o ¡¿¯i:(ã @s²ddlZddlmZddlmZmZmZddlmZGdd„dej ƒZ Gdd„dej ƒZ Gdd „d ej ƒZ e d krWe d d d dddddddd Ze ddd ¡ZeeeƒjƒdSdS)éN)Ú SingleRNNÚ merge_featureÚ split_feature)Ú choose_normcs@eZdZdZ    d ‡fdd„ Zdefd d „Zd d „Z‡ZS)ÚMemLSTMa5the Mem-LSTM of SkiM args: hidden_size: int, dimension of the hidden state. dropout: float, dropout ratio. Default is 0. bidirectional: bool, whether the LSTM layers are bidirectional. Default is False. mem_type: 'hc', 'h', 'c' or 'id'. It controls whether the hidden (or cell) state of SegLSTM will be processed by MemLSTM. In 'id' mode, both the hidden and cell states will be identically returned. norm_type: gLN, cLN. cLN is for causal implementation. çFÚhcÚcLNcs°tƒ ¡||_||_t|ƒd||_||_|dvs"Jd|›ƒ‚|dvr;td|j|j||d|_t ||jdd|_ |d vrVtd|j|j||d|_ t ||jdd|_ dSdS) Né)rÚhÚcÚidz4only support 'hc', 'h', 'c' and 'id', current type: )rr ÚLSTM)Ú input_sizeÚ hidden_sizeÚdropoutÚ bidirectionalÚBTD©Ú norm_typeÚ channel_sizeÚshape)rr ) ÚsuperÚ__init__rrÚintrÚmem_typerÚh_netrÚh_normÚc_netÚc_norm)Úselfrrrrr©Ú __class__©úK/home/ubuntu/.local/lib/python3.10/site-packages/espnet2/enh/layers/skim.pyrs>  ûûÿû ÿøzMemLSTM.__init__ÚreturncCsd|j›d|j›S)Nz Mem_type: z, bidirectional: )rr)r r#r#r$Ú extra_reprIszMemLSTM.extra_reprc Cs–|jdkr|}n‘|\}}|j\}}}||} | dd¡ ¡ | |||¡}| dd¡ ¡ | |||¡}|jdkrN|| | |¡¡}|| | |¡¡}n)|jdkrc|| | |¡¡}t   |¡}n|jdkrwt   |¡}|| | |¡¡}| | |||¡ dd¡ ¡}| | |||¡ dd¡ ¡}||f}|j sÉg} |D]$} t   | ¡} | dd…dd…dd…f| dd…dd…dd…f<|   | ¡q t | ƒ}|S)Nr r rrr r éÿÿÿÿ)rrÚ transposeÚ contiguousÚviewrrrrÚtorchÚ zeros_likerÚappendÚtuple) r rÚSÚret_valr r ÚdÚBSÚHÚBÚcausal_ret_valÚxÚx_r#r#r$ÚforwardLs6        0 zMemLSTM.forward)rFrr ) Ú__name__Ú __module__Ú __qualname__Ú__doc__rÚstrr&r8Ú __classcell__r#r#r!r$r sú,rcs,eZdZdZ d ‡fdd„ Zdd„Z‡ZS) ÚSegLSTMa¶the Seg-LSTM of SkiM args: input_size: int, dimension of the input feature. The input should have shape (batch, seq_len, input_size). hidden_size: int, dimension of the hidden state. dropout: float, dropout ratio. Default is 0. bidirectional: bool, whether the LSTM layers are bidirectional. Default is False. norm_type: gLN, cLN. cLN is for causal implementation. rFr csptƒ ¡||_||_t|ƒd|_tj||dd|d|_tj |d|_ t  ||j|¡|_ t ||dd|_dS)Nr T)Ú batch_firstr)Úprr)rrrrrÚ num_directionÚnnrÚlstmÚDropoutrÚLinearÚprojrÚnorm)r rrrrrr!r#r$rs û ÿzSegLSTM.