@jR ddlZddlZddlmZmZmZmZddlZddlmZddl m Z Gddejj Z Gddejj ZGd d ejj ZGd d ejj ZGd dejj ZGddejj ZGddej ZGddej ZdejdededejfdZdZd(dejdedededejf d Zd)d!ejded"ededejf d#Zd$eedefd%Zd$eedefd&Zd$eedefd'ZdS)*N)AnyDictListOptional)nn) functionalc eZdZdZd dedededeffd Zed e j fd Z d e j d e j fd Z xZ S)_ScaledEmbeddingaFMake continuous embeddings and boost learning rate Args: num_embeddings (int): number of embeddings embedding_dim (int): embedding dimensions scale (float, optional): amount to scale learning rate (Default: 10.0) smooth (bool, optional): choose to apply smoothing (Default: ``False``) $@Fnum_embeddings embedding_dimscalesmoothcttj|||_|rrt j|jjjd}|t j d|dz dddfz }||jjjdd<|jjxj|zc_||_ dS)Nrdim) super__init__r Embedding embeddingtorchcumsumweightdataarangesqrtr)selfr r rrr __class__s KC:\Projects\Historical\venv\Lib\site-packages\torchaudio/models/_hdemucs.pyrz_ScaledEmbedding.__init__-s nmDD  3\$."7"z%_HEncLayer.__init__..m BKMMr!r8c.tj|Sr$r GroupNormrIr=s r rJz%_HEncLayer.__init__..o [! < <r!r7rr)rrrConv1dr?r;r<r>rCConv2dconvnorm1rGrewritenorm2dconv_DConv)rr9r:r;r<r=r>r?r@rArBrCnorm_fnpad_valklassrs ` r rz_HEncLayer.__init__\sd   H))  $ $<<< ValueErrorgelurUpermutereshaperXrWrVglu) rr%r]BCFrTleyzs r r(z_HEncLayer.forwards"y !QUUWW\\'KAq"aq"a  Ay DB #q((E!aT[0@!ABCC IIaLL : H  |B172;.. !@AAAzz||q  QUUWW\\111d +F A F4::a== ! ! 9 'KAq"a !Q1%%--b!Q77A 1 Aq"a##++Aq!Q77AA 1 A JJt||A ' ' E!OOOr!) r6r7r7FTr8rNTr$r)r*r+r,r-r/strrrrrrr1r(r2r3s@r r5r5Is*%-1*3*3*3*3 *3  *3  *3*3*3*3*34S>**3*3*3*3*3*3*3X,,,x /E,QVQ],,,,,,,,r!r5ceZdZdZ dd ed ed ed ed ededededededeeee fdeffd Z de j dee j fdZ xZS) _HDecLayeraDecoder layer. This used both by the time and the frequency branches. Args: chin (int): number of input channels. chout (int): number of output channels. last (bool, optional): whether current layer is final layer (Default: ``False``) kernel_size (int, optional): Kernel size for encoder (Default: 8) stride (int): Stride for encoder layer (Default: 4) norm_groups (int, optional): number of groups for group norm. (Default: 1) empty (bool, optional): used to make a layer with just the first conv. this is used before merging the time and freq. branches. (Default: ``False``) freq (bool, optional): boolean for whether conv layer is for frequency (Default: ``True``) norm_type (str, optional): Norm type, either ``group_norm `` or ``none`` (Default: ``group_norm``) context (int, optional): context size for the 1x1 conv. (Default: 1) dconv_kw (Dict[str, Any] or None, optional): dictionary of kwargs for the DConv class. (Default: ``None``) pad (bool, optional): true to pad the input. Padding is done so that the output size is always the input size / stride. (Default: ``True``) Fr6r7rTr8Nr9r:lastr;r<r=r>r?r@rArBrCc t| i} d} | dkrfd} | r$||z dzdkrtd||z dz} nd} | |_||_||_||_||_||_||_ tj }tj }|r |dg}|dg}tj }tj}||||||_| ||_|jr2tj|_tj|_dS||d|zdd| zzd| |_| d|z|_dS)Nc(tjSr$rFrHs r rJz%_HDecLayer.__init__..rKr!r8c.tj|Sr$rMrOs r rJz%_HDecLayer.