9ijSSKrSSKrSSKrSSKrSSKJr SSKJs Jr SSKJ r J r SSK J r SrS*SjrSr"SS \R"5r"S S \ 5r"S S \R"5r"SS\R R"5r"SS\R"5rS+SjrS,Sjr"SS\R R"5r"SS\R R"5r"SS\R"5r"SS\R R"5r"SS\R R"5r"SS\R R"5r"S S!\R R"5r"S"S#\R R"5r "S$S%\R"5r!"S&S'\R"5r""S(S)\R"5r#g)-N)Conv1dConvTranspose1d) Generator皙?cv^^U4SjmUU4Sjn[R"[U5Vs/sH oC"U5PM sn5n[R"USS2SSS245USS2SSS24'[R"USS2SSS245USS2SSS24'UbSXR'[ R "U5$s snf)z Sinusoid position encoding tablecJ>U[R"SSUS--T- 5- $)N')nppower)positionhid_idxd_hids f/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/models/audio/vc/src/vocoder.py cal_angle.get_sinusoid_encoding_table..cal_angles'"((5!w!|*[T5Vs/sH nT"X5PM sn$s snfN)range)rhid_jrrs rget_posi_angle_vec7get_sinusoid_encoding_table..get_posi_angle_vecs$8=e E u (* EEEs$Nrr r)r arrayrsincostorch FloatTensor) n_positionr padding_idxrpos_isinusoid_tablers ` @rget_sinusoid_encoding_tabler%sFFXX05j0AB0Au E "0ABDN!ff^Aqt!tG%<=N1add7 ff^Aqt!tG%<=N1add7&)#   ^ ,, CsB6cVUR5SSnUR5SSup4[R"XA5nX-nXE-nXS- -U-nX-n UR"/UQSPUP76n [R "SU 5R SUU5n URU 5R5n U R5RS5n UR"/UQU PUP76n U Rn U RSU RU 5U 5 U R"/UQSP76n U $)Nrr)sizemathgcdviewrarangeunfold new_tensorlong contiguous new_zerosdevice index_add_to)signal frame_stepouter_dimensionsframes frame_lengthsubframe_length subframe_stepsubframes_per_frame output_sizeoutput_subframessubframe_signalframeresultdevice_of_results roverlap_and_addrD'sB{{}Sb)!;;=-Fhh|8O1M&9 +l:K"5kkI#3IRIIO LL, - 4 4Q8K5B DE   e $ ) ) +E     # #B 'E   ///1A/-/F}} b%((#34oF [[ /* /B /F Mrc.^\rSrSrU4SjrSrSrU=r$) LastLayerBc >[[U] 5 [[R U5"S0UD6Ul[[R U5"US- S-40UD6Ul[R RXXhS9Ul g)Nrr bias) superrF__init__getattrrnn activationpadrconv) self in_channels out_channelsnonlinear_activationnonlinear_activation_paramsrQ kernel_size pad_paramsrJ __class__s rrMLastLayer.__init__Dsz i')! HH*,K.IK588S);?q*@OJOHHOO {$? rclURU5nURU5nURU5nU$r)rPrQrRrSxs rforwardLastLayer.forwardNs/ OOA  HHQK IIaLr)rPrRrQ__name__ __module__ __qualname____firstlineno__rMr___static_attributes__ __classcell__rZs@rrFrFBs?rrFc,^\rSrSrSrU4SjrSrU=r$) Conv1d1x1Uz*1x1 Conv1d with customized initialization.c 2>[[U] UUSSSUS9 g)zInitialize 1x1 Conv1d module.rr)rXpaddingdilationrJN)rLrjrM)rSrTrUrJrZs rrMConv1d1x1.__init__Xs) i'   ( rrK)rbrcrdre__doc__rMrfrgrhs@rrjrjUs4rrjc2^\rSrSrSU4SjjrSrSrU=r$) LastLinearcc>[[U] 5 [R"SS9Ul[R "U5Ul[XUS9Ul [R "U5Ul [XUS9Ul g)N皙?)negative_sloperI) rLrrrMrO LeakyReLUrP BatchNorm1dbn_1rjlinear_1bn_2linear_2)rShidden_channel out_channelrJrZs rrMLastLinear.