o j@sddlZddlmZddlZddlZddlmZddlmm Z ddl m Z dee dejfddZde d e d ejdejfd d ZGd ddejZGdddejZGdddejZGdddejZGdddejZGdddejZGdddejZdS)N)Tuple)ConvNormshapereturncCs0t|dks Jdt|td|dS)Nz.Can only initialize 2-D embedding matrices ...)lentorchrandnnpsqrt)rr Y/home/kuhnn/.local/lib/python3.10/site-packages/TTS/tts/layers/delightful_tts/networks.pyinitialize_embeddings srd_modellengthdevicecCstj|||d}tjd|tj|dd}ttjd|d|dtd| }t|||dddddf<t |||dddddf<|d}|S)N)rr)dtyperrrg@) r zerosarangefloat unsqueezeexpmathlogsincos)rrrpepositiondiv_termr r rpositional_encodings,   r cs2eZdZdZ    d fdd Zdd ZZS) BottleneckLayera Bottleneck layer for reducing the dimensionality of a tensor. Args: in_dim: The number of input dimensions. reduction_factor: The factor by which to reduce the number of dimensions. norm: The normalization method to use. Can be "weightnorm" or "instancenorm". non_linearity: The non-linearity to use. Can be "relu" or "leakyrelu". kernel_size: The size of the convolutional kernel. use_partial_padding: Whether to use partial padding with the convolutional kernel. Shape: - Input: :math:`[N, in_dim]` where `N` is the batch size and `in_dim` is the number of input dimensions. - Output: :math:`[N, out_dim]` where `out_dim` is the number of output dimensions. weightnormreluFc stt|||_t||}||_|jdkrDt||||dkd}|dkr1t|tj |dd}||_ t |_ |dkrFt |_ dSdSdS)Nrr") kernel_sizeuse_weight_norm instancenormT)affine leakyrelu)superr!__init__reduction_factorintout_dimrnn SequentialInstanceNorm1d projection_fnReLU non_linearity LeakyReLU) selfin_dimr,normr4r%use_partial_padding reduced_dimfn __class__r rr+.s    zBottleneckLayer.__init__cCs"|jdkr||}||}|S)Nr)r,r2r4r6xr r rforwardFs   zBottleneckLayer.forward)r"r#r$F)__name__ __module__ __qualname____doc__r+r@ __classcell__r r r<rr!sr!cs<eZdZdZdeffdd ZdejdejfddZZ S) GLUActivationzClass that implements the Gated Linear Unit (GLU) activation function. The GLU activation function is a variant of the Leaky ReLU activation function, where the output of the activation function is gated by an input tensor. slopecstt||_dSN)r*r+r/r5lrelu)r6rGr<r rr+Us zGLUActivation.__init__r?rcCs$|jddd\}}|||}|S)Nrrdim)chunkrI)r6r?outgater r rr@YszGLUActivation.forward) rArBrCrDrr+r Tensorr@rEr r r<rrFMsrFcsJeZdZdedededeffdd Zdejdejd ejfd d ZZS) StyleEmbedAttention query_dimkey_dim num_units num_headscsVt||_||_||_tj||dd|_tj||dd|_tj||dd|_ dS)NF) in_features out_featuresbias) r*r+rSrTrRr/LinearW_queryW_keyW_value)r6rQrRrSrTr<r rr+`s zStyleEmbedAttention.__init__querykey_softrc Cs||}|j|j}tjtj||dddd}d}}||}||}tjtj||dddd}tjtj||dddd}t|| dd}||j d}t j |dd}t||}tj tj|dddddd}|S)NrrJrr$?r)r[rSrTr stacksplitrYrZmatmul transposerRFsoftmaxcatsqueeze) r6r\r]values split_sizeout_soft scores_softqueryskeysr r rr@js      zStyleEmbedAttention.forward rArBrCr-r+r rOr@rEr r r<rrP_s$ rPcs@eZdZdededeffdd Zdejdejfdd ZZS) EmbeddingPaddednum_embeddings embedding_dim padding_idxcsLttj|dftjd}d||<|d|tjt ||f|_ dS)Nr)rr padding_mult) r*r+r onesint64register_bufferr/ parameter Parameterr embeddings)r6rorprqrrr<r rr+s  zEmbeddingPadded.__init__idxrcCs|j|j}t||}|SrH)rxrrrc embedding)r6ryembeddings_zeroedr?r r rr@s  zEmbeddingPadded.forwardrmr r r<rrnsrncs8eZdZdeffdd ZdejdejfddZZS)EmbeddingProjBlockrpcs>ttt||tdt||tdg|_dS)Ng333333?)r*r+r/ ModuleListrXr5layers)r6rpr<r rr+s    zEmbeddingProjBlock.__init__r?rcCs$|}|jD]}||}q||}|SrH)r~)r6r?reslayerr r rr@s   zEmbeddingProjBlock.forwardrmr r r<rr|s r|csBeZdZd dededeffdd Zdejdejfd d ZZ S) LinearNormFrUrVrWcsHtt||||_tj|jj|r"tj|jj ddSdS)Ng) r*r+r/rXlinearinitxavier_uniform_weight constant_rW)r6rUrVrWr<r rr+s zLinearNorm.__init__r?rcCs||}|SrH)rr>r r rr@s zLinearNorm.forward)F) rArBrCr-boolr+r rOr@rEr r r<rrsrcs@eZdZdZdedeffdd Zdejdejfdd ZZ S) STLa0 A PyTorch module for the Style Token Layer (STL) as described in "A Style-Based Generator Architecture for Generative Adversarial Networks" (https://arxiv.org/abs/1812.04948) The STL applies a multi-headed attention mechanism over the learned style tokens, using the text input as the query and the style tokens as the keys and values. The output of the attention mechanism is used as the text's style embedding. Args: token_num (int): The number of style tokens. n_hidden (int): Number of hidden dimensions. n_hidden token_numcsrtt|d}|}||_tt|j|||_|d}||}t ||||d|_ tjj j |jddddS)Nrr)rQrRrSrTrr^)meanstd) r*rr+rr/rwr FloatTensorembedrP attentionrnormal_)r6rrrTEd_qd_kr<r rr+sz STL.__init__r?rcCs@|d}|d}t|jd|dd}|||}|S)Nrr)sizerr tanhrexpandr)r6r?Nr\ keys_softemotion_embed_softr r rr@s   z STL.forward) rArBrCrDr-r+r rOr@rEr r r<rrs r)rtypingrnumpyr r torch.nnr/torch.nn.functional functionalrc)TTS.tts.layers.delightful_tts.conv_layersrr-rOrrr Moduler!rFrPrnr|rrr r r rs     1'