__init__..rPr!rQrz#Kernel size and stride do not alignr)rrrdrCrtr?r9r>r<r;rrRConvTranspose1drSConvTranspose2dconv_trrWrGrVrU)rr9r:rtr;r<r=r>r?r@rArBrCrZr\klass_trrs ` r rz_HDecLayer.__init__s   H))  $ $<<<rcrhrUrVrdrWrzrCrtre) rr%r|lengthrirjrlrnros r r(z_HDecLayer.forwardsL. 9 ,AgGAq!q$)R++Az JDAdjja11q999AAA !HIII JJt||A ' ' 9 Lx 4c48txi/23#tx$(V"3334Awr{f$$ !JKKKy q A!t r!) Fr6r7rFTr8rNTrpr3s@r rsrss,%-10+0+0+0+ 0+  0+  0+0+0+0+0+0+4S>*0+0+0+0+0+0+0+d..Xel-C........r!rsc+eZdZdZ d3deedededededededededededededededededed ed!ed"ef*fd# Z d$Z d4d&Z d5d)e j d*ed+ed,ed-ef d.Zd/Zd0Zd1e j fd2ZxZS)6HDemucsa# Hybrid Demucs model from *Hybrid Spectrogram and Waveform Source Separation* :cite:`defossez2021hybrid`. See Also: * :class:`torchaudio.pipelines.SourceSeparationBundle`: Source separation pipeline with pre-trained models. Args: sources (List[str]): list of source names. List can contain the following source options: [``"bass"``, ``"drums"``, ``"other"``, ``"mixture"``, ``"vocals"``]. audio_channels (int, optional): input/output audio channels. (Default: 2) channels (int, optional): initial number of hidden channels. (Default: 48) growth (int, optional): increase the number of hidden channels by this factor at each layer. (Default: 2) nfft (int, optional): number of fft bins. Note that changing this requires careful computation of various shape parameters and will not work out of the box for hybrid models. (Default: 4096) depth (int, optional): number of layers in encoder and decoder (Default: 6) freq_emb (float, optional): add frequency embedding after the first frequency layer if > 0, the actual value controls the weight of the embedding. (Default: 0.2) emb_scale (int, optional): equivalent to scaling the embedding learning rate (Default: 10) emb_smooth (bool, optional): initialize the embedding with a smooth one (with respect to frequencies). (Default: ``True``) kernel_size (int, optional): kernel_size for encoder and decoder layers. (Default: 8) time_stride (int, optional): stride for the final time layer, after the merge. (Default: 2) stride (int, optional): stride for encoder and decoder layers. (Default: 4) context (int, optional): context for 1x1 conv in the decoder. (Default: 4) context_enc (int, optional): context for 1x1 conv in the encoder. (Default: 0) norm_starts (int, optional): layer at which group norm starts being used. decoder layers are numbered in reverse order. (Default: 4) norm_groups (int, optional): number of groups for group norm. (Default: 4) dconv_depth (int, optional): depth of residual DConv branch. (Default: 2) dconv_comp (int, optional): compression of DConv branch. (Default: 4) dconv_attn (int, optional): adds attention layers in DConv branch starting at this layer. (Default: 4) dconv_lstm (int, optional): adds a LSTM layer in DConv branch starting at this layer. (Default: 4) dconv_init (float, optional): initial scale for the DConv branch LayerScale. (Default: 1e-4) rQ0皙? Tr6r7rr-C6?sourcesaudio_channelschannelsgrowthnfftdepthfreq_emb emb_scale emb_smoothr; time_strider<rA context_enc norm_startsr= dconv_depth dconv_comp dconv_attn dconv_lstm dconv_initc t||_||_||_||_| |_| |_| |_||_ |jdz|_ d|_ tj |_tj |_tj |_tj |_|}|dz}|}|}|jdz}t%|jD]}||k}||k}||krdnd}|dk}| } | }!