o Si@sddlZddlmZmZmZmZddlZddlmZddl m Z ddl m Z m Z mZddlmZGdddeZGd d d eZGd d d eZdS) N)DictListOptionalTuple)Dataset)validate)CutCutSetMonoCut)EPSILONcsteZdZdZdedeffdd Zdedeee e ffdd Z d d Z d e deeejffd dZddZZS)SourceSeparationDataseta\ .. warning: Speech separation datasets are not yet updated to use the new Lhotse's sampling mechanism. An abstract base class, implementing PyTorch Dataset for the source separation task. It's created from two CutSets - one provides the audio cuts for the sources, and the other one the audio cuts for the signal mix. When queried for data samples, it returns a dict of: .. code-block:: { 'sources': (N x T x F) tensor, 'mixture': (T x F) tensor, 'real_mask': (N x T x F) tensor, 'binary_mask': (T x F) tensor } sources_set mixtures_setcs2ttd||_||_t|jj|_dS)NzZSpeech separation datasets are not yet updated to use the new Lhotse's sampling mechanism.) super__init__warningswarnr rlistidscut_idsselfr r __class__T/home/ubuntu/.local/lib/python3.10/site-packages/lhotse/dataset/source_separation.pyrs z SourceSeparationDataset.__init__cut_idreturncCstd)NzYou are using SpeechSeparationDataset, which is an abstract base class; instead, use one of its derived classes that specify whether the mix is pre-computed or done dynamically (on-the-fly).)NotImplementedError)rrrrr_obtain_mixture+sz'SourceSeparationDataset._obtain_mixturecCsJt|jt|j|jjD]}||j\}}t|dks"JqdS)N)rr r mixed_cutsvaluesridlen)rcut_ source_cutsrrrr2s  z SourceSeparationDataset.validateidxc Csv|j|}|j|d\}}t|}tjdd|Ddd}|}||jdddt}| d} |||| dS) N)rcSsg|] }t|qSr)torch from_numpy load_features).0 source_cutrrr @s z7SourceSeparationDataset.__getitem__..r)dimT)keepdim)sourcesmixture real_mask binary_mask) rrr)r*r+stackexpsumr argmax) rr(r mixture_cutr'r2r1 sources_expr3r4rrr __getitem__:s"   z#SourceSeparationDataset.__getitem__cCs t|jSN)r$rrrrr__len__Ts zSourceSeparationDataset.__len__)__name__ __module__ __qualname____doc__r rstrrrrr rrintrr)Tensorr;r> __classcell__rrrrr s r cs`eZdZdZ ddededeeffdd Zfdd Zd ed e e e e ffd d Z ZS)'DynamicallyMixedSourceSeparationDataseta A PyTorch Dataset for the source separation task. It's created from a number of CutSets: - ``sources_set``: provides the audio cuts for the sources that (the targets of source separation), - ``mixtures_set``: provides the audio cuts for the signal mix (the input of source separation), - ``nonsources_set``: *(optional)* provides the audio cuts for other signals that are in the mix, but are not the targets of source separation. Useful for adding noise. When queried for data samples, it returns a dict of: .. code-block:: { 'sources': (N x T x F) tensor, 'mixture': (T x F) tensor, 'real_mask': (N x T x F) tensor, 'binary_mask': (T x F) tensor } This Dataset performs on-the-fly feature-domain mixing of the sources. It expects the mixtures_set to contain MixedCuts, so that it knows which Cuts should be mixed together. Nr rnonsources_setcstj||d||_dS)Nr r)rrrH)rr rrHrrrrqs z0DynamicallyMixedSourceSeparationDataset.__init__cstt|jdSr<)rrrHr=rrrrzs z0DynamicallyMixedSourceSeparationDataset.validaterrcs(jj|}fdd|jD}||fS)Ncs g|] }|jjjvr|jqSr)r%r#r )r,trackr=rrr.szKDynamicallyMixedSourceSeparationDataset._obtain_mixture..)rr!tracksrrr9r'rr=rr~s  z7DynamicallyMixedSourceSeparationDataset._obtain_mixturer<)r?r@rArBr rrrrCrrrr rrFrrrrrGXs &rGcsHeZdZdZdedeffdd Zdedeee e ffdd Z Z S) PreMixedSourceSeparationDataseta A PyTorch Dataset for the source separation task. It's created from two CutSets - one provides the audio cuts for the sources, and the other one the audio cuts for the signal mix. When queried for data samples, it returns a dict of: .. code-block:: { 'sources': (N x T x F) tensor, 'mixture': (T x F) tensor, 'real_mask': (N x T x F) tensor, 'binary_mask': (T x F) tensor } It expects both CutSets to return regular Cuts, meaning that the signals were mixed in the time domain. In contrast to DynamicallyMixedSourceSeparationDataset, no on-the-fly feature-domain-mixing is performed. r rcs(fdd|D|_tj|ddS)Ncs"i|] jfddDqS)csg|] }|jjkr|jqSr) recording_idr#)r,cr%rrr.szGPreMixedSourceSeparationDataset.__init__...)r#)r,r rPr sz.rI)mixture_to_sourcerrrrrQrrs z(PreMixedSourceSeparationDataset.__init__rrcs.jj|}fddj|jD}||fS)Ncsg|]}jj|qSr)r cuts)r,r#r=rrr.s zCPreMixedSourceSeparationDataset._obtain_mixture..)rrTrSr#rLrr=rrs   z/PreMixedSourceSeparationDataset._obtain_mixture) r?r@rArBr rrCrrrr rrFrrrrrMs&rM)rtypingrrrrr)torch.utils.datarlhotser lhotse.cutrr r lhotse.utilsr r rGrMrrrrs   L1