o jH@sddlZddlmZddlmZdZGdddejjZGdddejjZGd d d ejjZ Gd d d ejjZ Gd ddejZ dS)N)nn) functionalg?cs*eZdZdZd fdd ZddZZS) DiscriminatorPaHiFiGAN Periodic Discriminator Takes every Pth value from the input waveform and applied a stack of convoluations. Note: if `period` is 2 `waveform = [1, 2, 3, 4, 5, 6 ...] --> [1, 3, 5 ... ] --> convs -> score, feat` Args: x (Tensor): input waveform. Returns: [Tensor]: discriminator scores per sample in the batch. [List[Tensor]]: list of features from each convolutional layer. Shapes: x: [B, 1, T] Fcst||_dd}|rtjjntjjj}t|tj dd|df|df||ddfd|tj dd|df|df||ddfd|tj dd|df|df||ddfd|tj dd |df|df||ddfd|tj d d |dfdd dg|_ |tj d dd dd d|_ dS) NcSst|||dS)N)int)kdr [/home/kuhnn/.local/lib/python3.10/site-packages/TTS/vocoder/models/hifigan_discriminator.py sz)DiscriminatorP.__init__.. rpadding)rr)rr)rr) super__init__periodrutils spectral_normparametrizations weight_norm ModuleListConv2dconvs conv_post)selfr kernel_sizestrideuse_spectral_norm get_paddingnorm_f __class__r r rs (((( zDiscriminatorP.__init__cCsg}|j\}}}||jdkr$|j||j}t|d|fd}||}|||||j|j}|jD]}||}t|t}||q3| |}||t |dd}||fS)a  Args: x (Tensor): input waveform. Returns: [Tensor]: discriminator scores per sample in the batch. [List[Tensor]]: list of features from each convolutional layer. Shapes: x: [B, 1, T] rreflectr) shaperFpadviewr leaky_relu LRELU_SLOPEappendrtorchflatten)r xfeatbctn_padlr r r forward-s      zDiscriminatorP.forward)rrF__name__ __module__ __qualname____doc__rr: __classcell__r r r&r r src*eZdZdZdfdd ZddZZS)MultiPeriodDiscriminatorzHiFiGAN Multi-Period Discriminator (MPD) Wrapper for the `PeriodDiscriminator` to apply it in different periods. Periods are suggested to be prime numbers to reduce the overlap between each discriminator. Fc sLtttd|dtd|dtd|dtd|dtd|dg|_dS)Nrr#rr )rrrrrdiscriminators)r r#r&r r rTs       z!MultiPeriodDiscriminator.__init__cCsDg}g}t|jD]\}}||\}}||||q ||fS)a* Args: x (Tensor): input waveform. Returns: [List[Tensor]]: list of scores from each discriminator. [List[List[Tensor]]]: list of list of features from each discriminator's each convolutional layer. Shapes: x: [B, 1, T] ) enumeraterFr0)r r3scoresfeats_r scorer4r r r r:`s    z MultiPeriodDiscriminator.forwardFr;r r r&r rBNs rBcrA)DiscriminatorSzHiFiGAN Scale Discriminator. It is similar to `MelganDiscriminator` but with a specific architecture explained in the paper. Args: use_spectral_norm (bool): if `True` swith to spectral norm instead of weight norm. Fcst|r tjjntjjj}t|tjdddddd|tjdddddd d |tjdd ddd d d |tjd d ddd d d |tjd dddd d d |tjddddd d d |tjddddddg|_ |tjdddddd|_ dS)NrrrDr)r)groupsrrrrr) rrrrrrrrConv1drr)r r#r%r&r r r~s  zDiscriminatorS.__init__cCsXg}|jD]}||}t|t}||q||}||t|dd}||fS)z Args: x (Tensor): input waveform. Returns: Tensor: discriminator scores. List[Tensor]: list of features from the convolutiona layers. rr))rr+r.r/r0rr1r2)r r3r4r9r r r r:s     zDiscriminatorS.forwardrLr;r r r&r rMusrMc(eZdZdZfddZddZZS)MultiScaleDiscriminatorzHiFiGAN Multi-Scale Discriminator. It is similar to `MultiScaleMelganDiscriminator` but specially tailored for HiFiGAN as in the paper. csRtttddttg|_ttjddddtjddddg|_dS)NTrCrPrr)rrrrrMrF AvgPool1d meanpoolsr r&r r rs ,z MultiScaleDiscriminator.__init__cCs^g}g}t|jD]!\}}|dkr|j|d|}||\}}||||q ||fS) Args: x (Tensor): input waveform. Returns: List[Tensor]: discriminator scores. List[List[Tensor]]: list of list of features from each layers of each discriminator. rr)rGrFrYr0)r r3rHrIir rKr4r r r r:s    zMultiScaleDiscriminator.forwardr;r r r&r rWs  rWcrV)HifiganDiscriminatorz+HiFiGAN discriminator wrapping MPD and MSD.cstt|_t|_dS)N)rrrBmpdrWmsdrZr&r r rs  zHifiganDiscriminator.__init__cCs,||\}}||\}}||||fS)r[)r^r_)r r3rHrIscores_feats_r r r r:s zHifiganDiscriminator.forwardr;r r r&r r]s r]) r1rtorch.nnrr+r/ModulerrBrMrWr]r r r r s  E'-$