__init__c Cs´|j\}}}|dur&|j}t |||j¡ |j¡}t |||j¡ |j¡}n|\}}| |||f¡\} \}}| | ¡} |  |   ¡  d| jd¡¡  |j¡} ||  | ¡} | ||ffS)Nr'é) rrBr+ÚzerosrÚtoÚdevicerDrrGr)r*rH) r Úinputrr4ÚTr3r1r r Úoutputr#r#r$r8—s  ÿ zSegLSTM.forward)rFr )r9r:r;r<rr8r>r#r#r!r$r?ss  ÿr?cs@eZdZdZ       d‡fd d „ Zd d „Zd d„Z‡ZS)ÚSkiMaþSkipping Memory Net args: input_size: int, dimension of the input feature. Input shape shoud be (batch, length, input_size) hidden_size: int, dimension of the hidden state. output_size: int, dimension of the output size. dropout: float, dropout ratio. Default is 0. num_blocks: number of basic SkiM blocks segment_size: segmentation size for splitting long features bidirectional: bool, whether the RNN layers are bidirectional. mem_type: 'hc', 'h', 'c', 'id' or None. It controls whether the hidden (or cell) state of SegLSTM will be processed by MemLSTM. In 'id' mode, both the hidden and cell states will be identically returned. When mem_type is None, the MemLSTM will be removed. norm_type: gLN, cLN. cLN is for causal implementation. seg_overlap: Bool, whether the segmentation will reserve 50% overlap for adjacent segments.Default is False. rrIéTrÚgLNFc sìtƒ ¡||_||_||_||_||_||_||_| |_ | |_ |dvs+Jd|›ƒ‚t   g¡|_ t|ƒD]} |j  t||||| d¡q5|jdurft   g¡|_t|dƒD]} |j t||||| d¡qVt  t  ¡t  ||d¡¡|_dS)N)rr r r Nz;only support 'hc', 'h', 'c', 'id', and None, current type: )rrrrrr )rrrr)rrrÚ output_sizerÚ segment_sizerÚ num_blocksrrÚ seg_overlaprCÚ ModuleListÚ seg_lstmsÚranger-r?Ú mem_lstmsrÚ SequentialÚPReLUÚConv1dÚ output_fc) r rrrSrrUrTrrrrVÚir!r#r$rÅsN  ú  ûÿ ûÿ  ÿz SkiM.__init__c Csj|j\}}}|jr!t| dd¡|jd\}}| dddd¡ ¡}n|j|d\}}| |d|j|¡}|j\}}}}||jks@J‚| ||||¡ ¡}d} t |j ƒD]} |j | || ƒ\}} |j rp| |j dkrp|j | | |ƒ} qR|jr’| ||||¡ dddd¡}t||ƒ}| |¡ dd¡}|S| ||||¡dd…d|…dd…f}| | dd¡¡ dd¡}|S)Nr rI)rTré)rMr')rrVrr(rTÚpermuter)Ú _padfeaturer*rYrUrXrrZrr^) r rMr4rNÚDÚrestr/ÚKrOrr_r#r#r$r8s2  ÿ€ (ýz SkiM.forwardcCsD|j\}}}|j||j}|dkrtjj |ddd|f¡}||fS)Nr)rrTr+rCÚ functionalÚpad)r rMr4rNrcrdr#r#r$rb"s zSkiM._padfeature)rrIrQTrrRF)r9r:r;r<rr8rbr>r#r#r!r$rP®sõ;"rPÚ__main__iMéoéÞgš™™™™™¹?r`rQFrr T)rrUrTrrrrVrIiê)r+Útorch.nnrCÚespnet2.enh.layers.dprnnrrrÚespnet2.enh.layers.tcnrÚModulerr?rPr9ÚmodelÚrandnrMÚprintrr#r#r#r$Ús.  f;}ö ñ