|s|dkrt'd| dz}!| } d}"d}#|r || kr|}!d}"d}#|!| ||"|||||||d d }$t)|$}%d |%d <| |%d <| |%d<d|%d<t)|$}&|#rt+||}|}t-||fd|i|$}'|r?|#dur|dkr d|%d<d|%d <t-||f||#d|%}(|j|(|j|'|d kr!|jt1|jz}|dz}t3||f|d k| d|&})|r1t3||f|#|d k| d|%}*|jd |*|jd |)|}|}t7||z}t7||z}|r|| krd}n|| z}|d kr!|rt9||| ||_ ||_t=|dS)Nr7rQr8nonerz$When freq is false, freqs must be 1.TF)lstmattnrcompressinit)r;r<r?rCr@r=rBrr?r;r<rCrA)rAr>)rtrA)r>rtrA)rr)rrrrrrr;rAr<r hop_lengthrr ModuleList freq_encoder freq_decoder time_encoder time_decoderrangerddictmaxr5appendlenrsinsertr-r freq_emb_scale_rescale_module),rrrrrrrrrrr;rr<rArrr=rrrrrr9chin_zr:chout_zfreqsindexrrr@r?strikerrC last_freqkwkwtkw_decenctencdectdecrs, r rzHDemucs.__init__Qs0   , &    )q. MOOMOOMOOMOO Q4:&&K /K /EJ&DJ&D(-(<(< &I19DDC #A::$%KLLL!Ao"CI !,,  #&*  ( *& Br((CCK!,C "CMCJ"XXF eW--VWHHkHRHHC /$$$%CM)*C &!$[{)[[WZ[[!((...   $ $S ) ) )zz*S->->>WfY5A:wYYRXYYC 2!%hYUaZY`hhdghh!((D111   $ $Q , , ,DF''E&7*++G %K''EEf$Ezzhz 0zYb c c c &.#r!c|j}|j}|}||dzkrtdtt j|jd|z }|dzdz}||||||zz|jdz d}t|||dddddf}|jd|dzkrtd |ddd|zf}|S) Nr7zHop length must be nfft // 4r_rQr`reflect)mode.zESpectrogram's last dimension must be 4 + input size divided by stride) rrrdr-mathceilra_pad1d_spectro)rr%hlrx0rmrCros r _specz HDemucs._specs _y  ??;<< < 172;+,, - -Agk KK3b2g  ;)K L L Qb ! !#ssAAA+ . 72;"q& dee e c1q2v:o r!Nc|j}tj|gd}tj|ddg}|dzdz}|tt j||z zd|zz}t |||}|d|||zf}|S)N)rrrrrQr`)r~.)rrcrCr-rr _ispectro)rror~rrCrmr%s r _ispeczHDemucs._ispecs _ E!\\\ " " E!aV  Agk #di ,,-- -C 7 aB ' ' ' c3v%% &r!zeror% padding_left padding_rightrvaluec|jd}|dkr3t||}||krtj|d||z dzf}tj|||f||S)zWrapper around F.pad, in order for reflect padding when num_frames is shorter than max_pad. Add extra zero padding around in order for padding to not break.r_rrr)rarrcrC)rr%rrrrr~max_pads r rzHDemucs._pad1dso 9  , 66G  E!a6!1A!5677uQ}5tUCCCr!c|j\}}}}tj|ddddd}|||dz||}|S)Nrrr7rQr`)rar view_as_realrfrg)rrorirjrkrlms r _magnitudezHDemucs._magnitudesWg 1b!  q ! ! ) )!Q1a 8 8 IIaQA & &r!c|j\}}}}}|||dd||dddddd}tj|}|S)Nr_rQrrr7r`)rarbrfrview_as_complex contiguous)rrriSrjrkrlr's r _maskz HDemucs._maskse1aQffQ2q"a((00Aq!QBB#CNN$4$455 r!inputct |jdkrtd|j|jd|jkrtd|jdd|}|jd}||}||}|}|j\}}}} |dd } |dd } || z d | zz }|} | d d } | d d }| | z d |zz } g}g}g}g}t|j D]@\}}| |jdd }|t|j krW| | jd|j |}|| } |j s| | n| }|||}|d kr|j{tj|jd|j}||d d d d d d f|}||j|zz}| |Btj|}tj|} t|jD]\}}|d}||||d\}}|jt|jz }||kr|j||z }|d}|j rH|jddkrtd|j|d d d d d f}||d |\} }|d}|| ||\} }t|d krt5dt|d krt5dt|d krt5dt|j} ||| d|| }|| d d d fz| d d d fz}||}!||!