__init__es\ j$(*,,c:NN>2 !.tL NN>2 !.DI rcURU5nURU5nURU5nURU5nURU5nUR U5nU$r)rPryrzr{r|r]s rr_LastLinear.forwardmsY OOA  IIaL MM!  OOA  IIaL MM! r)rPryr{rzr|Trarhs@rrrrrcsJrrrc6^\rSrSrSrSU4SjjrSrSrU=r$) Stretch2dwzStretch2d module.cN>[[U] 5 XlX lX0lg)zInitialize Stretch2d module. Args: x_scale (int): X scaling factor (Time axis in spectrogram). y_scale (int): Y scaling factor (Frequency axis in spectrogram). mode (str): Interpolation mode. N)rLrrMx_scaley_scalemode)rSrrrrZs rrMStretch2d.__init__zs! i')   rcl[R"XRUR4URS9$)zCalculate forward propagation. Args: x (Tensor): Input tensor (B, C, F, T). Returns: Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale), ) scale_factorr)F interpolaterrrr]s rr_Stretch2d.forwards-}} \\4<<8tyyJ Jr)rrr)nearest rbrcrdrerprMr_rfrgrhs@rrrws JJrrc6^\rSrSrSU4SjjrSrSrU=r$) UpsampleLayerc >[[U] 5 [USSS9Ul[ R "UUUUUUUS9Ulg)Nrr)rrnrJ)rLrrMrupsamplerOrrR) rS in_channelr~ upsample_raterXstridermrnrJrZs rrMUpsampleLayer.__init__sG mT+-!-C II      rcURURS55nURURS55nU$)Nr)r unsqueezerRsqueezer]s rr_UpsampleLayer.forwards3 MM!++a. ) IIaiil #r)rRr)rTrarhs@rrrs(rrcURRnURS5S:wa&URRR X5 gg)NConvr()rZrbfindweightdatanormal_)mmeanstd classnames r init_weightsrs= $$I~~f#  d($rc([X-U- S- 5$Nr )int)rXrns r get_paddingrs  &1Q6 77rc2^\rSrSrSU4SjjrSrSrU=r$) ResBlock1c>[[U] 5 [R"[ UUUSUS[ X#S5US9[ UUUSUS[ X#S5US9[ UUUSUS[ X#S5US9/5Ul[R"[ UUUSS[ US5US9[ UUUSS[ US5US9[ UUUSS[ US5US9/5Ulg)NrrrnrmrJr ) rLrrMrO ModuleListrrconvs1convs2rSchannelsrXrnrJrZs rrMResBlock1.__init__s. i')mm !!#K!=  !!#K!=  !!#K!= #%   6mm #K3  #K3  #K3 #%   rc[URUR5HOup#[R"U[ 5nU"U5n[R"U[ 5nU"U5nXA-nMQ U$r)ziprrr leaky_relu LRELU_SLOPE)rSr^c1c2xts rr_ResBlock1.forwards]$++t{{3FBa-BBBb+.BBBA 4 r)rr)rrTrarhs@rrrs6 prrc2^\rSrSrSU4SjjrSrSrU=r$) ResBlock2c>[[U] 5 [R"[ UUUSUS[ X#S5US9[ UUUSUS[ X#S5US9/5Ulg)Nrrr)rLrrMrOrrrconvsrs rrMResBlock2.__init__s{ i')]] !!#K!=  !!#K!= $   rczURH*n[R"U[5nU"U5nX1-nM, U$r)rrrr)rSr^crs rr_ResBlock2.forwards7Aa-B2BAr)r)r)rrTrarhs@rrrs *rrc6^\rSrSrSrSU4SjjrSrSrU=r$)BasisSignalLayeriz Basis Signalc>[[U] 5 [R"UR S5UR S5SS9Ul[R"U5UR lX l g)NrrFrI) rLrrMrOLinearr)layer ParameterrL)rSbasis_signal_weightrrZs rrMBasisSignalLayer.__init__s] .0YY  $ $Q '  $ $Q ' LL)<= rcXURU5n[X RS-5nU$r)rrDr)rSrsources rr_BasisSignalLayer.forward&s'F# 15 r)rr)@rrhs@rrrsrrcB^\rSrSrSrSSSSS04U4SjjrS rS rU=r$) CausalConv1di/z3CausalConv1d module with customized initialization.rT ConstantPad1dvaluerc>[[U] 5 [[R U5"US- U-40UD6Ul[R RUUUUUS9Ulg)zInitialize CausalConv1d module.rrN) rLrrMrNrrOrQrrR) rSrTrUrXrnrJrQrYrZs rrMCausalConv1d.