kyS{;qw45d&Hz;uj9Y0Q4vBsdBAaf()gF
zsDIYnJ1Sb2cWd(j?x_Bn`N*%!;)1>=EGB+ICNGQ6#&Me%(Lbb7%`QmqkUY$ayb(Sp
zJ-<~$=B_zp-U=a;{EtMb=&t2IksS8dX;pSKG6~DYFK_~kbTsm?DIs}-)*)ghex>&?
zT7F&P8^&x}{}Zo+&yUmoxoV0O2V^Dr80nH!h-DITLf%xm!2fY%7F-x$WG>MsMelGE
z*5S-Yge!W-0_miRR63Wk6VDM<k0<=n+j0VOpO6eK#_n!OnB&*cnAME4()2#kd@Wn$
z00C9btB*A`hu~6fqJ+GcFAtL}yK2s<xtQAWglZD;bh=AP9)ZR;$rpeGSVfq-w?0jQ
z2nX9<N{)`W0+GsxDG=FL8|~q=<A>3&Yf}8t&gSK*K!L~>NrBr`CDZ1@gyZYY1x@c&
zhQTT4H>6AHqim}DXdMWz4rdvh|9WKz<JgWke>^j^J(U$?a+Yyfho6AtT(WnVCE}wK
zPE9SU!p?6Q()B2o*MYBI6;l5mbvOqIG~s-~Im{{dtNH{iNpCy8X4QP-Yv@X_jHvw8
z6@bwusb*7F>fbAutE=RZsEg0nspy05MRx2!vu;J<_z1lpC5P$z(+*FX(!X#PD<mV<
z#!vopE2NE=D}vWuadQeOeUYxQVZ|naaGA1C(&oY-UdJnbXEeL;1jXBk`i#3EAf7lm
z1cKo>KO%m7NF~*lAZ{%8_~VcJTaFz&@wmV9g#Tnm7r1gQ$HuFae7X}_EH#Yb2BWi=
z6;@y3{1Sq2#Fs&-N!tY+g_NXv17$LV!i<$uD4cAOOfI1ZvP@|t&Vjg{3fd<?$%S}o
z1OEZb`~hFFN-g{<<?u^#`l-$-x}AWxLgX*f(>eOebob8F>r3=XvfS)k34!2m%rFv+
zS)qtCAi!|9iO$PndVazv1$&MhfgGkZNOCTWfKCwfe@7mtDCsUr%bjc{Mder__(|gr
z)=r0b+R0Xw0F(NE!X$mv2N)@zpySzHA>|90G9c>%w6dQ#Ph?aTw!Z%!5%bY7!G8^u
z8Y1a7m|ZZK_b_KMx9Ik{H_pw5rQA(&?j|vJ>zsj1+N7Pd+x+pS<|z&$pz&xdE<`}h
zdmsX;_DiLldLj~Tg;3bb&G+3clJd66dD|vWd_u+I%sZG9txg(w0Hs_R_0=VeIxw3L
zX(k|iTQy%FZ4m+l={v@E4vE!!rINi;!9KYF#>e-|p8YWYwvxSxrL07uSjnJxXx;(E
z!+1ecMNWHSm0lhSXH1*(#Jr1=X+Sm&h^B!>Q?8h|TQW7trY6zU#9mWhmrPc(OOe!V
zyIU=~wo0z8vTG|C6t?`i^KnH+F}F^UMiia(Nv|f!`Izi{Omse$^s17a)v~i%bXIFL
zAfXIk1Lq>)zz~5gABG6jeyOjiCsx7bp}zbQ=ao2_^rVXz8Hxj&)6mm|D6Wgq^IOeZ
z7CD@iC~?&?S1oeYi(Gl6CdqV&Iy^HWoD)J+xwB!8`?%?-<UA%jkBQD>59xeJqYc^<
zg%x7nGx*IFDEIpV%Kf7g${p_1!Ks6vP`D%bQ8>C&IJ)D9uNj%smtGsWIU;40$r)v^
z=dh(!tUeLB61nn`l};U5kKy+T>36kCT${|{kd!vYb819)6gA=}aoc2Wo5*eZgb7q@
zyD8JQZtgtOUtJ%~%Ad`RSaF8rVov@$-0R109be2Zzx5&<Ilm^FTL=pQ(VTo{tR90b
z2R7;9fD4kXgFY)v{?#hKXhF#x2b#bI>kn{dATynPn|p(s%^~sBnT+`B>-Jmr8T(>p
zIgIdUR?C^yFgbzW!>rufg>MwTUVN)~s&%Ox<y+DvoX_a#x5?MkhqNcw^NC2QZ(me0
zPDI=N^I}n(@{_oBnIqM<MHlhLY+$3niPklIvty=KhUV>M9A`;v9_0i3iX-!En{fc2
z+AkG1^~6fCLMVvOX3S*W+&Q&#(OG@hHn&T1Hp@;3**1Sk2AI)kW}MrRa9CgNn%g24
z?ZPj`6jRE}4*1v-t=pVlUgZN7W($>f3zb*p$3CPv@$#mJg12(vIB)Y7Ji-P}2IGds
zGvAoZd;q+DYAjU}Yt5XM1&ODXDed*kZ}y~oqW>%zKPpdr3pQYFn%divZG1o5)RJd?
zzfezZV{1iwx$(ntQ~L(%hc$Y*zbMK+SfKmGrk0$8PUA0iPQ3imX*!r`{bi<+o;`Yc
z&SB35_JbRYzpT`g>ucTJxkL9i6|I%WvyFdeapL9gvQ5YHtbdnhq~}6CJr}K`=bc%d
zImTac6y{esrp^NEuL>y4uZs2bTvABS+x1w(f3?GY(qa6yuJEMA`0K)~6Fbbmu65G$
z4%3My+pn99^t?w;&wJ@v)SD4Yw3rb~bQtM5H|u1hQEW7w+-Vh?^l&8|cWR4K;<8R{
zuu6qadfs3<<#R|rBRy}@({r5(&%&>PKtOdR1A7jmkns$_T2yZ^*azR_MVn#7@%Ws0
z3R*s1%8pwEZNL1|av_3RU2*7Krkj@3?h!-gkn&x2F|8!zm*OaCDO^Y#T06(>%(Q#`
z#n8~;`2OaLje8R+kY^4zH{i8-bZ;As9YEZ=nT|H!OGl9n^fjbnH8(K-`sRkwy#v2R
zevlD#P^hsW#QtHEEQu>aMlc7ZRJIC<fx$Ls(5yDfok>osWR+uPmWS}~ktB%`1fM|+
zoF|l7H2aj4>BC9r>1SJ+V6T>r?dpsc6k}?W#R51uQ7RK>b{W~3UCx48+d{#{`GSpe
zkBbExrGh<j!5;0&z2n&py`uvSOn1m1x0*(KJlDxEvPPhiczvwL$KkHNL%b4yb}i$8
z$@*(0&|m_FH{UVvmU~t$=MN)-WZ&Xfx+V*_!76E))inS3G#P&IgH;?m?by-UIbStJ
zn>H3wbSBD}&KU)d*q!>eYKm!G5pDY5r)H(n(9SBDgjMm5z~4>sg}|wGrXM?@ZC%uM
z^|-EZnq*m8T$5TqkMTV8YQ6){xVg}eRPiEsW*HENTB`jRUmBc0wV%T4Hfj7AfBWn4
zP1HQfv{tjKIh5LO;Fe}Wf5{Lw!<?*%{^@ua4WBa22oB`%ETQsL52?9}+Md*eBC<Gs
zX+!Z@cbtG?(CD_rEk_YM;lOyd@}{<N{+<D}<mH%|=^lPM1DGQG&*Yp&1fiQ8q7j)T
znonuee@a5q3h_uhh;C>r{;w1s3yDtIE@t7A(XqD<A{@uSjD#S0a)v^kg%k7iUV(yE
z?|GcMm>~JFlbk#pgW2|`t$dP&fQfQOI=5168pz7w9OcM2mUsGON6S(00sP%3x?7I<
z+dB@loH^D_2h|+yI87V^e>_Culkhi`!UyDdspNk`&Ir9eL5@JqQ{*>FPfT2c*qm%T
z9wu@Y>C<EIfHEZ{iP9VDKhS5Y;R>phE)DDy{&xf&BLMzSc$m~ZbY#Ew*v-dgu1OBB
z?C^>X91F2vuba2mN%jWW-T;*a+SO(z65Pd+?PB9WvE&f_&Ylz9J(9aec9WF^AeJ+y
z$-u*8`zN{B-+j0E?P77mcBycOT)0ClJo$0cNh$Y~oO=q#FcuVJhxq8*N5z`Qq>|?U
zueocDiR;Sm_soNNz;J<?ff;6aV_u1|V{ovs!_$EAgFL{AaT4Pg?D(0-U=o{6=){|D
zP!uUN^(qq=%d=arc2@OjXC-fis{KF`N9zy$a4$NkuaKx{SFNh{Lnqx`?RKk5{r%6K
zfjbvSlYT05`JXfAb?-fM@BjSI|H;GqmGJ17U88E?1P`20Z2lFwI^Oo<eLvinm{x1r
zc}=@2@8j}5gugCrR!evC(w&N}gw9fUt2b_m52@i69&XX&iRi<3-<pi?QtJ1p6?=Kb
