VhH*dZddlmZddlZddlZddlZddlmZddl m Z ddl Z ddl mZddlmZdd lmZdd lmZmZejeeefZGd d ejZGd dZdZ ddejeefdejejefdededededededej edejfdZ!dS) z Code to apply a model to a mix. It will handle chunking with overlaps and inteprolation between chunks, as well as the "shift trick". )ThreadPoolExecutorN)nn) functional)Demucs)HDemucs)HTDemucs) center_trimDummyPoolExecutorceZdZ d dejedejejejedejeffd Ze defdZ dZ xZ S) BagOfModelsNmodelsweightssegmentcRtt|dksJ|d|D]A}|jjksJ|jjksJ|jjksJ|||_Bj|_j|_j|_tj||_ |fd|D}nNt|t|ksJ|D])}t|tjksJ*||_ dS)a Represents a bag of models with specific weights. You should call `apply_model` rather than calling directly the forward here for optimal performance. Args: models (list[nn.Module]): list of Demucs/HDemucs models. weights (list[list[float]]): list of weights. If None, assumed to be all ones, otherwise it should be a list of N list (N number of models), each containing S floats (S number of sources). segment (None or float): overrides the `segment` attribute of each model (this is performed inplace, be careful is you reuse the models passed). rNc2g|]}djDS)cg|]}dS)?).0_s R/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/demucs/apply.py z3BagOfModels.__init__...<s222q222)sources)rrfirsts rrz(BagOfModels.__init__..<s*CCCq22EM222CCCr) super__init__lenr samplerateaudio_channelsrr ModuleListrr)selfrrrotherweightr __class__s @rrzBagOfModels.__init__sB 6{{Qq  ( (E=EM1111#u'77777'5+?????" ' #2*} mF++ ?CCCCFCCCGGw<<3v;;....! 9 96{{c%-&8&888888 rreturnctd}|jD]9}t|tr"t |t|j}:|S)Ninf)floatr isinstancer minr)r#max_allowed_segmentmodels rr-zBagOfModels.max_allowed_segmentCsV#Ell[ U UE%** U&)*=uU]?S?S&T&T#""rc td)NzCall `apply_model` on this.)NotImplementedError)r#xs rforwardzBagOfModels.forwardKs!"?@@@r)NN) __name__ __module__ __qualname__tpListModelOptionalr*rpropertyr-r2 __classcell__)r&s@rr r sAE/3%%rwu~%+bgbgen&=>%+e,%%%%%%N#U###X#AAAAAAArr c2eZdZddZedZdZdS) TensorChunkrNc|jd}|dksJ||ksJ|||z }nt||z |}t|tr|j|_||jz|_n||_||_||_|j|_dS)Nr)shaper,r+r=tensoroffsetlengthdevice)r#rArBrC total_lengths rrzTensorChunk.__init__Ps|B' {{{{ $$$$ >!F*FF.77F fk * * ! -DK 6=0DKK DK DK m rcLt|jj}|j|d<|S)Nr?)listrAr@rC)r#r@s rr@zTensorChunk.shapecs$T[&''Kb  rc8||jz }|jjd}|dksJ|j|dzz }||z}t d|}t ||}||z }||z } t j|jd||f|| f} | jd|ksJ| S)Nr?r.)rCrAr@rBmaxr,Fpad) r# target_lengthdeltarEstartend correct_start correct_endpad_left pad_rightouts rpaddedzTensorChunk.paddedis +{(, zzzz eqj(m#Au ,,,  5(+% eDK];%> >?(IAVWWy} ---- r)rN)r3r4r5rr:r@rVrrrr=r=OsR$$$$&X rr=ct|tr|St|tjsJt|S)N)r+r=thTensor)tensor_or_chunks r tensor_chunkr[|s@/;//,/2955555?+++rT?rFr.mixshiftssplitoverlaptransition_powerprogress num_workersrr'c  ||j}ntj|}| /|dkr|jdkrt|} nt } ||||||| | d} t |t rEd} dgt|jz} t|j |j D]\}}tt|j}||t!||fi| }||t#|D]/\}}|dd|ddddfxx|zcc<| |xx|z cc<0| |z } ~t | tjsJt'| jdD]"}| dd|ddddfxx| |zcc<#| S||||dks Jd|j\}}}|rd| d<t-d |jz}t1|}t |t2sJ||d |zz}d}t'|D]N}t7jd|}t3||||z|z }t!||fi| }||d ||z dfz }O||z}t |tjsJ|S|ryd | d <tj|t|j|||j}tj||j}| |j} | | dksJt-|j| z}t-d|z |z}t'd||} t?tA||jz d}!tj!tj"d|d zdz|tj"||d zz dd|g}"t|"|ksJ|"|"#z |z}"g}#| D]D}t3|||}$| j$t ||$fi| }%|#%|%|f||z }E|rtMj&|#|!dd}#|#D]\}%}|%'}&|&jd}'|d |||zfxx|"d|'|&z|jz cc<||||zxx|"d|'|jz cc<|(dksJ||z}t |tjsJ|St |tRr| t-| |jz}(n(tU|dr|+|}(n|}(t1|}t |t2sJ||(|}tj,5||}dddn #1swxYwYt |tjsJt[||S)a Apply model to a given mixture. Args: shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec and apply the oppositve shift to the output. This is repeated `shifts` time and all predictions are averaged. This effectively makes the model time equivariant and improves SDR by up to 0.2 points. split (bool): if True, the input will be broken down in 8 seconds extracts and predictions will be performed individually on each and concatenated. Useful for model with large memory footprint like Tasnet. progress (bool): if True, show a progress bar (requires split=True) device (torch.device, str, or None): if provided, device on which to execute the computation, otherwise `mix.device` is assumed. When `device` is different from `mix.device`, only local computations will be on `device`, while the entire tracks will be stored on `mix.device`. num_workers (int): if non zero, device is 'cpu', how many threads to use in parallel. segment (float or None): override the model segment parameter. 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