|}| || d|} | |d d d fz| d d d fz} | |z}|S)aHDemucs forward call Args: input (torch.Tensor): input mixed tensor of shape `(batch_size, channel, num_frames)` Returns: Tensor output tensor split into sources of shape `(batch_size, num_sources, channel, num_frames)` r`zDExpected 3D tensor with dimensions (batch, channel, frames). Found: rzZThe channel dimension of input Tensor must match `audio_channels` of HDemucs model. Found:.r_)rrQr`T)rkeepdimgh㈵>)rrQNr)devicerQz0If tdec empty is True, pre shape does not match zsaved is not emptyzlengths_t is not emptyzsaved_t is not empty)ndimrdrarrrmeanstd enumeraterrrrr>rrrrt expand_asr zeros_likerpoprrAssertionErrorrrbrr)"rrr%r~romagrirjFqrlrrxtmeantstdtsavedsaved_tlengths lengths_tidxencoder]rfrsembdecoder|preoffsetrlength_t_rzouts" r r(zHDemucs.forward s :??qdidoqqrr r ;q>T0 0 0+Q+++   JJu  ooa   g 1b!vv)Tv22ee 4e00 X$* %FD11vv&$v//5jTD[ )! $T%677  KC NN172; ' ' 'FS*++++  "...(-T"XXz NN2&&&& Fq&!!AaxxDM5l172;qx@@@mmC((**,,T111aaa-=>HHKK+c11 LLOOOO  Q    a %T%677 5 5KC99R==DVAtW[[__55FAsZ#d&7"8"88Ff}}(v6$==,,:5y|q((()g\_\e)g)ghhhaaaAg,C DdH55EB";;r??D DT844EB u::?? !566 6 y>>Q   !9:: : w<<1   !788 8    FF1aR # # AAAtG tAAAtG} ,zz!}} KKf % % WWQ2v & & $qqq$w- %4. 0 Fr!)rQrrQrrrrTr6rQr7rrr7r7rQr7r7r7rr$)rr)r)r*r+r,rrqr-r.r/rrrrr1rrrr(r2r3s@r rr-s-!!L  -~~c~~ ~  ~  ~~~~~~~~~~ !~"#~$%~&'~()~*+~,-~~~~~~@0DD DCDDSVDhmDDDDoU\oooooooor!rcfeZdZdZ dded ed ed ed ed ededededeffd ZdZ xZ S)rYa New residual branches in each encoder layer. This alternates dilated convolutions, potentially with LSTMs and attention. Also before entering each residual branch, dimension is projected on a smaller subspace, e.g. of dim `channels // compress`. Args: channels (int): input/output channels for residual branch. compress (float, optional): amount of channel compression inside the branch. (default: 4) depth (int, optional): number of layers in the residual branch. Each layer has its own projection, and potentially LSTM and attention.(default: 2) init (float, optional): initial scale for LayerNorm. (default: 1e-4) norm_type (bool, optional): Norm type, either ``group_norm `` or ``none`` (Default: ``group_norm``) attn (bool, optional): use LocalAttention. (Default: ``False``) heads (int, optional): number of heads for the LocalAttention. (default: 4) ndecay (int, optional): number of decay controls in the LocalAttention. (default: 4) lstm (bool, optional): use LSTM. (Default: ``False``) kernel_size (int, optional): kernel size for the (dilated) convolutions. (default: 3) r7rQrr8Fr`rrrrr@rheadsndecayrr;c t| dzdkrtd||_||_t ||_|dk} d} |dkrd} t||z } tj }tj g|_ t|jD] }| rtd|nd}|| dzz}tj|| | ||| | |tj| d|zd| d|ztjdt!||g}|r&|d t%| || | r&|d t'| dd tj|}|j | dS) NrQrz(Kernel size should not be divisible by 2c(tjSr$rFrHs r rJz!_DConv.__init__..rKr!r8c,tjd|S)NrrMrHs r rJz!_DConv.__init__..s Q 2 2r!r)dilationpaddingr`)rrT)layersr|)rrrdrrabsrr-rGELUrrrpowrRGLU _LayerScaler _LocalState_BLSTM Sequentialr)rrrrrr@rrrrr;dilaterZhiddenactrIrrmodslayerrs r rz_DConv.__init__s  ?a  GHH H    ZZ *)  $ $22GX())gmB'' tz"" & &A$*1s1ayyyH+"23G (FK(T[\\\ &!h,22H %%q Hd++D P A{6vNNNOOO D AvfQTBBBCCCM4(E K  u % % % %# & &r!c6|jD]}|||z}|S)zDConv forward call Args: x (torch.Tensor): input tensor for convolution Returns: Tensor Output after being run through layers. )r)rr%rs r r(z_DConv.forwards-[  EEE!HH AAr!) r7rQrr8Fr7r7Fr`) r)r*r+r,r-r.rqr/rr(r2r3s@r rYrY}s.