zUNz9p1K3;o#SYtUZ$r3xdz)5Xg-tUAy&;6NK<=XIEY-rbw2@X37_S;$6_xyQ6#??L
zJx<vw_&-O~`E+kF1RPJ;+u(S@SSBh=K+u32TS1w?L*eXsUQ@IWHg@)aY725(5Z)^x
zc$K|cagcUJzA)l0YGut@$u^~7uUfK~B4wWWq-nlMD=gM3c40nm9nodoYa6Rn&_k|o
zbpY45MR+#u`&#<E)-Sxaz7kNA3~)M6UtD!(q~mC__D4J`v6_-)V76^YHr{zV%OcHG
z$Bb?nh5Xd8E*fpnC)hd@JR7T!EnRj?A#ckrGdO(AYHM)*Xd1;J<IL(|u!J*86*uUD
z8Kf2)>I^Gq*4U1%?h)35&UyF$$w`=ND9x{3M}=!xQ1EaO%HV5J<vMoMS-ylyfN%st
z66i`GDS^ENrV{w+7ic658DPUeXeUCwmJ8BWu<N*$o}kK%!x@Ugmm%RIDRlS&p`qWS
z3Fyc?#LUuP1mjCCICc4U5R3pTq2`}XB~S1jU23SChq|FhqJ<w#zptxB#Ve&1@jPC-
zo%~aNhX)_g0u6|Qr_{Fc^4%*{bxM6ZuiCd#UKQW=$%Xj~EB;_?+n+su<N1|9_|~y_
zJ3`f}fgL=sL#uf_<<BJ|m4G92c5w*;d_@=YTLSB4o8`n?|8=dzTY0BP?v<>cOSWE{
z<8ucCoGuV=WjbI7Eo9Bh9)ul4CZdOaT6R-uI&UL~C0Vvz^p1TO|G!zVWCBi`<p^1_
z3d6ex+KP5g>A$R2hHzP?H`KOeAH#cT>O&#8@X4mmzQ)^w71kdH+(DE#VqRJ%9Cw}B
zW55;{-l;E&JDALYtjU&k)>>lf4;^IE``vOBW*CLNm2vrM9wnE&+iX1wXjy0;{Dlqr
z^h~7kEjwA9#BG^5Hm2Fw^ShXz?^qG?+xk7*+YZRC4Kj1kZkBcDiwGhBiT=izJg@A5
z1j(QS0g3n{h?A2wk473DA>i>^uJ2GcpBwUK0cn_P2EMQL)F2fzQ0Ccp^R}CG5ze!>
zoIhd{UL(D4mWrNdKY~RyqT}e4-5NUU|KnrOKNe2J&cJP!2p`&9*$aK1%8h$Oc+rA`
z+~&wCVX9vf5CrB9O!ss!>t(9EJOy)O#7uasxp}&H{lzktBc7Z7{vn>*u8CzkJRjg?
zEXS}2KuAFZ1dbgMxD+A9*XcT<7IotBBb-^d{a<#{3oCRyPg9wo^#r;l)Qa9;QJ?=H
z@EZa`=(aWq`q^S=2@k+$CZ{f4iF`?|1o1}?2+(cjPZx{i8W2h3ztHxt3GEgEHR$2o
z(=Mm2_Wi^Z+`r-r#vb9mx>*-uy4^aa)U@!jRyDYr2Y1iruY@apWLN6;^2&BKypM<X
zDb66YY13Urf9&$@`zda;KAb4q3<&JFNvjO+e42rKK=kHWCHyiSQ&$!Fnkrx8GOS~-
ztvY;juP-?QiX#v^niyINw=3cHbdRwIGfK-C9pO`Syz;stM^rh&<%r^ptim#``}$0D
zCRVD{wJinP5OE>XQ91JB*Y%Y9Vr7clryt4EwJb8xg9gU3k_*MOtRs3LWwF|ivX#QR
z_?5)S-4~VZ{ctw+^r%vJLM=SO3s21E%;sd=cpQbw9Ybp!ii`V2OY-%fKYs6V+$D|&
zRz*Id%15|-L~)uA%<LaX(r}a^L#`XvXiRu_o~u=IJ7aYKO7*T^^e)(ce(2sI`i8^S
zIk^*0(EHt%?tt~vfUP_1__Trne!eZ(+bMnC=|0eEz3-|#u)})4kx_n!9dJ5iT(8zP
zQ+fo%EEdh$!bJ9{hb3lW%n1S!&t>M%NE9MoC6LXxSTLB`T#!8WV!_zp9EcW-%~^KY
z2rLU7TY!#@OdNQJURUXd%AbiL^tM%?VgtKHpyI5BbHb9vaxt0oY<}%&RBW3;oJyc#
zqy|*HHS@(z?k0hXtw6<YBQwRU^pr-$i1SKVs6X;MLNz9oT1sXA0FXw1ZUg!g+vYXQ
z7x_<Wx*5{TF`zoaZIM4x%XP$-gEAef={T)8jnhmITjamd?e7S^J&lZx-o&1d+3V?D
zs$WNL^n&PRq5@(Bdp%?r#xhZ10s?Do(h3T?LgM$oVXbxHlL<lK>*hE@Egay51OGp;
zLlb_6_w#G5cWe!gzhVuJPb*8h_eh`aarbPuerBocX|R4)%_!es2b^{bydVVfS!jT4
z!0{tVzab4;;?%yz&Lgf$mgTn;$1k|?Px$#kuXW4OY>EZu^9IUFAQXCb(tpOl0NGqv
zqpt0QVb^FHJu9AMRv*KD($vRhL=;(l40}yeAG>ibvicbIkQ2_W_LB%>`|XgW?JYZT
z3t<_Y18qeEory>`2nP+cnR_>H1D#D4+1YJ9Teh`M<Y#Q9;WMYi)K8u$m?%VK$)b7t
zW@j?$2wqsDUX5Cp;Cqtq2=85AwyrDm9=8bKm5toNJ@rm_$;S?^Q#?VS*ZuQ$(4oac
z5mH_0*9kGfOI)YMp2L$BIRr2rSkv3d*t)@zj?{fj_;R{%0}-oxhO9?<2A@^F@#^_;
z5V<0V@MJm+Buss-ow?Gy>^0~hrQ>D~Pj`;KI#oFiUpeA+6OJcflw}$a4yoV@N5`FO
zgX@Gc+$azZkNhKnDY~n}l>29Z5jdL4(W4YD+YncKaye(>r85XSneGGPSPv5;W@y=;
zxd}wRy8Iff5yz$`VU9y{2wsGjMiqS3O^D=W*OhaSZcSj;mUBi#R_N|TKBUeroeN3>
zZT4F%7p$rNWM-bOewkg_EH0NzG=>?#n-LfA$-mH|p`tlBqWS*W%dsNZ_XNVR=WaiL
z^LYhfjL)cL2YA^5HPFWcee0}5__MX!JJy$#&1mLtMn<oMKw8Hy@XE)Mjl6Qd8hV0<
zo&bx3SdRKx#Hono*g-5uLz$>B0kKeR(kfr_Rw*^DYEf(S@TzXF^HPS=KB|O==y+jF
zkx!}eDK4K<oTpYvWq{dFWLotrVX%M)lw-#gc~q50xjd>kM^_#GSmBbRRKZcx3J9uo
zwK${&d$hoIE%bs`(S$(f9<Y)L<V(SDs?bF;L4e;9NJ%cp1YyfjgdiecR_<AJDTiNB
zI$u=IUQnFi&0gfri_vb)W`A$s`oKGb?+!)>btb8|MDv$xRXdVZOI0mklj1bQ`qV+1
z_4%{z{XK2g&)RG~`y8KjFu>`;^~DLgW+WdSPp=Xpp;beyQLh1J%a6sXLUrjs<BDS>
zSxq>7OES~!YYc0cfwbIe^vy}u4OS%VWs}VV);$Bx{+6Sgh*A)`CM|i`spQZnd#%`-
zuk$j9R{X>70=Ob9^1cz%Z;+ch-g0~sPoj~R&AgJ$J%whk02kKg-1NKU2oD1!Do_7`
zbhl%@z!e*93&4S~UKA}N@Y0G|F3GfqErG)*6U?3)NJ))036S5J>Ni;HKoV)7ag0yg
zgq3*Xkm_w!Zxg)pZ}kgJIN}BllHs0A&jR>dLjJjBd()FbTsm>JcI47lKFH<GTd?MO
zrcb+x-q+on>h2SV`kqU>Ifc7-ERMoy>T9op`_n+K7-1tjS$S-*astcS`Eh7Pz~XiC
zwU;ihc~wN69{p%C;}KOX-bZmux=!;`l*3v82dQLdyX-{Ju`vp0yj&o1<cm}|eLVA~
zP=_XWYhul$aT;ZP+w*-IL@>(5A}1K-RkUK!PhxhldYa0}vFWmJ-OuVs_u!GSBZEVI
z!@?_L<W<x+I$@L@S<a*DfPL#Zp&k3@)KPcIDIBc2$TC*Ab6j?vow{@>?WmLL#5y;D
zvuz?D%+psw9S25ym;fKHb@DJWuy=K*<B|0Vn^1Ue_Y{7ATg=UiYE(}x_tef>w36V*