__init__2sd lD*,588S);?h*F8,68HHOO    $ rcxURURU55SS2SS2SURS524$)zCalculate forward propagation. Args: x (Tensor): Input tensor (B, in_channels, T). Returns: Tensor: Output tensor (B, out_channels, T). Nr )rRrQr)r]s rr_CausalConv1d.forwardEs3yy!%aJQVVAYJ&677r)rRrQrrhs@rrr/s'= $$cN&88rrc8^\rSrSrSrSU4SjjrSrSrU=r$)CausalConvTranspose1diOz[[U] 5 [RR XX4US9UlX@lg)z(Initialize CausalConvTranspose1d module.rIN)rLrrMrrOrdeconvr)rSrTrUrXrrJrZs rrMCausalConvTranspose1d.__init__Rs; #T35hh.. {/G  rcRURU5SS2SS2SUR*24$)zCalculate forward propagation. Args: x (Tensor): Input tensor (B, in_channels, T_in). Returns: Tensor: Output tensor (B, out_channels, T_out). Nrrr]s rr_CausalConvTranspose1d.forward^s({{1~aMdkk\M122rrrrrhs@rrrOsF 33rrc L^\rSrSrSrSSSSSSS 0S 0S 4 U4S jjrS rSrU=r$) ResidualStackihz+Residual stack module introduced in MelGAN.r rTrwrvruReflectionPad1dFc R>[[U] 5 U (dUS- S-S:XdS5e[RR [ [RU5"S 0UD6[ [RU5"US- S-U-40UD6[RRUUUUUS9[ [RU5"S 0UD6[RRX"SUS95UlO[RR [ [RU5"S 0UD6[UUUUUUUS9[ [RU5"S 0UD6[RRX"SUS95Ul[RRX"SUS9Ul g) anInitialize ResidualStack module. Args: kernel_size (int): Kernel size of dilation convolution layer. channels (int): Number of channels of convolution layers. dilation (int): Dilation factor. bias (bool): Whether to add bias parameter in convolution layers. nonlinear_activation (str): Activation function module name. nonlinear_activation_params (dict): Hyperparameters for activation function. pad (str): Padding function module name before dilated convolution layer. pad_params (dict): Hyperparameters for padding function. use_causal_conv (bool): Whether to use causal convolution. rr rz$Not support even number kernel size.rrI)rnrJrQrYNrK) rLrrMrrO SequentialrNrstackr skip_layer) rSrXrrnrJrVrWrQrYuse_causal_convrZs rrMResidualStack.__init__ks0 mT+- !" J#I J",,,.M0KM#& aA'='H5)35%   ,.M0KMADADJ ,,,.M0KM%)+,.M0KMADADJ" ((//(ad/KrcHURU5URU5-$)zCalculate forward propagation. Args: c (Tensor): Input tensor (B, channels, T). Returns: Tensor: Output tensor (B, chennels, T). )rr)rSrs rr_ResidualStack.forwards zz!