]i)SSKJrJr SSKrSSKrSSKrSSKrSSKrSSK J r SSK J r SSK r SSKrSSKrSSKJr S\S\S\S \R&4S jrS \S \S \S\4Sjr"SS\5r"SS\5rS!SjrS"S\S\\S\S\S\\S\\\R<4S\S\4SjjrS#S\R@S\\S\\S \R@4S jjr!g)$)OptionalUnionN)Path)redirect_stderr)modelraten_fft bandwidthreturnc[R"SUS- US-S-SS9n[R"[R"X2:*5S5S-$)zConvert bandwidth to maximum bin count Assuming lapped transforms such as STFT Args: rate (int): Sample rate n_fft (int): FFT length bandwidth (float): Target bandwidth in Hz Returns: np.ndarray: maximum frequency bin rT)endpoint)nplinspacemaxwhere)rr r freqss L/mnt/rpi/tmp/demucs-venv-sys/lib/python3.13/site-packages/openunmix/utils.pybandwidth_to_max_binrsI KK4!8UaZ!^d CE 66"((5-.q1 2Q 66stateis_bestpathtargetc[R"U[RR X#S-55 U(a;[R"US[RR X#S-55 gg)aConvert bandwidth to maximum bin count Assuming lapped transforms such as STFT Args: state (dict): torch model state dict is_best (bool): if current model is about to be saved as best model path (str): model path target (str): target name z.chkpnt state_dictz.pthN)torchsaveosrjoin)rrrrs rsave_checkpointr"#sP JJubggll4));<= 5& TF?(KLrc.\rSrSrSrSrSrSSjrSrg) AverageMeter5z1Computes and stores the average and current valuec$UR5 gN)resetselfs r__init__AverageMeter.__init__8s  rc<SUlSUlSUlSUlg)Nr)valavgsumcountr)s rr(AverageMeter.reset;s rcXlU=RX-- slU=RU- slURUR- Ulgr')r.r0r1r/)r*r.ns rupdateAverageMeter.updateAs8 CG a 88djj(r)r/r1r0r.N)r) __name__ __module__ __qualname____firstlineno____doc__r+r(r5__static_attributes__rrr$r$5s; )rr$c.\rSrSrSrSSjrSrSrSrg) EarlyStoppingHzEarly Stopping MonitorcXlX lX0lSUlSUlSUlUR X5 US:Xa SUlgg)Nrcg)NTr=)abs r(EarlyStopping.__init__..Us$r)mode min_deltapatiencebestnum_bad_epochs is_better_init_is_better)r*rGrHrIs rr+EarlyStopping.__init__KsH "   T- q=.DN rc URcXlg[R"U5(agURXR5(aSUlXlOU=RS- slURUR :agg)NFTrr)rJrisnanrLrKrI)r*metricss rstepEarlyStopping.stepWso 99 I 88G   >>'99 - -"#D I   1 $    $-- /rcz^US;a[SU-S-5eUS:Xa U4SjUlUS:Xa U4SjUlgg)N>rminzmode z is unknown!rUc>XT- :$r'r=rCrJrHs rrE/EarlyStopping._init_is_better..n Q 1A-Arrc>XT-:$r'r=rWs rrErXprYr) ValueErrorrL)r*rGrHs `rrMEarlyStopping._init_is_betterjs@ ~ %Wt^n<= = 5=ADN 5=ADN r)rJrLrHrGrKrIN)rUr ) r7r8r9r:r;r+rRrMr<r=rrr?r?Hs  /&Brr?c[U[5(aU/n[U5R5nUR 5(dE[ [ US-5n[R"5n[U5 U"XUS9sSSS5 $0nUHn[[XHS-5S5n [R "U 5n SSS5 [#[U5R%SU-55n [&R "XS9n [(R*"W S S S -S -U S S U S SU SR,SS9Xx'U(aXxR/U SS9 XxR1U5 M U$!,(df  O=f[UR55 g![a [S5ef=f!,(df  N=f)zCore model loader target model path can be either .pth, or -sha256.pth (as used on torchub) The loader either loads the models from a known model string as registered in the __init__.py or loads from custom configs. _spec)targetsdevice pretrainedNz Model does not exist on torchhubz.jsonrz%s*.pth) map_locationargsnfftr r nb_channels hidden_size input_meanr)nb_binsrgrhmax_binF)strict) isinstancestrr expanduserexistsgetattr openunmixioStringIOrprintgetvalueAttributeError NameErroropenjsonloadnextglobrr OpenUnmixshapeload_state_dictto) r`model_str_or_pathrarb model_path hub_loadererrmodelsrstreamresultstarget_model_pathrs rload_target_modelsrss'3)'(335J      @ ,=,GHJ++-C %!'ZX&%Fd:'78#>&))F+?!%T*%5%:%:9v;M%N O JJ0FE"__/14q8#FOM:#FOM:l+11!4 FN..uU.C N  f %%& 7&% #,,. ! @>? ? @?