%1&1&1&1& 1&  1&  1&1&1&1&1&1&1&1&1&1&1&f       r!rYcReZdZdZd dedeffd Zdejdejfd Z xZ S) r ae BiLSTM with same hidden units as input dim. If `max_steps` is not None, input will be splitting in overlapping chunks and the LSTM applied separately on each chunk. Args: dim (int): dimensions at LSTM layer. layers (int, optional): number of LSTM layers. (default: 1) skip (bool, optional): (default: ``False``) rFrr|ctd|_tjd||||_tjd|z||_||_dS)NT) bidirectional num_layers hidden_size input_sizerQ) rr max_stepsrLSTMrLinearlinearr|)rrrr|rs r rz_BLSTM.__init__s] G$6s_bccc iC--  r!r%r"c |j\}}}|}d}d}d}d} |jd||jkrY|j}|dz}t|||} | jd} d}| ddddd||}|ddd}||d}||}|ddd}|rg} ||d||} |dz} t| D]} | dkr'| | dd| ddd| f/| | dz kr&| | dd| dd| df^| | dd| dd| | ftj | d} | d d|f} | }|j r||z}|S) aBLSTM forward call Args: x (torch.Tensor): input tensor for BLSTM shape is `(batch_size, dim, time_steps)` Returns: Tensor Output after being run through bidirectional LSTM. Shape is `(batch_size, dim, time_steps)` FrNrQTrr`r_.) rar_unfoldrfrgrrrrrcatr|)rr%rirjrlrnframedwidthr<nframesframesr'limitks r r(z_BLSTM.forwards '1a  > %!dn*<*<NEaZFQv..Fl1oGFq!Q**222q%@@A IIaA   IIaLLO KKNN IIaA    CYYq"a//FaKE7^^ > >66JJvaaaAAAww&678888'A+%%JJvaaaAAAuvvo67777JJvaaaAAAueV|&;<====)C$$Cc2A2g,CA 9 AAr!)rF) r)r*r+r,r-r/rrr1r(r2r3s@r r r sC4..%,........r!r cVeZdZdZd dededeffd Zdejdejfd ZxZ S) r aLocal state allows to have attention based only on data (no positional embedding), but while setting a constraint on the time window (e.g. decaying penalty term). Also a failed experiments with trying to provide some frequency based attention. r7rrrc\tt|||zdkrtd||_||_t j||d|_t j||d|_ t j||d|_ t j|||zd|_ |rK|j j xj dzc_ |j jtdd|j jj dd<t j||dzz|d|_dS)z Args: channels (int): Size of Conv1d layers. heads (int, optional): (default: 4) ndecay (int, optional): (default: 4) rz$Channels must be divisible by heads.rg{Gz?Nzbias must not be None.r)rr rrdrrrrRcontentquerykey query_decayrrbiasproj)rrrrrs r rz_LocalState.__init__s k4  ))+++ e q CDD D  y8Q77 Yx155 9Xx339Xuv~qAA  /   # ( (D 0 ( ($, !9:::,.D  ! &qqq )Ih2Ha@@ r!r%r"c|j\}}}|j}tj||j|j}|dddf|dddfz }||||d|}||||d|} tj d| |} | tj | jdz} |j rtjd|j dz|j|j} | |||d|} tj| dz } | ddd |ztj |j z } | tj d| | z } | tj|| jtjdtj| d }||||d|}tj d ||}||d|}|||zS) zLocalState forward call Args: x (torch.Tensor): input tensor for LocalState Returns: Tensor Output after being run through LocalState layer. )rdtypeNr_zbhct,bhcs->bhtsrQrzfts,bhfs->bhtsirzbhts,bhct->bhcs)rarrrrr/r)rbr*einsumrrrr+sigmoidr masked_fill_eyer/softmaxr(rgr-)rr%rirjrlrindexesdeltaquerieskeysdotsdecaysdecay_q decay_kernelweightsr(results r r(z_LocalState.forward6s%'1a ,qAAA4 747#33**Q--$$Qr155xx{{5"a00|-tW==  $*Q-((( ; J\!T[1_QXQWUUUF&&q))..q%Q??GmG,,q0G"KKAq111EIIKK?