}tq111r)rrrrhs@rrrhs95 (%5s$; ?LB22rrc z^\rSrSrS/SQ/SQSS/SQ/SQ/SQ/SQ/S S 4 U4S jjrS rS rS rSrSr Sr U=r $)HiFiGANGeneratoriPr )rrr 1) rrrTc x>[[U] 5 [U5Ul[U5Ul[ UUSSSU S9UlUS:Xa[O[n [R"5Ul [[X655Hqun upURRUSLa![!USU --USU S---U U SU S-U S9O)[#USU --USU S---U U U S-U S--U S-U S 95 Ms [R"5Ul['[UR55HPn USU S---n[[X'55H*unun nUR$RU "XUU S 95 M, MR [ WSSSSU S9UlUR+5 UR-5 g) NrrrrmrJrFr rrXrrmrJrmoutput_paddingrJrI)rLrrMlen num_kernels num_upsamplesrconv_prerrrOrups enumeraterappendrr resblocksr conv_postapply_weight_normreset_parameters)rSinput_channelsresblock_kernel_sizesupsample_ratesupsample_initial_channel resblock_typeupsample_kernel_sizesresblock_dilation_sizestransposedconvrJresblockiukchjdrZs rrMHiFiGANGenerator.__init__s .045 0  $   !. 49)==?"3~#MNIAv HHOO#1E"9,A6,QU<"# !F@O0QT:0QQZ@!"a!a%'(1u!@# $O$s488}%A)a!a%j9B&-GI 6Aq%%hrad&CDI&  Aq!QTB   rc,SnURU5 g)z:Remove weight normalization module from all of the layers.c[R"SUS35 [RRR U5 g![ a gf=f)NzWeight norm is removed from .)loggingdebugrrOutilsremove_weight_norm ValueErrorrs r_remove_weight_norm@HiFiGANGenerator.remove_weight_norm.._remove_weight_normsE  HiFiGANGenerator.apply_weight_norm.._apply_weight_normsc!UXX__--uxx//2121**1- :1#Q?@21rNr-)rSr4s rr"HiFiGANGenerator.apply_weight_norms A %&rc,SnURU5 g)zReset parameters. This initialization follows official implementation manner. https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py c*[U[RR5(d)[U[RR5(aAUR R RSS5 [R"SUS35 gg)Nr{Gz?zReset parameters in r$) r2rrOrrrrrr%r&r*s r_reset_parameters._reset_parameters se!UXX__--uxx//2121 %%c40 4QCq9:21rNr-)rSr:s rr!HiFiGANGenerator.reset_parameterss  ; $%rcURU5n[UR5Hn[R"U[ 5nUR U"U5nSn[UR5HRnUc&URX R-U-"U5nM,X0RX R-U-"U5- nMT X0R- nM [R"U5nURU5nU$r) r rr rrrr rrrrSr^rxsrs rr_HiFiGANGenerator.forwards MM! t))*A Q ,A AAB4++,:,<,<(s r inferenceHiFiGANGenerator.inference'@!U\\** &&(bdoo.?)@)G)G&H  KK1  ' ' * MM! t))*A Q ,A AAB4++,:,<,<(irrr rrrTFc N>[[U] 5 [U5Ul[U5Ul[ UUSSSU S9Ul[ SUSS5UlUS:Xa[O[n [RR[R"/SQ55Ul[R "5Ul[%['X655Hqun upUR"R)USLa![+US U --US U S---UUSUS -U S 9O)[-US U --US U S---UUUS -US --US -U S 95 Ms [R "5Ul[1[UR"55HQn US U S---n[%['X'55H+ununnUR.R)U "UUUU S 95 M- MS [ WS SSSU S9UlU (a[5SSSS9UlOSUlU (a[95UlgSUlg)Nrrrrr)rr rRFr rrrIrrr)dim_in style_dim max_conv_dim)rLrOrMrrr rr spk_fcrrrrOrrandnspk_inforr r rr rrrrrrunet FsmnEncoderV2 extra_layer)rSrrrrrrrrrY extra_inforJrrrrrrr rZs rrMConditionGenerator.__init__@s  $0245 0  $   S":AqA - 49)**5;;+GH ==?"3~#MNIAv HHOO#1E"9,A6,QU<"# !F@O0QT:0QQZ@!"a!a%'(1u!