>s03F";E7 F"+F;7 F F""F8; G rr`niterresidualwiener_win_lenrarb filterbankc [U5R5nUR5(aUc [S5e[ XUS9n [ [US5S5n [ R"U 5n SSS5 [R"U UUUW SU SU SU S US 9 RU5n U $[[U5n U "UUS UUUUS 9n U $!,(df  Ne=f) aSeparator loader Args: model_str_or_path (str): Model name or path to model _parent_ directory E.g. The following files are assumed to present when loading `model_str_or_path='mymodel', targets=['vocals']` 'mymodel/separator.json', mymodel/vocals.pth', 'mymodel/vocals.json'. Defaults to `umxl`. targets (list of str or None): list of target names. When loading a pre-trained model, all `targets` can be None as all targets will be loaded niter (int): Number of EM steps for refining initial estimates in a post-processing stage. `--niter 0` skips this step altogether (and thus makes separation significantly faster) More iterations can get better interference reduction at the price of artifacts. Defaults to `1`. residual (bool): Computes a residual target, for custom separation scenarios when not all targets are available (at the expense of slightly less performance). E.g vocal/accompaniment Defaults to `False`. wiener_win_len (int): The size of the excerpts (number of frames) on which to apply filtering independently. This means assuming time varying stereo models and localization of sources. None means not batching but using the whole signal. It comes at the price of a much larger memory usage. Defaults to `300` device (str): torch device, defaults to `cpu` pretrained (bool): determines if loading pre-trained weights filterbank (str): filterbank implementation method. Supported are `['torch', 'asteroid']`. `torch` is about 30% faster compared to `asteroid` on large FFT sizes such as 4096. However, asteroids stft can be exported to onnx, which makes is practical for deployment. Nz-For custom models, please specify the targets)r`rrbzseparator.jsonrc sample_raterfnhoprg) target_modelsrrrrr n_hoprgrT)r`rarbrrrr) rrorp UserWarningrryrzr{r Separatorrrqrr)rr`rrrrarbrrrrenc_conf separatorrs rload_separatorrsX'(335J ?MN N*7eop $z#34c :fyy(H;OO') /6"6" /!   "V* 2 Y(9: )!  9; :s C C!audio model_ratec[R"URURS9n[ U5S:XaUSnO3[ U5S:Xa$UR 5S::aUSnO USS2SS4nURSURS:aUR SS5nURSS:a[R"S5 USSS24nURSS:Xa[R"USSS 9nX:waU[R"S 5 [RRXS S 9RUR5nU"U5nU$) a From an input tensor, convert it to a tensor of shape shape=(nb_samples, nb_channels, nb_timesteps). This includes: - if input is 1D, adding the samples and channels dimensions. - if input is 2D o and the smallest dimension is 1 or 2, adding the samples one. o and all dimensions are > 2, assuming the smallest is the samples one, and adding the channel one - at the end, if the number of channels is greater than the number of time steps, swap those two. - resampling to target rate if necessary Args: audio (Tensor): input waveform rate (float): sample rate for the audio model_rate (float): sample rate for the model Returns: Tensor: [shape=(nb_samples, nb_channels=2, nb_timesteps)] )rar)NN.r )N.N.zBChannel count > 2!. Only the first two channels will be processed!)dimzresample to model sample ratesinc_interpolation) orig_freqnew_freqresampling_method)r as_tensorrralenrU transposewarningswarnrepeat_interleave torchaudio transformsResampler)rrrr resamplers r preprocessrs82 OOEKK =E 5zQo& Uq 99;! )$E!T3,'E {{1~ A&1% {{1~ ]^c2A2g {{1~''qa8  56))22CW3 "U\\  %  Lr)umxlcpuT)rNrFi,rTr)NN)"typingrrrr numpyrrrpathlibr contextlibrrsrzrrrfloatintndarrayrdictboolrnr"objectr$r?rlistrarTensorrr=rrrsa" & 7u7S7U7rzz7$M4M$McM3M$)6)&(BF(BV.d$"$'',QQ d^Q Q Q SM Q #u||# $ QQQl!"&8 <<8 5/88 \\ 8r