zZ+!Gd?1Ea-$V(b$^HzL;TYeEC>S3^XDcG#+=v0GUJlLfK`xkpu-vIXwfV%`8Kzr;0
z3^X4Gwl4*?Ctg(p9X!yX1bP>%RNn#aI{@FM2oaSzGZZh8rzlZ<jKV}^m+Q3~p;!mE
zXq+d7%b~auDg%%Uz`=1q0oP;iW-H*6gw?!qo>xvzEDcubxvp*oPL8K+B~%`J{q`F-
z-}vO}{8e7DLk;cZp`CNiIp-f%yyS4n1HHlw&_ZFVnR8;-=_#X2qS$q@#wEE%k!!SY
z`JC&Y0&peu<)d9{pqmG}X9rgTO^Ln9^J<`r2fAhl!N#Gua28U!1<;z?NbZ4%aG98I
zyVKueBIX;^l=X(9HK{2>V>hd8eXRAIn5#`%Ii+G<NBWxTZ{+^QXis$dogqj~Z;##_
zjbBsC9^+*&DOMZ^x>u?dSD^ko?>wm$6~}v&+<H1}^^=d|>6`7z@2SQf(biMv_^gou
zejcnl0Li43cR;q@&u4&N<W%*wSifko^|d*^XlH=aez8=8+X?xR9w`w7V)k;4Cstps
zPa*Sl;z}5d7?$qrCE#s1iZF^Y%z{0jC(As=+_DaVCf48mHQfRfoWZ$u!I=<}y^gO&
z4XOh39YiSb+7tA$5nCEUFk6LctKzmQgvDA}7l>gZe_E3`^OL5#O$$>>ZXX@CKHcZm
zIXdMn!Vpst8<L1Z08^Say8nC1Q7oQFD*-!B7)el>mA=wU2!r`^m(HIS7z%{`H445F
zAt_LVh@c4Vw<5G<kI)LaoIe%0gx1Xzt{x66BW|i8ielMu7H+JetQ8?PSA+zl5n4GS
zGF29)E74Ea*_(_MP$hAQmc5xuLPF=qg$Mpf5!E=ygnI-yE%_B)@L!6uXP$g=WbDA;
zGZ7-#_00r{9du5*&}l2CuK{Z8rm#cLj8T`!-%x3o0NMFP1X?bqTqS{C0`Jm@e@dW%
zzW$29ztPu!CEz3ACs0hlL$mn-a?4h%EW+sl`G1&#w}yk%k(PmFd2H;ZS6+c03av<!
zV`Iy<Bd?BM)EA4RG|Eq?=?etJ^K+SULL6|7a^EBH34u5PdSD|zBk&mkA(s7;a=#|<
z?*x7auxuA0iz70fKy0~6C|5_|5dst|Sx6&vr4wS$=;k5Bcre(0(Bh;oL&7D*&*&?8
zMbNipqzs6d_i!5{)JlX<SBMw{BTj6r#DXFobm6g3e`*JCiGOtNhXabKOJ_PFb(lwt
zBuz?qXn-OaTa{eV^he8N4Vm1i_-K+RDn6Q2sALXJlB43INd-#g(4@64Ynd`d$~rVD
zpkyDK)S`T|Lz5m+Hak|oL3d5sqil9;91~{xv#oPZ(z1D&TeXy1rRLUfI+~(RauHB~
zTFVXh$N3-S$EFi)YC#JxXi;-pd2Z{&-2F?r`*qby(Kx?oc4V&lM$^a5A2r9riOIxd
za?ip!wWVL(F~D~Ws6_{P(LvQS$UTD(Jx?!ro>o1>+%p_?<3t6%*(-BBHx7L~^wChf
zBiVw_Ri$S{Z5vTrj`5abs_$9udsda7<MMM4<*_AsOqE~a@=MV?bjCf@&PG0%{&42~
znb>*wjPxdsFWA-XPpFL@ys<--JGtDcy1Tf$>!JJLlKY_QKE&OJq7KdDk2zFNg;L$F
zdfKCI&07|Cs@?|t$XYNoUwXUzX1QhyYqtKc?AB5XL{Oy`MJmQpi(2>X{G0ijEu`5x
zP#dJ$AZq=nt*JvK!_#*zkhckC_p{3J7uC9xyzZo0J;tlY6sbZBRm55~Th6<O-W$3;
zq&X2dHh0!Kdk{KP@LOH|gqoL*7K@SXh(iPLmDL``IRJj|maOjU_3t#k+nCCgSP)y^
zaQS>Yxo}VrtGiweJ;OuKJPaLQ3LRHNCwS<Da`H_j^d=rft)gmv`u5Dt8KoIlHKtaa
z;uWVJR-9d`IIC8i;}z#pl60B%!&`<_*^iqEujb`{aOlIK_lGo_tl5swUR7+zaXcWP
zAGksf-MhyUb-{zaulPp$#~mMa#G8|~s<(}M+c24{;ffg4s_wXdod3goWk>%5n_794
zR~}WvLmbQjDX0a*F>mad`4hKK-aM&1+O=4tmi6<pel<A2g9Dgy&0nfWMOu+JDr;f9
zW2DU#2+d8#c4cn-z~UJ-bcBbFs3k{v$x%h}triF8#$(m<O}CqGHY<%ii@l4z58R(V
zdH+eZbcC0VsKv*4@i9g6uI6~6FDt%QC1<yqvzzDaj#^hG_d}^*Nh*l>;Qv-CP^Il$
z+OA03HOcy(>$+>Ukv!!mTJV|vN$1^8#lKJW@8kY`iw#kiDjnw1VMRKe%CWQ1TnqRp
zEELldkvD)M3CspWVJoOyEq+uB?piG_(L6Hl&L<SL{3Qsy28}HggvzvFY0B#^1Sf((
zNpTRr6F}4+y)xT7=bb(H?hFbTl`;$5g&?<6$`8+uesEItlyOfPo^ZKr_Sy$Cs$7Af
zaM3)?Uo$s)<D}}}#{Jtc!K+1nP4+^yx*mf7U~=Y0Vl}bJSi_BzD50?dAdp2aFNYO^
zDUty`3+ss6;=^}bApaP6N!h?{XNB>JM9ZD?boc0(4GQdRZ=x0`qc?H&ZZp;HT^JUL
z8I($1zgAe3@)R>z>tp~fd#*R;i;c%yV&~?|6<<A<Vc0>{<Y_+Tpqx{J;-QOjZYs;A
z9DFj%rm^%<cR5&V>>Na^aK@TAcxQ%}gZ@RC8TcH`mk1<llamX*$*cF8d1E&<0co8L
zd`{*`v?i^|;iU5}rUzfmpty|ff=_$QQqr2(bLSOawmT)E%nZ6%GxNqK;;jj5VmRTv
z)4|Kp24!Y&nmJfOye47$K@(k0K@u&Hn1RpFIukW12?_3FYGHV>X5sj~KjFIuM41`v
z%wd)EI3r15I*^z_Q4`Bqz`2ktPCRH)o*I6z=l(0=yBVBdd8}aWTx{~j<&=c18I%_=
zU+h>)LK454v?YfZYLdtA<@5SZQDz2pE|wDu#B1Y|iQf3tJIy=@%N~>^Cq!Zf0XN%~
z7{?36b|v*h<PD(I&2sb>7-OMj(Ymnb9_|FCW>8wmwk064L$WX_lEo>Jm_a`)quu~<
zyGZEN__lcb!T9|bMOHa^S{Xm9oV%!uU*sn*i83?jC}aquy#^X`SVOFrehIu5*`UbE
zs^j><kR<d(<PE?ll~rlA4XNB31_!_Z8bX|pe-KPrkzaLrQg*5!4bqf@a!$$PM+AnW
zDHnaij=pHF7TCCV?&=MML$A`u$&+$iDGxQE5e?u7^Rwq!9G;>mO^oR2CU9q%J;@Tb
zltgI~uN0-5zz!cPid{)bNRn2OEZ7!?e+f*45;JI)@hIT|xzm`EkWF4$=)HGMWEV#s
z)Z8BxSu<$xvgQOX07-J&f^*UL%lrpU<>_O8o3HG7mhXB_)R@7eZYIa@C?H9AMUuqy
z<J*Fsh`a%m_AvG|qn}7*7HZR(#nyCY^JK{K6l0GiaO0FNV!9~Z1fI2E$NpZ)^^#d{
z?1gBF%9=Q9N?9BX?-IaQR>|_nT8WR^2CfGbce%<cIIBom8t`tl#;x(;xN{yD3}4p)
zb*{{`O2LFbeI|4ca27~e3K<yk0MszJR?@I`maxjoI4fHXmJu5NBsd?`ic9Av5aRp%
zjq_SbXzmJpK3%(UE#(NaF7Xg_F*r3#S?TL4LYUe`Imki3u?ambU5rjzj?q&V(WqnX
zto15u;H*Jw-IF}9;QjgFy+P<*it!1?r;XqAJPp-|E-8y>SDhYe_ED9Ua8@E_NlXh}
zaNHX^cw=ULmPC$5z?`I;u1%HVN>4$`vW?zhfc49!lBdL^il3xPW!0QjgKxz8F&6}>
NZH=i+uMs<>{{e!3Wj+7^

