o }oia @sLddlmZmZddlZddlmZmZddlmZm Z GdddeZ dS))DictOptionalN) NeuralModule typecheck NeuralTypeSpectrogramTypecseZdZdZddeedeffdd Zede ee ffd d Z ede ee ffd d Z e d ejdejdejfddZZS)MixtureConsistencyProjectiona Ensure estimated sources are consistent with the input mixture. Note that the input mixture is assume to be a single-channel signal. Args: weighting: Optional weighting mode for the consistency constraint. If `None`, use uniform weighting. If `power`, use the power of the estimated source as the weight. eps: Small positive value for regularization Reference: Wisdom et al, Differentiable consistency constraints for improved deep speech enhancement, 2018 N:0yE> weightingepscs6t||_||_|jdvrtd|jddS)N)NpowerWeighting mode  not implemented)super__init__r r NotImplementedError)selfr r  __class__^/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/audio/modules/projections.pyr%s  z%MixtureConsistencyProjection.__init__returncCstdttdtdS)+Returns definitions of module output ports.BCDT)mixtureestimaterrrrr input_types-s  z(MixtureConsistencyProjection.input_typescCsdtdtiS)routputrrr!rrr output_types5s z)MixtureConsistencyProjection.output_typesrr cCs|ddkrtd|j|d}tj|ddd}|jdur&d|}n"|jdkr?|d}||jddd|j}n t d |jd ||||}|S) a)Enforce mixture consistency on the estimated sources. Args: mixture: Single-channel mixture, shape (B, 1, F, N) estimate: M estimated sources, shape (B, M, F, N) Returns: Source estimates consistent with the mixture, shape (B, M, F, N) z.Mixture must have a single channel, got shape T)dimkeepdimNr rr) size ValueErrorshapetorchsumr abspowr r)rrr Mestimated_mixtureweightconsistent_estimaterrrforward<s     z$MixtureConsistencyProjection.forward)Nr )__name__ __module__ __qualname____doc__rstrfloatrpropertyrrr"r$rr-Tensorr5 __classcell__rrrrr s &r ) typingrrr-nemo.core.classesrrnemo.core.neural_typesrrr rrrrs