o j@sXddlmZGdddejZGdddejZGdddejZGdd d ejZd S) )nnc(eZdZdZfddZddZZS)ZeroTemporalPadz7Pad sequences to equal lentgh in the temporal dimensioncs>t||d}|d}||}tdd||f|_dS)Nr)super__init__r ZeroPad2d pad_layer)self kernel_sizedilation total_padbeginend __class__U/home/kuhnn/.local/lib/python3.10/site-packages/TTS/tts/layers/generic/res_conv_bn.pyrs  zZeroTemporalPad.__init__cC ||SN)r r xrrrforwards zZeroTemporalPad.forward__name__ __module__ __qualname____doc__rr __classcell__rrrrrs rcr)Conv1dBNa1d convolutional with batch norm. conv1d -> relu -> BN blocks. Note: Batch normalization is applied after ReLU regarding the original implementation. Args: in_channels (int): number of input channels. out_channels (int): number of output channels. kernel_size (int): kernel size for convolutional filters. dilation (int): dilation for convolution layers. cs^t||d}|d}||}tj||||d|_t||ddf|_t||_dS)Nrr)r r) rrrConv1dconv1dr pad BatchNorm1dnorm)r in_channels out_channelsr r paddingpad_spad_errrr s  zConv1dBN.__init__cCs.||}||}tj|}||}|Sr)r"r#r functionalrelur%)r rorrrr)s    zConv1dBN.forwardrrrrrr s  r cs*eZdZdZdfdd ZddZZS) Conv1dBNBlocka1d convolutional block with batch norm. It is a set of conv1d -> relu -> BN blocks. Args: in_channels (int): number of input channels. out_channels (int): number of output channels. hidden_channels (int): number of inner convolution channels. kernel_size (int): kernel size for convolutional filters. dilation (int): dilation for convolution layers. num_conv_blocks (int, optional): number of convolutional blocks. Defaults to 2. rc sftg|_t|D]}t|dkr|n|||dkr|n|||}|j|q tj|j|_dSNrr)rrconv_bn_blocksranger appendr Sequential) r r&r'hidden_channelsr r num_conv_blocksidxlayerrrrr=s  zConv1dBNBlock.__init__cCr)z2 Shapes: x: (B, D, T) )r0rrrrrJs zConv1dBNBlock.forward)rrrrrrr.1s  r.cs.eZdZdZ d fdd Zd ddZZS) ResidualConv1dBNBlocka_Residual Convolutional Blocks with BN Each block has 'num_conv_block' conv layers and 'num_res_blocks' such blocks are connected with residual connections. conv_block = (conv1d -> relu -> bn) x 'num_conv_blocks' residuak_conv_block = (x -> conv_block -> + ->) x 'num_res_blocks' ' - - - - - - - - - ^ Args: in_channels (int): number of input channels. out_channels (int): number of output channels. hidden_channels (int): number of inner convolution channels. kernel_size (int): kernel size for convolutional filters. dilations (list): dilations for each convolution layer. num_res_blocks (int, optional): number of residual blocks. Defaults to 13. num_conv_blocks (int, optional): number of convolutional blocks in each residual block. Defaults to 2. rc sxtt||ks Jt|_t|D]#\}} t|dkr!|n||dt|kr,|n|||| |} |j| qdSr/) rrlenr ModuleList res_blocks enumerater.r2) r r&r'r4r dilationsnum_res_blocksr5r6r blockrrrrds  zResidualConv1dBNBlock.__init__NcCsH|durd}||}|jD]}|}||}||}|dur!||}q |S)Ng?)r<)r rx_maskr-r@resrrrrus zResidualConv1dBNBlock.forward)r9rrrrrrrr8Rs r8N)torchrModulerr r.r8rrrrs !