literal 0
HcmV?d00001

diff --git a/cli/calibrate_detector.py b/cli/calibrate_detector.py
new file mode 100644
index 0000000..5cd56da
--- /dev/null
+++ b/cli/calibrate_detector.py
@@ -0,0 +1,405 @@
+#!/usr/bin/env python3
+"""Choose detector metadata that better matches the trained model."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Iterable, Sequence
+
+import numpy as np
+import yaml
+
+from microwakeword.data import FeatureHandler
+from microwakeword.inference import Model
+
+
+DEFAULT_WINDOW_SIZES = [3, 4, 5, 6, 7]
+DEFAULT_TARGET_FAPH = float(os.environ.get("MWW_CALIBRATION_TARGET_FAPH", "1.0"))
+DEFAULT_COOLDOWN_SLICES = int(os.environ.get("MWW_CALIBRATION_COOLDOWN_SLICES", "25"))
+DEFAULT_POSITIVE_SKIP_SLICES = int(
+    os.environ.get("MWW_CALIBRATION_POSITIVE_SKIP_SLICES", "25")
+)
+DEFAULT_CUTOFF_STEP = float(os.environ.get("MWW_CALIBRATION_CUTOFF_STEP", "0.01"))
+DEFAULT_CUTOFF_MIN = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MIN", "0.00"))
+DEFAULT_CUTOFF_MAX = float(os.environ.get("MWW_CALIBRATION_CUTOFF_MAX", "1.00"))
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Calibrate microWakeWord detector metadata from validation data."
+    )
+    parser.add_argument(
+        "--training-config",
+        default="trained_models/wakeword/training_config.yaml",
+        help="Path to the saved microWakeWord training_config.yaml file.",
+    )
+    parser.add_argument(
+        "--model",
+        default=(
+            "trained_models/wakeword/tflite_stream_state_internal_quant/"
+            "stream_state_internal_quant.tflite"
+        ),
+        help="Path to the quantized streaming TFLite model.",
+    )
+    parser.add_argument(
+        "--output",
+        default=(
+            "trained_models/wakeword/tflite_stream_state_internal_quant/"
+            "detection_calibration.json"
+        ),
+        help="Where to write the selected detector settings as JSON.",
+    )
+    parser.add_argument(
+        "--window-sizes",
+        default=",".join(str(value) for value in DEFAULT_WINDOW_SIZES),
+        help="Comma-separated sliding window sizes to evaluate.",
+    )
+    parser.add_argument(
+        "--target-faph",
+        type=float,
+        default=DEFAULT_TARGET_FAPH,
+        help="Target ambient false accepts per hour for the selected operating point.",
+    )
+    parser.add_argument(
+        "--cooldown-slices",
+        type=int,
+        default=DEFAULT_COOLDOWN_SLICES,
+        help="Cooldown slices to use when estimating false accepts per hour.",
+    )
+    parser.add_argument(
+        "--positive-skip-slices",
+        type=int,
+        default=DEFAULT_POSITIVE_SKIP_SLICES,
+        help="Initial streaming slices to ignore when scoring positive examples.",
+    )
+    parser.add_argument(
+        "--cutoff-step",
+        type=float,
+        default=DEFAULT_CUTOFF_STEP,
+        help="Cutoff increment to evaluate between cutoff-min and cutoff-max.",
+    )
+    parser.add_argument(
+        "--cutoff-min",
+        type=float,
+        default=DEFAULT_CUTOFF_MIN,
+        help="Minimum cutoff to evaluate.",
+    )
+    parser.add_argument(
+        "--cutoff-max",
+        type=float,
+        default=DEFAULT_CUTOFF_MAX,
+        help="Maximum cutoff to evaluate.",
+    )
+    return parser.parse_args()
+
+
+def _parse_window_sizes(raw: str) -> list[int]:
+    values = []
+    for item in (raw or "").split(","):
+        item = item.strip()
+        if not item:
+            continue
+        value = int(item)
+        if value < 1:
+            raise ValueError("window sizes must be >= 1")
+        values.append(value)
+    if not values:
+        raise ValueError("at least one window size is required")
+    return sorted(set(values))
+
+
+def _moving_average(values: Sequence[float], window_size: int) -> np.ndarray:
+    array = np.asarray(values, dtype=np.float32)
+    if array.size == 0:
+        return array
+    if window_size <= 1:
+        return array
+    if array.size < window_size:
+        return np.asarray([float(array.mean())], dtype=np.float32)
+    cumsum = np.cumsum(np.insert(array, 0, 0.0))
+    averaged = (cumsum[window_size:] - cumsum[:-window_size]) / float(window_size)
+    return averaged.astype(np.float32)
+
+
+def _compute_false_accepts_per_hour(
+    probabilities_per_track: Iterable[np.ndarray],
+    cutoffs: np.ndarray,
+    cooldown_slices: int,
+    stride: int,
+    step_seconds: float,
+) -> tuple[np.ndarray, float]:
+    cutoffs = np.asarray(cutoffs, dtype=np.float32)
+    false_accepts = np.zeros(cutoffs.shape[0], dtype=np.float64)
+    duration_hours = 0.0
+
+    for track_probabilities in probabilities_per_track:
+        if track_probabilities.size == 0:
+            continue
+        duration_hours += (
+            len(track_probabilities) * stride * step_seconds / 3600.0
+        )
+        cooldown = np.full(cutoffs.shape[0], cooldown_slices, dtype=np.int32)
+        for probability in track_probabilities:
+            cooldown = np.maximum(cooldown - 1, 0)
+            accepted = (cooldown == 0) & (probability > cutoffs)
+            false_accepts += accepted.astype(np.float64)
+            cooldown = np.where(accepted, cooldown_slices, cooldown)
+
+    if duration_hours <= 0:
+        return np.full(cutoffs.shape[0], math.inf, dtype=np.float64), 0.0
+
+    return false_accepts / duration_hours, duration_hours
+
+
+def _select_best_candidate(
+    candidates: list[dict[str, float]],
+    target_faph: float,
+) -> tuple[dict[str, float], float]:
+    fallback_limits = [
+        target_faph,
+        max(target_faph * 2.0, target_faph + 0.5),
+        max(target_faph * 4.0, 2.0),
+    ]
+
+    def tier(candidate: dict[str, float]) -> int:
+        for index, limit in enumerate(fallback_limits):
+            if candidate["false_accepts_per_hour"] <= limit + 1e-9:
+                return index
+        return len(fallback_limits)
+
+    best = min(
+        candidates,
+        key=lambda candidate: (
+            tier(candidate),
+            -candidate["recall"],
+            candidate["false_accepts_per_hour"],
+            abs(candidate["sliding_window_size"] - 5),
+            -candidate["probability_cutoff"],
+        ),
+    )
+
+    tier_index = tier(best)
+    if tier_index < len(fallback_limits):
+        return best, fallback_limits[tier_index]
+    return best, float("inf")
+
+
+def _load_config(config_path: Path) -> dict:
+    with config_path.open("r", encoding="utf-8") as handle:
+        return yaml.load(handle.read(), Loader=yaml.Loader)
+
+
+def _load_eval_sets(
+    handler: FeatureHandler,
+    config: dict,
+) -> tuple[str, str, list[np.ndarray], list[np.ndarray]]:
+    for positive_mode, ambient_mode in (
+        ("validation", "validation_ambient"),
+        ("testing", "testing_ambient"),
+    ):
+        positive_tracks, labels, _ = handler.get_data(
+            positive_mode,
+            batch_size=config["batch_size"],
+            features_length=config["spectrogram_length"],
+            truncation_strategy="none",
+        )
+        ambient_tracks, _, _ = handler.get_data(
+            ambient_mode,
+            batch_size=config["batch_size"],
+            features_length=config["spectrogram_length"],
+            truncation_strategy="none",
+        )
+        positives = [
+            np.asarray(track)