@# $O$s488}%A)a!a%j9B&-GI 6Aq%%hr1ad&CDI&  B1adC !sMDIDI ,D #D rc&UR/SQ5n[R"URS5UR-SSS9n[R "USS9nX`R-n[R"USS9nX$-Xu--nUbUR U5n X- nURU5URURS55-n [UR5Hn [R"U [5n URU "U 5n Sn [UR5HRn U c&UR XR-U -"U 5n M,XR XR-U -"U 5- n MT XR- n M [R"U 5n UR#U 5n UR$b2UR%U RS5U5n U R'S5n U R/SQ5n U $)N)rr rrr(T)dimkeepdimr_)permutersumrrXsoftmaxr[r rVrr rrrr rrrrYr)rSinpsextra_mcabscorerspk_inpr\r^rr?rs rr_ConditionGenerator.forwardskk)$ !++a.4==8b$O e+ % %Q'%%)#  ))(3J  !G MM# W->->r-B!C Ct))*A Q ,A AAB4++,:,<,<(s rrKConditionGenerator.inferencerMr) rr r[rr rrVrXrYr )N?ro) rbrcrdrerMr_rKrfrgrhs@rrOrO>sK(1v!$ !f!*Iy A  =$~!FrrOc>^\rSrSrSrSS/S4U4SjjrSrSrU=r$)FeedForwardNetizA two-feed-forward-layer modulerrc>[TU]5 [R"UUUSUSS- S-S9Ul[R"UUUSUSS- S-SS9Ul[R "U5Ulg)Nrrr )rXrmF)rXrmrJ)rLrMrOrw_1w_2Dropoutdropout)rSd_inrd_outrXrvrZs rrMFeedForwardNet.__init__s 99  #A ^a'A-  99  #A ^a'A-  zz'* rcURSS5n[R"URU55nUR U5nUR U5nURSS5nU$)Nrr )rJrrelursrvrt)rSr^outputs rr_FeedForwardNet.forwards\Q"()f%&!!!!Q' r)rvrsrtrrhs@rrqrqs)891vs+,rrqc6^\rSrSrSU4SjjrSSjrSrU=r$) MemoryBlockV2ic *>[[U] 5 [[ US- S- 55n[US- S- 5nUS:aXS- nXc-nXVsUlUl[R"XUSSUSS9Ul [R"U5Ul g)Nrr rF)groupsrJ) rLrrMrroundlprprOrconv_dwrurv)rSr  filter_sizeshiftrv left_padding right_paddingrZs rrMMemoryBlockV2.__init__s mT+-5+/Q!678 [1_12 19  !L  "M'yy{AqO zz'* rc Ub!URURS5S5n[R"USSURUR SS4SSS9nUR UR5RSS55R5RSS5nXA- nURU5nUb!URURS5S5nU$)Nr(rconstantr)rrrr ) masked_fillrrrQrrrr1rJrv)rSinputmaskr^r|s rr_MemoryBlockV2.forwards  %%dnnR&8!F r)rrvrr)rrrarhs@rrrs +rrcF^\rSrSrSU4SjjrSSjrSrU=r$)rZic V>[[U] 5 XlX lX@lXPlX`lXl[U[5(d*[UR5V s/sHoPM sn Ul[R"5Ul[R"5Ul[R "X45UlURR%['UUUUS95 [SU5H(n URR%['UUUUS95 M* [R"5Ul[U5H6n UR(R%[+XAURU U55 M8 [,RR!XGSS9Ulgs sn f)N)rvrFrI)rLrZrMrfsmn_num_layersnum_memory_units ffn_inner_dimrvrr2listrrOradapterffn_lstrprojr rqmemory_block_lstrrfc) rSrr input_dimrrrvspk_dimr_rrZs rrMFsmnEncoderV2.__init__sY mT+-&. 0*  %&&).t/C/C)DE)DA%)DEDJ}} }} IIi:       ! " q/*A LL  $!$# % &+!# 'A  ! ! ( (.TZZ]%' (( ((//"2%/H3Fs$ F&c[R"XRUR5nURU5n[ UR UR 5HjupEU"U5nU"Xb5n[R"XpRUR5nURS5URS5:XdMfXs- nMl URU5n[R"USS9nU$)Nr(rra) rrvtrainingrrrrr)rrr)rSrrr^ffn memory_blockcontextmemorys rr_FsmnEncoderV2.forward/s IIe\\4== 9 IIaL!$T\\43H3H!I C!fG!'0FYYv||T]]CF{{2!&&*, "J GGAJ JJqa r) rrvrrrrrrrrr)rri0rirrRrrrarhs@rrZrZs.-I`rrZr)rr9)r)$r%r*numpyr rtorch.nnrOtorch.nn.functional functionalrrr Starganv3rrr%rDModulerFrjrrrrrrrrrrrrrrOrqrrZrKrrrso  ,  -,6 &  (JJ4BII8) 8AAH>ryy$'8588??8@3EHHOO32K2EHHOOK2\EuxxEPxxv RYY FBIIB@BII@r