$)DKBXBXXL EL!1<II ID %)AdkLLLdSSS-!,,,,,q//&&q%Q77/'BB2q))499V$$$$r!)r7r7) r)r*r+r,r-rrr1r(r2r3s@r r r s AAASAcAAAAAA2#%#%%,#%#%#%#%#%#%#%#%r!r cReZdZdZd dedeffd ZdejdejfdZ xZ S) rzLayer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf). This rescales diagonally residual outputs close to 0 initially, then learnt. rrrcttjt j|d|_||jjdd<dS)z Args: channels (int): Size of rescaling init (float, optional): Scale to default to (default: 0) T) requires_gradN)rrr Parameterrzerosrr)rrrrs r rz_LayerScale.__init__asS \%+hd"K"K"KLL ! r!r%r"c*|jdddf|zS)zLayerScale forward call Args: x (torch.Tensor): input tensor for LayerScale Returns: Tensor Output after rescaling tensor. N)r)rr%s r r(z_LayerScale.forwardksz!!!T'"Q&&r!)r) r)r*r+r,r-r.rrr1r(r2r3s@r rr\s"""E"""""" ' '%, ' ' ' ' ' ' ' 'r!rar;r<r"ctjdd}tjd}tj||z }|dz |z|z}t jd||z gfdtD}|ddkrtd|dd|dgz}| || | ||S)zGiven input of size [*OT, T], output Tensor of size [*OT, F, K] with K the kernel size, by extracting frames with the given stride. This will pad the input so that `F = ceil(T / K)`. see https://github.com/pytorch/pytorch/issues/60466 Nr_rr)rrCc:g|]}|S)r<).0rrEs r z_unfold..s#777qxx}}777r!zData should be contiguous.) listrar-rrrcrCrrrdr as_strided)rEr;r<rar~n_frames tgt_lengthstridess` r rrxs "  E   Fy&))HQ,&(;6J AAzF23444A7777aeegg777Gr{a5666crclfa[(G LL LL <<w ' ''r!ct|D]}t|tjtjtjtjfrd|j }|dz dz}|jxj |zc_ |j |j xj |zc_ dS)zI Rescales initial weight scale for all models within the module. g?g?N) modules isinstancerrRrxrSryrrdetachrr,)modulesubrrs r rrs~~'' cBIr'929bFXY Z Z '*..""))++C3Y3&E JOOu $OOx# & ''r!r%n_fftrrCc t|jdd}t|jd}|d|}t j||d|zz|t j|||dddd }|j\}}} ||| g| |S)Nr_rTr)window win_length normalizedcenterreturn_complexpad_mode) rKrar-rgrstft hann_windowtoextendrb) r%rWrrCotherr~rorrframes r rrs "  E   F "fA   S ''**1--   AgOAue LL%   66%==r!ror~c t|jdd}t|jd}t|jd}d|zdz }|d||}|d|zz}t j|||t j||j|d|d} | j\} }| || |S)Nrr_rQrT)rYrZr[r~r\) rKrar-rbristftr`rarealr) rorr~rCrcrr#rWrZr%rs r rrs "  E   E   F IME r5&!!A1s7#J    ,,//77   AIAv LL 66%==r!rc&t|ddS)zBuilds low nfft (1024) version of :class:`HDemucs`, suitable for sample rates around 8 kHz. Args: sources (List[str]): See :py:func:`HDemucs`. Returns: HDemucs: HDemucs model. irrrrrrs r hdemucs_lowrl 7Q 7 7 77r!c&t|ddS)aBuilds medium nfft (2048) version of :class:`HDemucs`, suitable for sample rates of 16-32 kHz. .. note:: Medium HDemucs has not been tested against the original Hybrid Demucs as this nfft and depth configuration is not compatible with the original implementation in https://github.com/facebookresearch/demucs Args: sources (List[str]): See :py:func:`HDemucs`. Returns: HDemucs: HDemucs model. rrrirjrks r hdemucs_mediumros 7Q 7 7 77r!c&t|ddS)zBuilds medium nfft (4096) version of :class:`HDemucs`, suitable for sample rates of 44.1-48 kHz. Args: sources (List[str]): See :py:func:`HDemucs`. Returns: HDemucs: HDemucs model. rrrirjrks r hdemucs_highrqrmr!)rVrr)rrr)rtypingtprrrrrrtorch.nnrrcModuler r5rsrrYr r rr1r-rrrrrqrlrorqrHr!r rvsS4 ,,,,,,,,,,,, $$$$$$#####ux###Lkkkkkkkk\sssssssslMMMMMehoMMM` TTTTTUX_TTTn@@@@@UX_@@@FB%B%B%B%B%")B%B%B%J'''''")'''8(u|(#(s(u|((((( ' ' ' SC#V[Vb(3C#V[Vb. 8c 8w 8 8 8 88DI8'8888& 8$s) 8 8 8 8 8 8 8r!