+            for track, label in zip(positive_tracks, labels)
+            if bool(label)
+        ]
+        ambient = [np.asarray(track) for track in ambient_tracks]
+        if positives and ambient:
+            return positive_mode, ambient_mode, positives, ambient
+    raise RuntimeError(
+        "No suitable validation/testing data was found for detector calibration."
+    )
+
+
+def _predict_tracks(
+    model: Model,
+    tracks: Sequence[np.ndarray],
+    label: str,
+) -> list[np.ndarray]:
+    predictions: list[np.ndarray] = []
+    total = len(tracks)
+    print(f"→ Running streaming inference on {total} {label} track(s)")
+    for index, track in enumerate(tracks, start=1):
+        values = np.asarray(model.predict_spectrogram(track), dtype=np.float32)
+        predictions.append(values)
+        if index == total or index % 25 == 0:
+            print(f"   {label}: {index}/{total}")
+    return predictions
+
+
+def main() -> int:
+    args = parse_args()
+    window_sizes = _parse_window_sizes(args.window_sizes)
+    if args.cutoff_step <= 0:
+        raise ValueError("cutoff-step must be > 0")
+    if args.cutoff_max < args.cutoff_min:
+        raise ValueError("cutoff-max must be >= cutoff-min")
+
+    config_path = Path(args.training_config)
+    model_path = Path(args.model)
+    output_path = Path(args.output)
+
+    if not config_path.exists():
+        raise FileNotFoundError(f"Training config not found: {config_path}")
+    if not model_path.exists():
+        raise FileNotFoundError(f"Streaming TFLite model not found: {model_path}")
+
+    cutoffs = np.arange(
+        args.cutoff_min,
+        args.cutoff_max + (args.cutoff_step / 2.0),
+        args.cutoff_step,
+        dtype=np.float32,
+    )
+    cutoffs = np.clip(cutoffs, 0.0, 1.0)
+    cutoffs = np.unique(np.round(cutoffs, 4))
+
+    print("===== Detector Calibration =====")
+    print(f"→ Model: {model_path}")
+    print(f"→ Training config: {config_path}")
+    print(
+        f"→ Evaluating window sizes {window_sizes} with target <= "
+        f"{args.target_faph:.2f} false accepts/hour"
+    )
+
+    config = _load_config(config_path)
+    config["flags"] = config.get("flags", {})
+    handler = FeatureHandler(config)
+
+    positive_mode, ambient_mode, positive_tracks, ambient_tracks = _load_eval_sets(
+        handler, config
+    )
+
+    print(
+        f"→ Using {positive_mode} positives ({len(positive_tracks)}) and "
+        f"{ambient_mode} ambient tracks ({len(ambient_tracks)})"
+    )
+
+    model = Model(str(model_path), stride=config["stride"])
+    positive_predictions = _predict_tracks(model, positive_tracks, "positive")
+    ambient_predictions = _predict_tracks(model, ambient_tracks, "ambient")
+
+    candidates: list[dict[str, float]] = []
+    best_by_window: list[dict[str, float]] = []
+    step_seconds = config["window_step_ms"] / 1000.0
+
+    for window_size in window_sizes:
+        ambient_averages = [
+            _moving_average(track, window_size) for track in ambient_predictions
+        ]
+        positive_maxima = []
+        for track in positive_predictions:
+            search = (
+                track[args.positive_skip_slices :]
+                if track.size > args.positive_skip_slices
+                else track
+            )
+            averaged = _moving_average(search, window_size)
+            if averaged.size == 0:
+                averaged = _moving_average(track, window_size)
+            positive_maxima.append(float(np.max(averaged)) if averaged.size else 0.0)
+
+        positive_maxima_array = np.asarray(positive_maxima, dtype=np.float32)
+        recall_by_cutoff = np.mean(
+            positive_maxima_array[None, :] > cutoffs[:, None], axis=1
+        )
+        faph_by_cutoff, ambient_hours = _compute_false_accepts_per_hour(
+            ambient_averages,
+            cutoffs,
+            args.cooldown_slices,
+            stride=config["stride"],
+            step_seconds=step_seconds,
+        )
+
+        window_candidates = []
+        for cutoff, recall, faph in zip(cutoffs, recall_by_cutoff, faph_by_cutoff):
+            candidate = {
+                "probability_cutoff": float(round(float(cutoff), 2)),
+                "sliding_window_size": int(window_size),
+                "recall": float(recall),
+                "false_accepts_per_hour": float(faph),
+                "ambient_hours": float(ambient_hours),
+            }
+            candidates.append(candidate)
+            window_candidates.append(candidate)
+
+        best_window, _ = _select_best_candidate(window_candidates, args.target_faph)
+        best_by_window.append(best_window)
+        print(
+            "   window={window}: cutoff={cutoff:.2f}; recall={recall:.2%}; "
+            "ambient_faph={faph:.3f}".format(
+                window=window_size,
+                cutoff=best_window["probability_cutoff"],
+                recall=best_window["recall"],
+                faph=best_window["false_accepts_per_hour"],
+            )
+        )
+
+    best, selected_limit = _select_best_candidate(candidates, args.target_faph)
+    if best["false_accepts_per_hour"] > args.target_faph + 1e-9:
+        print(
+            "⚠️  No candidate met the target false accepts/hour budget; "
+            "using the best fallback operating point."
+        )
+
+    print(
+        "✓ Selected cutoff={cutoff:.2f}, window={window}, recall={recall:.2%}, "
+        "ambient_faph={faph:.3f}".format(
+            cutoff=best["probability_cutoff"],
+            window=best["sliding_window_size"],
+            recall=best["recall"],
+            faph=best["false_accepts_per_hour"],
+        )
+    )
+
+    output = {
+        "probability_cutoff": best["probability_cutoff"],
+        "sliding_window_size": best["sliding_window_size"],
+        "target_false_accepts_per_hour": float(args.target_faph),
+        "selected_false_accepts_per_hour_limit": (
+            None if math.isinf(selected_limit) else float(selected_limit)
+        ),
+        "selected_metrics": {
+            "recall": round(best["recall"], 6),
+            "false_accepts_per_hour": round(best["false_accepts_per_hour"], 6),
+            "ambient_hours": round(best["ambient_hours"], 6),
+        },
+        "evaluation": {
+            "positive_dataset": positive_mode,
+            "ambient_dataset": ambient_mode,
+            "positive_tracks": len(positive_tracks),
+            "ambient_tracks": len(ambient_tracks),
+            "cooldown_slices": int(args.cooldown_slices),
+            "positive_skip_slices": int(args.positive_skip_slices),
+            "window_sizes": window_sizes,
+            "cutoff_min": round(float(cutoffs[0]), 4),
+            "cutoff_max": round(float(cutoffs[-1]), 4),
+            "cutoff_step": float(args.cutoff_step),
+        },
+        "per_window_best": best_by_window,
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+    }
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    output_path.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8")
+    print(f"📝 Wrote calibration to {output_path}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/cli/setup_audioset b/cli/setup_audioset
index 00c62a1..5af3a02 100755
--- a/cli/setup_audioset
+++ b/cli/setup_audioset
@@ -130,6 +130,73 @@ print(f"   AudioSet complete ({ok} ok, {skipped} skipped, {len(audioset_bad)} fa
 EOF
 }
 
+converter_from_dataset_api() {
+    # shellcheck source=/dev/null
+    source "${DATA_DIR}/.venv/bin/activate"
+
+    python - "${AUDIO16K_DIR}" <<-'EOF'
+import sys
+from pathlib import Path
+
+import librosa
+import numpy as np
+import scipy.io.wavfile
+from datasets import load_dataset
+
+def write_wav(dst: Path, data: np.ndarray, sr: int):
+    dst.parent.mkdir(parents=True, exist_ok=True)
+    x = np.clip(data, -1.0, 1.0)
+    scipy.io.wavfile.write(dst, sr, (x * 32767).astype(np.int16))
+
+audioset_out = Path(sys.argv[1])
+
+print("   AudioSet FLAC tarballs are unavailable; using Hugging Face datasets API instead.")
+dataset = load_dataset(
+    "agkphysics/AudioSet",
+    "balanced",
+    split="train",
+    streaming=True,
+)
+
+audioset_bad = []
+ok = 0
+skipped = 0
+heartbeat_every = 250
+
+for idx, sample in enumerate(dataset, start=1):
+    try:
+        video_id = str(sample.get("video_id") or f"audioset_{idx:06d}")
+        outfile = audioset_out / f"{video_id}.wav"
+        if outfile.exists():
+            skipped += 1
+            continue
+
+        audio = sample.get("audio") or {}
+        y = np.asarray(audio.get("array"))
+        sr = int(audio.get("sampling_rate") or 0)
+        if y.size == 0 or sr <= 0:
+            raise ValueError("missing decoded audio")
+        if y.ndim > 1:
+            y = np.mean(y, axis=-1)
+        if sr != 16000:
+            y = librosa.resample(y.astype(np.float32), orig_sr=sr, target_sr=16000)
+        if y.size == 0:
+            raise ValueError("empty audio")
+        write_wav(outfile, y, 16000)
+        ok += 1
+    except Exception as exc:
+        audioset_bad.append(f"{sample.get('video_id', idx)}:{exc}")
+
+    if idx == 1 or (idx % heartbeat_every) == 0:
+        print(f"   AudioSet API progress: {idx} clips processed (ok={ok}, skipped={skipped}, failed={len(audioset_bad)})")
+
+if audioset_bad:
+    (audioset_out / "audioset_corrupted_files.log").write_text("\n".join(audioset_bad))
+
+print(f"   AudioSet complete via datasets API ({ok} ok, {skipped} skipped, {len(audioset_bad)} failed)")
+EOF
+}
+
 expected_filecount=$(get_total_filecount filecounts)
 actual_filecount=$(find "${AUDIO16K_DIR}" -name "*.wav" 2>/dev/null | wc -l) || :
 write_filecount=false
@@ -139,40 +206,44 @@ if [ "${actual_filecount}" -ne 0 ] ; then
     echo "   Existing ${AUDIO16K_DIR} present (${actual_filecount} wav); skipping extract/convert"
 else
     dl=$(find_rev)
-    [ -n "$dl" ] || { echo "   Could not locate an AudioSet revision with FLAC tarballs still present on HF." ; exit 1 ; }
-    rev=${dl%%,*}
-    pattern=${dl##*,}
+    if [ -z "$dl" ] ; then
+        rm -rf "${AUDIO16K_DIR}/audioset_corrupted_files.log" || :
+        converter_from_dataset_api
+    else
+        rev=${dl%%,*}
+        pattern=${dl##*,}
 
-    echo "   Checking 10 tarballs"
-    for i in {0..9} ; do
-        fname="downloads/bal_train0${i}.tar"
-        if [ ! -f "${fname}" ] ; then
-            echo "   Downloading bal_train0${i}.tar"
-            url="${AUDIO_URL}/${rev}/${pattern}${i}.tar"
-            curl -L -s --fail "${url}" -o "${fname}" || { echo "Could not fetch ${fname} at rev ${rev}; continuing." ; continue ; }
+        echo "   Checking 10 tarballs"
+        for i in {0..9} ; do
+            fname="downloads/bal_train0${i}.tar"
+            if [ ! -f "${fname}" ] ; then
+                echo "   Downloading bal_train0${i}.tar"
+                url="${AUDIO_URL}/${rev}/${pattern}${i}.tar"
+                curl -L -s --fail "${url}" -o "${fname}" || { echo "Could not fetch ${fname} at rev ${rev}; continuing." ; continue ; }
+            fi
+
+            tarball_filecount=$(tar -tvf "${fname}" | wc -l )
+            filecounts["bal_train0${i}.tar"]=${tarball_filecount}
+            write_filecount=true
+
+            echo "   Untarring bal_train0${i}.tar"
+            tar -xf "${fname}" -C "${AUDIO_DIR}"
+            if "${CLEANUP_ARCHIVES}" && [ -f "${fname}" ] ; then
+                echo "   Cleaning up bal_train0${i}.tar"
+                rm -rf "${fname}"
+            fi
+        done
+
+        rm -rf "${AUDIO16K_DIR}/audioset_corrupted_files.log" || :
+        converter
+
+        # Recompute counts and warn (but do not fail)
+        expected_filecount=$(get_total_filecount filecounts)
+        actual_filecount=$(find "${AUDIO16K_DIR}" -name "*.wav" 2>/dev/null | wc -l) || :
+        if [ "${actual_filecount}" -ne "${expected_filecount}" ] ; then
+            echo "   Converted file count(${actual_filecount}) != expected file count(${expected_filecount})" >&2
+            echo "   WARNING: mismatch is expected if some AudioSet files are corrupted; continuing." >&2
         fi
-
-        tarball_filecount=$(tar -tvf "${fname}" | wc -l )
-        filecounts["bal_train0${i}.tar"]=${tarball_filecount}
-        write_filecount=true
-
-        echo "   Untarring bal_train0${i}.tar"
-        tar -xf "${fname}" -C "${AUDIO_DIR}"
-        if "${CLEANUP_ARCHIVES}" && [ -f "${fname}" ] ; then
-            echo "   Cleaning up bal_train0${i}.tar"
-            rm -rf "${fname}"
-        fi
-    done
-
-    rm -rf "${AUDIO16K_DIR}/audioset_corrupted_files.log" || :
-    converter
-
-    # Recompute counts and warn (but do not fail)
-    expected_filecount=$(get_total_filecount filecounts)
-    actual_filecount=$(find "${AUDIO16K_DIR}" -name "*.wav" 2>/dev/null | wc -l) || :
-    if [ "${actual_filecount}" -ne "${expected_filecount}" ] ; then
-        echo "   Converted file count(${actual_filecount}) != expected file count(${expected_filecount})" >&2
-        echo "   WARNING: mismatch is expected if some AudioSet files are corrupted; continuing." >&2
     fi
 fi
 
@@ -196,4 +267,4 @@ if "${CLEANUP_INTERMEDIATE_FILES}" && [ -d "${AUDIO_DIR}" ] ; then
 fi
 
 echo "   Audioset complete"
-exit 0
\ No newline at end of file
+exit 0
diff --git a/cli/setup_fma b/cli/setup_fma
index fe7f090..f9bbca5 100755
--- a/cli/setup_fma
+++ b/cli/setup_fma
@@ -27,8 +27,11 @@ cd "${DATA_DIR}/training_datasets"
 
 echo "***** Checking FMA *****"
 
-AUDIO_URL="https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/fma_xs.zip"
-AUDIO_ZIPFILE="fma_xs.zip"
+AUDIO_URLS=(
+    "https://os.unil.cloud.switch.ch/fma/fma_small.zip"
+    "https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/fma_xs.zip"
+)
+AUDIO_ZIPFILE="fma_small.zip"
 AUDIO_ZIP="./downloads/${AUDIO_ZIPFILE}"
 AUDIO_DIR="fma"
 mkdir -p "${AUDIO_DIR}" || :
@@ -81,6 +84,52 @@ EOF
 
 }
 
+extract_zip_with_python() {
+    local zip_path="$1"
+    local dest_dir="$2"
+
+    "${DATA_DIR}/.venv/bin/python" - "${zip_path}" "${dest_dir}" <<-'EOF'
+import sys
+import zipfile
+from pathlib import Path
+from tqdm import tqdm
+
+zip_path = Path(sys.argv[1])
+dest_dir = Path(sys.argv[2])
+
+if (not zip_path.exists()) or zip_path.stat().st_size == 0:
+    raise SystemExit(f"Archive missing or empty: {zip_path}")
+
+with zipfile.ZipFile(zip_path, "r") as zf:
+    members = zf.infolist()
+    size_gb = zip_path.stat().st_size / (1024 ** 3)
+    print(f"   Extracting {zip_path.name} ({len(members)} entries, {size_gb:.1f} GiB)...")
+    for member in tqdm(members, desc="   FMA zip extract", unit="file"):
+        zf.extract(member, dest_dir)
+EOF
+}
+
+download_with_fallbacks() {
+    local output="$1"
+    shift
+    local urls=( "$@" )
+    local rc=1
+
+    for url in "${urls[@]}" ; do
+        for attempt in 1 2 3 4 ; do
+            curl -sfL "${url}" -o "${output}" && [ -s "${output}" ] && return 0
+            rc=$?
+            rm -f "${output}" || :
+            if [ "${attempt}" -lt 4 ] ; then
+                echo "   Retry ${attempt}/3 after download failure"
+                sleep $(( attempt * 2 ))
+            fi
+        done
+    done
+
+    return "${rc}"
+}
+
 expected_filecount=${filecounts[${AUDIO_ZIPFILE}]}
 actual_filecount=$(find ${AUDIO16K_DIR} -name '*.wav' 2>/dev/null | wc -l) || :
 write_filecount=false
@@ -92,13 +141,16 @@ else
     if [ "${actual_filecount}" -eq 0 ] || [ "${actual_filecount}" -ne "${expected_filecount}" ] ; then
         if [ ! -f "${AUDIO_ZIP}" ]  ; then
             echo "   Downloading ${AUDIO_ZIPFILE}"
-            curl -sfL "${AUDIO_URL}" -o "${AUDIO_ZIP}"
+            download_with_fallbacks "${AUDIO_ZIP}" "${AUDIO_URLS[@]}" || {
+                echo "   Failed to download ${AUDIO_ZIPFILE} from all configured sources." >&2
+                exit 1
+            }
         fi
 
         rm -rf "${AUDIO_DIR}" || :
         mkdir "${AUDIO_DIR}"
-        echo "   Unzipping ${AUDIO_ZIPFILE}"
-        unzip -q -d "${AUDIO_DIR}" "${AUDIO_ZIP}"
+        echo "   Extracting ${AUDIO_ZIPFILE}"
+        extract_zip_with_python "${AUDIO_ZIP}" "${AUDIO_DIR}"
     fi
     if "${CLEANUP_ARCHIVES}" && [ -f "${AUDIO_ZIP}" ] ; then
         echo "   Cleaning up ${AUDIO_ZIPFILE}"
@@ -128,4 +180,3 @@ fi
 
 echo "   FMA complete"
 exit 0
-
diff --git a/cli/setup_python_venv b/cli/setup_python_venv
index 5ea750a..1122287 100755
--- a/cli/setup_python_venv
+++ b/cli/setup_python_venv
@@ -242,29 +242,7 @@ if [ ! -s "${MODEL_FILE}.json" ] ; then
     curl -sfL "${MODEL_URL}.json" -o "${MODEL_FILE}.json"
 fi
 
-# --- Dutch ONNX voices (single-speaker, used with --language=nl) ---
-# Working Dutch voices: pim, ronnie (nl_NL) and nathalie (nl_BE).
-# nl_NL-mls-medium is intentionally excluded (known Piper issue: outputs gibberish).
-HF_VOICES="https://huggingface.co/rhasspy/piper-voices/resolve/main"
-declare -a NL_VOICES=(
-    "nl/nl_NL/pim/medium/nl_NL-pim-medium"
-    "nl/nl_NL/ronnie/medium/nl_NL-ronnie-medium"
-    "nl/nl_BE/nathalie/medium/nl_BE-nathalie-medium"
-)
-echo "   ===== Checking Dutch Piper voices ====="
-for voice_path in "${NL_VOICES[@]}" ; do
-    voice_name="$(basename "${voice_path}")"
-    onnx_file="${VOICES_DIR}/${voice_name}.onnx"
-    json_file="${VOICES_DIR}/${voice_name}.onnx.json"
-    if [ ! -f "${onnx_file}" ] ; then
-        echo "      Downloading ${voice_name}.onnx"
-        curl -sfL "${HF_VOICES}/${voice_path}.onnx?download=true" -o "${onnx_file}"
-    fi
-    if [ ! -f "${json_file}" ] ; then
-        echo "      Downloading ${voice_name}.onnx.json"
-        curl -sfL "${HF_VOICES}/${voice_path}.onnx.json?download=true" -o "${json_file}"
-    fi
-done
+echo "   Non-English Piper voices will be downloaded on demand for the selected language."
 
 ${GPU} && onnxgpu='-gpu[cuda]' || onnxgpu=""
 echo "   ===== Installing onnxruntime${onnxgpu} ====="
diff --git a/cli/wake_word_sample_trainer b/cli/wake_word_sample_trainer
index 0ce01d8..f4fcda2 100644
--- a/cli/wake_word_sample_trainer
+++ b/cli/wake_word_sample_trainer
@@ -317,6 +317,7 @@ fi
 
 TRAINING_DONE="false"
 
+echo "🏋️ Starting model training and TFLite export (this is the longest stage)…"
 if run_attempt "Attempt 1/3: GPU training (default runtime profile)" ; then
   echo "✅ Training complete (GPU path)."
   TRAINING_DONE="true"
@@ -386,12 +387,24 @@ if [ "${TRAINING_DONE}" != "true" ]; then
 fi
 
 source_path="${WORK_DIR}/trained_models/wakeword/tflite_stream_state_internal_quant/stream_state_internal_quant.tflite"
+calibration_path="${WORK_DIR}/trained_models/wakeword/tflite_stream_state_internal_quant/detection_calibration.json"
 
 if [ ! -f "${source_path}" ] ; then
     echo "Output model not found! Training didn't complete successfully.  See ${TRAIN_LOG}"
     exit 1
 fi
 
+echo "🎯 Calibrating detector settings for on-device use…"
+if "${PYTHON_BIN:-python}" "${PROGDIR}/calibrate_detector.py" \
+    --training-config "${WORK_DIR}/trained_models/wakeword/training_config.yaml" \
+    --model "${source_path}" \
+    --output "${calibration_path}"; then
+    echo "✅ Detector calibration complete."
+else
+    echo "⚠️  Detector calibration failed; packaging with default detector settings."
+    rm -f "${calibration_path}" || :
+fi
+
 cp "${WORK_DIR}/trained_models/wakeword/model_summary.txt" "${OUTPUT_DIR}/logs/" || :
 cp -a "${WORK_DIR}/trained_models/wakeword/logs/train" "${OUTPUT_DIR}/logs/" || :
 cp -a "${WORK_DIR}/trained_models/wakeword/logs/validation" "${OUTPUT_DIR}/logs/" || :
@@ -404,24 +417,49 @@ tflite_path="${OUTPUT_DIR}/${tflite_filename}"
 cp "${source_path}" "${tflite_path}"
 
 json_path="${OUTPUT_DIR}/${wake_word_filename}.json"
-cat <<-EOF > "${json_path}"
-{
+export WAKE_WORD_TITLE LANGUAGE JSON_PATH="${json_path}" TFLITE_FILENAME="${tflite_filename}" CALIBRATION_PATH="${calibration_path}"
+echo "📦 Packaging final model artifacts…"
+"${PYTHON_BIN:-python}" - <<'PY'
+import json
+import os
+from pathlib import Path
+
+json_path = Path(os.environ["JSON_PATH"])
+calibration_path = Path(os.environ.get("CALIBRATION_PATH", ""))
+language = (os.environ.get("LANGUAGE", "en") or "en").strip().lower()
+probability_cutoff = 0.97
+sliding_window_size = 5
+
+if calibration_path.exists():
+    try:
+        calibration = json.loads(calibration_path.read_text(encoding="utf-8"))
+        probability_cutoff = float(calibration.get("probability_cutoff", probability_cutoff))
+        sliding_window_size = int(calibration.get("sliding_window_size", sliding_window_size))
+        print(
+            f"🎯 Using calibrated detector settings: "
+            f"cutoff={probability_cutoff:.2f}, window={sliding_window_size}"
+        )
+    except Exception as exc:
+        print(f"⚠️ Failed to read detector calibration ({exc}); using defaults.")
+
+meta = {
     "type": "micro",
-    "wake_word": "${WAKE_WORD_TITLE}",
+    "wake_word": os.environ["WAKE_WORD_TITLE"],
     "author": "Tater Totterson",
     "website": "https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git",
-    "model": "${tflite_filename}",
-    "trained_languages": ["en"],
+    "model": os.environ["TFLITE_FILENAME"],
+    "trained_languages": [language],
     "version": 2,
     "micro": {
-        "probability_cutoff": 0.97,
-        "sliding_window_size": 5,
+        "probability_cutoff": round(probability_cutoff, 2),
+        "sliding_window_size": sliding_window_size,
         "feature_step_size": 10,
         "tensor_arena_size": 30000,
-        "minimum_esphome_version": "2024.7.0"
-    }
+        "minimum_esphome_version": "2024.7.0",
+    },
 }
-EOF
+json_path.write_text(json.dumps(meta, indent=4) + "\n", encoding="utf-8")
+PY
 
 echo "Name:     ${WAKE_WORD_TITLE}"
 echo "Model:    ${tflite_path}"
diff --git a/dockerfile b/dockerfile
index bf3bfeb..37bb910 100644
--- a/dockerfile
+++ b/dockerfile
@@ -22,7 +22,7 @@ COPY --chown=root:root --chmod=0755 .bashrc /root/
 # Root-level entrypoints
 COPY --chown=root:root --chmod=0755 \
     train_wake_word \
-    run_recorder.sh \
+    run.sh \
     trainer_server.py \
     requirements.txt \
     /root/mww-scripts/
@@ -37,4 +37,4 @@ RUN chmod -R a+x /root/mww-scripts/cli
 COPY --chown=root:root --chmod=0644 static/index.html /root/mww-scripts/static/index.html
 
 # trainer server
-CMD ["/bin/bash", "-lc", "/root/mww-scripts/run_recorder.sh"]
+CMD ["/bin/bash", "-lc", "/root/mww-scripts/run.sh"]
diff --git a/run_recorder.sh b/run.sh
similarity index 97%
rename from run_recorder.sh
rename to run.sh
index 75d1ec9..1ef1fa8 100644
--- a/run_recorder.sh
+++ b/run.sh
@@ -8,7 +8,7 @@ DATA_DIR="${DATA_DIR:-/data}"
 HOST="${REC_HOST:-0.0.0.0}"
 PORT="${REC_PORT:-8888}"
 
-# Keep recorder deps separate from training venv
+# Keep trainer UI deps separate from the training venv
 VENV_DIR="${DATA_DIR}/.recorder-venv"
 PY="${VENV_DIR}/bin/python"
 PIP="${PY} -m pip"
diff --git a/trainer_server.py b/trainer_server.py
index 4e9eb70..cb69cf3 100644
--- a/trainer_server.py
+++ b/trainer_server.py
@@ -42,6 +42,12 @@ PIPER_VOICES_ROOT_URL = os.environ.get(
     "https://huggingface.co/rhasspy/piper-voices/resolve/main",
 )
 PIPER_CATALOG_CACHE_TTL_SECONDS = int(os.environ.get("PIPER_CATALOG_CACHE_TTL_SECONDS", "900"))
+PIPER_CATALOG_CACHE_FILE = Path(
+    os.environ.get(
+        "PIPER_CATALOG_CACHE_FILE",
+        str(ROOT_DIR / ".cache" / "piper_voices_catalog.json"),
+    )
+).resolve()
 
 DATASET_CLEANUP_ARCHIVES = os.environ.get("REC_DATASET_CLEANUP_ARCHIVES", "false").lower() in ("1", "true", "yes", "y")
 DATASET_CLEANUP_INTERMEDIATE = os.environ.get("REC_DATASET_CLEANUP_INTERMEDIATE_FILES", "false").lower() in ("1", "true", "yes", "y")
@@ -177,6 +183,27 @@ def _fetch_piper_catalog() -> Optional[Dict[str, Any]]:
     return data if isinstance(data, dict) else None
 
 
+def _read_cached_piper_catalog_file() -> Optional[Dict[str, Any]]:
+    try:
+        if not PIPER_CATALOG_CACHE_FILE.exists():
+            return None
+        data = json.loads(PIPER_CATALOG_CACHE_FILE.read_text(encoding="utf-8"))
+        return data if isinstance(data, dict) else None
+    except Exception:
+        return None
+
+
+def _write_cached_piper_catalog_file(data: Dict[str, Any]):
+    try:
+        PIPER_CATALOG_CACHE_FILE.parent.mkdir(parents=True, exist_ok=True)
+        PIPER_CATALOG_CACHE_FILE.write_text(
+            json.dumps(data, ensure_ascii=True),
+            encoding="utf-8",
+        )
+    except Exception:
+        pass
+
+
 def _load_piper_catalog() -> Optional[Dict[str, Any]]:
     now = time.time()
     with PIPER_CATALOG_LOCK:
@@ -185,6 +212,8 @@ def _load_piper_catalog() -> Optional[Dict[str, Any]]:
         if cached is not None and (now - fetched_at) < PIPER_CATALOG_CACHE_TTL_SECONDS:
             return cached
 
+    disk_cached = _read_cached_piper_catalog_file()
+
     try:
         fresh = _fetch_piper_catalog()
     except Exception:
@@ -194,9 +223,15 @@ def _load_piper_catalog() -> Optional[Dict[str, Any]]:
         if fresh is not None:
             PIPER_CATALOG_CACHE["entries"] = fresh
             PIPER_CATALOG_CACHE["fetched_at"] = now
+            _write_cached_piper_catalog_file(fresh)
             return fresh
-        if PIPER_CATALOG_CACHE.get("entries") is None:
-            PIPER_CATALOG_CACHE["entries"] = {}
+        if PIPER_CATALOG_CACHE.get("entries") is not None:
+            return PIPER_CATALOG_CACHE.get("entries")
+        if disk_cached is not None:
+            PIPER_CATALOG_CACHE["entries"] = disk_cached
+            PIPER_CATALOG_CACHE["fetched_at"] = now
+            return disk_cached
+        PIPER_CATALOG_CACHE["entries"] = {}
         PIPER_CATALOG_CACHE["fetched_at"] = now
         return PIPER_CATALOG_CACHE.get("entries")